Application of predictive biosimulation within pharmaceutical clinical development: examples of significance for translational medicine and clinical trial design.
The challenge of accurately predicting human clinical outcome based on preclinical data has led to a high failure rate of compounds in human clinical trials. A series of methods are described by which biosimulation can address these challenges and guide the design and evaluation of experimental and clinical protocols. Early compound development often proceeds on the basis of preclinical data from animal models. The systematic evaluation possible in a simulation can assist in the critical step of translating the preclinical outcomes to human physiology. Later in the process, clinical trials definitively establish a therapy's beneficial effects, as well as any adverse side effects. Biosimulation allows for the optimal design of clinical trials to ensure that key issues are addressed effectively and efficiently, and in doing so, improves the success rate of the trials.
Kansal
A R
AR
Entelos Inc, Foster City, CA 94404, USA. kansal@entelos.com
Trimmer
J
J
D000818:Animals; D001681:Biological Assay / Q000379:methods*; D002986:Clinical Trials as Topic / Q000379:methods*; D003198:Computer Simulation; D003920:Diabetes Mellitus / Q000188:drug therapy / Q000378:metabolism*; D004195:Disease Models, Animal; D015195:Drug Design; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D004345:Drug Industry / Q000379:methods; D006003:Glycogen / Q000378:metabolism*; D006801:Humans; D007328:Insulin / Q000008:administration & dosage*; D008099:Liver / Q000187:drug effects / Q000378:metabolism*; D008297:Male; D008954:Models, Biological; D051381:Rats; D017208:Rats, Wistar; D012107:Research Design; D013045:Species Specificity
D007328:Insulin; D006003:Glycogen