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There are a lot of statistics about drug development – and all of them show that the process is inefficient and expensive:
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.
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!
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.
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.
Gene therapy promises to hold the cure to a number of diseases which, until now, have been considered incurable. By replacing a faulty gene in a patient’s DNA or repairing a defective gene by gene editing, gene therapy can provide a one-time treatment option for diseases such as genetic blindness, muscular dystrophy, blood cancer. Read our recent whitepaper: Advancements in gene therapy – usage of AAV vectors here.
Chemicals & Drugs
Theses & Dissertation