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Bayesian Approach in Dosage Selection for Veterinary Pharmaceuticals

Learn about Bayesian forecasting methods in dosage selection, therapeutic drug monitoring, and the impact of population pharmacokinetics variability on exposure, efficacy, and toxicity. Understand how to predict exposure using a priori information. Discover the importance of population information in optimal dosage selection for veterinary pharmaceutical products.

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Bayesian Approach in Dosage Selection for Veterinary Pharmaceuticals

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  1. Round table: Principle of dosage selection for veterinary pharmaceutical productsBayesian approach in dosage selection NATIONAL VETERINARY S C H O O L T O U L O U S E D. Concordet National Veterinary School Toulouse,France EAVPT Torino September 2006

  2. Bayesian forecasting methods = Therapeutic drug monitoring

  3. Efficacy Toxicity Exposure Why a bayesian forecasting method ? Consequence of PK Variability : the same dose gives different exposures

  4. Efficacy Toxicity Exposure Why a bayesian forecasting method ? Consequence of PK Variability : the same dose gives different exposures We need to anticipate the "level" of exposure

  5. Exposure How to predict exposure ?

  6. POPULATION PK Cannot be predicted with covariates Need further information Exposure Covariate : e.g. Age How to predict exposure ?

  7. a priori information A blood sample at this time The bayesian approach Same dose animals with the same age Probably a high exposure

  8. Probably a small exposure A blood sample at this time The bayesian approach Same dose animals with the same age a priori information

  9. Exposure ? A blood sample at this time The bayesian approach Same dose animals with the same age a priori information

  10. Why population information is needed ? Concentration Exposure ? Time A blood sample at this time

  11. A blood sample at this time The bayesian approach Same dose animals with the same age

  12. Frequency Exposure A blood sample at this time The bayesian approach Same dose animals with the same age

  13. The a posteriori distribution Distribution of exposure for animals that received the same dose have the same age have the same drug concentation at the sampling time Frequency Exposure Maximum a posteriori (MAP) = Bayesian estimate = most common exposure

  14. Frequency Exposure The a priori information Same dose animals with the same age A blood sample at this time

  15. Frequency Exposure The a priori information Same dose animals with the same age A blood sample at this time

  16. Frequency Exposure The a priori information Same dose animals with the same age A blood sample at this time

  17. Exposure Covariate : e.g. Age How to predict exposure ? POP. PK

  18. Exposure Covariate : e.g. Age How to predict exposure ? POP. PK + 1 concentration POP. PK

  19. Exposure Covariate : e.g. Age How to predict exposure ? POP. PK + 2 concentrations POP. PK + 1 concentration POP. PK

  20. Problem of highly variable drugs ? 1st Administration: fixed dose Concentration A blood sample at this time Time

  21. Problem of highly variable drugs ? 2nd Administration: same animal, same dose as 1st Large inter-occasion variability Concentration A blood sample at this time Time

  22. How does it work ? A population model jth concentration measured on the ithanimal jth sample time of the ithanimal

  23. How does it work ? A set of concentrations on THE animal : (t1, Z1), (t2, Z2), … Maximize the a posteriori likelihood Minimize

  24. To summarize Bayesian forecasting can be useful for: pets touchy drugs (narrow therapeutic index) It requires: results of a pop PK study some concentrations on the animal a recent computer Can’t work for large inter-occasion variability

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