Search Results (388)

Background and Objective: Predicting drug–drug interactions (DDIs) in pediatric patients remains a major challenge in clinical pharmacology. This study aimed to evaluate and compare three empirical approaches for extrapolating adult cytochrome P450 (CYP)-mediated DDI pharmacokinetics (PK) data to predict the extent of the corresponding DDIs in children across different age groups. Methods: The approaches assessed were: (A) the direct use of adult area under the plasma concentration–time curve ratios (AUCRs) as estimators of pediatric values; (B) the application of a correction accounting for the ontogeny of the involved CYP enzyme; and (C) the application of corrections for both enzyme ontogeny and allometric scaling. Twenty-five pediatric AUCRs were predicted from adult AUCR data. Predictive performance was evaluated by comparing predicted AUCR_pediatric values with observed values, using a 50–200% acceptability range. Results: Approach C demonstrated superior predictive capability, with only one out of 25 predictions falling outside the acceptability range. In contrast, both approaches A and B resulted in three values each outside this range. Further visual exploration and detailed performance analyses confirmed the enhanced accuracy of approach C in predicting pediatric DDIs compared with the other approaches. Conclusions: This study demonstrates that the proposed approach of considering both ontogeny and allometric scaling represents a robust and reasonable method to anticipate the extent of pediatric CYP-based DDIs when adult PK data are available. Full article

Objectives: The co-administration of Bruton’s tyrosine kinase (BTK) inhibitors with antiretroviral drugs is challenging due to potential drug–drug interactions (DDIs). However, clinical trials specifically assessing such DDIs are lacking. We aimed to evaluate DDIs between the BTK inhibitors ibrutinib, zanubrutinib and acalabrutinib with representative antiretroviral drugs and to provide dose adjustment strategies using physiologically based pharmacokinetic (PBPK) models. Methods: PBPK models were developed in PK-Sim software. Model performance was verified by comparing simulated pharmacokinetic parameters and DDI magnitudes with probe drugs (midazolam or maraviroc) with reported clinical data. The validated models were subsequently applied to assess DDIs and explore dose adjustment strategies. Results: The developed PBPK model accurately describes the pharmacokinetics of each drug. Darunavir/ritonavir substantially increased the maximum plasma concentration (C_max) of ibrutinib, zanubrutinib, and acalabrutinib by 496%, 312%, and 160%, respectively. In contrast, efavirenz reduced C_max by 43%, 33%, and 37%, respectively, while etravirine caused smaller decreases of 5%, 0%, and 10%. Based on these predictions, recommended dose adjustment strategies include ibrutinib 105 mg once daily, zanubrutinib 40 mg twice daily, and acalabrutinib 50 mg twice daily when co-administered with darunavir/ritonavir or ibrutinib 980 mg once daily, zanubrutinib 240 mg twice daily, and acalabrutinib 150 mg twice daily when co-administered with efavirenz. No dose adjustment is required with etravirine. Conclusions: The PBPK models accurately predicted the in vivo pharmacokinetics of ibrutinib, zanubrutinib, acalabrutinib, and those of the antiretrovirals darunavir/ritonavir, efavirenz, and etravirine, and the DDIs between them. The dose adjustment strategies provided information valuable to the optimization of antineoplastic therapy in HIV-related lymphoma (HRL) patients. Full article

Background: The introduction of targeted therapy in oncology has led to several challenges. These medicines are relatively new in clinical practice and are not well known to specialists with regard to adverse drug reactions (ADRs) and potential drug–drug interactions (pDDIs). In addition, cancer affects multiple body systems, including weight loss, anemia, liver and kidney function, depression, and pain. Patients frequently have comorbidities, leading to polypharmacy and the use of special foods, nutritional supplements, and herbal products for self-medication. Identification of pDDIs is essential, as concomitant use of multiple medicinal products increases the risk of ADRs and may compromise treatment. Objective: This study aims to retrospectively review and analyze data on ADRs and pDDIs in the treatment of non-small cell lung cancer (NSCLC) with epidermal growth factor receptor (EGFR) inhibitors and to evaluate the relationship between them. Method: EudraVigilance and UpToDate^® Lexidrug™ application were used to screen suspected ADRs and pDDIs, respectively. Descriptive statistical analysis was performed. Results: After reviewing Line Listing Reports (LLRs) from 2021 to 2023 in EudraVigilance, the number of suspected adverse drug reactions (ADRs) reported was higher when drug interactions classified as risk categories D and X were identified, compared with cases involving EGFR inhibitor monotherapy or other drug combinations. Of the 144 cases involving category D and/or X interactions, 63 demonstrated a possible association with the reported ADRs of EGFR inhibitors. The most common pDDIs detected were erlotinib–ranitidine (14 cases, category D) and osimertinib–amiodarone (13 cases, category D). Conclusions: Although EGFR inhibitors improve overall and progression-free survival in NSCLC, screening for pDDIs before treatment is essential to improve safety and quality of life. Full article

Background and Objectives: Combining osimertinib (OSI) with pemetrexed (PEM) can enhance antitumor efficacy; however, the benefit is schedule-dependent. Our previous pharmacodynamic (PD) study showed that concurrent PEM + OSI is limited by OSI-induced G1 arrest, attenuating early PEM cytotoxicity. In contrast, sequential PEM→OSI allows PEM to fully induce S-phase arrest and DNA damage but elicits a pro-survival EGFR rebound; subsequent OSI suppresses this rebound and promotes apoptosis of damaged cells, yielding strong synergy. Here, we investigated whether pharmacokinetic (PK) drug–drug interactions (DDIs) contribute to this synergy and predicted the relative advantage of PEM→OSI versus PEM + OSI under clinically relevant conditions using a PK/PD approach. Method and Results: Potential PK-DDIs were assessed at cellular uptake, plasma exposure, and intratumoral distribution levels. No meaningful PK-DDIs were observed, supporting a primary PD-driven synergy. We integrated mouse PK with in vitro/in vivo PD data to build a mechanistic Quantitative System Pharmacology (QSP)–PK–PD model linking drug disposition to folate biology, Epidermal Growth Factor Receptor (EGFR) signaling, and tumor growth inhibition. The model recapitulated schedule dependence and explained PEM→OSI superiority: PEM initiates damage and EGFR compensatory rebound, after which OSI suppresses EGFR signaling and enhances apoptosis. In contrast, concurrent PEM + OSI induced G1 arrest, reduced the pool of damaged apoptosis-susceptible cells, and weakened the synergy. Global sensitivity analysis identified intrinsic OSI sensitivity and the pro-apoptotic protein Bim as key determinants; reduced OSI sensitivity or Bim activity diminished the advantage of the sequential strategy. The simulations indicated that OSI can start 48 h after PEM exposure (no extended drug holiday is needed) and that the PEM→OSI benefit remains robust across heterogeneity, including BIM-deletion polymorphisms and inter-individual variability in tumor drug sensitivity. Conclusions: This mechanism-based QSP–PK–PD framework connects whole-body PK to core PD processes, explains schedule-dependent synergy, and supports optimization of sequencing intervals and identification of likely responders. Full article

Background: Ribociclib, initially approved for HR+/HER2− advanced breast cancer (ABC) at a 600 mg dose, was recently approved for HR+/HER2− early breast cancer (EBC) at a 400 mg dose based on the NATALEE trial. Differences in dose and patient population warrant reassessment of ribociclib drug–drug interactions (DDIs) and the impact of hepatic or renal impairment (HI/RI) in EBC patients to guide co-medication management and subpopulation dose recommendations. Methods: Physiologically-based pharmacokinetic (PBPK) modeling based on a healthy volunteer population was conducted to assess ribociclib DDIs with CYP3A4 substrates/modulators in patients with EBC. Subgroup analysis from NATALEE assessed HI/RI impact on ribociclib PK in EBC patients. Existing data from ABC/advanced cancer patients and non-cancer subjects were also integrated to inform dose recommendations for EBC subpopulations. Results: PBPK modeling predicted that ritonavir or erythromycin (strong and moderate CYP3A4 inhibitors) would increase ribociclib steady-state area under the concentration–time curve (AUC) by 1.84-fold or show no meaningful impact, respectively. Steady-state ribociclib AUC was estimated to decrease by 83% and 74% with rifampicin and efavirenz, strong and moderate CYP3A4 inducers, respectively. Ribociclib was estimated to increase CYP3A4 substrate midazolam exposure by 280%. Mild HI or mild/moderate RI did not show an apparent impact on ribociclib PK. Conclusions: Using relevant data and methodology for EBC patients, this analysis informed the approved ribociclib label of no dose adjustment for EBC patients with concomitant use of a moderate CYP3A inhibitor, any degree of HI, or mild/moderate RI, and a reduced 200 mg dose for patients with concomitant use of a strong CYP3A inhibitor or severe RI. Full article

Background: Antiviral therapies are widely used during pregnancy and are generally considered safe, pregnancy-specific severe safety signals continue to be observed in post-marketing pharmacovigilance data. These signals are rarely interpreted within an integrated mechanistic framework. Methods: We analysed pregnancy-related EudraVigilance reports (2015–2025) using a previously network-based pharmacovigilance framework. Established ADR clusters were treated as fixed phenotypes and integrated with in silico ADMET liabilities, literature-derived pregnancy pharmacokinetic/pharmacodynamic (PK/PD) parameters, polypharmacy and co-medication network metrics, and exploratory statistical, machine-learning, and exposure–liability analyses for mechanistic prioritisation. Results: Phenotype membership explained 22.3% of the variance in composite ADMET risk (intraclass correlation coefficient = 0.223; p < 0.001), and all tested ADMET parameters differed significantly across phenotypes (FDR-adjusted p < 10⁻¹⁰). One phenotype showed pronounced enrichment, with 13 antivirals over-represented. Polypharmacy strongly modified seriousness, with odds of serious outcomes increasing by ~5% per additional co-reported active drug (OR 1.05, 95% CI 1.04–1.05). A composite mechanistic vulnerability index showed moderate concordance with empirical burden (Spearman’s ρ = 0.65), while regimen-level prioritisation of drug–drug interactions (DDIs) identified no high-priority combinations. Conclusions: Pregnancy-related antiviral ADRs cluster into reproducible phenotypes driven by mechanistic liability and system-level complexity, supporting mechanistically informed prioritisation and targeted pharmacometric follow-up. Full article

Objectives: To examine the prescribing of non-vitamin K-dependent oral anticoagulants (DOACs) among multidose drug dispensing (MDD) users aged ≥65 years, and to describe associated drug–drug interactions (DDIs), concomitant use of fall-risk increasing drugs (FRIDs) and anticholinergic drugs (AC). Methods: Cross-sectional analysis of anonymized MDD medication lists from 87,519 patients in 2018. DDIs were identified using The Norwegian Medical Products Agency interaction tool, FRIDs were defined using the Swedish National Board of Health and Welfare list, and the CRIDECO Anticholinergic Load Scale assessed anticholinergic burden. Results: Among the 13,215 patients aged 65 and older the mean number of prescribed medications was 10.3. At least one DDI involving the prescribed DOACs was present in 26.8% of patients, whereas severe DDIs were rare (0.2%). Almost all (96.7%) used at least one FRID, and nearly half (46.8%) had an anticholinergic score ≥ 3. Conclusions: DOACs are frequently prescribed together with medications that increase the risk of falls and bleeding. These findings highlight the need for individualized risk–benefit evaluations and deprescribing or substituting high impact FRIDS and ACs when clinically appropriate. Full article

Background/Objectives: Western societies introduce school-based or school-linked programs in order to improve the physical health status of students and prevent the negative impact of the late diagnosis of a series of diseases and conditions. Preschool visual acuity (VA) screening represents an established school-based approach aimed at the early detection of amblyopia risk factors and vision-related learning difficulties. In this study, we report the methods and outcomes of the first officially organized kindergarten-based VA screening program in Greece, implemented using the Democritus Digital Visual Acuity Test (DDiVAT) screening suite and involving trained educators as part of the screening workflow. The present analysis focuses on the operational performance and screening outcomes within this defined setting. Methods: This study was a kindergarten-based screening. Each kindergarten was equipped with the DDiVAT screening framework, which consisted of a 32-inch, 4K, Android Smart TV with the DDiVAT application preinstalled, a site-license granting access to the secure DDiVAT database, and two vouchers for teachers to participate in the official lifelong DDiVAT training program. Results: From 2476 enrolled students, 207 (8.36%) were referred due to suboptimal presenting VA in one or both eyes. Average VA ranged from logMAR 0.11 to 0.07, which is consistent with former reports. Conclusions: No major technical difficulties were encountered, suggesting that DDiVAT may represent a feasible digital approach for preschool VA screening in real-world educational settings. Full article

In many scientific fields (e.g., statistics, data science, machine learning, and image processing), effectively quantifying the statistical difference between two probability distributions is an important task. Although a wide variety of measures have been proposed in the literature, some of them (such as the chi-square divergence and the Kullback–Leibler divergence) do not satisfy one or both of two key axioms: normalization and symmetry. This paper proposes a new index for quantifying the statistical difference between two probability distributions, called the distribution discrepancy index (DDI). The proposed DDI is based on the recently developed concepts of informity and cross-informity in informity theory. Its value ranges from 0 to 1, with values close to 1 indicating a large discrepancy and values close to 0 indicating minimal discrepancy. The DDI satisfies the two key axioms and is applicable to both discrete and continuous distributions. This paper also proposes the distribution similarity index (DSI) as a complement to the DDI. Three examples are presented to compare the DDI with three existing discrepancy measures (the Hellinger distance, total variation distance, and Jensen–Shannon divergence) and the DSI with two existing similarity measures (the Bhattacharyya coefficient and overlapping index). Full article

Background and Clinical significance: This case describes a patient with a complex medical history who develops an active Mycobacterium tuberculosis (MTB) infection. The complex multidrug regimen has led to significant drug–drug interactions (DDIs) and adverse effects. This case highlights an urgent need for standardized guidelines on dose adjustment and therapeutic monitoring for opioid substitution therapy (OST) and antiretroviral therapy (ART) during MTB treatment to prevent adverse health outcomes and ensure clinical success. Case Presentation: A 43-year-old man with medical history including human immunodeficiency virus (HIV), chronic hepatitis C virus (HCV), psychotic disorder, and opioid dependence maintained on buprenorphine (24 mg/day) presented with acute psychosis and respiratory symptoms. During hospitalization, he was diagnosed with MTB infection and was started on an empirical rifampicin-based anti-MTB regimen. His clinical course was complicated by reduced buprenorphine efficacy caused by rifampicin, which precipitated opioid withdrawal symptoms. Conclusions: The successful clinical stabilization with resolution of withdrawal syndrome, reduced agitation, and normalization of vital signs, including heart rate and blood pressure of this patient, was achieved through targeted management of pervasive DDIs. A strategic ART switch and careful buprenorphine dose titration during rifampicin therapy was the key factor. This case highlights that co-managing HIV, MTB, and opioid use disorder presents a significant challenge where unaddressed DDIs directly threaten treatment efficacy, a patient’s safety, and adherence, and may result in increased toxicity. The case underscores the critical need for proactive DDI assessment, interdisciplinary collaboration, and guideline development for medication optimization in people living with HIV receiving OST. Full article

Deep learning methods have been extensively used for drug–drug interaction (DDI) prediction, aiding the development of effective and safe combination therapies. Most studies focus on either the internal molecular structure or external contextual information of individual drugs to improve feature diversity and validity. However, the latent similarities between drug pairs, which are essential for accurate predictions, have largely been overlooked. Therefore, we propose an interpretable predictive approach for graph embedding called PINGE, which relies solely on the interaction network of drugs. Specifically, we constrain the joint features of drug pairs to their interactions, allowing those with similar types to achieve cosine similarity. This similarity in direction helps the joint features converge to the same class during prediction. Additionally, each known drug can link to multiple others, enhancing its diversity. Extensive experiments demonstrate that PINGE outperforms current advanced prediction methods on both KEGG and Drugbank datasets, achieving improvements of 0.7% and 2.4% in ACC while providing network structure-based explanations for predictions. Furthermore, PINGE surpasses advanced baselines by 1% and 1.1% in AUC on the human drug–target dataset and HuRI protein–protein interaction dataset, showcasing excellent versatility. Full article

Background/Objectives: Potential drug–drug interaction (pDDI) refers to the co–administration of two or more drugs that interact with each other and may have therapeutic effects. Increasing rates of polypharmacy with age increase the risk of pDDIs in geriatric patients. This multicenter study aims to provide real–world data on the incidence of pDDI associated with antimicrobial therapy in hospitalized older adults. Methods: The study screened all hospitalized patients, including those aged 65 years and older. Using the Lexicomp^® Drug Interaction Online Database, researchers screened for pDDIs among all medications taken by patients. Results: 663 (24.0%) aged 65 and over were included in the study. Polypharmacy was present in 64.9%, and hyperpolypharmacy was present in 10.0% of the cases. 480 (72.4%) of the cases used antimicrobial therapy. The mean total number of drugs and antimicrobials used per case was 5.86 and 1.02, respectively. A total of 372 antimicrobial–related pDDIs were detected, and at least one antimicrobial–related pDDI was identified in 202 (42%) patients receiving antimicrobials. Ciprofloxacin (73.3%), clarithromycin (58.3%), and colistin (26.3%) had the highest numbers of D–type pDDIs. The antimicrobials with the highest incidence of X–type pDDIs were metronidazole (23.6%) and clarithromycin (8.3%), respectively. The logistic analysis found a significant association between antimicrobial–related pDDIs and an increase in the number of drugs, length of hospital stays, and ID departments. Conclusions: PDDI rates associated with antimicrobials, like the high pDDI rates associated with all drugs, support the literature. Therefore, strategies should be developed to reduce the risk of pDDI when prescribing antimicrobials to geriatric patients. Full article

Background/Objectives: Cytochrome P450 3A4 (CYP3A4) metabolizes approximately 30–50% of clinically used drugs; thus, accurate prediction of CYP3A4 inhibition is essential for early assessment of drug–drug interaction (DDI) risk and toxicity. This study evaluated an integrated artificial intelligence framework for predicting CYP3A4 inhibition (%) using a large, curated chemical dataset. Methods: A dataset of 23,713 compounds was compiled from the Korea Chemical Bank and multiple commercial and public databases. Vector-based machine learning (ML) models (LightGBM, XGBoost, CatBoost, and a weighted ML ensemble) and graph neural network (GNN) models (O-GNN with contrastive learning and manifold mixup (O-GNN + CL + Mixup), D-MPNN, GINE, and GATv2) were evaluated. Manifold mixup was applied during GNN training, and SMILES enumeration-based test-time augmentation was used at inference. The best-performing ML and GNN models were integrated using a weighted ensemble strategy. Model interpretability was examined using SHAP analysis for ML models and occlusion sensitivity analysis for O-GNN + CL + Mixup. Results: The weighted ML ensemble achieved the best performance among ML models (RMSE = 19.1031, Pearson correlation coefficient (PCC) = 0.7566); the O-GNN + CL + Mixup model performed the best among GNN models (RMSE = 20.1002, PCC = 0.7265). The hybrid model achieved improved predictive accuracy (RMSE = 19.0784, PCC = 0.7570). External validation on 100 newly generated experimental data points confirmed generalizability (Custom Metric = 0.8035). Conclusions: This study demonstrated that integrating ML and GNN models with data augmentation strategies improves the robustness and interpretability of CYP3A4 inhibition prediction and established a practical framework for metabolic screening and DDI risk assessment. Full article

Industrial gateways serve as critical data aggregation points within the Industrial Internet of Things (IIoT), enabling seamless data interoperability that empowers enterprises to extract value from equipment data more efficiently. However, their role exposes a fundamental trade-off between computational efficiency and prediction accuracy—a contradiction yet to be fully resolved by existing approaches. The rapid proliferation of IoT devices has led to a corresponding surge in network traffic, posing significant challenges for traffic forecasting methods, while deep learning models like Transformers and GNNs demonstrate high accuracy in traffic prediction, their substantial computational and memory demands hinder effective deployment on resource-constrained industrial gateways, while simple linear models offer relative simplicity, they struggle to effectively capture the complex characteristics of IIoT traffic—which often exhibits high nonlinearity, significant burstiness, and a wide distribution of time scales. The inherent time-varying nature of traffic data further complicates achieving high prediction accuracy. To address these interrelated challenges, we propose the lightweight and theoretically grounded DOA-MSDI-CrossLinear framework, redefining traffic forecasting as a hierarchical decomposition–interaction problem. Unlike existing approaches that simply combine components, we recognize that industrial traffic inherently exhibits scale-dependent temporal correlations requiring explicit decomposition prior to interaction modeling. The Multi-Scale Decomposable Mixing (MDM) module implements this concept through adaptive sequence decomposition, while the Dual Dependency Interaction (DDI) module simultaneously captures dependencies across time and channels. Ultimately, decomposed patterns are fed into an enhanced CrossLinear model to predict flow values for specific future time periods. The Dream Optimization Algorithm (DOA) provides bio-inspired hyperparameter tuning that balances exploration and exploitation—particularly suited for the non-convex optimization scenarios typical in industrial forecasting tasks. Extensive experiments on real industrial IoT datasets thoroughly validate the effectiveness of this approach. Full article

(1) Background: Drug–drug interactions (DDIs) are a frequent cause of medication-related harm, particularly in ambulatory care. Community pharmacists are uniquely positioned to identify and manage these risks. This study assessed DDI knowledge, practices, and barriers among community pharmacists in the Jazan Region, Saudi Arabia. (2) Methods: A structured, self-administered questionnaire was distributed to community pharmacists. The survey assessed DDI knowledge using 26 clinically relevant drug pairings and included questions on professional behavior, training exposure, software use, and educational needs. Descriptive and inferential statistics were applied to identify associations between knowledge scores and demographic or practice-related variables. (3) Results: A total of 219 pharmacists participated in the study. The mean knowledge score was (9.63 ± 4.58) out of 26, reflecting suboptimal to moderate awareness. Female pharmacists demonstrated significantly higher DDI knowledge scores than males (10.74 ± 5.4 vs. 9.08 ± 4.2; p = 0.016). Knowledge scores also differed significantly by academic qualification (p < 0.001), with PharmD holders scoring higher than B. Pharm and postgraduate degree holders. Pharmacists with less than 10 years of experience had significantly higher scores compared with those with longer practice duration (p = 0.002). Additionally, pharmacists who graduated from Saudi institutions scored higher than those trained outside Saudi Arabia (10.22 ± 4.7 vs. 8.44 ± 4.2; p = 0.005). Pharmacists who had received professional development training and those who attended workshops regularly also scored significantly higher. Familiarity with guidelines showed a positive trend. Reported barriers to effective DDI counseling included time constraints, limited patient understanding, and poor collaboration with prescribers. Self-rated awareness of DDIs was positively associated with actual knowledge scores. Pharmacists expressed strong preferences for workshops, online courses, and webinars as future training formats. (4) Conclusions: Pharmacists in the Jazan Region demonstrate moderate awareness of DDIs, with variation influenced by training, experience, and qualifications. Enhancing access to structured professional development and integrating clinical decision support tools could strengthen pharmacists’ role in preventing DDIs in community practice. Full article