Predictive Performance of Physiologically Based Pharmacokinetic Models for the Effect of Food on Oral Drug Absorption: Current Status

Li, Mengyao; Zhao, Ping; Pan, Yinming; Wagner, Christian

doi:10.1002/psp4.12260

Cited by 90 publications

(78 citation statements)

References 33 publications

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“…In a recent study, the FDA colleagues reviewed previously published food effect PBPK models. 11 The authors found that for 48 food effect AUC ratio predictions,~50% were within 1.25-fold. This accuracy is less than that which has been reported for PBPK predictions of enzyme induction (77%), or inhibition (81%) where modeling has been used to waive clinical studies.…”

Section: Discussionmentioning

confidence: 96%

“…The U.S. Food and Drug Administration (FDA) scientists recently summarized some of the limitations in a literature review across BCS I-IV classes, which highlighted the need for an agreed and optimized workflow for prediction of food effect. 11 In this study, we present a workflow for food effect predictions for immediate-release (IR) formulations of BCS I and BCS II compounds ( Fig. 1) for which clinical data are available in one prandial state, fasted or fed.…”

Section: Introductionmentioning

confidence: 99%

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Food Effect Projections via Physiologically Based Pharmacokinetic Modeling: Predictive Case Studies

Tistaert

Heimbach

Xia³

et al. 2019

Journal of Pharmaceutical Sciences

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Food can alter the absorption of orally administered drugs. Biopharmaceutics physiologically based pharmacokinetic (PBPK) modeling offers the possibility to simulate a compound's pharmacokinetics under fasted or fed states. To advance the utility of PBPK modeling, with a view to regulatory impact, we have pooled our experience across 4 pharmaceutical companies to propose a general multistep PBPK workflow leveraging pre-existing clinical data for immediate-release formulations of Biopharmaceutics Classification System I and II compounds. With this strategy, we wish to promote pragmatic PBPK approaches for compounds where absorption is well understood, that is, compounds with moderate-to-high permeability that are not substrates for uptake transporters. Five case studies demonstrate how food effect can be well predicted using appropriately established and validated models. The case studies integrate solubility and dissolution data for initial model development and apply a "middle-out" validation with clinical data in one prandial state. Then, whenever possible, a validation against both fasted and fed state data is recommended before application of the models prospectively for to-be-marketed formulations. Thus, when combined with limited clinical data, PBPK models could be used to simulate outcomes for new doses, formulations, or active pharmaceutical ingredient forms, in lieu of a clinical food-effect study.

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Section: Discussionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Food Effect Projections via Physiologically Based Pharmacokinetic Modeling: Predictive Case Studies

Tistaert

Heimbach

Xia³

et al. 2019

Journal of Pharmaceutical Sciences

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“…The use of in silico PBBM modelling to develop CRDS is becoming more widespread, and several examples of this have been presented by both Industry and Regulators [6,[14][15][16]. This approach offers enhanced mechanistic insight into the factors that influence absorption and the interplay between in vitro and in vivo performance.…”

Section: Clinically Relevant Specifications: Connecting Product Perfomentioning

confidence: 99%

Developing Clinically Relevant Dissolution Specifications for Oral Drug Products—Industrial and Regulatory Perspectives

McAllister

Flanagan

Boon³

et al. 2019

Pharmaceutics

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A meeting that was organized by the Academy of Pharmaceutical Sciences Biopharmaceutics and Regulatory Sciences focus groups focused on the challenges of Developing Clinically Relevant Dissolution Specifications (CRDS) for Oral Drug Products. Industrial Scientists that were involved in product development shared their experiences with in vitro dissolution and in silico modeling approaches to establish clinically relevant dissolution specifications. The regulators shared their perspectives on the acceptability of these different strategies for the development of acceptable specifications. The meeting also reviewed several collaborative initiatives that were relevant to regulatory biopharmaceutics. Following the scientific presentations, a roundtable session provided an opportunity for delegates to discuss the information that was shared during the presentations, debate key questions, and propose strategies to make progress in this critical area of regulatory biopharmaceutics. It was evident from the presentations and subsequent discussions that progress continues to be made with approaches to establish robust CRDS. Further dialogue between industry and regulatory agencies greatly assisted future developments and key areas for focused discussions on CRDS were identified.

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“…In this study, PBPK modelling described 50% of the observed food effect within a predefined boundary of 25%. The successful application of PBPK modelling in food‐effect predictions requires model verification against in vivo clinical data (Li, Zhao, Pan, & Wagner, ).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of food effect on the oral absorption of clarithromycin from immediate release tablet using physiological modelling

Radwan

Jayyousi

Shraim

et al. 2019

Biopharm & Drug Disp

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Predictive Performance of Physiologically Based Pharmacokinetic Models for the Effect of Food on Oral Drug Absorption: Current Status

Cited by 90 publications

References 33 publications

Food Effect Projections via Physiologically Based Pharmacokinetic Modeling: Predictive Case Studies

Food Effect Projections via Physiologically Based Pharmacokinetic Modeling: Predictive Case Studies

Developing Clinically Relevant Dissolution Specifications for Oral Drug Products—Industrial and Regulatory Perspectives

Evaluation of food effect on the oral absorption of clarithromycin from immediate release tablet using physiological modelling

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