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    Examination of the Impact of CYP3A4/5 on Drug-Drug Interaction between Schizandrol A/Schizandrol B and Tacrolimus (FK-506): A Physiologically Based Pharmacokinetic Modeling Approach. International journal of molecular sciences Schizandrol A (SZA) and schizandrol B (SZB) are two active ingredients of Wuzhi capsule (WZC), a Chinese proprietary medicine commonly prescribed to alleviate tacrolimus (FK-506)-induced hepatoxicity in China. Due to their inhibitory effects on cytochrome P450 (CYP) 3A enzymes, SZA/SZB may display drug-drug interaction (DDI) with tacrolimus. To identify the extent of this DDI, the enzymes' inhibitory profiles, including a 50% inhibitory concentration (IC) shift, reversible inhibition (RI) and time-dependent inhibition (TDI) were examined with pooled human-liver microsomes (HLMs) and CYP3A5-genotyped HLMs. Subsequently, the acquired parameters were integrated into a physiologically based pharmacokinetic (PBPK) model to quantify the interactions between the SZA/SZB and the tacrolimus. The metabolic studies indicated that the SZB displayed both RI and TDI on CYP3A4 and CYP3A5, while the SZA only exhibited TDI on CYP3A4 to a limited extent. Moreover, our PBPK model predicted that multiple doses of SZB would increase tacrolimus exposure by 26% and 57% in CYP3A5 expressers and non-expressers, respectively. Clearly, PBPK modeling has emerged as a powerful approach to examine herb-involved DDI, and special attention should be paid to the combined use of WZC and tacrolimus in clinical practice. 10.3390/ijms23094485
    Novel method for the prediction of drug-drug Interaction based on gene expression profiles. Taguchi Yh,Turki Turki European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences The accurate prediction of new interactions between drugs is important for avoiding unknown (mild or severe) adverse reactions to drug combinations. The development of effective in silico methods for evaluating drug interactions based on gene expression data requires an understanding of how various drugs alter gene expression. Current computational methods for the prediction of drug-drug interactions (DDIs) utilize data for known DDIs to predict unknown interactions. However, these methods are limited in the absence of known predictive DDIs. To improve DDIs interpretation, a recent study has demonstrated strong non-linear (i.e., dose-dependent) effects of DDIs. In this study, we present a new unsupervised learning approach involving tensor decomposition (TD)-based unsupervised feature extraction (FE) in 3D. We utilize our approach to reanalyze available gene expression profiles for Saccharomyces cerevisiae. We found that non-linearity is possible, even for single drugs. Thus, non-linear dose-dependence cannot always be attributed to DDIs. Our analysis provides a basis for the design of effective methods for evaluating DDIs. 10.1016/j.ejps.2021.105742
    A Simple UPLC/MS-MS Method for Simultaneous Determination of Lenvatinib and Telmisartan in Rat Plasma, and Its Application to Pharmacokinetic Drug-Drug Interaction Study. Cui Yanjun,Li Ying,Li Xiao,Fan Liju,He Xueru,Fu Yuhao,Dong Zhanjun Molecules (Basel, Switzerland) Lenvatinib is a multi-targeted tyrosine kinase inhibitor that inhibits tumor angiogenesis, but hypertension is the most common adverse reaction. Telmisartan is an angiotensin receptor blocker used to treat hypertension. In this study, a simple ultra-performance liquid chromatography-tandem mass spectrometry method was developed for the simultaneous determination of lenvatinib and telmisartan, and it was applied to the pharmacokinetic drug interaction study. Plasma samples were treated with acetonitrile to precipitate protein. Water (containing 5 mM of ammonium acetate and 0.1% formic acid) and acetonitrile (0.1% formic acid) were used as the mobile phases to separate the analytes with gradient elution using a column XSelect HSS T3 (2.1 mm × 100 mm, 2.5 μm). Multiple reaction monitoring in the positive ion mode was used for quantification. The method was validated and the precision, accuracy, matrix effect, recovery, and stability of this method were reasonable. The determination of analytes was not interfered with by other substances in the blank plasma, and the calibration curves of lenvatinib and telmisartan were linear within the range of 0.2-1000 ng/mL and 0.1-500 ng/mL, respectively. The results indicate that lenvatinib decreased the systemic exposure of telmisartan. Potential drug interactions were observed between lenvatinib and telmisartan. 10.3390/molecules27041291
    SSI-DDI: substructure-substructure interactions for drug-drug interaction prediction. Nyamabo Arnold K,Yu Hui,Shi Jian-Yu Briefings in bioinformatics A major concern with co-administration of different drugs is the high risk of interference between their mechanisms of action, known as adverse drug-drug interactions (DDIs), which can cause serious injuries to the organism. Although several computational methods have been proposed for identifying potential adverse DDIs, there is still room for improvement. Existing methods are not explicitly based on the knowledge that DDIs are fundamentally caused by chemical substructure interactions instead of whole drugs' chemical structures. Furthermore, most of existing methods rely on manually engineered molecular representation, which is limited by the domain expert's knowledge.We propose substructure-substructure interaction-drug-drug interaction (SSI-DDI), a deep learning framework, which operates directly on the raw molecular graph representations of drugs for richer feature extraction; and, most importantly, breaks the DDI prediction task between two drugs down to identifying pairwise interactions between their respective substructures. SSI-DDI is evaluated on real-world data and improves DDI prediction performance compared to state-of-the-art methods. Source code is freely available at https://github.com/kanz76/SSI-DDI. 10.1093/bib/bbab133