title: Use of predictive models in CNS diseases.
abstract: Today the CNS drug development poses serious challenges for developers given the low probability of success and the disproportionately high investment costs. This review demonstrates how predictive models can provide quantitative criteria for increasing the efficiency of drug development in CNS. Predictive models can be applied to characterize, understand, and predict a drug's PK and PD behavior; to quantify uncertainty of information about that behavior; to identify factors that could affect the outcomes of a clinical trial through Clinical Trial Simulation (CTS), to identify prognostic factors that could affect the disease progression, to implement optimal and adaptive clinical trial and finally to control the level of placebo response by implementing study designs that minimizes the impact of placebo on study outcomes.
authors.affiliation: PharmacoMetrica, Lieu-dit Longcol, La Fouillade, France. Electronic address: roberto.gomeni@pharmacometrica.com.
authors.forename: Roberto
authors.initials: R
authors.lastname: Gomeni
mesh_terms: D002491:Central Nervous System Agents / Q000493:pharmacokinetics / Q000494:pharmacology / Q000627:therapeutic use*; D002493:Central Nervous System Diseases / Q000188:drug therapy* / Q000503:physiopathology; D002986:Clinical Trials as Topic / Q000379:methods; D003198:Computer Simulation; D018450:Disease Progression; D015195:Drug Design; D006801:Humans; D008954:Models, Biological; D015990:Placebo Effect; D011379:Prognosis
chemical_list: D002491:Central Nervous System Agents