The clinical trial is a critical stage of drug development workflow, with an estimated average success rate of about 11% for drug candidates moving from Phase 1 towards approval. Even if the drug candidate is safe and efficacious, clinical trials might fail due to the lack of financing, insufficient enrollment, or poor study design. [Fogel DB. 2018].
Artificial Intelligence (AI) is increasingly perceived as a source of opportunities to improve the operational efficiency of clinical trials and minimize clinical development costs. Typically AI vendors offer their services and expertise in the three main areas. AI start-ups in the first area help to unlock information from disparate data sources, such as scientific papers, medical records, disease registries, and even medical claims by applying Natural Language Processing (NLP). This can support patient recruitment and stratification, site selection, and improve clinical study design and understanding of disease mechanisms. As an example, about 18 % of clinical studies fail due to insufficient recruitment, as a 2015 study reported.
Another aspect of success in clinical trials is improved patient stratification. Since trial patients are expensive - the average cost of enrolling one patient was $15,700-26,000 in 2017 -- it is important to be able to predict which patient will have greater benefit or risk from treatment. AI-driven companies operate with multiple data types, such as Electronic Health Records (EHR), omics, and imaging data to reduce population heterogeneity and increase clinical study power. Vendors could use speech biomarkers to identify neurological disease progression, imaging analyses to track treatment progression, or genetic biomarkers to identify patients with more severe symptoms.
AI is also streamlining the operational processes of clinical trials. AI vendors help to track patient health from their homes, monitor treatment response, and patient adherence to the trial procedures. By doing that AI companies decrease the risk of patient dropouts, which accounted for 30% on average. Usually, the Phase 3 clinical study stage requires 1000-3000 participants, with a part of them taking a placebo. That’s why the development of synthetic control arms - AI models that could replace the placebo-control groups of individuals thus reducing the number of individuals required for clinical trials - might become a novel trend.
Below we summarize a list of notable AI vendors providing advanced tools for clinical development.
Informational and analytical engines
ConcertAI (formerly, Concerto HealthAI) is a US-based company founded in 2017. The company provides real-world evidence (RWE) services for precision oncology. It has established the broadest clinical network through the partnership and licensing with community oncology networks, thus getting access to Electronic Medical Records, Results of NGS diagnostics, and patient-reported outcomes. Concerto then analyzes such data and generates evidence for new therapeutic approaches. The company raised a total of $300 million from a number of investors, with the latest $150 million Series C round from Sixth Street.
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Topics: Biotech Startups