Forecasting oral absorption across biopharmaceutics classification system classes with physiologically based pharmacokinetic models

Hansmann, Simone; Darwich, Adam S.; Margolskee, Alison; Aarons, Leon; Dressman, Jennifer B.

doi:10.1111/jphp.12618

Cited by 32 publications

(22 citation statements)

References 56 publications

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“…The low permeability cut‐off value of 1 × 10 −4 cm/s is close to the P eff of metoprolol, which is the generally accepted reference standard for high/low permeability. Based on the PBPK simulations performed, Hansmann, Darwich, Margolskee, Aarons, and Dressman () pointed to the use of a permeability value of 1.1 × 10 −4 cm/s as a minimum requirement for classification of a drug as highly permeable. The results of data classification obtained in the present study indicate this P eff value as a boundary between low and moderate permeability, whereas a high permeability cut‐off value is 2.7 × 10 −4 cm/s.…”

Section: Discussionmentioning

confidence: 99%

Application of data mining approach to identify drug subclasses based on solubility and permeability

Gatarić¹,

Parojčić

2019

Biopharm & Drug Disp

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Solubility and permeability are recognized as key parameters governing drug intestinal absorption and represent the basis for biopharmaceutics drug classification. The Biopharmaceutics Classification System (BCS) is widely accepted and adopted by regulatory agencies. However, currently established low/high permeability and solubility boundaries are the subject of the ongoing scientific discussion. The aim of the present study was to apply data mining analysis on the selected drugs data set in order to develop a human permeability predictive model based on selected molecular descriptors, and to perform data clustering and classification to identify drug subclasses with respect to dose/solubility ratio (D/S) and effective permeability (Peff). The Peff values predicted for 30 model drugs for which experimental human permeability data are not available were in good agreement with the reported fraction of drug absorbed. The results of clustering and classification analysis indicate the predominant influence of Peff over D/S. Two Peff cut‐off values (1 × 10−4 and 2.7 × 10−4 cm/s) have been identified indicating the existence of an intermediate group of drugs with moderate permeability. Advanced computational analysis employed in the present study enabled the recognition of complex relationships and patterns within physicochemical and biopharmaceutical properties associated with drug bioperformance.

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Section: Discussionmentioning

confidence: 99%

Application of data mining approach to identify drug subclasses based on solubility and permeability

Gatarić¹,

Parojčić

2019

Biopharm & Drug Disp

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“…An initial QSPR plus PBPK assessment, as when combining ADMETPredictor™ with GastroPlus, will alert the modeller to the major challenges of modelling for a specific molecule. Thus, if the Biopharmaceutics Classification System (BCS) [27] class is predicted as I and II, mechanistic oral absorption modelling predictions may be straightforward, while class III and IV compounds may be more challenging [19,28,29]. Furthermore, the Extended Clearance Classification System framework [30], recently incorporated in ADMETPredictor™ Version 9.0, can predict whether a compound is predominantly cleared by renal elimination or if hepatic transporters may affect elimination.…”

Section: Compound Assessment Using Quantitative Structure-property Rementioning

confidence: 99%

Physiologically Based Pharmacokinetic Modelling for First-In-Human Predictions: An Updated Model Building Strategy Illustrated with Challenging Industry Case Studies

et al. 2019

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Physiologically based pharmacokinetic modelling is well established in the pharmaceutical industry and is accepted by regulatory agencies for the prediction of drug-drug interactions. However, physiologically based pharmacokinetic modelling is valuable to address a much wider range of pharmaceutical applications, and new regulatory impact is expected as its full power is leveraged. As one example, physiologically based pharmacokinetic modelling is already routinely used during drug discovery for in-vitro to in-vivo translation and pharmacokinetic modelling in preclinical species, and this leads to the application of verified models for first-inhuman pharmacokinetic predictions. A consistent cross-industry strategy in this application area would increase confidence in the approach and facilitate further learning. With this in mind, this article aims to enhance a previously published first-inhuman physiologically based pharmacokinetic model-building strategy. Based on the experience of scientists from multiple companies participating in the GastroPlus™ User Group Steering Committee, new Absorption, Distribution, Metabolism and Excretion knowledge is integrated and decision trees proposed for each essential component of a first-inhuman prediction. We have reviewed many relevant scientific publications to identify new findings and highlight gaps that need to be addressed. Finally, four industry case studies for more challenging compounds illustrate and highlight key components of the strategy.

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“…BCS Class II-IV drugs with their solubility-and/or permeability-limited, transporter-dependent exposure are subject to absorption and DDI challenges that can be addressed with PBPK. Applications include absorption [20,21], PPI effect [22], food effect prediction [23,24], bioequivalence assessment through IVIVC for getting a biowaiver for formulation bridging, and DDI assessment [25][26][27][28][29][30], to name a few. A comprehensive list of applications is covered by Shebley et al [7].…”

Section: Requirements For Establishing Confidence In the Utility Of Pmentioning

confidence: 99%

Requirements to Establishing Confidence in Physiologically Based Pharmacokinetic (PBPK) Models and Overcoming Some of the Challenges to Meeting Them

Peters

Dolgos

2019

Clin Pharmacokinet

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When scientifically well-founded, the mechanistic basis of physiologically based pharmacokinetic (PBPK) models can help reduce the uncertainty and increase confidence in extrapolations outside the studied scenarios or studied populations. However, it is not always possible to establish mechanistically credible PBPK models. Requirements to establishing confidence in PBPK models, and challenges to meeting these requirements, are presented in this article. Parameter non-identifiability is the most challenging among the barriers to establishing confidence in PBPK models. Using case examples of small molecule drugs, this article examines the use of hypothesis testing to overcome parameter non-identifiability issues, with the objective of enhancing confidence in the mechanistic basis of PBPK models and thereby improving the quality of predictions that are meant for internal decisions and regulatory submissions. When the mechanistic basis of a PBPK model cannot be established, we propose the use of simpler models or evidence-based approaches.

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Forecasting oral absorption across biopharmaceutics classification system classes with physiologically based pharmacokinetic models

Abstract: For the four drugs studied, it appears that the forecasting accuracy of the PBPK models is related to the BCS class (BCS I > BCS II, BCS III > BCS IV). These results will need to be verified with additional drugs.

Cited by 32 publications

References 56 publications

Application of data mining approach to identify drug subclasses based on solubility and permeability

Application of data mining approach to identify drug subclasses based on solubility and permeability

Physiologically Based Pharmacokinetic Modelling for First-In-Human Predictions: An Updated Model Building Strategy Illustrated with Challenging Industry Case Studies

Requirements to Establishing Confidence in Physiologically Based Pharmacokinetic (PBPK) Models and Overcoming Some of the Challenges to Meeting Them

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