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СROs
Modern clinical trials have been introduced back in the 20th century and gradually have become the Gold standard for evidence generation in healthcare.
So, what can we do more in this field? The answer is both simple and complicated – harness the power of digitalization of healthcare system and provide even better (ie more effective) gold standard.
Trial design review and optimization via ML algorithms based on RWD by identifying patient subpopulations and by optimizing trial endpoints
Identification of best centers/geographies for patient enrollment
Traditional usage of Real World Evidence and Data has been focused on understanding disease burden, background risk and post approval pharmacovigilance. Recent analyses identify early treatment milestones and have provided a path to patient prospectives regarding treatment.
As a result, a better management patterns emerge that in turn help clinicians to better structure future search and set optimized trial goals.
Synthetic control arms and In silico clinical trials
The synthetic control arm is created by selecting a group of patients from historical data who have similar characteristics (e.g. demographics, disease stage, comorbidities) to the patients in the experimental group. The outcomes of the synthetic control group are then compared to the outcomes of the experimental group to assess the efficacy of the treatment.
Synthetic control arm can be used in various clinical trials such as in orphan drug development, rare disease, and in situations where it is difficult to find a suitable control group. However, it's important to note that synthetic control arm are not without limitations and the validity of the results obtained from this method should be carefully considered.This concept brings us closer to In silico trials – via simulating patients and using digital twins. Which can save us up to 75% of total costs of developing and commercializing a drug, let alone time constraints for recruitment and enrollment of patients.
See also
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