Literature DB >> 32985885

Predicting the Human Hepatic Clearance of Acidic and Zwitterionic Drugs.

David A Tess1, Heather Eng2, Amit S Kalgutkar1, John Litchfield1, David J Edmonds1, David A Griffith1, Manthena V S Varma2.   

Abstract

Prospective predictions of human hepatic clearance for anionic/zwitterionic compounds, which are oftentimes subjected to transporter-mediated uptake, are challenging in drug discovery. We evaluated the utility of preclinical species, rats and cynomolgus monkeys [nonhuman primates (NHPs)], to predict the human hepatic clearance using a diverse set of acidic/zwitterionic drugs. Preclinical clearance data were generated following intravenous dosing in rats/NHPs and compared to the human clearance data (n = 18/27). Single-species scaling of NHP clearance with an allometric exponent of 0.50 allowed for good prediction of human clearance (fold error ∼2.1, bias ∼1.0), with ∼86% predictions within 3-fold. In comparison, rats underpredicted the clearance of lipophilic acids, while overprediction was noted for hydrophilic acids. Finally, an in vitro clearance assay based on human hepatocytes, which is routinely used in discovery setting, markedly underpredicted human clearance (bias ∼0.12). Collectively, this study provides insights into the usefulness of the preclinical models in enabling pharmacokinetic optimization for acid/zwitterionic drug candidates.

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Year:  2020        PMID: 32985885     DOI: 10.1021/acs.jmedchem.0c01033

Source DB:  PubMed          Journal:  J Med Chem        ISSN: 0022-2623            Impact factor:   7.446


  4 in total

1.  In Vitro - in Vivo Extrapolation of Hepatic Clearance in Preclinical Species.

Authors:  David A Tess; Sangwoo Ryu; Li Di
Journal:  Pharm Res       Date:  2022-03-07       Impact factor: 4.200

Review 2.  Current Approaches for Predicting Human PK for Small Molecule Development Candidates: Findings from the IQ Human PK Prediction Working Group Survey.

Authors:  Carl Petersson; Xin Zhou; Joerg Berghausen; David Cebrian; Michael Davies; Kevin DeMent; Peter Eddershaw; Arian Emami Riedmaier; Alix F Leblanc; Nenad Manveski; Punit Marathe; Panteleimon D Mavroudis; Robin McDougall; Neil Parrott; Andreas Reichel; Charles Rotter; David Tess; Laurie P Volak; Guangqing Xiao; Zheng Yang; James Baker
Journal:  AAPS J       Date:  2022-07-19       Impact factor: 3.603

3.  LipMetE (Lipophilic Metabolism Efficiency) as a Simple Guide for Half-Life and Dosing Regimen Prediction of Oral Drugs.

Authors:  Giuseppe Cecere; Laura Guasch; Andres M Olivares-Morales; Kenichi Umehara; Antonia F Stepan
Journal:  ACS Med Chem Lett       Date:  2022-08-23       Impact factor: 4.632

4.  Predicting Total Drug Clearance and Volumes of Distribution Using the Machine Learning-Mediated Multimodal Method through the Imputation of Various Nonclinical Data.

Authors:  Hiroaki Iwata; Tatsuru Matsuo; Hideaki Mamada; Takahisa Motomura; Mayumi Matsushita; Takeshi Fujiwara; Kazuya Maeda; Koichi Handa
Journal:  J Chem Inf Model       Date:  2022-08-22       Impact factor: 6.162

  4 in total

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