인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
The purpose of this work was to formulate and characterize pH-sensitive, surfactant-based nanomicelles for the targeted delivery of Oxaliplatin to breast cancer cells. A secondary aim was to utilize machine learning (ML) models to interpolate and dissect the sophisticated, pH-dependent drug release kinetics. Nanomicelles of Oxaliplatin were prepared by Pluronic F-127 through a thin-film hydration technique. The nanomicelles were characterized for size, morphology, encapsulation efficiency, and drug release profile in physiological (pH 7.4) and acidic, tumor-mimicking (pH 5.4) media. The MTT assay was used to test cytotoxicity against L929 normal fibroblasts and MCF-7 breast cancer cells. ML models (Random Forest, Gradient Boosting, SVR) were trained on experimental release data to anticipate crucial release phase changes using SHAP analysis. Prepared nanomicelles were monodisperse and spherical with hydrodynamic diameter 290.3 nm and encapsulation efficiency 40.2%. They had good pH-responsive release with cumulative release 77.5% and 43.5% at pH 5.4 and 7.4, respectively, in 96 h. Kinetic modeling revealed a shift from Fickian diffusion at pH 7.4 to anomalous transport at pH 5.4. ML models showed great interpolation performance (R² > 0.97), and SHAP analysis showed remarkable release transitions. The cytotoxicity assays were different from free Oxaliplatin with improved activity against MCF-7 cells and lower toxicity against L929 cells. Surfactant-based nanomicelles are an effective delivery platform for pH-directed delivery of Oxaliplatin to enhance its therapeutic index. Nanomedicine formulation design is enabled by ML through comprehensive release kinetics analysis.
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오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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