This three-day, hands-on course provides a working knowledge of the theories and application of the state-of-the-art convolution-based modeling approach to characterize complex PK profiles, to establish in-vitro in-vivo correlation (IVIVC) and to optimize ER and LAI formulation development using the exposure-response relationship.

 

The course is structured in the following sections:

 

Convolution based model

• Development of convolution-based models

• Modeling single and repeated doses administration using convolution-based approach in NONMEM

• Development of an R based simulator for convolution-based models

 

Modeling complex absorption process

• Definition and implementation in NONMEM of typical and user defined input function models, to characterize delayed, multiple peaks and time dependent absorption process

• Characterize the complex in-vivo absorption processes of extended release formulations (ER) and Long Acting Injectable (LAI) formulations using convolution-based approach

 

Implementing Level A IVIVC

• Definition of Level A IVIVC

• Implementation of a Level A IVIVC using convolution-based models in NONMEM

• The concept of time-scaling

• Definition of time scaling functions for assessing IVIVC in NONMEM

• Evaluate the quality of an IVIVC model  and the predictive performances  of an IVIVC model

 

Optimizing drug performances

• Definition of drug/formulation optimization strategies using surface-response analysis, clinical trial simulation and convolution-based models

• Development of an R based optimizer

• Determine the relationship between drug delivery and clinical benefit using the outcome of an IVIVC analysis

• Determine dose, dosage regimen and dosing strategies for optimizing the clinical benefit using the outcomes of the IVIVC analysis

 

Who Should Attend?

The workshop is designed for pharmaceutical scientists, pharmacologists, physicians and other scientists involved in drug development wishing to learn and apply pharmacometrics as innovative decision supporting tool for accelerating drug discovery and development.

 

What Will You Learn?

Upon completion of this course, you should have a solid understanding of how to implement convolution-based models using NONMEM, how to implement IVIVC using NONMEM, how to simulate drug exposure as a function of the modified in-vivo delayed and time dependent drug release properties of ER and LAI formulations using R, and how to optimize the properties of ER and LAI formulations for maximizing the clinical benefit of a treatment.

 

References

• R Gomeni, F. Bressolle‐Gomeni, TJ Spencer, SV Faraone, L Fang, A Babiskin. Model‐Based Approach for Optimizing Study Design and Clinical Drug Performances of Extended‐Release Formulations of Methylphenidate for the Treatment of ADHD. Clin Pharmacol Ther. 2017 Dec;102 (6):951-960.

• R. Gomeni, F. Bressolle, M. Fava. Response surface analysis and non-linear optimization algorithm for maximization of clinical drug performance: Application to extended release and long-acting-injectable paliperidone. J Clin Pharmacol. 2016 Oct; 56(10):1296-306.