从基于点的分析转向基于系统的建模:知识整合以解决抗菌素耐药性的问题
Bhavatharini Arun1, Gauri G Rao1,2
1USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California, Los Angeles, California, USA.
优化抗生素治疗需要跨学科的合作. 整合实验和计算模型可以改善药物开发和患者治疗,以获得更好的结果.
科学领域:
- 药理学 药理学是指药理学的学科.
- 微生物学 微生物学
- 计算科学 计算科学
- 临床医学 临床医学
背景情况:
- 优化抗生素治疗是复杂的,需要一个全面的长椅到床边的战略.
- 药理学家,临床医生,微生物学家和计算科学家之间的跨学科合作至关重要.
研究的目的:
- 要强调如何将新型实验模型和多组数据与计算方法的整合可以增强抗生素治疗.
- 强调发展系统层面的观点,以改善药物开发和临床决策.
主要方法:
- 利用新的实验模型研究宿主环境中的药物-病原体相互作用.
- 使用多组学数据来理解细菌反应的分子机制.
- 应用药量计学和机器学习来构建in silico模型.
主要成果:
- 实验模型和多组学数据为药物-病原体相互作用和细菌反应提供了洞察力.
- 药理计量和机器学习使得这些见解可以整合到预测性in silico模型中.
- 系统层面的观点有助于明智的药物开发和临床决策.
结论:
- 结合实验和计算方法的整体,跨学科的方法是优化抗生素治疗的关键.
- 多样化的数据和建模技术的有效整合可以个性化抗生素治疗 (正确的药物,时间,剂量,持续时间).
- 这一综合战略有望推动抗菌药物开发和临床实践.
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