Adverse events related to drug-drug interactions in COVID-19 patients. A persistent concern in the post-pandemic era: a systematic review.

INTRODUCTION: Since COVID-19 patients are often polytreated, monitoring drug-drug interaction (DDIs) is necessary. We evaluated whether drugs used after the second COVID-19 pandemic wave were associated with DDI-related adverse events and the role of drug interaction checkers in identifying them. METHODS: The study (PROSPERO-ID: CRD42024507634) included: 1) consulting the drug interaction checkers Drugs.com, Liverpool COVID-19 Interactions, LexiComp, Medscape, and Micromedex; 2) systematic review; 3) reviewed studies analysis; 4) evaluating drug interaction checkers potential to anticipate DDI-related adverse events.The systematic review was performed searching PubMed, Scopus, ScienceDirect, and Cochrane databases from 1 March 2022 to 11 November 2023. Observational studies, and clinical trials were included. Article without reporting direct association between DDIs and adverse events were excluded. The risk of bias was assessed by Newcastle-Ottawa scale. RESULTS: The most frequent DDIs involved nirmatrelvir/ritonavir (N/R) and fluvoxamine. Fifteen studies, including 150 patients and 35 DDI-related outcomes, were analyzed. The most frequent DDIs involved tacrolimus with N/R, resulting in creatinine increase.Eighty percent of reported DDI-related adverse events would have been identified by all drug-interaction checkers, while the remaining 20% by at least 2 of them. CONCLUSIONS: Drug interaction checkers are useful but show inconsistencies. Multiple sources are needed to tailor treatment in the context of COVID-19.

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/ .

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.

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.

Predictors of potential clinically harmful drug-drug interactions at the medical wards in the University of Benin Teaching Hospital, Benin City.

Drug-drug interactions are preventable causes of adverse events. Different factors have been recognized as important predictors of drug-drug interactions but few studies have addressed these predictors in patients admitted into medical wards of a tertiary hospital in Nigeria hence this study. This was a retrospective study conducted using case records of patients admitted into the medical wards between January 1 and December 31, 2020. Patients were selected using a systematic random sampling method. Socio-demographic details including age, gender, number of comorbidities, and number of medications prescribed and diagnosis were collected on days 1, 3, and at discharge. Potential drug-drug interactions were checked using Lexi-interact® software. Analysis was set at p < 0.05. A total of 430 case records were included in this study based on the inclusion criteria. Lexi-interact recorded a prevalence of (217) 50.5% on day 1, (146) 34.0% on day 3, and (290) 67.4% at discharge. A significant association (p < 0.05) was found between the potential drug-drug interactions (DDI) and an increased number of medicines prescribed on all the days of admission. Also, patients without certain infectious or parasitic diseases have reduced odds of developing DDI. There is a need for continuous monitoring of medications from admission to discharge especially in the elderly, those on multiple medications, certain infectious or parasitic diseases, and comorbidities as these impact on DDIs.

CAT-DTI: cross-attention and Transformer network with domain adaptation for drug-target interaction prediction.

Accurate and efficient prediction of drug-target interaction (DTI) is critical to advance drug development and reduce the cost of drug discovery. Recently, the employment of deep learning methods has enhanced DTI prediction precision and efficacy, but it still encounters several challenges. The first challenge lies in the efficient learning of drug and protein feature representations alongside their interaction features to enhance DTI prediction. Another important challenge is to improve the generalization capability of the DTI model within real-world scenarios. To address these challenges, we propose CAT-DTI, a model based on cross-attention and Transformer, possessing domain adaptation capability. CAT-DTI effectively captures the drug-target interactions while adapting to out-of-distribution data. Specifically, we use a convolution neural network combined with a Transformer to encode the distance relationship between amino acids within protein sequences and employ a cross-attention module to capture the drug-target interaction features. Generalization to new DTI prediction scenarios is achieved by leveraging a conditional domain adversarial network, aligning DTI representations under diverse distributions. Experimental results within in-domain and cross-domain scenarios demonstrate that CAT-DTI model overall improves DTI prediction performance compared with previous methods.

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 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.

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.

A GraphSAGE-based model with fingerprints only to predict drug-drug interactions.

Drugs are an effective way to treat various diseases. Some diseases are so complicated that the effect of a single drug for such diseases is limited, which has led to the emergence of combination drug therapy. The use multiple drugs to treat these diseases can improve the drug efficacy, but it can also bring adverse effects. Thus, it is essential to determine drug-drug interactions (DDIs). Recently, deep learning algorithms have become popular to design DDI prediction models. However, most deep learning-based models need several types of drug properties, inducing the application problems for drugs without these properties. In this study, a new deep learning-based model was designed to predict DDIs. For wide applications, drugs were first represented by commonly used properties, referred to as fingerprint features. Then, these features were perfectly fused with the drug interaction network by a type of graph convolutional network method, GraphSAGE, yielding high-level drug features. The inner product was adopted to score the strength of drug pairs. The model was evaluated by 10-fold cross-validation, resulting in an AUROC of 0.9704 and AUPR of 0.9727. Such performance was better than the previous model which directly used drug fingerprint features and was competitive compared with some other previous models that used more drug properties. Furthermore, the ablation tests indicated the importance of the main parts of the model, and we analyzed the strengths and limitations of a model for drugs with different degrees in the network. This model identified some novel DDIs that may bring expected benefits, such as the combination of PEA and cannabinol that may produce better effects. DDIs that may cause unexpected side effects have also been discovered, such as the combined use of WIN 55,212-2 and cannabinol. These DDIs can provide novel insights for treating complex diseases or avoiding adverse drug events.

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.

Effect of Daridorexant on the Pharmacokinetics of Midazolam, and on the Pharmacokinetics and Pharmacodynamics of Warfarin in Healthy Male Subjects.

BACKGROUND AND OBJECTIVES: Daridorexant, a dual orexin receptor antagonist was recently approved for the treatment of insomnia at doses up to 50 mg once per night. This study investigated the effect of single-dose and multiple-dose daridorexant 50 mg at steady state on the pharmacokinetics (PK) of the cytochrome P450 (CYP) 3A4-sensitive substrate midazolam, and the effect of single-dose daridorexant 50 mg on the PK and pharmacodynamics (PD) of the CYP2C9-sensitive substrate warfarin. METHODS: In this prospective, single-center, open-label, fixed-sequence, phase I, drug-drug interaction study, 18 healthy male subjects sequentially received Treatment A, B, and C in three periods. Treatment A consisted of a single oral concomitant administration of midazolam 2 mg and warfarin 25 mg on day 1 of the first period. Treatment B consisted of one oral administration of daridorexant 50 mg followed 1 h later by a single oral dose of midazolam 2 mg concomitantly with a single oral dose of warfarin 25 mg on day 1 and a once-daily oral administration of daridorexant 50 mg for 6 days of the second period. Treatment C consisted of a single oral administration of daridorexant 50 mg at steady state followed 1 h later by a single oral administration of midazolam 2 mg on day 1 of the third period. Blood samples were assessed for midazolam and S-warfarin PK, and PD (international normalized ratio and factor VII). Noncompartmental  PK parameters and PD variables were evaluated with geometric mean ratios and 90% confidence intervals of Treatment B/A versus C/A for midazolam, and treatment B/A for warfarin. Safety and tolerability of each treatment were also assessed. RESULTS: Midazolam maximum plasma concentration (Cmax) and area under the plasma concentration-time curve from 0 to 24 h (AUC0-24) were 1.13- and 1.42-fold higher, respectively, after single-dose administration of daridorexant 50 mg compared to administration of midazolam alone, while Cmax and AUC0-24 were 1.12- and 1.35-fold higher, respectively, after administration of daridorexant 50 mg once daily at steady state. Terminal half-life and time to maximum plasma concentration were comparable between treatments. Daridorexant had no influence on the PK and PD of warfarin. All treatments were safe and well tolerated. CONCLUSIONS: Daridorexant at 50 mg is classified as a weak CYP3A4 inhibitor after single- and multiple-dose administration once daily at steady state. Daridorexant 50 mg did not induce CYP3A4 activity or inhibit CYP2C9 activity. CLINICAL TRIAL REGISTRATION: This trial (NCT05480488) was registered on 29 July, 2022.

Assessment of the Effects of Abrocitinib on the Pharmacokinetics of Probe Substrates of Cytochrome P450 1A2, 2B6 and 2C19 Enzymes and Hormonal Oral Contraceptives in Healthy Individuals.

BACKGROUND AND OBJECTIVE: Abrocitinib is an oral small-molecule Janus kinase (JAK)-1 inhibitor approved for the treatment of moderate-to-severe atopic dermatitis. In vitro studies indicated that abrocitinib is a weak time-dependent inhibitor of cytochrome P450 (CYP) 2C19/3A and a weak inducer of CYP1A2/2B6/2C19/3A. To assess the potential effect of abrocitinib on concomitant medications, drug-drug interaction (DDI) studies were conducted for abrocitinib with sensitive probe substrates of these CYP enzymes. The impact of abrocitinib on hormonal oral contraceptives (ethinyl estradiol and levonorgestrel), as substrates of CYP3A and important concomitant medications for female patients, was also evaluated. METHODS: Three Phase 1 DDI studies were performed to assess the impact of abrocitinib 200 mg once daily (QD) on the probe substrates of: (1) 1A2 (caffeine), 2B6 (efavirenz) and 2C19 (omeprazole) in a cocktail study; (2) 3A (midazolam); and (3) 3A (oral contraceptives). RESULTS: After multiple doses of abrocitinib 200 mg QD, there is a lack of effect on the pharmacokinetics of midazolam, efavirenz and contraceptives. Abrocitinib increased the area under the concentration time curve from 0 to infinity (AUCinf) and the maximum concentration (Cmax) of omeprazole by approximately 189 and 134%, respectively. Abrocitinib increased the AUCinf of caffeine by 40% with lack of effect on Cmax. CONCLUSIONS: Based on the study results, abrocitinib is a moderate inhibitor of CYP2C19. Caution should be exercised when using abrocitinib concomitantly with narrow therapeutic index medicines that are primarily metabolized by CYP2C19 enzyme. Abrocitinib is a mild inhibitor of CYP1A2; however, the impact is not clinically relevant, and no general dose adjustment is recommended for CYP1A2 substrates. Abrocitinib does not inhibit CYP3A or induce CYP1A2/2B6/2C19/3A and does not affect the pharmacokinetics of contraceptives. CLINICAL TRIALS REGISTRATION: ClinicalTrials.gov registration IDs: NCT03647670, NCT05067439, NCT03662516.

Anti-Calcitonin Gene-Related Peptide Monoclonal Antibodies in Migraine: Focus on Drug Interactions.

Calcitonin gene-related peptide neurotransmission was the target for recent development of monoclonal antibodies that effectively prevent attacks of both episodic and chronic migraine. The aim of this narrative review was to offer deeper insight into drug-drug, drug-food and drug-disease interactions of monoclonal antibodies approved for prevention of migraine attacks. For this narrative review, relevant literature was searched for in MEDLINE and Google Scholar databases, covering the 1966-2023 and 2006-2023 periods, respectively. The ClinicalTrials.gov database was also searched for relevant clinical studies whose results had not been published previously in medical journals, covering 2000-2023. Monoclonal antibodies (erenumab, fremanezumab, galcanezumab and eptinezumab) augment prophylactic action of gepants and onabotulinumtoxin A and somewhat increase efficacy of triptans used to abort migraine attacks; however, their adverse reactions may also be augmented. Pharmacokinetic interactions and interactions in general with drugs used for other indications except migraine are negligible, as are drug-food interactions. However, monoclonal antibodies may worsen diseases with already weakened CGRP neurotransmission, Raynaud phenomenon and constipation. Monoclonal antibodies used for prevention of migraine do not engage in significant pharmacokinetic interactions with other drugs; however, they do engage in pharmacodynamic interactions with other anti-migraine drugs, additively augmenting their prophylactic action, but also increasing frequency and severity of adverse reactions, which are a consequence of the CGRP neurotransmission interruption.

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.