Artificial Intelligence for Better Clinical Trial Prediction

The clinical trial prediction (CTP) engine empowers your decision-making

Predict-the-probability-of-clinical-trial-successPredict the probability of

clinical trial success

Evaluate-investment-decisionsEvaluate

investment decisions

Clinical-Trial-KPIsTrack 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 ManagementInvestment 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!

CROsCROs

PharmaPharma

DNABiotech

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

Deep Learning

 

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.

Neural Network

 

A neural network trained on various drug compound characteristics, clinical trial features, and sponsor track records, provides insights for optimizing study designs.

Real Time Analysis

 

Fully automated, continuous, and real time analysis enables Innoplexus to calculate predictions by accounting for new information which might impact a trial endpoint.

We connect more than 350+ Attributes based on our Life Science Data Ocean

35M+

Publications

580K+

Clinical Trials

9M+

Genes

5M+

Grants

3M+

Congress Articles

26M+

Patents

2M+

Chemicals & Drugs

1M+

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

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