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Application of predictive biosimulation within pharmaceutical clinical development: examples of significance for translational medicine and clinical trial design

Application of predictive biosimulation within pharmaceutical clinical development: examples of significance for translational medicine and clinical trial design

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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.

References

    1. 1)
      • M.J. Reed , K. Meszaros , L.J. Entes , M.D. Claypool , J.G. Pinkett , T.M. Gadbois , G.M. Reaven . A new rat model of type 2 diabetes: the fat-fed, streptozotocin-treated rat. Metabolism
    2. 2)
      • K. Hall , S.Q. Siler , R. Leipold , M. Hager , J.K. Trimmer , D. Polidori . Computer simulation of insulin therapy in type 2 diabetes: reduction of liver glycogen and counter-regulatory increase in food intake. Diabetes
    3. 3)
      • C.B. Newgard , L.J. Hirsch , D.W. Foster , J.D. McGarry . Studies on the mechanism by which exogenous glucose is converted into liver glycogen in the rat. A direct or an indirect pathway?. J. Biol. Chem.
    4. 4)
      • K. Hall , J. An , D. Polidori , A. Kansal , J. Trimmer , S. Siler , C. Newgard . Insulin therapy does not affect endogenous insulin secretion or liver glycogen despite decreased glucose in a novel rat model of type 2 diabetes. Diabetes
    5. 5)
      • Guidelines for computer modeling of diabetes and its complications. Diabetes Care
    6. 6)
      • J.A. DiMasi , R.W. Hansen , H.G. Grabowski . The price of innovation; new estimates of drug development costs. J. Health Econ. , 151 - 185
    7. 7)
      • T. Lindstrom , H.J. Amqvist , J. Ludvigsson , H.H. von Schenck . C-peptide profiles in patients with non-insulin-dependent diabetes mellitus before and during insulin treatment. Acta Endocrinol. (Copenh.)
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