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Artificial Intelligence for Better Clinical Trial Prediction
Predict the probability of
clinical trial success
Evaluate
investment decisions
Track clinical trial KPIs and
identify critical path
Leverage AI to predict clinical trial outcomes
There are a lot of statistics about drug development – and all of them show that the process is inefficient and expensive:
- Pharma companies spend more than USD 1 Billion on developing a new drug
- Drug candidates pass the clinical stage with a success rate as low as 10-15%
- Of all US clinical studies, 86% fail to meet the recruitment targets on time
- Dropout rates of clinical trials commonly range between 15-40%
- Of failed trials, 57% show limited efficacy; poor statistical endpoints or underpowered samples
However, clinical trial failures can be modeled by applying Artificial Intelligence techniques on real-world, outside-trial, and biomedical data sets. Innoplexus’ Clinical Trial Prediction engine leverages advanced deep learning techniques trained on publicly available trial data as well as on real-world events that are continuously crawled, aggregated, and analyzed by our proprietary technology.
The CTP engine serves various stakeholders keen on the prediction of clinical trial outcomes
Investment Management
A pharma or biotech company’s stock performance depends heavily on the outcome of its pipeline. Prior knowledge of approval probability of a trial can help investors gain an edge!
CROs
Pharma
Biotech
CTP helps CROs, pharma, and biotech companies to track clinical trial KPIs, optimize their clinical trial recruitment strategies, and mitigate operational and financial risks. CTP empowers companies to make the decisions and course corrections before obstacles delay a trial.
The CTP engine leverages advanced AI and analytics technology
Advanced deep learning techniques trained on publicly available trial data and real-world events that are continuously crawled, aggregated, and analyzed by Innoplexus’ proprietary technology.
A neural network trained on various drug compound characteristics, clinical trial features, and sponsor track records, provides insights for optimizing study designs.
Fully automated, continuous, and real time analysis enables Innoplexus to calculate predictions by accounting for new information which might impact a trial endpoint.
Leverage CTP engine to identify healthcare stock rises
Innoplexus’ CTP model forecasts the outcome of clinical studies and may serve as an early indicator for future stock movements.
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Theses & Dissertation
- World´s largest Life Science Ontology with 31M+ biomedical terms
- Connecting drugs, indications, study design, trial information, patients, authors, and sponsors with real-world events
- Structuring unstructured data
- Real time predictions
