Case Study: Effective precision medicine strategies with KerusCloud.
Over 90% of treatments undergoing trials currently fail to reach the market. Effective study planning and analysis is essential to reduce these failure rates and maximise the chances of a successful treatment development programme.
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 diverse and complex data to deliver advanced analytics to support key decision-making within clinical research programs. With unique second-generation simulation capabilities, KerusCloud ensures that the best design and analysis approach can be chosen to meet clinical objectives and significantly increase the probability of success.
This case study demonstrates how KerusCloud can improve the chances of success for a study where a retrospective Precision Medicine (PM) approach based on Pharmacogenetics (PGx) has been implemented following failure of the main study to meet its primary objective. In this case, PGx may offer key insight into the influence of genetic factors on variability of patient response, allowing the identification of genetic subgroups that drive a meaningful response to treatment. Here, KerusCloud shows that prospective optimisation of the study design to meet its objectives could dramatically increase the probability of study success in Precision Medicine.
- A Phase IIb study in Alzheimer’s disease was conducted, comprising 500 subjects randomised into 4 treatment groups receiving a placebo or 2mg, 4mg or 8mg of Rosiglitazone. Subjects were equally divided (1:1:1:1) with 125 subjects per group and the study was powered to detect a difference in all-comers. The study failed to meet its primary objective so a retrospective PGx study was undertaken using banked blood samples from around 60% of the study participants. This analysis did not form part of any prospective power calculations, so it was considered to be exploratory.
- The objective of the PGx study was to evaluate ability of numerous candidate genetic markers to explain the variability in response and ultimately to identify a predictive marker that could be used to progress the treatment in a genetic subgroup. In this study, we construct some what-if scenarios in KerusCloud and evaluate their likelihood of success.
Increasing PM success with KerusCloud
Statistics on the population response characteristics within each treatment group were extracted from the published study data.Additional information on the interaction between genetic markers, treatment and response was obtained from the literature. These statistics were used to randomly generate patient level data consistent with the original characteristics and assumptions.
Study data was simulated 1,000 times using KerusCloud for two scenarios: performing a retrospective analysis using the original study design; prospectively designing a smarter study where PGx was integrated into the design. For each scenario, we evaluated the probability to detect a genetically defined subgroup of patients who derive a clinically meaningful benefit.
KerusCloud showed that:
- The original study design had a low chance of success (~20%) to detect a clinically meaningful subgroup.
- Increasing the sample collection rate could nearly double the PoS.
- PoS could be further increased by reducing the number of treatment groups and implementing dose response models whilst maintaining the total number of subjects.
- Overall, study success rate can be improved by as much as 41% without increasing sample size and cost.
- Therefore, prospective study planning with KerusCloud can increase the likelihood of study success over the retrospective approach alone and improve the chances of meeting study objectives.
- Using KerusCloud offers the potential to rescue a development programme costing ~ £15-20M.
- KerusCloud enables the successful implementation of Precision Medicine Strategies.
- Published in Drug Discovery Today, 2011; Preventive and Predictive Genetics, 2015.
“The early R&D for KerusCloud was specifically directed towards enabling precision medicine strategies within clinical development programmes. This study demonstrated the power of simulation in complex study designs and the results were used as a proof of concept. This supported the continued development of the software platform which ultimately became KerusCloud .” CEO, Exploristics