title: In silico prediction of optimal in vivo delivery properties using convolution-based model and clinical trial simulation.
abstract: OBJECTIVE
  To develop a new strategy for the in silico evaluation of the optimal in vivo delivery properties of a drug, minimizing a cost function defined by the brain receptor occupancy obtained in positron-emission tomography experiments.
  
  
  METHODS
  A convolution-based model was formulated to link in vivo delivery rate to plasma concentrations whereas a second-stage model was used to link plasma concentrations to the pharmacodynamic effect. A feedback control approach was applied to identify the optimal in vivo delivery rate given an appropriate optimality criterion. Finally, clinical trial simulation was used as a supportive tool for decision-making by evaluating different scenarios accounting for pharmacokinetic/pharmacodynamic parameter uncertainty, inter-subject variability. and drug potency.
  
  
  RESULTS
  The results revealed that the mean in vivo delivery time significantly affects brain receptor occupancy whereas the fraction of the dose available for the systemic circulation shows the highest influence on brain receptor occupancy for a given in vivo delivery rate. Finally, variability on receptor occupancy seems to be more affected by the inter-individual variability on the disposition PK parameters.
  
  
  CONCLUSIONS
  The integration of convolution-based model. feedback control approach, and clinical trial simulation offers a unique tool for in ilico improvement of the drug development process by identifying critical issues on drug properties, optimal in vivo delivery rate, and potential problems related to the inter-individual variability.
authors.affiliation: GlaxoSmithKline, Verona, Italy. rog31390@gsk.com
authors.forename: Roberto
authors.initials: R
authors.lastname: Gomeni
authors.forename: Carla
authors.initials: C
authors.lastname: Dangeli
authors.forename: Alan
authors.initials: A
authors.lastname: Bye
mesh_terms: D002986:Clinical Trials as Topic; D003198:Computer Simulation; D003692:Delayed-Action Preparations / Q000493:pharmacokinetics*; D008954:Models, Biological
chemical_list: D003692:Delayed-Action Preparations