Literature DB >> 16353932

Pharmacokinetics/Pharmacodynamics and the stages of drug development: role of modeling and simulation.

Jenny Y Chien1, Stuart Friedrich, Michael A Heathman, Dinesh P de Alwis, Vikram Sinha.   

Abstract

Pharmacokinetic (PK) and pharmacodynamic (PD) modeling and simulation (M&S) are well-recognized powerful tools that enable effective implementation of the learn-and-confirm paradigm in drug development. The impact of PK/PD M&S on decision making and drug development risk management is dependent on the question being asked and on the availability and quality of data accessible at a particular stage of drug development. For instance, M&S methodologies can be used to capture uncertainty and use the expected variability in PK/PD data generated in preclinical species for projection of the plausible range of clinical dose; clinical trial simulation can be used to forecast the probability of achieving a target response in patients based on information obtained in early phases of development. Framing the right question and capturing the key assumptions are critical components of the "learn-and-confirm" paradigm in the drug development process and are essential to delivering high-value PK/PD M&S results. Selected works of PK/PD modeling and simulation from preclinical to phase III are presented as case examples in this article.

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Year:  2005        PMID: 16353932      PMCID: PMC2751257          DOI: 10.1208/aapsj070355

Source DB:  PubMed          Journal:  AAPS J        ISSN: 1550-7416            Impact factor:   4.009


  19 in total

Review 1.  Optimizing the science of drug development: opportunities for better candidate selection and accelerated evaluation in humans.

Authors:  L J Lesko; M Rowland; C C Peck; T F Blaschke
Journal:  Pharm Res       Date:  2000-11       Impact factor: 4.200

2.  Integration of pharmacokinetic and pharmacodynamic studies in the discovery, development, and review of protein therapeutic agents: a conference report.

Authors:  G R Galluppi; M C Rogge; L K Roskos; L J Lesko; M D Green; D W Feigal; C C Peck
Journal:  Clin Pharmacol Ther       Date:  2001-06       Impact factor: 6.875

3.  Optimal design of a population pharmacodynamic experiment for ivabradine.

Authors:  S B Duffull; F Mentré; L Aarons
Journal:  Pharm Res       Date:  2001-01       Impact factor: 4.200

Review 4.  Considerations in the design and development of transport inhibitors as adjuncts to drug therapy.

Authors:  Anne H Dantzig; Dinesh P de Alwis; Michael Burgess
Journal:  Adv Drug Deliv Rev       Date:  2003-01-21       Impact factor: 15.470

Review 5.  Hypothesis: a single clinical trial plus causal evidence of effectiveness is sufficient for drug approval.

Authors:  Carl C Peck; Donald B Rubin; Lewis B Sheiner
Journal:  Clin Pharmacol Ther       Date:  2003-06       Impact factor: 6.875

6.  Optimization of individual and population designs using Splus.

Authors:  Sylvie Retout; France Mentré
Journal:  J Pharmacokinet Pharmacodyn       Date:  2003-12       Impact factor: 2.745

7.  Use of prior information to stabilize a population data analysis.

Authors:  Per O Gisleskog; Mats O Karlsson; Stuart L Beal
Journal:  J Pharmacokinet Pharmacodyn       Date:  2002-12       Impact factor: 2.745

8.  Relation between QT and RR intervals is highly individual among healthy subjects: implications for heart rate correction of the QT interval.

Authors:  M Malik; P Färbom; V Batchvarov; K Hnatkova; A J Camm
Journal:  Heart       Date:  2002-03       Impact factor: 5.994

9.  A population pharmacokinetic model for doxorubicin and doxorubicinol in the presence of a novel MDR modulator, zosuquidar trihydrochloride (LY335979).

Authors:  Sophie Callies; Dinesh P de Alwis; James G Wright; Alan Sandler; Michael Burgess; Leon Aarons
Journal:  Cancer Chemother Pharmacol       Date:  2003-01-10       Impact factor: 3.333

10.  Population pharmacokinetic model for daunorubicin and daunorubicinol coadministered with zosuquidar.3HCl (LY335979).

Authors:  Sophie Callies; Dinesh P de Alwis; Atul Mehta; Michael Burgess; Leon Aarons
Journal:  Cancer Chemother Pharmacol       Date:  2004-03-24       Impact factor: 3.333

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  49 in total

Review 1.  Role of modelling and simulation: a European regulatory perspective.

Authors:  Siv Jönsson; Anja Henningsson; Monica Edholm; Tomas Salmonson
Journal:  Clin Pharmacokinet       Date:  2012-02-01       Impact factor: 6.447

2.  Developing and delivering clinical pharmacology in pharmaceutical companies.

Authors:  Duncan Richards
Journal:  Br J Clin Pharmacol       Date:  2012-06       Impact factor: 4.335

Review 3.  Prediction of exposure-response relationships to support first-in-human study design.

Authors:  John P Gibbs
Journal:  AAPS J       Date:  2010-10-22       Impact factor: 4.009

4.  Model-based drug development: the road to quantitative pharmacology.

Authors:  Liping Zhang; Vikram Sinha; S Thomas Forgue; Sophie Callies; Lan Ni; Richard Peck; Sandra R B Allerheiligen
Journal:  J Pharmacokinet Pharmacodyn       Date:  2006-06-13       Impact factor: 2.745

Review 5.  Overview of model-building strategies in population PK/PD analyses: 2002-2004 literature survey.

Authors:  C Dartois; K Brendel; E Comets; C M Laffont; C Laveille; B Tranchand; F Mentré; A Lemenuel-Diot; P Girard
Journal:  Br J Clin Pharmacol       Date:  2007-08-15       Impact factor: 4.335

Review 6.  Integrated pharmacokinetics and pharmacodynamics in drug development.

Authors:  Jasper Dingemanse; Silke Appel-Dingemanse
Journal:  Clin Pharmacokinet       Date:  2007       Impact factor: 6.447

7.  Model-based decision making in early clinical development: minimizing the impact of a blood pressure adverse event.

Authors:  Mark Stroh; Carol Addy; Yunhui Wu; S Aubrey Stoch; Nazaneen Pourkavoos; Michelle Groff; Yang Xu; John Wagner; Keith Gottesdiener; Craig Shadle; Hong Wang; Kimberly Manser; Gregory A Winchell; Julie A Stone
Journal:  AAPS J       Date:  2009-02-06       Impact factor: 4.009

8.  Literature mining on pharmacokinetics numerical data: a feasibility study.

Authors:  Zhiping Wang; Seongho Kim; Sara K Quinney; Yingying Guo; Stephen D Hall; Luis M Rocha; Lang Li
Journal:  J Biomed Inform       Date:  2009-04-02       Impact factor: 6.317

9.  Informative study designs to identify true parameter-covariate relationships.

Authors:  Phey Yen Han; Carl M J Kirkpatrick; Bruce Green
Journal:  J Pharmacokinet Pharmacodyn       Date:  2009-03-27       Impact factor: 2.745

10.  Translational pharmacokinetic-pharmacodynamic modeling from nonclinical to clinical development: a case study of anticancer drug, crizotinib.

Authors:  Shinji Yamazaki
Journal:  AAPS J       Date:  2012-12-19       Impact factor: 4.009

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