Response Surface Analysis and Nonlinear Optimization Algorithm for Maximization of Clinical Drug Performance: Application to Extended-Release and Long-Acting Injectable Paliperidone.

Model-based approach is recognized as a tool to make drug development more productive and to better support regulatory and therapeutic decisions. The objective of this study was to develop a novel model-based methodology based on the response surface analysis and a nonlinear optimizer algorithm to m...

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Publicado en:Journal of Clinical Pharmacology Vol. 56; no. 10; pp. 1296 - 1307
Autores principales: Gomeni, Roberto, Bressolle‐Gomeni, Françoise, Fava, Maurizio
Formato: case study equations & formulas research tables/charts Journal Article
Publicado: Wiley-Blackwell Oct2016
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Oct2016
      vid: 56
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      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        10.1002/jcph.724
        118246591
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        atl: Response Surface Analysis and Nonlinear Optimization Algorithm for Maximization of Clinical Drug Performance: Application to Extended-Release and Long-Acting Injectable Paliperidone.
      aug:
        au:
          Gomeni, Roberto
          Bressolle‐Gomeni, Françoise
          Fava, Maurizio
        affil: R&D Department, Pharmacometrica, Longcol, La Fouillade France
      sug:
        subj:
          Paliperidone Administration and Dosage
          Drug Design
          Paliperidone Pharmacodynamics
          Paliperidone Pharmacokinetics
          Clinical Effectiveness Evaluation
          Algorithms Utilization
          Human
      ab: Model-based approach is recognized as a tool to make drug development more productive and to better support regulatory and therapeutic decisions. The objective of this study was to develop a novel model-based methodology based on the response surface analysis and a nonlinear optimizer algorithm to maximize the clinical performances of drug treatments. The treatment response was described using a drug-disease model accounting for multiple components such as the dosage regimen, the pharmacokinetic characteristics of a drug (including the mechanism and the rate of drug delivery), and the exposure-response relationship. Then, the clinical benefit of a treatment was defined as a function of the diseases and the clinical endpoints and was estimated as a function of the target pharmacodynamic endpoints used to evaluate the treatment effect. A case study is presented to illustrate how the treatment performances of paliperidone extended release (ER) and paliperidone long-acting injectable (LAI) can be improved. A convolution-based approach was used to characterize the pharmacokinetics of ER and LAI paliperidone. The drug delivery properties and the dosage regimen maximizing the clinical benefit (defined as the target level of D2 receptor occupancy) were estimated using a nonlinear optimizer. The results of the analysis indicated that a substantial improvement in clinical benefit (from 15% to 27% for the optimization of the in vivo release and from ∼30% to ∼70% for the optimization of dosage regimen) was obtained when optimal strategies were deployed either for optimizing the in vivo drug delivery properties of ER formulations or for optimizing the dosage regimen of LAI formulations.
      pubtype: Academic Journal
      doctype:
        case study
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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