03: A multivariate approach to estimating the withdrawal time in food animal species

Music City Center 

In the US, the FDA uses linear regression and non-central t distribution to estimate the upper limit of the 95% CI for the 99% quantile (TLM) and define the time as withdrawal time (WDT) when this TLM falls at or below a safe concentration level (tolerance) following the administration of the approved drug in labeled or extra-label manner in food animal species. It involves only the concentrations at or above the limit of detections (LODs) and determines the WDT for each tissue separately. However, the tissues, namely, liver, kidney, muscle, and fat collected from an animal, may be correlated. Therefore, the multivariate linear regression model (MvLR) appropriately addresses this high inter-tissue correlation. In addition, involving only the concentrations above LOD, censored observations can also impact the correlations or covariance pattern among the tissues and result in biased and imprecise estimators. Therefore, we propose using ordinary least squares (OLS), generalized least squares (GLS) in MvLR, and expectation-conditional maximization (ECM) algorithm in censored MvLR along with multivariate t distribution to estimate will provide more precise and accurate estimates of WDT.

TLM

WDT

Multivariate t distribution

OLS

GLS

ECM 

Biopharmaceutical Section