Talk Abstract #2

October 11-12, 2005
eCheminfo & Innovation Well Conferences

An interactive environment for multiparameter optimization

James H Wikel, Coalesix Inc., Cambridge, MA

The challenge of developing useful drugs from active molecules is a Herculean effort. The task requires the simultaneous satisfaction of many criteria including: inherent potency, low side effect profile, good solubility, oral absorption, appropriate distribution in various tissues including the target tissue, metabolism at a rate that ensures sufficient residence time in vivo to be an effective drug, and excretion of parent drug and metabolites in order to minimize any toxic accumulation. The mantra of ‘fail fast’ is especially important in the pharmaceutical industry where the average new drug development costs are currently estimated in excess of $800 million. Associated with the high development costs are also the high risks of clinical failures. For example, an estimated 10,000 new structures are made and tested for every new drug reaching the market. The current success rate for clinical candidates to yield a marketed drug is only 1 in 10 or, put another way, the failure rate for clinical candidates is 90%. Boston Consulting Group estimates that if pharmaceutical companies could effect a 10% reduction in clinical failures they could realize a $100 million savings in development costs for every new drug. The most obvious way to achieve this result is to enter clinical trials with the best possible candidates, whose drug-like properties have been rigorously optimized, thereby improving the chances of a successful outcome. We have developed a novel optimization technology focused on providing an in silico environment enabling and synergizing multiparameter optimization with expert intuition and knowledge. The resulting environment enables the identification of a diverse selection of preclinical drug candidate molecules with better overall properties, thus supporting better decision making and providing a higher likelihood of clinical success.

Several components of the problem - such as synthetic tractability or IP status – are subjective or inherently implicit and so do not lend themselves to a purely computational approach. Coalesix's Candidate Design Environment is based on Interactive Evolutionary Computing (IEC). IEC is an optimization technique, which uses subjective human evaluation in problems where it is difficult or impossible to design a fitness function for automatic search of the input space.


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