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Simplifying the Extended Clearance Concept Classification System (EC3S) to Guide Clearance Prediction in Drug Discovery

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Abstract

Purpose

The Extended Clearance Concept Classification System was established as a development-stage tool to provide a framework for identifying fundamental mechanism(s) governing drug disposition in humans. In the present study, the applicability of the EC3S in drug discovery has been investigated. In its current format, the EC3S relies on low-throughput hepatocyte uptake data, which are not frequently generated in a discovery setting.

Methods

A relationship between hepatocyte uptake clearance and MDCK permeability was first established along with intrinsic clearance from human liver microsomes. The performance of this approach was examined by categorizing 64 drugs into EC3S classes and comparing the predicted major elimination pathway(s) to that observed in humans. As an extension of the work, the ability of the simplified EC3S to predict human systemic clearance based on intrinsic clearance generated using in-vitro metabolic systems was evaluated.

Results

The assessment enabled the use of MDCK permeability and unscaled unbound intrinsic clearance to generate cut-off criteria to categorize compounds into four EC3S classes: Class 12ab, 2cd, 34ab, and 34cd, with major elimination mechanism(s) assigned to each class. The predictivity analysis suggested that systemic clearance could generally be predicted within threefold for EC3S class 12ab and 34ab compounds. For classes 2cd and 34cd, systemic clearance was poorly predicted using in-vitro systems explored in this study.

Conclusion

Collectively, our simplified classification approach is expected to facilitate the identification of mechanism(s) involved in drug elimination, faster resolution of in-vitro to in-vivo disconnects, and better design of mechanistic pharmacokinetic studies in drug discovery.

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Data Availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

AFE:

Average fold error

AAFE:

Absolute average fold error

AO:

Aldehyde oxidase

BSA:

Bovine serum albumin

BCS:

Biopharmaceutical classification system

BDDCS:

Biopharmaceutics Drug Disposition Classification System

CES:

Carboxylesterase

CL:

Clearance

CLint :

Intrinsic clearance

CLhep :

Hepatic clearance

CYP:

Cytochrome P450

DMEM:

Dulbecco's Modified Eagle Medium

ECCS:

Extended Clearance Classification System

EC3S:

Extended Clearance Concept Classification System

ECM:

Extended Clearance Model

FBS:

Fetal bovine serum

FMO:

Flavin-containing monooxygenases

fuhep :

Fraction unbound in hepatocyte incubation

fuinc :

Fraction unbound in incubation

fumic :

Fraction unbound in microsomal incubation

fup :

Fraction unbound in plasma

HBSS:

Hank’s buffered salt solution

HHep:

Human hepatocytes

HLM:

Human liver microsomes

IVIVE:

In-vitro to –in-vivo extrapolation

MAO:

Monoamine oxidase

MDCK:

Madin-Darby canine kidney

MDCK-LE:

Low efflux MDCK cell line

Papp :

Apparent passive permeability

PSinf ,pass :

Passive sinusoidal hepatic uptake

QH :

Hepatic blood flow

Rb :

Blood to plasma ratio

RED:

Rapid equilibrium dialysis

SULT:

Sulfotransferase

UGT:

UDP-glucuronosyltransferase

XO:

Xanthine oxidase

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Acknowledgements

The authors would like to thank Drs. Gian Camenisch and Bernard Faller for their constructive critique of various aspects of this article. We would also like to thank Gaelle Chenal, PhongHung Nguyen, Gaurab KC, Markus Trunzer, and Linda Xiao for their contribution in in-vitro assays. Authors would also like to acknowledge Dr. Rowan Stringer for his help in the data compilation.

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Author notes

  1. Mitesh Patel and Julia Riede are the first two authors contributed equally.

Authors and Affiliations

  1. Pharmacokinetic Sciences, Novartis Institutes for BioMedical Research, Inc., 250 Massachusetts Avenue 2A/242, Cambridge, MA, 02139, USA

    Mitesh Patel, Dallas Bednarczyk & Sujal V. Deshmukh

  2. Pharmacokinetic Sciences, Novartis Institutes for BioMedical Research, Basel, Switzerland

    Julia Riede & Birk Poller

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  1. Mitesh Patel
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Contributions

Participated in research design: Patel, Riede, Poller.

Conducted experiments: Patel, Riede.

Performed data analysis: Riede, Patel, Deshmukh.

Wrote or contributed to the writing of the manuscript: Patel, Riede, Bednarczyk, Deshmukh.

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Correspondence to Sujal V. Deshmukh.

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Patel, M., Riede, J., Bednarczyk, D. et al. Simplifying the Extended Clearance Concept Classification System (EC3S) to Guide Clearance Prediction in Drug Discovery. Pharm Res 40, 937–949 (2023). https://doi.org/10.1007/s11095-023-03482-4

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