Search Results (257)

Background: Drug–drug interactions (DDIs) may occur when two or more drugs are taken together, leading to undesired side effects or potential synergistic effects. Most clinical effects of drug combinations have not been assessed in clinical trials. Therefore, predicting DDIs can provide better patient management, avoid drug combinations that can negatively affect patient care, and exploit potential synergistic combinations to improve current therapies in women’s healthcare. Methods: A DDI prediction model was built to describe relevant drug combinations affecting reproductive treatments. Approved drug features (chemical structure of drugs, side effects, targets, enzymes, carriers and transporters, pathways, protein–protein interactions, and interaction profile fingerprints) were obtained. A unified predictive score revealed unknown DDIs between reproductive and commonly used drugs and their associated clinical effects on reproductive health. The performance of the prediction model was validated using known DDIs. Results: This prediction model accurately predicted known interactions (AUROC = 0.9876) and identified 2991 new DDIs between 192 drugs used in different female reproductive conditions and other drugs used to treat unrelated conditions. These DDIs included 836 between drugs used for in vitro fertilization. Most new DDIs involved estradiol, acetaminophen, bupivacaine, risperidone, and follitropin. Follitropin, bupivacaine, and gonadorelin had the highest discovery rate (42%, 32%, and 25%, respectively). Some were expected to improve current therapies (n = 23), while others would cause harmful effects (n = 11). We also predicted twelve DDIs between oral contraceptives and HIV drugs that could compromise their efficacy. Conclusions: These results show the importance of DDI studies aimed at identifying those that might compromise or improve their efficacy, which could lead to personalizing female reproductive therapies. Full article

In clinical decision support systems (CDSSs), where accurate classification of drug–drug interactions (DDIs) can directly affect treatment safety and outcomes, identifying drug interactions is a major challenge, introducing a scalable approach for classifying DDIs utilizing a finely-tuned biomedical language model. The method shown here uses BiomedBERT, a domain-specific version of bidirectional encoder representations from transformers (BERT) that was pre-trained on biomedical literature, to reduce the number of resources needed during fine-tuning. Low-rank adaptation (LoRA) was used to fine-tune the model on the DrugBank dataset. The objective was to classify DDIs into two clinically distinct categories, that is, synergistic and antagonistic interactions. A pseudo-labeling strategy was created to deal with the problem of not having enough labeled data. A curated ground-truth dataset was constructed using polarity-labeled interaction entries from DrugComb and verified DrugBank antagonism pairs. The fine-tuned model is used to figure out what kinds of interactions there are in the rest of the unlabeled data. A checkpointing system saves predictions and confidence scores in small pieces, which means that the process can be continued and is not affected by system crashes. The framework is designed to log every prediction it makes, allowing results to be refined later, either manually or through automated updates, without discarding low-confidence cases, as traditional threshold-based methods often do. The method keeps a record of every output it generates, making it easier to revisit earlier predictions, either by experts or with improved tools, without depending on preset confidence cutoffs. It was built with efficiency in mind, so it can handle large amounts of biomedical text without heavy computational demands. Rather than focusing on model novelty, this research demonstrates how existing biomedical transformers can be adapted to polarity-aware DDI classification with minimal computational overhead, emphasizing deployment feasibility and clinical relevance. Full article

The significant burden of polypharmacy in clinical settings contrasts sharply with the narrow research focus on drug–drug interactions (DDIs), revealing an important gap in understanding the complexity of real-world multi-drug regimens. The present study addresses this gap by conducting a high-resolution, multidimensional bibliometric and network analysis of 19,151 DDI publications indexed in the Web of Science Core Collection (1975–2025). Using advanced tools, including VOSviewer version 1.6.20, Bibliometrix 5.0.0, and AI-enhanced terminology normalization, global research trajectories, knowledge clusters, and collaborative dynamics were systematically mapped. The analysis revealed an exponential growth in publication volume (from 55 in 1990 to 1194 in 2024), with output led by the United States and a marked acceleration in Chinese contributions after 2015. Key pharmacological agents frequently implicated in DDI research included CYP450-dependent drugs such as statins, antiretrovirals, and central nervous system drugs. Thematic clusters evolved from mechanistic toxicity assessments to complex frameworks involving clinical risk management, oncology co-therapies, and pharmacokinetic modeling. The citation impact peaked at 3.93 per year in 2019, reflecting the increasing integration of DDI research into mainstream areas of pharmaceutical science. The findings highlight a shift toward addressing polypharmacy risks in aging populations, supported by novel computational methodologies. This comprehensive assessment offers insights for researchers and academics aiming to navigate the evolving scientific landscape of DDIs and underlines the need for more nuanced system-level approaches to interaction risk assessment. Future studies should aim to incorporate patient-level real-world data, expand bibliometric coverage to underrepresented regions and non-English literature, and integrate pharmacogenomic and time-dependent variables to enhance predictive models of interaction risk. Cross-validation of AI-based approaches against clinical outcomes and prospective cohort data are also needed to bridge the translational gap and support precision dosing in complex therapeutic regimens. Full article

Background: Depression, a major global health issue, is commonly treated with selective serotonin reuptake inhibitors (SSRIs). Given the link between depression and inflammation, nonsteroidal anti-inflammatory drugs (NSAIDs) may have adjunctive benefits. Clinically, SSRIs and NSAIDs are often co-prescribed for comorbid pain or inflammatory conditions. However, both drug classes pose risks of adverse effects, and their interaction may lead to clinically significant drug–drug interactions. Objectives: This study analyzed FDA Adverse Event Reporting System (FAERS) data (2004–2024) to assess gastrointestinal bleeding, thrombocytopenia, and acute kidney injury (AKI) potential risks linked to SSRIs (citalopram, escitalopram, fluoxetine, paroxetine, fluvoxamine, and sertraline) and NSAIDs (propionic/acetic/enolic acid derivatives, COX-2 inhibitors) in depression patients, alone and combined. Methods: Disproportionality analysis (crude reporting odds ratios, cROR) identified possible associations; drug interactions were evaluated using Ω shrinkage, additive, multiplicative, and combination risk ratio (CRR) models. Results: Gastrointestinal bleeding risk was potentially elevated with citalopram (cROR = 2.81), escitalopram (2.27), paroxetine (2.17), fluvoxamine (3.58), sertraline (1.69), and propionic acid NSAIDs (3.17). Thrombocytopenia showed a potential correlation with fluoxetine (2.11) and paroxetine (2.68). AKI risk may be increased with citalopram (1.39), escitalopram (1.36), fluvoxamine (3.24), and COX-2 inhibitors (2.24). DDI signal analysis suggested that citalopram in combination with propionic acid derivatives (additive model = 0.01, multiplicative model = 1.14, and CRR = 3.13) might increase the risk of bleeding. Paroxetine combined with NSAIDs (additive model = 0.014, multiplicative model = 2.65, and CRR = 2.99) could potentially increase the risk of thrombocytopenia. Sertraline combined with NSAIDs (Ω₀₂₅ = 0.94, multiplicative model = 2.14) might be associated with an increasing risk of AKI. Citalopram combined with propionic acid derivatives (Ω₀₂₅ = 1.08, multiplicative model = 2.17, and CRR = 2.42) could be associated with an increased risk of acute kidney injury. Conclusions: Certain combinations of SSRIs and NSAIDs might further elevate these risks of gastrointestinal bleeding, thrombocytopenia, and acute kidney injury in patients with depression. Given the potential drug–drug interactions, heightened clinical vigilance is advised when prescribing SSRIs and NSAIDs in combination to patients with depression. Full article

Accurate prediction of drug–drug interactions (DDIs) is essential for ensuring medication safety and optimizing combination-therapy strategies. However, existing DDI models face limitations in handling interactions related to stereochemistry and precisely locating drug interaction sites. These limitations reduce the prediction accuracy for conformation-dependent interactions and the interpretability of molecular mechanisms, potentially posing risks to clinical safety. To address these challenges, we introduce LSA-DDI, a Spatial-Contrastive-Attention-Based Drug–Drug Interaction framework. Our 3D feature extraction method captures the spatial structure of molecules through three features—coordinates, distances, and angles—and fuses them to enhance the model of molecular spatial structures. Concurrently, we design and implement a Dynamic Feature Exchange (DFE) mechanism that dynamically regulates the flow of information across modalities via an attention mechanism, achieving bidirectional enhancement and semantic alignment of 2D topological and 3D spatial structure features. Additionally, we incorporate a dynamic temperature-regulated multiscale contrastive learning framework that effectively aligns multiscale features and enhances the model’s generalizability. Experiments conducted on public drug databases under both warm-start and cold-start scenarios demonstrated that LSA-DDI achieved competitive performance, with consistent improvements over existing methods. Full article

Background/Objective: Cerivastatin (Cer), a cholesterol-lowering statin, was withdrawn from the market due to fatal cases of rhabdomyolysis, particularly when co-administered with gemfibrozil (Gem), a strong CYP2C8 inhibitor. However, the pharmacokinetic (PK) mechanisms underlying these adverse events remain unclear. This study investigates the impact of drug–drug interactions (DDIs) involving Gem and itraconazole (Itr), a potent CYP3A4 inhibitor, on plasma concentrations of Cer and its major metabolites—M23, M1, and cerivastatin lactone (Cer-L)—with a focus on the risk of excessive Cer-L accumulation. Methods: We applied a newly developed Metabolite-Linked Model that simultaneously characterizes parent drug and metabolite kinetics by estimating metabolite formation fractions (f_M) and elimination rate constants (K_eM). The model was calibrated using observed DDI data from Cer + Gem and Cer + Itr scenarios and then used to predict outcomes in an untested Cer + Gem + Itr combination. Results: The model accurately reproduced observed metabolite profiles in single-inhibitor DDIs. Predicted AUCR values for Cer-L were 4.2 (Cer + Gem) and 2.1 (Cer + Itr), with reduced K_eM indicating CYP2C8 and CYP3A4 as primary elimination pathways. In the dual-inhibitor scenario, Cer-L AUCR reached ~70—far exceeding that of the parent drug—suggesting severe clearance impairment and toxic accumulation. Conclusions: Dual inhibition of CYP2C8 and CYP3A4 may cause dangerously elevated Cer-L levels, contributing to Cer-associated rhabdomyolysis. This modeling approach offers a powerful framework for evaluating DDI risks involving active or toxic metabolites, supporting safer drug development and regulatory assessment. Full article

Cancer remains a major global health burden driven by complex biological mechanisms, and while targeted therapies like tyrosine kinase inhibitors (TKIs) have revolutionized treatment, their efficacy and safety are significantly influenced by drug–drug interactions (DDIs). Tyrosine-kinase receptors (RTKs) regulate critical cellular processes, and their dysregulation through mutations or overexpression drives oncogenesis, with TKIs designed to inhibit these aberrant signaling pathways by targeting RTK phosphorylation. Pharmacokinetic DDIs can critically impact the efficacy and safety of TKIs such as erlotinib, gefitinib, and pazopanib by affecting their absorption, distribution, and metabolism. The modification of pH can influence drug absorption; furthermore, the inhibition or induction of metabolizing enzymes may affect biotransformation, while distribution can be altered through the modulation of transmembrane transporters. Additionally, ensuring quality of life during TKI treatment requires vigilant monitoring and management of adverse events, which range from mild (e.g., rash, diarrhea, fatigue) to severe (e.g., hepatotoxicity, cardiotoxicity). Drug-specific toxicities, such as hyperlipidemia with lorlatinib or visual disturbances with crizotinib, must be assessed using specific criteria, with dose adjustments and supportive care tailored to individual patient responses. Thus, optimal TKI therapy relies on managing drug interactions through multidisciplinary care, monitoring, and patient education to ensure safety and treatment efficacy. Full article

Objectives: The anticancer effects of PI3Kα inhibitors (PI3Ki) are constrained by their hyperglycemic side effects, while the efficacy of conventional hypoglycemic agents, such as insulin, metformin, and SGLT-2 inhibitors, in mitigating PI3Ki-induced hyperglycemia remains suboptimal. Dorzagliatin, a novel glucokinase activator, has been approved in China for the management of hyperglycemia, offering a promising alternative. This study aims to investigate the pharmacokinetic properties and potential mechanisms of drug interactions of dorzagliatin in the regulation of PI3K-induced hyperglycemia. Methods: Plasma concentrations of WX390, BYL719, and Dorz in mice were measured using high performance liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. Pharmacokinetic (PK) parameters and PK/PD models were derived by using Phoenix WinNonlin 8.3.5 software. Blood glucose levels at various time points and tumor volume changes over a four-week period were assessed to explore the interactions when PI3Ki were combined with dorzagliatin. Results: The results indicated that, compared to the Dorz group, the combination groups (Dorz + BYL719, Dorz + WX390) exhibited increases in ${\mathrm{A}\mathrm{U}\mathrm{C}}_{0\to t}$ of dorzagliatin by 41.65% and 20.25%, and in C_max by 33.48% and 13.32%, respectively. In contrast, co-administration of these PI3Ki with dorzagliatin resulted in minimal increase in their plasma exposure. The combination therapy group (Dorz+BYL719) exhibited superior antitumor efficacy compared to the BYL719 group. Conclusions: Our findings indicate that the drug–drug interactions (DDIs) between dorzagliatin and multiple PI3Ki (including WX390 and BYL719) may partially account for the enhanced antitumor efficacy observed in the combination therapy group compared to PI3Ki monotherapy. This interaction may be explained by the inhibition of P-glycoprotein (P-gp) and the pharmacological mechanism of dorzagliatin regarding the activation of insulin regulation. Full article

Drug transporters and metabolizing enzymes are integral components of drug disposition, governing the absorption, distribution, metabolism, and excretion (ADME) of pharmaceuticals. Their activities critically determine therapeutic efficacy and toxicity profiles, particularly for antimicrobial agents, one of the most widely prescribed drug classes frequently co-administered with other medications. Emerging evidence highlights the clinical significance of the drug–drug interactions (DDIs) mediated by these systems, which may alter antimicrobial pharmacokinetics, compromise treatment outcomes, or precipitate adverse events. With the continuous introduction of novel antimicrobial agents into clinical practice, the role of drug transporters and metabolizing enzymes in the pharmacokinetics of antibiotics and the DDIs between antibiotics and other drugs mediated by these transporters and enzymes are important to determine in order to provide a theoretical basis for the safe and effective use of antimicrobial drugs in clinical use. Full article

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. Full article

The characterization of a drug’s ADME (absorption, distribution, metabolism, and excretion) profile is crucial for accurately determining its safety and efficacy. The rising prevalence of polypharmacy has significantly increased the risk of drug-drug interactions (DDIs). These interactions can lead to altered drug exposure, potentially compromising efficacy or increasing the risk of adverse drug reactions (ADRs), thereby posing significant clinical and regulatory concerns. Traditional methods for assessing potential DDIs rely heavily on in vitro models, including enzymatic assays and transporter studies. While indispensable, these approaches have inherent limitations in scalability, cost, and ability to predict complex interactions. Recent advancements in analytical technologies, particularly the development of more sophisticated cellular models and computational modeling, have paved the way for more accurate and efficient DDI assessments. Emerging methodologies, such as organoids, physiologically based pharmacokinetic (PBPK) modeling, and artificial intelligence (AI), demonstrate significant potential in this field. A powerful and increasingly adopted approach is the integration of in vitro data with in silico modeling, which can lead to better in vitro-in vivo extrapolation (IVIVE). This review provides a comprehensive overview of both conventional and novel strategies for DDI predictions, highlighting their strengths and limitations. Equipping researchers with a structured framework for selecting optimal methodologies improves safety and efficacy evaluation and regulatory decision-making and deepens the understanding of DDIs. Full article

Cytochrome P450 (CYP450) enzymes are pivotal in the metabolism of numerous anticancer agents, with CYP3A4 being the predominant isoform involved. Inhibition of CYP450 enzymes is a major mechanism underlying clinically significant drug-drug interactions (DDIs), particularly in oncology, where polypharmacy is frequent. This review aims to provide a comprehensive and critical overview of CYP450 enzyme inhibition, focusing specifically on the impact of kinase inhibitors (KIs) and poly adenosine diphosphate-ribose polymerase (PARP) inhibitors. A systematic review of the current literature was conducted, focusing on the molecular mechanisms of CYP450 inhibition, including reversible, time-dependent, mechanism-based, and pseudo-irreversible inhibition. Specific attention was given to the inhibitory profiles of clinically relevant KIs and PARP inhibitors, with analysis of pharmacokinetic consequences and regulatory considerations. Many KIs, such as abemaciclib and ibrutinib, demonstrate time-dependent or quasi-irreversible inhibition of CYP3A4, while PARP inhibitors like olaparib and rucaparib exhibit moderate reversible and time-dependent CYP3A4 inhibition. These inhibitory activities can significantly alter the pharmacokinetics of co-administered drugs, leading to increased risk of toxicity or therapeutic failure. Regulatory guidelines now recommend early identification of time-dependent and mechanism-based inhibition using physiologically based pharmacokinetic) (PBPK) modeling. CYP450 inhibition by KIs and PARP inhibitors represents a critical but often underappreciated challenge in oncology pharmacotherapy. Understanding the mechanistic basis of these interactions is essential for optimizing treatment regimens, improving patient safety, and supporting personalized oncology care. Greater clinical vigilance and the integration of predictive modeling tools are necessary to mitigate the risks associated with CYP-mediated DDIs. Full article

Drug–drug interactions (DDIs) can pose significant risks in clinical practice and pharmacovigilance. Although traditional association rule mining techniques, such as the Apriori algorithm, have been applied to drug safety signal detection, their performance in DDI detection has not been systematically evaluated, especially in the Spontaneous Reporting System (SRS), which contains a large number of drugs and AEs with a complex correlation structure and unobserved latent factors. This study fills that gap through comprehensive simulation studies designed to mimic key features of SRS data. We show that latent confounding can substantially distort detection accuracy: for example, when using the reporting ratio (RR) as a secondary indicator, the area under the curve (AUC) for detecting main effects dropped by approximately 30% and for DDIs by about 15%, compared to settings without confounding. A real-world application using 2024 VAERS data further illustrates the consequences of unmeasured bias, including a potentially spurious association between COVID-19 vaccination and infection. These findings highlight the limitations of existing methods and emphasize the need for future tools that account for latent factors to improve the reliability of safety signal detection in pharmacovigilance analyses. Full article

Background/Objectives: Bradycardia, an uncharacteristically low heart rate below 60 bpm, is a commonly reported adverse drug event (ADE) in individuals administered dexmedetomidine for sedation. Dexmedetomidine is frequently used as a sedative and analgesic for both intubated and non-intubated patients due to its low risk of respiratory depression. The purpose of this study was to further characterize the safety profile of dexmedetomidine using safety reports collected from the FDA Adverse Event Reporting System (FAERS) and transcriptomic data. Methods: Association rule mining was used to both identify additional ADEs that presented concurrently with bradycardia in patients sedated with dexmedetomidine, as well as to characterize potential drug–drug interactions (DDIs). Furthermore, public transcriptomic data were analyzed to identify differentially expressed genes that may elucidate the genetic drivers of elevated bradycardia risk in those administered dexmedetomidine. Results: Bradycardia was the most frequently reported ADE for individuals administered dexmedetomidine. Other cardiovascular-related ADEs commonly associated with bradycardia included syncope (lift = 4.711), loss of consciousness (lift = 3.997), cardiac arrest (lift = 2.850), and hypotension (lift = 2.770). Several possible DDIs were identified, including Lactated Ringer’s solution (lift = 5.441), bupivacaine (lift = 2.984), and risperidone (lift = 2.434), which may elevate bradycardia risk. Finally, eight genes related to cardiac muscle contraction were identified as possible regulators of dexmedetomidine-induced bradycardia, including COX5B, COX6A2, COX8B, MYH7, MYH6, MYL2, UQCRQ, and UQCR11 in mouse cardiac cells. Conclusions: Key clinical takeaways include the co-presentation of multiple cardiovascular ADEs, including cardiac arrest, hypotension, and syncope, alongside dexmedetomidine-associated bradycardia. Furthermore, several possible DDIs with dexmedetomidine were also identified. Full article

Background: In intermediate care, older patients with polypharmacy are vulnerable to drug–drug interactions (DDI) and potentially inappropriate medication (PIM). Aims: To perform a consecutive intervention study to evaluate DDI/PIM. Methods: Clinically-relevant DDI/PIM were identified using AMeLI (electronic medication list) and PRISCUS 2.0 (PIM list). Consecutive patients (standard care group) were screened for DDI/PIM after admission (t0) and again before discharge (t1). In an interim period, physicians received general education about DDI/PIM. Then, consecutive patients (independent clinical pharmacy group) were screened for DDI/PIM after admission (t2). Physicians were then provided with patient-individualized recommendations by a clinical pharmacist to prevent DDI/PIM. The patients were then screened again for DDI/PIM before discharge (t3). Results: In each group, 100 patients were included with data available for evaluation from 97 (standard care group, median age: 78 years [Q25/Q75: 69/84]) and 89 (clinical pharmacy group, 76 years [67/84]). In the standard care group, DDI were identified in 55 (57%) patients after admission (t0) and 54 (56%) before discharge (t1, ARR[t0/t1] = 0.01, NNT[t0/t1] = 100, n.s.). In the clinical pharmacy group, DDI were identified in 32 (36%) after admission (t2; ARR[t0/t2] = 0.21/NNT[t0/t2] = 5, p < 0.01) and 26 (29%) before discharge (t3; ARR[t2/t3] = 0.07/NNT[t2/t3] = 15, n.s.; ARR[t1/t3] = 0.27/NNT[t1/t3] = 4, p < 0.001). PIM were identified in patients at t0: 34 (35%), t1: 35 (36%, ARR[t0/t1] = −0.01/NNH[t0/t1] = 100, n.s.), t2: 25 (26%, ARR[t0/t2] = 0.09/NNT[t0/t2] = 12, n.s.), t3: 23 (24%, ARR[t2/t3] = 0.11/NNT[t2/t3] = 10, n.s.; ARR[t1/t3] = 0.12/NNT[t1/t3] = 9, n.s.). Conclusions: In the standard care group, after admission, many DDI/PIM were identified in older intermediate care patients. Before discharge, their number was hardly influenced at all. General education for physicians led to DDI prevention after admission. In addition, the DDI frequency decreased by providing physicians with patient-individualized recommendations. Full article