Evaluation of potential drug-drug interactions with medical cannabis.

Cannabis-drug interactions have caused significant concerns, mainly due to their role in the cytochrome P450 (CYP) enzyme-mediated metabolic pathway of numerous medications. A systematic review was conducted to gain an overview of the potential interactions of cannabis with different drug classes by extracting pertinent information from published study data. From the inception of the study to October 1, 2023, we performed a systematic search of PubMed, Scopus, clinicaltrials.gov, and Web of Science. We included 54 out of 464 articles, and a total of 20 drug classes were identified to have interactions with medicinal cannabis. The cannabis-drug interactions were assessed and classified according to their probability and severity. The analysis revealed that antiepileptics had the most evidence of interaction with cannabis, followed by clobazam (CLB), warfarin, and tacrolimus. Generally, cannabis-drug interactions result in pharmacokinetic (PK) or pharmacodynamic (PD) changes. Therefore, careful monitoring should be performed to detect any unusual elevations in plasma levels. In addition, dose titrations or treatment withdrawal could help mitigate the adverse effects attributed to cannabis-drug interactions. Nevertheless, novel drugs are constantly emerging, and more research is needed to further identify potential interactions with cannabis.

Effect of Carbamazepine on Darunavir Trough Concentrations: When the Dose Can Make the Difference-A Case Study.

BACKGROUND: Carbamazepine (CBZ) is an antiseizure medication known to induce the expression of cytochrome P4503A metabolic enzymes. Here, we describe a man living with HIV who underwent several changes in the daily dose of CBZ, which resulted in different induction effects on darunavir trough concentrations. METHODS: A 59-year-old man with HIV, successfully undergoing maintenance antiretroviral treatment with darunavir/cobicistat once daily (combined with raltegravir), was prescribed CBZ for recurrent trigeminal neuralgia. Over subsequent months, the patient underwent various changes in the doses (from 200 to 800 mg/d) and trough concentrations (from 3.6 to 18.0 mg/L) of CBZ, guided by clinical response to trigeminal neuralgia. RESULTS: A highly significant inverse association was observed between darunavir trough concentration and both CBZ dose or trough concentration (coefficient of determination >0.75, P < 0.0001). Ultimately, the darunavir dose was increased to 600 mg twice daily with ritonavir and dolutegravir to ensure optimal antiretroviral coverage, anticipating potential further uptitration of CBZ doses. CONCLUSIONS: The impact of CBZ on boosted darunavir exposure seemed to be dose- and concentration-dependent. The management of such drug-drug interactions in daily practice was facilitated through therapeutic drug monitoring. This case underscores the importance of a multidisciplinary approach that incorporates both antiretroviral and nonantiretroviral comedications contributing to the optimal management of polypharmacy in individuals living with HIV.

Nirmatrelvir/ritonavir treatment of patients with COVID-19 taking tacrolimus: case series describing the results of drug-drug interactions.

Nirmatrelvir/ritonavir is a novel drug combination that is authorized by the Food and Drug Administration for the treatment of coronavirus disease 2019 (COVID-19). Ritonavir is a cytochrome P450 3A inhibitor and a P-glycoprotein inhibitor that increases the plasma concentration of tacrolimus and other medications. We describe the cases of two patients treated with nirmatrelvir/ritonavir: a patient who had undergone kidney transplantation and another with a history of hematopoietic stem cell transplantation. Toxic concentrations of tacrolimus were induced in both. This case series highlights the risk associated with the concomitant administration of tacrolimus and nirmatrelvir/ritonavir.

Effects of the selective AMPA modulator NBI-1065845 on the pharmacokinetics of midazolam or ethinyl estradiol-levonorgestrel in healthy adults.

This parallel-arm, phase I study investigated the potential cytochrome P450 (CYP)3A induction effect of NBI-1065845 (TAK-653), an investigational α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor potentiator in phase II development for major depressive disorder. The midazolam treatment arm received the sensitive CYP3A substrate midazolam on Day 1, followed by NBI-1065845 alone on Days 5-13; on Day 14, NBI-1065845 was administered with midazolam, then NBI-1065845 alone on Day 15. The oral contraceptive treatment arm received ethinyl estradiol-levonorgestrel on Day 1, then NBI-1065845 alone on Days 5-13; on Day 14, NBI-1065845 was administered with ethinyl estradiol-levonorgestrel, then NBI-1065845 alone on Days 15-17. Blood samples were collected for pharmacokinetic analyses. The midazolam treatment arm comprised 14 men and 4 women, of whom 16 completed the study. Sixteen of the 17 healthy women completed the oral contraceptive treatment arm. After multiple daily doses of NBI-1065845, the geometric mean ratios (GMRs) (90% confidence interval) for maximum observed concentration were: midazolam, 0.94 (0.79-1.13); ethinyl estradiol, 1.00 (0.87-1.15); and levonorgestrel, 0.99 (0.87-1.13). For area under the plasma concentration-time curve (AUC) from time 0 to infinity, the GMRs were as follows: midazolam, 0.88 (0.78-0.98); and ethinyl estradiol, 1.01 (0.88-1.15). For levonorgestrel, the GMR for AUC from time 0 to the last quantifiable concentration was 0.87 (0.78-0.96). These findings indicate that NBI-1065845 is not a CYP3A inducer and support its administration with CYP3A substrates. NBI-1065845 was generally well tolerated, with no new safety signals observed after coadministration of midazolam, ethinyl estradiol, or levonorgestrel.

Pharmacokinetic interactions between fexuprazan, a potassium-competitive acid blocker, and nonsteroidal anti-inflammatory drugs in healthy males.

Fexuprazan, a novel potassium-competitive acid blocker, is expected to be used for the prevention of nonsteroidal anti-inflammatory drugs (NSAIDs) induced ulcer. This study aimed to evaluate pharmacokinetic (PK) interactions between fexuprazan and NSAIDs in healthy subjects. A randomized, open-label, multicenter, six-sequence, one-way crossover study was conducted in healthy male subjects. Subjects randomly received one of the study drugs (fexuprazan 40 mg BID, celecoxib 200 mg BID, naproxen 500 mg BID, or meloxicam 15 mg QD) for 5 or 7 days in the first period followed by the combination of fexuprazan and one of NSAIDs for the same days and the perpetrator additionally administered for 1-2 days in the second period. Serial blood samples for PK analysis were collected until 48- or 72-h post-dose at steady state. PK parameters including maximum plasma concentration at steady state (Cmax,ss) and area under plasma concentration-time curve over dosing interval at steady state (AUCτ,ss) were compared between monotherapy and combination therapy. The PKs of NSAIDs were not significantly altered by fexuprazan. For fexuprazan, differences in PK parameters (22% in Cmax, 19% in AUCτ,ss) were observed when co-administered with naproxen, but not clinically significant. The geometric mean ratio (90% confidence interval) of combination therapy to monotherapy for Cmax,ss and AUCτ,ss was 1.22 (1.02-1.46) and 1.19 (1.00-1.43), respectively. There were no significant changes in the systemic exposure of fexuprazan by celecoxib and meloxicam. Fexuprazan and NSAIDs did not show clinically meaningful PK interactions.

DeepARV: ensemble deep learning to predict drug-drug interaction of clinical relevance with antiretroviral therapy.

Drug-drug interaction (DDI) may result in clinical toxicity or treatment failure of antiretroviral therapy (ARV) or comedications. Despite the high number of possible drug combinations, only a limited number of clinical DDI studies are conducted. Computational prediction of DDIs could provide key evidence for the rational management of complex therapies. Our study aimed to assess the potential of deep learning approaches to predict DDIs of clinical relevance between ARVs and comedications. DDI severity grading between 30,142 drug pairs was extracted from the Liverpool HIV Drug Interaction database. Two feature construction techniques were employed: 1) drug similarity profiles by comparing Morgan fingerprints, and 2) embeddings from SMILES of each drug via ChemBERTa, a transformer-based model. We developed DeepARV-Sim and DeepARV-ChemBERTa to predict four categories of DDI: i) Red: drugs should not be co-administered, ii) Amber: interaction of potential clinical relevance manageable by monitoring/dose adjustment, iii) Yellow: interaction of weak relevance and iv) Green: no expected interaction. The imbalance in the distribution of DDI severity grades was addressed by undersampling and applying ensemble learning. DeepARV-Sim and DeepARV-ChemBERTa predicted clinically relevant DDI between ARVs and comedications with a weighted mean balanced accuracy of 0.729 ± 0.012 and 0.776 ± 0.011, respectively. DeepARV-Sim and DeepARV-ChemBERTa have the potential to leverage molecular structures associated with DDI risks and reduce DDI class imbalance, effectively increasing the predictive ability on clinically relevant DDIs. This approach could be developed for identifying high-risk pairing of drugs, enhancing the screening process, and targeting DDIs to study in clinical drug development.

Potentially inappropriate prescriptions for poly-treated patients in long-term care facilities: retrospective pharmacoutilization analysis.

BACKGROUND This study aimed to investigate, among elderly patients in long-term care (LTC) facilities, potentially inappropriate drug prescriptions, potentially interactions and verify whether they can be traced back to hospitalisations or accesses to the Emergency Department (ED). The study data were acquired by means of a case report form investigating the medication management process in LTCs. MATERIAL AND METHODS Analysis of pharmacutilisation in LTCFs patients aged ≥65 years on polypharmacy or excessive polypharmacy, January-July 2023. Data was extracted from a database (DB) containing the monthly prescriptions of medicines supplied by direct distribution (DD) to LTCs. The prevalence of PIMs was evaluated by applying the Beers and STOPP criteria to the medication profile of each patient. RESULTS The overall prevalence of polypharmacy and hyperpolypharmacy was 83% and 17%, respectively. PIMs were defined using Beers and STOPP criteria. The most frequent PIMs were proton pump inhibitors (19% e 15%), antiplatelets agent (17% e 13%) and non-associated sulfonamides (14% e 12%). Of the 1,921 PIMs, 121 were contraindicated or very serious (6%) and 1,800 were major (94%).The most common medicaments involved in drug-drug interaction are furosemide (21%), sertraline (19%), pantoprazole (16%) e trazodone (15%). LTCs participating in the study (56%) excluded polypharmacy as a cause of access to the ED and ADRs. Therefore no case was ever reported (100%). CONCLUSIONS Polypharmacy or excessive polypharmacy among elderly patients may increase PIMs and ADRs. A constant review of the therapeutic regimens and deprescribing decrease inappropriate use of medications and interactions, ADRs, and accesses to the ED with consequent reduction of pharmaceutical spending.

Evaluation of medication use and polypharmacy in postoperative cardiac patients: The clinical pharmacist's imperative in a public institute of Pakistan.

As a major concern in the healthcare sector, polypharmacy is correlated with an increased risk of potential drug-drug interactions (pDDIs), treatment costs and adverse drug reactions (ADR). To assess the prevalence of polypharmacy and its associated factors among postoperative cardiac patients admitted to the National Institute of Cardiovascular Diseases (NICVD), a hospital-based cross-sectional study was conducted between November 2021 and April 2022. Medication charts of postoperative patients were reviewed for medication utilization and polypharmacy. Data was collected using a form approved by the Ethical Review Committee (ERC) regarding patient's clinical and demographic characteristics and medications administered. Statistical analysis was performed using the SPSS software version 25.0. Patients were taking an average of 10.3±1.7 medications. The minimum number of drugs taken per patient was 5, while the maximum was 15 drugs. Only 114 (29.7%) received polypharmacy (5-9 drugs) and hyper-polypharmacy (≥10 drugs) was 270 (70.3%). The mean±SD cardiovascular drugs used were 5.45±1.18 and the mean±SD non-cardiovascular drugs were 4.83±1.18. The prevalence of hyper-polypharmacy suggests a critical need for optimized medication management strategies in this population. Incorporating clinical pharmacists within public healthcare institutions can address polypharmacy-related challenges and enhance medication safety, adherence and patient outcomes.

Pharmacokinetic Interactions Between Tegoprazan and the Combination of Clarithromycin, Amoxicillin and Bismuth in Healthy Chinese Subjects: An Open-Label, Single-Center, Multiple-Dosage, Self-Controlled, Phase I Trial.

BACKGROUND: Tegoprazan is a potassium-competitive acid blocker that inhibits gastric acid and which may be used for eradicating Helicobacter pylori. This study focuses on the pharmacokinetic interaction and safety between tegoprazan and the combination of clarithromycin, amoxicillin and bismuth in healthy Chinese subjects. METHODS: An open-label, three-period, single-center, multiple-dosage, single-sequence, phase I trial was conducted in 22 healthy subjects. In period 1, the subjects took tegoprazan 50 mg twice daily for 7 days, and in period 2 they were administered clarithromycin 500 mg, amoxicillin 1000 mg and bismuth potassium citrate 600 mg twice daily for 7 days (days 14-20). Tegoprazan, clarithromycin, amoxicillin and bismuth potassium citrate were then administered in combination for 7 days (days 21-27) in period 3. Blood samples were collected up to 12 h after the last dose of each period. Safety assessments were performed in each period. RESULTS: The geometric mean ratios (GMRs) [90% confidence interval (CI)] of maximum plasma concentration at steady state (Cmax,ss) and area under the plasma concentration-time curve over the dosing interval (AUCτ) at steady state were 195.93% (175.52-218.71%) and 287.54% (263.28-314.04%) for tegoprazan and 423.23% (382.57-468.22%) and 385.61% (354.62-419.30%) for tegoprazan metabolite M1, respectively. The GMRs (90% CI) of Cmax,ss and AUCτ were 83.69% (77.44-90.45%) and 110.30% (102.74-118.41%) for clarithromycin, 126.25% (114.73-138.93%) and 146.94% (135.33-159.55%) for 14-hydroxyclarithromycin, 75.89% (69.73-82.60%) and 94.34% (87.94-101.20%) for amoxicillin, and 158.43% (125.43-200.11%) and 183.63% (156.42-215.58%) for bismuth, respectively. All reported adverse events were mild. The frequency of adverse events during the coadministration stage was not higher than that during the single- or triple-drug administration stages. CONCLUSION: The plasma exposure of tegoprazan, M1, 14-hydroxyclarithromycin and bismuth was increased after the coadministration of tegoprazan, clarithromycin, amoxicillin and bismuth. The coadministration exhibited favorable safety and tolerability. CLINICAL TRIALS REGISTRATION: CTR20230643.

Dietary Influence on Drug Efficacy: A Comprehensive Review of Ketogenic Diet-Pharmacotherapy Interactions.

It is widely acknowledged that the ketogenic diet (KD) has positive physiological effects as well as therapeutic benefits, particularly in the treatment of chronic diseases. Maintaining nutritional ketosis is of utmost importance in the KD, as it provides numerous health advantages such as an enhanced lipid profile, heightened insulin sensitivity, decreased blood glucose levels, and the modulation of diverse neurotransmitters. Nevertheless, the integration of the KD with pharmacotherapeutic regimens necessitates careful consideration. Due to changes in their absorption, distribution, metabolism, or elimination, the KD can impact the pharmacokinetics of various medications, including anti-diabetic, anti-epileptic, and cardiovascular drugs. Furthermore, the KD, which is characterised by the intake of meals rich in fats, has the potential to impact the pharmacokinetics of specific medications with high lipophilicity, hence enhancing their absorption and bioavailability. However, the pharmacodynamic aspects of the KD, in conjunction with various pharmaceutical interventions, can provide either advantageous or detrimental synergistic outcomes. Therefore, it is important to consider the pharmacokinetic and pharmacodynamic interactions that may arise between the KD and various drugs. This assessment is essential not only for ensuring patients' compliance with treatment but also for optimising the overall therapeutic outcome, particularly by mitigating adverse reactions. This highlights the significance and necessity of tailoring pharmacological and dietetic therapies in order to enhance the effectiveness and safety of this comprehensive approach to managing chronic diseases.

Learning with an evolving medicine label: how artificial intelligence-based medication recommendation systems must adapt to changing medication labels.

INTRODUCTION: Artificial intelligence or machine learning (AI/ML) based systems can help personalize prescribing decisions for individual patients. The recommendations of these clinical decision support systems must relate to the "label" of the medicines involved. The label of a medicine is an approved guide that indicates how to prescribe the drug in a safe and effective manner. AREAS COVERED: The label for a medicine may evolve as new information on drug safety and effectiveness emerges, leading to the addition or removal of warnings, drug-drug interactions, or to permit new indications. However, the speed at which these updates are made to these AI/ML recommendation systems may be delayed and could influence the safety of prescribing decisions. This article explores the need to keep AI/ML tools 'in sync' with any label changes. Additionally, challenges relating to medicine availability and geographical suitability are discussed. EXPERT OPINION: These considerations highlight the important role that pharmacoepidemiologists and drug safety professionals must play within the monitoring and use of these tools. Furthermore, these issues highlight the guiding role that regulators need to have in planning and oversight of these tools.

Artificial intelligence or machine learning (AI/ML) based systems that guide the prescription of medications have the potential to vastly improve patient care, but these tools should only provide recommendations that are in line with the label of a medicine. With a constantly evolving medication label, this is likely to be a challenge, and this also has implications for the off-label use of medicines.

A simulation-based comparison of drug-drug interaction signal detection methods.

Several statistical methods have been proposed to detect adverse drug reactions induced by taking two drugs together. These suspected adverse drug reactions can be discovered through post-market drug safety surveillance, which mainly relies on spontaneous reporting system database. Most previous studies have applied statistical models to real world data, but it is not clear which method outperforms the others. We aimed to assess the performance of various detection methods by implementing simulations under various conditions. We reviewed proposed approaches to detect signals indicating drug-drug interactions (DDIs) including the Ω shrinkage measure, the chi-square statistic, the proportional reporting ratio, the concomitant signal score, the additive model and the multiplicative model. Under various scenarios, we conducted a simulation study to examine the performances of the methods. We also applied the methods to Korea Adverse Event Reporting System (KAERS) data. Of the six methods considered in the simulation study, the Ω shrinkage measure and the chi-square statistic with threshold = 2 had higher sensitivity for detecting the true signals than the other methods in most scenarios while controlling the false positive rate below 0.05. When applied to the KAERS data, the two methods detected one known DDI for QT prolongation and one unknown (suspected) DDI for hyperkalemia. The performance of various signal detection methods for DDI may vary. It is recommended to use several methods together, rather than just one, to make a reasonable decision.

Opioid-induced respiratory depression suspected of drug interaction in a prostate cancer patient: a case report.

BACKGROUND: Many of the drugs used for the treatment and alleviation of symptoms in cancer patients are known to inhibit or induce cytochrome P450 (CYP). Therefore, it is important to pay attention to the drug interactions of opioid analgesics that are metabolized by CYPs, because for example when using oxycodone metabolized by CYP3A4, it is possible that the effect will be attenuated or enhanced by the concomitant use of drugs that induce or inhibit CYP3A4. Aprepitant, an antiemetic drug used in many patients receiving anticancer drugs, is known as a moderate competitive inhibitor of CYP3A4. We experienced a case of respiratory depression caused by opioids, which was suspected to be caused by a drug interaction with antiemetics especially aprepitant. CASE DESCRIPTION: The patient was a 72-year-old man. He had been treated with continuous oxycodone infusion for perianal pain associated with the rectal invasion of prostate cancer. No comorbidities other than renal dysfunction were observed. Oxycodone treatment was started at 48 mg/day, and was increased to 108 mg/day, and then the pain decreased. Once the pain was controlled, chemotherapy was planned. Antiemetics (dexamethasone, palonosetron, and aprepitant) were administered before anticancer drug administration. Approximately 3 hours after antiemetics administration and before the administration of the anticancer drugs, a ward nurse noticed that oversedation and respiratory depression had occurred. When the patient was called, he immediately woke up and was able to talk normally, so the anticancer drugs were administered as scheduled. About 2 hours after the nurse noticed oversedation, the attending physician reduced the dose of oxycodone infusion to 48 mg/day. After that, his drowsiness persisted, but his respiratory condition improved. Despite reducing the dose of oxycodone to less than half, the pain remained stable at numeric rating scale (NRS) 0-1, without the use of a rescue dose. The patient was discharged from the hospital 36 days after the administration of anticancer drugs, without any problems. CONCLUSIONS: The cause of respiratory depression in this case was thought to be a combination of factors, including drug interactions between oxycodone and antiemetics, and oxycodone accumulation due to renal dysfunction.

Electroanalysis in Pharmacogenomic Studies: Mechanisms of Drug Interaction with DNA.

The review discusses electrochemical methods for analysis of drug interactions with DNA. The electroanalysis method is based on the registration of interaction-induced changes in the electrochemical oxidation potential of heterocyclic nitrogenous bases in the DNA molecule and in the maximum oxidation current amplitude. The mechanisms of DNA-drug interactions can be identified based on the shift in the electrooxidation potential of heterocyclic nitrogenous bases toward more negative (cathodic) or positive (anodic) values. Drug intercalation into DNA shifts the electrochemical oxidation potential to positive values, indicating thermodynamically unfavorable process that hinders oxidation of nitrogenous bases in DNA. The potential shift toward the negative values indicates electrostatic interactions, e.g., drug binding in the DNA minor groove, since this process does not interfere with the electrochemical oxidation of bases. The concentration-dependent decrease in the intensity of electrochemical oxidation of DNA bases allows to quantify the type of interaction and calculate the binding constants.

Effects of cannabidiol and Δ9-tetrahydrocannabinol on cytochrome P450 enzymes: a systematic review.

Due to legal, political, and cultural changes, the use of cannabis has rapidly increased in recent years. Research has demonstrated that the cannabinoids cannabidiol (CBD) and Δ9-tetrahydrocannabinol (THC) inhibit and induce cytochrome P450 (CYP450) enzymes. The objective of this review is to evaluate the effect of CBD and THC on the activity of CYP450 enzymes and the implications for drug-drug interactions (DDIs) with psychotropic agents that are CYP substrates. A systematic search was conducted using PubMed, Scopus, Scientific Electronic Library Online (SciELO) and PsychINFO. Search terms included 'cannabidiol', 'tetrahydrocannabinol', and 'cytochrome P450'. A total of seven studies evaluating the interaction of THC and CBD with CYP450 enzymes and psychotropic drugs were included. Both preclinical and clinical studies were included. Results from the included studies indicate that both CBD and THC inhibit several CYP450 enzymes including, but not limited to, CYP1A2, CYP3C19, and CYP2B6. While there are a few known CYP450 enzymes that are induced by THC and CBD, the induction of CYP450 enzymes is an understudied area of research and lacks clinical data. The inhibitory effects observed by CBD and THC on CYP450 enzymes vary in magnitude and may decrease the metabolism of psychotropic agents, cause changes in plasma levels of psychotropic medications, and increase adverse effects. Our findings clearly present interactions between THC and CBD and several CYP450 enzymes, providing clinicians evidence of a high risk of DDIs for patients who consume both cannabis and psychotropic medication. However, more clinical research is necessary before results are applied to clinical settings.

Low-dose colchicine for atherosclerosis: long-term safety.

Low-dose colchicine (0.5 mg daily) is now FDA-approved for secondary prevention in patients with coronary disease and will be increasingly prescribed in clinical practice. In this State-of-the-Art Review, data were collated from contemporary systemic reviews of case reports, drug registries, and placebo-controlled trials that assessed specific issues of safety related to the continuous use of colchicine in a range of clinical settings to inform physicians, pharmacists, and patients of the absolute risks of continuous use of low-dose colchicine, including among individuals taking statin therapy. Based upon these collective data, it is concluded that aside mild diarrhoea on initiation of colchicine that typically subsides in the vast majority of patients within a week of therapy, continuous use of low-dose colchicine is well tolerated and very safe. It does not affect renal, liver, or cognitive function, has no adverse effects on bleeding, wound healing, fertility, or pregnancy, and does not increase risks of cancer, serious infection, or cause-specific mortality. When appropriately prescribed to patients without significant renal or hepatic impairment, reports of myelosuppression, myotoxicity, and serious drug-drug interactions are rare and no more frequent than placebo, including in patients taking statin therapy. Physicians, pharmacists, and patients can be reassured that in the absence of significant renal or hepatic impairment continuous use of low-dose colchicine can be used safely in patients with atherosclerosis for the purpose of reducing cardiovascular risk.

Clinical assessment of momelotinib drug-drug interactions via CYP3A metabolism and transporters.

Momelotinib-approved for treatment of myelofibrosis in adults with anemia-and its major active metabolite, M21, were assessed as drug-drug interaction (DDI) victims with a strong cytochrome P450 (CYP) 3A4 inhibitor (multiple-dose ritonavir), an organic anion transporting polypeptide (OATP) 1B1/1B3 inhibitor (single-dose rifampin), and a strong CYP3A4 inducer (multiple-dose rifampin). Momelotinib DDI perpetrator potential (multiple-dose) was evaluated with CYP3A4 and breast cancer resistance protein (BCRP) substrates (midazolam and rosuvastatin, respectively). DDI was assessed from changes in maximum plasma concentration (Cmax), area under the concentration-time curve (AUC), time to reach Cmax, and half-life. The increase in momelotinib (23% Cmax, 14% AUC) or M21 (30% Cmax, 24% AUC) exposure with ritonavir coadministration was not clinically relevant. A moderate increase in momelotinib (40% Cmax, 57% AUC) and minimal change in M21 was observed with single-dose rifampin. A moderate decrease in momelotinib (29% Cmax, 46% AUC) and increase in M21 (31% Cmax, 15% AUC) were observed with multiple-dose rifampin compared with single-dose rifampin. Due to potentially counteracting effects of OATP1B1/1B3 inhibition and CYP3A4 induction, multiple-dose rifampin did not significantly change momelotinib pharmacokinetics compared with momelotinib alone (Cmax no change, 15% AUC decrease). Momelotinib did not alter the pharmacokinetics of midazolam (8% Cmax, 16% AUC decreases) or 1'-hydroxymidazolam (14% Cmax, 16% AUC decreases) but increased rosuvastatin Cmax by 220% and AUC by 170%. Safety findings were mild in this short-term study in healthy volunteers. This analysis suggests that momelotinib interactions with OATP1B1/1B3 inhibitors and BCRP substrates may warrant monitoring for adverse reactions or dose adjustments.

Drug-Online: an online platform for drug-target interaction, affinity, and binding sites identification using deep learning.

BACKGROUND: Accurately identifying drug-target interaction (DTI), affinity (DTA), and binding sites (DTS) is crucial for drug screening, repositioning, and design, as well as for understanding the functions of target. Although there are a few online platforms based on deep learning for drug-target interaction, affinity, and binding sites identification, there is currently no integrated online platforms for all three aspects. RESULTS: Our solution, the novel integrated online platform Drug-Online, has been developed to facilitate drug screening, target identification, and understanding the functions of target in a progressive manner of "interaction-affinity-binding sites". Drug-Online platform consists of three parts: the first part uses the drug-target interaction identification method MGraphDTA, based on graph neural networks (GNN) and convolutional neural networks (CNN), to identify whether there is a drug-target interaction. If an interaction is identified, the second part employs the drug-target affinity identification method MMDTA, also based on GNN and CNN, to calculate the strength of drug-target interaction, i.e., affinity. Finally, the third part identifies drug-target binding sites, i.e., pockets. The method pt-lm-gnn used in this part is also based on GNN. CONCLUSIONS: Drug-Online is a reliable online platform that integrates drug-target interaction, affinity, and binding sites identification. It is freely available via the Internet at http://39.106.7.26:8000/Drug-Online/ .

Pharmacokinetics and Safety of Lurbinectedin Administrated with Itraconazole in Cancer Patients: A Drug-Drug Interaction Study.

This open-label, two-part, phase Ib drug-drug interaction study investigated whether the pharmacokinetic (PK) and safety profiles of lurbinectedin (LRB), a marine-derived drug, are affected by co-administration of itraconazole (ITZ), a strong CYP3A4 inhibitor, in adult patients with advanced solid tumors. In Part A, three patients were sequentially assigned to Sequence 1 (LRB 0.8 mg/m2, 1-h intravenous [IV] + ITZ 200 mg/day oral in Cycle 1 [C1] and LRB alone 3.2 mg/m2, 1 h, IV in Cycle 2 [C2]). In Part B, 11 patients were randomized (1:1) to receive either Sequence 1 (LRB at 0.9 mg/m2 + ITZ in C1 and LRB alone in C2) or Sequence 2 (LRB alone in C1 and LRB + ITZ in C2). Eleven patients were evaluable for PK analysis: three in Part A and eight in Part B (four per sequence). The systemic total exposure of LRB increased with ITZ co-administration: 15% for Cmax, area under the curve (AUC) 2.4-fold for AUC0-t and 2.7-fold for AUC0-∞. Co-administration with ITZ produced statistically significant modifications in the unbound plasma LRB PK parameters. The LRB safety profile was consistent with the toxicities described in previous studies. Co-administration with multiple doses of ITZ significantly altered LRB systemic exposure. Hence, to avoid LRB overexposure when co-administered with strong CYP3A4 inhibitors, an LRB dose reduction proportional to CL reduction should be applied.