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BACKGROUND: We evaluated dolutegravir pharmacokinetics in infants with human immunodeficiency virus (HIV) receiving dolutegravir twice daily (BID) with rifampicin-based tuberculosis (TB) treatment compared with once daily (OD) without rifampicin. METHODS: Infants with HIV aged 1-12 months, weighing ≥3 kg, and receiving dolutegravir BID with rifampicin or OD without rifampicin were eligible. Six blood samples were taken over 12 (BID) or 24 hours (OD). Dolutegravir pharmacokinetic parameters, HIV viral load (VL) data, and adverse events (AEs) were reported. RESULTS: Twenty-seven of 30 enrolled infants had evaluable pharmacokinetic curves. The median (interquartile range) age was 7.1 months (6.1-9.9), weight was 6.3 kg (5.6-7.2), 21 (78%) received rifampicin, and 11 (41%) were female. Geometric mean ratios comparing dolutegravir BID with rifampicin versus OD without rifampicin were area under curve (AUC)0-24h 0.91 (95% confidence interval, .59-1.42), Ctrough 0.95 (0.57-1.59), Cmax 0.87 (0.57-1.33). One infant (5%) receiving rifampicin versus none without rifampicin had dolutegravir Ctrough

Objective: The present study aims to explore the initial dosage optimization of sertraline in pediatric major depressive disorder (MDD) patients based on model-informed precision dosing (MIPD). Methods: A total of 111 pediatric MDD patients treated with sertraline were included for analysis using MIPD. Sertraline concentration levels, physiological and biochemical indexes of pediatric MDD patients, combined drug information were included in the construction of model. Results: Weight and zopiclone co-administration influenced sertraline clearance in pediatric MDD patients. With the same weight, the sertraline clearance rates were 0.453:1 in patients with, or without zopiclone, respectively. Furthermore, without zopiclone, for once-daily sertraline scheme, the dosages of 4.0, and 3.0 mg/kg/day were suggested for pediatric MDD patients with body weight of 30–38.5, and 38.5–80 kg, respectively; for twice-daily sertraline scheme, the dosage of 2.0 mg/kg/day was suggested for pediatric MDD patients with body weight of 30–80 kg. With zopiclone, for once-daily sertraline scheme, the dosage of 1.0 mg/kg/day was suggested for pediatric MDD patients with body weight of 30–80 kg; for twice-daily sertraline scheme, the dosage of 0.5 mg/kg/day was suggested for pediatric MDD patients with body weight of 30–80 kg. Conclusion: This study first explored the initial dosage optimization of sertraline in pediatric MDD patients based on MIPD, and recommended the optimal sertraline initial dosage in pediatric MDD patients based on zopiclone co-administration. [ABSTRACT FROM AUTHOR]

Background/objective: Preventive medications are crucial in migraine prevention. In cases of refractory migraine headaches, multiple medications may be required. We seek to identify a comprehensive list of preventive migraine headache medications that can be used as two, three, and four drug combinations without drug–drug interactions. Methods: We compiled a list of prevention medications from Szperka et al.'s "Migraine Care in the Era of COVID-19" as well as American Headache Society's 2018 and 2021 "Consensus Statements on Integrating New Migraine Treatments into Clinical Practice." We obtained all possible two to four combinations of prevention medications through this list. We then filtered out all combinations containing at least one interaction based on DrugBank database and also identified least to most interacting medications. Results: A total of 26 unique prevention medications are identified. This results in a total of 325 combinations of two preventives, 2,600 combinations of three preventives, and 14,950 combinations of four preventives. There are a total of 124, 146, and 0 non-interacting two, three, and four preventive combinations, respectively. All except 16 combinations of pick-twos can be placed within a pick-three combinations. The resulting distinct non-interacting medications can be represented by a condensed list of 162 unique combinations of medications. CGRP antagonists, Botulinum toxin A, melatonin, and candesartan are least interacting. Conclusion: This list of migraine preventive medications without drug–drug interactions is a useful tool for clinicians seeking to manage refractory headaches more effectively by implementing an evidence-based polypharmacy. [ABSTRACT FROM AUTHOR]

Mavacamten is a potential inducer of cytochrome P450 (CYP) 3A4 and could reduce the effectiveness of concomitant drugs that are metabolized by CYP3A4, such as midazolam. This study aimed to determine if repeat doses of mavacamten achieving clinically relevant exposures affected midazolam exposure. This was a single‐center, open‐label study in healthy participants. Participants received: on day 1, midazolam 5 mg; on days 2–3, mavacamten 25 mg; on days 4–16, mavacamten 15 mg; and on day 17, mavacamten 15 mg and midazolam 5 mg. Plasma concentrations of mavacamten, midazolam, and the midazolam metabolite 1′‐hydroxymidazolam were measured. A physiologically based pharmacokinetic (PBPK) model was used to simulate the effect of mavacamten‐mediated CYP3A4 induction on midazolam exposure by CYP2C19 phenotype. Thirteen adult participants were enrolled (46.2% were female; mean [SD] age: 34.0 [9.0] years). Compared with midazolam alone, midazolam coadministered with mavacamten decreased the maximum observed plasma concentration (Cmax), area under the drug concentration‐time curve (AUC) from time zero to infinity (AUC0‐inf), and AUC from time zero to last measurable concentration (AUC0‐last) for midazolam by 7%, 13%, and 24%, respectively; for 1′‐hydroxymidazolam, AUC0–inf and AUC0–last increased by 20% and 11%, respectively. Ten participants experienced adverse events and the majority were mild in severity. The PBPK model predicted the clinical trial data well. The PBPK simulation assessed that the overall impact of mavacamten on midazolam Cmax and AUC was predicted to be weak regardless of CYP2C19 phenotype. At clinically relevant exposures, mavacamten had a negligible effect on midazolam exposure. [ABSTRACT FROM AUTHOR]

Introduction: Sulfatinib is a novel oral tyrosine kinase inhibitor (TKI) with selective inhibition of fibroblast growth factor (FGFR), colony-stimulating factor 1 receptor (CSF-1R) and vascular endothelial growth factor receptor (VEGFR) 1, 2, and 3. It has been approved for the therapy of neuroendocrine tumors arising in the non-pancreatic (December 2020) and pancreatic (June 2021) glands. Until now, there has no research on the determination of sulfatinib in biological medium by ultra performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) method. Methods: The current study validated a sensitive and reliable quantitative detection of sulfatinib in plasma using UPLC-MS/MS for the first time, and investigated the interaction with myricetin in rats. Acetonitrile was used to precipitate the plasma protein, and lenvatinib was employed as the internal standard (IS). Results: The method demonstrated that sulfatinib presented high linearity over the concentration of 11–2,000 ng/mL with the lower limit of quantification (LLOQ) of 1 ng/mL. It was validated methodologically that the precision, matrix effect, stability, accuracy and extraction recovery were all within the allowable values. Moreover, male Sprague-Dawley (SD) rats were assigned randomly to assess the interaction between sulfatinib (30 mg/kg) and myricetin (50 mg/kg). Nevertheless, no significant differences of the main pharmacokinetic parameters were revealed. This may be due to insufficient doses of myricetin, or failure of myricetin to act in a timely manner in vivo. Discussion: The findings contributed to a better understanding of the metabolism and drug-drug interaction of sulfatinib, but the presence or absence of interactions needs to be confirmed by further studies. [ABSTRACT FROM AUTHOR]

Introduction: Tuberculosis (TB) treatment typically involves a tailored combination of four antibiotics based on the drug resistance profile of the infecting strain. The increasing drug resistance of Mycobacterium tuberculosis (Mtb) requires the development of novel antibiotics to ensure effective treatment regimens. Gallium (Ga) is being explored as a repurposed drug against TB due to its ability to inhibit Mtb growth and disrupt iron metabolism. Given the potential interactions between Ga and established antibiotics, we investigated how a combination of Ga with levofloxacin (Lfx) or linezolid (Lzd) affects the growth and metabolome of a multidrug-resistant (MDR) Mtb clinical strain. Methods: Mtb was cultured using a BACTEC 960 system with concentrations of Ga ranging from 125 to 1,000 μM and with 250 to 500 μM of Ga combined with 0.125 mg/L of Lfx or Lzd. For metabolome analysis, the antibacterials were used at concentrations that inhibited the growth of bacteria without causing cell death. Metabolites were extracted from Mtb cells and analyzed using chromatography-mass spectrometry. Results: The MDR Mtb strain exhibited a dose-dependent response to Ga. Notably, the enhancement in growth inhibition was statistically significant for the Ga/Lfx combination compared to Ga alone, while no such significance was observed for Ga/Lzd. Moreover, exposure to Ga/Lfx or Ga/Lzd resulted in distinct metabolite profiles. Ga treatment increased the level of aconitate, fumarate, and glucose in the cells, suggesting the inhibition of iron-dependent aconitase and fumarate hydratase, as well as disruption of the pentose phosphate pathway. The levels of glucose, succinic acid, citric acid, and hexadecanoic acid followed a similar pattern in cells exposed to Ga and Ga/Lfx at 500 μM Ga but exhibited different trends at 250 μM Ga. Discussion: In the presence of Lfx, the Mtb metabolome changes induced by Ga are more pronounced compared to those observed with Lzd. Lfx affects nucleic acids and transcription, which may enhance Ga-dependent growth inhibition by preventing the metabolic redirection that bacteria typically use to bypass iron-dependent enzymes. [ABSTRACT FROM AUTHOR]

Aims Methods Results Conclusions The aim of this study was to characterize the risk of death in older adults co‐prescribed low‐dose methotrexate and TMP‐SMX vs. low‐dose methotrexate and a cephalosporin.We conducted a retrospective, population‐based, new‐user cohort study in Ontario, Canada (April 1, 2002–August 1, 2022) using linked administrative healthcare data. Older adults taking low‐dose methotrexate who were newly co‐prescribed TMP‐SMX (n = 1602) were matched 1:1 with those who were newly co‐prescribed a cephalosporin. The primary outcome was death within 30 days of the antibiotic dispensing date. Secondary outcomes included all‐cause hospitalization, a hospital visit with myelosuppression and a hospitalization with persistent infection defined as the main diagnosis. Propensity score matching was used to balance comparison groups on indicators of baseline health. Risk ratios (RR) were obtained using modified Poisson regression.In a propensity‐score matched cohort of 3204 adults taking low‐dose methotrexate, the 30‐day risk of death was similar in adults co‐prescribed TMP‐SMX vs. a cephalosporin (14/1602 [0.87%] vs. 15/1602 [0.94%]; RR 0.93 [95% CI 0.45–1.93]). The risk of all‐cause hospitalization (RR 1.49 [95% CI 1.13–1.97]) and infection (RR 2.78 [95% CI 1.30–5.95]) was higher in adults treated with TMP‐SMX than those treated with cephalosporins.In older adults taking low‐dose methotrexate, co‐prescription of TMP‐SMX vs. a cephalosporin was not associated with a higher 30‐day risk of death but was associated with a higher 30‐day risk of all‐cause hospitalization and hospital admission with persistent infection. If verified, these risks should be balanced against the benefits of co‐prescribing TMP‐SMX and low‐dose methotrexate. [ABSTRACT FROM AUTHOR]

Introduction: Our aim was to analyze prevalence, profile and management of DDI detected during prescription review (PR) in community pharmacies. Methods: A cross-sectional study was carried out in ten pharmacies in Abidjan for 5 months. Physical prescription supports (PS) with least one prescription line were included. The PR integrating pharmaceutical anamnesis data was carried out for DDI detection and pharmaceutical intervention (PI) useful for their management. DDI's constraint levels were specified. Theriaque website and PI categorization tool of the French Society of Clinical Pharmacy were used. Results: This study included 1,034 PS. DDI's prevalence was 13.8% with 7% of DDI concerned by drugs taken outside PS. DDI detected were mainly «to be taken into account» level (47.6%) and concerned mainly H1 antihistamines (21.3%). PIs issued for DDI management were mainly «therapeutic monitoring» type (41.6%). Conclusion: PS received in community pharmacy are exposed to significant number of DDI. The community pharmacist must systematically carry out the PR and propose PIs for DDI's management. [ABSTRACT FROM AUTHOR]

Background: Olaparib (OLA) and regorafenib (REG) are metabolized by the CYP3A4 isoenzyme of cytochrome P450. Both drugs are also substrates and inhibitors of the membrane transporters P-glycoprotein and BCRP. Therefore, the potential concomitant use of OLA and REG may result in clinically relevant drug–drug interactions. Knowledge of the influence of membrane transporters and cytochrome P450 enzymes on the pharmacokinetics of drugs makes it possible to assess their impact on the efficacy and safety of therapy. Purpose: The study aimed to evaluate the bilateral pharmacokinetic interactions of OLA and REG and its active metabolites after a single administration in healthy rats. Methods: The study was performed in male Wistar rats (n = 24) randomly divided into three groups: one study group, IREG+OLA (n = 8), received REG with OLA, and two control groups, IIREG (n = 8) and IIIOLA (n = 8), received REG and OLA, respectively. The concentrations of OLA, REG, REG-N-oxide (M-2), and N-desmethyl-REG-N-oxide (M-5) were determined by ultra-performance liquid chromatography–tandem mass spectrometry (UPLC-MS/MS). The values of the pharmacokinetic parameters of OLA, REG, M-2, and M-5 were determined by non-compartmental analysis with linear interpolation. Results: After OLA administration, the pharmacokinetic parameters of REG (AUC0–∞, tmax, and t0.5) increased significantly by 3.38-, 2.66-, and 1.82-fold, respectively. On the other hand, REG elimination parameters, i.e., kel and Cl/F, were significantly reduced in the study group by 1.77- and 1.70-fold, respectively. In the study group, Cmax and AUC0–t values were also 7.22- and 8.86-fold higher for M-2 and 16.32- and 17.83-fold higher for M-5, respectively. The Metabolite M-2/Parent and Metabolite M-5/Parent ratios for Cmax and AUC0–t increased by 6.52-, 10.74-, 28-, and 13-fold, respectively. After administration of OLA with REG, the Cmax, AUC0–t, and AUC0–∞ of OLA increased by 2.0-, 3.4-, and 3.4-fold, respectively, compared to the control group. Meanwhile, Cl/F and Vd/F of OLA were significantly decreased in the presence of REG. Conclusions: OLA was shown to significantly affect the pharmacokinetics of REG and its active metabolites M-2 and M-5 in rats after co-administration of both drugs. There was also a significant effect of REG on the pharmacokinetics of OLA, which may have clinical relevance. The AUC ratios (study group/control group) were 3.41 and 3.39 for REG and OLA, respectively, indicating that REG and OLA were moderate inhibitors in this preclinical study. The results obtained need to be confirmed in clinical studies. This study may provide guidance on the safety of using both drugs in clinical practice. [ABSTRACT FROM AUTHOR]

Vatiquinone is a small molecule inhibitor of 15‐lipoxygenase in development for patients with Friedreich's ataxia. The objective of this analysis was to determine the effect of a cytochrome P450 isoform 3A4 (CYP3A4) inhibitor and inducer on vatiquinone pharmacokinetics (PKs). The coadministration of 400 mg of vatiquinone with 200 mg of itraconazole (a CYP3A4 inhibitor) resulted in increased maximum observed concentration (Cmax) of vatiquinone and systemic exposure (AUC0‐inf) by approximately 3.5‐ and 2.9‐fold, respectively. The coadministration of 400 mg of vatiquinone with 600 mg of rifampin (a CYP3A4 inducer) resulted in decreased vatiquinone Cmax and AUC0‐inf by approximately 0.64‐ and 0.54‐fold, respectively. The terminal half‐life of vatiquinone was not affected by itraconazole or rifampin. These clinical study results confirm the in vitro reaction phenotyping data that shows that CYP3A4 plays an important role in vatiquinone metabolism. The result of this analysis together with phase 3 efficacy and safety data, population PK analysis, and the exposure‐response relationship will determine if the extent of vatiquinone changes in the presence of CYP3A4 inhibitors and inducers are considered clinically relevant. [ABSTRACT FROM AUTHOR]

Introduction: Cyclin-dependent kinase (CDK) 4/6 inhibitors have significantly changed the treatment strategy for patients with locally advanced or metastatic hormone receptor positive (HR+), human epidermal growth factor 2 negative (HER2) breast cancer. The purpose of the study was to determine the prevalence of drug-drug interactions (DDI) in breast cancer patients using CDK 4/6 inhibitors and the extent of DDI reflected in the clinic and to increase clinical awareness among physicians. Method: The data of 115 metastatic breast cancer patients using CDK 4/6 inhibitors who were admitted to the Medical Oncology outpatient clinic between July 2021 and July 2023 were retrospectively reviewed. The Drugs.com® Drug Interaction Checker application was used for the interaction between the CDK 4/6 inhibitor and other drugs. Results: Among patients included in the study, 97.3% had at least one additional drug use. We have identified a total of 170 potential DDI risks in 63.5 % of patients. Among these, 50.5% had a major potential DDI. In our study, there was a potential risk of QT prolongation in 45.2% of 170 DDI, an increase in the potential toxicity of the additional drug in 44.1%, an increase in the potential toxicity of the CDK 4/6 inhibitor in 5.3%, a decrease in the potential efficacy of the CDK 4/6 inhibitor in 2.9%, a decrease in the potential efficacy of the additional drug in 1.1%, and a serious potential infection risk in 1.1%. Most of the drug interactions were QT prolongation and increased toxicity of the additional drug. In terms of cardiovascular events, grade-2 and grade-3 QTc prolongation was found in 4.3% and 1.7% of these interactions, respectively. When evaluated in terms of CDK 4/6 inhibitor subtype, there was a potential risk of DDI at major level with Ribocilib and at moderate level with Palbociclib. Conclusion: If CDK 4/6 inhibitors interact with concomitant drugs, they may cause an increase in the incidence of cardiac side effects and a decrease in the effect of the CDK 4/6 inhibitor or additional drug or an increase in toxicity. Increasing awareness of this issue will help to reduce the rates of side effects or toxicity and provide effective antitumour therapy. [ABSTRACT FROM AUTHOR]

Background: Zuranolone is an oral, once-daily, 14-day treatment course approved for adults with postpartum depression in the United States. Aims: To assess cognitive effects, pharmacokinetics, and safety of zuranolone, alone or with alprazolam/ethanol. Methods: This was a phase 1, two-part, two-period, randomized, double-blind, placebo-controlled crossover trial. Participants received zuranolone 50 mg or placebo once daily for 9 days, and additionally received alprazolam (1 mg, Part A), ethanol (males: 0.7 g/kg; females: 0.6 g/kg, Part B), or corresponding placebo on days 1, 5, and 9. Within each part, participants received all treatment combinations. Cognition was assessed using a computerized test battery; pharmacokinetics and safety were also evaluated. Results: All participants (Part A, N = 24; Part B, N = 25) received ⩾1 dose of zuranolone/placebo. Compared to placebo, zuranolone produced small-to-moderate cognitive decline (Cohen's | d | = 0.126–0.76); effects were larger with alprazolam (Cohen's | d | = 0.523–0.93) and ethanol (Cohen's | d | = 0.345–0.88). Zuranolone coadministration with alprazolam (Cohen's | d | = 0.6–1.227) or ethanol (Cohen's | d | = 0.054–0.5) generally worsened cognitive decline when compared with zuranolone alone. Maximal pharmacodynamic effects occurred at approximately 5 h and were resolved by 12 h postbaseline. No pharmacokinetic interactions were observed. Incidence of adverse events was similar between groups; most events were mild or moderate in severity. Conclusion: A general small-to-moderate magnitude decline in cognition occurred with zuranolone alone. Coadministration with alprazolam/ethanol increased the magnitude, but not the duration, of effects compared with single-agent administration. Zuranolone prescribers and patients should be aware of the potential for increased central nervous system-depressant effects if coadministered with GABAergic active compounds such as alprazolam and ethanol. [ABSTRACT FROM AUTHOR]

Mental health is an important segment in preserving overall health and represents a significant public health issue. In modern times, mental health disorders have risen, often requiring complex pharmacotherapy and chronic monitoring. The aim of this research was to determine the prevalence and clinical significance of potential psychotropic drug interactions in outpatient settings and compare the differences in potential drug–drug interaction (pDDIs) exposure with age. The psychotropic drugs included antipsychotics—N05A, anxiolytics—N05B, hypnotics and sedatives—N05C, and antidepressants—N06A. This retrospective study analyzed prescribed pharmacotherapy in 492 outpatients who were treated with at least one psychotropic drug. We determined 1.64 prescribed psychotropic drugs per patient and 2.2 pDDIs that involved psychotropic drugs. In total, 2285 pDDIs were recorded, of which almost half (47.6%) were pDDIs with psychotropic drugs. More prescribed psychotropic drugs were found in patients younger than 65 years, and equal exposure to pDDIs of psychotropic drugs (p = 0.5077) was found in both age groups. The most commonly identified psychotropics involved in pDDIs were benzodiazepines, promazine, and zolpidem. The results indicate that psychotropic drug interactions represent important drug-related problems for primary health care. The widespread use of psychotropic drugs and the determined clinical significance of their interactions require pharmacist interventions which can reduce the prevalence of pDDIs and increase patient safety. [ABSTRACT FROM AUTHOR]

Background: Thiopurine methyltransferase (TPMT) plays a crucial role in the detoxification of thiopurine drugs, including the antimetabolites azathioprine and 6-mercaptopurine (6-MP) used to treat autoimmune diseases and various cancers. These drugs interfere with DNA synthesis by inhibiting the production of purine-containing nucleotides, leading to the death of rapidly dividing cells. TPMT inactivates thiopurine drugs by methylating at the thiol group. The activity of TPMT can vary significantly between individuals, and its activity is impacted by co-administered drugs, altering the effectiveness and toxicity of thiopurine drugs. TPMT is inhibited by many drugs that are co-administered to treat symptoms associated with diseases treated with thiopurines. For example, aspirin and other anti-inflammatory drugs, including olsalazine, sulfasalazine, and balsalazide, inhibit TPMT. The impact of TPMT genotypes on its methylating activity is well defined, and genotyping patients to identify TPMT metabolizer status is common clinical practice. Unfortunately, there has been no concerted effort to comprehensively identify drugs on the market that impact TPMT activity. The inhibition of TPMT by co-administered drugs could in part be responsible for idiosyncratic toxicities associated with thiopurine drug therapy. Methods: Here, we report a facile approach to produce large quantities of recombinant TPMT and a high-throughput assay that utilizes the shift in absorbance due to the methylation of thiopurines to report on TPMT activity. Results and Conclusions: With purified TPMT on hand and the absorbance activity assay, we confirmed several compounds that inhibit TPMT, and the results were comparable to a mass spectral assay that measured 6-MP methylation. Understanding the impact of co-administered drugs on TPMT activity will improve the safety and efficacy of thiopurine-based treatment regimens. [ABSTRACT FROM AUTHOR]

Islatravir, a deoxyadenosine analog that inhibits HIV‐1 replication by multiple mechanisms of action, including reverse transcriptase translocation inhibition, is being developed for use in HIV‐1 treatment. People living with HIV often have comorbidities, such as dyslipidemia or type 2 diabetes mellitus, necessitating long‐term concomitant drug therapy. This nonrandomized, two‐period, fixed‐sequence, open‐label, phase 1, drug‐drug interaction study was conducted to evaluate the effects of islatravir coadministration on atorvastatin and metformin pharmacokinetics (PK) in healthy adults. In period 1, participants received a single dose of atorvastatin 20 mg and metformin 1000 mg. After a 5‐day washout, participants received atorvastatin 20 mg and metformin 1000 mg coadministered with a single oral dose of islatravir 60 mg (period 2). In both periods, blood samples were collected up to 72 h post dose to characterize the plasma PK of atorvastatin and metformin. Safety was monitored throughout the study. Fourteen participants were enrolled and completed the study. Atorvastatin and metformin plasma PK were similar after administration of atorvastatin and metformin with or without islatravir. The geometric mean ratio and 90% confidence interval of the area under the concentration‐time curve from time zero to infinity (AUC0‐∞) for atorvastatin and metformin with or without a single oral dose of islatravir were 1.04 (1.00‐1.10) and 0.87 (0.79‐0.96), respectively. Coadministration of islatravir with atorvastatin and metformin was well tolerated. Overall, coadministration of atorvastatin and metformin with a single oral dose of islatravir did not have a clinically meaningful effect on the PK profiles of either drug. [ABSTRACT FROM AUTHOR]

Dordaviprone (ONC201) is a novel, orally administered, anti‐cancer, small molecule imipridone with demonstrated antitumor effects in patients with glioma. Dordaviprone in vitro solubility is significantly reduced at pH >4.5. Concomitant use of acid reducing agents (ARAs) may therefore impact dordaviprone solubility and bioavailability. This open‐label, single‐sequence, three‐period crossover study evaluated the effect of proton‐pump inhibitor rabeprazole on dordaviprone pharmacokinetics (PK). Periods were consecutive and comprised of period 1 (days 1–3), period 2 (days 4–9), and period 3 (days 10–13). In period 1, participants received a single oral 625 mg dose of dordaviprone on day 1. In period 2, participants received six consecutive days of QD 20 mg rabeprazole alone. In period 3, patients received one oral dose of 20 mg rabeprazole (the seventh consecutive daily dose), followed 2 h later by a single 625 mg dordaviprone oral dose. PK blood samples were collected and analyzed from pre‐dose 72 h following dordaviprone administration in periods 1 and 3. Dordaviprone exposure PK parameters were similar following administration of dordaviprone alone or with rabeprazole. Geometric mean ratios and 90% CIs for dordaviprone exposure parameters with and without rabeprazole following dordaviprone administration fell within bioequivalence limits of 80.00%–125.00% for Cmax (97.19% [86.43–109.28]), AUClast (102.21% [95.19–109.75]), and AUCinf (102.27% [95.21–109.86]), indicating no effect of multiple oral doses of rabeprazole on dordaviprone relative bioavailability. Six of the 16 participants reported treatment‐emergent adverse events (TEAEs); dordaviprone‐related TEAEs were reported by three participants and were limited to mild nausea and dizziness. No dordaviprone dose adjustment or ARA treatment modification is warranted. [ABSTRACT FROM AUTHOR]

Fezolinetant is an oral, nonhormonal, neurokinin 3 receptor antagonist treatment option for moderate to severe vasomotor symptoms associated with menopause. An in vitro study using human recombinant cytochrome P450 (CYP) enzymes and human liver microsomes showed that fezolinetant is metabolized to its major but inactive metabolite, ES259564, predominantly through CYP1A2, with minor contributions from CYP2C9 and CYP2C19. The clinical impact of CYP1A2 inhibition and induction on single‐dose pharmacokinetics of fezolinetant was assessed in an open‐label, single‐sequence, phase 1 study in healthy postmenopausal women, where the impact of fluvoxamine, a strong CYP1A2 inhibitor, and smoking, a moderate CYP1A2 inducer, were evaluated. In total, 18 participants, 9 of whom were smokers, were enrolled. Fezolinetant pharmacokinetics were evaluated after a single 30‐mg dose on Day 1 and Day 7. Fluvoxamine 50 mg was administered as a single dose on Days 3 and 10 and twice daily from Days 4 to 9. Fluvoxamine increased geometric mean ratio of fezolinetant maximum plasma concentrations (Cmax) and area under the curve from time of dosing extrapolated to infinity (AUCinf) to 182% and 939%, respectively, while ES259564 Cmax decreased to 20.1% with no significant change in AUC. In smokers versus nonsmokers, when fezolinetant was administered alone, fezolinetant Cmax and AUCinf decreased to 71.7% and 48.3%, respectively, while ES259564 Cmax increased to 130.2% and AUCinf decreased to 81.8%. A single oral 30‐mg dose of fezolinetant was considered safe and well tolerated when co‐administered with fluvoxamine in healthy postmenopausal women. [ABSTRACT FROM AUTHOR]

Background: Clinical management of patients with chronic cardiometabolic disease is complicated by polypharmacy. Consequently, when patients clinically deteriorate, physicians are challenged to distinguish both medication nonadherence and drug-drug interactions (DDI) from chronic disease progression. Methods: In this randomized controlled trial, we enrolled U.S. board-certified Primary Care Physicians (PCPs) serving patients with cardiometabolic disease. PCPs were randomized and managed their patients with (intervention), or without (control), a novel chronic disease management test (CDMT) that can detect medication nonadherence and DDIs. Patients' medical records were abstracted at baseline and 3-month follow-up. Primary outcomes were the CDMT's impact on both the PCPs' detection of medication nonadherence and DDI, and the frequency of performing medication nonadherence- and DDI-related clinical actions. Secondary outcomes examined the types of clinical actions performed. Primary and secondary outcomes were analyzed by logistic regression using single variable and clustered multivariable modeling to adjust for similarities in patient characteristics, by PCP practice. Results: Sixteen intervention and 20 control PCPs shared de-identified records for 126 and 207 patients, respectively. There were no significant demographic differences between the two study arms, among PCPs or patients. At baseline, there was no significant difference between the intervention and control PCPs in the percentage of clinical actions performed for medication nonadherence (P = 0.98) and DDI (P = 0.41). At 3-month follow-up (after CDMT), 69.1% of intervention compared to 20.3% of control patients with medication nonadherence had a related clinical action performed (P < 0.001). Regarding DDI, 37.3% of intervention compared to 0.5% of control patients had a relevant clinical action performed in follow-up (P < 0.001). Across the range of medication nonadherence- and DDI-related actions, the intervention compared to the control PCPs were more likely to adjust the medication regimen (24.1% vs. 9.5%) and document medication nonadherence in the patient chart (31.0% vs. 14.3%) at follow-up (P = 0.04). Conclusions: Although intervention and control PCPs similarly detected and acted upon medication nonadherence and DDI at baseline, intervention PCPs' detection increased significantly after using the CDMT. Also, the clinical actions performed with CDMT support were more clinically rigorous. These outcomes support the clinical utility of CDMT in the management of symptomatic patients with cardiometabolic disease and polypharmacy. Trial registration: https://clinicaltrials.gov/study/NCT05910684. [ABSTRACT FROM AUTHOR]

Objective Background Methods Results Conclusion The potential for drug–drug interaction of multiple‐dose intranasal zavegepant on the single‐dose oral contraceptive ethinyl estradiol and levonorgestrel (EE‐LNG) was evaluated.Zavegepant (as a nasal spray) is a calcitonin gene‐related peptide receptor antagonist approved in the United States for treatment of acute migraine in adults.This single‐center, Phase 1, open‐label, fixed‐sequence study included healthy, nonsmoking females (18–45 years old). In treatment Period 1, a single oral dose of EE‐LNG 0.02–0.10 mg was administered on Day 1. In treatment Period 2, intranasal zavegepant (20 mg daily; 10 mg per nostril separated by 1 h) was administered on Days 1–5; 1 oral dose of EE‐LNG 0.02–0.10 mg was administered immediately after first 10 mg intranasal zavegepant dose on Day 2. Blood samples for EE‐LNG concentrations were collected on Day 1, treatment Period 1, and Day 2, treatment Period 2, and zavegepant concentrations on Day 2, treatment Period 2. Noncompartmental pharmacokinetic parameters included maximum observed concentration (Cmax), area under the concentration‐time curve (AUC) from Time 0 to last non‐zero concentration (AUC0‐t), and AUC from Time 0 to infinity (AUC0‐inf). The safety and pharmacokinetic sample sizes were 26 and 23, respectively.Statistical comparisons of pharmacokinetic exposure parameters after co‐administration of zavegepant and EE‐LNG versus EE‐LNG alone showed small, but statistically insignificant, changes in either EE or LNG exposure. EE comparison ratios (90% confidence intervals [CIs]) were 109.9% (105.3%, 114.8%) for AUC0‐inf and 110.2% (104.6%, 116.1%) for Cmax. LNG comparison ratios (90% CIs) were 107.0% (100.2%, 114.3%) for AUC0‐inf and 108.8% (99.9%, 118.4%) for Cmax. Frequently reported treatment‐emergent adverse events included dysgeusia (n = 25, 96%), throat irritation (n = 11, 42%), headache (n = 10, 39%), nasal discomfort (n = 7, 27%), pharyngeal paresthesia (n = 5, 19%), and nausea (n = 4, 15%).Co‐administration of zavegepant nasal spray with a single dose of an oral contraceptive resulted in no clinically meaningful changes (<12% increase) in EE‐LNG exposure. [ABSTRACT FROM AUTHOR]

We established a method based on ultra performance liquid chromatography tandem mass spectrometry (UPLC‒MS/MS) to quantitatively measure tepotinib, which was validated as acceptable and used in the evaluation of food-drug interactions between tepotinib and naringenin in rats. We used pemigatinib as the internal standard (IS), and acetonitrile and 0.1% formic acid aqueous solution constituted the mobile phase. To extract the target analyte, acetonitrile was used for protein precipitation (PPT). For UPLC‒MS/MS, we performed liquid chromatography using a C18 column, and mass spectrometry was performed in positive multiple reaction monitoring (MRM) mode. Excellent linearity was shown in the range of 0.1–500 ng/mL, and the coefficient of correlation was > 0.99. Notably, the lower limit of quantification (LLOQ) for tepotinib was determined to be 0.1 ng/mL. The intra- and inter-day accuracy of tepotinib ranged from − 1.7 to 7.3%, while the precision was ≤ 8.4%, at three concentrations except LLOQ. The recovery of each substance was ≥ 81.2%, and the matrix effects were within 90.5-98.6%. The stabilities of all analytes under different conditions met all requirements for quantitation in plasma samples. The relevant parameters, such as LLOQ, were evaluated in accordance with the principles of the Food and Drug Administration (FDA) biological verification method. Food-drug interaction study had shown that the plasma concentration of tepotinib could be significantly increased, accompanied by a decrease in clearance rate when administered with 50 mg/kg naringenin. The results showed that naringenin could increase the plasma concentration and decrease the clearance rate of tepotinib when naringenin and tepotinib were administered at the same time. [ABSTRACT FROM AUTHOR]

Background: Drug-drug interactions (DDIs) may influence the effectiveness and safety of medication treatment, which may require additional monitoring, dose adjustment or avoidance of certain drugs. DDIs involving P-glycoprotein (P-gp) affect many drugs, but current official product information is often insufficient to guide the management of these DDIs in clinical practice. The aim of this paper is to describe a protocol to assess DDIs involving P-gp and to develop and implement practice recommendations for clinically relevant P-gp-mediated DDIs that affect clinical outcomes through changes in systemic drug exposure. Methods: A combined literature review and expert opinion approach will be used according to the following seven steps: set up an expert panel (step 1), establish core concepts and definitions (step 2), select potential P-gp-modulators (i.e., P-gp-inducers and -inhibitors) and P-gp-substrates to be evaluated (step 3), select and extract evidence-based data, and present findings in standardized assessment reports (step 4), discuss and adopt classifications and practice recommendations with the expert panel (step 5), publish and integrate information and alerts in clinical decision support systems (CDSS) (step 6), (re)assessments of DDIs and potential new DDIs when new information is available or when initiated by healthcare providers (step 7). Anticipated results: The expert panel will classify potential P-gp-modulators and -substrates as clinically relevant P-gp-inducer, -inhibitor and/or -substrate and draw conclusions about which combinations of classified modulators and substrates will lead to clinically relevant DDIs. This may include the extrapolation of conclusions for DDIs where limited or no data are available, based on the pharmacological characteristics of these drugs. For (potential) DDIs that are considered to be clinically relevant, practice recommendations will be developed. Discussion: This protocol describes a standardized, evidence- and expert opinion-based assessment of P-gp-mediated DDIs that affect clinical outcomes. This approach will generate alerts with practice recommendations for clinically relevant DDIs and transparent rationales for DDIs that are considered to be irrelevant. These recommendations will improve individual patient care by supporting healthcare professionals to make consistent decisions on how to manage P-gp mediated DDIs. [ABSTRACT FROM AUTHOR]

Background: Voriconazole (VRZ) is involved in a variety of drug‒drug interactions (DDIs), but few studies have reported adverse events (AEs) associated with the DDIs of VRZ. The primary goal of this study was to analyse the potential risk factors for AEs caused by DDIs between VRZ and other drugs via the OpenVigil FDA platform and to provide a reference for preventing VRZ DDIs and monitoring clinically related adverse drug events. Methods: A retrospective pharmacovigilance study was conducted to investigate the AEs related to DDIs between VRZ and four categories of drugs: proton pump inhibitors (PPIs), non-steroidal anti-inflammatory drugs (NSAIDs), immunosuppressants, and other antibacterial drugs. AE information for the target drugs from the first quarter of 2004 to the third quarter of 2022 was downloaded from the OpenVigil FDA data platform. Four frequency statistical models—the reporting ratio method, Ω shrinkage measure model, combination risk ratio model, and the chi-square statistics model—were used to analyse the AEs related to DDIs and evaluate the correlation and influence of sex and age between the drug(s) and the target AEs detected. Results: A total of 38 drugs were included, with 262 AEs detected by at least one of the four models and 48 AEs detected by all four models. Some 77 detected AEs were significantly positively correlated with DDIs and were related to higher reporting rates of AEs than when used alone. Graft-versus-host disease was the AE that had the strongest correlation with the drug interaction between VRZ and immunosuppressants (tacrolimus, mycophenolate mofetil, cyclophosphamide, and cyclosporine), and multiple organ dysfunction syndrome was correlated with VRZ in combination with other antibacterial drugs (linezolid, meropenem, cefepime, and vancomycin). Significant sex and age differences in the target AEs were detected for five and nine target drugs, respectively. For VRZ in combination with linezolid, aggravated conditions and respiratory failure should be given more attention in male patients, and mycophenolate mofetil and respiratory failure in female patients. When conditions are aggravated, febrile neutropenia and septic shock should be of particular concern in patients over 18 years of age who use VRZ in combination with ceftazidime, ciprofloxacin, or cytarabine. In patients aged under 18, septic shock should be considered when VRZ is used in combination with meropenem and dexamethasone. Conclusion: AEs related to DDIs should receive more attention when VRZ is used in combination with PPIs (renal impairment), NSAIDs (constipation and renal failure), immunosuppressants (graft versus host disease, septic shock) and other antibacterial drugs (multiple organ dysfunction syndrome, febrile neutropenia, and respiratory failure). Considering the influence of sex and age differences in VRZ DDIs, these factors need to be considered when assessing the risk of AEs in patients receiving VRZ and other drugs. [ABSTRACT FROM AUTHOR]

Objective · To develop physiologically based pharmacokinetic (PBPK) models specifically designed for the Chinese population by utilizing the combination of clozapine and fluvoxamine as a case, and predict the drug-drug interaction (DDI) associated with the combination medication of clozapine, ultimately optimizing the dosage of clozapine. Methods · By obtaining the physicochemical parameters, absorption, distribution, metabolism, excretion (ADME)-related parameters, and physiologically relevant parameters of the Chinese population through literature and pharmacology-related databases, PBPK models for the clozapine and fluvoxamine were constructed by using PK-Sim® software. The models′ accuracy was evaluated by comparing predicted values of the area under the curve (AUC) and peak concentration (Cmax) to observed data, using the mean percentage error (MPE) and mean absolute percentage error (MAPE) as evaluation indicators. The models were validated against real-world plasma drug concentration data. Additionally, combining the inhibitory effect of fluvoxamine on clozapine, models for the combination therapy of clozapine and fluvoxamine were developed to predict the pharmacokinetic changes of clozapine. The presence of clinically significant DDI was determined by using the 90% confidence interval of the AUC ratio (AUCR) or Cmax ratio (CmaxR) as evaluation metrics, with a non-effect boundary set at 80%‒125%. The pharmacokinetic changes of clozapine upon co-administration with fluvoxamine based on PBPK models were quantified, and a dosage optimization for clozapine was developed. Results · The constructed model of clozapine and fluvoxamine was considered accurate if the absolute value of the MPE was ≤10% and the MAPE was <25% during validation, indicating that the predicted concentration-time curves were accurate. The PBPK model for the co-administration of clozapine and fluvoxamine was able to accurately predict pharmacokinetic parameters if the ratio of predicted AUC to observed AUC was within 1.25. The prediction of PBPK model for the co-administration showed that the 90% confidence intervals for AUCR and CmaxR of the combination therapy of clozapine and fluvoxamine were not entirely within the ineffective effect boundary, indicating a clinically significant DDI when these two drugs were used concomitantly. Moreover, the dose optimization according to the PBPK models indicated that when subjects were coadministered with clozapine and fluvoxamine, reducing the dose of clozapine to 50% of the original dose could maintain the exposure levels of clozapine consistent with monotherapy. Conclusion · The established PBPK model can effectively simulate the impact of combination therapy on pharmacokinetic changes of clozapine, providing valuable insights for predicting potential DDI and optimizing dosage regimens. If clozapine needs to be co-administered with fluvoxamine during the treatment, clinicians should remain vigilant for clinically significant DDI and contemplate optimizing the dosage of clozapine accordingly. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: In clinical practice, the prevalent problem of polypharmacy could result in increased risks of drug–drug interactions. Regorafenib (REG) is commonly co-administered with paracetamol (PA) as a treatment protocol in cancer patients with pain therapy. Purpose: This study aimed to demonstrate the effect of paracetamol on the pharmacokinetic parameters of regorafenib and its metabolites following a single administration of both substances in rats. Additionally, the influence of REG and its metabolites on the pharmacokinetics of paracetamol was also determined. Methods: Twenty-four rats were divided randomly into three groups: REG group (IIREG, regorafenib 20 mg/kg, n = 8), PA group (IIIPA, paracetamol 100 mg/kg, n = 8), and REG+PA co-administration group (IREG+PA, REG 20 mg/kg and PA 100 mg/kg, n = 8). The concentrations of regorafenib, regorafenib-N-oxide (M-2), and N-desmethyl-regorafenib-N-oxide (M-5) were determined using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC–MS/MS). The plasma concentrations of PA and its glucuronide (GPA) and sulfate (SPA) metabolites were measured using the validated high-performance liquid chromatography method with ultraviolet detection (HPLC–UV). The pharmacokinetic parameters were calculated using a non-compartmental model. The statistical evaluation was performed in the SAS program. Results: After the administration of PA, the Cmax and AUC0–∞ of REG increased by 890% and 1140%, respectively; for M-2, they increased by 220% and 170%, and for M-5, by 2130% and 1730% (Cmax and AUC0–∞, respectively). A difference in the ratio of M-2/REG for AUC0–∞ and Cmax between the groups was observed, but not for M-5/REG. The AUC0–∞ for PA and GPA decreased by 20.7% and 51.1%, respectively, when PA was co-administered with REG. But the AUC0–∞ for SPA increased by 91.35% in the IREG+PA group. A difference in the ratio of GPA/PA for Cmax and for SPA/PA for AUC0–t and AUC0–∞ between the groups was observed. Conclusions: Paracetamol increased the plasma exposure of regorafenib, M-2, and M-5, which may exacerbate the drug's side effects. In contrast, REG reduced paracetamol exposure and contributed to its faster elimination, which may reduce the analgesic and antipyretic effects of paracetamol. These findings suggest clinical relevance for oncology patients requiring analgesic treatment. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Aims: In patients with rheumatoid arthritis (RA), interleukin (IL)‐6 affects the activity of cytochrome P450 (CYP) enzymes. Treatment with anti‐IL‐6 therapy can reverse the IL‐6‐mediated downregulation of CYP enzymes, resulting in changes in plasma levels of CYP substrates. The primary objective of this study was to evaluate the impact of the IL‐6 inhibitor olokizumab on the pharmacokinetics of CYP probe substrates in subjects with active RA. Methods: Seventeen patients with active RA were orally administered a phenotyping cocktail of midazolam (CYP3A4 substrate), omeprazole (CYP2C19 substrate), warfarin (CYP2C9 substrate) and caffeine (CYP1A2 substrate) alone and 2 weeks after a single subcutaneous injection of 128 mg olokizumab. The pharmacokinetic parameters of each substrate were calculated using noncompartmental analysis. Results: Sixteen of 17 enrolled patients received the complete doses of the cocktail drugs and olokizumab and were eligible for the pharmacokinetic evaluations. After single‐dose administration of olokizumab, the exposure of midazolam and omeprazole decreased by 30‐33% and 26‐32%, respectively, compared to when the substrates were administered along via cocktail. In the presence of olokizumab, caffeine exposure increased by 19‐23% compared to caffeine administration alone. There were no significant changes in S‐warfarin exposure. Conclusion: In patients with active RA, olokizumab potentially reverses the IL‐6‐mediated suppression of CYP3A4 and CYP2C19. According to FDA guidance, olokizumab is considered a weak inducer of CYP3A4 and CYP2C19. [ABSTRACT FROM AUTHOR]

Asthma and Chronic Obstructive Pulmonary Disease (COPD) are major global health concerns, with inhalation therapy being a primary treatment method. Dry powder inhalers (DPIs) often face challenges related to particle aggregation, which can diminish drug delivery efficiency. This study investigates particle aggregation and aims to optimize the cohesion–adhesion balance to improve inhalation efficiency. Advanced techniques like atomic force microscopy and Raman imaging were used to analyze particle interactions, focusing on lactose ratios, particle morphology, and drug-drug interactions. The therapeutic efficacy of optimized formulations containing budesonide (BUD) and Arformoterol (AFT) was assessed using an asthma model, showing significant improvements in sRAW, neutrophil count, and tidal volume compared to the positive control, with p-values below 0.01. AFT exhibited comparable efficacy to Formoterol at half the dose. Additionally, pharmacokinetic studies demonstrated similar in vivo behavior between the drugs, confirming the therapeutic advantage of AFT, with p-values for AUC0−t and Cmax of.646 and.153, respectively. The fine particle fractions for AFT and BUD were 39.4% and 50.6%, respectively, indicating improved drug delivery efficiency and potential for better clinical outcomes in asthma and COPD patients. [ABSTRACT FROM AUTHOR]

P-glycoprotein (P-gp) is a key efflux transporter and may be involved in drug-drug interactions (DDIs) at the blood-brain barrier (BBB), which could lead to changes in central nervous system (CNS) drug exposure. Morphine is a P-gp substrate and therefore a potential victim drug for P-gp mediated DDIs. It is however unclear if P-gp inhibitors can induce clinically relevant changes in morphine CNS exposure. Here, we used a physiologically-based pharmacokinetic (PBPK) model-based approach to evaluate the potential impact of DDIs on BBB transport of morphine by clinically relevant P-gp inhibitor drugs. The LeiCNS-PK3.0 PBPK model was used to simulate morphine distribution at the brain extracellular fluid (brainECF) for different clinical intravenous dosing regimens of morphine, alone or in combination with a P-gp inhibitor. We included 34 commonly used P-gp inhibitor drugs, with inhibitory constants and expected clinical P-gp inhibitor concentrations derived from literature. The DDI impact was evaluated by the change in brainECF exposure for morphine alone or in combination with different inhibitors. Our analysis demonstrated that P-gp inhibitors had a negligible effect on morphine brainECF exposure in the majority of simulated population, caused by low P-gp inhibition. Sensitivity analyses showed neither major effects of increasing the inhibitory concentration nor changing the inhibitory constant on morphine brainECF exposure. In conclusion, P-gp mediated DDIs on morphine BBB transport for the evaluated P-gp inhibitors are unlikely to induce meaningful changes in clinically relevant morphine CNS exposure. The developed CNS PBPK modeling approach provides a general approach for evaluating BBB transporter DDIs in humans. [ABSTRACT FROM AUTHOR]

Abstract Objective Darunavir (DRV) is a P-glycoprotein (P-gp) inhibitor. Dabigatran etexilate, prodrug of the anticoagulant dabigatran, is a P-gp probe substrate. This study evaluated the effect of single and multiple doses of DRV, coadministered with cobicistat (COBI) or ritonavir (rtv), on the pharmacokinetics (PK) of single-dose dabigatran etexilate. Methods This was an open-label, fixed-sequence, single-center, 2-panel, phase 1 study in which healthy adult participants were equally divided over 2 panels. In panel 1, participants received single and multiple doses of DRV/COBI 800/150 mg coadministered with single-dose dabigatran etexilate 150 mg. In panel 2, participants received single and multiple doses of DRV 800 mg + rtv 100 mg coadministered with single-dose dabigatran etexilate 150 mg. Key PK parameters evaluated were maximum plasma concentration (Cmax) and area under the plasma concentration-time curve from time zero to infinity (AUCinf) for free and total dabigatran. Results Overall, 28 participants were enrolled and treated (n = 14 per panel). Dabigatran Cmax and AUCinf increased 2.64-fold after a single dose of DRV/COBI and 1.99- and 1.88-fold, respectively, after multiple doses of DRV/COBI. Dabigatran Cmax and AUCinf increased 1.64- and 1.72-fold, respectively, after a single dose of DRV + rtv and 1.22- and 1.18-fold, respectively, after multiple doses of DRV + rtv. In both panels, the most commonly reported adverse events were diarrhea and headache. Conclusion Findings of increased dabigatran exposure with DRV/COBI or DRV + rtv coadministration indicate an inhibitory effect of single-dose boosted DRV on P-gp, and a mixed inhibitory/inductive effect of multiple doses of boosted DRV on P-gp. Trial registration ClinicalTrials.gov, NCT04208061 . Registered December 19, 2019

Abstract Background Edoxaban, the only factor Xa inhibitor with active metabolites, is metabolized by carboxylesterase-1 to its main active metabolite, M-4, which is a substrate of organic anion transporting polypeptide 1B1 (OATP1B1) and is excreted in bile and urine. Because the area under the plasma concentration–time curve ratio of M-4 to parent compound is typically less than 10% in healthy subjects, M-4 is generally considered to exhibit negligible antithrombotic activity in patients treated with edoxaban. However, we identified a case in which drug interactions and kidney impairment led to a substantive increase in plasma M-4 concentrations. Case presentation This case report involved a 68-year-old man with pancreatic cancer who was orally administered edoxaban tablets for prevention of thrombus formation in non-valvular atrial fibrillation, in addition to rifampin and clarithromycin (CAM) for treatment of mycobacterium avium complex lung disease. These medications were temporarily discontinued for a pancreaticoduodenectomy but were resumed 8 days post-surgery (POD8). On POD9, the patient developed acute kidney injury, and the trough concentrations of edoxaban and M-4 were 131.1 ng/mL and 115.8 ng/mL, respectively (M-4 ratio: 88.3%). On POD11, the M-4 trough concentration and M-4 ratio increased to 216.2 ng/mL and 186.2%, respectively. The plasma concentration of coproporphyrin-I, an endogenous biomarker of OATP1B1 activity, increased during this period. Conclusions This case suggests that in patients with impaired renal function taking edoxaban, co-administration of carboxylesterase-1 inducers such as rifampin and/or OATP1B1 inhibitors such as rifampin or clarithromycin may increase plasma concentrations of M-4 to clinically non-negligible levels.

Abstract State‑of‑the‑art medical studies proved that predicting CYP450 enzyme inhibitors is beneficial in the early stage of drug discovery. However, accurate machine learning-based (ML) in silico methods for predicting CYP450 inhibitors remains challenging. Here, we introduce GTransCYPs, an improved graph neural network (GNN) with a transformer mechanism for predicting CYP450 inhibitors. This model significantly enhances the discrimination between inhibitors and non-inhibitors for five major CYP450 isozymes: 1A2, 2C9, 2C19, 2D6, and 3A4. GTransCYPs learns information patterns from molecular graphs by aggregating node and edge representations using a transformer. The GTransCYPs model utilizes transformer convolution layers to process features, followed by a global attention-pooling technique to synthesize the graph-level information. This information is then fed through successive linear layers for final output generation. Experimental results demonstrate that the GTransCYPs model achieved high performance, outperforming other state-of-the-art methods in CYP450 prediction. Scientific contribution The prediction of CYP450 inhibition via computational techniques utilizing biological information has emerged as a cost-effective and highly efficient approach. Here, we presented a deep learning (DL) architecture based on GNN with transformer mechanism and attention pooling (GTransCYPs) to predict CYP450 inhibitors. Four GTransCYPs of different pooling technique were tested on an experimental tasks on the CYP450 prediction problem for the first time. Graph transformer with attention pooling algorithm achieved the best performances. Comparative and ablation experiments provide evidence of the efficacy of our proposed method in predicting CYP450 inhibitors. The source code is publicly available at https://github.com/zonwoo/GTransCYPs .

Abstract 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.

Abstract Background We examined whether the pharmacodynamic drug–drug interaction between esaxerenone and trimethoprim enhances the hyperkalemic effect. Methods A retrospective observational study was conducted to identify patients >18 years undertaking esaxerenone alone or esaxerenone plus trimethoprim at Mie University Hospital from May 2019 to December 2022. We performed propensity score-matching (1:1) to compare between-group differences in the maximum change in serum potassium levels (ΔK) using the Mann–Whitney U test. For esaxerenone plus trimethoprim, Spearman's correlation coefficients were used to examine correlations between ΔK and variables, including changes in blood urea nitrogen (ΔBUN), serum creatinine levels (ΔCr), and weekly trimethoprim cumulative dose. Results Out of propensity score-matched groups (n=8 each), serum potassium levels significantly increased after administration of esaxerenone alone (4.4 [4.2 to 4.7] meq/L to 5.2 [4.7 to 5.4] meq/L, p=0.008) and esaxerenone plus trimethoprim (4.2 [4.0 to 5.1] meq/L to 5.4 [4.7 to 5.5] meq/L, p=0.023). ΔK did not significantly differ between the groups (esaxerenone alone; 0.6 [0.3 to 0.9] meq/L vs. esaxerenone plus trimethoprim; 1.0 [0.4 to 1.3] meq/L, p=0.342). ΔK positively correlated with ΔBUN (r=0.988, p

Abstract Background Drug-drug interactions (DDIs) increase the incidence of adverse drug reactions (ADRs). In a previous report, we revealed that the incidence of potential DDIs due to the same CYP molecular species in one prescription exceeds 90% among patients taking six or more drugs and that CYP3A4 markedly influences the increase in the number of potential DDIs in clinical practice. However, the factors contributing to an increased number of potential DDIs in prescriptions from multiple clinical departments remain poorly clarified. Methods This observational study was performed at five pharmacies in Okayama Prefecture, Japan. Patients who visited these pharmacies from 11 April 2022 to 24 April 2022 were included, except those who had prescriptions only from a single clinical department. A stratified analysis was performed to determine the incidence of CYP3A4-related potential DDIs according to the number of drugs taken. Additionally, factors associated with an increase in the number of drugs involved in CYP3A4-related potential DDIs were identified using multiple linear regression analysis. In this study, potential DDIs for the prescription data subdivided by clinical department, containing two or more drugs, were used as control data. Results Overall, 372 outpatients who received prescriptions from multiple clinical departments were included in the current study. The number of drugs contributing to CYP3A4-related potential DDIs increased with an increase in the number of clinical departments. Notably, in cases taking fewer than six drugs, prescriptions from multiple clinical departments had a higher frequency of CYP3A4-related potential DDIs than those in prescriptions subdivided by clinical department. Multiple regression analysis identified "Cardiovascular agents", "Agents affecting central nervous system", and "Urogenital and anal organ agents" as the top three drug classes that increase CYP3A4-related potential DDIs. Conclusion Collectively, these results highlight the importance of a unified management strategy for prescribed drugs and continuous monitoring of ADRs in outpatients receiving prescriptions from multiple clinical departments even if the number of drugs taken is less than six.

State‑of‑the‑art medical studies proved that predicting CYP450 enzyme inhibitors is beneficial in the early stage of drug discovery. However, accurate machine learning-based (ML) in silico methods for predicting CYP450 inhibitors remains challenging. Here, we introduce GTransCYPs, an improved graph neural network (GNN) with a transformer mechanism for predicting CYP450 inhibitors. This model significantly enhances the discrimination between inhibitors and non-inhibitors for five major CYP450 isozymes: 1A2, 2C9, 2C19, 2D6, and 3A4. GTransCYPs learns information patterns from molecular graphs by aggregating node and edge representations using a transformer. The GTransCYPs model utilizes transformer convolution layers to process features, followed by a global attention-pooling technique to synthesize the graph-level information. This information is then fed through successive linear layers for final output generation. Experimental results demonstrate that the GTransCYPs model achieved high performance, outperforming other state-of-the-art methods in CYP450 prediction. Scientific contribution The prediction of CYP450 inhibition via computational techniques utilizing biological information has emerged as a cost-effective and highly efficient approach. Here, we presented a deep learning (DL) architecture based on GNN with transformer mechanism and attention pooling (GTransCYPs) to predict CYP450 inhibitors. Four GTransCYPs of different pooling technique were tested on an experimental tasks on the CYP450 prediction problem for the first time. Graph transformer with attention pooling algorithm achieved the best performances. Comparative and ablation experiments provide evidence of the efficacy of our proposed method in predicting CYP450 inhibitors. The source code is publicly available at https://github.com/zonwoo/GTransCYPs. [ABSTRACT FROM AUTHOR]

Background: Edoxaban, the only factor Xa inhibitor with active metabolites, is metabolized by carboxylesterase-1 to its main active metabolite, M-4, which is a substrate of organic anion transporting polypeptide 1B1 (OATP1B1) and is excreted in bile and urine. Because the area under the plasma concentration–time curve ratio of M-4 to parent compound is typically less than 10% in healthy subjects, M-4 is generally considered to exhibit negligible antithrombotic activity in patients treated with edoxaban. However, we identified a case in which drug interactions and kidney impairment led to a substantive increase in plasma M-4 concentrations. Case presentation: This case report involved a 68-year-old man with pancreatic cancer who was orally administered edoxaban tablets for prevention of thrombus formation in non-valvular atrial fibrillation, in addition to rifampin and clarithromycin (CAM) for treatment of mycobacterium avium complex lung disease. These medications were temporarily discontinued for a pancreaticoduodenectomy but were resumed 8 days post-surgery (POD8). On POD9, the patient developed acute kidney injury, and the trough concentrations of edoxaban and M-4 were 131.1 ng/mL and 115.8 ng/mL, respectively (M-4 ratio: 88.3%). On POD11, the M-4 trough concentration and M-4 ratio increased to 216.2 ng/mL and 186.2%, respectively. The plasma concentration of coproporphyrin-I, an endogenous biomarker of OATP1B1 activity, increased during this period. Conclusions: This case suggests that in patients with impaired renal function taking edoxaban, co-administration of carboxylesterase-1 inducers such as rifampin and/or OATP1B1 inhibitors such as rifampin or clarithromycin may increase plasma concentrations of M-4 to clinically non-negligible levels. [ABSTRACT FROM AUTHOR]

Letermovir (LET) is a novel antiviral agent recently approved for cytomegalovirus (CMV) prophylaxis of renal transplant patients in Japan. However, its interactions with tacrolimus (TAC), an important immunosuppressant, remain ambiguous, warranting careful evaluation considering the unique genetic and physiological characteristics of Japanese patients. Therefore, in this study, we aimed to investigate the drug-drug interactions between LET and extended-release TAC (ER-TAC) in Japanese renal transplant patients via physiologically based pharmacokinetic (PBPK) modeling. We developed PBPK models for LET and TAC, including a new model for ER-TAC, using the Simcyp simulator. We also created a virtual Japanese post-transplant population by incorporating physiological parameters specific to Japanese patients, including CYP3A5 genotypes. Our model accurately predicted the pharmacokinetics of both immediate-release and ER-TAC co-administered with LET. In the Japanese population, LET significantly increased ER-TAC exposure, with the effect varying by CYP3A5 genotype. For CYP3A5*1 carrier, the area under the curve ratio ranged from 2.33 to 2.53, while for CYP3A5*3/*3 carriers, it ranged from 2.82 to 2.86. The maximum concentration ratio was approximately 1.50 across all groups. Our findings suggest reducing the ER-TAC dose by approximately 57-60% for CYP3A5*1 carrier and 65% for CYP3A5*3/*3 carriers when co-administered with LET for Japanese renal transplant patients. Moreover, the developed model incorporating population-specific factors, such as hematocrit values and CYP3A5 genotype frequencies, is a valuable tool to evaluate complex drug interactions and guide the dosing strategies for LET and TAC in Japanese patients. Overall, this study expands the application of PBPK modeling in transplant pharmacology, contributing to the development of effective immunosuppressive strategies for Japanese renal transplant patients. [ABSTRACT FROM AUTHOR]

The document is a corrigendum for an article titled "Inhibitory effects of calcium channel blockers nisoldipine and nimodipine on ivacaftor metabolism and their underlying mechanism." The correction addresses errors in Figure 7 and Table 2 related to the unit of concentration values used. The corrected Ki values are now 3.26 and 5.87, respectively. The authors assure that these corrections do not impact the scientific conclusions of the original article. [Extracted from the article]