Pharmacogenetics and Pharmacogenomics in Drug Discovery and Development: An Overview

Pharmacogenetics and Pharmacogenomics in Drug Discovery and Development: An Overview Introduction The advances made over the last 30 years in molecular biology, molecular genetics, and genomics, and the development and refinement of associated methods and technologies have had a major impact on our understanding of biology, including the action of drugs and other biologically active xenobiotics. The tools that have been developed to allow these advances, and the knowledge of fundamental principles underlying cellular function thus derived, have become quintessential, and indeed indispensable, for almost any field of biological research, including biomedicine and health care. One aspect of biology in particular, namely our understanding of genetics, and, especially, our cataloguing of genome sequences, has uniquely captured the imagination of both scientists and the public. This is quite understandable, given the austere beauty of Mendel’s laws of inheritance, the compelling esthetics of the double helix structure, the awe-inspiring accomplishment of cataloguing billions of base pairs, and, *E-mail of the corresponding author: klaus.lindpaintner@roche.com last but not least, the public relations campaign unprecedented in its scope in the history of scientific achievement. However, high expectations regarding the degree and timeframe of impact that these technologies will have on the practice of health care are almost certainly unrealistic. Situated at the interface between pharmacology and genetics/genomics, “pharmacogenetics and pharmacogenomics” (usually without any further definition what these terms mean) are commonly touted as heralding a “revolution” in medicine. It is important to realize that, with regard to pharmacology and drug discovery, accomplishments in basic biology, starting sometime in the last third of the past century, have already led to what may well be considered a rather fundamental shift from the “chemical paradigm” to the “biological paradigm”: historically, drug discovery was driven by medicinal chemistry, with biology serving an almost secondary, ancillary role that examined new molecules for biological function. The ability to comprehend cell biology and function, based on a newly developed set of tools to investigate the physiological effects of biomolecules and pathways on their molecular level, has since reversed this directionality: the biologist now drives the process, requesting from the chemist compounds that modulate the function of these biomolecules or pathways, with the expectation of a more predictable impact on physiological function and the correction of its pathological derailments. As pointed out above, the major change in how we discover drugs from the chemical to the biological paradigm already occurred some time ago; what the current advances, in due time, promise to allow us to do is to move from a physiology-based to a (molecular) pathology-based approach towards drug discovery, thus promising the advancement from a largely palliative to a more cause/contribution-targeting pharmacopoeia. This communication is intended to provide a necessarily somewhat subjective view of what the disciplines of genetics and genomics stand to contribute, and how they have already contributed over many years, to drug discovery and development, and more broadly to the practice of health care. Particular emphasis will be placed on examining the role of genetics – acquired or inherited variations at the level of DNA-encoded information – in “real life”, i.e., with regard to common complex disease; a realistic understanding of this role is absolutely essential for a balanced assessment of the impact of “genetics” on health care in the future. Definitions of some of the terms that are in wide-use today – almost always sorely missing from both acade- Lindpaintner: Pharmacogenetics and pharmacogenomics in drug discovery and development mic and public policy-related documents on the topic – will be provided, with an understanding that much of the field is still in flux, and that these may change. Particular emphasis will be given to pharmacogenetics, where a more systematic classification than generally found will be attempted. It is important to remain mindful that what will be discussed is to a large extent still uncharted territoryso, of necessity, many of the positions taken, reasoned on today’s understanding and knowledge, must be viewed as somewhat speculative. Where appropriate and possible, select examples will be provided, although it should be pointed out that much of the literature in the area of genetic epidemiology and pharmacogenetics lacks the stringent standards normally applied to peer-reviewed research, and replicate data are generally absent. Definition of Terms There is widespread indiscriminate use of, and thus confusion about, the terms “pharmacogenetics” and “pharmacogenomics”. While no universally accepted definition exists, there is an emerging consensus on the differential meaning and use of the two terms (see Table 1). Pharmacogenetics The term “genetics” relates etymologically to the presence of individual properties, and inter-individual differences in these properties, as a consequence of having inherited (or acquired) them. Thus, the term pharmacogenetics describes the interactions between a drug and an individual’s (or perhaps more accurately: groups’ of individuals) characteristics as they relate to differences in the DNA-based information. Pharmacogenetics, therefore, refers to the assessment of clinical efficacy and/or the safety and tolerability profile – the pharmacological, or reponse-phenotype – of a drug in groups of individuals who differ with regard to certain DNA-encoded characteristics, and tests the hypothesis that these differences, if indeed associated with a differential response-phenotype, may allow prediction of individual drug response. The DNA-encoded characteristics are most commonly assessed on the basis of the presence or absence of polymorphisms at the level of the nuclear DNA, but may be assessed at different levels where such DNA variation translates into different characteristics, such as differential mRNA expression or splicing, protein levels or functional characteristics, or even physiological phenotypes – all of which would be seen as surrogate, or more integrated markers, of the underlying genetic variant. It should be noted that some authors continue to subsume all applications of expression profiling under the term “pharmacogenomics”, in a definition of the terms that is more driven by the technology used rather than by functional context. Pharmacogenomics In contrast to the above, the terms pharmacogenomics, and its close relative, toxicogenomics, are etymologically linked to “genomics”, the study of the genome and of the entirety of expressed and non-expressed genes in any given physiologic state. These two fields of study are concerned with a comprehensive, genomewide assessment of the effects of pharmacological agents, including toxins/toxicants, on gene expression patterns. Pharmacogenomic studies are thus used to evaluate the differential effects of a number of chemical compounds, in the process of drug discovery commonly applied to lead selection, with regard to inducing or suppressing the expression of transcription of genes in an experimental setting. Except for situations in which pharmacogenetic considerations are “frontloaded” into the discovery process, inter-individual variations in gene sequence are not usually taken into account in this process. In contrast to pharmacogenetics, pharmacogenomics therefore does not focus on differences among individuals with regard to the drug’s effects but rather examines differences among several (prospective) drugs or compounds with regard to their biological effects using a “generic” set of expressed or non-expressed genes. The basis of comparison are quantitative measures of expression, using a number of more or less comprehensive gene-expression-profiling methods, commonly based on microarray formats. By extrapolation from the experimental results to theoretically desirable patterns of activation or inactivation of expression of genes in the setting of integrative pathophysiology, this approach is hoped to provide a faster, more comprehensive, and perhaps even more reliable way to assess the likelihood of finding an ultimately successful drug than previously available schemes involving mostly in vivo animal experimentation. Thus, although both pharmacogenetics and pharmacogenomics refer to the evaluation of drug effects using (primarily) nucleic acid markers and technology, Table 1 Terminology. • Pharmacogenetics – Differential effects of a drug – in vivo – in different patients, dependent on the presence of inherited gene variants – Assessed primarily genetic (SNP) and genomic (expression) approaches – A concept to provide more patient-/disease-specific health care – One drug – many genomes (i.e., differnt patients) – Focus: patient variability • Pharmacogenomics: – Differential effects of compounds – in vivo or in vitro – on gene expression, among the entirety of expressed genes – Assessed by expression profiling – A tool for compound selection/drug discovery – Many “drugs” (i.e., early-stage compounds) – one genome (i.e., “normative” genome [database, technology platform]) – Focus: compound variability Lindpaintner: Pharmacogenetics and pharmacogenomics in drug discovery and development the directionalities of their approaches are distinctly different: pharmacogenetics represents the study of differences among a number of individuals with regard to clinical response to a particular drug (“one drug, many genomes”), whereas pharmacogenomics represents the study of differences among a number of compounds with regard to gene expression response in a single (normative) genome/expressome (“many drugs, one genome”). Accordingly, the fields of intended use are distinct: the former will help in the clinical setting to find the medicine most likely to be optimal for a patient (or the patients most likely to respond to a drug), the latter will aid in the setting of pharmaceutical research to find the “best” drug candidate from a given series of compounds under evaluation. Pharmacogenomics: Discovering New Medicines Quicker and more Efficiently Once a screen (assay) has been set up in a drug discovery project and lead compounds are identified, the major task becomes the identification of an optimized clinical candidate molecule among the many compounds synthesized by medicinal chemists. Conventionally, such compounds are screened in a number of animal or cell models for efficacy and toxicity, experiments that, while having the advantage of being conducted in the in vivo setting, commonly take significant amounts of time and depend entirely on the validity of the model, i.e., the similarity between the experimental animal condition/setting and its human counterpart. Although such experiments will never be entirely replaced by expression profiling on either the nucleic acid (genomics) or the protein (proteomics) level, these techniques offer powerful advantages and complimentary information. First, efficacy and profile of induced changes can be assessed in a comprehensive fashion (within the limitations – primarily sensitivity and completeness of transcript representation – of the technology platform used). Second, these assessments of differential efficacy can be carried out much more expeditiously than in conventionally used, (patho-) physiology-based animal models. Third, the complex pattern of expression changes revealed by such experiments may provide new insights into possible biological interactions between the actual drug target and other biomolecules, and thus reveal new elements, or branch-points, of a biological pathway that may be useful as surrogate markers, novel diagnostic analytes, or as additional drug targets. Fourth, and increasingly important is that these tools serve to determine specificity of action among members of gene families that may be highly important for both efficacy and safety of a new drug. It must be borne in mind that any and all such experiments are limited by the coefficient of correlation with which the determined expression patterns are linked to the desired in vivo physiological action of the compound. A word of caution regarding microarray-based expression profiling would appear to be in order: the power of comprehensive (almost) genome-wide assessment of expression patterns has led to what may justly be described as something of an infatuation with this technology that at times leaves a degree of critical skepticism to be desired. In particular, the pair-wise comparison algorithms used in much of this work (competition staining of a case and a control sample on the same physical array) raise a number of questions regarding selection bias. These take on particular significance since the overall sample sizes are commonly (very) small. Biostatistical analytical approaches, if at all used, are commonly less than sophisticated. Additionally, it is important to remain aware of the fact that all microarray expression data are of only associative character and must be interpreted mindful of this limitation. As a subcategory of this approach, toxicogenomics is increasingly evolving as a powerful adjuvant to classic toxicological testing. As pertinent databases are being created from experiments with known toxicants, revealing expression patterns that may potentially be predictive of longer-term toxic liabilities of compounds, future drug discovery efforts should benefit by insights allowing earlier “killing” of compounds likely to cause such complications. If these approaches are used in drug discovery, even if implemented with proper biostatistics and analytical rigor, it is imperative to understand the probabilistic nature of such experiments: a promising profile on pharmacogenomic and toxicogenomic screening will enhance the likelihood of having selected an ultimately successful compound and will achieve this goal quicker than conventional animal experimentation, but will do so only with a certain likelihood of success. The less reductionist approach of the animal experiment will still be needed. It is to be anticipated, however, that such approaches will constitute an important, timeand resource-saving first evaluation or screening step, which will help to focus, and reduce the number of, the animal experiments that will ultimately need to be conducted. Pharmacogenetics: More Targeted, more Effective Medicines Genes and environment It is common knowledge that today’s pharmacopeia – in as much as it represents enormous progress compared with what physicians had only 15 or 20 years ago – is far from being perfect. Many patients respond only partially, or fail to respond altogether, to the drugs they are given, and others suffer adverse events that range form unpleasant to serious and life-threatening. There is an emerging consensus that all common complex diseases, i.e., the health problems that are by a large margin the main contributors to society’s disease burden as well as to public and private health spending, are multifactorial in nature, i.e., that they are brought upon by the coincidence of certain intrinsic (in- Lindpaintner: Pharmacogenetics and pharmacogenomics in drug discovery and development born or acquired) predispositions and susceptibilities on the one hand, and extrinsic, environment-derived influences on the other, with the relative importance of these two influences varying across a broad spectrum. In some diseases, external factors appear to be more important, while in others, intrinsic predispositions prevail. In the great majority, a number of both intrinsic (genetic) as well as extrinsic factors appear to contribute. Such complex causation exists in any one individual with the disease, with regard to the necessary coincidence of, as we assume today, several inherent predisposing susceptibility traits and commonly more than one environmental or lifestyle risk factor. This complexity is further accentuated by the fact that any one clinical diagnosis is bound to be etiologically heterogeneous at the level of molecular pathology. Thus, consensus exists that the same conventional clinical diagnosis given to different individuals is quite likely to reflect the outcome of different constellations of inborn susceptibility factors and/or of environmental and lifestyle-related risks. So, we may expect that both on the level of an individual patient, and even more so on the public health level, the disease-causing (or better: contributing) role that intrinsic, genetically encoded properties play with regard to the likelihood that disease occurs will be, by and large, quite modest. In common, complex diseases, the contribution of genetic factors is dramatically different from their effect in the rare, classic, monogenic, “Mendelian” diseases; while in the latter case, the impact of the genetic variant is typically categorical in nature, i.e., deterministic, in the former case, the presence of a disease-associated genetic variant is merely of probabilistic value, raising (or lowering) the likelihood of disease occurrence to some extent, but never predicting it in a black-and-white fashion. If we regard a pharmacological agent as an extrinsic, environmental factor with a potential to affect the health status of the individual to whom it is administered, then individually differing responses to such an agent would – under the paradigm just elaborated upon – be expected to be based on differences regarding the “intrinsic” characteristics of these patients, as long as we can exclude variation in the exposure to the drug (this is important, as in clinical practice non-adherence to prescribed regimens of administration, or drug-drug interactions interfering with bioavailability of the drug, are by far the most likely culprits when such differences in response-phenotype are observed). The influence of such intrinsic variation on drug response may be predicted to be more easily recognizable and more relevant the steeper the dose-response curve of a given drug is. The argument for the particular likelihood of observing environmental factor/gene interactions with drugs among all other “environmental influences” goes along the same lines. Among all these “environmental factors” that we are exposed to, drugs might be particularly likely to “interact” specifically and selectively with the genetic properties of a given individual, as their potency and – compared, say, to foodstuffs – nar- row therapeutic window make interactions with innate individual susceptibilities that affect the interaction with drugs more likely. Clearly, a better, more fundamental and mechanistic understanding of the molecular pathology of disease in general and of the role of intrinsic, biological properties regarding the predisposition to contract such diseases, as well as of drug action on the molecular level, will be essential for future progress in health care. Current progress in molecular biology and genetics has indeed provided us with some of the prerequisite tools that should help us reaching the goal of such more refined understanding. An Attempt at a Systematic Classification of Pharmacogenetics Two conceptually quite different scenarios of inter-individually differential drug response may be distinguished on the basis of the underlying biological variance (see Table 2): i. In the first case, the underlying biological variation is in itself not disease-causing or -contributing, and becomes clinically relevant only in response to the exposure to the drug in question (“classical pharmacogenetics”). ii. In the second case, the biological variation is directly disease-related, is per se of pathological importance, and represents a subgroup of the overall clinical disease/diagnostic entity. The differential response to a drug is thus related to how well this drug addresses, or is matched to, the presence or relative importance of the pathomechanism it targets in different patients, i.e., the “molecular differential diagnosis” of the patient (“disease-mechanism-related pharmacogenetics”). Although these two scenarios are conceptually rather different, they result in similar practical consequences Table 2 Pharmacogenetics. Systematic classification. • “Classical” pharmacogenetics – Pharmacokinetics Absorption Metabolism Activation of prodrugs De-activation Generation of biologically active metabolites Distribution Elimination – Pharmacodynamics Palliative drug action (modulation of diseasesymptoms or disease-signs by targeting physiologically relevant systems, without addressing those mechanism that cause or causally contribute to the disease) • “Molecular differential-diagnosis-related” Pharmacogenetics Causative drug action (modulation of actual causative of contributory mechanisms) Lindpaintner: Pharmacogenetics and pharmacogenomics in drug discovery and development with regard to the administration of a drug, namely stratification based on a particular, DNA-encoded marker. It seems therefore legitimate to subsume both under the umbrella of “pharmacogenetics”. “Classical pharmacogenetics” This category includes differential pharmacokinetics and pharmacodynamics. Pharmacokinetic effects are due to inter-individual differences in absorption, distribution, metabolism (with regard to both activation of pro-drugs, inactivation of the active molecule, and generation of derivative molecules with biological activity), or excretion of the drug. In any of these cases, differential effects observed are due to the presence at the intended site of action either of inappropriate concentrations of the pharmaceutical agent, or of inappropriate metabolites, Table 3 Pharmacogenetics: chronology and systematics. Date described ca. 1890 1957 – 60 1958 1957 – 60 1959 – 60 1960 – 62 1963 1969 1969 1977 1970 1980 1984 1988 or of both, resulting either in lack of efficacy or toxic effects. Pharmacogenetics, as it relates to pharmacokinetics, has been recognized as an entity for more than 100 years, going back to the observation, commonly credited to Archibald Garrod, that a subset of psychiatric patients treated with the hypnotic drug, sulphonal, developed porphyria. We have since come to understand the underlying genetic causes for many of the previously known differences in enzymatic activity, most prominently with regard to the P450 enzyme family, and these have been the subject of recent reviews (1, 2; Table 3). However, such pharmacokinetic effects are also seen with membrane transporters, such as in the case of differential activity of genetic variants of MDR-1 that affects the effective intracellular concentration of antiretrovirals (3), or of the purineanalogue-metabolizing enzyme, thiomethyl purine transferase (4). Pharmacogenetic phenotype Sulfonal-porphyria Suxamethonium hypersensitivity Primaquine hypersensitivity; favism Long QT-Syndrome Isoniazid slow/fast acetylation Malignant hyperthermia Fructose intolerance Vasopressin insensitivity Alcohol susceptibility Debrisoquine hypersensitivity Retinoic acid resistance 6-Mercaptopurine-toxicity Mephenytoin resistance Insulin resistance Underlying gene/mutation Porphobilinogen-deaminase? Pseudocholinesterase G-6-PD Herg etc. N-Acetyltranferase Ryanodine receptor Aldolase B Vasopressin receptor2 Aldehyde dehydrogenase CYP2D6 PML-RARA fusion gene Thiopurine methyltransferase CYP2C19 Insulin receptor Identified 1985 1990 – 92 1988 1991 – 97 1989 – 93 1991 – 97 1988 – 95 1992 1988 1988 – 93 1991 – 93 1995 1993 – 94 1988 – 93 Phase I enzyme Aldehyde dehydrogenase Alcohol dehydrogenase CYP1A2 CYP2A6 CYP2C9 CYP2C19 CYP2D6 CYP2E1 CYP3A4 CYP3A5 Serum cholinesterase Paraoxonase/arylesterase Phase II enzyme Acetyltransferase (NAT1) Acetyltransferase (NAT2) Dihydropyrimidine dehydrogenase Glutathione transferase (GST-M1) Thiomethyl transferase Thiopurine methyltransferase UDP-glucuronyl transferase (UGT1A) UDP-glucuronyl transferase (UGT2B7) Testing substance Acetaldehyde Ethanol Caffeine Nicotine, coumarin Warfarin Mephenytoin, omeprazole Dextromethorphan, debrisoquine, sparteine Chloroxazone, caffeine Erythromycin Midazolam Benzoylcholine, butyrylcholine Paraoxon Testing substance Para-aminosalicylic acid Isoniazid, sulfamethazine, caffeine 5-Fluorouracil trans-Stilbene-oxide 2-Mercaptoethanol, D-penicillamine, captopril 6-Mercaptopurine, 6-thioguanine, 8-azathioprine Bilirubin Oxazepam, ketoprofen, estradiol, morphine Lindpaintner: Pharmacogenetics and pharmacogenomics in drug discovery and development Notably, despite the widespread recognition of isoenzymes with differential metabolizing potential since the middle of the 20th century, the practical application and implementation of this knowledge has been minimal so far. This may be the consequence, on the one hand, of the irrelevance of such differences in the presence of relatively flat dose-effect curves (i.e., a sufficiently wide therapeutic window), and on the other, the fact that many drugs are subject to complex, parallel metabolizing pathways, where in the case of underperformance of one enzyme another one may compensate. Such compensatory pathways may well have somewhat different substrate affinities but allow plasma levels to remain within therapeutic concentrations. Thus, the number of such polymorphisms that have found practical applicability is rather limited and, by and large, restricted to determinations of the presence of functionally deficient variants of the enzyme thiopurinemethyltransferase in patients prior to treatment with purine-analogue chemotherapeutics. Pharmacodynamic effects, in contrast, may lead to inter-individual differences in a drug’s effects despite the presence of appropriate concentrations of the intended active (or activated) drug compound at the intended site of action. Here, DNA-based variation in how the target molecule, or another (downstream) member of the target molecule’s mechanistic pathway, can respond to the medicine modulates the effects of the drug. This will apply primarily to palliatively working medicines that improve a condition symptomatically by modulating disease-phenotype-relevant (but not disease-cause-relevant) pathways that are not dysfunctional but can be used to counterbalance the effect of a dysfunctional, disease-causing pathway, and therefore allow mitigation of symptoms. A classical example of such an approach is the acute treatment of thyrotoxicity with β-adrenergic blocking agents: even though the sympathetic nervous system in this case does not contribute causally to tachycardia and hypertension, dampening even its baseline tonus through this class of rapidly acting drugs can quickly and successfully relieve the cardiovascular symptoms and signs of this condition, and may well prevent a heart attack if the patient has underlying coronary disease, before the causal treatment (in this case available through partial chemical ablation of the hyperactive thyroid gland) can take effect. Notably, the majority of today’s pharmacopeia actually belongs to this class of palliatively acting medicines. A schematic (Figure 1) is provided to help to clarify these somewhat complex concepts, in which a hypothetical case of a complex trait/disease is depicted where excessive, dysregulated function of one of the trait-controlling/contributing pathways (Figure 1A and B) causes symptomatic disease – the example used refers to blood pressure as the trait, and hypertension as the disease in question, respectively (for the case of a defective or diminished function of a pathway, an analogous schematic could be constructed, and again for a deviant function). A palliative treatment would be one that addresses one of the pathways that – while not Figure 1 Modeling assumptions underlying pharmacogenetics classification. A: Normal physiology: 3 molecular mechanisms (M1, M2, M3) contribute to a trait. B: Diseased physiology D1: derailment (cause/contribution) of molecular mechanism 1 (M1). C: Diseased physiology D1: causal treatment T1 (aimed at M1). D: Diseased physiology D3: derailment (cause/contribution) of molecular mechanism 3 (M3). E: Diseased physiology D3, treatment T1: treatment does not address cause. F: Diseased physiology D1, palliative treatment T2 (aimed at M2). G: Diseased physiology D1, palliative treatment T2; T2-refractory gene variant in M2. H: Normal physiology variant: differential contribution of M1 and M2 to normal trait. I: Diseased physiology D1-variant: derailment of mechanism M1. J: Diseased physiology D1-variant: treatment with T2. Solid colors indicate normal function, stippling indicates pathologic dysfunction, hatching indicates therapeutic modulation. dysregulated – contributes to the overall deviant physiology (Figure 1F), while the respective pharmacogenetic-pharmacodynamic scenario would occur if this particular pathway was, due to a genetic variant, not responsive to the chosen drug (Figure 1G). A palliative treatment may also be ineffective if the particular mechanism targeted by the palliative drug, due to the presence of a molecular variant, provides less than the physiologically expected baseline contribution to the relevant phenotype (Figure 1H). In such a case, modulating an a priori unimportant pathway in the disease scenario will not yield successful palliative treatment results (Figure 1I and J). Several of the most persuasive examples we have accumulated to date for such palliative-drug-related pharmacogenetic effects have been observed in the field of asthma. The treatment of asthma relies on an array of drugs aimed at modulating different “generic” pathways, thus mediating bronchodilation or anti-inflammatory effects, often without regard to the possible causative contribution of the targeted mechanism to the disease. One of the mainstays of the treatment of asthma is activation of the β2-adrenergic receptor by specific agonists, which leads to relaxation of bronchial smooth muscles and, consequently, bronchodilation. Recently, several molecular variants of the β2-adrenoceptor have been shown to be associated with differential treatment response to such β2-agonists (5, 6). Individuals carrying one or two copies of a variant allele that contains a glycine in place of arginine in position Lindpaintner: Pharmacogenetics and pharmacogenomics in drug discovery and development 16 were found to have a 3- and 5-fold reduced response to the agonist, respectively. This was shown in both in vitro (7, 8) and in vivo (8) studies to correlate with an enhanced rate of agonist-induced receptor down-regulation, but not with any difference in transcriptional or translational activity of the gene, or with agonist binding. In contrast, a second polymorphism affecting position 19 of the β-upstream peptide was shown to affect translation (but not transcription) of the receptor itself, with a 50% decrease in receptor numbers associated with the variant allele – which happens to be in strong linkage disequilibrium with a variant allele at position 16 in the receptor. The simultaneous presence of both mutations would thus be predicted to result in low expression and enhanced down-regulation of an otherwise functionally normal receptor, depriving patients carrying such alleles of the benefits of effective bronchodilation as a “palliative” (i.e., non-causal) countermeasure affecting their pathological airway hyper-reactivity. Importantly, there is no evidence that any of the allelic variants encountered are associated with the prevalence or incidence, and thus potentially the etiology of the underlying disease (9, 10). This would reflect the scenario depicted in Figure 1H. Inhibition of leukotriene synthesis, another palliative approach toward the treatment of asthma, proved clinically ineffective in a small fraction of patients who carried only non-wild-type alleles of the 5-lipoxygenase promoter region (11). These allelic variants had previously been shown to be associated with decreased transcriptional activity of the gene (12). It stands to reason – consistent with the clinical observations – that in the presence of already reduced 5-lipoxygenase activity, pharmacological inhibition may be less effective (Figure 1H, I, J). Of note, again, is that there is no evidence for a primary, disease-causing or -contributing role of any 5-lipoxygenase variants; all of them were observed at equal frequencies in disease-affected and non-affected individuals (12). Pharmacogenetic effects may not only account for differential efficacy but also contribute to differential occurrence of adverse effects. An example for this scenario is provided by the well-documented “pharmacogenetic” association between molecular sequence variants of the 12S rRNA, a mitochondrion-encoded gene, and aminoglycoside-induced ototoxicity (13). Intriguingly, the mutation that is associated with susceptibility to ototoxicity renders the sequence of the human 12S rRNA similar to that of the bacterial 12S rRNA gene, and thus effectively turns the human 12S rRNA into the (bacterial) target for aminoglycoside drug action – presumably mimicking the structure of the bacterial binding site of the drug (14). As in the other examples, presence of the 12S rRNA mutation per se has no primary, drug treatment-independent pathologic effect. One may speculate that, analogously, such molecular mimicry may occur within one species: adverse events may arise if the selectivity of a drug is lost because a gene that belongs to the same gene family as the primary target, loses its “identity” vis-à -vis the drug and attains, based on its structural similarity to the principal target, similar to, or increased, affinity to the drug. Depending on the biological role of the “imposter” molecule, adverse events may occur – even though the variant molecule, again, may be quite silent with regard to contribution to disease causation. Although we currently have no obvious examples of this scenario, it is certainly imaginable for various classes of receptors and enzymes. Pharmacogenetics as a consequence of molecular differential diagnosis As alluded to earlier, there is general agreement today that any of the major clinical diagnoses in the field of common complex disease, such as diabetes, hypertension, or cancer, or others, are comprised of a number of etiologically (i.e., at the molecular level) more or less distinct subentities. In the case of a causally acting drug this may imply that the agent will only be appropriate, or will work best, in that fraction of all patients who carry the (all-inclusive and imprecise) clinical diagnosis in whom the dominant molecular etiology, or at least one of the contributing etiological factors, matches the biological mechanism of action that the drug in question modulates (Figure 1C). If the mechanism of action of the drug addresses a pathway that is not disease-relevant – perhaps already down-regulated as an appropriate physiologic response to the disease – then the drug may, logically, be expected no to show efficacy (Figure 1D, E). Thus, unrecognized and undiagnosed disease heterogeneity, disclosed indirectly by the presence or absence of response to a drug targeting a mechanism that contributes only to one of several molecular subgroups of the disease, provides an important explanation for differential drug response and likely represents a substantial fraction of what we today somewhat indiscriminately subsume under the term “pharmacogenetics”. Currently, the most frequently cited example for this category of pharmacogenetics is trastuzamab (Herceptin®), a humanized monoclonal antibody directed against the her-2-oncogene. This breast cancer treatment is prescribed based on the level of her-2-oncogene expression in the patient’s tumor tissue. Differential diagnosis at the molecular level not only provides an added level of diagnostic sophistication but also actually represents the prerequisite for choosing the appropriate therapy. Because tastuzamab specifically inhibits a “gain-of-function” variant of the oncogene, it is ineffective in the 2/3 of patients who do not overexpress the drug’s target, whereas it significantly improves survival in the 1/3 of patients that constitute the subentity within the broader diagnosis “breast cancer” and who express the gene at abnormally high levels (15). Some have argued against this being an example of pharmacogenetics, because the parameter for patient stratification (i.e., for differential diagnosis) is the somatic gene expression level rather than a particular “genotype” data (16). This is a difficult argument to follow, since in the case of a treatment-effect-modifying germ Lindpaintner: Pharmacogenetics and pharmacogenomics in drug discovery and development line mutation it would obviously not be the nuclear gene variant per se, but also its specific impact on either structure/function, or on expression of the respective gene/gene product, that would represent the actual physiological corollary underlying the differential drug action. Conversely, an a priori observed expression difference is highly likely to reflect a – as yet undiscovered – sequence variant. Indeed, as pointed out earlier, there are a number of examples in the field of pharmacogenomics where the connection between genotypic variant and altered expression has already been demonstrated (12, 17). Another example of how proper molecular diagnosis of relevant pathomechanisms will significantly influence drug efficacy, although still hypothetical, is in the evolving class of anti-AIDS/HIV drugs that target the CCR5 cell-surface receptor (18 – 20). These drugs would be predicted to be ineffective in those rare patients who carry the ∆-32 variant, but who nevertheless have contracted AIDS or test HIV-positive (most likely due to infection with an SI-virus phenotype that utilizes CXCR4) (21, 22). It should be noted that the pharmacogenetically relevant molecular variant needs not affect the primary drug target but may equally well be located in another molecule belonging to the system or pathway in question, both up- or downstream in the biological cascade with respect to the primary drug target. Different classes of markers Pharmacogenetic phenomena, as pointed out previously, need not be restricted to the observation of a direct association between allelic sequence variation and phenotype but may extend to a broad variety of indirect manifestations of underlying, but often (as yet) unrecognized sequence variation. Thus, differential methylation of the promoter region of O6-methylguanine-DNA methylase has recently been reported to be associated with differential efficacy of chemotherapy with alkylating agents. If methylation is present, the expression of the enzyme that rapidly reverses alkylation and induces drug resistance is inhibited, and therapeutic efficacy is greatly enhanced (23). Complexity is to be expected In the real world, it is likely that not only one of the scenarios depicted, but a combination of several ones, may affect how well a patient responds to a given treatment, or how likely it is that he or she will suffer an adverse event. Thus, a fast-metabolizing patient with poor-responder pharmacodynamics may be particularly unlikely to gain any benefit from taking the drug in question, while a slow-metabolizing status may counterbalance in another patient the same inopportune pharmacodynamics, while a third patient, who is a slow metabolizer and displays normal pharmacodynamics, may be more likely to suffer adverse events. In all of them, both the pharmacokinetic and pharmacodynamic properties may result from the interaction of several of the mechanisms described above. In addi- tion, we know of course that co-administration of other drugs, or even the consumption of certain foods, may affect and further complicate the picture for any given treatment. Incorporating Pharmacogenetics into Drug Development Strategy Diagnostics first, therapeutics second It is important to note that despite the public hyperbole and the high-strung expectations surrounding the use of pharmacogenetics to provide “personalized care” these approaches are likely to be applicable only to a fraction of medicines that are being developed. Further, if and when such approaches will be used, they will represent no radical new direction or concept in drug development but simply a stratification strategy as we have been using it all along. An increasingly sophisticated and precise diagnosis of disease arising from a deeper, more differentiated understanding of pathology at the molecular level, that will increasingly subdivide today’s clinical diagnoses into molecular subtypes, will foster medical advances which, if considered from the viewpoint of today’s clinical diagnosis, will appear as “pharmacogenetic” phenomena, as described above. However, the sequence of events that is today often presented as characteristic for a “pharmacogenetic scenario” – namely, exposing patients to the drug, recognizing a differential (quasibimodal) response pattern, discovering a marker that predicts this response, and creating a diagnostic product to be co-marketed with the drug henceforth – is likely to be reversed. Rather, in the case of “pharmacogenetics”, due to a match between drug action and dysregulation of a disease-contributing mechanism, we will likely search for a new drug specifically, and a priori, based on a new mechanistic understanding of disease causation or contribution (i.e., a newly found ability to diagnose a molecular subentity of a previously more encompassing, broader, and less precise clinical disease definition). Thus, pharmacogenetics will not be so much about finding the “right medicine for the right patient” but about finding the “right medicine for the disease(-subtype)”, as we have aspired to do all along throughout the history of medical progress. This is, in fact, good news: the conventional “pharmacogenetic scenario” would invariably present major challenges from both a regulatory and a business development and marketing standpoint, as it would confront development teams with a critical change in the drug’s profile at a very late point during the development process. In addition, the timely development of an approvable diagnostic in this situation is difficult at best, and its marketing as an “add-on” to the drug a less than attractive proposition to the diagnostics business. Thus, the “practice” of pharmacogenetics will, in many instances, be marked by progress along the very same path that has been one of the main avenues of medical progress for the last several Lindpaintner: Pharmacogenetics and pharmacogenomics in drug discovery and development 10000 years: differential diagnosis first, followed by the development of appropriate, more specific treatment modalities. Thus, the sequence of events in this case may well involve, first, the development of an in vitro diagnostic test as a stand-alone product that may be marketed on its own merits, allowing the physician to establish an accurate, state of the art diagnosis of the molecular subtype of the patient’s disease. Sometimes such a diagnostic may prove helpful even in the absence of specific therapy by guiding the choice of existing medicines and/or of non-drug treatment modalities such as specific changes in diet or lifestyle. Availability of such a diagnostic – as part of the more sophisticated understanding of disease – will undoubtedly foster and stimulate the search for new, more specific drugs; and once such drugs are found, availability of the specific diagnostic will be important for carrying out the appropriate clinical trials. This will allow a prospectively planned, much more systematic approach towards clinical and business development, with a commensurate greater chance of actual realization and success. Probability, not certainty In practice, some extent of guesswork will remain, due to the nature of common complex disease. First, all diagnostic approaches – including those based on DNA analysis in common complex disease, as stressed above – will ultimately only provide a measure of probability, not of certainty: thus, although the variances of drug response among patients who do (or do not) carry the drug-specific sub-diagnosis will be smaller, there will still be a distribution of differential responses: although by and large the drug will work better in the “responder” group, there will be some patients among this subgroup, who will respond less or not at all, and conversely, not everyone belonging to the “non-responder” group will completely fail to respond, depending perhaps on the relative magnitude with which the particular mechanism contributes to the disease. It is important to bear in mind, therefore, that even in the case of fairly obvious bimodality, patient responses will still show distribution patterns, and that all predictions as to responder- or non-responder status will only have a certain likelihood of being accurate (Figure 2).The terms “responder” and “non-responder” as applied to groups of patients stratified on the basis of a DNA marker represent, therefore, Mendelian-thinking-inspired misnomers that should be replaced by more appropriate terms that reflect the probabilistic nature of any such classification, e.g., “likely (non-)responder”. In addition, based on our current understanding of the polygenic and heterogeneous nature of these disorders, we will – even in an ideal world where we would know about all possible susceptibility gene variants for a given disease and have treatments for them – only be able to exclude, in any one patient, those that do not appear to contribute to the disease, and therefore deselect certain treatments. We will, however, most likely find ourselves left with a small number – Figure 2 Hypothetical example of bimodal distribution according to marker that indicates “non-responder” or “responder” status. Note that in both cases a distribution is present, with overlaps. Thus, the categorization into “responders” or “non-responders” based on the marker must be understood to convey only the probability to belong to one or the other group. two to four, perhaps – of potentially disease-contributing gene variants whose relative contribution to the disease will be very difficult, if not impossible, to rank in an individual patient. Likely then, trial and error, and this great intangible quantity, “physician experience”, will still play an important role, albeit on a more limited and sub-selective basis. The alternative situation, where differential drug response and/or safety issues are a consequence of a pathologically not relevant, purely drug response-related pharmacogenetics scenario, is more likely to present greater difficulty in planning and executing a clinical development program because, presumably, it will be more difficult to anticipate or predict differential responses a priori. When such a differential response occurs, it will also potentially be more difficult to find the relevant marker(s), unless it happens to be among the “obvious” candidate genes implicated in the disease physiopathology or the treatment’s mode of action. Although screening for molecular variants of these genes and testing for their possible associations with differential drug response is a logical first step, if unsuccessful, it may be necessary to embark on an unbiased genomewide screen for such a marker or markers. Despite recent progress in high-throughput genotyping, the obstacles that will have to be overcome on the technical, data analysis, and cost levels are formidable. They will limit the deployment of such programs, at least for the foreseeable future, to select cases in which there are very solid indications for doing so, based on clinical data showing a near-categorical (e.g., bimodal) distribution of treatment outcomes. Even then, we may expect to encounter for every success, that will be owed to a favorably strong linkage disequilibrium across considerable genomic distance in the relevant chromosomal region, as many or more failures, in cases where the Lindpaintner: Pharmacogenetics and pharmacogenomics in drug discovery and development culpable gene variant cannot be found due to the higher recombination rate or other characteristics of the stretch of genome on which it is located. Regulatory Aspects At this writing, regulatory agencies in both Europe and the USA are beginning to show keen interest in the potential role that pharmacogenetics approaches may play in the development and clinical use of new drugs, and the potential challenges that such approaches may present to the regulatory approval process. While no formal guidelines have been issued, the pharmaceutical industry has already been reproached – albeit in a rather nonspecific manner – for not being more proactive in the use of pharmacogenetic markers. It will be of key importance for all concerned to engage in an intensive dialogue at the end of which – it is hoped – a joint understanding will emerge that stratification according to DNA-based markers is fundamentally nothing new and not different from stratification according to any other clinical or demographic parameter, as has been used all along. Still, based on the perception (in the case of common complex diseases scientifically unjustified) that DNAbased markers represent a different class of stratification parameters, a number of important questions will need to be addressed and answered – hopefully always in analogy to “conventional” stratification parameters, including those referring to ethical aspects. Among the most important ones are questions concerning: • the need and/or ethical justification (or lack thereof) to include likely non-responders in a trial for the sake of meeting safety criteria, which, given the restricted indication of the drug, may indeed be excessively broad; • the need to carry out conventional size safety trials in the disease stratum eligible for the drug (if the stratum represents a relatively small fraction of all patients with the clinical diagnosis, it may be difficult to amass sufficient numbers and/or discourage companies from pursuing such drugs to the disadvantage of patients); • the need to use active controls if the patient/disease stratum is different from that in which the active control was originally tested; • the strategies to develop and gain approval for the applicable first-generation diagnostic, as well as for the regulatory approval of subsequent generations of tests to be used to determine eligibility for prescription of the drug; as well as • a number of ethical-legal questions relating to the unique requirements regarding privacy and confidentiality for “genetic testing” that may raise novel problems with regard to regulatory audits of patient data (see below). A concerted effort to avoid what has been termed “genetic exceptionalism” – the differential treatment of DNA-based markers as compared with other personal medical data – should be made so as to not further unnecessarily complicate the already very difficult process of obtaining regulatory approval. This seems justified based on the recognized fact that in the field of common complex disease DNA-based markers are not at all different from “conventional” medical data in all relevant aspects – namely specificity, sensitivity, and predictive value. Pharmacogenetic Testing for Drug Efficacy vs. Safety Greater efficacy: likely In principle, pharmacogenetic approaches may be useful both to raise efficacy and to avoid adverse events, by stratifying patient eligibility for a drug according to appropriate markers. In both cases, clinical decisions and recommendations must be supported by data that have undergone rigorous biostatistical scrutiny. Based on the substantially different prerequisites for, and opportunities to, acquiring such data, and to applying them to clinical decision-making, we expect the use of pharmacogenetics for enhanced efficacy to be considerably more common than for the avoidance of adverse events. The likelihood that adequate data on efficacy in a subgroup may be generated is reasonably high, given the fact that, unless the drug is viable in a reasonably sizeable number of patients, it will probably not be developed for lack of a viable business case, or at least only under the protected environment of orphan drug guidelines. Implementation of pharmacogenetic testing to stratify for efficacy, provided that safety in the non-responder group is not an issue, will primarily be a matter of physician preference and sophistication, and potentially of third-party payer directives but would appear less likely to become a matter of regulatory mandate, unless a drug has been developed selectively in a particular stratum of the overall indication (in which case a contra-indication label for other strata is likely to be issued). Indeed, an argument can be made against depriving those who carry the “likely non-responder” genotype regarding eligibility for the drug, but who individually, of course, may respond to the drug with a certain, albeit lower, probability. From the regulatory point of view, the use of pharmacogenetics for efficacy, if adequate safety data exist, appears largely unproblematic – the worst-case scenario (a genotypically inappropriate patient receiving the drug) would result in treatment without expected beneficial effect but with no increased odds to suffer adverse consequences, i.e., much of what one would expect under conventional paradigms. Avoidance of serious adverse effects: less likely – with exceptions The utility and clinical application of pharmacogenetic approaches towards improving safety, in particular with regard to serious adverse events, will meet with considerably greater hurdles and is therefore less likely expected to become reality. A number of reasons are Lindpaintner: Pharmacogenetics and pharmacogenomics in drug discovery and development cited for this: first, in the event of serious adverse events associated with the use of a widely prescribed medicine, withdrawal of the drug from the market is usually based almost entirely on anecdotal evidence from a rather small number of cases – in accordance with the Hippocratic mandate “primum non nocere”. If the sample size is insufficient to demonstrate a statistically significant association between drug exposure and event, as is typically the case, it will most certainly be insufficient to allow meaningful testing for genotype-phenotype correlations; the biostatistical hurdles become progressively more difficult as many markers are tested and the number of degrees of freedom applicable to the analysis for association continues to rise. Therefore, the fraction of attributable risk shown to be associated with a given at-risk (combination of) genotype(s) would have to be very substantial for regulators to accept such data. Indeed, the low prior probability of the adverse event, by definition, can be expected to yield an equally low positive (or negative) predictive value. Second, the very nature of safety issues raises the hurdles substantially because in this situation the worst-case scenario – administration of the drug to the “wrong” patient – will result in higher odds to harm to the patient. Therefore, it is likely that the practical application of successfully investigating and applying pharmacogenetics towards limiting adverse events will likely be restricted to diseases with dire prognosis, where a high medical need exists, where the drug in question offers unique potential advantages (usually bearing the characteristics of a “life-saving” drug), and where, therefore, the tolerance even for relatively severe side effects is much greater than for other drugs. This applies primarily to areas like oncology or HIV/AIDS, for which the recently reported highly specific and acceptably sensitive association between the MHC gene variant, HLA B5701, and occurrence of a severe hypersensitivity reaction is a prime example (24, 25). In most other indications, the sobering biostatistical and regulatory considerations represent barriers that are unlikely to be overcome easily; and the proposed, conceptually highly attractive, routine deployment of pharmacogenetics as a generalized drug surveillance or pharmaco-vigilance practice following the introduction of a new pharmaceutical agent (19) faces these scientific as well as formidable economic hurdles. Ethical-Societal Aspects of Pharmacogenetics No discussion about the use of genetic/genomic approaches to health care can be complete without considering their impact on the ethical, societal, and legal level. Much of the discussion about ethical and legal issues relating to pharmacogenetics is centered on the issue of “genetic testing”, a topic that has recently also been the focus of a number of guidelines, advisories, white papers, etc. issued by a number of committees in both Europe and the USA. It is interesting to note that the one characteristic that virtually all these documents share is an almost studious avoidance of defining what exactly a “genetic test” is. Where definitions are given, they tend to be very broad, including not only the analysis of DNA but also of transcription and translation products affected by inherited variation. In as much as the most sensible solution to this dilemma will ultimately, hopefully, be a consensus to treat all personal medical data in a similar fashion regardless of the degree to which DNA-encoded information affects it (noting that there really is not any medical data that are not to some extent affected by intrinsic patient properties), it may, for the time being, be helpful to let the definition of what constitutes “genetic data” be guided by the public perception of “genetic data” – in as much as the whole discussion of this topic is prompted by these public perceptions. In the public eye, “genetic test” is usually understood either (i) as any kind of test that establishes the diagnosis of, or predisposition for one of the classic monogenic, heritable disease, or (ii) as any kind of test based on structural nucleic acid analysis (sequence). This includes the (non-DNA-based) Guthrie test for phenylketonuria and forensic and paternity testing, as well as a DNA-based test for lipoprotein (a) (Lp(a)), but not the plasmaprotein-based test for the same marker (even though the information derived is identical). Since monogenic disease is, in effect, excluded from this discussion, it stands to reason to restrict the definition of “genetic testing” to the analysis of (human) DNA sequence. Based on the – perceived – particular sensitivity of “genetic” data, institutional review boards commonly apply a specific set of rules to grant permission to test for DNA-based markers in the course of drug trials or other clinical research, including (variably) separate informed consent forms, the anonymization of samples and data, specific stipulations about availability of genetic counseling, provision to be able to withdraw samples at any time in the future, etc. Arguments have been advanced (26) that genotype determinations for pharmacogenetic characterization, in contrast to “genetic” testing for primary disease risk assessment, are less likely to raise potentially sensitive issues with regard to patient confidentiality, the misuse of genotyping data, or other nucleic acid-derived information, and the possibility of stigmatization. While this is certainly true when pharmacogenetic testing is compared to predictive genotyping for highly penetrant Mendelian disorders, it is not apparent why in common complex disorders, issues surrounding predictors of primary disease risk would be any more or less sensitive than those pertaining to predictors of likely treatment success/failure. Both can be expected to provide, in most cases, a modicum of better probabilistic assessment, based on the modest degree of sensitivity, specificity, and positive/negative predictive value we are likely to see with tests for pharmacogenetic interactions. If, however, misguided this would be given the anticipated quite limited information content of such tests, such information was to be used “against” the Lindpaintner: Pharmacogenetics and pharmacogenomics in drug discovery and development patient, then two lines of reasoning may actually indicate an increased potential for ethical questions and complex confrontations among the various stakeholders to arise from pharmacogenetic data. First, while access to genotyping and other nucleic acid-derived data related to disease susceptibility can be strictly limited, the very nature of pharmacogenetic data calls for a rather more liberal position regarding use: if this information is to serve its intended purpose, i.e., improving the patients chance for successful treatment, then it is essential that it is shared among at least a somewhat wider circle of participants in the health care process. Thus, the prescription for a drug that is limited to a group of patients with a particular genotype will inevitably disclose the receiving patient’s genotype to anyone of a large number of individuals involved in the patients care at the medical and administrative level. The only way to limit this quasi-public disclosure of this patient’s genotype data would be if he or she were to sacrifice the benefits of the indicated treatment for the sake of data confidentiality. Second, patients profiled to carry a high disease probability along with a high likelihood for treatment response may be viewed, from the standpoint of, e.g., insurance risk, as quite comparable to patients displaying the opposite profile, i.e., a low risk to develop the disease but a high likelihood not to respond to medical treatment, if the disease indeed occurs. For any given disease risk, then, patients less likely to respond to treatment would be seen as a more unfavorable insurance risk, particularly if non-responder status is associated with chronic, costly illness rather than with early mortality, the first case having much more far-reaching economic consequences. The pharmacogenetic profile may thus, under certain circumstances, even become a more important (financial) risk assessment parameter than primary disease susceptibility, and would be expected – in as much as it represents but one stone in the complex-disease mosaic – to be treated with similar weight, or lack thereof, as other genetic and environmental risk factors. Practically speaking, the critical issue is not only, and perhaps not even predominantly, the real or perceived sensitive nature of the information, and how it is, if at all, disseminated and disclosed, but how and to what end it is used. Obviously, generation and acquisition of personal medical information must always be contingent on the individual’s free choice and consent, as must be all application of such data for specific purposes. Beyond this, however, there is today an urgent need for the requisite dialogue and discourse among all stakeholders within society to develop and endorse a set of criteria by which the use of genetic, and indeed of all personal medical information should occur. It will be critically important that society as a whole endorses, in an act of solidarity with those less fortunate, i.e., at higher risk of developing disease, or less likely to respond to treatment, rules that guarantee the beneficial and legitimate use of the data in the patient’s interest while at the same time prohibiting their use in ways that may harm the individual, personally, financially, or otherwise. As long as we trust our political decision processes to reflect societal consensus, and as long as such consensus reflects the principles of justice and equality, the resulting set of principles should ensure such proper use of medical information. Indeed, both aspects – data protection and patient/subject protection – are seminal components of the mandates included in the WHO’s “Proposed International Guidelines on Ethical Issues in Medical Genetics and Genetic Services” (27) which mandate autonomy, beneficence, non-maleficence, and justice. The essential requirement to reach such a consensus that will allow the use of genetics and genomics in the best interest of all concerned is an informed dialogue among the various stakeholders – which can only begin to take place once (mis-)perceptions are replaced by objective and neutral information as the basis to from informed opinions. Progress in the fields of genetic and genomics has been rapid and substantial over the last few decades, and it has been accompanied by a great deal of hyperbole in the popular press. Meanwhile, geneticists have continued to cultivate an arcane and forbidding vernacular that adds only to the appearance of purposeful secrecy that the public is reacting to, instead of having made extra efforts to reach out to the public in a concerted educational campaign. As part of the Human Genome Project substantial amounts of funding have been provided to work in the area of bioethics and much progress has been achieved there. Similar or even greater efforts need to be undertaken in the area of public information and education – which will surely go a long way towards resolving some of the fears as well as unrealistic hopes the public currently associates with genetics and genomics. The author of this communication and his colleagues have assembled an interactive CD-ROM-based educational program that is distributed freely upon request (28). Conclusion Pharmacogenetics, in all its various manifestations, will represent an important new avenue towards understanding disease pathology and drug action, and will offer new opportunities of stratifying patients to achieve better treatment success. As such, it represents a logical, consequent step in the history of medicine – evolutionary, rather than revolutionary. Its implementation will take time, and will not apply to all diseases and all treatments equally. Pharmacogenetic information will be probabilistic and relative, not deterministic or absolute. It will provide help, but no simple solutions. It will require society to find ways to sanction the proper use of this information, thus allowing and protecting its unencumbered use for the benefit of patients. A more realistic assessment of its actual potential to provide benefit or cause harm will likely quell much of the exalted hopes and exaggerated fears that are so often associated with the topic. Lindpaintner: Pharmacogenetics and pharmacogenomics in drug discovery and development 15. Baselga J, Tripathy D, Mendelsohn J, Baughman S, Benz CC, Dantis L, et al. Phase II study of weekly intravenous recombinant humanized anti-p185(HER2) monoclonal antibody in patients with HER2/neu-overexpressing metastatic breast cancer. J Clin Oncol 1996; 14:737 – 44. 16. Haseltine WA. Not quite pharmacogenomics [letter; comment]. Nat Biotechnol 1998; 16:1295. 17. McGraw DW, Forbes SL, Kramer LA, Liggett SB. Polymorphisms of the 5’ leader cistron of the human beta2-adrenergic receptor regulate receptor expression. J Clin Invest 1998;1 02:1927 – 32. 18. Huang Y, Paxton WA, Wolinsky SM, Neumann AU, Zhang L, He T, et al. The role of a mutant CCR5 allele in HIV-1 transmission and disease progression. Nat Med 1996; 2: 1240 – 3. 19. Dean M, Carrington M, Winkler C, Huttley GA, Smith MW, Allikmets R, et al. Genetic restriction of HIV-1 infection and progression to AIDS by a deletion of the CKR5 structural gene. Science 1996; 273:1856 – 62. 20. Samson M, Libert F, Doranz BJ, Rucker J, Liesnard C, Farber CM, et al. Resistance to HIV-1 infection in Caucasian individuals bearing mutant alleles of the CCR-5 chemokine receptor gene. Nature 1996; 382:722 – 5. 21. O’Brien TR, Winkler C, Dean M, Nelson JAE, Carrington M, Michael NL, et al. HIV-1 infection in a man homozygous for CCR5 32. Lancet 1997; 349:1219. 22. Theodorou I, Meyer L, Magierowska M, Katlama C, Rouzious C, Seroco Study Group. HIV-1 infection in an individual homozygous for CCR5 32. Lancet 1997; 349: 1219 – 20. 23. Esteller M, Garcia-Foncillas J, Andion E, Goodman, SN, OF Hidalgo, Vanaclocha V, et al. Inactivation of the DNA-repair gene mgmt and the clinical response of gliomas to alkylating agents. N Engl J Med 2000; 343:1350 – 4. 24. Mallal S, Nolan D, Witt C, Masel G, Martin AM, Moore C, et al. Association between presence of HLA-B*5701, HLADR7, and HLA-DQ3 and hypersensitivity to HIV-1 reversetranscriptase inhibitor abacavir. Lancet 2002; 359:727 – 32. 25. Hetherington S, Hughes AR, Mosteller M, Shortino D, Baker KL, Spreen W, et al. Genetic variations in HLA-B region and hypersensitivity reactions to abacavir. Lancet 2002; 359:1121 – 2. 26. Roses A. Pharmacogentics and future drug development and delivery. Lancet 2000; 355:1358 – 61. 27. Proposed International Guidelines on Ethical Issues in Medical Genetics and Genetic Services. http://www.who. int/ncd/hgn/hgnethic.htm 28. Roche Genetics Educational Program. Available upon request from http://www.rochegenetics.com Received 10 January 2003, accepted 20 January 2003 Corresponding author: Klaus Lindpaintner, MD, MPH, VP Research, Director, Roche Genetics, F. Hoffmann-La Roche, Bldg 93/532, 4070 Basel, Switzerland Phone: + 41-61-688.0254, Fax: + 41-61-688.1929, E-mail: klaus.lindpaintner@roche.com References 1. Dickins M, Tucker G: Drug disposition: to phenotype or genotype. Int J Pharm Med 2001; 15:70 – 3. Also see: http:// www.imm.ki.se/CYPalleles/ 2. Evans WE, Relling MV. Pharmacogenomics: translating functional genomics into rational therapies. Science 1999; 206:487 – 91. Also see: http://www.sciencemag.org/feature/ data/1044449.shl/ 3. Fellay J, Marzolini C, Meaden ER, Back DJ, Buclin T, Chave JP, et al. Response to antiretroviral treatment in HIV-1-infected individuals with allelic variants of the multidrug resistance transporter 1: a pharmacogenetics study. Lancet 2002; 359:30 – 6. 4. Dubinsky M, Lamothe S, Yang HY, Targan SR, Sinnett D, Theoret Y, et al. Pharmacogenomics and metabolite measurement for 6-mercaptopurine therapy in inflammatory bowel disease. Gastroenterology 2000; 118:705 – 13. 5. Martinez FD, Graves PE, Baldini M, Solomon S, Erickson R. Association between genetic polymorphisms of the beta 2-adrenoceptor and response to albuterol in children with and without a history of wheezing. J Clin Invest 1997; 100:3184 – 8. 6. Tan S, Hall IP, Dewar J, Dow E, Lipworth B. Association between beta 2-adrenoceptor polymorphism and susceptibility to bronchodilator desensitisation in moderately severe stable asthmatics. Lancet 1997; 350:995 – 9. 7. Green SA, Turki J, Innis M, Liggett SB. Amino-terminal polymorphisms of the human beta 2-adrenergic receptor impart distinct agonist-promoted regulatory properties. Biochemistry 1994; 33:9414 – 9. 8. Green SA, Turki J, Bejarano P, Hall IP, Liggett SB. Influence of beta 2-adrenergic receptor genotypes on signal transduction in human airway smooth muscle cells. Am J Respir Cell Mol Biol 1995; 13:25 – 33. 9. Reihsaus E, Innis M, MacIntyre N and Liggett SB. Mutations in the gene encoding for the beta 2-adrenergic receptor in normal and asthmatic subjects. Am J Respir Cell Mol Biol 1993; 8:334 – 49. 10. Dewar JC, Wheatley AP, Venn A, Morrison JFJ, Britton J, Hall IP. Beta2 adrenoceptor polymorphisms are in linkage disequilibrium, but are not associated with asthma in an adult population. Clin Exp All 1998; 28:442 – 8. 11. Drazen JM, Yandava CN, Dube L, Szczerback N, Hippensteel R, Pillari A, et al. Pharmacogenetic association between ALOX5 promoter genotype and the response to anti-asthma treatment. Nat Genet 1999; 22:168 – 70. 12. In KH, Asano K, Beier D, Grobholz J, Finn PW, Silverman EK, et al. Naturally occurring mutations in the human 5lipoxygenase gene promoter that modify transcription factor binding and reporter gene transcription. J Clin Invest 1997 1; 99:1130 – 7. 13. Fischel-Ghodsian N. Genetic factors in aminoglycoside toxicity. Ann NY Acad Sci 1999; 884:99 – 109. 14. Hutchin T, Cortopassi G. Proposed molecular and cellular mechanism for aminoglycoside ototoxicity. Antimicrob Agents Chemother 1994; 38:2517 – 20. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Clinical Chemistry and Laboratory Medicine (CCLM) de Gruyter

Pharmacogenetics and Pharmacogenomics in Drug Discovery and Development: An Overview

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de Gruyter
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1434-6621
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10.1515/CCLM.2003.063
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Abstract

Introduction The advances made over the last 30 years in molecular biology, molecular genetics, and genomics, and the development and refinement of associated methods and technologies have had a major impact on our understanding of biology, including the action of drugs and other biologically active xenobiotics. The tools that have been developed to allow these advances, and the knowledge of fundamental principles underlying cellular function thus derived, have become quintessential, and indeed indispensable, for almost any field of biological research, including biomedicine and health care. One aspect of biology in particular, namely our understanding of genetics, and, especially, our cataloguing of genome sequences, has uniquely captured the imagination of both scientists and the public. This is quite understandable, given the austere beauty of Mendel’s laws of inheritance, the compelling esthetics of the double helix structure, the awe-inspiring accomplishment of cataloguing billions of base pairs, and, *E-mail of the corresponding author: klaus.lindpaintner@roche.com last but not least, the public relations campaign unprecedented in its scope in the history of scientific achievement. However, high expectations regarding the degree and timeframe of impact that these technologies will have on the practice of health care are almost certainly unrealistic. Situated at the interface between pharmacology and genetics/genomics, “pharmacogenetics and pharmacogenomics” (usually without any further definition what these terms mean) are commonly touted as heralding a “revolution” in medicine. It is important to realize that, with regard to pharmacology and drug discovery, accomplishments in basic biology, starting sometime in the last third of the past century, have already led to what may well be considered a rather fundamental shift from the “chemical paradigm” to the “biological paradigm”: historically, drug discovery was driven by medicinal chemistry, with biology serving an almost secondary, ancillary role that examined new molecules for biological function. The ability to comprehend cell biology and function, based on a newly developed set of tools to investigate the physiological effects of biomolecules and pathways on their molecular level, has since reversed this directionality: the biologist now drives the process, requesting from the chemist compounds that modulate the function of these biomolecules or pathways, with the expectation of a more predictable impact on physiological function and the correction of its pathological derailments. As pointed out above, the major change in how we discover drugs from the chemical to the biological paradigm already occurred some time ago; what the current advances, in due time, promise to allow us to do is to move from a physiology-based to a (molecular) pathology-based approach towards drug discovery, thus promising the advancement from a largely palliative to a more cause/contribution-targeting pharmacopoeia. This communication is intended to provide a necessarily somewhat subjective view of what the disciplines of genetics and genomics stand to contribute, and how they have already contributed over many years, to drug discovery and development, and more broadly to the practice of health care. Particular emphasis will be placed on examining the role of genetics – acquired or inherited variations at the level of DNA-encoded information – in “real life”, i.e., with regard to common complex disease; a realistic understanding of this role is absolutely essential for a balanced assessment of the impact of “genetics” on health care in the future. Definitions of some of the terms that are in wide-use today – almost always sorely missing from both acade- Lindpaintner: Pharmacogenetics and pharmacogenomics in drug discovery and development mic and public policy-related documents on the topic – will be provided, with an understanding that much of the field is still in flux, and that these may change. Particular emphasis will be given to pharmacogenetics, where a more systematic classification than generally found will be attempted. It is important to remain mindful that what will be discussed is to a large extent still uncharted territoryso, of necessity, many of the positions taken, reasoned on today’s understanding and knowledge, must be viewed as somewhat speculative. Where appropriate and possible, select examples will be provided, although it should be pointed out that much of the literature in the area of genetic epidemiology and pharmacogenetics lacks the stringent standards normally applied to peer-reviewed research, and replicate data are generally absent. Definition of Terms There is widespread indiscriminate use of, and thus confusion about, the terms “pharmacogenetics” and “pharmacogenomics”. While no universally accepted definition exists, there is an emerging consensus on the differential meaning and use of the two terms (see Table 1). Pharmacogenetics The term “genetics” relates etymologically to the presence of individual properties, and inter-individual differences in these properties, as a consequence of having inherited (or acquired) them. Thus, the term pharmacogenetics describes the interactions between a drug and an individual’s (or perhaps more accurately: groups’ of individuals) characteristics as they relate to differences in the DNA-based information. Pharmacogenetics, therefore, refers to the assessment of clinical efficacy and/or the safety and tolerability profile – the pharmacological, or reponse-phenotype – of a drug in groups of individuals who differ with regard to certain DNA-encoded characteristics, and tests the hypothesis that these differences, if indeed associated with a differential response-phenotype, may allow prediction of individual drug response. The DNA-encoded characteristics are most commonly assessed on the basis of the presence or absence of polymorphisms at the level of the nuclear DNA, but may be assessed at different levels where such DNA variation translates into different characteristics, such as differential mRNA expression or splicing, protein levels or functional characteristics, or even physiological phenotypes – all of which would be seen as surrogate, or more integrated markers, of the underlying genetic variant. It should be noted that some authors continue to subsume all applications of expression profiling under the term “pharmacogenomics”, in a definition of the terms that is more driven by the technology used rather than by functional context. Pharmacogenomics In contrast to the above, the terms pharmacogenomics, and its close relative, toxicogenomics, are etymologically linked to “genomics”, the study of the genome and of the entirety of expressed and non-expressed genes in any given physiologic state. These two fields of study are concerned with a comprehensive, genomewide assessment of the effects of pharmacological agents, including toxins/toxicants, on gene expression patterns. Pharmacogenomic studies are thus used to evaluate the differential effects of a number of chemical compounds, in the process of drug discovery commonly applied to lead selection, with regard to inducing or suppressing the expression of transcription of genes in an experimental setting. Except for situations in which pharmacogenetic considerations are “frontloaded” into the discovery process, inter-individual variations in gene sequence are not usually taken into account in this process. In contrast to pharmacogenetics, pharmacogenomics therefore does not focus on differences among individuals with regard to the drug’s effects but rather examines differences among several (prospective) drugs or compounds with regard to their biological effects using a “generic” set of expressed or non-expressed genes. The basis of comparison are quantitative measures of expression, using a number of more or less comprehensive gene-expression-profiling methods, commonly based on microarray formats. By extrapolation from the experimental results to theoretically desirable patterns of activation or inactivation of expression of genes in the setting of integrative pathophysiology, this approach is hoped to provide a faster, more comprehensive, and perhaps even more reliable way to assess the likelihood of finding an ultimately successful drug than previously available schemes involving mostly in vivo animal experimentation. Thus, although both pharmacogenetics and pharmacogenomics refer to the evaluation of drug effects using (primarily) nucleic acid markers and technology, Table 1 Terminology. • Pharmacogenetics – Differential effects of a drug – in vivo – in different patients, dependent on the presence of inherited gene variants – Assessed primarily genetic (SNP) and genomic (expression) approaches – A concept to provide more patient-/disease-specific health care – One drug – many genomes (i.e., differnt patients) – Focus: patient variability • Pharmacogenomics: – Differential effects of compounds – in vivo or in vitro – on gene expression, among the entirety of expressed genes – Assessed by expression profiling – A tool for compound selection/drug discovery – Many “drugs” (i.e., early-stage compounds) – one genome (i.e., “normative” genome [database, technology platform]) – Focus: compound variability Lindpaintner: Pharmacogenetics and pharmacogenomics in drug discovery and development the directionalities of their approaches are distinctly different: pharmacogenetics represents the study of differences among a number of individuals with regard to clinical response to a particular drug (“one drug, many genomes”), whereas pharmacogenomics represents the study of differences among a number of compounds with regard to gene expression response in a single (normative) genome/expressome (“many drugs, one genome”). Accordingly, the fields of intended use are distinct: the former will help in the clinical setting to find the medicine most likely to be optimal for a patient (or the patients most likely to respond to a drug), the latter will aid in the setting of pharmaceutical research to find the “best” drug candidate from a given series of compounds under evaluation. Pharmacogenomics: Discovering New Medicines Quicker and more Efficiently Once a screen (assay) has been set up in a drug discovery project and lead compounds are identified, the major task becomes the identification of an optimized clinical candidate molecule among the many compounds synthesized by medicinal chemists. Conventionally, such compounds are screened in a number of animal or cell models for efficacy and toxicity, experiments that, while having the advantage of being conducted in the in vivo setting, commonly take significant amounts of time and depend entirely on the validity of the model, i.e., the similarity between the experimental animal condition/setting and its human counterpart. Although such experiments will never be entirely replaced by expression profiling on either the nucleic acid (genomics) or the protein (proteomics) level, these techniques offer powerful advantages and complimentary information. First, efficacy and profile of induced changes can be assessed in a comprehensive fashion (within the limitations – primarily sensitivity and completeness of transcript representation – of the technology platform used). Second, these assessments of differential efficacy can be carried out much more expeditiously than in conventionally used, (patho-) physiology-based animal models. Third, the complex pattern of expression changes revealed by such experiments may provide new insights into possible biological interactions between the actual drug target and other biomolecules, and thus reveal new elements, or branch-points, of a biological pathway that may be useful as surrogate markers, novel diagnostic analytes, or as additional drug targets. Fourth, and increasingly important is that these tools serve to determine specificity of action among members of gene families that may be highly important for both efficacy and safety of a new drug. It must be borne in mind that any and all such experiments are limited by the coefficient of correlation with which the determined expression patterns are linked to the desired in vivo physiological action of the compound. A word of caution regarding microarray-based expression profiling would appear to be in order: the power of comprehensive (almost) genome-wide assessment of expression patterns has led to what may justly be described as something of an infatuation with this technology that at times leaves a degree of critical skepticism to be desired. In particular, the pair-wise comparison algorithms used in much of this work (competition staining of a case and a control sample on the same physical array) raise a number of questions regarding selection bias. These take on particular significance since the overall sample sizes are commonly (very) small. Biostatistical analytical approaches, if at all used, are commonly less than sophisticated. Additionally, it is important to remain aware of the fact that all microarray expression data are of only associative character and must be interpreted mindful of this limitation. As a subcategory of this approach, toxicogenomics is increasingly evolving as a powerful adjuvant to classic toxicological testing. As pertinent databases are being created from experiments with known toxicants, revealing expression patterns that may potentially be predictive of longer-term toxic liabilities of compounds, future drug discovery efforts should benefit by insights allowing earlier “killing” of compounds likely to cause such complications. If these approaches are used in drug discovery, even if implemented with proper biostatistics and analytical rigor, it is imperative to understand the probabilistic nature of such experiments: a promising profile on pharmacogenomic and toxicogenomic screening will enhance the likelihood of having selected an ultimately successful compound and will achieve this goal quicker than conventional animal experimentation, but will do so only with a certain likelihood of success. The less reductionist approach of the animal experiment will still be needed. It is to be anticipated, however, that such approaches will constitute an important, timeand resource-saving first evaluation or screening step, which will help to focus, and reduce the number of, the animal experiments that will ultimately need to be conducted. Pharmacogenetics: More Targeted, more Effective Medicines Genes and environment It is common knowledge that today’s pharmacopeia – in as much as it represents enormous progress compared with what physicians had only 15 or 20 years ago – is far from being perfect. Many patients respond only partially, or fail to respond altogether, to the drugs they are given, and others suffer adverse events that range form unpleasant to serious and life-threatening. There is an emerging consensus that all common complex diseases, i.e., the health problems that are by a large margin the main contributors to society’s disease burden as well as to public and private health spending, are multifactorial in nature, i.e., that they are brought upon by the coincidence of certain intrinsic (in- Lindpaintner: Pharmacogenetics and pharmacogenomics in drug discovery and development born or acquired) predispositions and susceptibilities on the one hand, and extrinsic, environment-derived influences on the other, with the relative importance of these two influences varying across a broad spectrum. In some diseases, external factors appear to be more important, while in others, intrinsic predispositions prevail. In the great majority, a number of both intrinsic (genetic) as well as extrinsic factors appear to contribute. Such complex causation exists in any one individual with the disease, with regard to the necessary coincidence of, as we assume today, several inherent predisposing susceptibility traits and commonly more than one environmental or lifestyle risk factor. This complexity is further accentuated by the fact that any one clinical diagnosis is bound to be etiologically heterogeneous at the level of molecular pathology. Thus, consensus exists that the same conventional clinical diagnosis given to different individuals is quite likely to reflect the outcome of different constellations of inborn susceptibility factors and/or of environmental and lifestyle-related risks. So, we may expect that both on the level of an individual patient, and even more so on the public health level, the disease-causing (or better: contributing) role that intrinsic, genetically encoded properties play with regard to the likelihood that disease occurs will be, by and large, quite modest. In common, complex diseases, the contribution of genetic factors is dramatically different from their effect in the rare, classic, monogenic, “Mendelian” diseases; while in the latter case, the impact of the genetic variant is typically categorical in nature, i.e., deterministic, in the former case, the presence of a disease-associated genetic variant is merely of probabilistic value, raising (or lowering) the likelihood of disease occurrence to some extent, but never predicting it in a black-and-white fashion. If we regard a pharmacological agent as an extrinsic, environmental factor with a potential to affect the health status of the individual to whom it is administered, then individually differing responses to such an agent would – under the paradigm just elaborated upon – be expected to be based on differences regarding the “intrinsic” characteristics of these patients, as long as we can exclude variation in the exposure to the drug (this is important, as in clinical practice non-adherence to prescribed regimens of administration, or drug-drug interactions interfering with bioavailability of the drug, are by far the most likely culprits when such differences in response-phenotype are observed). The influence of such intrinsic variation on drug response may be predicted to be more easily recognizable and more relevant the steeper the dose-response curve of a given drug is. The argument for the particular likelihood of observing environmental factor/gene interactions with drugs among all other “environmental influences” goes along the same lines. Among all these “environmental factors” that we are exposed to, drugs might be particularly likely to “interact” specifically and selectively with the genetic properties of a given individual, as their potency and – compared, say, to foodstuffs – nar- row therapeutic window make interactions with innate individual susceptibilities that affect the interaction with drugs more likely. Clearly, a better, more fundamental and mechanistic understanding of the molecular pathology of disease in general and of the role of intrinsic, biological properties regarding the predisposition to contract such diseases, as well as of drug action on the molecular level, will be essential for future progress in health care. Current progress in molecular biology and genetics has indeed provided us with some of the prerequisite tools that should help us reaching the goal of such more refined understanding. An Attempt at a Systematic Classification of Pharmacogenetics Two conceptually quite different scenarios of inter-individually differential drug response may be distinguished on the basis of the underlying biological variance (see Table 2): i. In the first case, the underlying biological variation is in itself not disease-causing or -contributing, and becomes clinically relevant only in response to the exposure to the drug in question (“classical pharmacogenetics”). ii. In the second case, the biological variation is directly disease-related, is per se of pathological importance, and represents a subgroup of the overall clinical disease/diagnostic entity. The differential response to a drug is thus related to how well this drug addresses, or is matched to, the presence or relative importance of the pathomechanism it targets in different patients, i.e., the “molecular differential diagnosis” of the patient (“disease-mechanism-related pharmacogenetics”). Although these two scenarios are conceptually rather different, they result in similar practical consequences Table 2 Pharmacogenetics. Systematic classification. • “Classical” pharmacogenetics – Pharmacokinetics Absorption Metabolism Activation of prodrugs De-activation Generation of biologically active metabolites Distribution Elimination – Pharmacodynamics Palliative drug action (modulation of diseasesymptoms or disease-signs by targeting physiologically relevant systems, without addressing those mechanism that cause or causally contribute to the disease) • “Molecular differential-diagnosis-related” Pharmacogenetics Causative drug action (modulation of actual causative of contributory mechanisms) Lindpaintner: Pharmacogenetics and pharmacogenomics in drug discovery and development with regard to the administration of a drug, namely stratification based on a particular, DNA-encoded marker. It seems therefore legitimate to subsume both under the umbrella of “pharmacogenetics”. “Classical pharmacogenetics” This category includes differential pharmacokinetics and pharmacodynamics. Pharmacokinetic effects are due to inter-individual differences in absorption, distribution, metabolism (with regard to both activation of pro-drugs, inactivation of the active molecule, and generation of derivative molecules with biological activity), or excretion of the drug. In any of these cases, differential effects observed are due to the presence at the intended site of action either of inappropriate concentrations of the pharmaceutical agent, or of inappropriate metabolites, Table 3 Pharmacogenetics: chronology and systematics. Date described ca. 1890 1957 – 60 1958 1957 – 60 1959 – 60 1960 – 62 1963 1969 1969 1977 1970 1980 1984 1988 or of both, resulting either in lack of efficacy or toxic effects. Pharmacogenetics, as it relates to pharmacokinetics, has been recognized as an entity for more than 100 years, going back to the observation, commonly credited to Archibald Garrod, that a subset of psychiatric patients treated with the hypnotic drug, sulphonal, developed porphyria. We have since come to understand the underlying genetic causes for many of the previously known differences in enzymatic activity, most prominently with regard to the P450 enzyme family, and these have been the subject of recent reviews (1, 2; Table 3). However, such pharmacokinetic effects are also seen with membrane transporters, such as in the case of differential activity of genetic variants of MDR-1 that affects the effective intracellular concentration of antiretrovirals (3), or of the purineanalogue-metabolizing enzyme, thiomethyl purine transferase (4). Pharmacogenetic phenotype Sulfonal-porphyria Suxamethonium hypersensitivity Primaquine hypersensitivity; favism Long QT-Syndrome Isoniazid slow/fast acetylation Malignant hyperthermia Fructose intolerance Vasopressin insensitivity Alcohol susceptibility Debrisoquine hypersensitivity Retinoic acid resistance 6-Mercaptopurine-toxicity Mephenytoin resistance Insulin resistance Underlying gene/mutation Porphobilinogen-deaminase? Pseudocholinesterase G-6-PD Herg etc. N-Acetyltranferase Ryanodine receptor Aldolase B Vasopressin receptor2 Aldehyde dehydrogenase CYP2D6 PML-RARA fusion gene Thiopurine methyltransferase CYP2C19 Insulin receptor Identified 1985 1990 – 92 1988 1991 – 97 1989 – 93 1991 – 97 1988 – 95 1992 1988 1988 – 93 1991 – 93 1995 1993 – 94 1988 – 93 Phase I enzyme Aldehyde dehydrogenase Alcohol dehydrogenase CYP1A2 CYP2A6 CYP2C9 CYP2C19 CYP2D6 CYP2E1 CYP3A4 CYP3A5 Serum cholinesterase Paraoxonase/arylesterase Phase II enzyme Acetyltransferase (NAT1) Acetyltransferase (NAT2) Dihydropyrimidine dehydrogenase Glutathione transferase (GST-M1) Thiomethyl transferase Thiopurine methyltransferase UDP-glucuronyl transferase (UGT1A) UDP-glucuronyl transferase (UGT2B7) Testing substance Acetaldehyde Ethanol Caffeine Nicotine, coumarin Warfarin Mephenytoin, omeprazole Dextromethorphan, debrisoquine, sparteine Chloroxazone, caffeine Erythromycin Midazolam Benzoylcholine, butyrylcholine Paraoxon Testing substance Para-aminosalicylic acid Isoniazid, sulfamethazine, caffeine 5-Fluorouracil trans-Stilbene-oxide 2-Mercaptoethanol, D-penicillamine, captopril 6-Mercaptopurine, 6-thioguanine, 8-azathioprine Bilirubin Oxazepam, ketoprofen, estradiol, morphine Lindpaintner: Pharmacogenetics and pharmacogenomics in drug discovery and development Notably, despite the widespread recognition of isoenzymes with differential metabolizing potential since the middle of the 20th century, the practical application and implementation of this knowledge has been minimal so far. This may be the consequence, on the one hand, of the irrelevance of such differences in the presence of relatively flat dose-effect curves (i.e., a sufficiently wide therapeutic window), and on the other, the fact that many drugs are subject to complex, parallel metabolizing pathways, where in the case of underperformance of one enzyme another one may compensate. Such compensatory pathways may well have somewhat different substrate affinities but allow plasma levels to remain within therapeutic concentrations. Thus, the number of such polymorphisms that have found practical applicability is rather limited and, by and large, restricted to determinations of the presence of functionally deficient variants of the enzyme thiopurinemethyltransferase in patients prior to treatment with purine-analogue chemotherapeutics. Pharmacodynamic effects, in contrast, may lead to inter-individual differences in a drug’s effects despite the presence of appropriate concentrations of the intended active (or activated) drug compound at the intended site of action. Here, DNA-based variation in how the target molecule, or another (downstream) member of the target molecule’s mechanistic pathway, can respond to the medicine modulates the effects of the drug. This will apply primarily to palliatively working medicines that improve a condition symptomatically by modulating disease-phenotype-relevant (but not disease-cause-relevant) pathways that are not dysfunctional but can be used to counterbalance the effect of a dysfunctional, disease-causing pathway, and therefore allow mitigation of symptoms. A classical example of such an approach is the acute treatment of thyrotoxicity with β-adrenergic blocking agents: even though the sympathetic nervous system in this case does not contribute causally to tachycardia and hypertension, dampening even its baseline tonus through this class of rapidly acting drugs can quickly and successfully relieve the cardiovascular symptoms and signs of this condition, and may well prevent a heart attack if the patient has underlying coronary disease, before the causal treatment (in this case available through partial chemical ablation of the hyperactive thyroid gland) can take effect. Notably, the majority of today’s pharmacopeia actually belongs to this class of palliatively acting medicines. A schematic (Figure 1) is provided to help to clarify these somewhat complex concepts, in which a hypothetical case of a complex trait/disease is depicted where excessive, dysregulated function of one of the trait-controlling/contributing pathways (Figure 1A and B) causes symptomatic disease – the example used refers to blood pressure as the trait, and hypertension as the disease in question, respectively (for the case of a defective or diminished function of a pathway, an analogous schematic could be constructed, and again for a deviant function). A palliative treatment would be one that addresses one of the pathways that – while not Figure 1 Modeling assumptions underlying pharmacogenetics classification. A: Normal physiology: 3 molecular mechanisms (M1, M2, M3) contribute to a trait. B: Diseased physiology D1: derailment (cause/contribution) of molecular mechanism 1 (M1). C: Diseased physiology D1: causal treatment T1 (aimed at M1). D: Diseased physiology D3: derailment (cause/contribution) of molecular mechanism 3 (M3). E: Diseased physiology D3, treatment T1: treatment does not address cause. F: Diseased physiology D1, palliative treatment T2 (aimed at M2). G: Diseased physiology D1, palliative treatment T2; T2-refractory gene variant in M2. H: Normal physiology variant: differential contribution of M1 and M2 to normal trait. I: Diseased physiology D1-variant: derailment of mechanism M1. J: Diseased physiology D1-variant: treatment with T2. Solid colors indicate normal function, stippling indicates pathologic dysfunction, hatching indicates therapeutic modulation. dysregulated – contributes to the overall deviant physiology (Figure 1F), while the respective pharmacogenetic-pharmacodynamic scenario would occur if this particular pathway was, due to a genetic variant, not responsive to the chosen drug (Figure 1G). A palliative treatment may also be ineffective if the particular mechanism targeted by the palliative drug, due to the presence of a molecular variant, provides less than the physiologically expected baseline contribution to the relevant phenotype (Figure 1H). In such a case, modulating an a priori unimportant pathway in the disease scenario will not yield successful palliative treatment results (Figure 1I and J). Several of the most persuasive examples we have accumulated to date for such palliative-drug-related pharmacogenetic effects have been observed in the field of asthma. The treatment of asthma relies on an array of drugs aimed at modulating different “generic” pathways, thus mediating bronchodilation or anti-inflammatory effects, often without regard to the possible causative contribution of the targeted mechanism to the disease. One of the mainstays of the treatment of asthma is activation of the β2-adrenergic receptor by specific agonists, which leads to relaxation of bronchial smooth muscles and, consequently, bronchodilation. Recently, several molecular variants of the β2-adrenoceptor have been shown to be associated with differential treatment response to such β2-agonists (5, 6). Individuals carrying one or two copies of a variant allele that contains a glycine in place of arginine in position Lindpaintner: Pharmacogenetics and pharmacogenomics in drug discovery and development 16 were found to have a 3- and 5-fold reduced response to the agonist, respectively. This was shown in both in vitro (7, 8) and in vivo (8) studies to correlate with an enhanced rate of agonist-induced receptor down-regulation, but not with any difference in transcriptional or translational activity of the gene, or with agonist binding. In contrast, a second polymorphism affecting position 19 of the β-upstream peptide was shown to affect translation (but not transcription) of the receptor itself, with a 50% decrease in receptor numbers associated with the variant allele – which happens to be in strong linkage disequilibrium with a variant allele at position 16 in the receptor. The simultaneous presence of both mutations would thus be predicted to result in low expression and enhanced down-regulation of an otherwise functionally normal receptor, depriving patients carrying such alleles of the benefits of effective bronchodilation as a “palliative” (i.e., non-causal) countermeasure affecting their pathological airway hyper-reactivity. Importantly, there is no evidence that any of the allelic variants encountered are associated with the prevalence or incidence, and thus potentially the etiology of the underlying disease (9, 10). This would reflect the scenario depicted in Figure 1H. Inhibition of leukotriene synthesis, another palliative approach toward the treatment of asthma, proved clinically ineffective in a small fraction of patients who carried only non-wild-type alleles of the 5-lipoxygenase promoter region (11). These allelic variants had previously been shown to be associated with decreased transcriptional activity of the gene (12). It stands to reason – consistent with the clinical observations – that in the presence of already reduced 5-lipoxygenase activity, pharmacological inhibition may be less effective (Figure 1H, I, J). Of note, again, is that there is no evidence for a primary, disease-causing or -contributing role of any 5-lipoxygenase variants; all of them were observed at equal frequencies in disease-affected and non-affected individuals (12). Pharmacogenetic effects may not only account for differential efficacy but also contribute to differential occurrence of adverse effects. An example for this scenario is provided by the well-documented “pharmacogenetic” association between molecular sequence variants of the 12S rRNA, a mitochondrion-encoded gene, and aminoglycoside-induced ototoxicity (13). Intriguingly, the mutation that is associated with susceptibility to ototoxicity renders the sequence of the human 12S rRNA similar to that of the bacterial 12S rRNA gene, and thus effectively turns the human 12S rRNA into the (bacterial) target for aminoglycoside drug action – presumably mimicking the structure of the bacterial binding site of the drug (14). As in the other examples, presence of the 12S rRNA mutation per se has no primary, drug treatment-independent pathologic effect. One may speculate that, analogously, such molecular mimicry may occur within one species: adverse events may arise if the selectivity of a drug is lost because a gene that belongs to the same gene family as the primary target, loses its “identity” vis-à -vis the drug and attains, based on its structural similarity to the principal target, similar to, or increased, affinity to the drug. Depending on the biological role of the “imposter” molecule, adverse events may occur – even though the variant molecule, again, may be quite silent with regard to contribution to disease causation. Although we currently have no obvious examples of this scenario, it is certainly imaginable for various classes of receptors and enzymes. Pharmacogenetics as a consequence of molecular differential diagnosis As alluded to earlier, there is general agreement today that any of the major clinical diagnoses in the field of common complex disease, such as diabetes, hypertension, or cancer, or others, are comprised of a number of etiologically (i.e., at the molecular level) more or less distinct subentities. In the case of a causally acting drug this may imply that the agent will only be appropriate, or will work best, in that fraction of all patients who carry the (all-inclusive and imprecise) clinical diagnosis in whom the dominant molecular etiology, or at least one of the contributing etiological factors, matches the biological mechanism of action that the drug in question modulates (Figure 1C). If the mechanism of action of the drug addresses a pathway that is not disease-relevant – perhaps already down-regulated as an appropriate physiologic response to the disease – then the drug may, logically, be expected no to show efficacy (Figure 1D, E). Thus, unrecognized and undiagnosed disease heterogeneity, disclosed indirectly by the presence or absence of response to a drug targeting a mechanism that contributes only to one of several molecular subgroups of the disease, provides an important explanation for differential drug response and likely represents a substantial fraction of what we today somewhat indiscriminately subsume under the term “pharmacogenetics”. Currently, the most frequently cited example for this category of pharmacogenetics is trastuzamab (Herceptin®), a humanized monoclonal antibody directed against the her-2-oncogene. This breast cancer treatment is prescribed based on the level of her-2-oncogene expression in the patient’s tumor tissue. Differential diagnosis at the molecular level not only provides an added level of diagnostic sophistication but also actually represents the prerequisite for choosing the appropriate therapy. Because tastuzamab specifically inhibits a “gain-of-function” variant of the oncogene, it is ineffective in the 2/3 of patients who do not overexpress the drug’s target, whereas it significantly improves survival in the 1/3 of patients that constitute the subentity within the broader diagnosis “breast cancer” and who express the gene at abnormally high levels (15). Some have argued against this being an example of pharmacogenetics, because the parameter for patient stratification (i.e., for differential diagnosis) is the somatic gene expression level rather than a particular “genotype” data (16). This is a difficult argument to follow, since in the case of a treatment-effect-modifying germ Lindpaintner: Pharmacogenetics and pharmacogenomics in drug discovery and development line mutation it would obviously not be the nuclear gene variant per se, but also its specific impact on either structure/function, or on expression of the respective gene/gene product, that would represent the actual physiological corollary underlying the differential drug action. Conversely, an a priori observed expression difference is highly likely to reflect a – as yet undiscovered – sequence variant. Indeed, as pointed out earlier, there are a number of examples in the field of pharmacogenomics where the connection between genotypic variant and altered expression has already been demonstrated (12, 17). Another example of how proper molecular diagnosis of relevant pathomechanisms will significantly influence drug efficacy, although still hypothetical, is in the evolving class of anti-AIDS/HIV drugs that target the CCR5 cell-surface receptor (18 – 20). These drugs would be predicted to be ineffective in those rare patients who carry the ∆-32 variant, but who nevertheless have contracted AIDS or test HIV-positive (most likely due to infection with an SI-virus phenotype that utilizes CXCR4) (21, 22). It should be noted that the pharmacogenetically relevant molecular variant needs not affect the primary drug target but may equally well be located in another molecule belonging to the system or pathway in question, both up- or downstream in the biological cascade with respect to the primary drug target. Different classes of markers Pharmacogenetic phenomena, as pointed out previously, need not be restricted to the observation of a direct association between allelic sequence variation and phenotype but may extend to a broad variety of indirect manifestations of underlying, but often (as yet) unrecognized sequence variation. Thus, differential methylation of the promoter region of O6-methylguanine-DNA methylase has recently been reported to be associated with differential efficacy of chemotherapy with alkylating agents. If methylation is present, the expression of the enzyme that rapidly reverses alkylation and induces drug resistance is inhibited, and therapeutic efficacy is greatly enhanced (23). Complexity is to be expected In the real world, it is likely that not only one of the scenarios depicted, but a combination of several ones, may affect how well a patient responds to a given treatment, or how likely it is that he or she will suffer an adverse event. Thus, a fast-metabolizing patient with poor-responder pharmacodynamics may be particularly unlikely to gain any benefit from taking the drug in question, while a slow-metabolizing status may counterbalance in another patient the same inopportune pharmacodynamics, while a third patient, who is a slow metabolizer and displays normal pharmacodynamics, may be more likely to suffer adverse events. In all of them, both the pharmacokinetic and pharmacodynamic properties may result from the interaction of several of the mechanisms described above. In addi- tion, we know of course that co-administration of other drugs, or even the consumption of certain foods, may affect and further complicate the picture for any given treatment. Incorporating Pharmacogenetics into Drug Development Strategy Diagnostics first, therapeutics second It is important to note that despite the public hyperbole and the high-strung expectations surrounding the use of pharmacogenetics to provide “personalized care” these approaches are likely to be applicable only to a fraction of medicines that are being developed. Further, if and when such approaches will be used, they will represent no radical new direction or concept in drug development but simply a stratification strategy as we have been using it all along. An increasingly sophisticated and precise diagnosis of disease arising from a deeper, more differentiated understanding of pathology at the molecular level, that will increasingly subdivide today’s clinical diagnoses into molecular subtypes, will foster medical advances which, if considered from the viewpoint of today’s clinical diagnosis, will appear as “pharmacogenetic” phenomena, as described above. However, the sequence of events that is today often presented as characteristic for a “pharmacogenetic scenario” – namely, exposing patients to the drug, recognizing a differential (quasibimodal) response pattern, discovering a marker that predicts this response, and creating a diagnostic product to be co-marketed with the drug henceforth – is likely to be reversed. Rather, in the case of “pharmacogenetics”, due to a match between drug action and dysregulation of a disease-contributing mechanism, we will likely search for a new drug specifically, and a priori, based on a new mechanistic understanding of disease causation or contribution (i.e., a newly found ability to diagnose a molecular subentity of a previously more encompassing, broader, and less precise clinical disease definition). Thus, pharmacogenetics will not be so much about finding the “right medicine for the right patient” but about finding the “right medicine for the disease(-subtype)”, as we have aspired to do all along throughout the history of medical progress. This is, in fact, good news: the conventional “pharmacogenetic scenario” would invariably present major challenges from both a regulatory and a business development and marketing standpoint, as it would confront development teams with a critical change in the drug’s profile at a very late point during the development process. In addition, the timely development of an approvable diagnostic in this situation is difficult at best, and its marketing as an “add-on” to the drug a less than attractive proposition to the diagnostics business. Thus, the “practice” of pharmacogenetics will, in many instances, be marked by progress along the very same path that has been one of the main avenues of medical progress for the last several Lindpaintner: Pharmacogenetics and pharmacogenomics in drug discovery and development 10000 years: differential diagnosis first, followed by the development of appropriate, more specific treatment modalities. Thus, the sequence of events in this case may well involve, first, the development of an in vitro diagnostic test as a stand-alone product that may be marketed on its own merits, allowing the physician to establish an accurate, state of the art diagnosis of the molecular subtype of the patient’s disease. Sometimes such a diagnostic may prove helpful even in the absence of specific therapy by guiding the choice of existing medicines and/or of non-drug treatment modalities such as specific changes in diet or lifestyle. Availability of such a diagnostic – as part of the more sophisticated understanding of disease – will undoubtedly foster and stimulate the search for new, more specific drugs; and once such drugs are found, availability of the specific diagnostic will be important for carrying out the appropriate clinical trials. This will allow a prospectively planned, much more systematic approach towards clinical and business development, with a commensurate greater chance of actual realization and success. Probability, not certainty In practice, some extent of guesswork will remain, due to the nature of common complex disease. First, all diagnostic approaches – including those based on DNA analysis in common complex disease, as stressed above – will ultimately only provide a measure of probability, not of certainty: thus, although the variances of drug response among patients who do (or do not) carry the drug-specific sub-diagnosis will be smaller, there will still be a distribution of differential responses: although by and large the drug will work better in the “responder” group, there will be some patients among this subgroup, who will respond less or not at all, and conversely, not everyone belonging to the “non-responder” group will completely fail to respond, depending perhaps on the relative magnitude with which the particular mechanism contributes to the disease. It is important to bear in mind, therefore, that even in the case of fairly obvious bimodality, patient responses will still show distribution patterns, and that all predictions as to responder- or non-responder status will only have a certain likelihood of being accurate (Figure 2).The terms “responder” and “non-responder” as applied to groups of patients stratified on the basis of a DNA marker represent, therefore, Mendelian-thinking-inspired misnomers that should be replaced by more appropriate terms that reflect the probabilistic nature of any such classification, e.g., “likely (non-)responder”. In addition, based on our current understanding of the polygenic and heterogeneous nature of these disorders, we will – even in an ideal world where we would know about all possible susceptibility gene variants for a given disease and have treatments for them – only be able to exclude, in any one patient, those that do not appear to contribute to the disease, and therefore deselect certain treatments. We will, however, most likely find ourselves left with a small number – Figure 2 Hypothetical example of bimodal distribution according to marker that indicates “non-responder” or “responder” status. Note that in both cases a distribution is present, with overlaps. Thus, the categorization into “responders” or “non-responders” based on the marker must be understood to convey only the probability to belong to one or the other group. two to four, perhaps – of potentially disease-contributing gene variants whose relative contribution to the disease will be very difficult, if not impossible, to rank in an individual patient. Likely then, trial and error, and this great intangible quantity, “physician experience”, will still play an important role, albeit on a more limited and sub-selective basis. The alternative situation, where differential drug response and/or safety issues are a consequence of a pathologically not relevant, purely drug response-related pharmacogenetics scenario, is more likely to present greater difficulty in planning and executing a clinical development program because, presumably, it will be more difficult to anticipate or predict differential responses a priori. When such a differential response occurs, it will also potentially be more difficult to find the relevant marker(s), unless it happens to be among the “obvious” candidate genes implicated in the disease physiopathology or the treatment’s mode of action. Although screening for molecular variants of these genes and testing for their possible associations with differential drug response is a logical first step, if unsuccessful, it may be necessary to embark on an unbiased genomewide screen for such a marker or markers. Despite recent progress in high-throughput genotyping, the obstacles that will have to be overcome on the technical, data analysis, and cost levels are formidable. They will limit the deployment of such programs, at least for the foreseeable future, to select cases in which there are very solid indications for doing so, based on clinical data showing a near-categorical (e.g., bimodal) distribution of treatment outcomes. Even then, we may expect to encounter for every success, that will be owed to a favorably strong linkage disequilibrium across considerable genomic distance in the relevant chromosomal region, as many or more failures, in cases where the Lindpaintner: Pharmacogenetics and pharmacogenomics in drug discovery and development culpable gene variant cannot be found due to the higher recombination rate or other characteristics of the stretch of genome on which it is located. Regulatory Aspects At this writing, regulatory agencies in both Europe and the USA are beginning to show keen interest in the potential role that pharmacogenetics approaches may play in the development and clinical use of new drugs, and the potential challenges that such approaches may present to the regulatory approval process. While no formal guidelines have been issued, the pharmaceutical industry has already been reproached – albeit in a rather nonspecific manner – for not being more proactive in the use of pharmacogenetic markers. It will be of key importance for all concerned to engage in an intensive dialogue at the end of which – it is hoped – a joint understanding will emerge that stratification according to DNA-based markers is fundamentally nothing new and not different from stratification according to any other clinical or demographic parameter, as has been used all along. Still, based on the perception (in the case of common complex diseases scientifically unjustified) that DNAbased markers represent a different class of stratification parameters, a number of important questions will need to be addressed and answered – hopefully always in analogy to “conventional” stratification parameters, including those referring to ethical aspects. Among the most important ones are questions concerning: • the need and/or ethical justification (or lack thereof) to include likely non-responders in a trial for the sake of meeting safety criteria, which, given the restricted indication of the drug, may indeed be excessively broad; • the need to carry out conventional size safety trials in the disease stratum eligible for the drug (if the stratum represents a relatively small fraction of all patients with the clinical diagnosis, it may be difficult to amass sufficient numbers and/or discourage companies from pursuing such drugs to the disadvantage of patients); • the need to use active controls if the patient/disease stratum is different from that in which the active control was originally tested; • the strategies to develop and gain approval for the applicable first-generation diagnostic, as well as for the regulatory approval of subsequent generations of tests to be used to determine eligibility for prescription of the drug; as well as • a number of ethical-legal questions relating to the unique requirements regarding privacy and confidentiality for “genetic testing” that may raise novel problems with regard to regulatory audits of patient data (see below). A concerted effort to avoid what has been termed “genetic exceptionalism” – the differential treatment of DNA-based markers as compared with other personal medical data – should be made so as to not further unnecessarily complicate the already very difficult process of obtaining regulatory approval. This seems justified based on the recognized fact that in the field of common complex disease DNA-based markers are not at all different from “conventional” medical data in all relevant aspects – namely specificity, sensitivity, and predictive value. Pharmacogenetic Testing for Drug Efficacy vs. Safety Greater efficacy: likely In principle, pharmacogenetic approaches may be useful both to raise efficacy and to avoid adverse events, by stratifying patient eligibility for a drug according to appropriate markers. In both cases, clinical decisions and recommendations must be supported by data that have undergone rigorous biostatistical scrutiny. Based on the substantially different prerequisites for, and opportunities to, acquiring such data, and to applying them to clinical decision-making, we expect the use of pharmacogenetics for enhanced efficacy to be considerably more common than for the avoidance of adverse events. The likelihood that adequate data on efficacy in a subgroup may be generated is reasonably high, given the fact that, unless the drug is viable in a reasonably sizeable number of patients, it will probably not be developed for lack of a viable business case, or at least only under the protected environment of orphan drug guidelines. Implementation of pharmacogenetic testing to stratify for efficacy, provided that safety in the non-responder group is not an issue, will primarily be a matter of physician preference and sophistication, and potentially of third-party payer directives but would appear less likely to become a matter of regulatory mandate, unless a drug has been developed selectively in a particular stratum of the overall indication (in which case a contra-indication label for other strata is likely to be issued). Indeed, an argument can be made against depriving those who carry the “likely non-responder” genotype regarding eligibility for the drug, but who individually, of course, may respond to the drug with a certain, albeit lower, probability. From the regulatory point of view, the use of pharmacogenetics for efficacy, if adequate safety data exist, appears largely unproblematic – the worst-case scenario (a genotypically inappropriate patient receiving the drug) would result in treatment without expected beneficial effect but with no increased odds to suffer adverse consequences, i.e., much of what one would expect under conventional paradigms. Avoidance of serious adverse effects: less likely – with exceptions The utility and clinical application of pharmacogenetic approaches towards improving safety, in particular with regard to serious adverse events, will meet with considerably greater hurdles and is therefore less likely expected to become reality. A number of reasons are Lindpaintner: Pharmacogenetics and pharmacogenomics in drug discovery and development cited for this: first, in the event of serious adverse events associated with the use of a widely prescribed medicine, withdrawal of the drug from the market is usually based almost entirely on anecdotal evidence from a rather small number of cases – in accordance with the Hippocratic mandate “primum non nocere”. If the sample size is insufficient to demonstrate a statistically significant association between drug exposure and event, as is typically the case, it will most certainly be insufficient to allow meaningful testing for genotype-phenotype correlations; the biostatistical hurdles become progressively more difficult as many markers are tested and the number of degrees of freedom applicable to the analysis for association continues to rise. Therefore, the fraction of attributable risk shown to be associated with a given at-risk (combination of) genotype(s) would have to be very substantial for regulators to accept such data. Indeed, the low prior probability of the adverse event, by definition, can be expected to yield an equally low positive (or negative) predictive value. Second, the very nature of safety issues raises the hurdles substantially because in this situation the worst-case scenario – administration of the drug to the “wrong” patient – will result in higher odds to harm to the patient. Therefore, it is likely that the practical application of successfully investigating and applying pharmacogenetics towards limiting adverse events will likely be restricted to diseases with dire prognosis, where a high medical need exists, where the drug in question offers unique potential advantages (usually bearing the characteristics of a “life-saving” drug), and where, therefore, the tolerance even for relatively severe side effects is much greater than for other drugs. This applies primarily to areas like oncology or HIV/AIDS, for which the recently reported highly specific and acceptably sensitive association between the MHC gene variant, HLA B5701, and occurrence of a severe hypersensitivity reaction is a prime example (24, 25). In most other indications, the sobering biostatistical and regulatory considerations represent barriers that are unlikely to be overcome easily; and the proposed, conceptually highly attractive, routine deployment of pharmacogenetics as a generalized drug surveillance or pharmaco-vigilance practice following the introduction of a new pharmaceutical agent (19) faces these scientific as well as formidable economic hurdles. Ethical-Societal Aspects of Pharmacogenetics No discussion about the use of genetic/genomic approaches to health care can be complete without considering their impact on the ethical, societal, and legal level. Much of the discussion about ethical and legal issues relating to pharmacogenetics is centered on the issue of “genetic testing”, a topic that has recently also been the focus of a number of guidelines, advisories, white papers, etc. issued by a number of committees in both Europe and the USA. It is interesting to note that the one characteristic that virtually all these documents share is an almost studious avoidance of defining what exactly a “genetic test” is. Where definitions are given, they tend to be very broad, including not only the analysis of DNA but also of transcription and translation products affected by inherited variation. In as much as the most sensible solution to this dilemma will ultimately, hopefully, be a consensus to treat all personal medical data in a similar fashion regardless of the degree to which DNA-encoded information affects it (noting that there really is not any medical data that are not to some extent affected by intrinsic patient properties), it may, for the time being, be helpful to let the definition of what constitutes “genetic data” be guided by the public perception of “genetic data” – in as much as the whole discussion of this topic is prompted by these public perceptions. In the public eye, “genetic test” is usually understood either (i) as any kind of test that establishes the diagnosis of, or predisposition for one of the classic monogenic, heritable disease, or (ii) as any kind of test based on structural nucleic acid analysis (sequence). This includes the (non-DNA-based) Guthrie test for phenylketonuria and forensic and paternity testing, as well as a DNA-based test for lipoprotein (a) (Lp(a)), but not the plasmaprotein-based test for the same marker (even though the information derived is identical). Since monogenic disease is, in effect, excluded from this discussion, it stands to reason to restrict the definition of “genetic testing” to the analysis of (human) DNA sequence. Based on the – perceived – particular sensitivity of “genetic” data, institutional review boards commonly apply a specific set of rules to grant permission to test for DNA-based markers in the course of drug trials or other clinical research, including (variably) separate informed consent forms, the anonymization of samples and data, specific stipulations about availability of genetic counseling, provision to be able to withdraw samples at any time in the future, etc. Arguments have been advanced (26) that genotype determinations for pharmacogenetic characterization, in contrast to “genetic” testing for primary disease risk assessment, are less likely to raise potentially sensitive issues with regard to patient confidentiality, the misuse of genotyping data, or other nucleic acid-derived information, and the possibility of stigmatization. While this is certainly true when pharmacogenetic testing is compared to predictive genotyping for highly penetrant Mendelian disorders, it is not apparent why in common complex disorders, issues surrounding predictors of primary disease risk would be any more or less sensitive than those pertaining to predictors of likely treatment success/failure. Both can be expected to provide, in most cases, a modicum of better probabilistic assessment, based on the modest degree of sensitivity, specificity, and positive/negative predictive value we are likely to see with tests for pharmacogenetic interactions. If, however, misguided this would be given the anticipated quite limited information content of such tests, such information was to be used “against” the Lindpaintner: Pharmacogenetics and pharmacogenomics in drug discovery and development patient, then two lines of reasoning may actually indicate an increased potential for ethical questions and complex confrontations among the various stakeholders to arise from pharmacogenetic data. First, while access to genotyping and other nucleic acid-derived data related to disease susceptibility can be strictly limited, the very nature of pharmacogenetic data calls for a rather more liberal position regarding use: if this information is to serve its intended purpose, i.e., improving the patients chance for successful treatment, then it is essential that it is shared among at least a somewhat wider circle of participants in the health care process. Thus, the prescription for a drug that is limited to a group of patients with a particular genotype will inevitably disclose the receiving patient’s genotype to anyone of a large number of individuals involved in the patients care at the medical and administrative level. The only way to limit this quasi-public disclosure of this patient’s genotype data would be if he or she were to sacrifice the benefits of the indicated treatment for the sake of data confidentiality. Second, patients profiled to carry a high disease probability along with a high likelihood for treatment response may be viewed, from the standpoint of, e.g., insurance risk, as quite comparable to patients displaying the opposite profile, i.e., a low risk to develop the disease but a high likelihood not to respond to medical treatment, if the disease indeed occurs. For any given disease risk, then, patients less likely to respond to treatment would be seen as a more unfavorable insurance risk, particularly if non-responder status is associated with chronic, costly illness rather than with early mortality, the first case having much more far-reaching economic consequences. The pharmacogenetic profile may thus, under certain circumstances, even become a more important (financial) risk assessment parameter than primary disease susceptibility, and would be expected – in as much as it represents but one stone in the complex-disease mosaic – to be treated with similar weight, or lack thereof, as other genetic and environmental risk factors. Practically speaking, the critical issue is not only, and perhaps not even predominantly, the real or perceived sensitive nature of the information, and how it is, if at all, disseminated and disclosed, but how and to what end it is used. Obviously, generation and acquisition of personal medical information must always be contingent on the individual’s free choice and consent, as must be all application of such data for specific purposes. Beyond this, however, there is today an urgent need for the requisite dialogue and discourse among all stakeholders within society to develop and endorse a set of criteria by which the use of genetic, and indeed of all personal medical information should occur. It will be critically important that society as a whole endorses, in an act of solidarity with those less fortunate, i.e., at higher risk of developing disease, or less likely to respond to treatment, rules that guarantee the beneficial and legitimate use of the data in the patient’s interest while at the same time prohibiting their use in ways that may harm the individual, personally, financially, or otherwise. As long as we trust our political decision processes to reflect societal consensus, and as long as such consensus reflects the principles of justice and equality, the resulting set of principles should ensure such proper use of medical information. Indeed, both aspects – data protection and patient/subject protection – are seminal components of the mandates included in the WHO’s “Proposed International Guidelines on Ethical Issues in Medical Genetics and Genetic Services” (27) which mandate autonomy, beneficence, non-maleficence, and justice. The essential requirement to reach such a consensus that will allow the use of genetics and genomics in the best interest of all concerned is an informed dialogue among the various stakeholders – which can only begin to take place once (mis-)perceptions are replaced by objective and neutral information as the basis to from informed opinions. Progress in the fields of genetic and genomics has been rapid and substantial over the last few decades, and it has been accompanied by a great deal of hyperbole in the popular press. Meanwhile, geneticists have continued to cultivate an arcane and forbidding vernacular that adds only to the appearance of purposeful secrecy that the public is reacting to, instead of having made extra efforts to reach out to the public in a concerted educational campaign. As part of the Human Genome Project substantial amounts of funding have been provided to work in the area of bioethics and much progress has been achieved there. Similar or even greater efforts need to be undertaken in the area of public information and education – which will surely go a long way towards resolving some of the fears as well as unrealistic hopes the public currently associates with genetics and genomics. The author of this communication and his colleagues have assembled an interactive CD-ROM-based educational program that is distributed freely upon request (28). Conclusion Pharmacogenetics, in all its various manifestations, will represent an important new avenue towards understanding disease pathology and drug action, and will offer new opportunities of stratifying patients to achieve better treatment success. As such, it represents a logical, consequent step in the history of medicine – evolutionary, rather than revolutionary. Its implementation will take time, and will not apply to all diseases and all treatments equally. Pharmacogenetic information will be probabilistic and relative, not deterministic or absolute. It will provide help, but no simple solutions. It will require society to find ways to sanction the proper use of this information, thus allowing and protecting its unencumbered use for the benefit of patients. A more realistic assessment of its actual potential to provide benefit or cause harm will likely quell much of the exalted hopes and exaggerated fears that are so often associated with the topic. Lindpaintner: Pharmacogenetics and pharmacogenomics in drug discovery and development 15. Baselga J, Tripathy D, Mendelsohn J, Baughman S, Benz CC, Dantis L, et al. Phase II study of weekly intravenous recombinant humanized anti-p185(HER2) monoclonal antibody in patients with HER2/neu-overexpressing metastatic breast cancer. J Clin Oncol 1996; 14:737 – 44. 16. Haseltine WA. Not quite pharmacogenomics [letter; comment]. Nat Biotechnol 1998; 16:1295. 17. McGraw DW, Forbes SL, Kramer LA, Liggett SB. Polymorphisms of the 5’ leader cistron of the human beta2-adrenergic receptor regulate receptor expression. J Clin Invest 1998;1 02:1927 – 32. 18. Huang Y, Paxton WA, Wolinsky SM, Neumann AU, Zhang L, He T, et al. The role of a mutant CCR5 allele in HIV-1 transmission and disease progression. Nat Med 1996; 2: 1240 – 3. 19. Dean M, Carrington M, Winkler C, Huttley GA, Smith MW, Allikmets R, et al. Genetic restriction of HIV-1 infection and progression to AIDS by a deletion of the CKR5 structural gene. Science 1996; 273:1856 – 62. 20. Samson M, Libert F, Doranz BJ, Rucker J, Liesnard C, Farber CM, et al. Resistance to HIV-1 infection in Caucasian individuals bearing mutant alleles of the CCR-5 chemokine receptor gene. Nature 1996; 382:722 – 5. 21. O’Brien TR, Winkler C, Dean M, Nelson JAE, Carrington M, Michael NL, et al. HIV-1 infection in a man homozygous for CCR5 32. Lancet 1997; 349:1219. 22. Theodorou I, Meyer L, Magierowska M, Katlama C, Rouzious C, Seroco Study Group. HIV-1 infection in an individual homozygous for CCR5 32. Lancet 1997; 349: 1219 – 20. 23. Esteller M, Garcia-Foncillas J, Andion E, Goodman, SN, OF Hidalgo, Vanaclocha V, et al. Inactivation of the DNA-repair gene mgmt and the clinical response of gliomas to alkylating agents. N Engl J Med 2000; 343:1350 – 4. 24. Mallal S, Nolan D, Witt C, Masel G, Martin AM, Moore C, et al. Association between presence of HLA-B*5701, HLADR7, and HLA-DQ3 and hypersensitivity to HIV-1 reversetranscriptase inhibitor abacavir. Lancet 2002; 359:727 – 32. 25. Hetherington S, Hughes AR, Mosteller M, Shortino D, Baker KL, Spreen W, et al. Genetic variations in HLA-B region and hypersensitivity reactions to abacavir. Lancet 2002; 359:1121 – 2. 26. Roses A. Pharmacogentics and future drug development and delivery. Lancet 2000; 355:1358 – 61. 27. Proposed International Guidelines on Ethical Issues in Medical Genetics and Genetic Services. http://www.who. int/ncd/hgn/hgnethic.htm 28. Roche Genetics Educational Program. Available upon request from http://www.rochegenetics.com Received 10 January 2003, accepted 20 January 2003 Corresponding author: Klaus Lindpaintner, MD, MPH, VP Research, Director, Roche Genetics, F. Hoffmann-La Roche, Bldg 93/532, 4070 Basel, Switzerland Phone: + 41-61-688.0254, Fax: + 41-61-688.1929, E-mail: klaus.lindpaintner@roche.com References 1. Dickins M, Tucker G: Drug disposition: to phenotype or genotype. Int J Pharm Med 2001; 15:70 – 3. Also see: http:// www.imm.ki.se/CYPalleles/ 2. Evans WE, Relling MV. Pharmacogenomics: translating functional genomics into rational therapies. Science 1999; 206:487 – 91. Also see: http://www.sciencemag.org/feature/ data/1044449.shl/ 3. Fellay J, Marzolini C, Meaden ER, Back DJ, Buclin T, Chave JP, et al. Response to antiretroviral treatment in HIV-1-infected individuals with allelic variants of the multidrug resistance transporter 1: a pharmacogenetics study. Lancet 2002; 359:30 – 6. 4. Dubinsky M, Lamothe S, Yang HY, Targan SR, Sinnett D, Theoret Y, et al. Pharmacogenomics and metabolite measurement for 6-mercaptopurine therapy in inflammatory bowel disease. Gastroenterology 2000; 118:705 – 13. 5. Martinez FD, Graves PE, Baldini M, Solomon S, Erickson R. Association between genetic polymorphisms of the beta 2-adrenoceptor and response to albuterol in children with and without a history of wheezing. J Clin Invest 1997; 100:3184 – 8. 6. Tan S, Hall IP, Dewar J, Dow E, Lipworth B. Association between beta 2-adrenoceptor polymorphism and susceptibility to bronchodilator desensitisation in moderately severe stable asthmatics. Lancet 1997; 350:995 – 9. 7. Green SA, Turki J, Innis M, Liggett SB. Amino-terminal polymorphisms of the human beta 2-adrenergic receptor impart distinct agonist-promoted regulatory properties. Biochemistry 1994; 33:9414 – 9. 8. Green SA, Turki J, Bejarano P, Hall IP, Liggett SB. Influence of beta 2-adrenergic receptor genotypes on signal transduction in human airway smooth muscle cells. Am J Respir Cell Mol Biol 1995; 13:25 – 33. 9. Reihsaus E, Innis M, MacIntyre N and Liggett SB. Mutations in the gene encoding for the beta 2-adrenergic receptor in normal and asthmatic subjects. Am J Respir Cell Mol Biol 1993; 8:334 – 49. 10. Dewar JC, Wheatley AP, Venn A, Morrison JFJ, Britton J, Hall IP. Beta2 adrenoceptor polymorphisms are in linkage disequilibrium, but are not associated with asthma in an adult population. Clin Exp All 1998; 28:442 – 8. 11. Drazen JM, Yandava CN, Dube L, Szczerback N, Hippensteel R, Pillari A, et al. Pharmacogenetic association between ALOX5 promoter genotype and the response to anti-asthma treatment. Nat Genet 1999; 22:168 – 70. 12. In KH, Asano K, Beier D, Grobholz J, Finn PW, Silverman EK, et al. Naturally occurring mutations in the human 5lipoxygenase gene promoter that modify transcription factor binding and reporter gene transcription. J Clin Invest 1997 1; 99:1130 – 7. 13. Fischel-Ghodsian N. Genetic factors in aminoglycoside toxicity. Ann NY Acad Sci 1999; 884:99 – 109. 14. Hutchin T, Cortopassi G. Proposed molecular and cellular mechanism for aminoglycoside ototoxicity. Antimicrob Agents Chemother 1994; 38:2517 – 20.

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Clinical Chemistry and Laboratory Medicine (CCLM)de Gruyter

Published: Apr 25, 2003

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