A Molecular Fragment Representation Learning Framework for Drug-Drug Interaction Prediction.

The concurrent use of multiple drugs may result in drug-drug interactions, increasing the risk of adverse reactions. Hence, it is particularly crucial to propose computational methods for precisely identifying unknown drug-drug interactions, which is of great significance for drug development and health. However, most recent studies have limited the drug-drug interaction prediction task to identifying interactions between substructures, overlooking molecular hierarchical information. Moreover, the extracted substructures in these methods are always restricted to have the same number of atoms as contained in the molecular graph, which does not align with real-world facts. In this study, a molecular fragment representation learning framework for drug-drug interaction prediction is introduced. Initially, a fragment extraction module is designed to acquire a series of molecular fragments. Subsequently, to capture more comprehensive features, molecular hierarchical information is effectively integrated, enabling drug-drug interaction prediction by identifying pairwise interactions between molecular fragments of each drug. Comprehensive evaluations demonstrate that the proposed method achieved state-of-the-art performance in both DrugBank and Twosides datasets, particularly achieving an improved accuracy of over 20% for unseen drugs in both two datasets. Furthermore, case studies and visual analysis confirm that the proposed method can accurately identify crucial substructures influencing the interactions, which are basically consistent with functional group structures in reality. In conclusion, this method not only enhances the performance of drug-drug interaction prediction but also offers high interpretability. Source code is freely available at https://github.com/kennysyp/MFR-DDI .

The Prevalent New-User Design to Study Drug-Drug Interactions: The Example of Sulfonylureas and Warfarin Interaction on the Risk of Severe Hypoglycemia.

PURPOSE: The optimal design for pharmacoepidemiologic drug-drug interactions (DDIs) studies is unclear. Using the association between concomitant use of sulfonylureas and warfarin and the risk of severe hypoglycemia as a case study, a DDI with little or no clinical impact, we tested whether the prevalent new-user design can be applied in the area. METHODS: Among all patients initiating sulfonylureas in the UK's Clinical Practice Research Datalink (1998-2020), we identified those adding-on warfarin while on a sulfonylurea. For each co-exposed patient, we defined a prescription-based exposure set including other sulfonylurea users not adding-on warfarin (comparators). Within each exposure set, we matched each co-exposed patient to five comparators on time-conditional propensity scores (TCPS) and followed them using an as-treated approach. Cox proportional hazards models estimated hazard ratios (HRs) and 95% confidence intervals (CIs) of severe hypoglycemia associated with concomitant use of sulfonylureas and warfarin compared to use of sulfonylureas alone. Sensitivity analyses addressed the impact of different potential sources of bias. RESULTS: The study cohort included 17 890 patients co-exposed to sulfonylureas and warfarin and 88 749 matched comparators. After TCPS matching, patient characteristics were well-balanced between groups. Compared to use of sulfonylureas alone, concomitant use of sulfonylureas and warfarin was not associated with the risk of severe hypoglycemia (HR, 1.04; 95% CI, 0.92-1.17). Sensitivity analyses were consistent with the primary analysis (HRs ranging from 1.01 to 1.15, all not statistically significant). CONCLUSIONS: Our study suggests that the prevalent new-user design could be used for the assessment of clinical effects of DDIs.

Evaluating the capability of ChatGPT in predicting drug-drug interactions: Real-world evidence using hospitalized patient data.

Drug-drug interactions (DDIs) present a significant health burden, compounded by clinician time constraints and poor patient health literacy. We assessed the ability of ChatGPT (generative artificial intelligence-based large language model) to predict DDIs in a real-world setting. Demographics, diagnoses and prescribed medicines for 120 hospitalized patients were input through three standardized prompts to ChatGPT version 3.5 and compared against pharmacist DDI evaluation to estimate diagnostic accuracy. Area under receiver operating characteristic and inter-rater reliability (Cohen's and Fleiss' kappa coefficients) were calculated. ChatGPT's responses differed based on prompt wording style, with higher sensitivity for prompts mentioning 'drug interaction'. Confusion matrices displayed low true positive and high true negative rates, and there was minimal agreement between ChatGPT and pharmacists (Cohen's kappa values 0.077-0.143). Low sensitivity values suggest a lack of success in identifying DDIs by ChatGPT, and further development is required before it can reliably assess potential DDIs in real-world scenarios.

Analysis of riociguat and desmethyl riociguat by UPLC-MS/MS and its interaction with quercetin.

Riociguat, an orally soluble guanylate cyclase (sGC)-promoting drug, is mainly used in the clinical treatment of pulmonary hypertension (PH). In this study, a novel ultra-performance liquid chromatography-tandem mass spectrometry method was developed to quantify the concentrations of riociguat and its metabolite (M1) in plasma. The precision, stability, accuracy, matrix effect, and recovery of the methodology were satisfactory. Quercetin, a well-recognized compound, functions as a novel anticancer agent with the potential to alleviate symptoms of PH. Therefore, the potential interaction between quercetin and riociguat was investigated in this study. The levels of riociguat and M1 in rat plasma were measured using the method developed in this study to evaluate the interactions between riociguat and quercetin in rats. The results revealed that quercetin significantly inhibited riociguat and M1 metabolism with increased systemic exposure.

Assessment of pharmacokinetic and pharmacodynamic interactions between zavegepant and sumatriptan: A phase 1, randomized, placebo-controlled study in healthy adults.

OBJECTIVE: To evaluate the pharmacodynamic (PD) and pharmacokinetic (PK) interactions between zavegepant and sumatriptan in healthy adults. BACKGROUND: Zavegepant is a high-affinity, selective, small-molecule calcitonin gene-related peptide receptor antagonist administered as a nasal spray approved in the United States for the acute treatment of migraine. Triptans, including sumatriptan, are a different class of drugs for acute migraine treatment and are associated with a risk of increased blood pressure (BP). Hence, it is important to study the drug-drug interactions between zavegepant and sumatriptan due to potential coadministration in clinical settings. METHODS: This was a Phase 1, single-center, partially blind, randomized, placebo-controlled, single-arm study. Eligible participants were males aged ≥ 18 and ≤ 40 years or females aged ≥ 18 and ≤ 50 years. On Day 1, participants received sumatriptan 2 × 6 mg subcutaneous injections (1 h apart) and were then randomized (6:1 ratio) to receive zavegepant 2 × 10 mg nasal spray (1 in each nostril) or placebo on Days 2 and 3. On Day 4, zavegepant or placebo was coadministered with sumatriptan after the second sumatriptan injection. BP, PK, and safety were evaluated at pre-specified time points. RESULTS: Forty-two participants enrolled in the study received at least one dose of any treatment and were included in the safety analyses. Forty-one participants who completed the study were included in the BP and PK analyses. The mean (standard deviation) time-weighted average (TWA) of mean arterial pressure (MAP [sumatriptan + zavegepant 87.2 (6.8) vs. sumatriptan 86.9 (6.0)]), diastolic BP (DBP [sumatriptan + zavegepant 72.3 (6.8) vs. sumatriptan 72.1 (6.2)]), and systolic BP (SBP [sumatriptan + zavegepant 116.8 (10.2) vs. sumatriptan 116.2 (8.6)]) did not change following zavegepant and sumatriptan coadministration on Day 4 compared to sumatriptan alone on Day 1. Statistical comparisons of the TWA of MAP, DBP, and SBP between sumatriptan and zavegepant coadministration and sumatriptan alone were similar; the differences observed were 0.04 mmHg for MAP (90% confidence interval [CI]: -0.69, 0.77 mmHg), 0.00 mmHg for DBP (90% CI: -0.76, 0.76 mmHg), and 0.33 mmHg for SBP (90% CI: -0.97, 1.63 mmHg). Sumatriptan PK after sumatriptan and zavegepant coadministration versus sumatriptan alone was similar; the comparison ratios were 102.5% (90% CI: 100.7%, 104.2%) for AUC0-inf and 104.1% (90% CI: 98.0%, 110.6%) for Cmax. A small difference in zavegepant PK exposure after sumatriptan and zavegepant coadministration versus zavegepant alone was not considered clinically relevant: the comparison ratios were 112.4% (90% CI: 103.4%, 122.3%) for AUC0-24 and 96.7% (90% CI: 88.9%, 105.2%) for Cmax. Overall, 90% (38/42) of participants experienced ≥ 1 treatment-emergent adverse event that was mild or moderate in severity. All treatments were generally safe and well tolerated. CONCLUSION: Coadministration of zavegepant with sumatriptan was safe and without PD or PK interactions in healthy adults.

Inhibitory effects of calcium channel blockers nisoldipine and nimodipine on ivacaftor metabolism and their underlying mechanism.

Ivacaftor is the first potentiator of the cystic fibrosis transmembrane conductance regulator (CFTR) protein approved for use alone in the treatment of cystic fibrosis (CF). Ivacaftor is primarily metabolized by CYP3A4 and therefore may interact with drugs that are CYP3A4 substrates, resulting in changes in plasma exposure to ivacaftor. The study determined the levels of ivacaftor and its active metabolite M1 by ultra performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS). We screened 79 drugs and 19 severely inhibited ivacaftor metabolism, particularly two cardiovascular drugs (nisoldipine and nimodipine). In rat liver microsomes (RLM) and human liver microsomes (HLM), the half-maximal inhibitory concentrations (IC50) of nisoldipine on ivacaftor metabolism were 6.55 µM and 9.10 µM, respectively, and the inhibitory mechanism of nisoldipine on ivacaftor metabolism was mixed inhibition; the IC50 of nimodipine on ivacaftor metabolism in RLM and HLM were 4.57 µM and 7.15 µM, respectively, and the inhibitory mechanism of nimodipine on ivacaftor was competitive inhibition. In pharmacokinetic experiments in rats, it was observed that both nisoldipine and nimodipine significantly altered the pharmacokinetic parameters of ivacaftor, such as AUC(0-t) and CLz/F. However, this difference may not be clinically relevant. In conclusion, this paper presented the results of studies investigating the interaction between these drugs and ivacaftor in vitro and in vivo. The objective is to provide a rationale for the safety of ivacaftor in combination with other drugs.

Drug-drug interactions of simnotrelvir/ritonavir: an open-label, fixed-sequence, two-period clinical trial.

OBJECTIVES: Simnotrelvir is a small-molecule highly specific 3C-like protease inhibitor for anti-SARS-CoV-2 and was approved as a combination drug with ritonavir (simnotrelvir/ritonavir) in China. Simnotrelvir is a substrate of cytochrome P450 3A (CYP3A) and P-glycoprotein (P-gp), and a weak inhibitor of CYP3A. Ritonavir is a substrate and inhibitor of CYP3A and an inhibitor of P-gp. Hence, the drug-drug interaction potential of simnotrelvir/ritonavir should be investigated. METHODS: This drug-drug interaction study was an open-label, fixed-sequence, two-period phase I clinical trial in Chinese healthy adult subjects, divided into three cohorts, including simnotrelvir/ritonavir co-administrated with a strong CYP3A and P-gp inhibitor (itraconazole) and inducer (rifampicin), and with a specific CYP3A substrate (midazolam). RESULTS: The results demonstrated that compared with administration of simnotrelvir/ritonavir alone, the co-administration with itraconazole increased the geometric least-square mean ratio (GMR) of the expose (area under the plasma concentration-time curve from time zero to the lowest detectable plasma concentration [AUC0-t]) of simnotrelvir by 25% (GMR 125%, 90% CI 114-137%), whereas co-administration with rifampicin significantly decreased the AUC0-t of simnotrelvir by 81.5% (GMR 18.5%, 90% CI 16.4-20.9%). Notably, simnotrelvir/ritonavir increased the AUC0-t of midazolam by 16.69-fold (GMR 1769%, 90% CI 1551-2018%). The co-administration of simnotrelvir/ritonavir and rifampicin caused the increased amount and severity of treatment-emergent adverse events, especially hepatotoxicity. DISCUSSION: The co-administration of simnotrelvir/ritonavir with CYP3A and P-gp inhibitors can be safely used, whereas the co-administration with CYP3A and P-gp strong inducer should be avoided to minimize the risk of under-exposure. Co-administration of midazolam with simnotrelvir/ritonavir increased systemic exposure of midazolam. CLINICALTRIALS: gov Identifier: NCT05665647.

Drug transporters in drug disposition - highlights from the year 2023.

Drug transporter field is rapidly evolving with significant progress in in vitro and in vivo tools and, computational models to assess transporter-mediated drug disposition and drug-drug interactions (DDIs) in humans. On behalf of all coauthors, I am pleased to share the fourth annual review highlighting articles published and deemed influential in the field of drug transporters in the year 2023. Each coauthor independently selected peer-reviewed articles published or available online in the year 2023 and summarized them as shown previously (Chothe et al. 2021; Chothe et al. 2022, 2023) with unbiased perspectives. Based on selected articles, this review was categorized into four sections: (1) transporter structure and in vitro evaluation, (2) novel in vitro/ex vivo models, (3) endogenous biomarkers, and (4) PBPK modeling for evaluating transporter DDIs (Table 1). As the scope of this review is not to comprehensively review each article, readers are encouraged to consult original paper for specific details. Finally, I appreciate all the authors for their time and continued support in writing this review.

Concomitant Administration of Psychotropic and Prostate Cancer Drugs: A Pharmacoepidemiologic Study Using Drug-Drug Interaction Databases.

Prostate cancer (PC) represents the second most common diagnosed cancer in men. The burden of diagnosis and long-term treatment may frequently cause psychiatric disorders in patients, particularly depression. The most common PC treatment option is androgen deprivation therapy (ADT), which may be associated with taxane chemotherapy. In patients with both PC and psychiatric disorders, polypharmacy is frequently present, which increases the risk of drug-drug interactions (DDIs) and drug-related adverse effects. Therefore, this study aimed to conduct a pharmacoepidemiologic study of the concomitant administration of PC drugs and psychotropics using three drug interaction databases (Lexicomp®, drugs.com®, and Medscape®). This study assayed 4320 drug-drug combinations (DDCs) and identified 814 DDIs, out of which 405 (49.63%) were pharmacokinetic (PK) interactions and 411 (50.37%) were pharmacodynamic (PD) interactions. The most common PK interactions were based on CYP3A4 induction (n = 275, 67.90%), while the most common PD interactions were based on additive torsadogenicity (n = 391, 95.13%). Proposed measures for managing the identified DDIs included dose adjustments, drug substitutions, supplementary agents, parameters monitoring, or simply the avoidance of a given DDC. A significant heterogenicity was observed between the selected drug interaction databases, which can be mitigated by cross-referencing multiple databases in clinical practice.

Drug-Drug Interactions in Nosocomial Infections: An Updated Review for Clinicians.

Prevention, assessment, and identification of drug-drug interactions (DDIs) represent a challenge for healthcare professionals, especially in nosocomial settings. This narrative review aims to provide a thorough assessment of the most clinically significant DDIs for antibiotics used in healthcare-associated infections. Complex poly-pharmaceutical regimens, targeting multiple pathogens or targeting one pathogen in the presence of another comorbidity, have an increased predisposition to result in life-threatening DDIs. Recognising, assessing, and limiting DDIs in nosocomial infections offers promising opportunities for improving health outcomes. The objective of this review is to provide clinicians with practical advice to prevent or mitigate DDIs, with the aim of increasing the safety and effectiveness of therapy. DDI management is of significant importance for individualising therapy according to the patient, disease status, and associated comorbidities.

Adverse reactions of immune checkpoint inhibitors combined with Proton pump inhibitors: a pharmacovigilance analysis of drug-drug interactions.

BACKGROUND: Combining immune checkpoint and proton pump inhibitors is widely used in cancer treatment. However, the drug-drug interactions of these substances are currently unknown. This study aimed to explore drug-drug interactions associated with concomitant immune checkpoint and proton pump inhibitors. METHODS: Data were obtained from the US Food and Drug Administration Adverse Event Reporting System from 2014 to 2023. Disproportionality analysis was used for data mining by calculating the reporting odds ratios (RORs) with 95% confidence intervals (95%Cls). The adjusted RORs (RORadj) were then analysed using logistic regression analysis, considering age, sex, and reporting year. Drug-drug interactions occur when a combination treatment enhances the frequency of an event. Further confirmation of the robustness of the findings was achieved using additive and multiplicative models, which are the two statistical methodologies for signal detection of DDIs using spontaneous reporting system. RESULTS: The total number of reports on immune checkpoint combined with proton pump inhibitors was 4,276. Median patient age was 66 years (interquartile range [IQR]: 60-74 years). Significant interaction signals were observed for congenital, familial and genetic disorders (RORadj = 2.66, 95%CI, 1.38-5.14, additive models = 0.7322, multiplicative models = 3.5142), hepatobiliary disorders (RORcrude = 6.64, 95%CI, 5.82-7.58, RORadj = 7.10, 95%CI, 6.16-8.18, additive models = 2.0525, multiplicative models = 1.1622), metabolism and nutrition disorders (RORcrude = 3.27, 95%CI, 2.90-3.69, RORadj = 2.66, 95%CI, 2.30-3.08, additive models = 0.6194), and skin and subcutaneous tissue disorders (RORcrude = 1.41, 95%CI, 1.26-1.58, RORadj = 1.53, 95%CI, 1.34-1.75, additive models = 0.6927, multiplicative models = 5.3599). Subset data analysis showed that programmed death-1 combined with proton pump inhibitors was associated with congenital, familial, and genetic disorders; hepatobiliary disorders; and skin and subcutaneous tissue disorders. Programmed death ligand-1 combined with proton pump inhibitors was associated with adverse reactions of metabolism and nutrition disorders. Cytotoxic T-lymphocyte antigen-4 combined with proton pump inhibitors was associated with congenital, familial, and genetic disorders, and skin and subcutaneous tissue disorders. CONCLUSIONS: Based on real-world data, four Standardized MedDRA Query System Organ Class toxicities were identified as drug-drug interactions associated with combining immune checkpoint and proton pump inhibitors. Clinicians should be cautious when administering these drugs concomitantly. Preclinical trials and robust clinical studies are required to explore the mechanisms and relationships underlying interactions, thus improving understanding of drug-drug interactions associated with this combination therapy.

Determination of Vatiquinone Drug-Drug Interactions, as CYP450 Perpetrator and Victim, Using Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation.

Vatiquinone, a 15-lipoxygenase inhibitor, is in development for patients with Friedreich's ataxia. Physiologically based pharmacokinetic (PBPK) modeling addressed drug-drug interaction gaps without additional studies. A PBPK model (Simcyp Simulator version 21, full model) was developed using parameters obtained from in vitro studies, in silico estimation and optimization, and two clinical studies. A venous blood dosing model best characterized vatiquinone lymphatic absorption. Apparent oral clearance (CL/F) was used to optimize intrinsic clearance (CLint). Intestinal availability (Fg) was estimated using the hybrid flow term (Qgut), unbound fraction in the enterocytes (fugut), and gut intrinsic metabolic clearance (CLuG,int). Renal clearance (CLR) was set to zero. Assuming an Fa of 1, CYP3A4 contribution (fmCYP3A4) was further optimized. The PBPK model was verified with two clinical studies and demonstrated that it adequately characterized vatiquinone PK. As a perpetrator, the model predicted no risk for vatiquinone to significantly alter the drug exposures of CYP3A4 and CYP1A2 substrates as evident bynegligible reduction in both midazolam and caffeine area under the curve (AUC)inf and Cmax. As a victim, the model predicted that vatiquinone exposures are weakly influenced by moderate CYP3A4 inhibitors and inducers. With fluconazole coadministration, vatiquinone AUCinf and Cmax increased by nearly 50% and 25%, respectively. With efavirenz coadministration, vatiquinone AUCinf and Cmax decreased by approximately 20% and 10%, respectively. Results suggested that vatiquinone does not significantly impact CYP3A4 and CYP1A2 substrates and that moderate CYP3A4 inhibitors and inducers weakly impact vatiquinone AUC.

Letter to the editor regarding the paper by A. Boileve et al.: Safety of direct oral anticoagulants in patients with advanced solid tumors receiving anti­VEGF agents: a retrospective study.

Concomitant direct oral anticoagulants (DOACs) and tyrosine kinase inhibitor targeting vascular endothelial growth factor receptor (anti-VEGF TKI) have been associated with a higher risk of bleeding. Nevertheless, concomitant administration seems frequent in clinical practice in patients with cancer-associated thrombosis and appears to be safe according to the retrospective study by Boileve A. et al. But the risk of an additional pharmacokinetic interaction between anti-VEGF TKI and DOACs must be considered, in case of P-glycoprotein (P-gp) inhibition by the TKI. We describe a case report with a major bleeding event in a renal metastatic cancer patient treated with cabozantinib and rivaroxaban. This case highlights the difficult therapeutic decision in a complex patient with cancer-associated thrombosis, who refused the anticoagulant subcutaneous route. Accumulation of bleeding risk factors (genito-urinary tumor localization) was additive to several pharmacodynamic interactions (acetylsalicylic acid, venlafaxine) and a potential pharmacokinetic interaction between cabozantinib and rivaroxaban. Indeed, cabozantinib-related P-glycoprotein inhibition could have led to a supratherapeutic level of rivaroxaban, contributing partly to the bleeding event. Before combining an anti-VEGF TKI and DOACs, a multidisciplinary pretherapeutic assessment seems crucial to evaluate the patient's bleeding risk factors, pharmacodynamic interactions, and the risk of pharmacokinetic interactions mediated by P-gp.

Assessment of drug-drug interactions among patients with hematologic malignancy: A clinical pharmacist-led study.

INTRODUCTION: Patients with hematologic malignancies often receive multiple medications, leading to potential drug-drug interactions (DDIs). Identifying and managing these DDIs is crucial for ensuring patient safety and effective care. This study aimed to identify and describe DDIs and associated factors in hematologic malignancy patients. METHODS: This prospective interventional study was conducted at a referral center and included hospitalized patients with hematologic malignancies who were receiving at least four concurrent medications. A pharmacist initially compiled a comprehensive list of all medications through patient interviews and medication reviews, and subsequently, identified and categorized potential DDIs using the Lexi-interact® and Micromedex® databases. The clinical pharmacist then evaluated the clinical impact of the identified DDIs in every individual patient and provided appropriate interventions to resolve them. RESULTS: A total of 200 patients met the inclusion criteria for the study, with 1281 DDIs identified across 337 distinct types. The majority of identified DDIs exhibited major severity (52.1%) and pharmacokinetic mechanisms (50.3%), with an unspecified onset (79.4%) and fair evidence (67%). Of the identified DDIs, 81.1% were considered clinically significant, prompting 1059 pharmacotherapy interventions by the clinical pharmacist. Additionally, a significant relationship was observed between the number of drugs used during hospitalization and the occurrence of DDIs (P < 0.001, r = 0.633). CONCLUSION: DDIs are highly prevalent among hospitalized patients with hematologic malignancies, with their occurrence increasing alongside the number of medications administrated. The intervention of a clinical pharmacist is crucial to evaluate the clinical impact of these DDIs and implement effective interventions for their management.

Data-driven drug treatment: enhancing clinical decision-making with SalpPSO-optimized GraphSAGE.

Safe drug recommendation systems play a crucial role in minimizing adverse drug reactions and enhancing patient safety. In this research, we propose an innovative approach to develop a safety drug recommendation system by integrating the Salp Swarm Optimization-based Particle Swarm Optimization (SalpPSO) with the GraphSAGE algorithm. The goal is to optimize the hyper parameters of GraphSAGE, enabling more accurate drug-drug interaction prediction and personalized drug recommendations. The research begins with data collection from real-world datasets, including MIMIC-III, Drug Bank, and ICD-9 ontology. The databases provide comprehensive and diverse clinical data related to patients, diseases, and drugs, forming the foundation of a knowledge graph. It represents drug-related entities and their relationships, such as drugs, indications, adverse effects, and drug-drug interactions. The knowledge graph's integration of patient data, disease ontology, and drug information enhances the system's accuracy to predict drug-drug interactions as well as identifying potential detrimental drug reactions. The GraphSAGE algorithm is employed as the base model for learning node embeddings in the knowledge graph. To enhance its performance, we propose the SalpPSO algorithm for hyper parameter optimization. SalpPSO combines features from Salp Swarm Optimization and Particle Swarm Optimization, offering a robust and effective optimization process. The optimized hyper parameters lead to more reliable and accurate drug recommendation system. For evaluation, the dataset is split into training and validation sets and compared the performance of the modified GraphSAGE model with SalpPSO-optimized hyper parameters to the standard models. The experimental analysis conducted in terms of various measures proves the efficiency of the proposed safe recommendation system, offering valuable for healthcare experts in making more informed and personalized drug treatment decisions for patients.

Pharmacovigilance of drug-drug interactions: A pharmacokinetic study on the combined oral administration of lurasidone and clozapine in rats by using LC-MS/MS.

In recent years, the expanding array of psychotropic medications has led to an increase in drug-drug interactions, particularly with combinations of different antipsychotics or psychotropic medications in clinical practice. However, the potential pharmacokinetic interactions between Lurasidone and Clozapine have not been extensively studied. Thus, this study aims to investigate these potential interactions by analyzing their pharmacokinetics in rat plasma after single oral administrations using developed LC-MS/MS methods. The study revealed notable changes in Lurasidone's pharmacokinetic parameters between single and combination administrations. Specifically, there were significant reductions in t1/2 and Vd by 3.3 and 1.5-fold (p < 0.05) respectively, while Cmax and AUC0-t proved a significant increase by 1.8 and 1.6-fold (p < 0.05) respectively following the combination administration. Furthermore, separate co-administration markedly decreased Clozapine's Cmax and AUC 0-t by 1.6 and 1.3-fold (p < 0.05) respectively, after the combination administration. Moreover, the AUC ratio for Lurasidone was 0.2, indicating a diminished therapeutic effect, whereas the AUC ratio for Clozapine suggested an elevated risk of adverse effects. These findings confirm the presence of drug-drug interactions between Lurasidone and Clozapine, suggesting potential implications for treatment efficacy. Recommendations for future clinical research include conducting pharmacodynamic studies to evaluate the impact of Lurasidone and Clozapine combination therapy. This underscores the importance of thoroughly assessing these interactions for clinical relevance and provides a scientific foundation for future evaluations of this drug combination.

Drug-drug interaction between diltiazem and tacrolimus in relation to CYP3A5 genotype status in Chinese pediatric patients with nephrotic range proteinuria: a retrospective study.

Background: Tacrolimus is widely used to treat pediatric nephrotic range proteinuria (NRP). Diltiazem, a CYP3A4/5 inhibitor, is often administered with tacrolimus, affecting its pharmacokinetic profile. The impact of this combination on tacrolimus exposure, particularly in CYP3A5*3 genetic polymorphism, remains unclear in pediatric NRP patients. This study aimed to evaluate the effects of diltiazem on tacrolimus pharmacokinetics, focusing on the CYP3A5*3 polymorphism. Methods: We conducted a retrospective clinical study involving pediatric NRP patients, divided into two groups: those receiving tacrolimus with diltiazem and those receiving tacrolimus alone. Propensity score matching (PSM) was used to balance the baseline characteristics between the groups. We compared daily dose-adjusted trough concentrations (C0/D) of tacrolimus in both the original and PSM cohorts. The influence of diltiazem on tacrolimus C0/D, stratified by CYP3A5*3 genetic polymorphism, was assessed in a self-controlled case series study. Results: Before PSM, the tacrolimus C0/D in patients taking diltiazem was significantly higher compared to those with tacrolimus alone (75.84 vs. 56.86 ng/mL per mg/kg, P = 0.034). This finding persisted after PSM (75.84 vs. 46.93 ng/mL per mg/kg, P= 0.028). In the self-controlled case study, tacrolimus C0/D elevated about twofold (75.84 vs. 34.76 ng/mL per mg/kg, P < 0.001) after diltiazem administration. CYP3A5 expressers (CYP3A5*1/*1 and *1/*3) and CYP3A5 non-expressers (CYP3A5*3/*3) experienced a 1.8-fold and 1.3-fold increase in tacrolimus C0/D when combined with diltiazem, respectively. Conclusion: Diltiazem significantly increased tacrolimus C0/D, with CYP3A5*3 expressers showing higher elevations than non-expressers among pediatric NRP patients. These findings highlight the importance of personalized tacrolimus therapy based on CYP3A5*3 genotypes in pediatric patients taking diltiazem.

Influence of age and co-medication on dolutegravir glucuronidation in paediatric patients.

Dolutegravir (DTG) is primarily metabolized by uridine diphosphate glucuronosyltransferases, forming the pharmacologically inactive DTG glucuronide (DTG-gluc). We described the dolutegravir metabolic ratio (DTG-MR; DTG-gluc AUC0-24h divided by DTG AUC0-24h) in 85 children with HIV aged 3 months to 18 years receiving DTG in the CHAPAS-4 (ISRCTN22964075) and ODYSSEY (NCT02259127) trials. Additionally, we assessed the influence of age, body weight, nucleoside/nucleotide reverse transcriptase inhibitor (NRTI) backbone, rifampicin use and kidney function on DTG-MR. The overall geometric mean (CV%) DTG-MR was 0.054 (52%). Rifampicin use was the only significant factor associated with DTG-MR (P < .001) in multiple linear regression. DTG-MR geometric mean ratio was 1.81 (95% CI: 1.57-2.08) for children while on vs. off rifampicin. This study showed that overall DTG-MR in children was similar to adults, unaffected by age or NRTI backbone, and increased with rifampicin co-administration. These findings support future paediatric pharmacokinetic modelling and extrapolation from adult data.

In vitro assessment of inhibitory effects of kinase inhibitors on CYP2C9, 3A and 1A2: Prediction of drug-drug interaction risk with warfarin and direct oral anticoagulants.

OBJECTIVE: This study aimed to evaluate the cytochrome P450 (CYP)-mediated drug-drug interaction (DDI) potential of kinase inhibitors with warfarin and direct oral anticoagulants (DOACs). METHODS: An in vitro CYP probe substrate cocktail assay was used to study the inhibitory effects of fifteen kinase inhibitors on CYP2C9, 3A, and 1A2. Then, DDI predictions were performed using both mechanistic static and physiologically-based pharmacokinetic (PBPK) models. RESULTS: Linsitinib, masitinib, regorafenib, tozasertib, trametinib, and vatalanib were identified as competitive CYP2C9 inhibitors (Ki = 1.4, 1.0, 1.1, 3.8, 0.5, and 0.1 µM, respectively). Masitinib and vatalanib were competitive CYP3A inhibitors (Ki = 1.3 and 0.2 µM), and vatalanib noncompetitively inhibited CYP1A2 (Ki = 2.0 µM). Moreover, linsitinib and tozasertib were CYP3A time-dependent inhibitors (KI = 26.5 and 400.3 µM, kinact = 0.060 and 0.026 min-1, respectively). Only linsitinib showed time-dependent inhibition of CYP1A2 (KI = 13.9 µM, kinact = 0.018 min-1). Mechanistic static models identified possible DDI risks for linsitinib and vatalanib with (S)-/(R)-warfarin, and for masitinib with (S)-warfarin. PBPK simulations further confirmed that vatalanib may increase (S)- and (R)-warfarin exposure by 4.37- and 1.80-fold, respectively, and that linsitinib may increase (R)-warfarin exposure by 3.10-fold. Mechanistic static models predicted a smaller risk of DDIs between kinase inhibitors and apixaban or rivaroxaban. The greatest AUC increases (1.50-1.74) were predicted for erlotinib in combination with apixaban and rivaroxaban. Linsitinib, masitinib, and vatalanib were predicted to have a smaller effect on apixaban and rivaroxaban AUCs (AUCR 1.22-1.53). No kinase inhibitor was predicted to increase edoxaban exposure. CONCLUSIONS: Our results suggest that several kinase inhibitors, including vatalanib and linsitinib, can cause CYP-mediated drug-drug interactions with warfarin and, to a lesser extent, with apixaban and rivaroxaban. The work provides mechanistic insights into the risk of DDIs between kinase inhibitors and anticoagulants, which can be used to avoid preventable DDIs in the clinic.