Hybrid Modified League Championship Algorithm and its Applications in Compartmental Pharmacokinetic-Pharmacodynamic Data Analysis

We present herein, the application of a hybrid modified league championship (HMLCA) algorithm and its applications for the reliable estimation of Compartmental PKPD parameters, for the first time in literature. For this purpose, we have modified the original League Championship algorithm, combined it with local optimization methods such as Nelder-Mead and Gauss-Newton, such that the final compartmental PK-PD parameters are estimated in a single step, to facilitate ease of analysis and to avoid laborious procedures. In HMCLA, the initial PK-PD parameters are estimated by modified LCA and then, they are optimized by the local optimization methods. We have studied the performance of the new approach using thirty-seven datasets that comprises of real-world PK-PD datasets fitted with 19 algebraic equations PK-PD models, 18 differential equations PK-PD models. These datasets represent different studies performed during pre-clinical and Phase I clinical stages of drug discovery and development. The performance of our models measured in terms weighted residual sum of squares (WRSS) is found to be better than the original LCA. Our approach eliminates the laborious procedure of estimating initial parameters and the need for domain expertise, thus it is a potential approach for automated compartmental PK-PD analysis