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.

Prevalence and differences in the co-administration of drugs known to interact: an analysis of three distinct and large populations.

BACKGROUND: The co-administration of drugs known to interact greatly impacts morbidity, mortality, and health economics. This study aims to examine the drug-drug interaction (DDI) phenomenon with a large-scale longitudinal analysis of age and gender differences found in drug administration data from three distinct healthcare systems. METHODS: This study analyzes drug administrations from population-wide electronic health records in Blumenau (Brazil; 133 K individuals), Catalonia (Spain; 5.5 M individuals), and Indianapolis (USA; 264 K individuals). The stratified prevalences of DDI for multiple severity levels per patient gender and age at the time of administration are computed, and null models are used to estimate the expected impact of polypharmacy on DDI prevalence. Finally, to study actionable strategies to reduce DDI prevalence, alternative polypharmacy regimens using drugs with fewer known interactions are simulated. RESULTS: A large prevalence of co-administration of drugs known to interact is found in all populations, affecting 12.51%, 12.12%, and 10.06% of individuals in Blumenau, Indianapolis, and Catalonia, respectively. Despite very different healthcare systems and drug availability, the increasing prevalence of DDI as patients age is very similar across all three populations and is not explained solely by higher co-administration rates in the elderly. In general, the prevalence of DDI is significantly higher in women - with the exception of men over 50 years old in Indianapolis. Finally, we show that using proton pump inhibitor alternatives to omeprazole (the drug involved in more co-administrations in Catalonia and Blumenau), the proportion of patients that are administered known DDI can be reduced by up to 21% in both Blumenau and Catalonia and 2% in Indianapolis. CONCLUSIONS: DDI administration has a high incidence in society, regardless of geographic, population, and healthcare management differences. Although DDI prevalence increases with age, our analysis points to a complex phenomenon that is much more prevalent than expected, suggesting comorbidities as key drivers of the increase. Furthermore, the gender differences observed in most age groups across populations are concerning in regard to gender equity in healthcare. Finally, our study exemplifies how electronic health records' analysis can lead to actionable interventions that significantly reduce the administration of known DDI and its associated human and economic costs.

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.

Predicting drug-drug interactions in breast cancer patients treated with CDK4/6 inhibitors and forward planning.

INTRODUCTION: Cyclin-dependent kinase (CDK) 4/6 inhibitors are cornerstones in the treatment of Hormone Receptor (HR) positive and Human Epidermal Growth factor (HER2) negative metastatic breast cancer. Given their widespread use in the metastatic setting and emerging use in the adjuvant setting, studying drug-drug interactions (DDI) of these medications is of utmost importance. AREAS COVERED: This review provides key background information on the CDK4/6 inhibitors, palbociclib, ribociclib, and abemaciclib. We discuss drug-drug interactions including those with proton pump inhibitors as well as CYP3A substrates, inhibitors, and inducers. We describe the effect of these drugs on membrane transporters and their substrates as well as those drugs that increase risk of CDK4/6 toxicities. Finally, we explore future directions for strategies to minimize drug-drug interactions. EXPERT OPINION: It is crucial to be mindful of medications that may interfere with drug absorption, such as proton pump inhibitors, as well as those that interfere with drug metabolism, such as CYP3A4 inhibitors and inducers. Additionally, special consideration should be given to populations at higher risk for polypharmacy, such as older patients with greater comorbidities. These interactions and patient characteristics should be considered when developing individual treatment plans with CDK4/6 inhibitors.

A Real-world Toxicity Atlas Shows that Adverse Events of Combination Therapies Commonly Result in Additive Interactions.

PURPOSE: Combination therapies are a promising approach for improving cancer treatment, but it is challenging to predict their resulting adverse events in a real-world setting. EXPERIMENTAL DESIGN: We provide here a proof-of-concept study using 15 million patient records from the FDA Adverse Event Reporting System (FAERS). Complex adverse event frequencies of drugs or their combinations were visualized as heat maps onto a two-dimensional grid. Adverse event frequencies were shown as colors to assess the ratio between individual and combined drug effects. To capture these patterns, we trained a convolutional neural network (CNN) autoencoder using 7,300 single-drug heat maps. In addition, statistical synergy analyses were performed on the basis of BLISS independence or χ2 testing. RESULTS: The trained CNN model was able to decode patterns, showing that adverse events occur in global rather than isolated and unique patterns. Patterns were not likely to be attributed to disease symptoms given their relatively limited contribution to drug-associated adverse events. Pattern recognition was validated using trial data from ClinicalTrials.gov and drug combination data. We examined the adverse event interactions of 140 drug combinations known to be avoided in the clinic and found that near all of them showed additive rather than synergistic interactions, also when assessed statistically. CONCLUSIONS: Our study provides a framework for analyzing adverse events and suggests that adverse drug interactions commonly result in additive effects with a high level of overlap of adverse event patterns. These real-world insights may advance the implementation of new combination therapies in clinical practice.

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.

Application of fluorocarbon nanoparticles of 131I-fulvestrant as a targeted radiation drug for endocrine therapy on human breast cancer.

BACKGROUND: Breast cancer is the most prevalent malignant tumor among women, with hormone receptor-positive cases constituting 70%. Fulvestrant, an antagonist for these receptors, is utilized for advanced metastatic hormone receptor-positive breast cancer. Yet, its inhibitory effect on tumor cells is not strong, and it lacks direct cytotoxicity. Consequently, there's a significant challenge in preventing recurrence and metastasis once cancer cells develop resistance to fulvestrant. METHOD: To address these challenges, we engineered tumor-targeting nanoparticles termed 131I-fulvestrant-ALA-PFP-FA-NPs. This involved labeling fulvestrant with 131I to create 131I-fulvestrant. Subsequently, we incorporated the 131I-fulvestrant and 5-aminolevulinic acid (ALA) into fluorocarbon nanoparticles with folate as the targeting agent. This design facilitates a tri-modal therapeutic approach-endocrine therapy, radiotherapy, and PDT for estrogen receptor-positive breast cancer. RESULTS: Our in vivo and in vitro tests showed that the drug-laden nanoparticles effectively zeroed in on tumors. This targeting efficiency was corroborated using SPECT-CT imaging, confocal microscopy, and small animal fluorescence imaging. The 131I-fulvestrant-ALA-PFP-FA-NPs maintained stability and showcased potent antitumor capabilities due to the synergism of endocrine therapy, radiotherapy, and CR-PDT. Throughout the treatment duration, we detected no notable irregularities in hematological, biochemical, or histological evaluations. CONCLUSION: We've pioneered a nanoparticle system loaded with radioactive isotope 131I, endocrine therapeutic agents, and a photosensitizer precursor. This system offers a combined modality of radiotherapy, endocrine treatment, and PDT for breast cancer.

Pharmacokinetic Effects of Different Models of Nonalcoholic Fatty Liver Disease in Transgenic Humanized OATP1B Mice.

Organic anion transporting polypeptide (OATP) 1B1 and OATP1B3 (collectively, OATP1B) transporters encoded by the solute carrier organic anion transporter (SLCO) genes mediate uptake of multiple pharmaceutical compounds. Nonalcoholic steatohepatitis (NASH), a severe form of nonalcoholic fatty liver disease (NAFLD), decreases OATP1B abundance. This research characterized the pathologic and pharmacokinetics effects of three diet- and one chemical-induced NAFLD model in male and female humanized OATP1B mice, which comprises knock-out of rodent Oatp orthologs and insertion of human SLCO1B1 and SLCO1B3. Histopathology scoring demonstrated elevated steatosis and inflammation scores for all NAFLD-treatment groups. Female mice had minor changes in SLCO1B1 expression in two of the four NAFLD treatment groups, and pitavastatin (PIT) area under the concentration-time curve (AUC) increased in female mice in only one of the diet-induced models. OATP1B3 expression decreased in male and female mice in the chemical-induced NAFLD model, with a coinciding increase in PIT AUC, indicating the chemical-induced model may better replicate changes in OATP1B3 expression and OATP substrate disposition observed in NASH patients. This research also tested a reported multifactorial pharmacokinetic interaction between NAFLD and silymarin, an extract from milk thistle seeds with notable OATP-inhibitory effects. Males showed no change in PIT AUC, whereas female PIT AUC increased 1.55-fold from the diet alone and the 1.88-fold from the combination of diet with silymarin, suggesting that female mice are more sensitive to pharmacokinetic changes than male mice. Overall, the humanized OATP1B model should be used with caution for modeling NAFLD and multifactorial pharmacokinetic interactions. SIGNIFICANCE STATEMENT: Advanced stages of NAFLD cause decreased hepatic OATP1B abundance and increase systemic exposure to OATP substrates in human patients. The humanized OATP1B mouse strain may provide a clinically relevant model to recapitulate these observations and predict pharmacokinetic interactions in NAFLD. This research characterized three diet-induced and one drug-induced NAFLD model in a humanized OATP1B mouse model. Additionally, a multifactorial pharmacokinetic interaction was observed between silymarin and NAFLD.

A case report of drug interaction between co-packaged nirmatrelvir-ritonavir and tacrolimus causing hyponatremia in a lung transplant recipient.

BACKGROUND: Coronavirus disease 2019 (COVID-19) infection in lung transplant recipients can be lethal owing to the use of immunosuppressants. Antiviral agents may be administered to these patients. Co-packaged nirmatrelvir-ritonavir is a new agent currently being used in combination. CASE PRESENTATION: In this report, we present a case of a 64-year-old woman, a lung transplant recipient, who experienced hyponatremia and showed a high serum tacrolimus concentration following the administration of the co-packaged nirmatrelvir-ritonavir combination. CONCLUSION: Although the nirmatrelvir-ritonavir and tacrolimus combination is not contraindicated, other treatment strategies should be considered first, if available, and the dose of tacrolimus should be reduced when using the nirmatrelvir-ritonavir combination. In cases where combination therapy is necessary, serum tacrolimus levels should be closely monitored in lung transplant recipients. Documentation of more such reports is important to identify drug interactions between nirmatrelvir-ritonavir and other agents, with the aim of preventing severe adverse effects.

HIV-TB Coinfection: Current Therapeutic Approaches and Drug Interactions.

The co-occurrence of human immunodeficiency virus (HIV) and tuberculosis (TB) infection poses a significant global health challenge. Treatment of HIV and TB co-infection often necessitates combination therapy involving antiretroviral therapy (ART) for HIV and anti-TB medications, which introduces the potential for drug-drug interactions (DDIs). These interactions can significantly impact treatment outcomes, the efficacy of treatment, safety, and overall patient well-being. This review aims to provide a comprehensive analysis of the DDIs between anti-HIV and anti-TB drugs as well as potential adverse effects resulting from the concomitant use of these medications. Furthermore, such findings may be used to develop personalized therapeutic strategies, dose adjustments, or alternative drug choices to minimize the risk of adverse outcomes and ensure the effective management of HIV and TB co-infection.

Clinical Analysis and in Vitro Correlation of BCRP-Mediated Drug-Drug Interaction in the Gastrointestinal Tract.

Breast cancer resistance protein (BCRP) is a drug efflux transporter expressed on the epithelial cells of the small intestine and on the lateral membrane of the bile duct in the liver; and is involved in the efflux of substrate drugs into the gastrointestinal lumen and secretion into bile. Recently, the area under the plasma concentration-time curve (AUC) of rosuvastatin (ROS), a BCRP substrate drug, has been reported to be increased by BCRP inhibitors, and BCRP-mediated drug-drug interaction (DDI) has attracted attention. In this study, we performed a ROS uptake study using human colon cancer-derived Caco-2 cells and confirmed that BCRP inhibitors significantly increased the intracellular accumulation of ROS. The correlation between the cell to medium (C/M) ratio of ROS obtained by the in vitro study and the absorption rate constant (ka) ratio obtained by clinical analysis was examined, and a significant positive correlation was observed. Therefore, it is suggested that the in vitro study using Caco-2 cells could be used to quantitatively estimate BCRP-mediated DDI with ROS in the gastrointestinal tract.

Risk factors associated with high-dose methotrexate induced toxicities.

INTRODUCTION: High-dose methotrexate (HDMTX) therapy poses challenges in various neoplasms due to individualized pharmacokinetics and associated adverse effects. Our purpose is to identify early risk factors associated with HDMTX-induced toxicities, paving the way for personalized treatment. AREAS COVERED: A systematic review of PubMed and Cochrane databases was conducted for articles from inception to July 2023. Eligible studies included reviews, clinical trials, and real-world analyses. Irrelevant studies were excluded, and manual searches and citation reviews were performed. Factors such as MTX exposure, drug interactions, demographics, serum albumin, urine pH, serum calcium, and genetic polymorphisms affecting MTX transport (e.g. SLCO1B1), intracellular folate metabolism (MTHFR), cell development (ARID5B), metabolic pathways (UGT1A1, PNPLA3), as well as epigenetics were identified. EXPERT OPINION: This comprehensive review aids researchers and clinicians in early identification of HDMTX toxicity risk factors. By understanding the multifaceted risk factors associated with hematologic malignancies, personalized treatment approaches can be tailored to optimize therapeutic outcomes.

Pharmacology and pharmacokinetics of tazemetostat.

Tazemetostat, a novel oral selective inhibitor of enhancer of zeste homolog 2 (EZH2), was approved by the Food and Drug Administration (FDA) in 2020 for use in patients with advanced epithelioid sarcoma or relapsed/refractory (R/R) EZH2-mutated follicular lymphoma. These indications were approved by the FDA trough accelerated approval based on objective response rate and duration of response that resulted from phase 2 clinical trials. Tazemetostat competes with S-adenosylmethionine (SAM) cofactor to inhibit EZH2, reducing the levels of trimethylated lysine 27 of histone 3 (H3K27me3), considered as pharmacodynamic marker. Tazemetostat is orally bioavailable, characterized by rapid absorption and dose-proportional exposure, which is not influenced by coadministration with food or gastric acid reducing agents. It highly distributes in tissues, but with limited access to central nervous system. Tazemetostat is metabolized by CYP3A in the liver to 3 major inactive metabolites (M1, M3, and M5), has a short half-life and is mainly excreted in feces. Drug-drug interactions were shown with moderate CYP3A inhibitors as fluconazole, leading the FDA to recommend a 50% dose reduction, while studies investigating coadministration of tazemetostat with strong inhibitors/inducers are ongoing. No dosage modifications are recommended based on renal or hepatic dysfunctions. Overall, tazemetostat is the first-in-class EZH2 inhibitor approved by the FDA for cancer treatment. Current clinical studies are evaluating combination therapies in patients with several malignancies.

Drug interactions of carbonic anhydrase inhibitors and activators.

INTRODUCTION: Carbonic anhydrases (CAs, EC 4.2.1.1) have been established drug targets for decades, with their inhibitors and activators possessing relevant pharmacological activity and applications in various fields. At least 11 sulfonamides/sulfamates are clinically used as diuretics, antiglaucoma, antiepileptic, or antiobesity agents and one derivative, SLC-0111, is in clinical trials as antitumor/antimetastatic agent. The activators were less investigated with no clinically used agent. AREAS COVERED: Drug interactions between CA inhibitors/activators and various other agents are reviewed in publications from the period March 2020 - January 2024. EXPERT OPINION: Drug interactions involving these agents revealed several interesting findings. Acetazolamide plus loop diuretics is highy effective in acute decompensated heart failure, whereas ocular diseases such as X-linked retinoschisis and macular edema were treated by acetazolamide plus bevacizumab or topical NSAIDs. Potent anti-infective effects of acetazolamide and other CAIs, alone or in combination with other agents were demonstrated for the management of Neisseria gonorrhoea, vancomycin resistant enterococci, Acanthamoeba castellanii, Trichinella spiralis, and Cryptococcus neoformans infections. Topiramate, in combination with phentermine is incresingly used for the management of obesity, whereas zonisamide plus levodopa is highly effective for Parkinson's disease. Acetazolamide, methazolamide, ethoxzolamide, and SLC-0111 showed synergistic antitumor/antimetastatic action in combination with many other antitumor drugs.

A comprehensive review on potential drug-drug interactions of proton pump inhibitors with antidiabetic drugs metformin and DPP-4 inhibitors.

A drug interaction is a condition in which two or more drugs are taken at the same time. Type 2 diabetes mellitus is a significant contributor to polypharmacy. Proton pump inhibitors (PPIs) are often prescribed in combination with metformin or DPP-4 inhibitors (sitagliptin, saxagliptin, linagliptin, and alogliptin) or a combined dose of metformin and DPP-4 inhibitor to treat gastritis in diabetic patients. This review article mainly focused on evaluating the potential drug-drug interactions (DDIs) between PPIs (i.e. esomeprazole, lansoprazole, omeprazole, pantoprazole, and rabeprazole) with metformin and PPIs with DPP-4 inhibitors. The findings demonstrated the existence of pharmacokinetic and pharmacodynamic DDIs between the aforementioned PPIs with metformin and DPP-4 inhibitors, which could impact the biological activities (i.e., hypoglycemia) of these drugs. Moreover, this review suggested that esomeprazole could be the best drug in the PPI group to be prescribed simultaneously with metformin and DPP-4 inhibitors, as most of the antidiabetic drugs of this study did not show any interaction with esomeprazole. The findings of this study also revealed that both antidiabetic drugs and PPIs could have positive interactions as PPIs have the potential to lessen the gastrointestinal side effects of metformin and DPP-4 inhibitors. To achieve the greatest therapeutic impact with the fewest side effects, careful dose control of these drugs is required. So, more extensive research on both human and animal subjects are needed to ascertain the veracity of this hypothesis.

Understanding CYP3A4 and P-gp mediated drug-drug interactions through PBPK modeling - Case example of pralsetinib.

Pralsetinib, a potent and selective inhibitor of oncogenic RET fusion and RET mutant proteins, is a substrate of the drug metabolizing enzyme CYP3A4 and a substrate of the efflux transporter P-gp based on in vitro data. Therefore, its pharmacokinetics (PKs) may be affected by co-administration of potent CYP3A4 inhibitors and inducers, P-gp inhibitors, and combined CYP3A4 and P-gp inhibitors. With the frequent overlap between CYP3A4 and P-gp substrates/inhibitors, pralsetinib is a challenging and representative example of the need to more quantitatively characterize transporter-enzyme interplay. A physiologically-based PK (PBPK) model for pralsetinib was developed to understand the victim drug-drug interaction (DDI) risk for pralsetinib. The key parameters driving the magnitude of pralsetinib DDIs, the P-gp intrinsic clearance and the fraction metabolized by CYP3A4, were determined from PBPK simulations that best captured observed DDIs from three clinical studies. Sensitivity analyses and scenario simulations were also conducted to ensure these key parameters were determined with sound mechanistic rationale based on current knowledge, including the worst-case scenarios. The verified pralsetinib PBPK model was then applied to predict the effect of other inhibitors and inducers on the PKs of pralsetinib. This work highlights the challenges in understanding DDIs when enzyme-transporter interplay occurs, and demonstrates an important strategy for differentiating enzyme/transporter contributions to enable PBPK predictions for untested scenarios and to inform labeling.

Clinical Evaluation of the Effect of Encorafenib on Bupropion, Rosuvastatin, and Coproporphyrin I and Considerations for Statin Coadministration.

BACKGROUND AND OBJECTIVES: Encorafenib is a kinase inhibitor indicated for the treatment of patients with unresectable or metastatic melanoma or metastatic colorectal cancer, respectively, with selected BRAF V600 mutations. A clinical drug-drug interaction (DDI) study was designed to evaluate the effect of encorafenib on rosuvastatin, a sensitive substrate of OATP1B1/3 and breast cancer resistance protein (BCRP), and bupropion, a sensitive CYP2B6 substrate. Coproporphyrin I (CP-I), an endogenous substrate for OATP1B1, was measured in a separate study to deconvolute the mechanism of transporter DDI. METHODS: DDI study participants received a single oral dose of rosuvastatin (10 mg) and bupropion (75 mg) on days - 7, 1, and 14 and continuous doses of encorafenib (450 mg QD) and binimetinib (45 mg BID) starting on day 1. The CP-I data were collected from participants in a phase 3 study who received encorafenib (300 mg QD) and cetuximab (400 mg/m2 initial dose, then 250 mg/m2 QW). Pharmacokinetic and pharmacodynamic analysis was performed using noncompartmental and compartmental methods. RESULTS: Bupropion exposure was not increased, whereas rosuvastatin Cmax and area under the receiver operating characteristic curve (AUC) increased approximately 2.7 and 1.6-fold, respectively, following repeated doses of encorafenib and binimetinib. Increase in CP-I was minimal, suggesting that the primary effect of encorafenib on rosuvastatin is through BCRP. Categorization of statins on the basis of their metabolic and transporter profile suggests pravastatin would have the least potential for interaction when coadministered with encorafenib. CONCLUSION: The results from these clinical studies suggest that encorafenib does not cause clinically relevant CYP2B6 induction or inhibition but is an inhibitor of BCRP and may also inhibit OATP1B1/3 to a lesser extent. Based on these results, it may be necessary to consider switching statins or reducing statin dosage accordingly for coadministration with encorafenib. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov NCT03864042, registered 6 March 2019.

Investigation of pharmacokinetic drug interaction between clesacostat and DGAT2 inhibitor ervogastat in healthy adult participants.

Co-administration of clesacostat (acetyl-CoA carboxylase inhibitor, PF-05221304) and ervogastat (diacylglycerol O-acyltransferase inhibitor, PF-06865571) in laboratory models improved non-alcoholic fatty liver disease (NAFLD)/non-alcoholic steatohepatitis (NASH) end points and mitigated clesacostat-induced elevations in circulating triglycerides. Clesacostat is cleared via organic anion-transporting polypeptide-mediated hepatic uptake and cytochrome P450 family 3A (CYP3A); in vitro clesacostat is identified as a potential CYP3A time-dependent inactivator. In vitro ervogastat is identified as a substrate and potential inducer of CYP3A. Prior to longer-term efficacy trials in participants with NAFLD, safety and pharmacokinetics (PK) were evaluated in a phase I, non-randomized, open-label, fixed-sequence trial in healthy participants. In Cohort 1, participants (n = 7) received clesacostat 15 mg twice daily (b.i.d.) alone (Days 1-7) and co-administered with ervogastat 300 mg b.i.d. (Days 8-14). Mean systemic clesacostat exposures, when co-administered with ervogastat, decreased by 12% and 19%, based on maximum plasma drug concentration and area under the plasma drug concentration-time curve during the dosing interval, respectively. In Cohort 2, participants (n = 9) received ervogastat 300 mg b.i.d. alone (Days 1-7) and co-administered with clesacostat 15 mg b.i.d. (Days 8-14). There were no meaningful differences in systemic ervogastat exposures when administered alone or with clesacostat. Clesacostat 15 mg b.i.d. and ervogastat 300 mg b.i.d. co-administration was overall safe and well tolerated in healthy participants. Cumulative safety and no clinically meaningful PK drug interactions observed in this study supported co-administration of these two novel agents in additional studies exploring efficacy and safety in the management of NAFLD.

A low-cost machine learning framework for predicting drug-drug interactions based on fusion of multiple features and a parameter self-tuning strategy.

Poly-drug therapy is now recognized as a crucial treatment, and the analysis of drug-drug interactions (DDIs) offers substantial theoretical support and guidance for its implementation. Predicting potential DDIs using intelligent algorithms is an emerging approach in pharmacological research. However, the existing supervised models and deep learning-based techniques still have several limitations. This paper proposes a novel DDI analysis and prediction framework called the Multi-View Semi-supervised Graph-based (MVSG) framework, which provides a comprehensive judgment by integrating multiple DDI features and functions without any time-consuming training process. Unlike conventional approaches, MVSG can search for the most suitable similarity (or distance) measurement among DDI data and construct graph structures for each feature. By employing a parameter self-tuning strategy, MVSG fuses multiple graphs according to the contributions of features' information. The actual anticancer drug data are extracted from the authoritative public database for evaluating the effectiveness of our framework, including 904 drugs, 7730 DDI records and 19 types of drug interactions. Validation results indicate that the prediction is more accurate when multiple features are adopted by our framework. In comparison to conventional machine learning techniques, MVSG can achieve higher performance even with less labeled data and without a training process. Finally, MVSG is employed to narrow down the search for potential valuable combinations.