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polyPK: an R package for pharmacokinetic analysis of multi-component drugs using a metabolomics approach

polyPK: an R package for pharmacokinetic analysis of multi-component drugs using a metabolomics... Summary: Pharmacokinetics (PK) is a long-standing bottleneck for botanical drug and traditional medicine research. By using an integrated phytochemical and metabolomics approach coupled with multivariate statistical analysis, we propose a new strategy, Poly-PK, to simultaneously moni- tor the performance of drug constituents and endogenous metabolites, taking into account both the diversity of the drug’s chemical composition and its complex effects on the mammalian meta- bolic pathways. Poly-PK is independent of specific measurement platforms and has been success- fully applied in the PK studies of Puerh tea, a traditional Chinese medicine Huangqi decoction and many other multi-component drugs. Here, we introduce an R package, polyPK, the first and only automation of the data analysis pipeline of Poly-PK strategy. polyPK provides 10 functions for data pre-processing, differential compound identification and grouping, traditional PK parameters calcu- lation, multivariate statistical analysis, correlations, cluster analyses and resulting visualization. It may serve a wide range of users, including pharmacologists, biologists and doctors, in understand- ing the metabolic fate of multi-component drugs. Availability and implementation: polyPK package is freely available from the R archive CRAN (https://CRAN.R-project.org/package¼polyPK). Contact: wjia@cc.hawaii.edu or chentianlu@sjtu.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online. 1 Introduction (Jia et al., 2015; Lan et al., 2010, 2013; Xie et al., 2012, 2017). The pharmacokinetics (PK) of multi-component drugs such as Using this strategy, researchers will be able to monitor the perform- herbal medicines and other natural products is more complex than ance of both drug constituents and endogenous metabolites. Its core compounds containing a single active component. From 2010 on, step is to identify numerous differential metabolites and to divide our group has proposed, step by step, a metabolomics-based strat- them into three groups: (i) altered endogenous metabolites caused egy, Poly-PK, which is very comprehensive in exploring complex ef- by exogenous drugs, (ii) absorbed drug-derived compounds and (iii) fects of multi-component drugs in the mammalian metabolic system secondary metabolites generated by the chemical transformation of V The Author(s) 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com 1792 polyPK 1793 drug compounds by hepatic enzymes and gut microbes (Jia et al., descriptions are provided in SI. The default setting is optimal for 2015). Currently, this strategy has been successfully applied in most cases. For left-censored missing values (e.g. quantitative examining the complex PK and pharmacodynamics profiles of a metabolomics data), ‘QRILC’ is the best imputation method. Chinese tea (Xie et al., 2012), a Chinese herbal medicine Huangqi decoction (Xie et al., 2017) as well as, various multi-component drugs and shows prominent advantages over conventional 2.2 Differential compounds screening and grouping approaches (Lan et al., 2013). Considering the hard and complex The core part of polyPK is noted in a white dashed box in Figure data analysis burden, especially for those not familiar to metabolo- 1C. Differential metabolites of all time points are identified by para- mics and PK study, we developed polyPK, an R package, to auto- metric or nonparametric hypothesis tests (p< 0.05) between the pre- mate the data analysis pipeline of Poly-PK strategy (Fig. 1). To our dose and every post-dose metabolomics data respectively (function knowledge, this is the first and only integrated tool that uses metab- ‘GetDiffData’) and are listed in weight rank order as calculated by olomics approach for pharmacokinetic analysis and visualization on the SAM (Significance analysis of microarrays) method. It is sug- multi-component drugs. gested to adjust the raw p values by the ‘FDR’ (or stricter ‘Holm’) method to eliminate possible false positive results induced by mul- tiple tests. Subsequently, functions ‘GetEndo’, ‘GetAbso’ and 2 Implementation and main functions ‘GetSecdAbso’ divide the differential metabolites into three groups by similarity analysis: (i) altered endogenous metabolites, (ii) ab- 2.1 Data importation and pre-processing sorbed drug compounds and (iii) secondary metabolites of the ab- Three data files, derived from any measurement platforms (e.g. sorbed drug compounds. These findings are the crucial basis for the NMR, GC/LC-MS), are required for a complete polyPK study (Fig. following analysis on the interrelationships between drug com- 1A): (i) list of drug constituents, (ii) pre- and (iii) post-dose (multiple pounds and metabolites derived from drug as well as the metabolic time-points) metabolomics data. A file of basic information is op- impact of the drug to the mammalian metabolism. tional. The formats and demo contents of the four data files are illustrated in Supplementary Figures S1–S4. All the functions are in- dependent and if some of the data files are not imported, only the related functions and results will be affected. 2.3 Analysis and visualization of differential compounds The pre- and post-dose metabolomics datasets are processed by The resulting differential compounds or groups of compounds are the function ‘DataPre’. Outliers and sparse variables are removed analyzed in four independent ways (Fig. 1C). Seven conventional PK and then missing values are imputed (Fig. 1B). Various control par- parameters (Cmax, Tmax, AUC, CL, Tlast, Tfirst and Cmin) and ameters are ready for users with diverse aims and detailed the concentration-time profiles of every user-specified compound are calculated and plotted by the function ‘PKs’ [Fig. 1D(a, b)]. This is an extension of the traditional PK analysis as both drug com- pounds and endogenous metabolites can be analyzed. A) data importation drug compounds pre-dose metabolome post-dose metabolome basic information The scores and trajectory plots [Fig. 1D(c)] of principal component B) data pretreatment DataPre analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) sparse variables elimination outliers removal missing values imputation logarithm which are commonly used in metabolomics studies are generated by the C) Poly-PK analyses PKs GetEndo PK parameters calculation function ‘ScatPlot’. These figures are effective ways to show the classifi- (Tmax, Cmax, AUC, CL, Tlast, Tfirst, Cmin) altered endogenous ScatPlot cation and alteration trend of metabolic profiles across time-points of metabolites multivariate statistical analyses GetAbso GetDiffData (PCA, PLS-DA) interest. Permutation plot [Fig. 1D(d)] and error rates of PLS-DA are absorbed drug CorrPlot pretreated differential compounds also generated to check the model performance. data metabolites correlation analyses (Spearman, Pearson, Kendall) GetSecdAbso HeatMap The relationships between any two groups of differential metab- secondary metabolites of the cluster analyses absorbed drug compounds (HCA) olites are evaluated by correlation coefficients (Pearson, Spearman D) results and visualization Name ID Tmax Tlast Tfirst Cmax Cmin AUC CL or Kendall) and diverse forms of correlation diagrams [Fig. 1D(f– glycolithocholic acid HMDB00698 3 5 0 1577 281.98 4024.41 3.73E-03 fructose HMDB00660 3 5 0 1660600 401000 5447994.68 2.75E-06 myrisc acid HMDB00806 2 5 0 497400 268300 2027495.56 7.40E-06 h)], which may provide additional insights to the interplay of multi- n-octadecanoic acid HMDB00827 2 5 0 3206000 2065000 13553175.68 1.11E-06 tryptophan HMDB00929 0 5 0 103120 71510 427260.47 3.51E-05 phenylpyruvic acid HMDB00205 0 5 0 18070 12380 71589.80 2.10E-04 component drugs and the metabolic system of mammalian. octanoylrac-glycerol inx0001 0 5 0 4127 2458 17010.01 8.82E-04 (b) (c) (d) (e) 3-indolelacc acid HMDB00671 5 5 0 110970 15000 218756.92 6.86E-05 gancaonin V HMDB37586 4 5 1 39819 0 117523.29 1.28E-04 p-value inx0001 HMDB34146 HMDB02259 HMDB02142 isoquercitrin HMDB37362 1 5 1 2239 0 6807.59 2.20E-03 inx0001 0.0000 0.0040 0.0047 0.0041 Additionally, heatmap with Hierarchical Cluster Analysis (HCA) ceanothic acid HMDB36851 4 5 1 126.35 0 550.23 2.73E-02 HMDB34146 0.0040 0.0000 6.5498E-44 5.2388E-55 ganoderic acid H HMDB35987 4 5 1 3367000 0 7609937.75 1.97E-06 HMDB02259 0.0047 6.5498E-44 0.0000 8.5590E-46 isoformononen HMDB33994 3 5 1 821 0 2608.54 5.75E-03 HMDB02142 0.0041 5.2388E-55 8.5590E-46 0.0000 [Fig. 1D(e)] is also a good way to visualize the similarity of groups liquirin HMDB29520 4 5 1 2.537 0 7.73 1.94E+00 formononen HMDB05808 3 5 1 53.49 0 97.30 1.54E-01 HMDB00827 0.6172 0.7940 0.5513 0.8079 (a) (f) (g) (h) or compounds by detailed data (e.g. all or any group of differential compounds). Fig. 1. Workflow of polyPK. (A) Data importation. Four data files, three Gender, daily routine and diet may impact the performance and effi- required (solid boxes) and one optional (black dashed box), are imported for analysis. (B) Data pretreatment. (C) Poly-PK analyses. Differential metabolites cacy of drugs. So, data analysis is conducted in all, male and female are identified and are classified into three groups (white dashed box). The re- samples, separately. Times of meal and sleep are marked in the figures if sulting compounds or groups of compounds are analyzed in four independ- corresponding information is given in the basic information file. ent ways. (D) Results and visualization. Some figures (.pdf) and tables (.xlsx) are generated during step C. (a) PK parameters of user-specified compounds (e.g. absorbed drug compounds). (b) Concentration-time profiles of user- specified compounds. (c) Trajectory of user-specified groups of compounds 3 Conclusion (e.g. altered endogenous metabolites). (d) R2-Q2 scatter plot of the permuta- tion test. (e) Heatmap and cluster of user-specified groups of compounds. (f) An R package, polyPK, was developed to automate the complete Pairwise correlation coefficients of user-specified compounds (e.g. altered en- data analysis and resulting visualization step of Poly-PK pipeline. It dogenous metabolites and absorbed drug compounds). Bubble (g) and chord will serve as an easy and effective tool for PK study of multi- (h) diagrams of correlation coefficients. The functions of each step are in component drugs by using a systematic omics approach. More de- white italic. All the intermediate and final results are saved separately in cor- responding files and folders scriptions are provided in the Supplementary Material. 1794 M.Li et al. Lan,K. et al. (2010) An integrated metabolomics and pharmacokinetics strategy Funding for multi-component drugs evaluation. Curr. Drug Metab., 11, 105–114. This work was supported by National Key R&D Program of China Lan,K. et al. (2013) Towards polypharmacokinetics: pharmacokinetics of [2017YFC0906800] and the National Natural Science Foundation of China multicomponent drugs and herbal medicines using a metabolomics ap- [31501079 and 31500954]. proach. Evid. Based Complement. Alternat. Med., 2013, 819147. Xie,G. et al. (2017) Poly-pharmacokinetic study of a multicomponent herbal Conflict of Interest: none declared. medicine in healthy Chinese volunteers. Clin. Pharmacol. Ther., doi: 10. 1002/cpt.784 Xie,G. et al. (2012) Metabolic fate of tea polyphenols in humans. J. Proteome References Res., 11, 3449–3457. Jia,W. et al. (2015) The polypharmacokinetics of herbal medicines. Science, 350, S76–S79. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Bioinformatics Oxford University Press

polyPK: an R package for pharmacokinetic analysis of multi-component drugs using a metabolomics approach

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Oxford University Press
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© The Author(s) 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com
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1367-4803
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Abstract

Summary: Pharmacokinetics (PK) is a long-standing bottleneck for botanical drug and traditional medicine research. By using an integrated phytochemical and metabolomics approach coupled with multivariate statistical analysis, we propose a new strategy, Poly-PK, to simultaneously moni- tor the performance of drug constituents and endogenous metabolites, taking into account both the diversity of the drug’s chemical composition and its complex effects on the mammalian meta- bolic pathways. Poly-PK is independent of specific measurement platforms and has been success- fully applied in the PK studies of Puerh tea, a traditional Chinese medicine Huangqi decoction and many other multi-component drugs. Here, we introduce an R package, polyPK, the first and only automation of the data analysis pipeline of Poly-PK strategy. polyPK provides 10 functions for data pre-processing, differential compound identification and grouping, traditional PK parameters calcu- lation, multivariate statistical analysis, correlations, cluster analyses and resulting visualization. It may serve a wide range of users, including pharmacologists, biologists and doctors, in understand- ing the metabolic fate of multi-component drugs. Availability and implementation: polyPK package is freely available from the R archive CRAN (https://CRAN.R-project.org/package¼polyPK). Contact: wjia@cc.hawaii.edu or chentianlu@sjtu.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online. 1 Introduction (Jia et al., 2015; Lan et al., 2010, 2013; Xie et al., 2012, 2017). The pharmacokinetics (PK) of multi-component drugs such as Using this strategy, researchers will be able to monitor the perform- herbal medicines and other natural products is more complex than ance of both drug constituents and endogenous metabolites. Its core compounds containing a single active component. From 2010 on, step is to identify numerous differential metabolites and to divide our group has proposed, step by step, a metabolomics-based strat- them into three groups: (i) altered endogenous metabolites caused egy, Poly-PK, which is very comprehensive in exploring complex ef- by exogenous drugs, (ii) absorbed drug-derived compounds and (iii) fects of multi-component drugs in the mammalian metabolic system secondary metabolites generated by the chemical transformation of V The Author(s) 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com 1792 polyPK 1793 drug compounds by hepatic enzymes and gut microbes (Jia et al., descriptions are provided in SI. The default setting is optimal for 2015). Currently, this strategy has been successfully applied in most cases. For left-censored missing values (e.g. quantitative examining the complex PK and pharmacodynamics profiles of a metabolomics data), ‘QRILC’ is the best imputation method. Chinese tea (Xie et al., 2012), a Chinese herbal medicine Huangqi decoction (Xie et al., 2017) as well as, various multi-component drugs and shows prominent advantages over conventional 2.2 Differential compounds screening and grouping approaches (Lan et al., 2013). Considering the hard and complex The core part of polyPK is noted in a white dashed box in Figure data analysis burden, especially for those not familiar to metabolo- 1C. Differential metabolites of all time points are identified by para- mics and PK study, we developed polyPK, an R package, to auto- metric or nonparametric hypothesis tests (p< 0.05) between the pre- mate the data analysis pipeline of Poly-PK strategy (Fig. 1). To our dose and every post-dose metabolomics data respectively (function knowledge, this is the first and only integrated tool that uses metab- ‘GetDiffData’) and are listed in weight rank order as calculated by olomics approach for pharmacokinetic analysis and visualization on the SAM (Significance analysis of microarrays) method. It is sug- multi-component drugs. gested to adjust the raw p values by the ‘FDR’ (or stricter ‘Holm’) method to eliminate possible false positive results induced by mul- tiple tests. Subsequently, functions ‘GetEndo’, ‘GetAbso’ and 2 Implementation and main functions ‘GetSecdAbso’ divide the differential metabolites into three groups by similarity analysis: (i) altered endogenous metabolites, (ii) ab- 2.1 Data importation and pre-processing sorbed drug compounds and (iii) secondary metabolites of the ab- Three data files, derived from any measurement platforms (e.g. sorbed drug compounds. These findings are the crucial basis for the NMR, GC/LC-MS), are required for a complete polyPK study (Fig. following analysis on the interrelationships between drug com- 1A): (i) list of drug constituents, (ii) pre- and (iii) post-dose (multiple pounds and metabolites derived from drug as well as the metabolic time-points) metabolomics data. A file of basic information is op- impact of the drug to the mammalian metabolism. tional. The formats and demo contents of the four data files are illustrated in Supplementary Figures S1–S4. All the functions are in- dependent and if some of the data files are not imported, only the related functions and results will be affected. 2.3 Analysis and visualization of differential compounds The pre- and post-dose metabolomics datasets are processed by The resulting differential compounds or groups of compounds are the function ‘DataPre’. Outliers and sparse variables are removed analyzed in four independent ways (Fig. 1C). Seven conventional PK and then missing values are imputed (Fig. 1B). Various control par- parameters (Cmax, Tmax, AUC, CL, Tlast, Tfirst and Cmin) and ameters are ready for users with diverse aims and detailed the concentration-time profiles of every user-specified compound are calculated and plotted by the function ‘PKs’ [Fig. 1D(a, b)]. This is an extension of the traditional PK analysis as both drug com- pounds and endogenous metabolites can be analyzed. A) data importation drug compounds pre-dose metabolome post-dose metabolome basic information The scores and trajectory plots [Fig. 1D(c)] of principal component B) data pretreatment DataPre analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) sparse variables elimination outliers removal missing values imputation logarithm which are commonly used in metabolomics studies are generated by the C) Poly-PK analyses PKs GetEndo PK parameters calculation function ‘ScatPlot’. These figures are effective ways to show the classifi- (Tmax, Cmax, AUC, CL, Tlast, Tfirst, Cmin) altered endogenous ScatPlot cation and alteration trend of metabolic profiles across time-points of metabolites multivariate statistical analyses GetAbso GetDiffData (PCA, PLS-DA) interest. Permutation plot [Fig. 1D(d)] and error rates of PLS-DA are absorbed drug CorrPlot pretreated differential compounds also generated to check the model performance. data metabolites correlation analyses (Spearman, Pearson, Kendall) GetSecdAbso HeatMap The relationships between any two groups of differential metab- secondary metabolites of the cluster analyses absorbed drug compounds (HCA) olites are evaluated by correlation coefficients (Pearson, Spearman D) results and visualization Name ID Tmax Tlast Tfirst Cmax Cmin AUC CL or Kendall) and diverse forms of correlation diagrams [Fig. 1D(f– glycolithocholic acid HMDB00698 3 5 0 1577 281.98 4024.41 3.73E-03 fructose HMDB00660 3 5 0 1660600 401000 5447994.68 2.75E-06 myrisc acid HMDB00806 2 5 0 497400 268300 2027495.56 7.40E-06 h)], which may provide additional insights to the interplay of multi- n-octadecanoic acid HMDB00827 2 5 0 3206000 2065000 13553175.68 1.11E-06 tryptophan HMDB00929 0 5 0 103120 71510 427260.47 3.51E-05 phenylpyruvic acid HMDB00205 0 5 0 18070 12380 71589.80 2.10E-04 component drugs and the metabolic system of mammalian. octanoylrac-glycerol inx0001 0 5 0 4127 2458 17010.01 8.82E-04 (b) (c) (d) (e) 3-indolelacc acid HMDB00671 5 5 0 110970 15000 218756.92 6.86E-05 gancaonin V HMDB37586 4 5 1 39819 0 117523.29 1.28E-04 p-value inx0001 HMDB34146 HMDB02259 HMDB02142 isoquercitrin HMDB37362 1 5 1 2239 0 6807.59 2.20E-03 inx0001 0.0000 0.0040 0.0047 0.0041 Additionally, heatmap with Hierarchical Cluster Analysis (HCA) ceanothic acid HMDB36851 4 5 1 126.35 0 550.23 2.73E-02 HMDB34146 0.0040 0.0000 6.5498E-44 5.2388E-55 ganoderic acid H HMDB35987 4 5 1 3367000 0 7609937.75 1.97E-06 HMDB02259 0.0047 6.5498E-44 0.0000 8.5590E-46 isoformononen HMDB33994 3 5 1 821 0 2608.54 5.75E-03 HMDB02142 0.0041 5.2388E-55 8.5590E-46 0.0000 [Fig. 1D(e)] is also a good way to visualize the similarity of groups liquirin HMDB29520 4 5 1 2.537 0 7.73 1.94E+00 formononen HMDB05808 3 5 1 53.49 0 97.30 1.54E-01 HMDB00827 0.6172 0.7940 0.5513 0.8079 (a) (f) (g) (h) or compounds by detailed data (e.g. all or any group of differential compounds). Fig. 1. Workflow of polyPK. (A) Data importation. Four data files, three Gender, daily routine and diet may impact the performance and effi- required (solid boxes) and one optional (black dashed box), are imported for analysis. (B) Data pretreatment. (C) Poly-PK analyses. Differential metabolites cacy of drugs. So, data analysis is conducted in all, male and female are identified and are classified into three groups (white dashed box). The re- samples, separately. Times of meal and sleep are marked in the figures if sulting compounds or groups of compounds are analyzed in four independ- corresponding information is given in the basic information file. ent ways. (D) Results and visualization. Some figures (.pdf) and tables (.xlsx) are generated during step C. (a) PK parameters of user-specified compounds (e.g. absorbed drug compounds). (b) Concentration-time profiles of user- specified compounds. (c) Trajectory of user-specified groups of compounds 3 Conclusion (e.g. altered endogenous metabolites). (d) R2-Q2 scatter plot of the permuta- tion test. (e) Heatmap and cluster of user-specified groups of compounds. (f) An R package, polyPK, was developed to automate the complete Pairwise correlation coefficients of user-specified compounds (e.g. altered en- data analysis and resulting visualization step of Poly-PK pipeline. It dogenous metabolites and absorbed drug compounds). Bubble (g) and chord will serve as an easy and effective tool for PK study of multi- (h) diagrams of correlation coefficients. The functions of each step are in component drugs by using a systematic omics approach. More de- white italic. All the intermediate and final results are saved separately in cor- responding files and folders scriptions are provided in the Supplementary Material. 1794 M.Li et al. Lan,K. et al. (2010) An integrated metabolomics and pharmacokinetics strategy Funding for multi-component drugs evaluation. Curr. Drug Metab., 11, 105–114. This work was supported by National Key R&D Program of China Lan,K. et al. (2013) Towards polypharmacokinetics: pharmacokinetics of [2017YFC0906800] and the National Natural Science Foundation of China multicomponent drugs and herbal medicines using a metabolomics ap- [31501079 and 31500954]. proach. Evid. Based Complement. Alternat. Med., 2013, 819147. Xie,G. et al. (2017) Poly-pharmacokinetic study of a multicomponent herbal Conflict of Interest: none declared. medicine in healthy Chinese volunteers. Clin. Pharmacol. Ther., doi: 10. 1002/cpt.784 Xie,G. et al. (2012) Metabolic fate of tea polyphenols in humans. J. Proteome References Res., 11, 3449–3457. Jia,W. et al. (2015) The polypharmacokinetics of herbal medicines. Science, 350, S76–S79.

Journal

Bioinformatics – Oxford University Press

Published: Dec 23, 2017

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