Impact of mathematical pharmacology on practice and theory: four case studies

Impact of mathematical pharmacology on practice and theory: four case studies Drug-discovery has become a complex discipline in which the amount of knowledge about human biology, physiology, and biochemistry have increased. In order to harness this complex body of knowledge mathematics can play a critical role, and has actually already been doing so. We demonstrate through four case studies, taken from previously published data and analyses, what we can gain from mathematical/analytical techniques when nonlinear concentration-time courses have to be transformed into their equilibrium concentration-response (target or complex) relationships and new structures of drug potency have to be deciphered; when pattern recognition needs to be carried out for an unconventional response-time dataset; when what-if? predictions beyond the observational concentration-time range need to be made; or when the behaviour of a semi-mechanistic model needs to be elucidated or challenged. These four examples are typical situations when standard approaches known to the general community of pharmacokineticists prove to be inadequate. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Pharmacokinetics and Pharmacodynamics Springer Journals

Impact of mathematical pharmacology on practice and theory: four case studies

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Publisher
Springer US
Copyright
Copyright © 2017 by The Author(s)
Subject
Biomedicine; Pharmacology/Toxicology; Pharmacy; Veterinary Medicine/Veterinary Science; Biomedical Engineering; Biochemistry, general
ISSN
1567-567X
eISSN
1573-8744
D.O.I.
10.1007/s10928-017-9539-8
Publisher site
See Article on Publisher Site

Abstract

Drug-discovery has become a complex discipline in which the amount of knowledge about human biology, physiology, and biochemistry have increased. In order to harness this complex body of knowledge mathematics can play a critical role, and has actually already been doing so. We demonstrate through four case studies, taken from previously published data and analyses, what we can gain from mathematical/analytical techniques when nonlinear concentration-time courses have to be transformed into their equilibrium concentration-response (target or complex) relationships and new structures of drug potency have to be deciphered; when pattern recognition needs to be carried out for an unconventional response-time dataset; when what-if? predictions beyond the observational concentration-time range need to be made; or when the behaviour of a semi-mechanistic model needs to be elucidated or challenged. These four examples are typical situations when standard approaches known to the general community of pharmacokineticists prove to be inadequate.

Journal

Journal of Pharmacokinetics and PharmacodynamicsSpringer Journals

Published: Sep 7, 2017

References

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