Evaluation of bottom‐up modeling of the blood–brain barrier to improve brain penetration prediction via physiologically based pharmacokinetic modeling

Bowman, Christine M.; Ma, Fang; Mao, Jialin; Plise, Emile G.; Chen, Eugene; Liu, Liling; Zhang, Shu; Chen, Yuan

doi:10.1002/bdd.2344

Cited by 3 publications

References 32 publications

(56 reference statements)

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Dose Optimization Informed by PBPK Modeling: State‐of‐the Art and Future

Rowland Yeo,

Gil Berglund,

Chen

2024

Clin Pharma and Therapeutics

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Model‐informed drug development (MIDD) is a powerful quantitative approach that plays an integral role in drug development and regulatory review. While applied throughout the life cycle of the development of new drugs, a key application of MIDD is to inform clinical trial design including dose selection and optimization. To date, physiologically‐based pharmacokinetic (PBPK) modeling, an established component of the MIDD toolkit, has mainly been used for assessment of drug–drug interactions (DDIs) and consequential dose adjustments in regulatory submissions. As a result of recent scientific advances and growing confidence in the utility of the approach, PBPK models are being increasingly applied to provide dose recommendations for subjects with differing ages, genetics, and disease states. In this review, we present our perspective on the current landscape of regulatory acceptance of PBPK applications supported by relevant case studies. We also discuss the recent progress and future challenges associated with expanding the utility of PBPK models into emerging areas for regulatory decision making, especially dose optimization in highly vulnerable and understudied populations and facilitating diversity in clinical trials.

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Dose Optimization Informed by PBPK Modeling: State‐of‐the Art and Future

Rowland Yeo,

Gil Berglund,

Chen

2024

Clin Pharma and Therapeutics

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Membrane transporters in drug development and as determinants of precision medicine

Galetin,

Brouwer,

Tweedie

et al. 2024

Nat Rev Drug Discov

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Reliability of in vitro data for the mechanistic prediction of brain extracellular fluid pharmacokinetics of P-glycoprotein substrates in vivo; are we scaling correctly?

Valkengoed,

Hirasawa,

Rottschäfer

et al. 2024

Preprint

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Plasma pharmacokinetic (PK) profiles often do not resemble the PK within the central nervous system (CNS) because of blood-brain-border (BBB) processes, like active efflux by P-glycoprotein (P-gp). Methods to predict CNS-PK are therefore desired. Here we investigate whether in vitro apparent permeability (Papp) and corrected efflux ratio (ERc) extracted from literature can be repurposed as input for the LeiCNS-PK3.4 physiologically-based PK model to confidently predict rat brain extracellular fluid (ECF) PK of P-gp substrates. Literature values of in vitro Caco-2, LLC-PK1-mdr1a/MDR1, and MDCKII-MDR1 cell line transport data were used to calculate P-gp efflux clearance (CLPgp). Subsequently, CLPgp was scaled from in vitro to in vivo through a relative expression factor (REF) based on P-gp expression differences. BrainECF PK was predicted well (within 2-fold error of the observed data) for 2 out of 4 P-gp substrates after short infusions and 3 out of 4 P-gp substrates after continuous infusions. Prediction accuracy was influenced by variability in reported ERc and in vitro P-gp expression. Notably, use of Papp and in vitro P-gp expression obtained from a single study did not guarantee an accurate prediction; it often resulted in worse predictions than when using in vitro expression values reported by other labs. Using Papp and P-gp expression as input, LeiCNS-PK3.4 shows promise in predicting brainECF PK but this study highlights that the in vitro to in vivo translation is not yet robust. We conclude that more information is needed about context and drug dependency of in vitro data for robust brainECF PK predictions.

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Evaluation of bottom‐up modeling of the blood–brain barrier to improve brain penetration prediction via physiologically based pharmacokinetic modeling

Cited by 3 publications

References 32 publications

Dose Optimization Informed by PBPK Modeling: State‐of‐the Art and Future

Dose Optimization Informed by PBPK Modeling: State‐of‐the Art and Future

Membrane transporters in drug development and as determinants of precision medicine

Reliability of in vitro data for the mechanistic prediction of brain extracellular fluid pharmacokinetics of P-glycoprotein substrates in vivo; are we scaling correctly?

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