Drug–drug interactions (DDIs) can either enhance or diminish the positive or negative effects of the associated drugs. Multiple drug combinations create difficulties in identifying clinically relevant drug interactions; this is why electronic drug interaction checkers frequently report DDI results inconsistently. Our paper aims to analyze drug interactions in cardiovascular diseases by selecting drugs from pharmacotherapeutic subcategories of interest according to Level 2 of the Anatomical Therapeutic Chemical (ATC) classification system. We checked DDIs between 9316 pairs of cardiovascular drugs and 25,893 pairs of cardiovascular and other drugs. We then evaluated the overall agreement on DDI severity results between two electronic drug interaction checkers. Thus, we obtained a fair agreement for the DDIs between drugs in the cardiovascular category, as well as for the DDIs between drugs in the cardiovascular and other (i.e., non-cardiovascular) categories, as reflected by the Fleiss’ kappa coefficients of $\kappa =0.3363$ and $\kappa =0.3572$, respectively. The categorical analysis of agreement between ATC-defined subcategories reveals Fleiss’ kappa coefficients that indicate levels of agreement varying from poor agreement ($\kappa <0$) to perfect agreement ($\kappa =1$). The main drawback of the overall agreement assessment is that it includes DDIs between drugs in the same subcategory, a situation of therapeutic duplication seldom encountered in clinical practice. Our main conclusion is that the categorical analysis of the agreement on DDI is more insightful than the overall approach, as it allows a more thorough investigation of the disparities between DDI databases and better exposes the factors that influence the different responses of electronic drug interaction checkers. Using categorical analysis avoids potential inaccuracies caused by particularizing the results of an overall statistical analysis in a heterogeneous dataset.

Background: Drug-drug interactions (DDIs) are a growing safety concern in dental care, particularly among patients with comorbidities and polypharmacy. However, real-world data (RWD) on the prevalence and severity of DDIs in dental settings remain scarce. Objectives: This study aimed to assess the frequency, severity, and clinical relevance of DDIs in dental patients and to identify age- and comorbidity-related risk patterns. Methods: This retrospective study analyzed a cohort of 105 dental patients, considering demographics, preexisting diseases, dental procedures, and prescribed medications. We examined drug-drug interactions (DDIs) employing the DrugBank Drug Interaction Checker, which yields DDI severity into major, moderate, or minor. Results: 45.7% of patients had preexisting diseases, with cardiovascular diseases most prevalent (19.0%). Higher prevalent dental diagnoses and procedures included apical lesions (47.6%) and tooth extractions (53.3%), suggesting frequent pharmacotherapy exposure. We identified 542 DDIs out of 1332 drug pairs and found 2.3% major, 25.0% moderate, and 13.4% minor, with 59.3% showing no interactions. Key high-risk DDIs included epinephrine with beta-blockers. Fifteen patients aged 31–60 years experienced the most major DDIs of 61.3%, patients ≥ 61 years faced 38.7%, and the 0–30 group had none, highlighting age-specific risks. The higher DDIs burden in the 31–60 age group may reflect better knowledge of the drugs they used and accurate reporting of them. Conclusions: Our retrospective study addresses the paucity of dental DDIs real-world data (RWD) studies, pleading for improved drug reconciliation, systematic screening, and age- and comorbidity-tailored strategies to enhance patient safety.

This study aims to assess the potential anticancer activity of lemongrass essential oil (LEO) using in vitro and in silico methods. The steam hydrodistillation of the aerial parts yielded 3.2% (wt) LEO. The GC-MS analysis of the LEO revealed the presence of α-citral (37.44%), β-citral (36.06%), linalool acetate (9.82%), and d-limonene (7.05%) as major components, accompanied by several other minor compounds. The antioxidant activity, assessed using the DPPH assay, revealed that LEO exhibits an IC₅₀ value of 92.30 μg/mL. The cytotoxic effect of LEO, as well as LEO solubilized with Tween-20 (LEO-Tw) and PEG-400 (LEO-PEG), against a series of cancer cell lines (A375, RPMI-7951, MCF-7, and HT-29) was assessed using the Alamar Blue assay; the results revealed a high cytotoxic effect against all cell lines used in this study. Moreover, neither one of the tested concentrations of LEO, LEO-PG, or LEO-TW significantly affected the viability of healthy HaCaT cells, thus showing promising selectivity characteristics. Furthermore, LEO, LEO-PG, and LEO-TW increased ROS production and decreased the mitochondrial membrane potential (MMP) in all cancer cell lines. Moreover, LEO treatment decreased all mitochondrial respiratory rates, thus suggesting its ability to induce impairment of mitochondrial function. Molecular docking studies revealed that LEO anticancer activity, among other mechanisms, could be attributed to PDK1 and PI3Kα, where the major contributors are among the minor components of the essential oil. The highest active theoretical inhibitor against both proteins was β-caryophyllene oxide.

Breast cancer faces significant therapeutic challenges, particularly for triple-negative breast cancer (TNBC), due to limited targeted therapies and drug resistance. Drug repositioning leverages existing safety and pharmacokinetic data to expedite the identification of new indications with cost-effective benefits compared to de novo drug discovery. In this critical narrative review, we examine recent advances in computational repositioning strategies for breast cancer, focusing on network-based methods, computer-aided drug design, artificial intelligence and machine learning, transcriptomic signature matching, and multi-omics integration. We highlight key case studies that have progressed to preclinical validation or clinical evaluation. We assess comparative performance metrics, experimental validation outcomes, and real-world success rates. We also present critical methodological challenges, including data heterogeneity, bias in real-world data, and the need for study reproducibility. Our review emphasizes the importance of window-of-opportunity trials and the need for standardized data sharing and reproducible pipelines. These insights highlight the groundbreaking potential of in silico repositioning in addressing unmet needs in breast cancer therapy.

We introduce the Onco-Hem Connectome (OHC), a patient similarity network (PSN) designed to organize real-world hemato-oncology inpatients by exploratory phenotypes with potential clinical utility. Background: Polypharmacy and drug–drug interactions (DDIs) are pervasive in hemato-oncology and vary with comorbidity and treatment intensity. Methods: We retrospectively analyzed a 2023 single-center cohort of 298 patients (1158 hospital episodes). Standardized feature vectors combined demographics, comorbidity (Charlson, Elixhauser), comorbidity polypharmacy score (CPS), aggregate DDI severity score (ADSS), diagnoses, and drug exposures. Cosine similarity defined edges (threshold ≥ 0.6) to build an undirected PSN; communities were detected with modularity-based clustering and profiled by drugs, diagnosis codes, and canonical chemotherapy regimens. Results: The OHC comprised 295 nodes and 4179 edges (density 0.096, modularity Q = 0.433), yielding five communities. Communities differed in comorbidity burden (Kruskal–Wallis ${\epsilon }^2$: Charlson 0.428, Elixhauser 0.650, age 0.125, all FDR-adjusted p < 0.001) but not in utilization (LOS, episodes) after FDR (${\epsilon }^2$ ≈ 0.006–0.010). Drug enrichment (e.g., enoxaparin $\Delta$ = +0.13 in Community 2; vinblastine $\Delta$ = +0.09 in Community 3) and principal diagnoses (e.g., C90.0 23%, C91.1 15%, C83.3 15% in Community 1) supported distinct clinical phenotypes. Robustness analyses showed block-equalized features preserved communities (ARI 0.946; NMI 0.941). Community drug signatures and regimen signals aligned with diagnosis patterns, reflecting the integration of resource-use variables in the feature design. Conclusions: The Onco-Hem Connectome yields interpretable, phenotype-level insights that can inform supportive care bundles, DDI-aware prescribing, and stewardship, and it provides a foundation for phenotype-specific risk models (e.g., prolonged stay, infection, high-DDI episodes) in hemato-oncology.

Background: This study evaluates the translational relevance of free computer-assisted ADMET platforms in drug discovery by comparing SwissADME and FAF-Drugs4 predictions with regulatory and curated reference data for approved anticancer drugs. Methods: Fourteen approved anticancer agents representing diverse chemical classes were analyzed using SwissADME and FAF-Drugs4. We compared predicted physicochemical, pharmacokinetic, and toxicity-related properties with information extracted from FDA- and EMA-approved product information, DrugBank, and PubChem. We evaluated the concordance for oral absorption, permeability, metabolic stability, and toxicity-related trends. Results: The platforms showed good concordance for broad descriptor-driven properties, particularly oral suitability and physicochemical trends. Strong agreement was observed for intravenously administered taxanes, which displayed unfavorable oral drug characteristics, and for several orally active small molecules with generally compatible profiles. Partial concordance was observed for compounds such as temozolomide, whose clinical behavior is influenced by factors not fully captured by descriptor-based models. Toxicity outputs were informative as early warning signals, with vandetanib showing the clearest alignment between predicted elevated risk and documented safety concerns. Conclusions: Free computational ADMET tools are valuable computer-assisted drug discovery resources for early triage, predictive toxicology, and prioritization of repositioning candidates, but they should complement rigorous experimental and clinical evaluation.