PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 2: Comparative assessment of prediction methods of human volume of distribution

Jones, Rhys D.O.; Jones, Hannah M.; Rowland, Malcolm; Gibson, Christopher R; Yates, James; Chien, Jenny Y.; Ring, Barbara J.; Adkison, Kimberly K.; Ku, M. Sherry; He, Handan; Vuppugalla, Ragini; Marathe, Punit; Fischer, Volker; Dutta, Sandeep; Sinha, Vikash; Bjornsson, Thorir D.; Lavé, Thierry; Poulin, Patrick

doi:10.1002/jps.22553

Cited by 119 publications

(128 citation statements)

References 31 publications

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“…The prediction accuracy of preclinical V ss can then be assessed as in vivo data become available and can be used to indicate the level of confidence (uncertainty) in the human V ss prediction. In cases where confidence is low, allometric methods may be applied [28].…”

Section: Distributionmentioning

confidence: 99%

Application of IVIVE and PBPK modeling in prospective prediction of clinical pharmacokinetics: strategy and approach during the drug discovery phase with four case studies

Chen¹,

Jin²,

Mukadam³

et al. 2012

Biopharm & Drug Disp

102

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Prospective simulations of human pharmacokinetic (PK) parameters and plasma concentration-time curves using in vitro in vivo extrapolation (IVIVE) and physiologically based pharmacokinetic (PBPK) models are becoming a vital part of the drug discovery and development process. This paper presents a strategy to deliver prospective simulations in support of clinical candidate nomination. A three stage approach of input parameter evaluation, model selection and multiple scenario simulation is utilized to predict the key components influencing human PK; absorption, distribution and clearance. The Simcyp W simulator is used to illustrate the approach and four compounds are presented as case studies. In general, the prospective predictions captured the observed clinical data well. Predicted C max was within 2-fold of observed data for all compounds and AUC was within 2-fold for all compounds following a single dose and three out of four compounds following multiple doses. Similarly, t max was within 2-fold of observed data for all compounds. However, C last was less accurately captured with two of the four compounds predicting C last within 2-fold of observed data following a single dose. The trend in results was towards overestimation of AUC and C last , this was particularly apparent for compound 2 for which clearance was likely underestimated via IVIVE. The prospective approach to simulating human PK using IVIVE and PBPK modeling outlined here attempts to utilize all available in silico, in vitro and in vivo preclinical data in order to determine the most appropriate assumptions to use in prospective predictions of absorption, distribution and clearance to aid clinical candidate nomination.

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Section: Distributionmentioning

confidence: 99%

Application of IVIVE and PBPK modeling in prospective prediction of clinical pharmacokinetics: strategy and approach during the drug discovery phase with four case studies

Chen¹,

Jin²,

Mukadam³

et al. 2012

Biopharm & Drug Disp

102

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“…Of course, this is not a trivial problem [1][2][3][4][5] because different values for the same parameter can be found in the literature due, in some cases to considerable inter-subjects variability [23 -29]. Accordingly, in order to simplify comparison of the three different drugs considered in this paper (theophylline, temazepam and nimesulide), we decided to rely exclusively on the information reported in [10] as source of PK parameters values.…”

Section: Resultsmentioning

confidence: 99%

“…Quite recently, an initiative of the Pharmaceutical Research and Manufacturers of America (PhRMA) tried to evaluate the reliability of predictive models in terms of of drug efficacy, safety and properties estimation [1][2][3]. For this purpose, 108 clinical compounds (22 % acids, 46 % bases, 18 % zwitterions and 14 % neutrals), supplied by 12 PhRMA member companies, have been considered.…”

Section: Introductionmentioning

confidence: 99%

A physiologically oriented mathematical model for the description of in vivo drug release and absorption

Cont¹,

Abrami²,

Hasa³

et al. 2014

ADMET DMPK

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This paper focuses on a physiologically-oriented mathematical model aimed at studying the in vivo drug release, absorption, distribution, metabolism and elimination (ADME). To this purpose, the model accounts for drug delivery from an ensemble of non-eroding poly-disperse polymeric particles and the subsequent ADME processes. The model outcomes are studied with reference to three widely used drugs: theophylline, temazepam and nimesulide. One of the most important results of this study is the quantitative evaluation of the interplay between the release kinetics and the subsequent ADME processes. Indeed, it is usually assumed that in vivo drug release coincides with in vitro so that the effect exerted by the ADME processes is neglected. In addition, the proposed model may be an important tool for the design of delivery systems since, through proper changes, it could also account for different oral delivery systems.

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“…Since one of the traditional foundations of PBPK modelling is the IVIVE of ADME processes, the selection of the extrapolation model by software developers or model-writers can have a huge impact on the success of predictions. A recently published initiative of the Pharmaceutical Research and Manufacturers of America (PhRMA) aimed to assess the performance of various ADME prediction methods and models in the prediction of human pharmacokinetics from preclinical and in vitro data [54][55][56][57][58]. This extensive evaluation concluded that, for the compound dataset studied, the PBPK approach generally poorly predicted the distribution, and the absorption, or intestinal and hepatic first-pass clearance.…”

Section: Discussionmentioning

confidence: 99%

Physiologically based pharmacokinetic (PBPK) modelling tools: how to fit with our needs?

Bouzom

Ball

Perdaems

et al. 2012

Biopharm & Drug Disp

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In 2005, a survey compared a number of commercial PBPK software available at the time, mainly focusing on 'ready to use' modelling tools. Since then, these tools and software have been further developed and improved to allow modellers to perform WB-PBPK modelling including ADME processes at a high level of sophistication. This review presents a comparison of the features, values and limitations of both the 'ready to use' software and of the traditional user customizable software that are frequently used for the building and use of PBPK models, as well as the challenges associated with the various modelling approaches regarding their current and future use. PBPK models continue to be used more and more frequently during the drug development process since they represent a quantitative, physiologically realistic platform with which to simulate and predict the impact of various potential scenarios on the pharmacokinetics and pharmacodynamics of drugs. The 'ready to use' PBPK software has been a major factor in the increasing use of PBPK modelling in the pharmaceutical industry, opening up the PBPK approach to a broader range of users. The challenge is now to educate and to train scientists and modellers to ensure their appropriate understanding of the assumptions and the limitations linked both to the physiological framework of the 'virtual body' and to the scaling methodology from in vitro to in vivo (IVIVE).

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PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 2: Comparative assessment of prediction methods of human volume of distribution

Cited by 119 publications

References 31 publications

Application of IVIVE and PBPK modeling in prospective prediction of clinical pharmacokinetics: strategy and approach during the drug discovery phase with four case studies

Application of IVIVE and PBPK modeling in prospective prediction of clinical pharmacokinetics: strategy and approach during the drug discovery phase with four case studies

A physiologically oriented mathematical model for the description of in vivo drug release and absorption

Physiologically based pharmacokinetic (PBPK) modelling tools: how to fit with our needs?

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