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Predicting Clearance Mechanism in Drug Discovery: Extended Clearance Classification System (ECCS)

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Abstract

Early prediction of clearance mechanisms allows for the rapid progression of drug discovery and development programs, and facilitates risk assessment of the pharmacokinetic variability associated with drug interactions and pharmacogenomics. Here we propose a scientific framework – Extended Clearance Classification System (ECCS) – which can be used to predict the predominant clearance mechanism (rate-determining process) based on physicochemical properties and passive membrane permeability. Compounds are classified as: Class 1A – metabolism as primary systemic clearance mechanism (high permeability acids/zwitterions with molecular weight (MW) ≤400 Da), Class 1B – transporter-mediated hepatic uptake as primary systemic clearance mechanism (high permeability acids/zwitterions with MW >400 Da), Class 2 – metabolism as primary clearance mechanism (high permeability bases/neutrals), Class 3A –renal clearance (low permeability acids/zwitterions with MW ≤400 Da), Class 3B – transporter mediated hepatic uptake or renal clearance (low permeability acids/zwitterions with MW >400 Da), and Class 4 – renal clearance (low permeability bases/neutrals). The performance of the ECCS framework was validated using 307 compounds with single clearance mechanism contributing to ≥70% of systemic clearance. The apparent permeability across clonal cell line of Madin − Darby canine kidney cells, selected for low endogenous efflux transporter expression, with a cut-off of 5 × 10−6 cm/s was used for permeability classification, and the ionization (at pH7) was assigned based on calculated pKa. The proposed scheme correctly predicted the rate-determining clearance mechanism to be either metabolism, hepatic uptake or renal for ~92% of total compounds. We discuss the general characteristics of each ECCS class, as well as compare and contrast the framework with the biopharmaceutics classification system (BCS) and the biopharmaceutics drug disposition classification system (BDDCS). Collectively, the ECCS framework is valuable in early prediction of clearance mechanism and can aid in choosing the right preclinical tool kit and strategy for optimizing drug exposure and evaluating clinical risk of pharmacokinetic variability caused by drug interactions and pharmacogenomics.

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ACKNOWLEDGMENTS AND DISCLOSURES

The authors acknowledge Drs. R. Scott Obach, Tristan Maurer, David Tess, Shinji Yamazaki and Larry Tremaine for critical review of the manuscript and valuable suggestions.

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  1. Pfizer Global Research and Development, Pfizer Inc., Groton, Connecticut, 06340, USA

    Manthena V. Varma & Charlotte Allerton

  2. Pfizer Global Research and Development, Pfizer Inc., Cambridge, Massachusetts, 02139, USA

    Stefanus J. Steyn & Ayman F. El-Kattan

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Varma, M.V., Steyn, S.J., Allerton, C. et al. Predicting Clearance Mechanism in Drug Discovery: Extended Clearance Classification System (ECCS). Pharm Res 32, 3785–3802 (2015). https://doi.org/10.1007/s11095-015-1749-4

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