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Mechanistic Modeling of Intra‐Tumor Spatial Distribution of Antibody‐Drug Conjugates: Insights into Dosing Strategies in Oncology

机译:抗体 - 药物缀合物内肿瘤内空间分布的机理建模:肿瘤学中的给药策略见解

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摘要

Antibody drug conjugates (ADCs) provide targeted delivery of cytotoxic agents directly inside tumor cells. However, many ADCs targeting solid tumors have exhibited limited clinical efficacy, in part, due to insufficient penetration within tumors. To better understand the relationship between ADC tumor penetration and efficacy, previously applied Krogh cylinder models that explore tumor growth dynamics following ADC administration in preclinical species were expanded to a clinical framework by integrating clinical pharmacokinetics, tumor penetration, and tumor growth inhibition. The objective of this framework is to link ADC tumor penetration and distribution to clinical efficacy. The model was validated by comparing virtual patient population simulations to observed overall response rates from trastuzumab‐DM1 treated patients with metastatic breast cancer. To capture clinical outcomes, we expanded upon previous Krogh cylinder models to include the additional mechanism of heterogeneous tumor growth inhibition spatially across the tumor. This expansion mechanistically captures clinical response rates by describing heterogeneous ADC binding and tumor cell killing; high binding and tumor cell death close to capillaries vs. low binding, and high tumor cell proliferation far from capillaries. Sensitivity analyses suggest that clinical efficacy could be optimized through dose fractionation, and that clinical efficacy is primarily dependent on the ADC‐target affinity, payload potency, and tumor growth rate. This work offers a mechanistic basis to predict and optimize ADC clinical efficacy for solid tumors, allowing dosing strategy optimization to improve patient outcomes.
机译:抗体药物缀合物(ADCs)在肿瘤细胞内直接提供细胞毒性剂的靶向递送。然而,由于肿瘤内的渗透性不足,许多靶向实体瘤的ADC具有有限的临床疗效。为了更好地理解ADC肿瘤渗透和功效之间的关系,通过整合临床药代动力学,肿瘤渗透和肿瘤生长抑制,将探测ADC施用后肿瘤生长动力学探索肿瘤生长动力学的Krogh气缸模型。该框架的目的是将ADC肿瘤渗透率链接到临床疗效。通过比较虚拟患者人口模拟来验证模型,从而观察到从曲妥珠单抗-DM1治疗的转移性乳腺癌患者的整体反应率。为了捕获临床结果,我们扩展了以前的Krogh气缸模型,以包括在肿瘤上空间上的异质肿瘤生长抑制的额外机制。这种扩展通过描述异质ADC结合和肿瘤细胞杀伤来机械化地捕获临床反应率;高结合和肿瘤细胞死亡与毛细血管与低结合,以及远离毛细血管的高肿瘤细胞增殖。敏感性分析表明,可以通过剂量分馏优化临床疗效,并且临床疗效主要取决于ADC-靶亲和力,有效载荷效力和肿瘤生长速率。这项工作提供了一种机制基础,以预测和优化ADC对实体肿瘤的临床疗效,从而允许给药策略优化改善患者结果。

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