Detecting drug-drug interactions between therapies for COVID-19 and concomitant medications through the FDA adverse event reporting system

Effects of nirmatrelvir/ritonavir (Paxlovid) on the nervous system: analysis on adverse events released by FDA

BACKGROUND

Nirmatrelvir/ritonavir, commonly known as Paxlovid, is one of the main drugs used to treat COVID-19. Neurological disorders are among the adverse drug reactions (ADRs) linked to Paxlovid, yet comprehensive data-mining studies based on real-world neurological adverse events induced by Paxlovid are lacking.

METHODS

It is an observational study, to reduce the risk of bias affected by COVID-19 disease, our study included only patients with COVID-19 disease. In this case, disproportionate analysis is performed using the Report Odds Ratio (ROR) and its 95% Confidence Interval (CI).

RESULTS

We screened and compared all medications associated with COVID-19 (N = 439) and found that 22 of these were linked to neurological adverse reactions. Paxlovid was associated with a threefold greater number of neurological adverse events compared to all other drugs combined (N = 11,792), with a strong signal value (ROR = 2.27).

CONCLUSIONS

Compared to all other COVID-19-related drugs, Paxlovid has the highest number and stronger signal value for neurologic-related adverse reactions. Clinicians should pay special attention to female patients taking Paxlovid within the first 30 days, monitoring for symptoms such as dysgeusia, ageusia, headache, and anosmia. In addition, headache and anosmia are not uncommon occurrences as mentioned in the instructions and should be noted.

Exploring the impact of co-exposure timing on drug-drug interactions in signal detection through spontaneous reporting system databases: a scoping review

INTRODUCTION

Drug-drug interactions (DDIs) are defined as the pharmacological effects produced by the concomitant administration of two or more drugs. To minimize false positive signals and ensure their validity when analyzing Spontaneous Reporting System (SRS) databases, it has been suggested to incorporate key pharmacological principles, such as temporal plausibility.

AREAS COVERED

The scoping review of the literature was completed using MEDLINE from inception to March 2023. Included studies had to provide detailed methods for identifying DDIs in SRS databases. Any methodological approach and adverse event were accepted. Descriptive analyzes were excluded as we focused on automatic signal detection methods. The result is an overview of all the available methods for DDI signal detection in SRS databases, with a specific focus on the evaluation of the co-exposure time of the interacting drugs. It is worth noting that only a limited number of studies (n = 3) have attempted to address the issue of overlapping drug administration times.

EXPERT OPINION

Current guidelines for signal validation focus on factors like the number of reports and temporal association, but they lack guidance on addressing overlapping drug administration times, highlighting a need for further research and method development.

The therapeutic landscape for COVID-19 and post-COVID-19 medications from genetic profiling of the Vietnamese population and a predictive model of drug-drug interaction for comorbid COVID-19 patients

Despite the raised awareness of the role of pharmacogenomic (PGx) in personalized medicines for COVID-19, data for COVID-19 drugs is extremely scarce and not even a publication on this topic for post-COVID-19 medications to date. In the current study, we investigated the genetic variations associated with COVID-19 and post-COVID-19 therapies by using whole genome sequencing data of the 1000 Vietnamese Genomes Project (1KVG) in comparison with other populations retrieved from the 1000 Genomes Project Phase 3 (1KGP3) and the Genome Aggregation Database (gnomAD). Moreover, we also evaluated the risk of drug interactions in comorbid COVID-19 and post-COVID-19 patients based on pharmacogenomic profiles of drugs using a computational approach. For COVID-19 therapies, variants related to the response of two causal treatment agents (tolicizumab and ritonavir) and antithrombotic drugs are common in the Vietnamese cohort. Regarding post-COVID-19, drugs for mental manipulations possess the highest number of clinical annotated variants carried by Vietnamese individuals. Among the superpopulations, East Asian populations shared the most similar genetic structure with the Vietnamese population, whereas the African population showed the most difference. Comorbid patients are at an increased drug-drug interaction (DDI) risk when suffering from COVID-19 and after recovering as well due to a large number of potential DDIs which have been identified. Our results presented the population-specific understanding of the pharmacogenomic aspect of COVID-19 and post-COVID-19 therapy to optimize therapeutic outcomes and promote personalized medicine strategy. We also partly clarified the higher risk in COVID-19 patients with underlying conditions by assessing the potential drug interactions.

Adverse Event Profiles of Antiseizure Medications and the Impact of Coadministration on Drug Tolerability in Adults with Epilepsy

BACKGROUND

Antiseizure medication (ASM) as monotherapy or in combination is the treatment of choice for most patients with epilepsy. Therefore, knowledge about the typical adverse events (AEs) for ASMs and other coadministered drugs (CDs) is essential for practitioners and patients. Due to frequent polypharmacy, it is often difficult to clinically assess the AE profiles of ASMs and differentiate the influence of CDs.

OBJECTIVE

This retrospective analysis aimed to determine typical AE profiles for ASMs and assess the impact of CDs on AEs in clinical practice.

METHODS

The Liverpool AE Profile (LAEP) and its domains were used to identify the AE profiles of ASMs based on data from a large German multicenter study (Epi2020). Following established classifications, drugs were grouped according to their mode of action (ASMs) or clinical indication (CDs). Bivariate correlation, multivariate ordinal regression (MORA), and artificial neural network (ANNA) analyses were performed. Bivariate correlation with Fisher's z-transformation was used to compare the correlation strength of LAEP with the Hospital Anxiety and Depression Scale (HADS) and Neurological Disorders Depression Inventory for Epilepsy (NDDI-E) to avoid LAEP bias in the context of antidepressant therapy.

RESULTS

Data from 486 patients were analyzed. The AE profiles of ASM categories and single ASMs matched those reported in the literature. Synaptic vesicle glycoprotein 2A (SV2A) and voltage-gated sodium channel (VGSC) modulators had favorable AE profiles, while brivaracetam was superior to levetiracetam regarding psychobehavioral AEs. MORA revealed that, in addition to seizure frequency, α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) modulators and antidepressants were the only independent predictors of high LAEP values. After Fisher's z-transformation, correlations were significantly lower between LAEP and antidepressants than between LAEP and HADS or NDDI-E. Therefore, a bias in the results toward over interpreting the impact of antidepressants on LAEP was presumed. In the ANNA, perampanel, zonisamide, topiramate, and valproic acid were important nodes in the network, while VGSC and SV2A modulators had low relevance for predicting relevant AEs. Similarly, cardiovascular agents, analgesics, and antipsychotics were important CDs in the ANNA model.

CONCLUSION

ASMs have characteristic AE profiles that are highly reproducible and must be considered in therapeutic decision-making. Therapy using perampanel as an AMPA modulator should be considered cautiously due to its relatively high AE profile. Drugs acting via VGSCs and SV2A receptors are significantly better tolerated than other ASM categories or substances (e.g., topiramate, zonisamide, and valproate). Switching to brivaracetam is advisable in patients with psychobehavioral AEs who take levetiracetam. Because CDs frequently pharmacokinetically interact with ASMs, the cumulative AE profile must be considered.

TRIAL REGISTRATION

DRKS00022024, U1111-1252-5331.

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.

Drug-Drug Interactions among Patients Hospitalized with COVID-19 in Greece

The modulation of the pharmacological action of drugs due to drug-drug interactions (DDIs) is a critical issue in healthcare. The aim of this study was to evaluate the prevalence and the clinical significance of potential DDIs in patients admitted to the University Hospital of Heraklion in Greece with coronavirus disease 2019 (COVID-19). Cardiovascular disorders (58.4%) and diabetes (types I and II) (29.6%) were the most common comorbidities. A high occurrence of DDIs was observed, and clinically significant DDIs that may hamper response to treatment represented 40.3% of cases on admission, 21% during hospitalization, and 40.7% upon discharge. Polypharmacy and comorbidities were associated with a higher prevalence of DDIs in a statistically significant way (p < 0.05, 95% CI). Clinically significant DDIs and increased C-reactive protein values upon admission were associated with prolonged hospitalization. The results reveal that patients admitted due to COVID-19 in Greece often have an additional burden of DDIs that healthcare teams should approach and resolve.