Abstract: The pharmaceutical industry is experiencing a major change in analytical method development with the use of computational and In-silico modeling approaches. Traditional experimental methods, while essential to analytical science, consume a lot of resources, take a long time, and can harm the environment. Recently, new computational techniques like molecular modeling, computational fluid dynamics (CFD), chemometric analysis, and machine learning (ML) have become effective, precise, and sustainable options for designing and optimizing analyses. Molecular modeling helps explain drug-excipient interactions, which supports rational formulation and method development. CFD simulations give detailed insights into chromatographic flow dynamics, aiding in the systematic optimization of column performance and resolution. Chemometric tools, such as Principal Component Analysis (PCA) and Partial Least Squares (PLS), enable multivariate analysis and data-driven optimization of analytical factors. The use of machine learning algorithms improves the ability to predict and control key chromatographic elements like retention time, selectivity, and mobile phase composition. By combining these computational methods with Quality by Design (QbD) and Design of Experiments (DoE) frameworks, researchers can ensure structured and scientifically sound method development that follows ICH Q14 and FDA guidelines. Studies show significant decreases in method development time, resource use, and environmental impact. Overall, the shift to In-silico modeling and computational simulation represents a significant step toward a more sustainable, efficient, and digitally focused future in pharmaceutical analytical science.
Sharma et al. (Wed,) studied this question.