Enabling lead optimization with a focus on optimal dose selection by utilizing multi-parametric data visualization techniques.

For the development of low molecular weight oral drugs, optimizing absorption, distribution, metabolism, and excretion (ADME) and pharmacokinetic (PK) properties are crucial. Historically, optimization strategies often emphasized individual ADME properties over integrated PK optimization. Such individual parameter optimization approaches in rank ordering compounds focus on parameters other than PK and dose drivers. Emphasizing individual thresholds and indices may result in discarding compounds that do not meet these specific criteria but could have a balanced profile suitable as a drug. Integrated approaches pivoted on dose tend to utilize a one-compartmental model intended to guide a balanced parameter optimization towards a desired product profile. By employing various visualizations of PK parameter combinations alongside project-specific thresholds, the concept outlines how property combinations influence early dose estimation and related PK parameters, supporting the identification of clinical candidates. The primary objective herein is to provide a framework for integrating basic ADME/PK principles with model predictions and custom visualizations for systematic PK and dose optimization, whereby enabling ideation prior to synthesis and subsequently integrating experimental results during lead optimization.