Drug discovery and development processes are under increased pressure to deliver medicines and vaccines to patients faster than ever. The demand to have robust and efficient clinical chemistry, manufacturing, and control (CMC) strategies is the main driving factor in the implementation of new approaches which allow for faster experimentation without sacrificing the quality of the results. Inspired by biological screening, chemical development of new active pharmaceutical ingredients (APIs) has been leveraging parallel experimentation over the past few decades to disrupt the approach that scientists adopt to investigate the chemical and formulation space. Design of Experiments and advanced statistical tools are essential to design and evaluate results in an efficient manner . In fact, the results of these studies generate comprehensive datasets across multiple continuous variables and factors which can be fed to modeling algorithms . This provides further insight and knowledge on the effect of multiple variables on the target process and can help identify critical operating parameters. Combining High Throughput Experimentation (HTE) with computational modeling may prove to be an effective tool for visualizing and reporting results with a fully traceable and consistent methodology . As these benefits are realized, the reach of HTE has extended into other areas of research such as chemical synthesis optimization and conducting drug solubility assessments in various media .