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Clinical Trial Prediction

Clinical Trial Prediction

Pharma companies spend more than 1 Billion USD on developing a new drug. Drug candidates pass the clinical stage with a success rate as low as 10-15%. Of all clinical studies, 86% fail to meet recruitment targets on time. Dropout rates of clinical trials commonly range between 15-40%. Of failed trials, 57% show limited efficacy i.e. due to poor statistical endpoints or underpowered samples and thus making clinical trials inherently more time consuming and expensive.

Predicting the probability or likelihood of success (PoS) for a clinical trial is important for pharma investors and to the clinical investigators that help them in evaluating the economic and scientific decisions. Without the proper estimate of PoS there is a chance for misjudging the risk and value proposition for the drug development that may lead to loss of opportunities for both investors and patients. Furthermore, to help prioritize the proper resource allocations, accurate and timely assessment of risk is critical and is the need of the hour for the drug development companies conducting the clinical trials.

Innoplexus, an AI-based company, leveraged its life sciences data ocean and proprietary AI technologies to build a clinical trial prediction (CTP) engine that can predict the probability of success for a clinical trial. Innoplexus continuously crawls, aggregates, and structures data for the prediction engine by using the computer vision technology. The algorithms auto-label the clinical trials based on, if the clinical trial met, its endpoints to build a training dataset for the prediction engine. The predicted outcome of a clinical trial is based on its likelihood of meeting its endpoints. The model continually refines its predictions as more information is publicly disclosed.

Using the CTP engine, Innoplexus can help answer the following questions:

  • What are the most important parameters for a clinical trial in a given indication?
  • Which investigators will improve success probability?
  • Which sites have the right “trial load” or are best to execute trials?
  • Is the anticipated enrollment time reasonable?
  • How successful is a sponsor in conducting CTs in a given therapeutic area?
  • What is the supporting biological evidence for the drug-indication being investigated