Radiopharmaceutical cocktails have been developed over the years to treat cancer. Cocktails of agents are attractive because 1 radiopharmaceutical is unlikely to have the desired therapeutic effect because of nonuniform uptake by the targeted cells. Therefore, multiple radiopharmaceuticals targeting different receptors on a cell is warranted. However, past implementations in vivo have not met with convincing results because of the absence of optimization strategies. Here we present artificial intelligence (AI) tools housed in a new version of our software platform, MIRDcell V4, that optimize a cocktail of radiopharmaceuticals by minimizing the total disintegrations needed to achieve a given surviving fraction (SF) of tumor cells. Methods: AI tools are developed within MIRDcell V4 using an optimizer based on the sequential least-squares programming algorithm. The algorithm determines the molar activities for each drug in the cocktail that minimize the total disintegrations required to achieve a specified SF. Tools are provided for populations of cells that do not cross-irradiate (e.g., circulating or disseminated tumor cells) and for multicellular clusters (e.g., micrometastases). The tools were tested using model data, flow cytometry data for suspensions of single cells labeled with fluorochrome-labeled antibodies, and 3-dimensional spatiotemporal kinetics in spheroids for fluorochrome-loaded liposomes. Results: Experimental binding distributions of 4 ²¹¹At-antibodies were considered for treating suspensions of MDA-MB-231 human breast cancer cells. A 2-drug combination reduced the number of ²¹¹At decays required by a factor of 1.6 relative to the best single antibody. In another study, 2 radiopharmaceuticals radiolabeled with ^195mPt were each distributed lognormally in a hypothetical multicellular cluster. Here, the 2-drug combination required 1.7-fold fewer decays than did either drug alone. Finally, 2 ²²⁵Ac-labeled drugs that provide different radial distributions within a spheroid require about one half of the disintegrations required by the best single agent. Conclusion: The MIRDcell AI tools determine optimized drug combinations and corresponding molar activities needed to achieve a given SF. This approach could be used to analyze a sample of cells obtained from cell culture, animal, or patient to predict the best combination of drugs for maximum therapeutic effect with the least total disintegrations.

多年来，人们一直在开发放射性药物混合物来治疗癌症。药物混合物很有吸引力，因为 1 放射性药物不太可能具有理想的治疗效果，因为目标细胞的摄取不均匀。因此，需要针对细胞上不同受体的多种放射性药物。然而，由于缺乏优化策略，过去的体内实现并没有得到令人信服的结果。在这里，我们展示了位于我们新版软件平台 MIRDcell V4 中的人工智能 （AI） 工具，它通过最大限度地减少实现给定存活部分 （SF） 肿瘤细胞所需的总崩解来优化放射性药物混合物。方法：AI 工具是在 MIRDcell V4 中使用基于顺序最小二乘编程算法的优化器开发的。该算法确定混合物中每种药物的摩尔活性，以最小化达到指定 SF 所需的总崩解。为不交叉照射的细胞群（例如，循环或播散的肿瘤细胞）和多细胞簇（例如，微转移）提供了工具。这些工具使用模型数据、流式细胞术数据（用于用荧光染料标记的抗体标记的单细胞悬液）以及载有荧光染料的脂质体的球状体中的 3 维时空动力学进行了测试。结果：4 ²¹¹个 At 抗体的实验结合分布被考虑用于治疗 MDA-MB-231 人乳腺癌细胞的悬浮液。相对于最佳单一抗体，2 药物组合将所需的 ²¹¹At 衰变数量减少了 1.6 倍。 在另一项研究中，用 ^195mPt 放射性标记的 2 种放射性药物分别对数正态分布在假设的多细胞簇中。在这里，2 种药物组合需要的蛀牙比单独使用任何一种药物少 1.7 倍。最后，2 ²²⁵个 Ac 标记的药物在球体内提供不同的径向分布，需要的崩解能力约为最佳单一药物所需崩解能力的一半。结论：MIRDcell AI 工具确定实现给定 SF 所需的优化药物组合和相应的摩尔活性。这种方法可用于分析从细胞培养物、动物或患者获得的细胞样品，以预测最佳药物组合，以最少的总崩解获得最大的治疗效果。