Prediction of Drug Clearance from Enzyme and Transporter Kinetics

Kulkarni, Priyanka; Youssef, Amir S; Argikar, Aneesh A.

doi:10.1007/978-1-0716-1554-6_14

Cited by 5 publications

(4 citation statements)

References 230 publications

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“…Potentially we could also apply our bias model to improve predictions of human clearance. Without the ML models, traditionally scalars had been developed for IVIVE scaling per compound class . While working out such scalars for every new project or compound class might be tedious, they can practically be transferred to IVIVE for different species.…”

Section: Discussionmentioning

confidence: 99%

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A Machine Learning Framework to Improve Rat Clearance Predictions and Inform Physiologically Based Pharmacokinetic Modeling

Andrews-Morger,

Reutlinger,

Parrott

et al. 2023

Mol. Pharmaceutics

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During drug discovery and development, achieving appropriate pharmacokinetics is key to establishment of the efficacy and safety of new drugs. Physiologically based pharmacokinetic (PBPK) models integrating in vitro-to-in vivo extrapolation have become an essential in silico tool to achieve this goal. In this context, the most important and probably most challenging pharmacokinetic parameter to estimate is the clearance. Recent work on high-throughput PBPK modeling during drug discovery has shown that a good estimate of the unbound intrinsic clearance (CL int,u, ) is the key factor for useful PBPK application. In this work, three different machine learning-based strategies were explored to predict the rat CL int,u as the input into PBPK. Therefore, in vivo and in vitro data was collected for a total of 2639 proprietary compounds. The strategies were compared to the standard in vitro bottom-up approach. Using the well-stirred liver model to back-calculate in vivo CL int,u from in vivo rat clearance and then training a machine learning model on this CL int,u led to more accurate clearance predictions (absolute average fold error (AAFE) 3.1 in temporal cross-validation) than the bottom-up approach (AAFE 3.6-16, depending on the scaling method) and has the advantage that no experimental in vitro data is needed. However, building a machine learning model on the bias between the back-calculated in vivo CL int,u and the bottom-up scaled in vitro CL int,u also performed well. For example, using unbound hepatocyte scaling, adding the bias prediction improved the AAFE in the temporal cross-validation from 16 for bottom-up to 2.9 together with the bias prediction. Similarly, the log Pearson r 2 improved from 0.1 to 0.29. Although it would still require in vitro measurement of CL int,u. , using unbound scaling for the bottom-up approach, the need for correction of the f u,inc by f u,p data is circumvented. While the above-described ML models were built on all data points available per approach, it is discussed that evaluation comparison across all approaches could only be performed on a subset because ca. 75% of the molecules had missing or unquantifiable measurements of the fraction unbound in plasma or in vitro unbound intrinsic clearance, or they dropped out due to the blood-flow limitation assumed by the well-stirred model. Advantageously, by predicting CL int,u as the input into PBPK, existing workflows can be reused and the prediction of the in vivo clearance and other PK parameters can be improved.

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Section: Discussionmentioning

confidence: 99%

“…Without the ML models, traditionally scalars had been developed for IVIVE scaling per compound class. 29 While working out such scalars for every new project or compound class might be tedious, they can practically be transferred to IVIVE for different species. However, this hypothesis needs to be confirmed in future studies.…”

Section: Molecularmentioning

confidence: 99%

A Machine Learning Framework to Improve Rat Clearance Predictions and Inform Physiologically Based Pharmacokinetic Modeling

Andrews-Morger,

Reutlinger,

Parrott

et al. 2023

Mol. Pharmaceutics

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“…The advantages of hepatocyte suspension include encompassing a full spectrum of drugmetabolizing enzymes and the absence of a requirement for cofactors to initiate metabolic physiological reactions (Kulkarni et al, 2021). To monitor parent compound depletion over time, a liquid chromatography (LC) system coupled with a triple quad mass spectrometer (QqQ) is normally used with the high sensitivity and selectivity of multiple reaction monitoring (MRM) methods.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Hepatocyte Stability Assay for Simultaneous Metabolic Stability Assessment and Metabolite Profiling

Leung,

Liu,

Cunico

et al. 2024

Drug Metab Dispos

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The determination of metabolic stability is critical for drug discovery programs, allowing for the optimization of chemical entities and compound prioritization. As such, it is common to perform high-volume in vitro metabolic stability experiments early in the lead optimization process to understand metabolic liabilities. Additional metabolite identification experiments are subsequently performed for a more comprehensive understanding of the metabolic clearance routes to aid medicinal chemists in the structural design of compounds. Collectively, these experiments require extensive sample preparation and a substantial amount of time and resources.To overcome the challenges, a high-throughput integrated assay for simultaneous hepatocyte metabolic stability assessment and metabolite profiling was developed. This assay platform consists of four parts: 1) an automated liquid-handling system for sample preparation and incubation; 2) a liquid chromatography and high-resolution mass spectrometry-based system to simultaneously monitor the parent compound depletion and metabolite formation; 3) an automated data analysis and report system for hepatic clearance assessment; and 4) a streamlined auto-batch processing for software-based metabolite profiling. The assay platform was evaluated using eight control compounds with various metabolic rates and biotransformation routes in hepatocytes across three species. Multiple sample preparation and data analysis steps were evaluated and validated for accuracy, repeatability, and metabolite coverage. The combined utility of an automated liquid-handling instrument, a high-resolution mass spectrometer, and multiple streamlined data processing software improves the process of these highly demanding screening assays, and allows for simultaneous determination of metabolic stability and metabolite profiles for more efficient lead optimization during early drug discovery.

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“…In vitro tests using human organotypic cells and tissue are at the basis of next generation risk assessment (Bell et al 2018;Bessems et al 2014;Breen et al 2021;Coecke et al 2013;Krewski et al 2020;NRC 2007;Rotroff et al 2010;Wetmore 2015). The application of these in vitro assays in quantitative hazard characterisation depends on the accurate extrapolation of concentration-response relationships from in vitro to in vivo, a process referred to as in vitro to in vitro extrapolation (IVIVE) (Kulkarni et al 2021). The process entails translating in vitro concentration-response relationships to an external dose-response relationship in humans and animals, like an oral dose in a repeat dose pharmacological or toxicological study.…”

Section: Introductionmentioning

confidence: 99%

Towards best use and regulatory acceptance of generic physiologically based kinetic (PBK) models for in vitro-to-in vivo extrapolation (IVIVE) in chemical risk assessment

et al. 2022

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With an increasing need to incorporate new approach methodologies (NAMs) in chemical risk assessment and the concomitant need to phase out animal testing, the interpretation of in vitro assay readouts for quantitative hazard characterisation becomes more important. Physiologically based kinetic (PBK) models, which simulate the fate of chemicals in tissues of the body, play an essential role in extrapolating in vitro effect concentrations to in vivo bioequivalent exposures. As PBK-based testing approaches evolve, it will become essential to standardise PBK modelling approaches towards a consensus approach that can be used in quantitative in vitro-to-in vivo extrapolation (QIVIVE) studies for regulatory chemical risk assessment based on in vitro assays. Based on results of an ECETOC expert workshop, steps are recommended that can improve regulatory adoption: (1) define context and implementation, taking into consideration model complexity for building fit-for-purpose PBK models, (2) harmonise physiological input parameters and their distribution and define criteria for quality chemical-specific parameters, especially in the absence of in vivo data, (3) apply Good Modelling Practices (GMP) to achieve transparency and design a stepwise approach for PBK model development for risk assessors, (4) evaluate model predictions using alternatives to in vivo PK data including read-across approaches, (5) use case studies to facilitate discussions between modellers and regulators of chemical risk assessment. Proof-of-concepts of generic PBK modelling approaches are published in the scientific literature at an increasing rate. Working on the previously proposed steps is, therefore, needed to gain confidence in PBK modelling approaches for regulatory use.

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Prediction of Drug Clearance from Enzyme and Transporter Kinetics

Cited by 5 publications

References 230 publications

A Machine Learning Framework to Improve Rat Clearance Predictions and Inform Physiologically Based Pharmacokinetic Modeling

A Machine Learning Framework to Improve Rat Clearance Predictions and Inform Physiologically Based Pharmacokinetic Modeling

An Integrated Hepatocyte Stability Assay for Simultaneous Metabolic Stability Assessment and Metabolite Profiling

Towards best use and regulatory acceptance of generic physiologically based kinetic (PBK) models for in vitro-to-in vivo extrapolation (IVIVE) in chemical risk assessment

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