Lopinavir/ritonavir for treatment of non-hospitalized patients with COVID-19: a randomized clinical trial

COVID-19 therapeutics

SUMMARYSince the emergence of COVID-19 in 2020, an unprecedented range of therapeutic options has been studied and deployed. Healthcare providers have multiple treatment approaches to choose from, but efficacy of those approaches often remains controversial or compromised by viral evolution. Uncertainties still persist regarding the best therapies for high-risk patients, and the drug pipeline is suffering fatigue and shortage of funding. In this article, we review the antiviral activity, mechanism of action, pharmacokinetics, and safety of COVID-19 antiviral therapies. Additionally, we summarize the evidence from randomized controlled trials on efficacy and safety of the various COVID-19 antivirals and discuss unmet needs which should be addressed.

Upstrapping to determine futility: predicting future outcomes nonparametrically from past data

BACKGROUND

Clinical trials often involve some form of interim monitoring to determine futility before planned trial completion. While many options for interim monitoring exist (e.g., alpha-spending, conditional power), nonparametric based interim monitoring methods are also needed to account for more complex trial designs and analyses. The upstrap is one recently proposed nonparametric method that may be applied for interim monitoring.

METHODS

Upstrapping is motivated by the case resampling bootstrap and involves repeatedly sampling with replacement from the interim data to simulate thousands of fully enrolled trials. The p-value is calculated for each upstrapped trial and the proportion of upstrapped trials for which the p-value criteria are met is compared with a pre-specified decision threshold. To evaluate the potential utility for upstrapping as a form of interim futility monitoring, we conducted a simulation study considering different sample sizes with several different proposed calibration strategies for the upstrap. We first compared trial rejection rates across a selection of threshold combinations to validate the upstrapping method. Then, we applied upstrapping methods to simulated clinical trial data, directly comparing their performance with more traditional alpha-spending and conditional power interim monitoring methods for futility.

RESULTS

The method validation demonstrated that upstrapping is much more likely to find evidence of futility in the null scenario than the alternative across a variety of simulations settings. Our three proposed approaches for calibration of the upstrap had different strengths depending on the stopping rules used. Compared to O'Brien-Fleming group sequential methods, upstrapped approaches had type I error rates that differed by at most 1.7% and expected sample size was 2-22% lower in the null scenario, while in the alternative scenario power fluctuated between 15.7% lower and 0.2% higher and expected sample size was 0-15% lower.

CONCLUSIONS

In this proof-of-concept simulation study, we evaluated the potential for upstrapping as a resampling-based method for futility monitoring in clinical trials. The trade-offs in expected sample size, power, and type I error rate control indicate that the upstrap can be calibrated to implement futility monitoring with varying degrees of aggressiveness and that performance similarities can be identified relative to considered alpha-spending and conditional power futility monitoring methods.

The relationship between viral clearance rates and disease progression in early symptomatic COVID-19: a systematic review and meta-regression analysis

BACKGROUND

Effective antiviral drugs accelerate viral clearance in acute COVID-19 infections; the relationship between accelerating viral clearance and reducing severe clinical outcomes is unclear.

METHODS

A systematic review was conducted of randomized controlled trials (RCTs) of antiviral therapies in early symptomatic COVID-19, where viral clearance data were available. Treatment benefit was defined clinically as the relative risk of hospitalization/death during follow-up (≥14 days), and virologically as the SARS-CoV-2 viral clearance rate ratio (VCRR). The VCRR is the ratio of viral clearance rates between the intervention and control arms. The relationship between the clinical and virological treatment effects was assessed by mixed-effects meta-regression.

RESULTS

From 57 potentially eligible RCTs, VCRRs were derived for 44 (52 384 participants); 32 had ≥1 clinical endpoint in each arm. Overall, 9.7% (R2) of the variation in clinical benefit was explained by variation in VCRRs with an estimated linear coefficient of -0.92 (95% CI: -1.99 to 0.13; P = 0.08). However, this estimate was highly sensitive to the inclusion of the recent very large PANORAMIC trial. Omitting this outlier, half the variation in clinical benefit (R2 = 50.4%) was explained by variation in VCRRs [slope -1.47 (95% CI -2.43 to -0.51); P = 0.003], i.e. higher VCRRs were associated with an increased clinical benefit.

CONCLUSION

Methods of determining viral clearance in COVID-19 studies and the relationship to clinical outcomes vary greatly. As prohibitively large sample sizes are now required to show clinical treatment benefit in antiviral therapeutic assessments, viral clearance is a reasonable surrogate endpoint.

A practical guide to adopting Bayesian analyses in clinical research

Background

Bayesian statistical approaches are extensively used in new statistical methods but have not been adopted at the same rate in clinical and translational (C&T) research. The goal of this paper is to accelerate the transition of new methods into practice by improving the C&T researcher's ability to gain confidence in interpreting and implementing Bayesian analyses.

Methods

We developed a Bayesian data analysis plan and implemented that plan for a two-arm clinical trial comparing the effectiveness of a new opioid in reducing time to discharge from the post-operative anesthesia unit and nerve block usage in surgery. Through this application, we offer a brief tutorial on Bayesian methods and exhibit how to apply four Bayesian statistical packages from STATA, SAS, and RStan to conduct linear and logistic regression analyses in clinical research.

Results

The analysis results in our application were robust to statistical package and consistent across a wide range of prior distributions. STATA was the most approachable package for linear regression but was more limited in the models that could be fitted and easily summarized. SAS and R offered more straightforward documentation and data management for the posteriors. They also offered direct programming of the likelihood making them more easily extendable to complex problems.

Conclusion

Bayesian analysis is now accessible to a broad range of data analysts and should be considered in more C&T research analyses. This will allow C&T research teams the ability to adopt and interpret Bayesian methodology in more complex problems where Bayesian approaches are often needed.

Outpatient randomized controlled trials to reduce COVID-19 hospitalization: Systematic review and meta-analysis

This COVID-19 outpatient randomized controlled trials (RCTs) systematic review compares hospitalization outcomes amongst four treatment classes over pandemic period, geography, variants, and vaccine status. Outpatient RCTs with hospitalization endpoint were identified in Pubmed searches through May 2023, excluding RCTs <30 participants (PROSPERO-CRD42022369181). Risk of bias was extracted from COVID-19-NMA, with odds ratio utilized for pooled comparison. Searches identified 281 studies with 61 published RCTs for 33 diverse interventions analyzed. RCTs were largely unvaccinated cohorts with at least one COVID-19 hospitalization risk factor. Grouping by class, monoclonal antibodies (mAbs) (OR = 0.31 [95% CI = 0.24-0.40]) had highest hospital reduction efficacy, followed by COVID-19 convalescent plasma (CCP) (OR = 0.69 [95% CI = 0.53-0.90]), small molecule antivirals (OR = 0.78 [95% CI = 0.48-1.33]), and repurposed drugs (OR = 0.82 [95% CI: 0.72-0.93]). Earlier in disease onset interventions performed better than later. This meta-analysis allows approximate head-to-head comparisons of diverse outpatient interventions. Omicron sublineages (XBB and BQ.1.1) are resistant to mAbs Despite trial heterogeneity, this pooled comparison by intervention class indicated oral antivirals are the preferred outpatient treatment where available, but intravenous interventions from convalescent plasma to remdesivir are also effective and necessary in constrained medical resource settings or for acute and chronic COVID-19 in the immunocompromised.

Impact of some antiviral drugs on health care utilization for patients with COVID-19: a systematic review and meta-analysis

BACKGROUND

We aimed to assess the impact of antiviral drugs (fluvoxamine,remdesivir, lopinavir/ritonavir (LPV/r), molnupiravir, andnirmatrelvir/ritonavir (NRV/r)) on health care utilization (HCU) inCOVID-19 patients. We summarized findings from randomized controlledtrials (RCTs) and observational studies.

METHODS

We systematically searched four medical databases (PubMed, Web of Science, Embase, Cochrane Library) for COVID-19 studies up to February 15, 2023. A comprehensive review, meta-analysis, sensitivity analysis, and subgroup analysis were conducted. Pooled effects with 95% confidence intervals (CIs) were calculated for antiviral drugs' impact on hospitalization, mechanical ventilation (MV), and intensive care unit (ICU) outcomes.

RESULTS

Our analysis included 34 studies (584,978 patients). Meta-analysisindicated potential benefits: remdesivir and molnupiravir potentiallyreduced MV risk, and NRV/r correlated with lower hospitalizationrates. However, LPV/r did not notably curb HCU. Remdesivir waspreferable for high-risk COVID-19 patients, while molnupiravir andNRV/r were recommended for those aged 60 and above.

CONCLUSION

Remdesivir, molnupiravir, and NRV/r may reduce HCU during the COVID-19 pandemic. However, due to limited study details and significant heterogeneity in effect estimates, further precise evidence is crucial, especially concerning emerging variants.

Remote and semi-automated methods to conduct a decentralized randomized clinical trial

Introduction

Designing and conducting clinical trials is challenging for some institutions and researchers due to associated time and personnel requirements. We conducted recruitment, screening, informed consent, study product distribution, and data collection remotely. Our objective is to describe how to conduct a randomized clinical trial using remote and automated methods.

Methods

A randomized clinical trial in healthcare workers is used as a model. A random group of workers were invited to participate in the study through email. Following an automated process, interested individuals scheduled consent/screening interviews. Enrollees received study product by mail and surveys via email. Adherence to study product and safety were monitored with survey data review and via real-time safety alerts to study staff.

Results

A staff of 10 remotely screened 406 subjects and enrolled 299 over a 3-month period. Adherence to study product was 87%, and survey data completeness was 98.5% over 9 months. Participants and study staff scored the System Usability Scale 93.8% and 90%, respectively. The automated and remote methods allowed the study maintenance period to be managed by a small study team of two members, while safety monitoring was conducted by three to four team members. Conception of the trial to study completion was 21 months.

Conclusions

The remote and automated methods produced efficient subject recruitment with excellent study product adherence and data completeness. These methods can improve efficiency without sacrificing safety or quality. We share our XML file for researchers to use as a template for learning purposes or designing their own clinical trials.