Translational pharmacokinetic-pharmacodynamic analysis in the pharmaceutical industry: an IQ Consortium PK-PD Discussion Group perspective

[Display omitted] •Preclinical pharmacokinetic-pharmacodynamic modeling (PK-PD) is an important translational tool to understand drug efficacy in the pharmaceutical industry.•Applications of PK-PD modeling in different therapeutic areas are reviewed.•Case studies illustrate impact of preclinical PK-...

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Veröffentlicht in:Drug discovery today 2017-10, Vol.22 (10), p.1447-1459
Hauptverfasser: Wong, Harvey, Bohnert, Tonika, Damian-Iordache, Valeriu, Gibson, Christopher, Hsu, Cheng-Pang, Krishnatry, Anu Shilpa, Liederer, Bianca M., Lin, Jing, Lu, Qiang, Mettetal, Jerome T., Mudra, Daniel R., Nijsen, Marjoleen J.M.A., Schroeder, Patricia, Schuck, Edgar, Suryawanshi, Satyendra, Trapa, Patrick, Tsai, Alice, Wang, Haiqing, Wu, Fan
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Sprache:eng
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Zusammenfassung:[Display omitted] •Preclinical pharmacokinetic-pharmacodynamic modeling (PK-PD) is an important translational tool to understand drug efficacy in the pharmaceutical industry.•Applications of PK-PD modeling in different therapeutic areas are reviewed.•Case studies illustrate impact of preclinical PK-PD modeling.•Best practices are described.•Future opportunities for preclinical PK-PD modeling. With inadequate efficacy being the primary cause for the attrition of drug candidates in clinical development, the need to better predict clinical efficacy earlier in the drug development process has increased in importance in the pharmaceutical industry. Here, we review current applications of translational pharmacokinetic-pharmacodynamic (PK-PD) modeling of preclinical data in the pharmaceutical industry, including best practices. Preclinical translational PK-PD modeling has been used in many therapeutic areas and has been impactful to drug development. The role of preclinical translational PK-PD modeling in drug discovery and development will continue to evolve and broaden, given that its broad implementation in the pharmaceutical industry is relatively recent and many opportunities still exist for its further application.
ISSN:1359-6446
1878-5832
DOI:10.1016/j.drudis.2017.04.015