Artificial intelligence-powered approach for strategic pharmaceutical portfolio management based on a priori estimation of clinical trial success evolution

Drug development process is time consuming, expensive, complex and highly risky. The nature of risk in this industry is multidimensional. One important dimension involves clinical development since late-stage development failures are the costliest. If pharmacological efficacy and safety remain well-known failure factors, the literature abounds on a multitude of important failure risk factors of a strategic, operational and commercial nature. In addition to being difficult for humans to control, the decisions associated with these risk factors are major contributors to failure in late stages. Less than 10% of potential medicines that start a Phase I will eventually enter the market and not all marketed drugs will generate revenues that match or exceed R&D costs. Solving this problem requires the development of efficient portfolio management approaches. AI offers capability to streamline late-stages drug development by enabling intelligent control of the multitude of avoidable strategic, commercial and operational risk factors for failure

Faculty Supervisor:

Catherine Beauchemin

Student:

Iness Halimi, Inès Benchaar

Partner:

SORINTELLIS

Discipline:

Life Science

Sector:

Artificial Intelligence, Pharmaceuticals, Biotechnology

University:

Université de Montréal