Thomas Dennis G, Chikkagoudar Satish, Heredia-Langer Alejandro, Tardiff Mark F, Xu Zhixiang, Hourcade Dennis E, Pham Christine T N, Lanza Gregory M, Weinberger Kilian Q, Baker Nathan A
Knowledge Discovery and Informatics, Pacific Northwest National Laboratory, Richland, WA 99352, USA.
Applied Statistics and Computational Modeling, Pacific Northwest National Laboratory, Richland, WA 99352, USA.
Comput Sci Discov. 2014 Mar 21;7(1):015003. doi: 10.1088/1749-4699/7/1/015003.
Nanoparticles are potentially powerful therapeutic tools that have the capacity to target drug payloads and imaging agents. However, some nanoparticles can activate complement, a branch of the innate immune system, and cause adverse side-effects. Recently, we employed an tro hemolysis assay to measure the serum complement activity of perfluorocarbon nanoparticles that differed by size, surface charge, and surface chemistry, quantifying the nanoparticle-dependent complement activity using a metric called Residual Hemolytic Activity (RHA). In the present work, we have used a decision tree learning algorithm to derive the rules for estimating nanoparticle-dependent complement response based on the data generated from the hemolytic assay studies. Our results indicate that physicochemical properties of nanoparticles, namely, size, polydispersity index, zeta potential, and mole percentage of the active surface ligand of a nanoparticle, can serve as good descriptors for prediction of nanoparticle-dependent complement activation in the decision tree modeling framework.
纳米颗粒是具有潜在强大功能的治疗工具，有能力靶向药物有效载荷和成像剂。然而，一些纳米颗粒可激活补体（先天免疫系统的一个分支）并引起不良副作用。最近，我们采用了一种溶血试验来测量不同尺寸、表面电荷和表面化学性质的全氟化碳纳米颗粒的血清补体活性，使用一种称为残余溶血活性（RHA）的指标来量化纳米颗粒依赖性补体活性。在本研究中，我们使用决策树学习算法，根据溶血试验研究产生的数据，推导估算纳米颗粒依赖性补体反应的规则。我们的结果表明，纳米颗粒的物理化学性质，即尺寸、多分散指数、zeta电位和纳米颗粒活性表面配体的摩尔百分比，可作为决策树建模框架中预测纳米颗粒依赖性补体激活的良好描述符。
