Clinical trials with antiviral drugs against COVID-19: some progress and many shattered hopes

Vaccines and drugs are the cornerstones in the fight against the SARS-CoV-2 pandemic. While vaccines were a success story, the development of antiviral drugs against SARS-CoV-2 turned out to be difficult. For an accelerated use of antivirals in the clinic, most SARS-CoV-2 antivirals represented repurposed drugs. The present article summarizes the outcomes of clinical trials with antiviral drugs in COVID-19 patients. Many antiviral drugs failed to demonstrate beneficial effects or showed mixed results. One reason for the low success rate of clinical trials was shortcomings of antiviral tests in cell culture systems and another reason was the abundance of ill-coordinated and underpowered clinical trials. However, large pragmatic clinical trials particularly of the British RECOVERY trial series demonstrated that even under emergency situation drug trials can be conducted in a timely way such that the therapy of COVID-19 patients can be based on evidence basis instead on expert opinion or even worse on political pressure.     

Improved confidence intervals for a difference of two cause-specific cumulative incidence functions estimated in the presence of competing risks and random censoring

A cause-specific cumulative incidence function (CIF) is the probability of failure from a specific cause as a function of time. In randomized trials, a difference of cause-specific CIFs (treatment minus control) represents a treatment effect. Cause-specific CIF in each intervention arm can be estimated based on the usual non-parametric Aalen–Johansen estimator which generalizes the Kaplan–Meier estimator of CIF in the presence of competing risks. Under random censoring, asymptotically valid Wald-type confidence intervals (CIs) for a difference of cause-specific CIFs at a specific time point can be constructed using one of the published variance estimators. Unfortunately, these intervals can suffer from substantial under-coverage when the outcome of interest is a rare event, as may be the case for example in the analysis of uncommon adverse events. We propose two new approximate interval estimators for a difference of cause-specific CIFs estimated in the presence of competing risks and random censoring. Theoretical analysis and simulations indicate that the new interval estimators are superior to the Wald CIs in the sense of avoiding substantial under-coverage with rare events, while being equivalent to the Wald CIs asymptotically. In the absence of censoring, one of the two proposed interval estimators reduces to the well-known Agresti–Caffo CI for a difference of two binomial parameters. The new methods can be easily implemented with any software package producing point and variance estimates for the Aalen–Johansen estimator, as illustrated in a real data example.     

Impact of and Correction for Outcome Misclassification in Cumulative Incidence Estimation

Cohort studies and clinical trials may involve multiple events. When occurrence of one of these events prevents the observance of another, the situation is called “competing risks”. A useful measure in such studies is the cumulative incidence of an event, which is useful in evaluating interventions or assessing disease prognosis. When outcomes in such studies are subject to misclassification, the resulting cumulative incidence estimates may be biased. In this work, we study the mechanism of bias in cumulative incidence estimation due to outcome misclassification. We show that even moderate levels of misclassification can lead to seriously biased estimates in a frequently unpredictable manner. We propose an easy to use estimator for correcting this bias that is uniformly consistent. Extensive simulations suggest that this method leads to unbiased estimates in practical settings. The proposed method is useful, both in settings where misclassification probabilities are known by historical data or can be estimated by other means, and for performing sensitivity analyses when the misclassification probabilities are not precisely known.     

Emerging trends in COVID-19 treatment: learning from inflammatory conditions associated with cellular therapies

Coronavirus disease 2019 (SARS-CoV2) is an active global health threat for which treatments are desperately being sought. Even though most people infected experience mild to moderate respiratory symptoms and recover with supportive care, certain vulnerable hosts develop severe clinical deterioration. While several drugs are currently being investigated in clinical trials, there are currently no approved treatments or vaccines for COVID-19 and hence there is an unmet need to explore additional therapeutic options. At least three inflammatory disorders or syndromes associated with immune dysfunction have been described in the context of cellular therapy. Specifically, Cytokine Release Syndrome (CRS), Immune Reconstitution Inflammatory Syndrome (IRIS), and Secondary Hemophagocytic Lymphohistiocytosis (sHLH) all have clinical and laboratory characteristics in common with COVID19 and associated therapies that could be worth testing in the context of clinical trials. Here we discuss these diseases, their management, and potential applications of these treatment in the context of COVID-19. We also discuss current cellular therapies that are being evaluated for the treatment of COVID-19 and/or its associated symptoms.     

Potential natural candidates in the treatment of coronavirus infections

Many viral infections do not have treatments or resistant to existing antiviral therapeutic interventions, and a novel strategy is required to combat virus-mediated fatalities. A novel coronavirus (coronavirus disease 2019 [COVID-19]) emerged in Wuhan, China, in late 2019 and rapidly spread across the globe. COVID-19 has impacted human society with life-threatening and unprecedented health, social, and economic issues, and it continues to affect millions of people. More than 5,800 clinical trials are in place worldwide to develop treatments to eradicate COVID-19. Historically, traditional medicine or natural products, such as medicinal plants, marine organisms and microbes, have been efficacious in treating viral infections. Nevertheless, important parameters for natural products, including clinical trial information, pharmacokinetic data, potency and toxicity profiles, in vivo and in vitro data, and product safety require validation. In this review article, an evaluation is performed of the potential application of natural product-based antiviral compounds, including crude extracts and bioactive chemical compounds obtained from medicinal plants, marine organisms, and microbes, to treat the viral infections COVID-19.     

The impact of COVID-19 on clinical research for Neglected Tropical Diseases (NTDs): A case study of bubonic plague

BackgroundAmong the many collaterals of the COVID-19 pandemic is the disruption of health services and vital clinical research. COVID-19 has magnified the challenges faced in research and threatens to slow research for urgently needed therapeutics for Neglected Tropical Diseases (NTDs) and diseases affecting the most vulnerable populations. Here we explore the impact of the pandemic on a clinical trial for plague therapeutics and strategies that have been considered to ensure research efforts continue.MethodsTo understand the impact of the COVID-19 pandemic on the trial accrual rate, we documented changes in patterns of all-cause consultations that took place before and during the pandemic at health centres in two districts of the Amoron’I Mania region of Madagascar where the trial is underway. We also considered trends in plague reporting and other external factors that may have contributed to slow recruitment.ResultsDuring the pandemic, we found a 27% decrease in consultations at the referral hospital, compared to an 11% increase at peripheral health centres, as well as an overall drop during the months of lockdown. We also found a nation-wide trend towards reduced number of reported plague cases.DiscussionCOVID-19 outbreaks are unlikely to dissipate in the near future. Declining NTD case numbers recorded during the pandemic period should not be viewed in isolation or taken as a marker of things to come. It is vitally important that researchers are prepared for a rebound in cases and, most importantly, that research continues to avoid NTDs becoming even more neglected.     

新型冠状病毒肺炎:实验室检测、治疗及疫苗

新型冠状病毒肺炎(2019 novel coronavirus disease,COVID-19),一种由动物来源的新型冠状病毒(severe acute respiratory syndrome coronavirus 2,SRAS-CoV-2)感染所致的疾病在全球范围内急速传播,严重的危害人类的健康.快速、准确的诊...     

Temporary derogation from European environmental legislation for clinical trials of genetically modified organisms for coronavirus disease 2019

Attempts to streamline environmental procedures for those products containing or consisting of genetically modified organisms (GMOs) among the European Union (EU) Member States are ongoing but still need to be further developed. These procedures can be complex, resource-intensive and time-consuming. Some candidate vaccines currently under development for COVID-19 include genetically modified viruses, which may be considered GMOs. Given the public health emergency caused by the COVID-19 outbreak, on July 15, 2020, the European Parliament approved a temporary derogation of the European environmental requirements to facilitate that those clinical trials with GMOs intended to treat or prevent COVID-19 can start as soon as possible in Europe. This measure has been very controversial, since it could entail risks to human health and the environment, and could be seen as unfair for other products targeting unmet medical needs. With the adoption of this measure, the bottlenecks and obstacles for the development of innovative GMO-based medicines in the EU that the environmental legislation entails have become even more evident.     

益生菌缓解新型冠状病毒感染症状的研究进展

由急性呼吸道综合征冠状病毒2(severeacuterespiratorysyndromecoronavirus2,SARS-CoV-2)引起的新型冠状病毒感染(coronavirusdisease2019,COVID-19)从2020年初迅速扩展至全球,成为人类历史上最严重的大流行之一。已有证据证明当SARS-CoV-2的刺突蛋白(S蛋白)与细胞表面受体血管紧张素转化酶2 (angiotensin converting enzyme 2, ACE2)结合时,可感染宿主细胞,引起肠道菌群失调,并引发不同的并发症。益生菌是活的微生物,已被证明对人体健康有益。因其在调节肠道菌群、治疗多种疾病和抗病毒方面的功效而被考虑用来改善COVID-19。本文基于目前公开的临床前和临床试验结果,总结了益生菌在缓解COVID-19临床症状及胃肠道不良反应的效果,并讨论了益生菌在改善COVID-19后遗症方面的潜力,从而为后续管理COVID-19提供新的方向,进一步为呼吸系统疾病提供理论依据。     

Plant Derived Antiviral Products for Potential Treatment of COVID-19: A Review

COVID-19 caused by SARS-CoV-2 is declared global pandemic. The virus owing high resemblance with SARS-CoV and MERS-CoV has been placed in family of beta-coronavirus. However, transmission and infectivity rate of COVID-19 is quite higher as compared to other members of family. Effective management strategy with potential drug availability will break the virus transmission chain subsequently reduce the pressure on the healthcare system. Extensive research trials are underway to develop novel efficient therapeutics against SARS-CoV-2. In this review, we have discussed the origin and family of coronavirus, structure, genome and pathogenesis of virus SARS-CoV-2 inside human host cell; comparison among SARS, MERS, SARS-CoV-2 and common flu; effective management practices; treatment with immunity boosters; available medication with ongoing clinical trials. We suggest medicinal plants could serve as potential candidates for drug development against COVID-19 infection.     

Evaluating prognostic accuracy of biomarkers under competing risk

To develop more targeted intervention strategies, an important research goal is to identify markers predictive of clinical events. A crucial step toward this goal is to characterize the clinical performance of a marker for predicting different types of events. In this article, we present statistical methods for evaluating the performance of a prognostic marker in predicting multiple competing events. To capture the potential time-varying predictive performance of the marker and incorporate competing risks, we define time- and cause-specific accuracy summaries by stratifying cases based on causes of failure. Such definition would allow one to evaluate the predictive accuracy of a marker for each type of event and compare its predictiveness across event types. Extending the nonparametric crude cause-specific receiver operating characteristics curve estimators by Saha and Heagerty (2010), we develop inference procedures for a range of cause-specific accuracy summaries. To estimate the accuracy measures and assess how covariates may affect the accuracy of a marker under the competing risk setting, we consider two forms of semiparametric models through the cause-specific hazard framework. These approaches enable a flexible modeling of the relationships between the marker and failure times for each cause, while efficiently accommodating additional covariates. We investigate the asymptotic property of the proposed accuracy estimators and demonstrate the finite sample performance of these estimators through simulation studies. The proposed procedures are illustrated with data from a prostate cancer prognostic study.     

Monitoring clinical trials with multiple arms

In order to benefit from the substantial overhead expenses of a large group sequential clinical trial, the simultaneous investigation of several competing treatments becomes more popular. If at some interim analysis any treatment arm reveals itself to be inferior to any other treatment under investigation, this inferior arm may be or may even need to be dropped for ethical and/or economic reasons. Recently proposed methods for monitoring and analysis of group sequential clinical trials with multiple treatment arms are compared and discussed. The main focus of the article is on the application and extension of (adaptive) closed testing procedures in the group sequential setting that strongly control the familywise error rate. A numerical example is given for illustration.     

Exact statistical inference for group sequential trials.

This paper considers clinical trials comparing two treatments with dichotomous responses where the data are examined periodically for early evidence of treatment difference. The existing group sequential methods for such trials are based on the large-sample normal approximation to the joint distribution of the estimators of treatment difference over interim analyses. We demonstrate through extensive numerical studies that, for small and even moderate-sized trials, these approximate procedures may lead to tests with supranominal size (mainly when unpooled estimators of variance are used) and confidence intervals with under-nominal coverage probability. We then study exact methods for group sequential testing, repeated interval estimation, and interval estimation following sequential testing. The new procedures can accommodate any treatment allocation rules. An example using real data is provided.     

Multiple imputation methods for treatment noncompliance and nonresponse in randomized clinical trials

Summary .  Randomized clinical trials are a powerful tool for investigating causal treatment effects, but in human trials there are oftentimes problems of noncompliance which standard analyses, such as the intention-to-treat or as-treated analysis, either ignore or incorporate in such a way that the resulting estimand is no longer a causal effect. One alternative to these analyses is the complier average causal effect (CACE) which estimates the average causal treatment effect among a subpopulation that would comply under any treatment assigned. We focus on the setting of a randomized clinical trial with crossover treatment noncompliance (e.g., control subjects could receive the intervention and intervention subjects could receive the control) and outcome nonresponse. In this article, we develop estimators for the CACE using multiple imputation methods, which have been successfully applied to a wide variety of missing data problems, but have not yet been applied to the potential outcomes setting of causal inference. Using simulated data we investigate the finite sample properties of these estimators as well as of competing procedures in a simple setting. Finally we illustrate our methods using a real randomized encouragement design study on the effectiveness of the influenza vaccine.     

Semiparametric inference for surrogate endpoints with bivariate censored data

Summary .   Considerable attention has been recently paid to the use of surrogate endpoints in clinical research. We deal with the situation where the two endpoints are both right censored. While proportional hazards analyses are typically used for this setting, their use leads to several complications. In this article, we propose the use of the accelerated failure time model for analysis of surrogate endpoints. Based on the model, we then describe estimation and inference procedures for several measures of surrogacy. A complication is that potentially both the independent and dependent variable are subject to censoring. We adapt the Theil–Sen estimator to this problem, develop the associated asymptotic results, and propose a novel resampling-based technique for calculating the variances of the proposed estimators. The finite-sample properties of the estimation methodology are assessed using simulation studies, and the proposed procedures are applied to data from an acute myelogenous leukemia clinical trial.     

On the COLTON Model for Clinical Trials with Delayed Observations-Dichotomous Responses

In the COLTON model for clinical trials a choice is to be made between two medical treatments where there is a known patient horizon N. In an earlier paper we studied the COLTON model under the additional assumption that there is a time lag between the administration of the treatments and the availability of the responses where the responses are normally distributed. Here we extend the results of the earlier paper to the case where responses are dichotomous. The relative performance of two simple procedures for dealing with the patients who arrive during the waiting period between the end of the administration of treatment in the trial phase and observation of the final trial response is discussed within a BAYESIAN framework.     

Mesenchymal stromal cells to fight SARS-CoV-2: Taking advantage of a pleiotropic therapy

The devastating global impact of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has prompted scientists to develop novel strategies to fight Coronavirus Disease of 2019 (COVID-19), including the examination of pre-existing treatments for other viral infections in COVID-19 patients. This review provides a reasoned discussion of the possible use of Mesenchymal Stromal Cells (MSC) or their products as a treatment in SARS-CoV-2-infected patients. The main benefits and concerns of using this cellular therapy, guided by preclinical and clinical data obtained from similar pathologies will be reviewed. MSC represent a highly immunomodulatory cell population and their use may be safe according to clinical studies developed in other pathologies. Notably, four clinical trials and four case reports that have already been performed in COVID-19 patients obtained promising results. The clinical application of MSC in COVID-19 is very preliminary and further investigational studies are required to determine the efficacy of the MSC therapy. Nevertheless, these preliminary studies were important to understand the therapeutic potential of MSC in COVID-19. Based on these encouraging results, the United States Food and Drug Administration (FDA) authorized the compassionate use of MSC, but only in patients with Acute Respiratory Distress Syndrome (ARDS) and a poor prognosis. In fact, patients with severe SARS-CoV-2 can present infection and tissue damage in different organs, such as lung, heart, liver, kidney, gut and brain, affecting their function. MSC may have pleiotropic activities in COVID-19, with the capacity to fight inflammation and repair lesions in several organs.     

Clinical progress in MSC-based therapies for the management of severe COVID-19

Considering the high impact that severe Coronavirus disease 2019 (COVID-19) cases still pose on public health and their complex pharmacological management, the search for new therapeutic alternatives is essential. Mesenchymal stromal cells (MSCs) could be promising candidates as they present important immunomodulatory and anti-inflammatory properties that can combat the acute severe respiratory distress syndrome (ARDS) and the cytokine storm occurring in COVID-19, two processes that are mainly driven by an immunological misbalance. In this review, we provide a comprehensive overview of the intricate inflammatory process derived from the immune dysregulation that occurs in COVID-19, discussing the potential that the cytokines and growth factors that constitute the MSC-derived secretome present to treat the disease. Moreover, we revise the latest clinical progress made in the field, discussing the most important findings of the clinical trials conducted to date, which follow 2 different approaches: MSC-based cell therapy or the administration of the secretome by itself, as a cell-free therapy.     

Nonparametric estimation of the cumulative incidences of competing risks under double truncation

Registry data typically report incident cases within a certain calendar time interval. Such interval sampling induces double truncation on the incidence times, which may result in an observational bias. In this paper, we introduce nonparametric estimation for the cumulative incidences of competing risks when the incidence time is doubly truncated. Two different estimators are proposed depending on whether the truncation limits are independent of the competing events or not. The asymptotic properties of the estimators are established, and their finite sample performance is investigated through simulations. For illustration purposes, the estimators are applied to childhood cancer registry data, where the target population is peculiarly defined conditional on future cancer development. Then, in our application, the cumulative incidences inform on the distribution by age of the different types of cancer.     

抗新型冠状病毒单克隆中和抗体药物研发进展*

随着新型冠状病毒肺炎(COVID-19)疫情在全球的不断蔓延,开发有效的治疗药物迫在眉睫。中和抗体作为最有希望的新型冠状病毒特异性治疗药物,已经在临床研究中展现很好的治疗效果。对抗新冠病毒单克隆中和抗体药物研发的进展、涉及的主要技术和主要临床试验结果进行了总结,以期为包括COVID-19在内的新发、突发传染病中和抗体药物研发提供参考。