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General Framework to Quantitatively Predict Pharmacokinetic Induction Drug–Drug Interactions Using In Vitro Data

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Abstract

Introduction

Metabolic inducers can expose people with polypharmacy to adverse health outcomes. A limited fraction of potential drug–drug interactions (DDIs) have been or can ethically be studied in clinical trials, leaving the vast majority unexplored. In the present study, an algorithm has been developed to predict the induction DDI magnitude, integrating data related to drug-metabolising enzymes.

Methods

The area under the curve ratio (AUCratio) resulting from the DDI with a victim drug in the presence and absence of an inducer (rifampicin, rifabutin, efavirenz, or carbamazepine) was predicted from various in vitro parameters and then correlated with the clinical AUCratio (N = 319). In vitro data including fraction unbound in plasma, substrate specificity and induction potential for cytochrome P450s, phase II enzymes and uptake, and efflux transporters were integrated. To represent the interaction potential, the in vitro metabolic metric (IVMM) was generated by combining the fraction of substrate metabolised by each hepatic enzyme of interest with the corresponding in vitro fold increase in enzyme activity (E) value for the inducer.

Results

Two independent variables were deemed significant and included in the algorithm: IVMM and fraction unbound in plasma. The observed and predicted magnitudes of the DDIs were categorised accordingly: no induction, mild, moderate, and strong induction. DDIs were assumed to be well classified if the predictions were in the same category as the observations, or if the ratio between these two was < 1.5-fold. This algorithm correctly classified 70.5% of the DDIs.

Conclusion

This research presents a rapid screening tool to identify the magnitude of potential DDIs utilising in vitro data which can be highly advantageous in early drug development.

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Authors and Affiliations

  1. Pharmacology and Therapeutics, University of Liverpool, Liverpool, UK

    Sandra Grañana-Castillo, Angharad Williams, Thao Pham, Saye Khoo, Daryl Hodge & Marco Siccardi

  2. Institute of Life Course and Medical Sciences, University of Liverpool and Liverpool University Hospitals NHS FT, Liverpool, UK

    Asangaedem Akpan

  3. NIHR Clinical Research Network, Northwest Coast, Liverpool, UK

    Asangaedem Akpan

  4. Mathematical Sciences, University of Liverpool, Liverpool, UK

    Rachel Bearon

  5. Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, 3rd Floor, William Henry Duncan Building, 6 West Derby Street, Liverpool, L7 8TX, UK

    Marco Siccardi

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Funding

This project is funded by the Engineering and Physical Sciences Research Council (EPSRC).

Conflict of interest

Marco Siccardi has received research grant funding from Janssen and ViiV unrelated to this work. M.S. is currently employed by Labcorp. Saye Khoo has received grant support for the Liverpool drug interaction website (http://www.covid-druginteractions.org) from AbbVie, Gilead, MSD, Novartis, and Sobi. S.K. has also received speakers’ honoraria from ViiV Healthcare, Gilead Sciences, and AbbVie; consultancy fees from ViiV Healthcare and Merck; and research funding from Gilead Sciences and ViiV Healthcare unrelated to this work. Sandra Grañana-Castillo, Angharad Williams, Thao Pham, Daryl Hodge, Asangaedem Akpan, and Rachel Bearon declare that they have no potential conflicts of interest that might be relevant to the contents of this manuscript.

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

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All authors contributed to the study conception and design. Data collection was performed by SGC and analysis were performed by SGC with additional contribution of AW. The first draft of the manuscript was written by SGC, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Grañana-Castillo, S., Williams, A., Pham, T. et al. General Framework to Quantitatively Predict Pharmacokinetic Induction Drug–Drug Interactions Using In Vitro Data. Clin Pharmacokinet 62, 737–748 (2023). https://doi.org/10.1007/s40262-023-01229-3

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