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.

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.

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.

Cardiovascular Drug Interactions With Nirmatrelvir/Ritonavir in Patients With COVID-19: JACC Review Topic of the Week.

Nirmatrelvir-ritonavir (NMVr) is used to treat symptomatic, nonhospitalized patients with coronavirus disease-2019 (COVID-19) who are at high risk of progression to severe disease. Patients with cardiovascular risk factors and cardiovascular disease are at a high risk of developing adverse events from COVID-19 and as a result have a higher likelihood of receiving NMVr. Ritonavir, the pharmaceutical enhancer used in NMVr, is an inhibitor of the enzymes of CYP450 pathway, particularly CYP3A4 and to a lesser degree CYP2D6, and affects the P-glycoprotein pump. Co-administration of NMVr with medications commonly used to manage cardiovascular conditions can potentially cause significant drug-drug interactions and may lead to severe adverse effects. It is crucial to be aware of such interactions and take appropriate measures to avoid them. In this review, we discuss potential drug-drug interactions between NMVr and commonly used cardiovascular medications based on their pharmacokinetics and pharmacodynamic properties.

A biomedical knowledge graph-based method for drug-drug interactions prediction through combining local and global features with deep neural networks.

Drug-drug interactions (DDIs) prediction is a challenging task in drug development and clinical application. Due to the extremely large complete set of all possible DDIs, computer-aided DDIs prediction methods are getting lots of attention in the pharmaceutical industry and academia. However, most existing computational methods only use single perspective information and few of them conduct the task based on the biomedical knowledge graph (BKG), which can provide more detailed and comprehensive drug lateral side information flow. To this end, a deep learning framework, namely DeepLGF, is proposed to fully exploit BKG fusing local-global information to improve the performance of DDIs prediction. More specifically, DeepLGF first obtains chemical local information on drug sequence semantics through a natural language processing algorithm. Then a model of BFGNN based on graph neural network is proposed to extract biological local information on drug through learning embedding vector from different biological functional spaces. The global feature information is extracted from the BKG by our knowledge graph embedding method. In DeepLGF, for fusing local-global features well, we designed four aggregating methods to explore the most suitable ones. Finally, the advanced fusing feature vectors are fed into deep neural network to train and predict. To evaluate the prediction performance of DeepLGF, we tested our method in three prediction tasks and compared it with state-of-the-art models. In addition, case studies of three cancer-related and COVID-19-related drugs further demonstrated DeepLGF's superior ability for potential DDIs prediction. The webserver of the DeepLGF predictor is freely available at http://120.77.11.78/DeepLGF/.

COVID-19 and antiepileptic drugs: an approach to guide practices when nirmatrelvir/ritonavir is co-prescribed.

Management and dose adjustment are a major concern for clinicians in the absence of specific clinical outcome data for patients on antiepileptic drugs (AEDs), in the event of short-term (5 days) nirmatrelvir/ritonavir co-exposure. Therefore, in this report, we identified drugs that require dose adjustment because of drug-drug interactions (DDIs) between nirmatrelvir/ritonavir and AEDs. We hereby used four databases (Micromedex Drug Interaction, Liverpool Drug Interaction Group for COVID-19 Therapies, Medscape Drug Interaction Checker, and Lexicomp Drug Interactions) and DDI-Predictor.In the light of applying the DDI-Predictor, for carbamazepine, clobazam, oxcarbazepine, eslicarbazepine, phenytoin, phenobarbital, pentobarbital, rufinamide, and valproate as CYP3A4 inducers, we recommend that a dose adjustment of short-term nirmatrelvir/ritonavir as a substrate (victim) drug would be more appropriate instead of these AEDs to avoid impending DDI-related threats in patients with epilepsy.

Drug-drug interaction between remdesivir and immunosuppressant agents in a kidney transplant recipient.

A 60-year-old man was treated with a regimen of controlled-release tacrolimus (2 mg once daily), everolimus (0.5 mg twice daily), methylprednisolone (4 mg once daily), and mizoribine (100 mg twice daily) as an anti-rejection regimen following living-donor kidney transplantation. One year after transplantation, the recipient was admitted to Mie University Hospital (day X; admission date) to treat coronavirus disease 2019 pneumonia. The latest trough concentrations of tacrolimus and everolimus before admission (day X-65) were 4.5 ng/mL and 4.4 ng/mL, respectively. Since tacrolimus concentration was 4.2 ng/mL on day X+3, the dose was adjusted to 1.5 mg once daily to reach the target concentration of 3.0 ng/mL due to the introduction of remdesivir. After starting remdesivir on day X+4, the increased trough concentrations of tacrolimus on day X+6 (6.9 ng/mL) and everolimus on day X+7 (9.2 ng/mL) were observed, which resulted in dose reduction of tacrolimus (0.5 mg once daily) and discontinuation of everolimus. After discontinuation of remdesivir on day X+9, dose titration of controlled-release tacrolimus and restart of everolimus (0.5 mg twice daily) were performed from day X+15. The dose of controlled-release tacrolimus was titrated and fixed to 2 mg once daily at discharge (day X+21). There was no toxicity due to immunosuppressive agents during hospitalization. This case report indicated that remdesivir might interact with cytochrome P450 3A4 substrates, such as tacrolimus and everolimus, and elevate their blood concentrations under high inflammatory conditions.

Development and Validation of an HPLC-UV Method for the Quantification of 4'-Hydroxydiclofenac Using Salicylic Acid: Future Applications for Measurement of In Vitro Drug-Drug Interaction in Rat Liver Microsomes.

Salicylic acid is a key compound in nonsteroidal anti-inflammatory drugs that has been recently used for preventing the risk of hospitalization and death among COVID-19 patients and in preventing colorectal cancer (CRC) by suppressing two key proteins. Understanding drug-drug interaction pathways prevent the occurrence of adverse drug reactions in clinical trials. Drug-drug interactions can result in the variation of the pharmacodynamics and pharmacokinetic of the drug. Inhibition of the Cytochrome P450 enzyme activity leads to the withdrawal of the drug from the market. The aim of this paper was to develop and validate an HPLC-UV method for the quantification of 4'-hydroxydiclofenac as a CYP2C9 metabolite using salicylic acid as an inhibitor in rat liver microsomes. A CYP2C9 assay was developed and validated on the reversed phase C18 column (SUPELCO 25 cm × 4.6 mm × 5 µm) using a low-pressure gradient elution programming at T = 30 °C, a wavelength of 282 nm, and a flow rate of 1 mL/min. 4'-hydroxydiclofenac demonstrated a good linearity (R2 > 0.99), good reproducibility, low detection, and quantitation limit, and the inter and intra-day precision met the ICH guidelines (<15%). 4'-hydroxydiclofenac was stable for three days and showed an acceptable accuracy and recovery (80-120%) within the ICH guidelines in a rat liver microsome sample. This method will be beneficial for future applications of the in vitro inhibitory effect of salicylic acid on the CYP2C9 enzyme activity in rat microsomes and the in vivo administration of salicylic acid in clinical trials.

Oral Drugs Against COVID-19.

BACKGROUND: Five-day oral therapies against early COVID-19 infection have recently been conditionally approved in Europe. In the drug combination nirmatrelvir + ritonavir (nirmatrelvir/r), the active agent, nirmatrelvir, is made bioavailable in clinically adequate amounts by the additional administration of a potent inhibitor of its first-pass metabolism by way of cytochrome P450 [CYP] 3A in the gut and liver. In view of the central role of CYP3A in the clearance of many different kinds of drugs, and the fact that many patients with COVID-19 are taking multiple drugs to treat other conditions, it is important to assess the potential for drug interactions when nirmatrelvir/r is given, and to minimize the risks associated with such interactions. METHODS: We defined the interaction profile of ritonavir on the basis of information derived from two databases (Medline, GoogleScholar), three standard electronic texts on drug interactions, and manufacturer-supplied drug information. We compiled a list of drugs and their potentially relevant interactions, developed a risk min - imization algorithm, and applied it to the substances in question. We also compiled a list of commonly prescribed drugs for which there is no risk of interaction with nirmatrelvir/r. RESULTS: Out of 190 drugs and drug combinations, 57 do not need any special measures when given in combination with brief, low-dose ritonavir treatment, while 15 require dose modification or a therapeutic alternative, 8 can be temporarily discontinued, 9 contraindicate ritonavir use, and 102 should preferably be combined with a different treatment. CONCLUSION: We have proposed measures that are simple to carry out for the main types of drug that can interact with ritonavir. These measures can be implemented under quarantine conditions before starting a 5-day treatment with nirmatrelvir/r.

Interactions of the protease inhibitor, ritonavir, with common anesthesia drugs.

The protease inhibitor, ritonavir, is a strong inhibitor of CYP 3A. The drug is used for management of the human immunovirus and is currently part of an oral antiviral drug combination (nirmatrelvir-ritonavir) for the early treatment of SARS-2 COVID-19-positive patients aged 12 years and over who have recognized comorbidities. The CYP 3A enzyme system is responsible for clearance of numerous drugs used in anesthesia (e.g., alfentanil, fentanyl, methadone, rocuronium, bupivacaine, midazolam, ketamine). Ritonavir will have an impact on drug clearances that are dependent on ritonavir concentration, anesthesia drug intrinsic hepatic clearance, metabolic pathways, concentration-response relationship, and route of administration. Drugs with a steep concentration-response relationship (ketamine, midazolam, rocuronium) are mostly affected because small changes in concentration have major changes in effect response. An increase in midazolam concentration is observed after oral administration because CYP 3A in the gastrointestinal wall is inhibited, causing a large increase in relative bioavailability. Fentanyl infusion may be associated with a modest increase in plasma concentration and effect, but the large between subject variability of pharmacokinetic and pharmacodynamic concentration changes suggests it will have little impact on an individual patient, especially when used with adverse effect monitoring. It has been proposed that drugs that have no or only a small metabolic pathway involving the CYP 3A enzyme be used during anesthesia, for example, propofol, atracurium, remifentanil, and the volatile agents. That anesthesia approach denies children of drugs with considerable value. It is better that the inhibitory changes in clearance of these drugs are understood so that rational drug choices can be made to tailor drug use to the individual patient. Altered drug dose, anticipation of duration of effect, timing of administration, use of reversal agents and perioperative monitoring would better behoove children undergoing anesthesia.

The COVID-19 Oral Drug Molnupiravir Is a CES2 Substrate: Potential Drug-Drug Interactions and Impact of CES2 Genetic Polymorphism In Vitro.

Molnupiravir is one of the two coronavirus disease 2019 (COVID-19) oral drugs that were recently granted the emergency use authorization by the Food and Drug Administration (FDA). Molnupiravir is an ester and requires hydrolysis to exert antiviral activity. Carboxylesterases constitute a class of hydrolases with high catalytic efficiency. Humans express two major carboxylesterases (CES1 and CES2) that differ in substrate specificity. Based on the structural characteristics of molnupiravir, this study was performed to test the hypothesis that molnupiravir is preferably hydrolyzed by CES2. Several complementary approaches were used to test this hypothesis. As many as 24 individual human liver samples were tested and the hydrolysis of molnupiravir was significantly correlated with the level of CES2 but not CES1. Microsomes from the intestine, kidney, and liver, but not lung, all rapidly hydrolyzed molnupiravir and the magnitude of hydrolysis was related closely to the level of CES2 expression among these organs. Importantly, recombinant CES2 but not CES1 hydrolyzed molnupiravir, collectively establishing that molnupiravir is a CES2-selective substrate. In addition, several CES2 polymorphic variants (e.g., R180H) differed from the wild-type CES2 in the hydrolysis of molnupiravir. Molecular docking revealed that wild-type CES2 and its variant R180H used different sets of amino acids to interact with molnupiravir. Furthermore, molnupiravir hydrolysis was significantly inhibited by remdesivir, the first COVID-19 drug granted the full approval by the FDA. The results presented raise the possibility that CES2 expression and genetic variation may impact therapeutic efficacy in clinical situations and warrants further investigation. SIGNIFICANCE STATEMENT: COVID-19 remains a global health crisis, and molnupiravir is one of the two recently approved oral COVID-19 therapeutics. In this study, we have shown that molnupiravir is hydrolytically activated by CES2, a major hydrolase whose activity is impacted by genetic polymorphic variants, disease mediators, and many potentially coadministered medicines. These results presented raise the possibility that CES2 expression and genetic variation may impact therapeutic efficacy in clinical situations and warrants further investigation.

Prescription cannabidiol for seizure disorder management: Initial drug-drug interaction management by specialty pharmacists.

PURPOSE: To describe the presence, type, and management of drug-drug interactions (DDIs) at prescription cannabidiol (CBD) therapy initiation. METHODS: We conducted a single-center, retrospective study of patients prescribed CBD from a medical center's neurology clinic for seizure management from January 2019 through April 2020. Patients were excluded if they were enrolled in a CBD clinical trial or the insurance approval or medication fulfillment process was not completed by the center's specialty pharmacy. The primary outcomes were the numbers, types, and management of DDIs identified at the time of CBD prescribing. RESULTS: Of the 136 patients included, 109 (80%) had a DDI identified at baseline. Of the 260 DDIs, 71% (n = 184) were pharmacodynamic and 29% (n = 76) were pharmacokinetic in nature. Management of the 260 DDIs detected included counseling only (89% [n = 232 interactions]), discontinuation of the interacting agent [9% (n = 22 interactions]), and dosage change for the interacting agent [2% (n = 6 interactions]). Clobazam was the most commonly identified interacting medication (n = 63, 24%), while valproic acid accounted for 10% (n = 26) of the DDIs. The population was predominantly white (n = 115, 85%), 18 years of age or younger (n = 92, 68%), and had an indication for prescription CBD treatment of Lennox-Gastaut syndrome (n = 117, 86%). CONCLUSION: This study provides new information on the role that integrated specialty pharmacists can play in identifying and managing initial DDIs in patients starting prescription CBD.

UnbiasedDTI: Mitigating Real-World Bias of Drug-Target Interaction Prediction by Using Deep Ensemble-Balanced Learning.

Drug-target interaction (DTI) prediction through in vitro methods is expensive and time-consuming. On the other hand, computational methods can save time and money while enhancing drug discovery efficiency. Most of the computational methods frame DTI prediction as a binary classification task. One important challenge is that the number of negative interactions in all DTI-related datasets is far greater than the number of positive interactions, leading to the class imbalance problem. As a result, a classifier is trained biased towards the majority class (negative class), whereas the minority class (interacting pairs) is of interest. This class imbalance problem is not widely taken into account in DTI prediction studies, and the few previous studies considering balancing in DTI do not focus on the imbalance issue itself. Additionally, they do not benefit from deep learning models and experimental validation. In this study, we propose a computational framework along with experimental validations to predict drug-target interaction using an ensemble of deep learning models to address the class imbalance problem in the DTI domain. The objective of this paper is to mitigate the bias in the prediction of DTI by focusing on the impact of balancing and maintaining other involved parameters at a constant value. Our analysis shows that the proposed model outperforms unbalanced models with the same architecture trained on the BindingDB both computationally and experimentally. These findings demonstrate the significance of balancing, which reduces the bias towards the negative class and leads to better performance. It is important to note that leaning on computational results without experimentally validating them and by relying solely on AUROC and AUPRC metrics is not credible, particularly when the testing set remains unbalanced.

Recommendations for the Management of Drug-Drug Interactions Between the COVID-19 Antiviral Nirmatrelvir/Ritonavir (Paxlovid) and Comedications.

The coronavirus disease 2019 (COVID-19) antiviral nirmatrelvir/ritonavir (Paxlovid) has been granted authorization or approval in several countries for the treatment of patients with mild to moderate COVID-19 at high risk of progression to severe disease and with no requirement for supplemental oxygen. Nirmatrelvir/ritonavir will be primarily administered outside the hospital setting as a 5-day course oral treatment. The ritonavir component boosts plasma concentrations of nirmatrelvir through the potent and rapid inhibition of the key drug-metabolizing enzyme cytochrome P450 (CYP) 3A4. Thus nirmatrelvir/ritonavir, even given as a short treatment course, has a high potential to cause harm from drug-drug interactions (DDIs) with other drugs metabolized through this pathway. Options for mitigating risk from DDIs with nirmatrelvir/ritonavir are limited due to the clinical illness, the short window for intervention, and the related difficulty of implementing clinical monitoring or dosage adjustment of the comedication. Pragmatic options are largely confined to preemptive or symptom-driven pausing of the comedication or managing any additional risk through counseling. This review summarizes the effects of ritonavir on drug disposition (i.e., metabolizing enzymes and transporters) and discusses factors determining the likelihood of having a clinically significant DDI. Furthermore, it provides a comprehensive list of comedications likely to be used in COVID-19 patients which are categorized according to their potential DDI risk with nirmatrelvir/ritonavir. It also discusses recommendations for the management of DDIs which balance the risk of harm from DDIs with a short course of ritonavir, against unnecessary denial of nirmatrelvir/ritonavir treatment.

Identification of Drug Interaction Adverse Events in Patients With COVID-19: A Systematic Review.

Importance: During the COVID-19 pandemic, urgent clinical management of patients has mainly included drugs currently administered for other diseases, referred to as repositioned drugs. As a result, some of these drugs have proved to be not only ineffective but also harmful because of adverse events associated with drug-drug interactions (DDIs). Objective: To identify DDIs that led to adverse clinical outcomes and/or adverse drug reactions in patients with COVID-19 by systematically reviewing the literature and assessing the value of drug interaction checkers in identifying such events. Evidence Review: After identification of the drugs used during the COVID-19 pandemic, the drug interaction checkers Drugs.com, COVID-19 Drug Interactions, LexiComp, Medscape, and WebMD were consulted to analyze theoretical DDI-associated adverse events in patients with COVID-19 from March 1, 2020, through February 28, 2022. A systematic literature review was performed by searching the databases PubMed, Scopus, and Cochrane for articles published from March 1, 2020, through February 28, 2022, to retrieve articles describing actual adverse events associated with DDIs. The drug interaction checkers were consulted again to evaluate their potential to assess such events. Findings: The DDIs identified in the reviewed articles involved 46 different drugs. In total, 575 DDIs for 58 drug pairs (305 associated with at least 1 adverse drug reaction) were reported. The drugs most involved in DDIs were lopinavir and ritonavir. Of the 6917 identified studies, 20 met the inclusion criteria. These studies, which enrolled 1297 patients overall, reported 115 DDI-related adverse events: 15 (26%) were identifiable by all tools analyzed, 29 (50%) were identifiable by at least 1 of them, and 14 (24%) remained nonidentifiable. Conclusions and Relevance: The main finding of this systematic review is that the use of drug interaction checkers could have identified several DDI-associated adverse drug reactions, including severe and life-threatening events. Both the interactions between the drugs used to treat COVID-19 and between the COVID-19 drugs and those already used by the patients should be evaluated.

Potential Therapeutic Roles for Direct Factor Xa Inhibitors in Coronavirus Infections.

Human factor Xa (FXa) is a serine protease of the common coagulation pathway. FXa is known to activate prothrombin to thrombin, which eventually leads to the formation of cross-linked blood clots. While this process is important in maintaining hemostasis, excessive thrombin generation results in a host of thrombotic conditions. FXa has also been linked to inflammation via protease-activated receptors. Together, coagulopathy and inflammation have been implicated in the pathogenesis of viral infections, including the current coronavirus pandemic. Direct FXa inhibitors have been shown to possess anti-inflammatory and antiviral effects, in addition to their established anticoagulant activity. This review summarizes the pharmacological activities of direct FXa inhibitors, their pharmacokinetics, potential drug-drug interactions and adverse effects, and the details of clinical trials involving direct FXa inhibitors in coronavirus disease 2019 (COVID-19) patients.