Role of artificial intelligence in quality profiling and optimization of drug products

Basit Öğe Kaydı
dc.contributor.authorMesut, Burcu
dc.contributor.authorBaşkor, Atakan
dc.contributor.authorAksu, Neşe Buket
dc.date.accessioned2023-06-10T09:25:19Z
dc.date.available2023-06-10T09:25:19Z
dc.date.issued2023en_US
dc.departmentFakülteler, Eczacılık Teknolojisi Bilimleri Bölümü, Farmasötik Teknoloji Ana Bilim Dalıen_US
dc.description.abstractIn the pharmaceutical industry, one of the sectors with the highest research and development potential today, the development of safe and effective new drug active ingredients and formulations is an expensive and long process. It has rapidly adapted to the developing technologies; modern production tools have been developed to help increase the production quality as a result of the important developments in quality and risk management systems in recent years. The development of these modern tools has resulted in positive outcomes such as increase in quality and decrease in cost of the products, advocating quality while designing instead of testing it as stated in the ICH Q8 guide. With these approaches, it is attempted to reduce the uncertainty and pharmaceutical authorities have published the ICH Q9 guide, which will meet this need and be used to identify risks and made it available to the pharmaceutical industry. This guide defines risk and describes the methodologies that can be applied to identify risks in processes and drug design. The ICH Q8 and ICH Q9 guidelines should be applied together and recommend using statistical tools in the formulation development process. During the formulation development phase, both the design areas and the determination of the most appropriate formulation create very complex data and models while trying to analyze studies in theory and practice, and although there is sufficient knowledge, the human capacity to evaluate the whole data at once is limited. Therefore, it is necessary to get support from various software in order to manage the drug development process more efficiently. Machine learning methods can quickly help create and improve products, increase productivity, consistency, and quality. Machine learning models make predictions based on empirical data and provide an excellent opportunity to develop the most appropriate and effective formulations.en_US
dc.identifier.citationMesut, B., Başkor, A., Aksu, N. B. (2023). Role of artificial intelligence in quality profiling and optimization of drug products. A Handbook of Artificial Intelligence in Drug Delivery, 35-54.en_US
dc.identifier.endpage54en_US
dc.identifier.isbn9780323899253
dc.identifier.isbn9780323903738
dc.identifier.scopus2-s2.0-85160470887
dc.identifier.scopusqualityN/A
dc.identifier.startpage35en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12939/3530
dc.indekslendigikaynakScopus
dc.institutionauthorAksu, Neşe Buket
dc.language.isoen
dc.publisherElsevieren_US
dc.relation.ispartofA Handbook of Artificial Intelligence in Drug Delivery
dc.relation.isversionof10.1016/B978-0-323-89925-3.00003-4en_US
dc.relation.publicationcategoryKitap Bölümü - Uluslararasıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial intelligenceen_US
dc.subjectMachine learningen_US
dc.subjectOptimizationen_US
dc.subjectQbden_US
dc.subjectQualityen_US
dc.subjectRisk assessmenten_US
dc.titleRole of artificial intelligence in quality profiling and optimization of drug products
dc.typeBook Part

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