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Photostability considerations in preclinical studies: Mini-Review

김윤균
한국분석과학회|2024.12|분석과학 (Analytical Science & Technology)|제37권 제6호|pp. 347-353|DOI 10.5806/AST.2024.37.6.347

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키워드

artificial intelligence (AI)pharmacokineticsphotodegradationphotostabilitydrug development
주제분류화학

초록

Photostability is a critical parameter influencing the stability and efficacy of pharmaceuticals and other scientific materials when exposed to light. In preclinical pharmacokinetic studies, photostability is often overlooked, leading to inaccurate data and potential challenges in drug development. This mini-review explores key considerations for photostability during dosing, administration, sample collection, storage, preparation, and analysis in preclinical studies. Factors such as solution concentration, pH, and light exposure are discussed, along with strategies to mitigate photodegradation risks, such as using UV-shielded materials and optimizing sample handling procedures. Additionally, the review highlights advancements in photostability enhancement, including encapsulation techniques, antioxidants, and the use of LED light sources. The integration of artificial intelligence (AI) in photostability research is also examined, showcasing its potential to predict photoreactive properties, optimize molecular stability, and improve drug development efficiency. Ensuring photostability throughout preclinical studies is essential to maintain data reliability, reduce development costs, and enhance the overall success of therapeutic products.

Abstract

Photostability is a critical parameter influencing the stability and efficacy of pharmaceuticals and other scientific materials when exposed to light. In preclinical pharmacokinetic studies, photostability is often overlooked, leading to inaccurate data and potential challenges in drug development. This mini-review explores key considerations for photostability during dosing, administration, sample collection, storage, preparation, and analysis in preclinical studies. Factors such as solution concentration, pH, and light exposure are discussed, along with strategies to mitigate photodegradation risks, such as using UV-shielded materials and optimizing sample handling procedures. Additionally, the review highlights advancements in photostability enhancement, including encapsulation techniques, antioxidants, and the use of LED light sources. The integration of artificial intelligence (AI) in photostability research is also examined, showcasing its potential to predict photoreactive properties, optimize molecular stability, and improve drug development efficiency. Ensuring photostability throughout preclinical studies is essential to maintain data reliability, reduce development costs, and enhance the overall success of therapeutic products.

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