Drug RescueOrigent works with pharmaceutical companies, biotech firms, and CROs to analyze past trial results in order to identify patient subgroups for which efficacy can be proven.
The Problem: Late Stage Clinical Trials Fail Unexpectedly
Drug development, particularly for CNS diseases like amyotrophic lateral sclerosis (ALS), Parkinson’s (PD), and Huntington’s (HD), has historically been plagued by high clinical trial failure rates. Due to the extraordinarily high cost of drug development, approximately $22B in R&D investment is written off annually from failed trials.
Biological and clinical heterogeneity are often cited as contributing factors for the dismal success rate of Phase III trials in this class of diseases. Too often, the overall heterogeneity of large Phase III patient populations creates statistical noise that masks the significance of an otherwise demonstrable treatment effect.
The Solution: Post Hoc Analyses and Simulations
Origent utilizes machine learning and AI to optimize human clinical trials. Our patient-level predictive analytics platform is designed to identify failed drug programs that can be rescued. By employing our validated disease progression predictive models, Origent can pinpoint the characteristics of patient cohorts where treatment effects can be demonstrated. This insight is used to revive failed drug programs and return them on a path toward regulatory approval for specific patient subgroups.
Origent’s ForecastOne® DEC platform determines whether a prognostic prediction-defined subgroup of patients in a failed clinical trial demonstrates a statistically significant treatment effect. Detectable Effect Cluster (DEC) analysis provides an unbiased, innovative new framework for re-examining failed ALS drug trials and uncovering “hot spots” where a prediction defined patient subgroup could form the basis of a subsequent successful trial.
The Outcome: New Opportunities to Generate Returns from Lost Assets
ForecastOne® DEC allows Origent to identify past failed drug programs that could be returned to active development and quickly enter registration studies. These new studies use algorithmic patient screening criteria to enroll a more homogenous patient population in which efficacy can be demonstrated. Based on this insight the new trial enjoys a greater degree of confidence, allowing a previously-abandoned drug program to make it to registration.
Learn more about the use of DEC analysis in ALS by viewing our poster from the 31st International Symposium on ALS/MDN.
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