Literature DB >> 19003542

Concepts and challenges in quantitative pharmacology and model-based drug development.

Liping Zhang1, Marc Pfister, Bernd Meibohm.   

Abstract

Model-based drug development (MBDD) has been recognized as a concept to improve the efficiency of drug development. The acceptance of MBDD from regulatory agencies, industry, and academia has been growing, yet today's drug development practice is still distinctly distant from MBDD. This manuscript is aimed at clarifying the concept of MBDD and proposing practical approaches for implementing MBDD in the pharmaceutical industry. The following concepts are defined and distinguished: PK-PD modeling, exposure-response modeling, pharmacometrics, quantitative pharmacology, and MBDD. MBDD is viewed as a paradigm and a mindset in which models constitute the instruments and aims of drug development efforts. MBDD covers the whole spectrum of the drug development process instead of being limited to a certain type of modeling technique or application area. The implementation of MBDD requires pharmaceutical companies to foster innovation and make changes at three levels: (1) to establish mindsets that are willing to get acquainted with MBDD, (2) to align processes that are adaptive to the requirements of MBDD, and (3) to create a closely collaborating organization in which all members play a role in MBDD. Pharmaceutical companies that are able to embrace the changes MBDD poses will likely be able to improve their success rate in drug development, and the beneficiaries will ultimately be the patients in need.

Entities:  

Mesh:

Substances:

Year:  2008        PMID: 19003542      PMCID: PMC2628212          DOI: 10.1208/s12248-008-9062-3

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


  30 in total

1.  Quantized surface complementarity diversity (QSCD): a model based on small molecule-target complementarity.

Authors:  E A Wintner; C C Moallemi
Journal:  J Med Chem       Date:  2000-05-18       Impact factor: 7.446

2.  Impact of population pharmacokinetic-pharmacodynamic analyses on the drug development process: experience at Parke-Davis.

Authors:  S C Olson; H Bockbrader; R A Boyd; J Cook; J R Koup; R L Lalonde; P H Siedlik; J R Powell
Journal:  Clin Pharmacokinet       Date:  2000-05       Impact factor: 6.447

3.  Clinical trial simulation of docetaxel in patients with cancer as a tool for dosage optimization.

Authors:  C Veyrat-Follet; R Bruno; R Olivares; G R Rhodes; P Chaikin
Journal:  Clin Pharmacol Ther       Date:  2000-12       Impact factor: 6.875

Review 4.  Pharmacokinetics/pharmacodynamics in drug development: an industrial perspective.

Authors:  P Chaikin; G R Rhodes; R Bruno; S Rohatagi; C Natarajan
Journal:  J Clin Pharmacol       Date:  2000-12       Impact factor: 3.126

Review 5.  Pharmacokinetic/pharmacodynamic studies in drug product development.

Authors:  Bernd Meibohm; Hartmut Derendorf
Journal:  J Pharm Sci       Date:  2002-01       Impact factor: 3.534

Review 6.  Can the pharmaceutical industry reduce attrition rates?

Authors:  Ismail Kola; John Landis
Journal:  Nat Rev Drug Discov       Date:  2004-08       Impact factor: 84.694

Review 7.  Learning versus confirming in clinical drug development.

Authors:  L B Sheiner
Journal:  Clin Pharmacol Ther       Date:  1997-03       Impact factor: 6.875

Review 8.  Relevance of the application of pharmacokinetic-pharmacodynamic modelling concepts in drug development. The "wooden shoe' paradigm.

Authors:  D D Breimer; M Danhof
Journal:  Clin Pharmacokinet       Date:  1997-04       Impact factor: 6.447

9.  Optimizing dose selection with modeling and simulation: application to the vasopeptidase inhibitor M100240.

Authors:  Marc Pfister; Nancy E Martin; Lloyd P Haskell; Jeffrey S Barrett
Journal:  J Clin Pharmacol       Date:  2004-06       Impact factor: 3.126

10.  Effectiveness of accelerated perioperative care and rehabilitation intervention compared to current intervention after hip and knee arthroplasty. A before-after trial of 247 patients with a 3-month follow-up.

Authors:  Kristian Larsen; Karen Elisabeth Hvass; Torben B Hansen; Per B Thomsen; Kjeld Søballe
Journal:  BMC Musculoskelet Disord       Date:  2008-04-28       Impact factor: 2.362

View more
  32 in total

1.  Population pharmacokinetic/pharmacodynamic analyses as the basis for dosing of therapeutic monoclonal antibodies.

Authors:  Bernd Meibohm
Journal:  Clin Pharmacokinet       Date:  2011-12-01       Impact factor: 6.447

2.  Population pharmacokinetics and pharmacodynamics of bivalirudin in young healthy Chinese volunteers.

Authors:  Dong-mei Zhang; Kun Wang; Xia Zhao; Yun-fei Li; Qing-shan Zheng; Zi-ning Wang; Yi-min Cui
Journal:  Acta Pharmacol Sin       Date:  2012-06-04       Impact factor: 6.150

Review 3.  Pharmacokinetics and pharmacokinetic-pharmacodynamic correlations of therapeutic peptides.

Authors:  Lei Diao; Bernd Meibohm
Journal:  Clin Pharmacokinet       Date:  2013-10       Impact factor: 6.447

Review 4.  At the bench: the key role of PK-PD modelling in enabling the early discovery of biologic therapies.

Authors:  Mark Penney; Balaji Agoram
Journal:  Br J Clin Pharmacol       Date:  2014-05       Impact factor: 4.335

5.  Systems pharmacology: bridging systems biology and pharmacokinetics-pharmacodynamics (PKPD) in drug discovery and development.

Authors:  Piet H van der Graaf; Neil Benson
Journal:  Pharm Res       Date:  2011-05-11       Impact factor: 4.200

6.  Dose selection using a semi-mechanistic integrated glucose-insulin-glucagon model: designing phase 2 trials for a novel oral glucokinase activator.

Authors:  Xin Zhang; Karen Schneck; Juliana Bue-Valleskey; Kwee Poo Yeo; Michael Heathman; Vikram Sinha
Journal:  J Pharmacokinet Pharmacodyn       Date:  2012-12-22       Impact factor: 2.745

Review 7.  Dashboard systems: implementing pharmacometrics from bench to bedside.

Authors:  Diane R Mould; Richard N Upton; Jessica Wojciechowski
Journal:  AAPS J       Date:  2014-06-20       Impact factor: 4.009

Review 8.  Model-based clinical drug development in the past, present and future: a commentary.

Authors:  Holly Kimko; José Pinheiro
Journal:  Br J Clin Pharmacol       Date:  2015-01       Impact factor: 4.335

9.  Application of a physiologically based pharmacokinetic model informed by a top-down approach for the prediction of pharmacokinetics in chronic kidney disease patients.

Authors:  Hiroyuki Sayama; Hiroaki Takubo; Hiroshi Komura; Motohiro Kogayu; Masahiro Iwaki
Journal:  AAPS J       Date:  2014-06-11       Impact factor: 4.009

10.  Translational Framework Predicting Tumour Response in Gemcitabine-Treated Patients with Advanced Pancreatic and Ovarian Cancer from Xenograft Studies.

Authors:  Maria Garcia-Cremades; Celine Pitou; Philip W Iversen; Iñaki F Troconiz
Journal:  AAPS J       Date:  2019-01-31       Impact factor: 4.009

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.