Penerapan Kecerdasan Buatan dalam Pengembangan Formulasi Farmasi: Tren, Tantangan, dan Prospek Masa Depan (Tinjauan Literatur)

Muhammad Faisal, Shinta Puspitasari, Eryona Azizun Rosyida

Abstract


The development of digital technology and the increasing complexity of drug development have encouraged the use of artificial intelligence as an innovative approach in modern pharmacy. This study aims to conduct a systematic analysis of scientific literature to identify key trends, evaluate implementation challenges, and formulate future prospects for the application of artificial intelligence (AI) in pharmaceutical formulation development in a comprehensive and evidence-based manner. The study uses a qualitative approach through the Systematic Literature Review method with literature sources obtained from the DOAJ, Scopus, and Google Scholar databases for publications published between 2017 and 2026. The selection process was carried out in stages based on topic relevance, methodological quality, and suitability to the research objectives. The results of the study show a paradigmatic shift from a conventional experimental (trial-and-error) approach to a data-driven formulation system that is predictive, precise, and digitally integrated. The application of AI has been proven to improve the efficiency of formulation design, accelerate the optimization of excipient composition, and support the development of drug delivery systems and quality-based pharmaceutical manufacturing through integration with the concepts of Quality by Design and digital formulation. However, the implementation of AI still faces challenges related to data standardization, model validation in real conditions, as well as regulatory requirements and the readiness of the technology ecosystem. Overall, AI has the potential to become the main foundation for the transformation of pharmaceutical formulation development that is more efficient, adaptive, and patient-oriented in the future.

Keywords


Artificial Intelligence, Pharmaceutical Formulation, Machine Learning, Drug Delivery System

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References


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