Biotechnological drug development is an extensive area still growing and coming into prominence day by day. Since biotechnological product manufacturing is irreversible, highly expensive, and contains so many critical parameters throughout the process, quality control tests applied to the finished product become inefficacious; therefore, maintaining predefined quality is crucial. Quality by Design (QbD), a systematic approach, is designing and optimizing of formulation and production processes in order to provide a predefined product quality by following a risk and scientific-based path. Determining the critical variables for biotechnological products and their manufacturing via risk assessment is the first and most vital stage of QbD approach, before exploring the multivariate relations among the independent and dependent critical variables by mathematical modeling with the assistive technologies. Response Surface Method (RSM), Artificial Neural Network (ANN), and Genetic Algorithm (GA) are some of the assistive technologies used to perform mathematical modeling. After modeling, additional knowledge is vested and this provides the chance to find a range in which the product quality is always ensured, called as “Design space”. So, product quality is procured all along the process by keeping the critical variables under control with less effort, money, and mistakes.
Part of the book: Special Topics in Drug Discovery