Quantitative risk modelling for new pharmaceutical compounds.
The process of discovering and developing new drugs is long, costly and risk-laden. Faced with a wealth of newly discovered compounds, industrial scientists need to target resources carefully to discern the key attributes of a drug candidate and to make informed decisions. Here, we describe a quantitative approach to modelling the risk associated with drug development as a tool for scenario analysis concerning the probability of success of a compound as a potential pharmaceutical agent. We bring together the three strands of manufacture, clinical effectiveness and financial returns. This approach involves the application of a Bayesian Network. A simulation model is demonstrated with an implementation in MS Excel using the modelling engine Crystal Ball.
Tang
Zhengru
Z
School of Computing and Mathematical Sciences, Liverpool John Moores University, Liverpool L3 3AF, UK.
Taylor
Mark J
MJ
Lisboa
Paulo
P
Dyas
Mark
M
D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D015195:Drug Design; D004341:Drug Evaluation / Q000706:statistics & numerical data; D004353:Drug Evaluation, Preclinical / Q000706:statistics & numerical data; D004345:Drug Industry / Q000191:economics / Q000706:statistics & numerical data*; D009010:Monte Carlo Method; D011336:Probability; D018570:Risk Assessment