Evidence map›Paper›PMID 21740074›Full record

ArticleClinical pharmacokinetics2011

Prediction of human oral plasma concentration-time profiles using preclinical data: comparative evaluation of prediction approaches in early pharmaceutical discovery.

An Van den Bergh, Vikash Sinha, Ron Gilissen, Roel Straetemans, Koen Wuyts, Denise Morrison, Luc Bijnens, Claire Mackie

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Article in Clinical pharmacokinetics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

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9citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

An Van den BerghADME-TOX Department, Johnson and Johnson Pharmaceutical Research and Development, Beerse, Belgium.
Vikash Sinha
Ron Gilissen
Roel Straetemans
Koen Wuyts
Denise Morrison
Luc Bijnens
Claire Mackie

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

objectivesEmpirically based methods remain one of our tools in human pharmacokinetic predictions. The Dedrick approach and the steady-state plasma drug concentration (C(ss))-mean residence time (MRT) approach are based on the assumption that concentration-time profiles are similar among species, including man, and that curves derived from a variety of animal species can be superimposed after mathematical transformation. In the Dedrick approach the transformation is based on the slope and intercept of the allometric relationship. The C(ss)-MRT approach is based on the implementation of measured animal and predicted human MRT and dose/volume of distribution at steady state (V(ss)). The aims of the present study were to compare the predictive performance of concentration-time profiles obtained by these approaches, to evaluate the prediction of individual pharmacokinetic parameters by these approaches and to further refine these approaches incorporating the experience from our previous work.

methodsA retrospective analysis using 35 proprietary compounds developed at Johnson & Johnson Pharmaceutical Research and Development was conducted to compare the accuracies of the Dedrick and C(ss)-MRT approaches for predicting oral concentration-time profiles and pharmacokinetic parameters in man. In the first step, input for the transformation was based on simple allometry. Then we assessed whether both methods could be fine-tuned by systematically incorporating correction factors (maximum life span potential, brain weight and plasma protein binding), depending on the interspecies relationship. In addition, for the C(ss)-MRT approach, we used formulas based on multivariate regression analysis as input for the transformation.

resultsInclusion of correction factors significantly improved the profile predictability for the Dedrick and C(ss)-MRT approaches. This was mainly linked to an improved prediction of terminal elimination half-life (t(½)), MRT and the ratio between the maximum plasma concentration and the concentration at the last observed time point (C(max)/C(last)). No significant differences were observed between the Dedrick approach with correction factors, the C(ss)-MRT approach with correction factors and the C(ss)-MRT approach, based on the regression equations.

conclusionsBased on the dataset evaluated in this study, we demonstrated that human plasma concentration-time profiles and pharmacokinetic parameters could be predicted with the Dedrick and C(ss)-MRT approaches and that if correction factors were implemented, the predictions improved significantly. With the requirement of only a limited preclinical in vivo pharmacokinetic dataset, these empirical methods could offer potential in the early stages of drug discovery.

Indexed as

Models, BiologicalPharmacokineticsAnimalsDrug DesignDrug DiscoveryDrug Evaluation, PreclinicalHalf-LifeHumansMultivariate AnalysisPharmaceutical PreparationsRegression AnalysisRetrospective StudiesSpecies SpecificityTissue DistributionPharmaceutical Preparations

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.