From Big Data to Smart Data. Quantitative Systems Pharmacology as a novel mechanism-based computer-modeling approach to support CNS Research & Development

Hugo Geerts is co-founder and Chief Scientific Officer of In Silico Biosciences, a company that provides mechanistic Quantitative Systems Pharmacology disease modeling services in CNS Drug discovery & Development since 2002. After an undergraduate degree in physics (theoretical quantum-mechanics) and a PhD in Biophysics he got a Bachelor Degree in Medicine, a Master in Pharmaceutical Sciences, and a Pharma Executive MBA at Drexel University in Philadelphia. He worked for 17 years with Dr. Paul Janssen, probably the greatest drug hunter in history at the Janssen Research Foundation (part of J&J) where he headed the Alzheimer Discovery research with programs in tangle and b-amyloid pathology, identified galantamine as an inlicensing candidate and supported its successful clinical development. Galantamine is known as Razadyne in the US and Reminyl in Europe. He is passionate about the need for combining Big Data and Smart Data in order to develop more translational, quantitative and predictive computer modeling & simulation along the whole Drug R&D process by creating individualized human ‘avatars’. This hopefully will reduce the failure rate of clinical trials and get better drugs faster to the right patients. He is also on the faculty of the Drexel University Dept. of Pharmacology.

Massive investment and technological advances in the collection of extensive and longitudinal information on thousands of Alzheimer patients results in large amounts of data. These “Big-Data” databases can potentially advance CNS research and drug development. However, although necessary they are not sufficient and we posit that they must be matched with analytical methods that can generate actionable and quantitative predictive models.
While these empirically-derived associations can identify novel and useful hypotheses, they need to be organically integrated in a quantitative understanding of the pathology with practical use for drug discovery and development. Mechanism-based modeling and simulation approaches, where existing domain knowledge is formally integrated using complexity science and quantitative systems pharmacology can be combined with data-driven analytics in predictive models for target validation, drug discovery programs and optimization of clinical development. We will give examples of Quantitative Systems Pharmacology applications in psychiatry and neurology with proven value both for the pharma industry and physicians treating patients in real-life clinical settings.