Bayesian data-driven models for pharmaceutical process development

The primary objectives of pharmaceutical development encompass identifying the routes, processes, and conditions for producing medicines while establishing a control strategy to ensure acceptable quality attributes throughout the commercial manufacturing lifecycle. However, achieving these goals is challenged by uncertainties surrounding design decisions for the manufacturing process and variations in manufacturing methods resulting in distributions of outcomes during production. In this discussion, we focus on Bayesian approaches to quantify uncertainty and guide decision -making in process development. Bayesian modeling with Markov chain Monte Carlo proves effective in estimating process reliability. Recent advancements in surrogate models (e.g. Gaussian process, decision trees, and neural networks) offer novel means to quantify uncertainty and have shown success in designing experimental plans that reduce the number of required experiments to determine the optimal process design. By leveraging Bayesian approaches, chemical engineers can enhance their ability to navigate complex decision landscapes and optimize processes for improved efficiency and reliability.