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.

Physiologically based pharmacokinetic modelling to predict potential drug-drug interactions of dersimelagon (MT-7117).

AIMS: Dersimelagon is a novel, investigational, orally administered, selective agonist of the melanocortin-1 receptor that has demonstrated efficacy at increasing symptom-free light exposure and an acceptable safety profile in patients with protoporphyria. A phase 1 drug-drug interaction (DDI) study demonstrated that dersimelagon 300 mg has the potential for clinically relevant DDIs with drugs that are substrates for breast cancer resistance protein, such as atorvastatin and rosuvastatin. This study uses physiologically based pharmacokinetic (PBPK) modelling to further investigate the DDI effects at lower doses of dersimelagon with substrate drugs. METHODS: The data from in silico, in vitro and in vivo studies were used to construct a PBPK model for dersimelagon to assess the DDI potential between dersimelagon and substrate drugs for cytochrome P450 3A, P-glycoprotein, organic anion transporting polypeptide 1B1/1B3, organic anion transporter 3 and breast cancer resistance protein, including atorvastatin and rosuvastatin. RESULTS: The systemic exposure of atorvastatin based on the maximum plasma concentration and area under the plasma concentration-time curve was predicted to increase 1.21-fold and 1.25-fold, respectively, if coadministered with dersimelagon 100 mg, and 1.42-fold and 1.45-fold with dersimelagon 200 mg. The systemic exposure of rosuvastatin followed trends similar to atorvastatin (1.67-fold and 1.34-fold increase in maximum plasma concentration and area under the plasma concentration-time curve, respectively, with dersimelagon 100 mg, and 2.40-fold and 1.69-fold with dersimelagon 200 mg). CONCLUSION: Overall, PBPK modelling results indicate that the simulated changes in plasma exposure of atorvastatin and rosuvastatin following coadministration with dersimelagon 100 or 200 mg are not clinically significant, but caution and appropriate clinical monitoring should be recommended.

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

[This corrects the article DOI: 10.3389/fphar.2024.1403649.].

Investigation of Ponatinib Metabolism and Drug-Drug Interactions with Lycopene and Shikonin in vitro and in vivo.

Ponatinib is approved for use in patients with chronic myeloid leukaemia (CML) who are resistant to or intolerant to prior tyrosine kinase inhibitor (TKI) therapy. Given that ponatinib can induce significant cardiotoxicity when taken and that most Chinese medicines have cardioprotective effects, so it is possible to give them in combination in clinic to alleviate adverse effects. The quantitative determination of ponatinib and its metabolite N-desmethyl ponatinib was optimized and fully verified by ultra performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS), and the drug-drug interactions (DDI) of ponatinib with lycopene and shikonin in vivo and in vitro were studied. The results of bioanalytical methodology showed that ponatinib and N-desmethyl ponatinib had good linearity in plasma samples, and their selectivity, accuracy, precision, stability, matrix effect and recovery were all satisfied with the need of quantitative analysis of samples. In animal experiments, compared with the control group, lycopene and shikonin significantly changed the pharmacokinetic parameters of ponatinib, including AUC(0-t), AUC(0-∞) and CLz/F, while had no effect on the pharmacokinetic parameters of N-desmethyl ponatinib. In vitro interaction studies showed that lycopene showed mixed inhibition mechanism on ponatinib metabolism in both rat liver microsomes (RLM) and human liver microsomes (HLM). And, shikonin displayed mixed inhibition mechanism and competitive inhibition mechanism in RLM and HLM, respectively. In summary, the UPLC-MS/MS method can accurately and sensitively quantify ponatinib and N-desmethyl ponatinib, and provide further reference for clinical drug combination between ponatinib and lycopene or shikonin.

Retrospective analysis of lithium treatment: examination of blood levels.

Introduction: Lithium is a key medication for treating various neuropsychiatric disorders, with a narrow therapeutic index and significant drug interactions. Monitoring lithium blood levels is crucial. This study aims to investigate the relationship between lithium blood levels and demographic characteristics such as age and gender, as well as possible drug interactions, in patients with a history of lithium use who applied to various services and outpatient clinics. Materials & methods: The files of 438 patients who were admitted to various services and outpatient clinics of Kirklareli Training and Research Hospital between January 1 and December 31, 2023, were retrospectively reviewed. Patients' blood lithium levels, gender, age, service/outpatient clinic they admitted to, other medications used, urea, creatinine, and eGFR values were recorded. Results: When the demographic characteristics of 438 patients were examined, 62% were female (270), 38% were male (168), and the average age was 46.3 ± 14.8 years, showing a normal distribution. It was found that 192 patients (71 males, 121 females) had therapeutic lithium blood levels, while 244 patients (97 males, 147 females) had levels below 0.6 mmol/L. Two female patients had blood levels above the therapeutic range (1.23 and 1.43 mmol/L). Among the clinics and services, the four most frequented were the psychiatry clinic (314 patients), internal medicine clinic (36 patients), emergency service (27 patients), and medical oncology clinic (17 patients). Of the 314 patients admitted to the psychiatry clinic, 168 had therapeutic drug levels; only 7 of the 36 admitted to internal medicine had therapeutic levels; 12 of the 27 patients in the emergency service had therapeutic levels; and all 17 patients in medical oncology had levels below therapeutic limits. Discussion: The data emphasize the importance of regular blood level monitoring to ensure lithium treatment's efficacy and patient safety. It is noteworthy that most patients in the psychiatry clinic had therapeutic drug levels, while those in other clinics had lower levels. Conclusion: In conclusion, this study highlights the importance of regular blood level monitoring to ensure the efficacy and safety of lithium treatment.

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.

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.

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.

Bioavailability and interactions of schisandrin B with 5-fluorouracil in a xenograft mouse model of colorectal cancer.

Schisandrin B (Sch B) is a predominant bioactive lignan from the fruit of a Chinese medicine food homology plant, Schisandra chinensis. Previously, we observed potent anti-tumor effect of Sch-B in colorectal cancer (CRC) and enhanced chemotherapy efficacy with fluorouracil (5-FU). However, their bioavailability and reciprocal interactions under CRC conditions are unclear. In this study, we first compared the bioavailability, metabolism and tissue distribution of Sch-B between non-tumor-bearing and xenograft CRC tumor-bearing mice. Next, we examined SchB-5-FU interactions via investigating alterations in drug metabolism and multidrug resistance. Using a validated targeted metabolomics approach, five active metabolites, including Sch-B and fluorodeoxyuridine triphosphate, were found tumor-accumulative. Co-treatment resulted in higher levels of Sch-B and 5-FU metabolites, showing improved phytochemical and drug bioavailability. Multidrug resistance gene (MDR1) was significantly downregulated upon co-treatment. Overall, we demonstrated the potential of Sch-B to serve as a promising chemotherapy adjuvant via improving drug bioavailability and metabolism, and attenuating MDR.

In vitro mechanistic study on mycophenolate mofetil drug interactions: effect of prednisone, cyclosporine, and others.

Objective: The metabolism- and transporter-based drug-drug interactions (DDIs) between mycophenolate mofetil (MMF) and co-administered medications may be key factors for the high individual variability in MMF exposure. This study systematically assessed the influence of co-medications on the mycophenolic acid (MPA) pharmacokinetic (PK) process in vitro, particularly to provide mechanistic evidence of the metabolic interaction among steroids, cyclosporine (CsA), and MMF. Methods: Based on a previous study, we hypothesized that there are three main DDI pathways affecting MMF PK in vivo. A human hepatocyte induction study, transporter substrate/inhibition study using human embryonic kidney 293 cells, and multidrug resistance-associated protein 2 (MRP2) substrate/inhibition study using vesicle membrane were conducted to assess the mechanistic evidence of the metabolic interaction in triple therapies. The potential DDI risks associated with seven medications commonly co-administered with MMF in clinical practice were further evaluated. Results: The in vitro results suggested that prednisolone, the active metabolite of prednisone, induces the enzymatic activity of uridine 5'-diphospho-glucuronosyltransferase (UGT), particularly the UGT1A9 and UGT2B7 isoforms, resulting in increased metabolism of MPA to MPA glucuronide (MPAG). This induction potential was not observed in CsA-treated human hepatocytes. CsA inhibits organic anion-transporting polypeptide (OATP) 1B1- and OATP1B3-mediated MPAG. Prednisolone and CsA showed no inhibitory effect on MRP2-mediated MPAG efflux. Salvia miltiorrhiza significantly inhibited organic anion-transporting polypeptide and OAT 3 activities, suggesting that it affects the hepatic uptake and renal excretion of MPAG, causing increased MPAG exposure in vivo. Conclusion: These identified factors may contribute to the high inter-individual variability in MMF exposure and facilitate further development of mechanistic MMF PK models and individualized therapies.

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.

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.

Early follow-up of outpatients with oral anticancer therapy in the ONCORAL multidisciplinary community-hospital program.

BACKGROUND: Healthcare professionals are faced with the new challenges of preventing and managing drug-related problems (DRPs) with oral anticancer therapy (OAT): side-effects, drug-drug interactions (DDIs), non-adherence, or medication errors. This study aims to assess the impact of ONCORAL, a real-life multidisciplinary care plan for cancer patients based on community and hospital follow-up, for the first OAT cycle. METHODS: A prospective cohort study was conducted between October 1, 2021 and October 1, 2022 including all outpatients starting OAT treatment. During the first OAT cycle, the program consists of 6 weekly scheduled face-to-face or phone consultations to prevent and manage DRPs. Nurse and pharmacist interventions (NPIs) are realized to optimize treatments (primary outcomes). Secondary outcomes included the relative dose intensity (RDI) of the first cycle. RESULTS: A total of 562 NPIs were performed by the ONCORAL team: that is, 87.1% of the 209 patients included, for a mean of 3.1 ±â€…2.2 NPIs/patient. NPIs-concerned DRPs detected by the nurse and pharmacist (346, 61.6%), symptoms and/or adverse effects reported as PROs by the patient or family (138, 24.6%), or pathway issues (78, 13.9%). Seventy-three DDIs were detected and managed during medication review, in a quarter of patients (n = 54/209), leading to the discontinuation of a daily concomitant medication in 30 cases. The mean RDI at the end of the first cycle, calculated for 209 patients, was 83.1 ±â€…23.9% (17.56-144.23). CONCLUSION: In these ambulatory cancer patients, the interest in tailored monitoring of DRPs as a whole, including the prevention and management of drug interactions in addition to symptoms and adverse effects, is highlighted.

The CYP3A inducer dexamethasone affects the pharmacokinetics of sunitinib by accelerating its metabolism in rats.

Sunitinib, a novel anti-tumor small molecule targeting VEGFR, is prescribed for advanced RCC and GISTs. Sunitinib is primarily metabolized by the CYP3A enzyme. It is well-known that dexamethasone serves as a potent inducer of this enzyme system. Nonetheless, the effect of dexamethasone on sunitinib metabolism remains unclear. This study examined the effect of dexamethasone on the pharmacokinetics of sunitinib and its metabolite N-desethyl sunitinib in rats. The plasma levels of both compounds were measured using UHPLC-MS/MS. Pharmacokinetic parameters and metabolite ratio values were calculated. Compare to control group, the low-dose dexamethasone group and high-dose dexamethasone group decreased the AUC(0-t) values of sunitinib by 47 % and 45 %, respectively. Meanwhile, the AUC(0-t) values of N-desethyl sunitinib were increased by 2.2-fold and 2.4-fold in low-dose dexamethasone group and high-dose dexamethasone group, respectively. The CL values for sunitinib were both approximately 45 % higher in the two dexamethasone groups. Remarkably, metabolite ratio values increased over 5-fold in both low-dose dexamethasone group and high-dose dexamethasone group, indicating a significant enhancement of sunitinib metabolism by dexamethasone. Moreover, the total levels of sunitinib and its metabolite are also significantly increased. The impact of interactions on sunitinib metabolism, as observed with CYP3A inducers such as dexamethasone, is a crucial consideration for clinical practice. To optimize the dosage and prevent adverse drug events, therapeutic drug monitoring can be employed to avoid the toxicity from such interactions.

Prevalence of Potential Drug Interactions With Direct-Acting Antivirals for COVID-19 Among Hospitalized Patients.

PURPOSE: Clinicians consider polypharmacy, comorbidities, and other factors including the potential for drug-drug interactions (DDIs) when evaluating therapeutic options for specific clinical diagnoses. Contemporary treatment for coronavirus disease 2019 (COVID-19) includes direct-acting antivirals (DAAs). We sought to characterize patients' characteristics, comorbidities, and medications received during their hospitalization for COVID-19 and quantify potential DDIs that clinicians consider in selecting appropriate DAAs. METHODS: Patients hospitalized with a primary diagnosis of COVID-19 between May 2020 and December 2022 from the PINC AI Healthcare Database were identified. Medications administered during the hospitalization with the potential to cause DDIs with nirmatrelvir/ritonavir, remdesivir, or molnupiravir (per the Emergency Use Authorization factsheet or package insert) were assessed. For DDIs with nirmatrelvir/ritonavir, medications are categorized as "Contraindicated," "Avoid Concomitant Use," or "Other DDIs" (includes recommendation for dose modification or clinical and laboratory monitoring). For remdesivir, coadministration with chloroquine phosphate and hydroxychloroquine sulfate was not recommended. For molnupiravir, no drugs are listed as having potential DDIs. In a subset of patients, a multivariable logistic regression model was used to examine the association between documented patient/hospital characteristics and the likelihood of being "Contraindicated" to receive nirmatrelvir/ritonavir. FINDINGS: Of the 788,238 patients hospitalized for COVID-19 in 920 hospitals, 53% were ≥ 65 years old, and 31% had Charlson Comorbidity Index (CCI) ≥ 3. During the study period, about half of the patients received medications categorized as "Contraindicated" (11%) and/or "Avoid Concomitant Use" (41%) with nirmatrelvir/ritonavir. The frequency of administered drugs was higher in those aged ≥ 65 years (68%), CCI ≥ 3 (78%), with high-risk underlying conditions (55%). About 1% of patients received medications that were not recommended to be coadmistered with remdesivir. Among a subset of patients hospitalized for COVID-19 in 2022, those who were older, had higher CCI, high-risk underlying conditions, severe hepatic impairment, Medicare insurance, and hospitalized in larger hospitals were significantly more likely to be categorized as "Contraindicated" when considering nirmatrelvir/ritonavir as a therapeutic option to manage COVID-19. IMPLICATIONS: A significant proportion of patients hospitalized for COVID-19 receive medications for other conditions that have the potential to result in DDIs with DAAs; most predominantly with nirmatrelvir/ritonavir, a strong CYP3A enzyme inhibitor, fewer with remdesivir, and none with molnupiravir. Higher age and comorbidity burden were significantly associated with a higher likelihood of receiving medications that are "Contraindicated" with nirmatrelvir/ritonavir. In the evolving COVID-19 era, these findings provide insights into patients hospitalized for COVID-19, and the polypharmacy evaluations that clinicians may encounter when selecting among DAAs to manage COVID-19.

Inhibition of human UDP-glucuronosyltransferase (UGT) enzymes by darolutamide: Prediction of in vivo drug-drug interactions.

Darolutamide is a potent second-generation, selective nonsteroidal androgen receptor inhibitor (ARI), which has been approved by the US Food and Drug Administration (FDA) in treating castrate-resistant, non-metastatic prostate cancer (nmCRPC). Whether darolutamide affects the activity of UDP-glucuronosyltransferases (UGTs) is unknown. The purpose of the present study is to evaluate the inhibitory effect of darolutamide on recombinant human UGTs and pooled human liver microsomes (HLMs), and explore the potential for drug-drug interactions (DDIs) mediated by darolutamide through UGTs inhibition. The product formation rate of UGTs substrates with or without darolutamide was determined by HPLC or UPLC-MS/MS to estimate the inhibitory effect and inhibition modes of darolutamide on UGTs were evaluated by using the inhibition kinetics experiments. The results showed that 100 µM darolutamide exhibited inhibitory effects on most of the 12 UGTs tested. Inhibition kinetic studies of the enzyme revealed that darolutamide noncompetitively inhibited UGT1A1 and competitively inhibited UGT1A7 and 2B15, with the Ki of 14.75 ± 0.78 µM, 14.05 ± 0.42 µM, and 6.60 ± 0.08 µM, respectively. In particular, it also potently inhibited SN-38, the active metabolite of irinotecan, glucuronidation in HLMs with an IC50 value of 3.84 ± 0.46 µM. In addition, the in vitro-in vivo extrapolation (IVIVE) method was used to quantitatively predict the risk of darolutamide-mediated DDI via inhibiting UGTs. The prediction results showed that darolutamide may increase the risk of DDIs when administered in combination with substrates of UGT1A1, UGT1A7, or UGT2B15. Therefore, the combined administration of darolutamide and drugs metabolized by the above UGTs should be used with caution to avoid the occurrence of potential DDIs.

Clinical Significance and Patterns of Potential Drug-Drug Interactions in Cardiovascular Patients: Focus on Low-Dose Aspirin and Angiotensin-Converting Enzyme Inhibitors.

Objective: This study assessed the patterns and clinical significance of potential drug-drug interactions (pDDIs) in patients with diseases of the cardiovascular system. Methods: Electronic health records (EHRs), established in 2018-2023, were selected using the probability serial nested sampling method (n = 1030). Patients were aged 27 to 95 years (65.0% men). Primary diagnosis of COVID-19 was present in 17 EHRs (1.7%). Medscape Drug Interaction Checker was used to characterize pDDIs. The Mann-Whitney U test and chi-square test were used for statistical analysis. Results: Drug numbers per record ranged from 1 to 23 in T-List and from 1 to 20 in P-List. In T-List, 567 drug combinations resulted in 3781 pDDIs. In P-List, 584 drug combinations resulted in 5185 pDDIs. Polypharmacy was detected in 39.0% of records in T-List versus 65.9% in P-List (p-value < 0.05). The rates of serious and monitor-closely pDDIs due to 'aspirin + captopril' combinations were significantly higher in P-List than in T-List (p-value < 0.05). The rates of serious pDDIs due to 'aspirin + enalapril' and 'aspirin + lisinopril' combinations were significantly lower in P-List compared with the corresponding rates in T-List (p-value < 0.05). Serious pDDIs due to administration of aspirin with fosinopril, perindopril, and ramipril were detected less frequently in T-List (p-value < 0.05). Conclusions: Obtained data may suggest better patient adherence to 'aspirin + enalapril' and 'aspirin + lisinopril' combinations, which are potentially superior to the combinations of aspirin with fosinopril, perindopril, and ramipril. An abundance of high-order pDDIs in real-world clinical practice warrants the development of a decision support system aimed at reducing pharmacotherapy-associated risks while integrating patient pharmacokinetic, pharmacodynamic, and pharmacogenetic information.

Innovative Approaches to Optimize Clinical Transporter Drug-Drug Interaction Studies.

Of the 450 cell membrane transporters responsible for shuttling substrates, nutrients, hormones, neurotransmitters, antioxidants, and signaling molecules, approximately nine are associated with clinically relevant drug-drug interactions (DDIs) due to their role in drug and metabolite transport. Therefore, a clinical study evaluating potential transporter DDIs is recommended if an investigational product is intestinally absorbed, undergoes renal or hepatic elimination, or is suspected to either be a transporter substrate or perpetrator. However, many of the transporter substrates and inhibitors administered during a DDI study also affect cytochrome P450 (CYP) activity, which can complicate data interpretation. To overcome these challenges, the assessment of endogenous biomarkers can help elucidate the mechanism of complex DDIs when multiple transporters or CYPs may be involved. This perspective article will highlight how creative study designs are currently being utilized to address complex transporter DDIs and the role of physiology-based -pharmacokinetic (PBPK) models can play.

Effect of midostaurin on the pharmacokinetics of P-gp, BCRP, and CYP2D6 substrates: assessing potential drug-drug interactions in healthy participants : Brief title: Drug-drug interaction of midostaurin.

PURPOSE: Midostaurin, approved for FLT3-mutated acute myeloid leukemia and advanced systemic mastocytosis, is mainly metabolized by cytochrome P450 (CYP) 3A4. Midostaurin exhibited potential inhibitory effects on P-glycoprotein (P-gp), breast cancer resistance protein (BCRP), organic anion-transporting polyprotein 1B1, and CYP2D6 in in vitro studies. This study investigated the pharmacokinetic (PK) effects of midostaurin on P-gp (digoxin), BCRP (rosuvastatin) and CYP2D6 (dextromethorphan) substrates in healthy adults. METHODS: This was an open-label, single-sequence, phase I clinical study evaluating the effect of single-dose midostaurin (100 mg) on the PK of digoxin and rosuvastatin (Arm 1), and dextromethorphan (Arm 2). Participants were followed up for safety 30 days after last dose. In addition, the effect of midostaurin on the PK of dextromethorphan metabolite (dextrorphan) was assessed in participants with functional CYP2D6 genes in Arm 2. RESULTS: The effect of midostaurin on digoxin was minor and resulted in total exposure (AUC) and peak plasma concentration (Cmax) that were only 20% higher. The effect on rosuvastatin was mild and led to an increase in AUCs of approximately 37-48% and of 100% in Cmax. There was no increase in the primary PK parameters (AUCs and Cmax) of dextromethorphan in the presence of midostaurin. The study treatments were very well tolerated with no occurance of severe adverse events (AEs), AEs of grade ≥ 2, or deaths. CONCLUSION: Midostaurin showed only a minor inhibitory effect on P-gp, a mild inhibitory effect on BCRP, and no inhibitory effect on CYP2D6. Study treatments were well tolerated in healthy adults.

Physiologically based pharmacokinetic modeling of CYP2C8 substrate rosiglitazone and its metabolite to predict metabolic drug-drug interaction.

Rosiglitazone is an activator of nuclear peroxisome proliferator-activated (PPAR) receptor gamma used in the treatment of type 2 diabetes mellitus. The elimination of rosiglitazone occurs mainly via metabolism, with major contribution by enzyme cytochrome P450 (CYP) 2C8. Primary routes of rosiglitazone metabolism are N-demethylation and hydroxylation. Modulation of CYP2C8 activity by co-administered drugs lead to prominent changes in the exposure of rosiglitazone and its metabolites. Here, we attempt to develop mechanistic parent-metabolite physiologically based pharmacokinetic (PBPK) model for rosiglitazone. Our goal is to predict potential drug-drug interaction (DDI) and consequent changes in metabolite N-desmethyl rosiglitazone exposure. The PBPK modeling was performed in the PKSim® software using clinical pharmacokinetics data from literature. The contribution to N-desmethyl rosiglitazone formation by CYP2C8 was delineated using vitro metabolite formation rates from recombinant enzyme system. Developed model was verified for prediction of rosiglitazone DDI potential and its metabolite exposure based on observed clinical DDI studies. Developed model exhibited good predictive performance both for rosiglitazone and N-desmethyl rosiglitazone respectively, evaluated based on commonly acceptable criteria. In conclusion, developed model helps with prediction of CYP2C8 DDI using rosiglitazone as a substrate, as well as changes in metabolite exposure. In vitro data for metabolite formation can be successfully utilized to translate to in vivo conditions.