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基于跨人群图表的优化覆盖范围的全原细胞疗法.

Sapir Israeli¹, Elizabeth F Krakow^2,3, Martin Maiers^4,5

¹Department of Mathematics, Bar-Ilan University, Ramat Gan, Israel.

Frontiers in immunology

|June 1, 2023

概括

为现成的免疫效应细胞疗法 (IECT) 银行选择最佳捐赠者至关重要. 基于图形的算法有效地识别了捐赠者,优于随机选择,使基因编辑成本效益降低.

关键词:

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科学领域:

免疫学免疫学免疫学
遗传学是一个遗传学.
生物信息学是一种生物信息学.

背景情况:

现成的免疫效应细胞疗法 (IECT) 显示出与自身治疗相比更有前途.
对IECT的HLA匹配标准根据GvHD减少和持久性策略而有所不同.
为IECT产品银行优化捐助者选择是一个重大挑战.

研究的目的:

开发一种最佳的供体选择策略,用于现成的IECT.
为了比较招募更多捐赠者的成本效益与对HLA表达的基因编辑.
评估不同优化算法对供体选择的效率.

主要方法:

开发了一个最佳覆盖问题和基于图形的供体选择算法.
比较贪,线性编程 (LP) 和整数线性编程 (ILP) 算法与随机选择.
评估了不同HLA匹配优先级和人口规模的场景.

主要成果:

优化算法 (贪,LP,ILP) 通过比随机选择更少的捐赠者实现了最佳覆盖.
基因编辑通常比招募额外的捐赠者更昂贵.
最佳的供体选择保持了不同人群的覆盖率,与随机选择不同.

结论:

基于图形的算法为开发现成IECT银行提供了一种高效,灵活的方法.
这些方法提高了IECT产品设计的透明度和标准化.
最佳的供体选择是最大限度地覆盖患者群体并最大限度地降低成本的关键.