Drug RescueOrigent works with pharmaceutical companies, biotech firms, and CROs to analyze past trial results in order to identify patient sub-groups for which efficacy can be proven. By employing our disease progression models, Origent can pinpoint the characteristics of patient cohorts benefitted from an intervention. Clients utilize this data to re-start or re-orient trials and seek regulatory approval for specific patient groups.
The Problem: Late Stage Clinical Trials Fail Unexpectedly
Early stage clinical trials may be unknowingly biased due to the enrollment of patients who were predisposed to progress slowly. This slow progression creates an artificial positive result within the early stage clinical trials. Phase 2B and Phase 3 trials are undertaken on the basis of the artificial positive results, and consequently fail as a result.
These late stage failures result in massive financial losses, lost time, and wasted goodwill.
The Solution: Post Hoc Analyses and Simulations
Evaluate the expected progression of each patient in both the early and late-stage clinical trials to determine whether the trials unknowingly enrolled patients with different expected disease progression profiles. Analyze the late-stage trial population to simulate trial results using only those patients with predicted disease progression profiles similar to the early-stage trials.
Identify probable responders within the late-stage clinical trial by comparing their disease progression following an intervention to their personalized predicted progression profiles. Isolate likely responders to develop hypotheses about patient responder subgroups and design experiments to test those hypotheses.
The Outcome: New Opportunities to Generate Returns from Lost Assets
- Pharmaceutical managers find new submarket opportunities to recoup value from otherwise lost investments in failed drugs.
- Clinical researchers gain clearer insight into why the trials failed, and develop strategies for avoiding similar failures in the future.