HOME- Bryn Mawr Conference
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- Blank, L
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- Lett, G.S.
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| G. Scott Lett, Bioanalytics Group |
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| Dr. G. Scott Lett is President and a co-founder of The BioAnalytics Group LLC, a company that provides computer simulation, data analysis and visualization products and services to pharmaceutical and medical device companies worldwide. Dr. Lett also serves on the National Institutes of Health Modeling and Analysis of Biological Systems (MABS) Study Section. Prior to co-founding BioAnalytics, he served as Director of Software Development and Distinguished Scientist at Physiome Sciences, a company that pioneered the use of computer simulations in drug discovery. At Physiome, he led numerous software development, modeling and data analysis research efforts, and is the inventor or co-inventor of 6 pending patents. He is co-author of the first CellML language standard for describing biological data. Prior to joining Physiome, Dr. Lett made numerous award-winning contributions in the petroleum and spacecraft industries. An expert in large-scale scientific computing, numerical analysis and data visualization, he received his Ph.D. in Applied Mathematics at the University of Colorado.
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Computational Models are Needed to Make Drugs Safer and More Effective
G. Scott Lett, Bioanalytics Group
Biological simulations are used with increasing frequency to aid in drug discovery and development. Some of the more popular applications include target validation, compound optimization, experimental design and biomarker development. More recently, mechanistic biological models have been used in clinical trials, assisting with trial design, optimal dosing strategies, patient stratification and data interpretation. In recent years, computer simulations of biological processes have graduated from theoretical studies to decision support tools. The reasons for the increasing use of computer simulations as decision support tools will be discussed in this talk, focusing on the following questions:
1. What can be done with a computer that can't be done with laboratory experiments or clinical trials alone?
2. Mathematical models must be imperfect representations of biological processes; under what conditions can we rely upon these models to make decisions?
These questions will be discussed in the light of two example applications; preclinical cardiac drug safety assessment and cancer drug discovery.
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