探索人工智能支持的便携式系统在抗癌药物输送中的预测潜力:与修改的戈珀茨 (Gompertz) 进行比较研究,如双相响应模型
Subeel Shah1, Kapil Saraswat2, Charu Misra1
1Department of Pharmacy, School of Chemical Sciences and Pharmacy, Central University of Rajasthan, Bandarsindri, Ajmer, Rajasthan, India, 305817.
AAPS PharmSciTech
|July 16, 2025
概括
这项研究引入了修改后的Gompertz模型和单板计算机上的AI系统,以预测乳腺癌中的抗瘤活性. 这两种模型都准确地将药物的疗效与瘤反应相关联,提供了改进的预测能力.
科学领域:
- 在瘤学瘤学.
- 计算生物学 计算生物学
- 纳米技术纳米技术
背景情况:
- 传统的数学模型很难准确地将药物的疗效与癌症管理中的瘤反应相关起来.
- 人工智能 (AI) 提供了一个新的途径,但需要大量的计算资源.
- 预测抗瘤活性,特别是新型药物配方,如多塞塔克塞尔-棕酸盐固体脂质纳米颗粒 (DTX-PL-SLN),仍然是一个挑战.
研究的目的:
- 开发和验证一个修改的Gompertz-like双相反应模型 (MGBRM) 来预测抗瘤活性.
- 介绍一个支持人工智能的单板计算机 (SBC) 系统,用于实时预测瘤行为.
- 将MGBRM和AI-SBC系统的预测精度与乳腺癌治疗的体内实验数据进行比较.
主要方法:
- 开发一个修改的戈珀茨样双相反应模型 (MGBRM).
- 实现一个支持人工智能的单板电脑 (SBC) 系统.
- 使用线性回归算法与C++库和实体实验数据超过20天的模型验证.
主要成果:
- 在没有治疗,DTX-PL和DTX-PL-SLN组的实际和预测瘤体积之间,MGBRM显示出出色的相关性 (r2值为0.999,0.986和0.998).
- 支持人工智能的SBC系统也显示了高相关性 (r2接近1) 与体内实验中的瘤体积.
- 这两种模型都成功地捕获了双相反应,在治疗期间预测瘤体积方面表现优于传统模型.
结论:
- 修改后的Gompertz-like双相反应模型 (MGBRM) 在预测治疗动物的瘤体积方面取得了重大进展.
- 支持人工智能的SBC系统为实时瘤行为监测和预测提供了一种计算效率高的方法.
- 这些模型增强了对数值系统参数的理解,以及针对个性化癌症治疗策略的黑子AI预测.
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