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Background Critical drug-drug interactions (DDI) and hepatotoxicity complicate concurrent use of rifampicin and protease inhibitors. We investigated whether dose escalation of atazanavir/ritonavir could safely overcome the DDI with rifampicin. Methods DERIVE (NCT04121195, EDCTP) was a dose-escalation trial in people with human immunodeficiency virus (HIV) on atazanavir/ritonavir-based antiretroviral therapy (ART) in Uganda. Four intensive pharmacokinetic (PK) visits were performed: PK1 300/100 mg OD (baseline); PK2 300/100 mg OD with rifampicin 600 mg; PK3 300/100 mg twice a day (BID) with rifampicin 600 mg OD; PK4 300/100 mg BID with rifampicin 1200 mg OD. Dolutegravir 50 mg BID throughout the study period ensured participants remained protected from subtherapeutic atazanavir concentrations. The data were interpreted with noncompartmental analysis. The target minimum concentration was atazanavir's protein-adjusted IC90 (PA-IC90), 0.014 mg/L. Results We enrolled 26 participants (23 female) with median (range) age 44 (28–61) years and weight 67 (50–75) kg. Compared with PK1, atazanavir Ctau, and AUC were significantly reduced at PK2 by 96% and 85%, respectively. The escalation to BID dosing (PK3) reduced this difference in Ctau, and AUC24 to 18% lower and 8% higher, respectively. Comparable exposures were maintained with double doses of rifampicin. Lowest Ctau during PK1, PK3, and PK4 were 12.7-, 4.8-, and 8.6-fold higher than PA-IC90, respectively, whereas 65% of PK2 Ctau were below the limit of quantification (0.03 mg/L), hence likely below PA-IC90. No participant developed significant elevation of liver enzymes, reported a serious adverse event (SAE) or experienced rebound viraemia. Conclusions Twice daily atazanavir/ritonavir during rifampicin co-administration was well tolerated and achieved plasma concentrations above the target. Clinical Trials Registration NCT04121195. Registered on 09 October 2019, https://clinicaltrials.gov/ct2/show/NCT04121195. [ABSTRACT FROM AUTHOR]

Drug-drug interaction (DDI) may result in clinical toxicity or treatment failure of antiretroviral therapy (ARV) or comedications. Despite the high number of possible drug combinations, only a limited number of clinical DDI studies are conducted. Computational prediction of DDIs could provide key evidence for the rational management of complex therapies. Our study aimed to assess the potential of deep learning approaches to predict DDIs of clinical relevance between ARVs and comedications. DDI severity grading between 30,142 drug pairs was extracted from the Liverpool HIV Drug Interaction database. Two feature construction techniques were employed: 1) drug similarity profiles by comparing Morgan fingerprints, and 2) embeddings from SMILES of each drug via ChemBERTa, a transformer-based model. We developed DeepARV-Sim and DeepARV-ChemBERTa to predict four categories of DDI: i) Red: drugs should not be co-administered, ii) Amber: interaction of potential clinical relevance manageable by monitoring/dose adjustment, iii) Yellow: interaction of weak relevance and iv) Green: no expected interaction. The imbalance in the distribution of DDI severity grades was addressed by undersampling and applying ensemble learning. DeepARV-Sim and DeepARV-ChemBERTa predicted clinically relevant DDI between ARVs and comedications with a weighted mean balanced accuracy of 0.729 ± 0.012 and 0.776 ± 0.011, respectively. DeepARV-Sim and DeepARV-ChemBERTa have the potential to leverage molecular structures associated with DDI risks and reduce DDI class imbalance, effectively increasing the predictive ability on clinically relevant DDIs. This approach could be developed for identifying high-risk pairing of drugs, enhancing the screening process, and targeting DDIs to study in clinical drug development. [ABSTRACT FROM AUTHOR]