Literature DB >> 21105864

Quantitative structure-pharmacokinetic relationships.

Chao Xu1, Donald E Mager.   

Abstract

IMPORTANCE OF THE FIELD: Quantitative structure-pharmacokinetic relationships (QSPKR) modeling is a valuable tool in drug discovery and development. It can provide insights into the molecular determinants of processes governing the time course of drug exposure and response. AREAS COVERED IN THIS REVIEW: Empirical and mechanism-based QSPKR models are discussed, including specific examples for oral absorption, nonspecific protein binding, volume of distribution, total metabolic stability and specific interactions with drug metabolizing enzymes. Emphasis is placed on state-of-the-art techniques, including new approaches for the direct simulation of concentration-time profiles from molecular descriptors (temporal QSPKR). WHAT THE READER WILL GAIN: Reviewing the application of current QSPKR modeling techniques will place these methods in context and highlight their respective advantages and limitations, as well as opportunities for further refinement. TAKE HOME MESSAGE: The expansion of readily available molecular descriptors and advanced algorithms has improved empirical models and enabled the development of robust models for non-congeneric series. Empirical models focus on point estimates of global PK processes and physiologically-based models may be more desirable than data-driven methods. Further integration of relevant biological and pharmacological mechanisms will improve the ability to predict the full time course of drug concentration and effect profiles for diverse compounds and experimental conditions.

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Year:  2010        PMID: 21105864     DOI: 10.1517/17425255.2011.537257

Source DB:  PubMed          Journal:  Expert Opin Drug Metab Toxicol        ISSN: 1742-5255            Impact factor:   4.481


  4 in total

1.  Quantitative structure-pharmacokinetic relationships for the prediction of renal clearance in humans.

Authors:  Rutwij A Dave; Marilyn E Morris
Journal:  Drug Metab Dispos       Date:  2014-10-28       Impact factor: 3.922

2.  How effective are ionization state-based QSPKR models at predicting pharmacokinetic parameters in humans?

Authors:  Anish Gomatam; Blessy Joseph; Poonam Advani; Mushtaque Shaikh; Krishna Iyer; Evans Coutinho
Journal:  Mol Divers       Date:  2022-10-11       Impact factor: 3.364

3.  The use of pseudo-equilibrium constant affords improved QSAR models of human plasma protein binding.

Authors:  Xiang-Wei Zhu; Alexander Sedykh; Hao Zhu; Shu-Shen Liu; Alexander Tropsha
Journal:  Pharm Res       Date:  2013-04-09       Impact factor: 4.200

4.  Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients.

Authors:  Alex A Freitas; Kriti Limbu; Taravat Ghafourian
Journal:  J Cheminform       Date:  2015-02-26       Impact factor: 5.514

  4 in total

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