Literature DB >> 34888713

Current Approaches of Building Mechanistic Pharmacodynamic Drug-Target Binding Models.

Jingyi Liang1,2,3, Vi Ngoc-Nha Tran1, Colin Hemez4, Pia Abel Zur Wiesch5,6,7,8.   

Abstract

Mechanistic pharmacodynamic models that incorporate the binding kinetics of drug-target interactions have several advantages in understanding target engagement and the efficacy of a drug dose. However, guidelines on how to build and interpret mechanistic pharmacodynamic drug-target binding models considering both biological and computational factors are still missing in the literature. In this chapter, current approaches of building mechanistic PD models and their advantages are discussed. We also present a methodology on how to select a suitable model considering both biological and computational perspectives, as well as summarize the challenges of current mechanistic PD models.
© 2022. Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Differential equations; Drug–target binding kinetics; Mathematical biology; Mechanistic models; Model selection; Occupancy–efficacy relationship; PK-PD simulation tools; Parameter estimation; Pharmacodynamics

Mesh:

Substances:

Year:  2022        PMID: 34888713     DOI: 10.1007/978-1-0716-1767-0_1

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  21 in total

1.  A spatially distributed computational model of brain cellular metabolism.

Authors:  Daniela Calvetti; Yougan Cheng; Erkki Somersalo
Journal:  J Theor Biol       Date:  2015-04-08       Impact factor: 2.691

2.  Prediction of drug-induced catalepsy based on dopamine D1, D2, and muscarinic acetylcholine receptor occupancies.

Authors:  K Haraguchi; K Ito; H Kotaki; Y Sawada; T Iga
Journal:  Drug Metab Dispos       Date:  1997-06       Impact factor: 3.922

3.  Translating slow-binding inhibition kinetics into cellular and in vivo effects.

Authors:  Grant K Walkup; Zhiping You; Philip L Ross; Eleanor K H Allen; Fereidoon Daryaee; Michael R Hale; John O'Donnell; David E Ehmann; Virna J A Schuck; Ed T Buurman; Allison L Choy; Laurel Hajec; Kerry Murphy-Benenato; Valerie Marone; Sara A Patey; Lena A Grosser; Michele Johnstone; Stephen G Walker; Peter J Tonge; Stewart L Fisher
Journal:  Nat Chem Biol       Date:  2015-04-20       Impact factor: 15.040

4.  Animal-to-human extrapolation of the pharmacokinetic and pharmacodynamic properties of buprenorphine.

Authors:  Ashraf Yassen; Erik Olofsen; Jingmin Kan; Albert Dahan; Meindert Danhof
Journal:  Clin Pharmacokinet       Date:  2007       Impact factor: 6.447

5.  Population pharmacodynamic modelling of aspirin- and Ibuprofen-induced inhibition of platelet aggregation in healthy subjects.

Authors:  Ying Hong; Fran M Gengo; Michelle M Rainka; Vernice E Bates; Donald E Mager
Journal:  Clin Pharmacokinet       Date:  2008       Impact factor: 6.447

6.  Pharmacodynamic modelling of biomarker data in oncology.

Authors:  Robert C Jackson
Journal:  ISRN Pharmacol       Date:  2012-02-16

7.  Using Chemical Reaction Kinetics to Predict Optimal Antibiotic Treatment Strategies.

Authors:  Pia Abel Zur Wiesch; Fabrizio Clarelli; Ted Cohen
Journal:  PLoS Comput Biol       Date:  2017-01-06       Impact factor: 4.475

8.  In vitro and in silico analysis of the effects of D2 receptor antagonist target binding kinetics on the cellular response to fluctuating dopamine concentrations.

Authors:  Wilhelmus E A de Witte; Joost W Versfelt; Maria Kuzikov; Solene Rolland; Victoria Georgi; Philip Gribbon; Sheraz Gul; Dymphy Huntjens; Piet Hein van der Graaf; Meindert Danhof; Amaury Fernández-Montalván; Gesa Witt; Elizabeth C M de Lange
Journal:  Br J Pharmacol       Date:  2018-09-21       Impact factor: 8.739

9.  Virus neutralisation: new insights from kinetic neutralisation curves.

Authors:  Carsten Magnus
Journal:  PLoS Comput Biol       Date:  2013-02-28       Impact factor: 4.475

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