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

Granana-Castillo, Sandra, Williams, Angharad, Pham, Thao, Khoo, Saye ORCID: 0000-0002-2769-0967, Hodge, Daryl, Akpan, Asangaedem ORCID: 0000-0002-1764-8669, Bearon, Rachel and Siccardi, Marco ORCID: 0000-0002-3539-7867
(2023) General Framework to Quantitatively Predict Pharmacokinetic Induction Drug-Drug Interactions Using In Vitro Data. CLINICAL PHARMACOKINETICS, 62 (5). pp. 737-748.

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<h4>Introduction</h4>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.<h4>Methods</h4>The area under the curve ratio (AUC<sub>ratio</sub>) 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 AUC<sub>ratio</sub> (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.<h4>Results</h4>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.<h4>Conclusion</h4>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.

Item Type: Article
Uncontrolled Keywords: Humans, Carbamazepine, Rifampin, Cytochrome P-450 Enzyme System, Drug Interactions, Models, Biological, Cytochrome P-450 CYP3A
Divisions: Faculty of Health and Life Sciences
Faculty of Health and Life Sciences > Institute of Systems, Molecular and Integrative Biology
Depositing User: Symplectic Admin
Date Deposited: 31 Mar 2023 10:25
Last Modified: 09 Jun 2023 09:02
DOI: 10.1007/s40262-023-01229-3
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