The investigation of the complex population-drug-drug interaction between ritonavir-boosted lopinavir and chloroquine or ivermectin using physiologically-based pharmacokinetic modeling.

OBJECTIVES: Therapy failure caused by complex population-drug-drug (PDDI) interactions including CYP3A4 can be predicted using mechanistic physiologically-based pharmacokinetic (PBPK) modeling. A synergy between ritonavir-boosted lopinavir (LPVr), ivermectin, and chloroquine was suggested to improve COVID-19 treatment. This work aimed to study the PDDI of the two CYP3A4 substrates (ivermectin and chloroquine) with LPVr in mild-to-moderate COVID-19 adults, geriatrics, and pregnancy populations. METHODS: The PDDI of LPVr with ivermectin or chloroquine was investigated. Pearson's correlations between plasma, saliva, and lung interstitial fluid (ISF) levels were evaluated. Target site (lung epithelial lining fluid [ELF]) levels of ivermectin and chloroquine were estimated. RESULTS: Upon LPVr coadministration, while the chloroquine plasma levels were reduced by 30, 40, and 20%, the ivermectin plasma levels were increased by a minimum of 425, 234, and 453% in adults, geriatrics, and pregnancy populations, respectively. The established correlation equations can be useful in therapeutic drug monitoring (TDM) and dosing regimen optimization. CONCLUSIONS: Neither chloroquine nor ivermectin reached therapeutic ELF levels in the presence of LPVr despite reaching toxic ivermectin plasma levels. PBPK modeling, guided with TDM in saliva, can be advantageous to evaluate the probability of reaching therapeutic ELF levels in the presence of PDDI, especially in home-treated patients.

Structure-Property Relationships and Machine Learning Models for Addressing CYP3A4-Mediated Victim Drug-Drug Interaction Risk in Drug Discovery.

Among the FDA-approved small molecule drugs (2005-2016) that are primarily metabolized by cytochrome P450 (CYP), 64% are primarily metabolized by CYP3A4. As the proportion of an individual drug's fraction metabolized through CYP3A4 increases, the risk for the drug to be a victim of an interaction with CYP3A4 inhibitors or inducers increases. Therefore, it is important to assess the extent of involvement of individual CYP enzymes in the overall clearance for a scaffold early in discovery and mitigate the CYP3A4-mediated victim-drug-drug interaction (DDI) risk, if warranted by the desired clinical profile of the drug. To mitigate the CYP3A4-mediated victim DDI risk in discovery, we analyzed the physicochemical properties of the CYP3A4 substrates and found that molecular weight was the property that provided the best separation of the CYP3A4 substrates from other CYP substrates. In addition, neutral and basic compounds with MW ≥ 360 g/mol tend to be primarily metabolized by CYP3A4, whereas acidic compounds with MW < 360 g/mol are most likely to be primarily metabolized by other CYP enzymes. We then developed Support Vector Machine based on fingerprints (SVM-FP) and Deep-Learning (DL) models to predict if a molecule will be primarily metabolized by CYP3A4. Our models were trained on 2306 compounds, which is the largest training set among published models for this endpoint. Both models showed positive predictive values (PPV) > 80% in predicting a CYP3A4 substrate on a prospective testing set. Given the high PPV of the models, project teams can confidently deprioritize compounds predicted to be CYP3A4 substrates to avoid the potential liability of CYP3A4 victim DDI. Teams can then focus time and resources on synthesizing compounds that are predicted to have a lower dependency on CYP3A4 metabolism and confirm that experimentally. Through such iterative in silico-in vitro learning circles, drug discovery teams can decide if metabolism through non-CYP3A4 pathways could be achieved in the SAR of a chemical series to mitigate the CYP3A4 victim DDI risk.

Prediction methods of drug-drug interactions of non-oral CYP3A4 substrates based on clinical interaction data after oral administrations - Validation with midazolam, alfentanil, and verapamil after intravenous administration and prediction for blonanserin transdermal patch.

Drug-drug interactions (DDI) have been examined for various drugs for oral use, but less for non-oral applications. This study provides DDI prediction methods for non-orally administered CYP3A4 substrates based on clinical DDI data of oral dosages. Gut availability (Fg) and fraction contribution of CYP3A4 to hepatic intrinsic clearance (fmCYP3A4) were predicted by AUC ratio (AUCR) in oral DDI study with/without grapefruit juice, and alteration in intrinsic clearances with/without ketoconazole, respectively. AUCRs of non-orally administered CYP3A4 substrates with/without inhibitors or inducers were predicted with the estimated Fg, fmCYP3A4 and changes in liver CYP3A4 activities with inhibitors/inducers predicted using Simcyp library. DDIs of intravenously administered midazolam and alfentanil with CYP3A4 inhibitors/inducers could be predicted well by this method with predicted AUCRs within ±64% of observed values. Moreover, maximum DDIs with strong CYP3A4 inducers could be predicted by comparing hepatic clearance with hepatic blood flow, as hepatic blood flow indicates the possible maximum hepatic clearance after strong enzyme induction. Predicted AUCRs of midazolam, alfentanil and R- and S-verapamil were less than, but not far from observed ratios, suggesting good conservative prediction. These methods were applied to blonanserin transdermal patch, suggesting much smaller interaction with CYP3A4 inhibitors/inducers compared to oral dosage of blonanserin.

Progress in the Consideration of Possible Sex Differences in Drug Interaction Studies.

BACKGROUND: Anecdotal evidence suggests that there may be sex differences in Drug-drug Interactions (DDI) involving specific drugs. Regulators have provided general guidance for the inclusion of females in clinical studies. Some clinical studies have reported sex differences in the Pharmacokinetics (PK) of CYP3A4 substrates, suggesting that DDI involving CYP3A4 substrates could potentially show sex differences. OBJECTIVE: The aim of this review was to investigate whether recent prospective DDI studies have included both sexes and whether there was evidence for the presence or absence of sex differences with the DDIs. METHODS: The relevant details from 156 drug interaction studies within 124 papers were extracted and evaluated. RESULTS: Only eight studies (five papers) compared the outcome of the DDI between males and females. The majority of the studies had only male volunteers. Five studies had females only while 60 had males only, with 7.7% of the studies having an equal proportion of both sexes. Surprisingly, four studies did not specify the sex of the subjects. Based on the limited number of studies comparing males and females, no specific trends or conclusions were evident. Sex differences in the interaction were reported between ketoconazole and midazolam as well as clarithromycin and midazolam. However, no sex difference was observed with the interaction between clarithromycin and triazolam or erythromycin and triazolam. No sex-related PK differences were observed with the interaction between ketoconazole and domperidone, although sex-related differences in QT prolongation were observed. CONCLUSION: This review has shown that only limited progress had been made with the inclusion of both sexes in DDI studies.