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Computational transport analysis of antibody-drug conjugate bystander effects and payload tumoral distribution: implications for therapy
Molecular Systems Design & Engineering ( IF 3.6 ) Pub Date : 2017-10-17 00:00:00 , DOI: 10.1039/c7me00093f
Eshita Khera 1, 2, 3, 4 , Cornelius Cilliers 1, 2, 3, 4 , Sumit Bhatnagar 1, 2, 3, 4 , Greg M. Thurber 1, 2, 3, 4, 5
Affiliation  

Antibody drug conjugates (ADCs) have a proven clinical record with four FDA approved drugs and dozens more in clinical trials. However, a better understanding of the relationship between delivery and efficacy of ADCs is needed to improve the rate of successful clinical development. Recent evidence indicates that heterogeneous distribution can play a large role in the efficacy of these drugs. However, the impact of the drug payload, particularly the ability of the payload to diffuse outside of the original targeted cell into adjacent cells (the bystander effect), is not completely understood. Given the challenges in directly measuring the payload distribution within tumors, we developed a predictive computational model to study payload distribution as a function of antibody dose, payload dose, and payload properties. The computational results indicate that: 1) the heterogeneous tumoral distribution of ADCs impacts efficacy, and increasing the antibody dose improves penetration and efficacy. 2) The increased penetration of payloads with bystander effects can partially compensate for poor antibody penetration, but larger antibody doses still result in further improvement. This occurs because of the higher efficiency of direct cell killing than bystander killing. 3) Bystander effects are important for killing antigen negative cells, and an optimum in physicochemical properties exists. Payloads with a balance in cellular uptake versus tissue diffusion enter cells fast enough to avoid tumor washout but slow enough to reach distant cells. Therefore, optimizing the antibody dose, payload dose, and payload physicochemical properties results in ideal delivery to the site of action and maximum efficacy.

中文翻译:

抗体-药物偶联物旁观者效应和有效载荷肿瘤分布的计算转运分析:对治疗的意义

抗体药物偶联物(ADC)在4种FDA批准的药物中都有可靠的临床记录,在临床试验中还有数十种。但是,需要更好地了解ADC的传递与功效之间的关系,以提高成功的临床开发速度。最近的证据表明,异质分布可在这些药物的功效中发挥重要作用。但是,药物有效载荷的影响,特别是有效载荷扩散到原始目标细胞之外并进入相邻细胞的能力(旁观者效应),还没有被完全理解。鉴于直接测量肿瘤内有效载荷分布所面临的挑战,我们开发了一种预测性计算模型来研究有效载荷分布与抗体剂量,有效载荷剂量和有效载荷特性之间的关系。计算结果表明:1)ADCs的异质性肿瘤分布影响疗效,增加抗体剂量可提高渗透率和疗效。2)具有旁观者效应的有效载荷穿透力的增加可以部分弥补不良的抗体穿透力,但更大的抗体剂量仍可带来进一步的改善。发生这种情况是因为直接细胞杀死的效率比旁观者杀死的效率更高。3)旁观者效应对于杀死抗原阴性细胞很重要,并且在物理化学性质上存在最佳。有效负载与蜂窝式摄取的平衡 2)具有旁观者效应的有效载荷穿透力的增加可以部分弥补不良的抗体穿透力,但更大的抗体剂量仍可带来进一步的改善。发生这种情况是因为直接细胞杀死的效率比旁观者杀死的效率更高。3)旁观者效应对于杀死抗原阴性细胞很重要,并且在物理化学性质上存在最佳。有效负载与蜂窝式摄取的平衡 2)具有旁观者效应的有效载荷穿透力的增加可以部分弥补不良的抗体穿透力,但更大的抗体剂量仍可带来进一步的改善。发生这种情况是因为直接细胞杀死的效率比旁观者杀死的效率更高。3)旁观者效应对于杀死抗原阴性细胞很重要,并且在物理化学性质上存在最佳。有效负载与蜂窝式摄取的平衡组织扩散相比,细胞进入细胞的速度要足够快以避免肿瘤的冲刷,但是要足够慢才能到达远处的细胞。因此,优化抗体剂量,有效载荷剂量和有效载荷理化特性可实现理想的作用部位传递和最大功效。
更新日期:2017-10-25
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