Best Endpoint

Identifying the most efficient way to generate clinical evidence given multiple uncertainties.

Best Endpoint

Best Endpoint 558 344 Exploristics
Case Study: Identifying the most efficient way to generate clinical evidence given multiple uncertainties

 

KerusCloud is a ground-breaking new clinical study design and analytics software platform which delivers smarter real-time studies for today’s clinical research challenges.

Using powerful cloud-based processing, KerusCloud can handle the diverse and complex data now collected routinely, to deliver advanced analytics which simplify the study planning and decision-making process.

With unique second-generation study simulation capabilities, KerusCloud provides exceptional support in developing robust evidence packages for drug approval.

 

The Challenge

Respiratory Syncytial Virus (RSV) infection is a disease with high unmet medical need. It is the most common cause of bronchiolitis in infants <2yrs and patients with underlying conditions can develop serious complications. A biopharmaceutical company developing inhaled formulations was designing a first-time-in-patient study with multiple sources of uncertainty. These included:

A biopharmaceutical company developing inhaled formulations was designing a first-time-in-patient study with multiple sources of uncertainty. These included:

      • Planning multiple doses due to uncertainty about exposure in infants

      • Primary endpoint to be derived based on viral load measured in nasal aspirate samples but sample collection must be minimized due to discomfort for infants

With Exploristics’ support, this developer could examine what optimal controllable design features are needed to maximize the chance of success in this vulnerable population.

Best endpoint and patient subgroup selection with KerusCloud

The Approach

KerusCloud was used to develop and implement a study simulation framework. The information for simulations were derived from natural history data.

Scenarios were evaluated covering:

  1. Number of dose groups
  2. Size of each cohort
  3. Ratio of active treatment to placebo in each cohort
  4. Viral Load endpoint (AUC or slope)
  5. Frequency of sampling

With KerusCloud it was possible to quantify the potential for a study to detect differential treatments in subgroups based on patient age or virus type and investigate analysis options such as exposure-response modelling. KerusCloud also simulated the differential efficacy based on virus type and PK exposure.

 

The Impact

KerusCloud established the appropriate sample-size, number of dose-groups and allocation ratios required.

Its simulation results identified key factors for study success:

  • Quantifying the minimum number of nasal aspirate samples and best choice of endpoint and statistical analysis
  • Confirming that the exposure-response modelling was viable

Overall, this approach reduced the duration of the study by one year.

“The simulations were beautiful. They exploited the full potential of a historical data set to inform the design of our study. Options were ruled in or out and critical elements identified. This allowed us to simplify, enhance and de-risk the study design.” Chief Medical Officer, Small Biotech, UK