Human CYP3A4 blocking peptide

Racemic praziquantel (PZQ) is the drug of choice for the treatment of schistosomiasis. R-Praziquantel (R-PZQ) has been shown as the therapeutic form, whereas S-PZQ is less efficacious and responsible for the bitter taste of the tablet. This study aimed at investigating the metabolism of R- and S-PZQ as this could have implications on efficacy and safety of racemate and R-PZQ specific formulations under development. In vitro CYP reaction phenotyping assay using 10 recombinant CYP (rCYP) isoenzymes showed hepatic CYP1A2, 2C19, 2D6, 3A4, and 3A5 were the major enzymes involved in metabolism of PZQ. Enzyme kinetic studies were performed by substrate depletion and metabolite formation methods, by incubating PZQ and its R- or S-enantiomers in human liver microsomes (HLM) and the rCYP enzymes. The effect of selective CYP inhibitors on PZQ metabolism was assessed in HLM. CYP1A2, 2C19, and 3A4 exhibited different catalytic activity toward PZQ, R- and S-enantiomers. Metabolism of R-PZQ was mainly catalyzed by CYP1A2 and CYP2C19, whereas metabolism of S-PZQ was mainly by CYP2C19 and CYP3A4. Based on metabolic CLint obtained through formation of hydroxylated metabolites, CYP3A4 was estimated to contribute 89.88% to metabolism of S-PZQ using SIMCYP (R) IVIVE prediction. Reanalysis of samples from a human PZQ-ketoconazole (KTZ) drug-drug interaction pharmacokinetic study confirmed these findings in that KTZ, a potent inhibitor of CYP3A, selectively increased area under the curve of S-PZQ by 68% and that of R-PZQ by just 9%. Knowledge of enantioselective metabolism will enable better understanding of variable efficacy of PZQ in patients and the R-PZQ formulation under development.

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.