Evaluation of the Drug-Drug Interaction Potential of Ensitrelvir Fumaric Acid with Cytochrome P450 3A Substrates in Healthy Japanese Adults.

BACKGROUND: Management of drug-drug interactions (DDIs) for ensitrelvir, a novel 3-chymotrypsin-like protease inhibitor of SARS-CoV-2 infection is crucial. A previous clinical DDI study of ensitrelvir with midazolam, a clinical index cytochrome P450 (CYP) 3A substrate, demonstrated that ensitrelvir given for 5 days orally with a loading/maintenance dose of 750/250 mg acted as a strong CYP3A inhibitor. OBJECTIVES: The objectives of this study were to investigate the effect of ensitrelvir on the pharmacokinetics of CYP3A substrates, dexamethasone, prednisolone and midazolam, and to assess the pharmacokinetics, safety, and tolerability of ensitrelvir following multiple-dose administration of ensitrelvir. METHODS: This was a Phase 1, multicenter, single-arm, open-label study in healthy Japanese adult participants. The effects of multiple doses of ensitrelvir in the fasted state on the pharmacokinetics of dexamethasone, prednisolone, and midazolam were investigated. Ensitrelvir was administered from Day 1 through Day 5, with a loading/maintenance dose of 750/250 mg for the dexamethasone and prednisolone cohorts whereas 375/125 mg for the midazolam cohort. Either dexamethasone, prednisolone, or midazolam was administered alone (Day - 2) or in combination with ensitrelvir (Day 5) in each of the cohorts. Additionally, dexamethasone or prednisolone was administered on Days 9 and 14. The pharmacokinetic parameters of ensitrelvir, dexamethasone, prednisolone, and midazolam were calculated based on their plasma concentration data with non-compartmental analysis. In safety assessments, the nature, frequency, and severity of treatment-emergent adverse events were evaluated and recorded. RESULTS: The area under the concentration-time curve (AUC) ratio of dexamethasone on Day 5 was 3.47-fold compared with the corresponding values for dexamethasone alone on Day - 2 and the effect diminished over time after the last dose of ensitrelvir. No clinically meaningful effect was observed for prednisolone. The AUC ratio of midazolam was 6.77-fold with ensitrelvir 375/125 mg suggesting ensitrelvir at 375/125 mg strongly inhibits CYP3A similar to that at 750/250 mg. No new safety signals with ensitrelvir were reported during the study. CONCLUSION: The inhibitory effect for CYP3A was confirmed after the last dose of ensitrelvir, and the effect diminished over time. In addition, ensitrelvir at 375/125 mg showed CYP3A inhibitory potential similar to that at 750/250 mg. These findings can be used as a clinical recommendation for prescribing ensitrelvir with regard to concomitant medications. CLINICAL TRIAL REGISTRATION: Japan Registry of Clinical Trials identifier: jRCT2031210202.

Results From a Randomized, Open-Label, Crossover Study Evaluating the Effect of the Aldosterone Synthase Inhibitor Baxdrostat on the Pharmacokinetics of Metformin in Healthy Human Subjects.

BACKGROUND: Due to the high comorbidity of diabetes and hypertension, co-administration of metformin with anti-hypertensive drugs is likely. Baxdrostat is an aldosterone synthase inhibitor in development for the potential treatment of hypertension. In vitro data indicated that baxdrostat inhibits the multidrug and toxin extrusion 1 (MATE1) and MATE2-K renal transporters. Metformin is a MATE substrate, so this study assessed potential effects of baxdrostat on the pharmacokinetics of metformin. METHODS: Twenty-seven healthy volunteers received 1000 mg metformin alone and 1000 mg metformin in the presence of 10 mg baxdrostat in a randomized, crossover manner. Each treatment was separated by 10 or more days. Blood and urine samples were collected over a 3-day period after each treatment to measure plasma and urine concentrations of metformin. Safety was assessed by adverse events (AEs), physical examinations, electrocardiograms, vital signs, and clinical laboratory evaluations. RESULTS: There were no deaths, serious AEs, discontinuations due to treatment-emergent AEs, or noteworthy increases in AEs with either treatment, indicating that metformin and baxdrostat were well-tolerated when co-administered. Baxdrostat did not significantly affect plasma concentrations or renal clearance of metformin. CONCLUSION: The results of this study suggest that diabetic patients with hypertension receiving both metformin and baxdrostat are unlikely to require dose adjustment. REGISTRATION: ClinicalTrials.gov identifier no. NCT05526690.

Role of Cytochrome P450 2C9 in COVID-19 Treatment: Current Status and Future Directions.

The major human liver drug metabolising cytochrome P450 (CYP) enzymes are downregulated during inflammation and infectious disease state, especially during coronavirus disease 2019 (COVID-19) infection. The influx of proinflammatory cytokines, known as a 'cytokine storm', during severe COVID-19 leads to the downregulation of CYPs and triggers new cytokine release, which further dampens CYP expression. Impaired drug metabolism, along with the inevitable co-administration of drugs or 'combination therapy' in patients with COVID-19 with various comorbidities, could cause drug-drug interactions, thus worsening the disease condition. Genetic variability or polymorphism in CYP2C9 across different ethnicities could contribute to COVID-19 susceptibility. A number of drugs used in patients with COVID-19 are inducers or inhibitors of, or are metabolised by, CYP2C9, and co-administration might cause pharmacokinetic and pharmacodynamic interactions. It is also worth mentioning that some of the COVID-19 drug interactions are due to altered activity of other CYPs including CYP3A4. Isoniazid/rifampin for COVID-19 and tuberculosis co-infection; lopinavir/ritonavir and cobicistat/remdesivir combination therapy; or multi-drug therapy including ivermectin, azithromycin, montelukast and acetylsalicylic acid, known as TNR4 therapy, all improved recovery in patients with COVID-19. However, a combination of CYP2C9 inducers, inhibitors or both, and plausibly different CYP isoforms could lead to treatment failure, hepatotoxicity or serious side effects including thromboembolism or bleeding, as observed in the combined use of azithromycin/warfarin. Further, herbs that are CYP2C9 inducers and inhibitors, showed anti-COVID-19 properties, and in silico predictions postulated that phytochemical compounds could inhibit SARS-CoV-2 virus particles. COVID-19 vaccines elicit immune responses that activate cytokine release, which in turn suppresses CYP expression that could be the source of compromised CYP2C9 drug metabolism and the subsequent drug-drug interaction. Future studies are recommended to determine CYP regulation in COVID-19, while recognising the involvement of CYP2C9 and possibly utilising CYP2C9 as a target gene to tackle the ever-mutating SARS-CoV-2.

Predicting activatory and inhibitory drug-target interactions based on structural compound representations and genetically perturbed transcriptomes.

A computational approach to identifying drug-target interactions (DTIs) is a credible strategy for accelerating drug development and understanding the mechanisms of action of small molecules. However, current methods to predict DTIs have mainly focused on identifying simple interactions, requiring further experiments to understand mechanism of drug. Here, we propose AI-DTI, a novel method that predicts activatory and inhibitory DTIs by combining the mol2vec and genetically perturbed transcriptomes. We trained the model on large-scale DTIs with MoA and found that our model outperformed a previous model that predicted activatory and inhibitory DTIs. Data augmentation of target feature vectors enabled the model to predict DTIs for a wide druggable targets. Our method achieved substantial performance in an independent dataset where the target was unseen in the training set and a high-throughput screening dataset where positive and negative samples were explicitly defined. Also, our method successfully rediscovered approximately half of the DTIs for drugs used in the treatment of COVID-19. These results indicate that AI-DTI is a practically useful tool for guiding drug discovery processes and generating plausible hypotheses that can reveal unknown mechanisms of drug action.

Evaluation of Drug-Drug Interactions of Ensitrelvir, a SARS-CoV-2 3CL Protease Inhibitor, With Transporter Substrates Based on In Vitro and Clinical Studies.

Drug-drug interaction potentials of ensitrelvir, a novel oral inhibitor of 3C-like protease of severe acute respiratory syndrome coronavirus 2, for drug transporters were evaluated by in vitro and clinical studies. The target drug transporters assessed were P-glycoprotein (P-gp), breast cancer resistance protein (BCRP), organic anion transporting polypeptide (OATP) 1B1, OATP1B3, organic anion transporter (OAT) 1, OAT3, organic cation transporter (OCT) 1, OCT2, and multidrug and toxin extrusion 1 and 2K. In vitro study revealed that ensitrelvir is a substrate for P-gp and BCRP and inhibits P-gp, BCRP, OATP1B1, OATP1B3, OCT1, and OAT3. Based on these results, a clinical drug-drug interaction study to evaluate the effect of ensitrelvir on the pharmacokinetics of P-gp, BCRP, OATP1B1, OATP1B3, and OCT1 substrates was conducted with a cocktail approach using digoxin (P-gp substrate), rosuvastatin (BCRP, OATP1B1, and OATP1B3 substrate), and metformin (OCT1 substrate). The cocktail was administered first, and after the washout period, the cocktail was coadministered with 500 mg of ensitrelvir. No treatment-emergent adverse events were observed. Pharmacokinetic analyses demonstrated that the ratios (90% confidence intervals) of "cocktail with ensitrelvir" to "cocktail without ensitrelvir" for maximum plasma concentration and area under the plasma concentration-time curve were, respectively, 2.17 (1.72-2.73) and 1.31 (1.13-1.52) for digoxin, 1.97 (1.73-2.25) and 1.65 (1.47-1.84) for rosuvastatin, and 1.03 (0.91-1.16) and 1.02 (0.94-1.11) for metformin. The results indicate that the exposure levels of digoxin and rosuvastatin increased when coadministered with ensitrelvir, but those of metformin were not changed. In conclusion, ensitrelvir has an impact on the exposure levels of P-gp, BCRP, OATP1B1, and OATP1B3 substrates.

Adaptogens on Depression-Related Outcomes: A Systematic Integrative Review and Rationale of Synergism with Physical Activity.

Depression is considered the most important disorder affecting mental health. The aim of this systematic integrative review was: (i) to describe the effects of supplementation with adaptogens on variables related to depression in adults; and (ii) to discuss the potential combination with physical exercise to aid planning and commissioning future clinical research. An integrative review was developed complementing the Preferred Reporting Items for Systematic reviews and Meta-Analyses statement (PROSPERO registration: CRD42021249682). A total of 41 articles met the inclusion criteria. With a Price index of 46.4%, we found that: (i) Hypericum perforatum (St. John's Wort) is the most studied and supported adaptogen (17/41 [41.46%], three systematic reviews with meta-analysis) followed by Crocus sativus L. or saffron (6/41 [14.63%], three systematic reviews with meta-analysis and two systematic reviews); (ii) it is possible that the significantly better performance of adaptogens over placebo is due to the reduction of allostatic load via the action of secondary metabolites on BDNF regulation; and, (iii) the number of studies reporting physical activity levels is limited or null for those that combine an exercise program with the consumption of adaptogens. Aware of the need for a multidisciplinary approach for depression treatment, this systematic integrative review provides an up-to-date view for supporting the use of St. John's Wort and saffron as non-pharmacological strategies while also help commissioning future research on the efficacy of other adaptogens. It also contributes to the design of future clinical research studies that evaluate the consumption of herbal extracts plus physical exercise, mainly resistance training, as a potentially safe and powerful strategy to treat depression.

Prevalence of Potential Drug-drug Interactions With Ritonavir-containing COVID-19 Therapy in the United States: An Analysis of the National Health and Nutrition Examination Survey.

PURPOSE: The evolving epidemiology and treatment landscape of COVID-19 necessitates research into potential drug-drug interactions (pDDIs) from the use of new treatments for COVID-19, particularly those that contain ritonavir, a potent inhibitor of the cytochrome P350 3A4 (CYP3A4) metabolic pathway. In this study, we assessed the prevalence of pDDIs between medications for chronic conditions metabolized through the CYP3A4 metabolic pathway and ritonavir-containing COVID-19 medications in the US general population. METHODS: This study combined National Health and Nutrition Examination Survey (NHANES) waves 2015 to 2016 and 2017 to March 2020 to observe pDDI prevalence between ritonavir-containing therapy and coadministered medications among US adults 18 years or older. CYP3A4-mediated medications were identified from affirmative medication questionnaire response and associated prescription examination by surveyors. CYP3A4-mediated medications with associated pDDIs with ritonavir and assessed pDDI severity (minor, major, moderate, and severe) were obtained from the University of Liverpool's COVID-19 online drug interaction checker, Lexicomp, and US Food and Drug Administration fact sheets. pDDI prevalence and severity were evaluated by demographic characteristics and COVID-19 risk factors. FINDINGS: A total of 15,685 adult participants were identified during the 2015 to 2020 NHANES waves. Survey participants used a mean (SD) of 2.7 (1.8) drugs with likelihood of a pDDI. The weighted prevalence of major to contraindicated pDDIs among the US population was 29.3%. Prevalence rates among those 60 years and older, with serious heart conditions, with moderate chronic kidney disease (CKD), with severe CKD, with diabetes, and with HIV were 60.2%, 80.7%, 73.9%, 69.5%, 63.4%, and 68.5%, respectively. Results remained largely unchanged after removal of statins from the list of drugs associated with ritonavir-based pDDIs. IMPLICATIONS: Approximately one-third of the US population would be at risk for a major or contraindicated pDDI should they receive a ritonavir-containing regimen, and this risk increases significantly among individuals 60 years or older and with comorbidities such as serious heart conditions, CKD, diabetes, and HIV. The state of polypharmacy in the US population and the quickly changing COVID-19 landscape indicate significant risk of pDDIs among those requiring treatment with ritonavir-containing COVID-19 medications. Practitioners should take polypharmacy, age, and comorbidity profile into account when prescribing COVID-19 therapies. Alternative treatment regimens should be considered, especially for those of older age and those with risk factors for progression to severe COVID-19.

Prevalence of potential drug-drug interactions with ritonavir-containing COVID-19 therapy.

BACKGROUND: Patients with COVID-19 receiving ritonavir-containing therapies are at risk of potential drug-drug interactions (pDDIs) because of ritonavir's effects on cytochrome P450 3A4. OBJECTIVE: To assess the prevalence of pDDIs with ritonavir-containing COVID-19 therapy in adults with COVID-19 using the Optum Clinformatics Data Mart database. METHODS: In this retrospective, observational cohort study, patients with COVID-19 aged 18 years or older prescribed cytochrome P450 3A4-mediated medications with supply days overlapping the date of COVID-19 diagnosis between January 1, 2020, and June 30, 2021, were classified as having pDDIs. pDDI was classified as contraindicated, major, moderate, or mild using established drug interaction resources. Prevalence of pDDIs with ritonavir-containing COVID-19 therapy was estimated for the entire cohort and in patient groups with high risk of severe COVID-19 progression or pDDIs. Actual COVID-19 treatments received by the patients, if any, were not considered. Outcomes were presented descriptively without adjusted comparisons. RESULTS: A total of 718,387 patients with COVID-19 were identified. The age-sex standardized national prevalence of pDDIs of any severity was estimated at 52.2%. Approximately 34.5% were at risk of contraindicated or major pDDIs. Compared with patients without pDDI, patients exposed to pDDIs were older and more likely to be female, reside in long-term care facilities, and have risk factors for progression to severe COVID-19. Higher prevalence of major/contraindicated pDDIs was observed in older patients (76.1%), female patients (65.0%), and patients with multiple morbidities (84.6%). CONCLUSIONS: Study findings demonstrate that more than one-third of patients with COVID-19 were at risk of significant pDDIs if treated with ritonavir-containing COVID-19 therapy and highlight the need to assess all patients with COVID-19 for pDDIs. Ritonavir-based therapies may not be appropriate for certain patient groups, and alternative therapies should be considered. DISCLOSURES: Drs Igho-Osagie, Puenpatom, and Grifasi Williams are employees of Merck Sharp & Dohme LLC, a subsidiary of Merck & Co., Inc. Dr Song and Ms He are employees of Analysis Group, Inc., and served as paid consultants for Merck Sharp & Dohme LLC, a subsidiary of Merck & Co., Inc. Drs Yi and Wang, and Mr Berman, and Ms Gu were employees of Analysis Group, Inc., at the time of study conduct. Financial support for this study was provided by Merck Sharp & Dohme LLC, a subsidiary of Merck & Co., Inc. The study sponsor was involved in the design and conduct of the study; collection, management, analysis, interpretation of data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

DRaW: prediction of COVID-19 antivirals by deep learning-an objection on using matrix factorization.

BACKGROUND: Due to the high resource consumption of introducing a new drug, drug repurposing plays an essential role in drug discovery. To do this, researchers examine the current drug-target interaction (DTI) to predict new interactions for the approved drugs. Matrix factorization methods have much attention and utilization in DTIs. However, they suffer from some drawbacks. METHODS: We explain why matrix factorization is not the best for DTI prediction. Then, we propose a deep learning model (DRaW) to predict DTIs without having input data leakage. We compare our model with several matrix factorization methods and a deep model on three COVID-19 datasets. In addition, to ensure the validation of DRaW, we evaluate it on benchmark datasets. Furthermore, as an external validation, we conduct a docking study on the COVID-19 recommended drugs. RESULTS: In all cases, the results confirm that DRaW outperforms matrix factorization and deep models. The docking results approve the top-ranked recommended drugs for COVID-19. CONCLUSIONS: In this paper, we show that it may not be the best choice to use matrix factorization in the DTI prediction. Matrix factorization methods suffer from some intrinsic issues, e.g., sparsity in the domain of bioinformatics applications and fixed-unchanged size of the matrix-related paradigm. Therefore, we propose an alternative method (DRaW) that uses feature vectors rather than matrix factorization and demonstrates better performance than other famous methods on three COVID-19 and four benchmark datasets.

Probing the Neuro-psychological Changes Observed with the Administration of COVID-19 Drugs.

The COVID-19 virus caused countless significant alterations in the human race, the most challenging of which was respiratory and neurological disorders. Several studies were conducted to find a robust therapy for the virus, which led to a slew of additional health issues. This study aims to understand the changes in the neurological system brought about by COVID-19 drugs and highlights the drug-drug interaction between COVID-19 drugs and psychiatric drugs. Alongside this, the study focuses on the neuropsychological changes in three critical mental disorders, such as schizophrenia, Alzheimer's disease, and Parkinson's disease. The comprehensive and narrative review being performed in this paper, has brought together the relevant work done on the association of COVID-19 drugs and changes in the neurological system. For this study, a systematic search was performed on several databases such as PubMed, Scopus, and Web of Science. This study also consolidates shreds of evidence about the challenges confronted by patients having disorders like Schizophrenia, Alzheimer's disease, and Parkinson's disease. This review is based on the studies done on COVID-19 drugs from mid-2020 to date. We have identified some scopes of crucial future opportunities which could add more depth to the current knowledge on the association of COVID- 19 drugs and the changes in the neurological system. This study may present scope for future work to investigate the pathophysiological changes of these disorders due to COVID-19.

A pharmacokinetic drug-drug interaction study between rosuvastatin and emvododstat, a potent anti-SARS-CoV-2 (COVID-19) DHODH (dihydroorotate dehydrogenase) inhibitor.

A therapeutic agent that targets both viral replication and the hyper-reactive immune response would offer a highly desirable treatment for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; COVID-19) management. Emvododstat (PTC299) was found to be a potent inhibitor of immunomodulatory and inflammation-related processes by the inhibition of dihydroorotate dehydrogenase (DHODH) to reduce SARS-CoV-2 replication. DHODH is the rate-limiting enzyme of the de novo pyrimidine nucleotide biosynthesis pathway. This drug interaction study was performed to determine whether emvododstat was an inhibitor of breast cancer resistance protein (BCRP) transporters in humans. Potential drug-drug interactions (DDIs) between emvododstat and a BCRP transporter substrate (rosuvastatin) were investigated by measuring plasma rosuvastatin concentrations before and after emvododstat administration. There was no apparent difference in rosuvastatin plasma exposure. The geometric means of maximum plasma rosuvastatin concentrations (Cmax ) were 4369 (rosuvastatin) and 5141 pg/mL (rosuvastatin + emvododstat) at 4 h postdose. Geometric mean rosuvastatin area under the concentration-time curve (AUC) from time 0 to the last measurable plasma concentration was 45 616 and 48 975 h·pg/mL when administered alone and after 7 days of b.i.d. emvododstat dosing, respectively. Geometric least squares mean ratios for Cmax and AUC were approximately equal to 1. Overall, administration of multiple doses of 100 mg emvododstat b.i.d. for 7 days in combination with a single dose of rosuvastatin was safe and well tolerated. Emvododstat can be safely administered with other BCRP substrate drugs. Hence, pharmacokinetic DDI mediated via BCRP inhibition is not expected when emvododstat and BCRP substrates are coadministered.

The inhibitory and inducing effects of ritonavir on hepatic and intestinal CYP3A and other drug-handling proteins.

Ritonavir, originally developed as HIV protease inhibitor, is widely used as a booster in several HIV pharmacotherapy regimens and more recently in Covid-19 treatment (e.g., Paxlovid). Its boosting capacity is due to the highly potent irreversible inhibition of the cytochrome P450 (CYP) 3 A enzyme, thereby enhancing the plasma exposure to coadministered drugs metabolized by CYP3A. Typically used booster doses of ritonavir are 100-200 mg once or twice daily. This review aims to address several aspects of this booster drug, including the possibility to use lower ritonavir doses, 20 mg for instance, resulting in partial CYP3A inactivation in patients. If complete CYP3A inhibition is not needed, lower ritonavir doses could be used, thereby reducing unwanted side effects. In this context, there are contradictory reports on the actual recovery time of CYP3A activity after ritonavir discontinuation, but probably this will take at least one day. In addition to ritonavir's CYP3A inhibitory effect, it can also induce and/or inhibit other CYP enzymes and drug transporters, albeit to a lesser extent. Although ritonavir thus exhibits gene induction capacities, with respect to CYP3A activity the inhibition capacity clearly predominates. Another potent CYP3A inhibitor, the ritonavir analog cobicistat, has been reported to lack the ability to induce enzyme and transporter genes. This might result in a more favorable drug-drug interaction profile compared to ritonavir, although the actual benefit appears to be limited. Indeed, ritonavir is still the clinically most used pharmacokinetic enhancer, indicating that its side effects are well manageable, even in chronic administration regimens.

Understanding the Risk of Drug Interactions Between Ritonavir-Containing COVID-19 Therapies and Small-Molecule Kinase Inhibitors in Patients With Cancer.

PURPOSE: The introduction of COVID-19 therapies containing ritonavir has markedly expanded the scope of use for this medicine. As a strong cytochrome P450 3A4 inhibitor, the use of ritonavir is associated with a high drug interaction risk. There are currently no data to inform clinician regarding the likely magnitude and duration of interaction between ritonavir-containing COVID-19 therapies and small-molecule kinase inhibitors (KIs) in patients with cancer. METHODS: Physiologically based pharmacokinetic modeling was used to conduct virtual clinical trials with a parallel group study design in the presence and absence of ritonavir (100 mg twice daily for 5 days). The magnitude and time course of changes in KI exposure when coadministered with ritonavir was evaluated as the primary outcome. RESULTS: Dosing of ritonavir resulted in a > 2-fold increase in steady-state area under the plasma concentration-time curve and maximal concentration for six of the 10 KIs. When the KI was coadministered with ritonavir, dose reductions to between 10% and 75% of the original dose were required to achieve an area under the plasma concentration-time curve within 1.25-fold of the value in the absence of ritonavir. CONCLUSION: To our knowledge, this study provides the first data to assist clinicians' understanding of the drug interaction risk associated with administering ritonavir-containing COVID-19 therapies to patients with cancer who are currently being treated with KIs. These data may support clinicians to make more informed dosing decisions for patients with cancer undergoing treatment with KIs who require treatment with ritonavir-containing COVID-19 antiviral therapies.

Tacrolimus Drug-Drug Interaction with Nirmatrelvir/Ritonavir (Paxlovid™) Managed with Phenytoin.

INTRODUCTION: The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 or COVID-19) pandemic has had a significant impact on communities and health systems. The Federal Drug Administration (FDA) authorized Pfizer's nirmatrelvir/ritonavir (Paxlovid™) through an EUA for the treatment of mild to moderate cases of COVID-19 at high risk for progression to severe disease. Patients with a history of transplant who test positive for COVID-19 are considered high risk because of their immunosuppression and are therefore candidates for nirmatrelvir/ritonavir. CASE REPORT: This is a case of a 67-year-old female with a past medical history of orthotopic heart transplant who received tacrolimus as part of her immunosuppressive regimen. She originally presented with complaints of dyspnea and cough for several days in the setting of COVID-19. The patient was started on nirmatrelvir/ritonavir due to her high risk for progression to severe disease. Four days after starting nirmatrelvir/ritonavir, she presented to the ED for slowed speech, fatigue, weakness, and loss of appetite. Upon admission she was found to have a supratherapeutic tacrolimus level of 176.4 ng/mL and an acute kidney injury. In this case, phenytoin was used as a CYP3A4 inducer to quickly decrease the tacrolimus level to within therapeutic range. CONCLUSION: This case highlights the strong and important drug-drug interaction between tacrolimus and nirmatrelvir/ritonavir leading to toxic levels of tacrolimus. It also demonstrates the utility and effectiveness of phenytoin as a "rescue" medication for tacrolimus toxicity.

A dual graph neural network for drug-drug interactions prediction based on molecular structure and interactions.

Expressive molecular representation plays critical roles in researching drug design, while effective methods are beneficial to learning molecular representations and solving related problems in drug discovery, especially for drug-drug interactions (DDIs) prediction. Recently, a lot of work has been put forward using graph neural networks (GNNs) to forecast DDIs and learn molecular representations. However, under the current GNNs structure, the majority of approaches learn drug molecular representation from one-dimensional string or two-dimensional molecular graph structure, while the interaction information between chemical substructure remains rarely explored, and it is neglected to identify key substructures that contribute significantly to the DDIs prediction. Therefore, we proposed a dual graph neural network named DGNN-DDI to learn drug molecular features by using molecular structure and interactions. Specifically, we first designed a directed message passing neural network with substructure attention mechanism (SA-DMPNN) to adaptively extract substructures. Second, in order to improve the final features, we separated the drug-drug interactions into pairwise interactions between each drug's unique substructures. Then, the features are adopted to predict interaction probability of a DDI tuple. We evaluated DGNN-DDI on real-world dataset. Compared to state-of-the-art methods, the model improved DDIs prediction performance. We also conducted case study on existing drugs aiming to predict drug combinations that may be effective for the novel coronavirus disease 2019 (COVID-19). Moreover, the visual interpretation results proved that the DGNN-DDI was sensitive to the structure information of drugs and able to detect the key substructures for DDIs. These advantages demonstrated that the proposed method enhanced the performance and interpretation capability of DDI prediction modeling.

Drug-drug interactions of ritonavir-boosted SARS-CoV-2 protease inhibitors in solid organ transplant recipients: experience from the initial use of lopinavir-ritonavir.

OBJECTIVES: To review the drug-drug interactions between tacrolimus and lopinavir/ritonavir in 23 patients who received solid organ transplant during the first wave of COVID-19 and to determine the efficacy as well as safety of prednisone monotherapy. METHODS: Observational study performed between March and June 2020 in solid organ transplant recipients admitted with an established diagnosis of SARS-CoV-2 infection who received lopinavir/ritonavir (≥2 doses). Once lopinavir/ritonavir therapy was initiated, calcineurin inhibitor treatment was temporarily switched to prednisone monotherapy (15-20 mg/d) to avoid drug-drug interactions and toxicity. After lopinavir/ritonavir treatment completion, immunosuppressive treatment was restarted with reduced doses of prednisone-tacrolimus (target minimum blood concentration -C0- approximately 5 ng/mL). Patients were observed for 3 months to confirm the absence of rejection. RESULTS: The median time from discontinuation of tacrolimus to initiation of lopinavir/ritonavir was 14 hours (interquartile range [IQR], 12-15) and from discontinuation of lopinavir/ritonavir to resumption of tacrolimus 58 hours (IQR, 47-81). The duration of lopinavir/ritonavir treatment was 7 days (IQR, 5-7). Nine of the 21 (42.8%) patients on tacrolimus treatment had C0 above the cutoff point after lopinavir/ritonavir initiation, despite having been substituted with prednisone before lopinavir/ritonavir initiation. Three patients had very high concentrations (>40 ng/mL) and developed toxicity. No episodes of acute rejection were diagnosed. DISCUSSION: We did not observe toxicity in patients for whom tacrolimus was discontinued 24 hours before starting lopinavir/ritonavir and reintroduced at half dose 48 to 72 hours after lopinavir/ritonavir discontinuation. Prednisone monotherapy during lopinavir/ritonavir therapy was safe with no episodes of acute rejection. Experience with lopinavir/ritonavir may be applicable to the use of nirmatrelvir/ritonavir, but larger multicentre studies are needed to confirm these findings.

Drug-drug interaction between dexamethasone and direct-acting oral anticoagulants: a nested case-control study in the National COVID Cohort Collaborative (N3C).

OBJECTIVE: The goal of this work is to evaluate if there is an increase in the risk of thromboembolic events (TEEs) due to concomitant exposure to dexamethasone and apixaban or rivaroxaban. Direct oral anticoagulants (DOACs), as well as corticosteroid dexamethasone, are commonly used to treat individuals hospitalised with COVID-19. Dexamethasone induces cytochrome P450-3A4 enzyme that also metabolises DOACs apixaban and rivaroxaban. This raises a concern about possible interaction between dexamethasone and DOACs that may reduce the efficacy of the DOACs and result in an increased risk of TEE. DESIGN: We used nested case-control study design. SETTING: This study was conducted in the National COVID Cohort Collaborative (N3C), the largest electronic health records repository for COVID-19 in the USA. PARTICIPANTS: Study participants were adults over 18 years who were exposed to a DOAC for 10 or more consecutive days. Exposure to dexamethasone was at least 5 or more consecutive days. PRIMARY AND SECONDARY OUTCOME MEASURES: Our primary exposure variable was concomitant exposure to dexamethasone for 5 or more days after exposure to either rivaroxaban or apixaban for 5 or more consecutive days. We used McNemar's Χ2 test and adjusted logistic regression to evaluate association between concomitant use of dexamethasone with either apixaban or rivaroxaban. RESULTS: McNemar's Χ2 test did not find a discernible association of TEE in patients concomitantly exposed to dexamethasone and a DOAC (χ2=0.5, df=1, p=0.48). In addition, a conditional logistic regression model did not find an increase in the risk of TEE (adjusted OR 1.15, 95% CI 0.32 to 4.18). CONCLUSION: This nested case-control study did not find evidence of an association between concomitant exposure to dexamethasone and a DOAC with an increase in risk of TEE. Due to small sample size, an association cannot be completely ruled out.

Drug interactions between common dermatological medications and the oral anti-COVID-19 agents nirmatrelvir-ritonavir and molnupiravir.

INTRODUCTION: The oral antiviral agents nirmatrelvir-ritonavir (NMV/r) and molnupiravir are used to treat mild-to-moderate COVID-19 infection in outpatients. However, the use of NMV/r is complicated by significant drug-drug interactions (DDIs) with frequently prescribed medications. Healthcare professionals should be aware of the possible risk of DDIs, given the emergence of COVID-19 variants and the widespread use of oral COVID-19 treatments. We reviewed available data on DDIs between NMV/r, molnupiravir and common dermatological medications; summarised the potential side effects; and suggest strategies for safe COVID-19 treatment. METHOD: A systematic review using PubMed was conducted on data published from inception to 18 July 2022 to find clinical outcomes of DDIs between NMV/r, molnupiravir and dermatological medications. We also searched the Lexicomp, Micromedex, Liverpool COVID-19 Drug Interactions database and the National Institutes of Health COVID-19 Treatment Guidelines for interactions between NMV/r and molnupiravir, and commonly used dermatological medications. RESULTS: NMV/r containing the cytochrome P-450 (CYP) 3A4 inhibitor ritonavir has DDIs with other medications similarly dependent on CYP3A4 metabolism. Dermatological medications that have DDIs with NMV/r include rifampicin, clofazimine, clarithromycin, erythromycin, clindamycin, itraconazole, ketoconazole, fluconazole, bilastine, rupatadine, dutasteride, ciclosporin, cyclophosphamide, tofacitinib, upadacitinib, colchicine and systemic glucocorticoids. With no potential DDI identified yet in in vitro studies, molnupiravir may be an alternative COVID-19 therapy in patients taking medications that have complicated interactions with NMV/r, which cannot be stopped or dose adjusted. CONCLUSION: NMV/r has significant DDIs with many common dermatological medications, which may require temporary discontinuation, dosage adjustment or substitution with other anti-COVID-19 agents such as molnupiravir.

Evaluation of potential drug-drug interactions and polypharmacy in hospitalized COVID-19 patients.

Background: Drugs that are used in COVID-19 infection, may interact with each other, as well as with the drugs for comorbidities, used concomitantly with COVID-19 treatment. Objectives: It is quite important to calculate and present the patients' exposure to clinically important potential drug-drug interactions (pDDIs). We aimed to investigate the pDDIs and the burden of polypharmacy in COVID-19. Methods: The medical records of 126 consecutive inpatients with COVID-19 treatment were retrospectively analyzed. The Lexi-interact database was used to investigate pDDIs. Results: According to the Lexi-interact database, 605 pDDIs were detected. Of these pDDIs, 23 (3.8%) were A risk category interaction, 186 (30.7%) were B risk category interaction, 339 (56%) were C risk category interaction, 54 (8.9%) were D risk category interaction, and 3 (0.5%) were X risk category interaction. Sixty-five-point five percent of pDDIs (n=396) were clinically important pDDIs (C, D, and X categories), and 69 patients (54.8%) had at least one clinically important pDDIs. The most interacting drug was hydroxychloroquine (n=171, 28.3%). Hydroxychloroquine was also the most interacting drug in the C risk category (n=101, 29.8%) and had 19 pDDIs with metformin, 16 pDDIs with beta-blockers, 13 pDDIs with acetylsalicylic acid, and 10 pDDIs with insulin in the C risk category. Enoxaparin was the most interacting drug (n=25, 46.3%) in the D risk category and most of them were with acetylsalicylic acid (n=12). The most common possible clinical manifestations of pDDIs were QT prolongation, hypoglycemia, and hemorrhage. One hundred and eighteen patients (93.6%) used five or more drugs daily. There was a significant positive correlation between the number of drugs prescribed to patients and the number of clinically important pDDIs (r=0.80, p<0.001). Conclusions: Clinically important pDDIs are common among COVID-19 patients and the majority of pDDIs require monitoring of therapy. COVID-19 patients should be closely observed for QT prolongation, hypoglycemia, and hemorrhage due to pDDIs during treatment.

Pharmacologic Treatment of Transplant Recipients Infected With SARS-CoV-2: Considerations Regarding Therapeutic Drug Monitoring and Drug-Drug Interactions.

BACKGROUND: COVID-19 is a novel infectious disease caused by the severe acute respiratory distress (SARS)-coronavirus-2 (SARS-CoV-2). Several therapeutic options are currently emerging but none with universal consensus or proven efficacy. Solid organ transplant recipients are perceived to be at increased risk of severe COVID-19 because of their immunosuppressed conditions due to chronic use of immunosuppressive drugs (ISDs). It is therefore likely that solid organ transplant recipients will be treated with these experimental antivirals. METHODS: This article is not intended to provide a systematic literature review on investigational treatments tested against COVID-19; rather, the authors aim to provide recommendations for therapeutic drug monitoring of ISDs in transplant recipients infected with SARS-CoV-2 based on a review of existing data in the literature. RESULTS: Management of drug-drug interactions between investigational anti-SARS-CoV-2 drugs and immunosuppressants is a complex task for the clinician. Adequate immunosuppression is necessary to prevent graft rejection while, if critically ill, the patient may benefit from pharmacotherapeutic interventions directed at limiting SARS-CoV-2 viral replication. Maintaining ISD concentrations within the desired therapeutic range requires a highly individualized approach that is complicated by the pandemic context and lack of hindsight. CONCLUSIONS: With this article, the authors inform the clinician about the potential interactions of experimental COVID-19 treatments with ISDs used in transplantation. Recommendations regarding therapeutic drug monitoring and dose adjustments in the context of COVID-19 are provided.