Co authored by Janet Woodcock last month . Copy a

Co authored by Janet Woodcock last month . Copy and google for the rest . Why trials are tough .

“”Discussion and conclusions
A therapeutic trial ecosystem should possess two key capabilities in order to respond efficiently and effectively to an outbreak of a previously unknown disease such as COVID-19. First, a robust screening mechanism is needed, whereby repurposed drug candidates can be prioritized via mechanistic or nonclinical information and rapidly evaluated for the outbreak-related indications. The second requirement is a system to rapidly and efficiently generate definitive, highly actionable information on safety, efficacy and target population, of a quality that would be deemed acceptable by regulators and expert groups charged with establishing standard of care. Both these capabilities should be highly responsive to emerging information relevant to standard of care or from trial results. Furthermore, when the course of disease is as complex as it is in the case of COVID-19, multiple stages and presentations of disease must be adequately evaluated with respect to candidate drugs that enter clinical trials.

The analysis of our database has revealed gaps in these capabilities. The most important finding in our assessment is that the vast majority of trials of therapeutics for COVID-19 are not designed to yield actionable information; low randomization rates and underpowered outcome data render matters of safety and efficacy generally uninterpretable. Many of these trials are classified as phase II (Fig. 3), indicating procedural barriers to the generation of pivotal data. Especially within the urgent context of the pandemic, rapid screening and seamless phase II–III transitions should facilitate efficient go/no-go decision-making that will preserve resources and optimize enrolment. Notably, we observed great duplication of effort among registered trial arms, with multiple small trials studying similar interventions in similar populations.

The data analysis in this assessment also allows us to evaluate the ecosystem’s reaction to critical findings, such as lack of evidence of efficacy for hydroxychloroquine. Additionally, we can assess the type and speed of response from different sponsors (academic compared with industry; Supplementary Fig. 4), growth in early-stage or late-stage trials, and representation of different geographies. Our data allow us to probe gaps in the clinical development landscape; for example, the insufficiency of investigation we see into pre-exposure and post-exposure prophylaxis (Fig. 2). Finally, we can ask fundamental questions about trial performance during a pandemic. How does trial design affect trial status and results when under the pressures of a rapidly changing public health emergency, and what factors are correlated with any notable trends?

As the COVID-19 pandemic continues, we will continue to assess pertinent factors of the trials landscape as a way of informing national and global COVID-19 response efforts. At the same time, we must continue to identify opportunities for readying our clinical development environment for greater patient impact in the context of public health emergencies.””