Literature DB >> 24136298

Systems pharmacology modeling: an approach to improving drug safety.

Jane P F Bai1, Robert J Fontana, Nathan D Price, Vineet Sangar.   

Abstract

Advances in systems biology in conjunction with the expansion in knowledge of drug effects and diseases present an unprecedented opportunity to extend traditional pharmacokinetic and pharmacodynamic modeling/analysis to conduct systems pharmacology modeling. Many drugs that cause liver injury and myopathies have been studied extensively. Mitochondrion-centric systems pharmacology modeling is important since drug toxicity across a large number of pharmacological classes converges to mitochondrial injury and death. Approaches to systems pharmacology modeling of drug effects need to consider drug exposure, organelle and cellular phenotypes across all key cell types of human organs, organ-specific clinical biomarkers/phenotypes, gene-drug interaction and immune responses. Systems modeling approaches, that leverage the knowledge base constructed from curating a selected list of drugs across a wide range of pharmacological classes, will provide a critically needed blueprint for making informed decisions to reduce the rate of attrition for drugs in development and increase the number of drugs with an acceptable benefit/risk ratio.
Copyright © 2013 John Wiley & Sons, Ltd.

Entities:  

Keywords:  biomarkers/clinical phenotype; cellular phenotype; gene drug interaction; mitochondria; molecular networks; molecular pathways; organ injury; systems biology

Mesh:

Substances:

Year:  2013        PMID: 24136298     DOI: 10.1002/bdd.1871

Source DB:  PubMed          Journal:  Biopharm Drug Dispos        ISSN: 0142-2782            Impact factor:   1.627


  7 in total

Review 1.  Harnessing Big Data for Systems Pharmacology.

Authors:  Lei Xie; Eli J Draizen; Philip E Bourne
Journal:  Annu Rev Pharmacol Toxicol       Date:  2016-10-13       Impact factor: 13.820

Review 2.  Providing data science support for systems pharmacology and its implications to drug discovery.

Authors:  Thomas Hart; Lei Xie
Journal:  Expert Opin Drug Discov       Date:  2016-01-09       Impact factor: 6.098

3.  MatVPC: A User-Friendly MATLAB-Based Tool for the Simulation and Evaluation of Systems Pharmacology Models.

Authors:  K Biliouris; M Lavielle; M N Trame
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2015-08-22

4.  The impact of composite AUC estimates on the prediction of systemic exposure in toxicology experiments.

Authors:  Tarjinder Sahota; Meindert Danhof; Oscar Della Pasqua
Journal:  J Pharmacokinet Pharmacodyn       Date:  2015-04-14       Impact factor: 2.745

Review 5.  The promises of quantitative systems pharmacology modelling for drug development.

Authors:  V R Knight-Schrijver; V Chelliah; L Cucurull-Sanchez; N Le Novère
Journal:  Comput Struct Biotechnol J       Date:  2016-09-23       Impact factor: 7.271

6.  Aging effects on QT interval: Implications for cardiac safety of antipsychotic drugs.

Authors:  Simon W Rabkin
Journal:  J Geriatr Cardiol       Date:  2014-03       Impact factor: 3.327

7.  Using quantitative systems pharmacology to evaluate the drug efficacy of COX-2 and 5-LOX inhibitors in therapeutic situations.

Authors:  Christoph Thiel; Ines Smit; Vanessa Baier; Henrik Cordes; Brigida Fabry; Lars Mathias Blank; Lars Kuepfer
Journal:  NPJ Syst Biol Appl       Date:  2018-08-03
  7 in total

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