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Background/ObjectivesNew computational methods, based on statistical, machine learning, and deep learning techniques using drug-related entities (e.g., genes, protein bindings, etc.), help reduce the costs of in-vitro experiments through drug-drug interaction prediction (DDIp). This review examines recent advances in DDIp. It presents an in-depth review of the state-of-the-art studies relating to semi-supervised, supervised, self-supervised learning, and other techniques such as graph-based learning and matrix factorization methods for predicting DDIs. All possible interactions between drugs are not known, and accurately predicting interactions is even more difficult due to the complex nature of drug-drug interactions (DDI).MethodsOf the 49 papers published in Web of Science in the last 6 years, 24 papers were considered relevant based on information presented in their titles and abstracts. The included articles focus specifically on predicting DDIs using a type of machine learning algorithm. Excluded articles focused on drug discovery, drug repurposing, molecular representation, or the extraction of biomedical interactions. The methodology, results limitations, and future research directions were studied for each paper. Common challenges, limitations, and future research directions were analyzed.Results and conclusionThe main limitations are class imbalance, poor performance on new drugs, limited explainability, and the need for additional data sources. Show less

The use of multiple medications increases the risk of harmful drug-drug interactions (DDIs). Conventional DDI screening databases vary in coverage and often trigger low-relevance alerts, contributing to alert fatigue. Large language models (LLMs) have emerged as potential tools for DDI identification, however, their performance compared to established databases using real-world patient data remains under-explored. Show less

An analysis of dose, dose frequency, human pharmacokinetics, and potential drug-drug interactions (DDI) was performed on small-molecule oral drugs approved by the FDA from 2020 to 2024 (n = 104). Although most oral drugs are administered QD (67%), BID and TID regimens are also regularly approved (32%). First-in-class (FIC) drugs and drugs with Orphan Drug Designation (ODD) have a higher frequency of BID or TID administration compared to drugs without those designations (BID and TID = 50% for FIC drugs vs 19% for non-FIC; BID and TID = 41% for ODD vs 20% non-ODD). Most drugs are >95% plasma protein bound (58%), with a large fraction >99% bound (29%). Of these drugs, 22% have black box warnings and 42% list contraindications. An examination of DDI revealed the most frequent warning around CYP3A4 induction (60%). These findings will help medicinal chemists better understand and predict typical and nontypical profiles of oral drugs. Show less

Background The co-administration of drugs known to interact greatly impacts morbidity, mortality, and health economics. This study aims to examine the drug–drug interaction (DDI) phenomenon with a large-scale longitudinal analysis of age and gender differences found in drug administration data from three distinct healthcare systems. Methods This study analyzes drug administrations from population-wide electronic health records in Blumenau (Brazil; 133 K individuals), Catalonia (Spain; 5.5 M individuals), and Indianapolis (USA; 264 K individuals). The stratified Show less

Cell viability and metabolic activity are ubiquitous parameters used in biochemistry, molecular biology, and biotechnological studies. Virtually all toxicology and pharmacological projects include at some point the evaluation of cell viability and/or metabolic activity. Among the methods used to address cell metabolic activity, resazurin reduction is probably the most common. At variance with resazurin, resorufin is intrinsically fluorescent, which simplifies its detection. Resazurin conversion to resorufin in the presence of cells is used as a reporter of metabolic activity of cells and can be detected by a simple fluorometric assay. UV–Vis absorbance is an alternative technique but is not as sensitive. In contrast to its wide empirical “black box” use, the chemical and cell biology fundamentals of the resazurin assay are underexplored. Resorufin is further converted to other species, which jeopardizes the linearity of the assays, and the interference of extracellular processes has to be accounted for when quantitative bioassays are aimed at. In this work, we revisit the fundamentals of metabolic activity assays based on the reduction of resazurin. Deviation to linearity both in calibration and kinetics, as well as the existence of competing reactions for resazurin and resorufin and their impact on the outcome of the assay, are addressed. In brief, fluorometric ratio assays using low resazurin concentrations obtained from data collected at short time intervals are proposed to ensure reliable conclusions. Show less

AIMS: Gastro-resistant dimethyl fumarate (DMF) is an oral therapeutic indicated for the treatment of relapsing multiple sclerosis. Recent data suggest that a primary pharmacodynamic response to DMF treatment is activation of the nuclear factor (erythroid-derived 2)-like 2 (NRF2) pathway; however, the gene targets modulated downstream of NRF2 that contribute to DMF-dependent effects are poorly understood. RESULTS: Using wild-type and NRF2 knockout mice, we characterized DMF transcriptional responses throughout the brain and periphery to understand DMF effects in vivo and to explore the necessity of NRF2 in this process. Our findings identified tissue-specific expression of NRF2 target genes as well as NRF2-dependent and -independent gene regulation after DMF administration. Furthermore, using gene ontology, we identified common biological pathways that may be regulated by DMF and contribute to in vivo functional effects. INNOVATION: Together, these data suggest that DMF modulates transcription through multiple pathways, which has implications for the cytoprotective, immunomodulatory, and clinical properties of DMF. CONCLUSION: These findings provide further understanding of the DMF mechanism of action and propose potential therapeutic targets that warrant further investigation for treating neurodegenerative diseases. Antioxid. Redox Signal. 24, 1058-1071. Show less