Development of a novel method for updating the predicted partition coefficient values generated by an existing in silico prediction method
Helen Graham (1), James Yates (2), Aleksandra Galetin (1), Leon Aarons (1)
(1) School of Pharmacy and Pharmaceutical Sciences, University of Manchester, M13 9PL; (2) AstraZeneca, Alderley Edge, SK10 4TG
Objective: The use of PBPK modelling is becoming an increasingly important step in the drug development process as it aims to reduce the amount of in vivo and in vitro work needed during the early stages. Partition coefficients (Kps) are a vital input parameter for these models, as they help to describe the distribution of a drug within the body, and can be used to predict volume of distribution. Many in silico methods exist in the literature for the prediction of these Kp values, with varying degrees of accuracy. Six of these methods have been compared in previous work, with the Rodgers & Rowland method [1,2] found to be the most accurate across all drug classes and in all tissues. Therefore this method has been chosen as the basis for a novel Kp predictor which takes the predictions made by the Rodgers & Rowland method and updates them, taking into account experimental data gathered during the early stages of drug development. These updated Kp predictions can then be used to generate predictions for other pharmacokinetic parameters, such as concentration-time profiles, Vss, and t1/2 in both rat and human.
Methods: A covariance matrix was generated from prior knowledge of the error of the Rodgers & Rowland Kp predictions when compared to experimental values. A Monte Carlo simulation was performed to produce randomly generated sets of Kp predictions (using the Rodgers & Rowlands predictions as the mean), and these values were then used within a PBPK model to produce a set of predicted concentration-time profiles. Using a conditional log likelihood function, information taken from the Monte Carlo simulations was used along with prior knowledge from the experimentally derived concentration-time profile to produce a set of updated Kp values. This work was performed using the modelling tool AcslX®.
Results: The updated Kp values for certain tissues (such as liver and adipose) were shown to be an improvement upon the Kp predictions generated by the Rodgers & Rowland method when compared to experimental values, and they were shown to produce improved predictions for Vss, in addition to predicted concentration-time profiles that mimic more closely the experimental data.
Conclusion: A novel method has been described that can generate updated Kp values that are an update of the predictions generated by the Rodgers & Rowland method, using information about the error of the method and experimentally-derived iv profiles as prior knowledge.
 Rodgers et al. (2005) J Pharm Sci 94(6):1259-1276
 Rodgers and Rowland (2006) J Pharm Sci 95(6):1238-1257