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Round table: Principle of dosage selection for veterinary pharmaceutical products Bayesian approach in dosage selection

Round table: Principle of dosage selection for veterinary pharmaceutical products Bayesian 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.

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Round table: Principle of dosage selection for veterinary pharmaceutical products Bayesian approach in dosage selection

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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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