Purpose: To treat malaria, HIV-infected patients normally receive artemether (80 mg twice daily) concurrently with\r\nantiretroviral therapy and drug-drug interactions can potentially occur. Artemether is a substrate of CYP3A4 and\r\nCYP2B6, antiretrovirals such as efavirenz induce these enzymes and have the potential to reduce artemether\r\npharmacokinetic exposure. The aim of this study was to develop an in vitro in vivo extrapolation (IVIVE) approach to\r\nmodel the interaction between efavirenz and artemether. Artemether dose adjustments were then simulated in\r\norder to predict optimal dosing in co-infected patients and inform future interaction study design.\r\nMethods: In vitro data describing the chemical properties, absorption, distribution, metabolism and elimination of\r\nefavirenz and artemether were obtained from published literature and included in a physiologically based\r\npharmacokinetic model (PBPK) to predict drug disposition simulating virtual clinical trials. Administration of\r\nefavirenz and artemether, alone or in combination, were simulated to mirror previous clinical studies and facilitate\r\nvalidation of the model and realistic interpretation of the simulation. Efavirenz (600 mg once daily) was\r\nadministered to 50 virtual subjects for 14 days. This was followed by concomitant administration of artemether\r\n(80 mg eight hourly) for the first two doses and 80 mg (twice daily) for another two days.\r\nResults: Simulated pharmacokinetics and the drug-drug interaction were in concordance with available clinical\r\ndata. Efavirenz induced first pass metabolism and hepatic clearance, reducing artemether Cmax by 60% and AUC by\r\n80%. Dose increases of artemether, to correct for the interaction, were simulated and a dose of 240 mg was\r\npredicted to be sufficient to overcome the interaction and allow therapeutic plasma concentrations of artemether.\r\nConclusions: The model presented here provides a rational platform to inform the design for a clinical drug\r\ninteraction study that may save time and resource while the optimal dose is determined empirically. Wider\r\napplication of IVIVE could help researchers gain a better understanding of the molecular mechanisms underpinning\r\nvariability in drug disposition.
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