The US Food and Drug Administration (FDA) guidance has recommended several model-based predictions to determine potential drug-drug interactions (DDIs) mediated by cytochrome P450 (CYP) induction. In particular, the ratio of substrate area under the plasma concentration-time curve (AUCR) under and not under the effect of inducers is predicted by the Michaelis-Menten (MM) model, where the MM constant (K m ) of a drug is implicitly assumed to be sufficiently higher than the concentration of CYP enzymes that metabolize the drug (E T ) in both the liver and small intestine. Furthermore, the fraction absorbed from gut lumen (F a ) is also assumed to be one because F a is usually unknown. Here, we found that such assumptions lead to serious errors in predictions of AUCR. To resolve this, we propose a new framework to predict AUCR. Specifically, F a was re-estimated from experimental permeability values rather than assuming it to be one. Importantly, we used the total quasi-steady-state approximation to derive a new equation, which is valid regardless of the relationship between K m and E T , unlike the MM model. Thus, our framework becomes much more accurate than the original FDA equation, especially for drugs with high affinities, such as midazolam or strong inducers, such as rifampicin, so that the ratio between K m and E T becomes low (i.e., the MM model is invalid). Our work greatly improves the prediction of clinical DDIs, which is critical to preventing drug toxicity and failure.Cytochrome P450 (CYP) is the most important superfamily of enzymes, playing a crucial role in the metabolic clearance of an enormous number of compounds in humans. Approximately 70-80% of marketed drugs are metabolized by this superfamily of enzymes, especially CYP1A2, CYP2C, CYP2D6, and CYP3A4. 1 Such CYP enzymes can be induced by xenobiotic substances, including drugs, resulting in the increased metabolic activity of these enzymes. For instance, rifampicin, a prototype CYP3A4 enzyme inducer, enhances the metabolic process of midazolam, a probe substrate of CYP3A4, decreasing its plasma concentration and thus its therapeutic efficacy. 2 Therefore, the induction of CYP enzymes is one of the major reasons for clinical drug-drug interactions (DDIs).

Five compounds originating from marine fungi species Aspergillus sp. and Penicillium sp. were found to be highly potent inhibitors of cancer therapy target, Hsp90, using molecular docking and FPL calculations.

The SARS-CoV-2 pandemic requires a new therapeutic target for viral infection, and papain-like protease (Plpro) has been suggested as a druggable target. This in-vitro study was conducted to examine the drug metabolism of the GRL0617 and HY-17542, Plpro inhibitors. Metabolism of these inhibitors was studied to predict the pharmacokinetics in human liver microsomes. The hepatic cytochrome P450 (CYP) isoforms responsible for their metabolism were identified using recombinant enzymes. The drug–drug interaction potential mediated by cytochrome P450 inhibition was estimated. In human liver microsomes, the Plpro inhibitors had phase I and phase I + II metabolism with half-lives of 26.35 and 29.53 min, respectively. Hydroxylation (M1) and desaturation (-H2, M3) of the para-amino toluene side chain were the predominant reactions mediated with CYP3A4 and CYP3A5. CYP2D6 is responsible for the hydroxylation of the naphthalene side ring. GRL0617 inhibits major drug-metabolizing enzymes, including CYP2C9 and CYP3A4. HY-17542 is structural analog of GRL0617 and it is metabolized to GRL0617 through non-cytochrome P450 reactions in human liver microsomes without NADPH. Like GRL0617 and HY-17542 undergoes additional hepatic metabolism. The in-vitro hepatic metabolism of the Plpro inhibitors featured short half-lives; preclinical metabolism studies are needed to determine therapeutic doses for these inhibitors.

Heat shock protein 90 (HSP90) is known as one of the most potential target in cancer therapy. In this context, we have demonstrated that marine fungi derivatives can play as possible inhibitors for preventing the biological activity of HSP90 using a combination of molecular docking and fast pulling of ligand (FPL) simulations. In particular, the computational approaches were validated since compared with the respective experiments. Based on a benchmark on available inhibitors of HsP90, GOLD docking package using ChemPLP scoring function was found to be dominated over both Autodock Vina and Autodock4 in preliminary estimation the ligand binding affinity and binding pose with the Pearson correlation, R=-0.62. Moreover, FPL calculations were also indicated to be a suitable approach to refine docking simulations with a correlation coefficient with the respective experimental data of R=-0.81. Therefore, the binding affinity of marine fungi derivatives to Hsp90 was evaluated. Docking and FPL calculations suggested that five compounds including 23, 40, 46, 48, and 52 are as highly potential inhibitors for HSP90. The obtained results probably enhance the cancer therapy. <br>