自动化主动学习优化水凝药物释放概况.
Eugene Cheong1, D Christopher Radford1, Adam J Gormley1
1Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.
概括
这项研究引入了一种自动化,机器学习 (ML) 引导的框架,以优化酸盐水凝配方用于药物输送. ML方法显著加快了用于治疗的受控释放系统的发展.
科学领域:
- 生物材料科学 生物材料科学
- 药物输送系统 药物输送系统
- 计算化学的计算化学
背景情况:
- 水凝提供了卓越的生物相容性和可调节的释放动力学,用于治疗.
- 优化凝配方针对特定的药物释放特征是传统上耗时和劳动密集的.
研究的目的:
- 开发一个自动化,高吞吐量和机器学习 (ML) 引导的框架,以实现高效的酸盐凝配方优化.
- 为了加快对敏感治疗药物的控制释放系统的设计.
主要方法:
- 使用液体处理机器人创建了120种含牛血清白蛋白 (BSA) 的酸盐水凝配方的多样化图书馆.
- 采用高斯过程回归 (GPR) ML模型来预测随着时间的推移累积释放配置文件.
- 应用沙普利添加剂解释 (SHAP) 进行特征重要性分析,以确定关键的释放动力学因素.
- 实施贝叶斯优化和主动学习,用于代的配方选择和改进.
主要成果:
- 确定了阿尔金酸盐分子量,度和时间作为影响药物释放动态的关键因素.
- 通过代的ML引导优化实现了接近零顺序的释放配置文件.
- 成功翻译了优化的配方,以实现长期释放的chondroitinase ABC单酶纳米颗粒 (chABC-SENs).
结论:
- 展示了一个可扩展的,数据驱动的战略,用于快速优化水凝配方.
- 强调了ML在加速先进可控释放技术发展方面的巨大潜力.
- 验证了该框架在优化释放复杂的治疗有效载荷 (如酶纳米粒子) 的有效性.
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