Important Drug-Drug Interactions in HIV-Infected Persons on Antiretroviral Therapy: An Update on New Interactions Between HIV and Non-HIV Drugs

2011 ◽  
Vol 14 (1) ◽  
pp. 67-82 ◽  
Author(s):  
Alice Tseng ◽  
Michelle Foisy
Keyword(s):  
Antiretroviral Therapy ◽  
Drug Interactions ◽  
Hiv Drugs ◽  
New Interactions ◽  
Important Drug

scholarly journals In Silico Prediction of Drug–Drug Interactions Mediated by Cytochrome P450 Isoforms

Pharmaceutics ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 538
Author(s):  
Alexander V. Dmitriev ◽  
Anastassia V. Rudik ◽  
Dmitry A. Karasev ◽  
Pavel V. Pogodin ◽  
Alexey A. Lagunin ◽  
...  
Keyword(s):  
Cytochrome P450 ◽  
Drug Interactions ◽  
Cytochromes P450 ◽  
Investigational Drug ◽  
Web Resource ◽  
Average Accuracy ◽  
Structural Formulas ◽  
Cytochrome P450 Isoforms ◽  
Leave One Out ◽  
Important Drug

Drug–drug interactions (DDIs) can cause drug toxicities, reduced pharmacological effects, and adverse drug reactions. Studies aiming to determine the possible DDIs for an investigational drug are part of the drug discovery and development process and include an assessment of the DDIs potential mediated by inhibition or induction of the most important drug-metabolizing cytochrome P450 isoforms. Our study was dedicated to creating a computer model for prediction of the DDIs mediated by the seven most important P450 cytochromes: CYP1A2, CYP2B6, CYP2C19, CYP2C8, CYP2C9, CYP2D6, and CYP3A4. For the creation of structure–activity relationship (SAR) models that predict metabolism-mediated DDIs for pairs of molecules, we applied the Prediction of Activity Spectra for Substances (PASS) software and Pairs of Substances Multilevel Neighborhoods of Atoms (PoSMNA) descriptors calculated based on structural formulas. About 2500 records on DDIs mediated by these cytochromes were used as a training set. Prediction can be carried out both for known drugs and for new, not-yet-synthesized substances. The average accuracy of the prediction of DDIs mediated by various isoforms of cytochrome P450 estimated by leave-one-out cross-validation (LOO CV) procedures was about 0.92. The SAR models created are publicly available as a web resource and provide predictions of DDIs mediated by the most important cytochromes P450.

Clinically important drug interactions

Reactions ◽  
1988 ◽  
Vol 215 (1) ◽  
pp. 3-4
Keyword(s):  
Drug Interactions ◽  
Important Drug

scholarly journals Novel analysis of drug-drug interactions reveals the appearance of non-linear dose dependence under the single drug treatment

2020 ◽  
Author(s):  
Yh. Taguchi ◽  
Turki Turki
Keyword(s):  
Gene Expression ◽  
Drug Interactions ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Tensor Decomposition ◽  
Dose Dependence ◽  
Linear Dose ◽  
Non Linear ◽  
New Interactions ◽  
Dose Dependent

ABSTRACTThe 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 under-standing 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.

Principles of Clinically Important Drug Interactions with Carbamazepine. Part II

1991 ◽  
Vol 11 (5) ◽  
pp. 306???312
Author(s):  
TERENCE A. KETTER ◽  
ROBERT M. POST ◽  
KATHY WORTHINGTON
Keyword(s):  
Drug Interactions ◽  
Important Drug
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