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.
Predicting Absorption-Distribution Properties of Neuroprotective Phosphine-Borane Compounds Using In Silico Modeling and Machine Learning
