Prof. Serge Guzy acquired his chemical engineering degree in 1982 from the University of Brussels and received his master degree in chemistry and biophysics in 1985 from the Weizmann Institute. Serge obtained his Doctorate in biomedical engineering on 1990 from the Technion and one year later got his post-doctorate form UC Berkeley in chemical engineering and held a faculty position UCSF School of Pharmacy between 1991-1996. Since that time Serge served as a consultant for Pharsight, headed the Pharmacometrics at XOMA, Biotech, and founded POP-PHARM with the end of providing PK/PD consulting and software development in support of drug development (
see Serge CV). Serge developed the MC-PEM engine of the PDX-MC-PEM software which allows to characterize PK/PD information for a specific population based on PK/PD observations. 

With 20 years of experience with modeling and simulation, Serge Guzy is currently President, CEO of POP-PHARM, a consulting and software Development Company. He established new methods for statistical population approaches in drug development, based on Monte Carlo simulation algorithms. The resulting MC-PEM methodology and Population software development made him now internationally recognized. These new tools have already been well utilized in drug development, from the early stage of antibody discovery and lead selection programs as a useful tool to guide antibody design goals and inform teams for better decision making , as well as in clinical development as a tool for guiding optimally new trial designs.

Beyond supporting Preclinical R&D and Clinical development for PK/PD modeling and analysis, Serge has branched out to the use of Decision tree analysis combined with Monte Carlo simulation and Real Option Analysis to evaluate stochastically company portfolio.

This approach is useful to:

- balance optimally the different projects across the company

- prevent with high probability the company to remain without financial resources

- provides a better long range forecast of the company financial future



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