Literature DB >> 28476536

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

Harvey Wong1, Tonika Bohnert2, Valeriu Damian-Iordache3, Christopher Gibson4, Cheng-Pang Hsu5, Anu Shilpa Krishnatry3, Bianca M Liederer6, Jing Lin7, Qiang Lu8, Jerome T Mettetal9, Daniel R Mudra10, Marjoleen J M A Nijsen11, Patricia Schroeder2, Edgar Schuck12, Satyendra Suryawanshi13, Patrick Trapa14, Alice Tsai15, Haiqing Wang13, Fan Wu16.   

Abstract

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.
Copyright © 2017 Elsevier Ltd. All rights reserved.

Mesh:

Year:  2017        PMID: 28476536     DOI: 10.1016/j.drudis.2017.04.015

Source DB:  PubMed          Journal:  Drug Discov Today        ISSN: 1359-6446            Impact factor:   7.851


  7 in total

1.  Application of Pharmacokinetic-Pharmacodynamic Modeling to Inform Translation of In Vitro NaV1.7 Inhibition to In Vivo Pharmacological Response in Non-human Primate.

Authors:  Jeanine E Ballard; Parul Pall; Joshua Vardigan; Fuqiang Zhao; Marie A Holahan; Richard Kraus; Yuxing Li; Darrell Henze; Andrea Houghton; Christopher S Burgey; Christopher Gibson
Journal:  Pharm Res       Date:  2020-09-04       Impact factor: 4.200

2.  Translational Model-Informed Dose Selection for a Human Positron Emission Tomography Imaging Study of JNJ-54175446, a P2X7 Receptor Antagonist.

Authors:  Yan Xu; Xin Miao; Paulien Ravenstijn; Anja Hijzen; Mark E Schmidt; Partha Nandy; Honghui Zhou
Journal:  Clin Transl Sci       Date:  2019-11-09       Impact factor: 4.689

3.  Applications of Quantitative Systems Pharmacology in Model-Informed Drug Discovery: Perspective on Impact and Opportunities.

Authors:  Erica L Bradshaw; Mary E Spilker; Richard Zang; Loveleena Bansal; Handan He; Rhys D O Jones; Kha Le; Mark Penney; Edgar Schuck; Brian Topp; Alice Tsai; Christine Xu; Marjoleen J M A Nijsen; Jason R Chan
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2019-10-25

4.  Mechanistic Multilayer Quantitative Model for Nonlinear Pharmacokinetics, Target Occupancy and Pharmacodynamics (PK/TO/PD) Relationship of D-Amino Acid Oxidase Inhibitor, TAK-831 in Mice.

Authors:  Tomoki Yoneyama; Sho Sato; Andy Sykes; Rosa Fradley; Stuart Stafford; Shyam Bechar; Eimear Howley; Toshal Patel; Yoshihiko Tagawa; Toshiya Moriwaki; Satoru Asahi
Journal:  Pharm Res       Date:  2020-08-05       Impact factor: 4.200

5.  Translational Pharmacokinetic-Pharmacodynamic Modeling of NaV1.7 Inhibitor MK-2075 to Inform Human Efficacious Dose.

Authors:  Jeanine E Ballard; Parul S Pall; Joshua Vardigan; Fuqiang Zhao; Marie A Holahan; Xiaoping Zhou; Nina Jochnowitz; Richard L Kraus; Rebecca M Klein; Darrell A Henze; Andrea K Houghton; Christopher S Burgey; Christopher Gibson; Arie Struyk
Journal:  Front Pharmacol       Date:  2021-12-24       Impact factor: 5.810

6.  A quantitative systems pharmacology approach to predict the safe-equivalent dose of doxorubicin in patients with cardiovascular comorbidity.

Authors:  Lan Sang; Yi Yuan; Ying Zhou; Zhengying Zhou; Muhan Jiang; Xiaoquan Liu; Kun Hao; Hua He
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2021-10-13

7.  Translational and pharmacokinetic-pharmacodynamic application for the clinical development of GDC-0334, a novel TRPA1 inhibitor.

Authors:  Phyllis Chan; Han Ting Ding; Bianca M Liederer; Jialin Mao; Paula Belloni; Liuxi Chen; Simon S Gao; Victory Joseph; Xiaoying Yang; Joseph S Lin; Mayur S Mitra; Wendy S Putnam; Angelica Quartino; Rebecca N Bauer; Lin Pan
Journal:  Clin Transl Sci       Date:  2021-05-31       Impact factor: 4.689

  7 in total

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