UNASSIGNED: The aim of this review was to provide all the pharmacokinetic data for semaglutide in humans concerning its pharmacokinetics after subcutaneously and oral applications in healthy and diseased populations, to provide recommendations for clinical use.
UNASSIGNED: The PubMed and Embase databases were searched to screen studies associated with the pharmacokinetics of semaglutide. The pharmacokinetic parameters included area under the curve plasma concentrations (AUC), maximal plasma concentration (Cmax), time to Cmax, half-life (t1/2), and clearance. The systematic literature search retrieved 17 articles including data on pharmacokinetic profiles after subcutaneously and oral applications of semaglutide, and at least one of the above pharmacokinetic parameter was reported in all included studies.
UNASSIGNED: Semaglutide has a predictable pharmacokinetic profile with a long t1/2 that allows for once-weekly subcutaneous administration. The AUC and Cmax of both oral and subcutaneous semaglutide increased with dose. Food and various dosing conditions including water volume and dosing schedules can affect the oral semaglutide exposure. There are limited drug-drug interactions and no dosing adjustments in patients with upper gastrointestinal disease, renal impairment or hepatic impairment. Body weight may affect semaglutide exposure, but further studies are needed to confirm this.
UNASSIGNED: This review encompasses all the pharmacokinetic data for subcutaneous and oral semaglutide in both healthy and diseased participants. The existing pharmacokinetic data can assist in developing and evaluating pharmacokinetic models of semaglutide and will help clinicians predict semaglutide dosages. In addition, it can also help optimize future clinical trials.

Venetoclax (VEN) is used in patients with acute myeloid leukemia (AML) and is primarily metabolized by CYP3A4, a major drug-metabolizing enzyme. Patients with AML simultaneously administered VEN and CYP3A4 inhibitors require a more appropriate management of drug-drug interactions (DDIs). Here, we report two cases of patients with AML (54-year-old man and 22-year-old woman) administrated VEN and CYP3A4 inhibitors, such as posaconazole, cyclosporine, or danazol. In the first case, we evaluated the appropriateness of timing for adjusting VEN dosage subsequent to the cessation of posaconazole. Consequently, modifying the VEN dosage in conjunction with the cessation of Posaconazole simultaneously may result in elevated plasma VEN levels. In the second case, plasma VEN concentrations were markedly elevated when co-administered with several CYP3A4 inhibitors. Additionally, in vitro assays were conducted for reverse translational studies to analyze CYP3A4 inhibition. CYP3A4 inhibition by combinatorial administration of cyclosporine A and danazol was demonstrated in vitro, which potentially explains the increasing plasma VEN concentrations observed in clinical settings. Although the acquisition of therapeutic effects is a major priority for patients, frequent therapeutic drug monitoring and dosage adjustments considering DDIs would be important factors in chemotherapy.

The Japanese package insert (J-PI) for nirmatrelvir/ritonavir (N/r) (specially approved pharmaceutical) includes numerous warnings about drug interactions. However, discrepancies in the information on drug interaction are reported between J-PI and foreign databases. This study aimed to evaluate various information sources on N/r drug interactions. We categorized and compared information on N/r drug interactions from the J-PI, prescribing information from foreign regulatory agencies, guidance from the National Institutes of Health and University Health Network, the Ontario coronavirus disease 2019 (COVID-19) Science Advisory Table, University of Liverpool, Lexicomp, and the Japanese Society of Pharmaceutical Health Care and Sciences (JSPHCS). We assessed information quantity, missing data in J-PI, predicted change of the area under the blood concentration-time curve (AUC) for nirmatrelvir or co-administered drugs, and the information source consistency. From these information sources, we compiled a dataset with 115 contraindications and 203 precautions for N/r co-administration, and 51 contraindications are missing in J-PI. Among them, at least 12 drugs have large predicted AUC changes with N/r (AUC ≥5-fold or <1/5 of the baseline value). Nine of these 12 drugs are included as contraindications in Lexicomp and the JSPHCS. The consistency among the information sources is low. Information in the J-PI alone may be insufficient and Lexicomp or the JSPHCS guidelines should be useful because of their large amounts of information and wide coverage of drugs with large AUC changes. Due to low source consistency, multiple sources are needed for clinical management.

Mucosal-delivered drugs have to pass through the mucus layer before absorption through the epithelial cell membrane. Although there has been increasing interest in polymeric mucins, a major structural component of mucus, potentially acting as important physiological regulators of mucosal drug absorption, there are no reports that have systematically evaluated the interaction between mucins and drugs. In this study, we assessed the potential interaction between human polymeric mucins (MUC2, MUC5B, and MUC5AC) and various drugs with different chemical profiles by simple centrifugal method and fluorescence analysis. We found that paclitaxel, rifampicin, and theophylline likely induce the aggregation of MUC5B and/or MUC2. In addition, we showed that the binding affinity of drugs for polymeric mucins varied, not only between individual drugs but also among mucin subtypes. Furthermore, we demonstrated that deletion of MUC5AC and MUC5B in A549 cells increased the cytotoxic effects of cyclosporin A and paclitaxel, likely due to loss of mucin-drug interaction. In conclusion, our results indicate the necessity to determine the binding of drugs to mucins and their potential impact on the mucin network property.

(1) Background: Geriatric patients are at high risk of complications of Coronavirus disease-2019 (COVID-19) and are good candidates for antiviral drugs. (2) Methods: A retrospective study of electronic health records (EHRs) aiming to describe antiviral (nirmatrelvir and ritonavir (nirmatrelvir/r) or remdesivir) use, drug-drug interactions (DDIs) and adverse drug reactions (ADRs) in elderly patients (75 and over), hospitalized with mild-to-moderate COVID-19 between July 2022 and June 2023. (3) Results: Out of 491 patients (mean age: 86.9 years), 180 (36.7%) received nirmatrelvir/r, 78 (15.9%) received remdesivir, and 233 (47.4%) received no antiviral therapy. No association was found between the choice of antiviral and the demographic or medical data. No serious ADR was observed. Nirmatrelvir/r dosage adjustment was inadequate in 65% of patients with renal impairment. In total, 128 patients (71%) on nirmatrelvir/r had potential pharmacokinetic DDIs, with 43 resulting in a possibly related ADR. In the remdesivir group, pharmacodynamic DDIs were more frequent, with QTc prolongation risk in 56 patients (72%). Only 20 patients underwent follow-up ECG, revealing QTc prolongation in 4. (4) Conclusions: There is an underutilization of antivirals despite their justified indications. Nirmatrelvir/r dosage was rarely adjusted to renal function. Dose adjustments and closer monitoring are needed due to the high risk of drug interactions.

OBJECTIVE: Large Language Models (LLMs) like Generative Pre-trained Transformer (GPT) from OpenAI and LLaMA (Large Language Model Meta AI) from Meta AI are increasingly recognized for their potential in the field of cheminformatics, particularly in understanding Simplified Molecular Input Line Entry System (SMILES), a standard method for representing chemical structures. These LLMs also have the ability to decode SMILES strings into vector representations.
METHODS: We investigate the performance of GPT and LLaMA compared to pre-trained models on SMILES in embedding SMILES strings on downstream tasks, focusing on two key applications: molecular property prediction and drug-drug interaction prediction.
RESULTS: We find that SMILES embeddings generated using LLaMA outperform those from GPT in both molecular property and DDI prediction tasks. Notably, LLaMA-based SMILES embeddings show results comparable to pre-trained models on SMILES in molecular prediction tasks and outperform the pre-trained models for the DDI prediction tasks.
CONCLUSIONS: The performance of LLMs in generating SMILES embeddings shows great potential for further investigation of these models for molecular embedding. We hope our study bridges the gap between LLMs and molecular embedding, motivating additional research into the potential of LLMs in the molecular representation field. GitHub: https://github.com/sshaghayeghs/LLaMA-VS-GPT .

Unknown interactions between drugs remain the limiting factor for clinical application of drugs, and the induction and inhibition of drug-metabolizing CYP enzymes are considered the key to examining the drug-drug interaction (DDI). In this study, using human HepaRG cells as an in vitro model system, we analyzed the potential DDI based on the expression levels of CYP3A4 and CYP1A2. Rifampicin and omeprazole, the potent inducers for CYP3A4 and CYP1A2, respectively, induce expression of the corresponding CYP enzymes at both the mRNA and protein levels. We noticed that, in addition to inducing CYP1A2, omeprazole induced CYP3A4 mRNA expression in HepaRG cells. However, unexpectedly, CYP3A4 protein expression levels were not increased after omeprazole treatment. Concurrent administration of rifampicin and omeprazole showed an inhibitory effect of omeprazole on the CYP3A4 protein expression induced by rifampicin, while its mRNA induction remained intact. Cycloheximide chase assay revealed increased CYP3A4 protein degradation in the cells exposed to omeprazole. The data presented here suggest the potential importance of broadening the current DDI examination beyond conventional transcriptional induction and enzyme-activity inhibition tests to include post-translational regulation analysis of CYP enzyme expression.

Physiologically based pharmacokinetic (PBPK) models were utilized to investigate potential interactions between aflatoxin B1 (AFB1) and efavirenz (EFV), a non-nucleoside reverse transcriptase inhibitor drug and inducer of several CYP enzymes, including CYP3A4. PBPK simulations were conducted in a North European Caucasian and Black South African population, considering different dosing scenarios. The simulations predicted the impact of EFV on AFB1 metabolism via CYP3A4 and CYP1A2. In vitro experiments using human liver microsomes (HLM) were performed to verify the PBPK predictions for both single- and multiple-dose exposures to EFV. Results showed no significant difference in the formation of AFB1 metabolites when combined with EFV (0.15 µM) compared to AFB1 alone. However, exposure to 5 µM of EFV, mimicking chronic exposure, resulted in increased CYP3A4 activity, affecting metabolite formation. While co-incubation with EFV reduced the formation of certain AFB1 metabolites, other outcomes varied and could not be fully attributed to CYP3A4 induction. Overall, this study provides evidence that EFV, and potentially other CYP1A2/CYP3A4 perpetrators, can impact AFB1 metabolism, leading to altered exposure to toxic metabolites. The results emphasize the importance of considering drug interactions when assessing the risks associated with mycotoxin exposure in individuals undergoing HIV therapy in a European and African context.

UNASSIGNED: Few real-world studies have investigated drug-drug interactions (DDIs) involving non-vitamin-K antagonist oral anticoagulants (NOACs) in patients with nonvalvular atrial fibrillation (NVAF). The interactions encompass drugs inducing or inhibiting cytochrome P450 3A4 and permeability glycoprotein. These agents potentially modulate the breakdown and elimination of NOACs. This study investigated the impact of DDIs on thromboembolism in this clinical scenario.
UNASSIGNED: Patients who had NVAF and were treated with NOACs were selected as the study cohort from the National Health Insurance Research Database of Taiwan. Cases were defined as patients hospitalised for a thromboembolic event and who underwent a relevant imaging study within 7 days before hospitalisa-tion or during hospitalisation. Each case was matched with up to 4 controls by using the incidence density sampling method. The concurrent use of a cytochrome P450 3A4/permeability glycoprotein inducer or inhibitor or both with NOACs was identified. The effects of these interactions on the risk of thromboembolic events were examined with univariate and multivariate conditional logistic regressions.
UNASSIGNED: The study cohort comprised 60,726 eligible patients. Among them, 1288 patients with a thromboembolic event and 5144 matched control patients were selected for analysis. The concurrent use of a cytochrome P450 3A4/permeability glycoprotein inducer resulted in a higher risk of thromboembolic events (adjusted odds ratio [AOR] 1.23, 95% confidence interval [CI] 1.004-1.51).
UNASSIGNED: For patients with NVAF receiving NOACs, the concurrent use of cytochrome P450 3A4/ permeability glycoprotein inducers increases the risk of thromboembolic events.

BACKGROUND: Polypharmacy in older adults with cancer receiving chemotherapy leads to increased risks of drug interactions, translating in potential hazardous health outcomes. This study aims to assess the prevalence of polypharmacy, drug-drug interactions (DDIs), and severe-drug interactions (SDIs) in older patients with cancer. Antineoplastic agents (ANAs) involvement and possible risk contexts (comorbidities with cardiac risk, and high-risk medications) were also analysed.
METHODS: Observational study with older adults (≥ 65 years) diagnosed with cancer, who were treated with antineoplastic agents (ANAs); it was conducted in three hospitals from the north of Portugal. Data collection was obtained using self-reports and medical records. DDIs were identified and classified using Micromedex® software. Descriptive and association analyze statistics were performed. Statistical hypothesis tests with p value less than 0.05 were considered significant. All statistical procedures and analysis were performed with R version 4.1.3.
RESULTS: We enrolled 552 patients. Polypharmacy prevalence was 88.40%; 76.45% and 56.16% of the patients presented with DDIs and SDIs, respectively. SDIs with ANAs were found in 21.20% of the patients. High-risk medications were associated with a higher risk of polypharmacy, DDIs, and SDIs. Polypharmacy and DDIs were higher in patients with hypertension or diabetes. SDIs were higher in patients with diabetes.
CONCLUSIONS: Polypharmacy, potential DDIs and SDIs were highly prevalent in older adults with cancer. A careful review of the medication administered is necessary to decrease it. These findings warrant further research to optimize medication in this population and decrease problems related to medication, which may lead to emergency room visits and hospitalisations, compromising patient safety and/or ongoing treatments.