Clinical Development
Clinical Development optimization
Accelerate and de-risk your clinical trials.
Our validated and transparent patient-level models and simulators accounting for disease and care diversity and evolution will help you turn disease and care heterogeneity from a threat into an opportunity to accelerate and de-risk your upcoming clinical trials.
Traditional approach : Constrained eligibility criteria to control safety and efficacy.
• High uncertainty due to disease heterogeneity
• Limited generalizability
• Recruitment difficulties
• Limited probability of success
Opportunity for improvement : Patient-centric simulations accounting for disease and care diversity and evolution.
• Identification of high responders with unmet needs
• Trial enrichment to speed-up recruitment while maintaining probability of success
• Endpoint surrogacy optimization to secure correlation between efficacy and effectiveness
• State-of-the-art synthetic control arms
OUR USE-CASES
Trial enrichment for faster recruitment and higher generalizability
Real-World Data-driven eligibility criteria relaxation is key to accelerate and de-risk clinical trials. Trial design enrichment improves trials’ results generalizability and regulatory acceptance. Mapping inclusion/exclusion criteria on Real-World Data allowed to simulate enrichment scenarii and select the optimal one. This helped reduce the recruitment time by several months while maintaining probability of success.
Trial enrichment for faster recruitment and higher generalizability
Real-World Data-driven eligibility criteria relaxation is key to accelerate and de-risk clinical trials. Trial design enrichment improves trials’ results generalizability and regulatory acceptance. Mapping inclusion/exclusion criteria on Real-World Data allowed to simulate enrichment scenarii and select the optimal one. This helped reduce the recruitment time by several months while maintaining probability of success.
Modeling Huntington's disease progression to de-risk Phase III trials
Modeling patient’s heterogeneity in Huntington's disease, a monogenic neurological pathology that inexorably progresses to motor, cognitive and behavioral decline, allowed for maximizing the probability of trial success and duration. A clustering algorithm and a predictive random forest model were used to characterize the endotypes and progression factors and to determine the causal and temporal relationship between biomarkers and clinical signs.
Modeling Huntington's disease progression to de-risk Phase III trials
Modeling patient’s heterogeneity in Huntington's disease, a monogenic neurological pathology that inexorably progresses to motor, cognitive and behavioral decline, allowed for maximizing the probability of trial success and duration. A clustering algorithm and a predictive random forest model were used to characterize the endotypes and progression factors and to determine the causal and temporal relationship between biomarkers and clinical signs.
Historical control arm with adaptive design to optimize prevention of HIV mother-to-child transmission
Intensification of antiretroviral therapy in late pregnancy aims to reduce the risk of mother-to-child transmission of HIV. Such prevention would have a significant impact in low and middle-income countries. However, recruiting at-risk pregnant women remains difficult. Adaptive trial design with historical control arm based on Bayesian modeling resulted in a 5-fold reduction in total sample size and trial duration compared to a standard randomized trial.
Historical control arm with adaptive design to optimize prevention of HIV mother-to-child transmission
Intensification of antiretroviral therapy in late pregnancy aims to reduce the risk of mother-to-child transmission of HIV. Such prevention would have a significant impact in low and middle-income countries. However, recruiting at-risk pregnant women remains difficult. Adaptive trial design with historical control arm based on Bayesian modeling resulted in a 5-fold reduction in total sample size and trial duration compared to a standard randomized trial.