Druglikeness 평가 도구의 본초(本草)에의 활용성 평가
Application of the Druglikeness Measurement Tool to the Medical Herb to Evaluate its Usability
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초록
신약 개발의 고비용 저효율로 인해 제약회사들은 천연물에서 신약후보 물질을 찾고 약물 개발 단계에서 후보물질의 ADME, druglikeness, 독성 등의 특성을 early discovery stage에 배치하고 있다. 그런데 천연물은 다성분의 특징으로 인해 유효성분을 찾는 것이 중요하다. 이에 본 연구에서는 durglikeness를 이용하여 유효 물질이 실험적으로 검증된 천연물 본초에 적용하여 합성의약품을 기반으로 개발된 durglikeness 모델의 활용성을 평가하였다. 평가 결과 본초에 대한druglikeness평가 결과는 비교적 낮은 결과를 보였다. 하지만 druglikeness를 평가하는 각 물리화학적 특성별로 결과는 달랐으며 이를 활용하여 본초의 druglikeness를 평가하는 방법의 연구에 활용하거나 인공지능의 feature 및 feature의 가중치로 활용할 수 있을 것으로 보인다.
Abstract
Due to the high cost and low efficiency of new drug development, pharmaceutical companies are looking for new drug candidate in natural products and placing the properties of drug candidate such as ADME, drug tolerance, and toxicity on the early discovery stage. However, it is important to find active ingredients in natural products due to the characteristics of multi-component. Druglikeness is a concept used to filter out compounds with inappropriate properties as drugs. Lipinski's rules, Gose's rules, Oprea's rules, Veber's rules, Varma's rules, and QED values are used as tools to evaluate druglikeness and artificial intelligence techniques are recently used to evaluate druglikeness. Therefore, in this study, the utilization of the druglikeness model which is based on synthetic drugs are evaluated by applying the natural product active ingredients which was experimentally verified. The results of the druglikeness evaluation of natural products showed relatively low. However, the results were different for each physicochemical characteristic of evaluating druglikeness, and it can be used to study how to evaluate druglikeness or as a weight for artificial intelligence's feature to evaluate druglikeness of natural products.
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