Drug-drug interaction (DDI) is the phenomenon of alteration of the pharmacological activity of a drug(s) when another drug(s) is co-administered in cases of so-called polypharmacy. There are three types of DDIs: pharmacokinetic (PK), pharmacodynamic, and pharmaceutical. PK is the most frequent type of DDI, which often appears as a result of the inhibition or induction of drug-metabolising enzymes (DME). In this review, we summarise in silico methods that may be applied for the prediction of the inhibition or induction of DMEs and describe appropriate computational methods for DDI prediction, showing the current situation and perspectives of these approaches in medicinal and pharmaceutical chemistry. We review sources of information on DDI, which can be used in pharmaceutical investigations and medicinal practice and/or for the creation of computational models. The problem of the inaccuracy and redundancy of these data are discussed. We provide information on the state-of-the-art physiologically- based pharmacokinetic modelling (PBPK) approaches and DME-based in silico methods. In the section on ligand-based methods, we describe pharmacophore models, molecular field analysis, quantitative structure-activity relationships (QSAR), and similarity analysis applied to the prediction of DDI related to the inhibition or induction of DME. In conclusion, we discuss the problems of DDI severity assessment, mention factors that influence severity, and highlight the issues, perspectives and practical using of in silico methods.

Drug transporters are considered to be determinants of drug disposition and effects/toxicities by affecting the absorption, distribution, and excretion of drugs. Drug transporters are generally divided into solute carrier (SLC) family and ATP binding cassette (ABC) family. Widely studied ABC family transporters include P-glycoprotein (P-GP), breast cancer resistance protein (BCRP), and multidrug resistance proteins (MRPs). SLC family transporters related to drug transport mainly include organic anion-transporting polypeptides (OATPs), organic anion transporters (OATs), organic cation transporters (OCTs), organic cation/carnitine transporters (OCTNs), peptide transporters (PEPTs), and multidrug/toxin extrusions (MATEs). These transporters are often expressed in tissues related to drug disposition, such as the small intestine, liver, and kidney, implicating intestinal absorption of drugs, uptake of drugs into hepatocytes, and renal/bile excretion of drugs. Most of therapeutic drugs are their substrates or inhibitors. When they are comedicated, serious drug-drug interactions (DDIs) may occur due to alterations in intestinal absorption, hepatic uptake, or renal/bile secretion of drugs, leading to enhancement of their activities or toxicities or therapeutic failure. This chapter will illustrate transporter-mediated DDIs (including food drug interaction) in human and their clinical significances.

It is now established that a drug's pharmacokinetics (PK) absorption, distribution, metabolism, excretion (ADME) and drug-drug interaction (DDI) profile can be modulated by age, disease, and genotype. In order to facilitate subject phenotyping and clinical DDI assessment, therefore, various endogenous compounds (in plasma and urine) have been pursued as drug-metabolizing enzyme and transporter biomarkers. Compared with biomarkers, however, the topic of circulating extracellular vesicles as "liquid biopsy" has received little attention within the ADME community; most organs secrete nanovesicles (e.g., exosomes) into the blood that contain luminal "cargo" derived from the originating organ (proteins, messenger RNA, and microRNA). As such, ADME profiling of plasma exosomes could be leveraged to better define genotype-phenotype relationships and the study of ontogeny, disease, and complex DDIs. If methods to support the isolation of tissue-derived plasma exosomes are successfully developed and validated, it is envisioned that they will be used jointly with genotyping, biomarkers, and modeling tools to greatly progress translational PK-ADME-DDI science.

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.

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.

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.

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.