Literature DB >> 19252332

Incorporating receptor theory in mechanism-based pharmacokinetic-pharmacodynamic (PK-PD) modeling.

Bart A Ploeger1, Piet H van der Graaf, Meindert Danhof.   

Abstract

Pharmacokinetic-Pharmacodynamic (PK-PD) modeling helps to better understand drug efficacy and safety and has, therefore, become a powerful tool in the learning-confirming cycles of drug-development. In translational drug research, mechanism-based PK-PD modeling has been recognized as a tool for bringing forward early insights in drug efficacy and safety into the clinical development. These models differ from descriptive PK-PD models in that they quantitatively characterize specific processes in the causal chain between drug administration and effect. This includes target site distribution, binding and activation, pharmacodynamic interactions, transduction and homeostatic feedback mechanisms. Compared to descriptive models mechanism-based PK-PD models that utilize receptor theory concepts for characterization of target binding and target activation processes have improved properties for extrapolation and prediction. In this respect, receptor theory constitutes the basis for 1) prediction of in vivo drug concentration-effect relationships and 2) characterization of target association-dissociation kinetics as determinants of hysteresis in the time course of the drug effect. This approach intrinsically distinguishes drug- and system specific parameters explicitly, allowing accurate extrapolation from in vitro to in vivo and across species. This review provides an overview of recent developments in incorporating receptor theory in PK-PD modeling with a specific focus on the identifiability of these models.

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Year:  2009        PMID: 19252332     DOI: 10.2133/dmpk.24.3

Source DB:  PubMed          Journal:  Drug Metab Pharmacokinet        ISSN: 1347-4367            Impact factor:   3.614


  21 in total

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3.  PKPD modelling of the interrelationship between mean arterial BP, cardiac output and total peripheral resistance in conscious rats.

Authors:  N Snelder; B A Ploeger; O Luttringer; D F Rigel; R L Webb; D Feldman; F Fu; M Beil; L Jin; D R Stanski; M Danhof
Journal:  Br J Pharmacol       Date:  2013-08       Impact factor: 8.739

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

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Journal:  Pharm Res       Date:  2011-05-11       Impact factor: 4.200

5.  Inhibition of snowshoe hare succinate dehydrogenase activity as a mechanism of deterrence for papyriferic acid in birch.

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6.  Estimation of binding rate constants using a simultaneous mixed-effects method: application to monoamine transporter reuptake inhibitor reboxetine.

Authors:  Neil Benson; Nelleke Snelder; Bart Ploeger; Carolyn Napier; Harriet Sale; Nigel J M Birdsall; Richard P Butt; Piet H van der Graaf
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7.  Pharmacokinetic-pharmacodynamic modeling of alpha interferon response induced by a Toll-like 7 receptor agonist in mice.

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Review 8.  Clozapine, atypical antipsychotics, and the benefits of fast-off D2 dopamine receptor antagonism.

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Review 9.  Molecular mechanism matters: Benefits of mechanistic computational models for drug development.

Authors:  Lindsay E Clegg; Feilim Mac Gabhann
Journal:  Pharmacol Res       Date:  2015-06-18       Impact factor: 7.658

10.  Drug effects on the CVS in conscious rats: separating cardiac output into heart rate and stroke volume using PKPD modelling.

Authors:  N Snelder; B A Ploeger; O Luttringer; D F Rigel; F Fu; M Beil; D R Stanski; M Danhof
Journal:  Br J Pharmacol       Date:  2014-09-05       Impact factor: 8.739

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