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In Vitro-In Vivo Extrapolation and Scaling Factors for Clearance of Human and Preclinical Species with Liver Microsomes and Hepatocytes

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Abstract

In vitro-in vivo extrapolation ((IVIVE) and empirical scaling factors (SF) of human intrinsic clearance (CLint) were developed using one of the largest dataset of 455 compounds with data from human liver microsomes (HLM) and human hepatocytes (HHEP). For extended clearance classification system (ECCS) class 2/4 compounds, linear SFs (SFlin) are approximately 1, suggesting enzyme activities in HLM and HHEP are similar to those in vivo under physiological conditions. For ECCS class 1A/1B compounds, a unified set of SFs was developed for CLint. These SFs contain both SFlin and an exponential SF (SFβ) of fraction unbound in plasma (fu,p). The unified SFs for class 1A/1B eliminate the need to identify the transporters involved prior to clearance prediction. The underlying mechanisms of these SFs are not entirely clear at this point, but they serve practical purposes to reduce biases and increase prediction accuracy. Similar SFs have also been developed for preclinical species. For HLM-HHEP disconnect (HLM > HHEP) ECCS class 2/4 compounds that are mainly metabolized by cytochrome P450s/FMO, HLM significantly overpredicted in vivo CLint, while HHEP slightly underpredicted and geometric mean of HLM and HHEP slightly overpredicted in vivo CLint. This observation is different than in rats, where rat liver microsomal CLint correlates well with in vivo CLint for compounds demonstrating permeability-limited metabolism. The good CLint IVIVE developed using HLM and HHEP helps build confidence for prospective predictions of human clearance and supports the continued utilization of these assays to guide structure–activity relationships to improve metabolic stability.

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Data Availability

All data generated or analysed during this study are included in this published article.

Abbreviations

ADME:

Absorption, distribution, metabolism, and excretion

AFE:

Average fold error

ALQ:

Above the limit of quantification

BLQ:

Below the limit of quantification

CLh :

Hepatic clearance

CLint :

Intrinsic clearance

CO2 :

Carbon dioxide

CYP:

Cytochrome P450

DDI:

Drug-drug interaction

DI90% :

Total deviation index (a measure of the fold range that captures 90% of prediction errors)

ECCS:

Extended clearance classification system

ELogD:

Chromatographic LogD

FMO:

Flavin-containing monooxygenase

f u,lm :

Fraction unbound in liver microsomes

f u,p :

Fraction unbound in plasma

HEK-293:

Immortalized human embryonic kidney cell line

HEP:

Hepatocytes

HHEP:

Human hepatocytes

HLM:

Human liver microsomes

HTD 96:

96-Well high throughput equilibrium dialysis device

IV :

Intravenous

IVIVE :

In vitro-in vivo Extrapolation

k w :

Chromatographic capacity factor in aqueous

LC–MS/MS:

Liquid chromatography with tandem mass spectrometry

LM :

Liver microsomes

logD7.4 :

Log10 of distribution coefficient between octanol and pH 7.4 buffer

MDCK:

Madin-Darby canine kidney cell line

MDCK-LE:

Low efflux MDCK cell line (i.e., RRCK)

MgCl2 :

Magnesium chloride

NADPH:

Reduced nicotinamide adenine dinucleotide phosphate

NHP:

Non-human primate

OAT:

Organic anion transporter

OATP:

Organic anion transporting polypeptides

P app :

Apparent permeability

PBPK:

Physiologically based pharmacokinetic modeling

PBS:

Phosphate-buffered saline

PK:

Pharmacokinetics

pKa :

Negative log10 of acid dissociation constant

Rbp :

Blood-to-plasma ratio

rpm:

Revolutions per minute

RRCK:

Ralph and Russ canine kidney cell line (i.e., MDCK-LE)

SAR:

Structure-activity relationships

SF:

Scaling factor

SFβ :

Exponential scaling factor

SFlin :

Linear scaling factor

SFLogD:

Shake-flask LogD

UV:

Ultraviolet

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Acknowledgements

Authors greatly appreciate the input and discussion from Stefanus Steyn, Manthena Varma, and many Pfizer colleagues.

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Authors and Affiliations

  1. Modeling and Simulation, Pfizer Worldwide Research and Development, Cambridge, MA, USA

    David Tess

  2. Modeling and Simulation, Pfizer Worldwide Research and Development, Groton, CT, USA

    George C. Chang & Christopher Keefer

  3. Discovery Sciences, Pfizer Worldwide Research and Development, Groton, CT, USA

    Anthony Carlo

  4. Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, La Jolla, CA, USA

    Rhys Jones

  5. Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, Groton, CT, 06340, USA

    Li Di

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David Tess, George C. Chang, Christopher Keefer, Anthony Carlo, Rhys Jones, and Li Di all contributed to the following areas: substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work; and drafting the work or revising it critically for important intellectual content; and final approval of the version to be published; and agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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Tess, D., Chang, G.C., Keefer, C. et al. In Vitro-In Vivo Extrapolation and Scaling Factors for Clearance of Human and Preclinical Species with Liver Microsomes and Hepatocytes. AAPS J 25, 40 (2023). https://doi.org/10.1208/s12248-023-00800-x

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