DOI: 10.53600/ajesa.1382037 ISSN: 2564-6397


Gizem YEĞEN, Cagla SENEL, Saadet Kevser PABUCCUOĞLU, Buket AKSU
  • General Medicine
Pharmaceutical development and authorization stages have high requirements that increase labour and costs. Risks for product quality and process robustness also increase in parallel with complex practices existing in the pharmaceutical industry and emerging as a result of developments. Although it is challenging to eliminate parameters leading to increased risks, there is need to appropriately manage the risks likewise arrange decision making processes. Designing and optimizing formulation and production processes to deliver the predetermined product quality is known as Quality by Design (QbD) in pharmaceutical development. In terms of data and knowledge, QbD can be carried out using a variety of technologies in this process. Mathematical modelling is one of these tools allows for the quick formation of subject knowledge, which may subsequently be used in an independent or integrated manner and to produce Design of Experiments (DOE). Response surface method (RSM), Artificial Neural Network (ANN), Genetic Algorithm (GA) are some of the assistive technologies used in mathematical modelling that enables to enlighten the effect of formulation and process variables on product quality attributes. The use of advanced mathematical modelling techniques in formulation and process development has become widespread and it appears to be beneficial in different areas of pharmaceutical development.

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