PO.ET09.03 · 实验与分子治疗

应用机制性临床前PK/PD/疗效建模以支持AZD9750(一种新型口服雄激素受体降解剂,PROTAC)的联合策略

Application of mechanistic preclinical PK/PD/efficacy modeling to support combination strategy for AZD9750, a novel oral androgen receptor degrader (PROTAC)

海报缩略图:应用机制性临床前PK/PD/疗效建模以支持AZD9750(一种新型口服雄激素受体降解剂,PROTAC)的联合策略
编号 4612 展板 22 时间 4/21 09:00–12:00 区域 Section 18 主讲 Ana Quiroga, B Eng;M Eng;PhD
分会场 Proximity-Induced Drug Discovery 1
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作者与单位 Authors & Affiliations

Ana Quiroga1, Pablo Morentin Gutierrez1, Antonio Ramos-Montoya1, Chrysiis Michaloglou1, Nuria Galeano-Dalmau1, Claire Crafter1, Aaron Smith1, Jamie Scott1, Michael Niedbala2

1AstraZeneca, Cambridge, United Kingdom,2AstraZeneca, Waltham, MA

摘要 Abstract

中文摘要
雄激素受体(AR)在前列腺癌中高表达,是肿瘤学中经临床验证的靶点。AZD9750是一种新型强效口服选择性AR蛋白水解靶向嵌合体(PROTAC),具有适合与多种其他疗法联合使用的药理学特征,例如capivasertib(一种强效的pan-AKT激酶抑制剂,对携带PIK3CA和PTEN突变的肿瘤具有抗肿瘤活性)和saruparib(一种PARP1选择性抑制剂,对携带BRCA1和BRCA2等基因突变的肿瘤尤为有效)。我们在此展示用于理解AZD9750与AKT和PARP抑制剂联用抗肿瘤机制的临床前PK/PD/疗效建模工作。我们开发了一种新型机制性数学模型,应用于体内临床前激素敏感、ARwt前列腺PDX模型C901和MR041。C901具有BRCA2的纯合缺失,MR041为PTEN缺失,使它们分别成为与PARP和AKT抑制剂联用的适宜候选。模型的PK模块描述化合物在单药治疗和联合治疗中的暴露量。PD模块描述通过Western blot测定的AR、AKT、GSK3beta和S6的总水平及磷酸化水平,以及通过ELISA测定的PARylation水平。在疗效模块中,AR、pS6和PARylation水平与肿瘤生长抑制相关联,而pGSK3beta和PARylation水平与凋亡诱导相关联;随后,这些参数决定肿瘤大小。所有模型参数均来自内部研究;部分参数通过对取自多项研究的个体纵向PK、PD生物标志物和肿瘤大小数据进行非线性混合效应建模来估计。该模型很好地描述了不同化合物的血浆浓度与PD生物标志物调节之间的关系,无论是在单药治疗还是联合治疗中。此外,该数学模型能够将联合治疗中增强的抗肿瘤疗效解释为不同生物标志物调节的函数。本研究提供了对AZD9750与AKT和PARP抑制剂联用的定量机制性见解。该研究丰富了我们对与AR-PROTAC、PARP抑制剂和AKT抑制剂相关的生物标志物的理解,为临床试验中监测生物标志物的选择提供了信息。此外,它量化了实现最大抗肿瘤活性所需的生物标志物调节程度,并支持理性的联合策略,以及临床开发中的剂量和给药方案优化。
查看英文原文 English abstract
The androgen receptor (AR) is highly expressed in prostate cancers and is a clinically validated target in oncology. AZD9750 is a novel potent oral selective AR Proteolysis-targeting chimera (PROTAC) with a suitable pharmacological profile to be used in combination with a variety of other therapeutics such as capivasertib, a potent pan-AKT kinase inhibitor with anti-tumor activity in tumors with PIK3CA and PTEN mutations, and saruparib, a PARP1-selective inhibitor especially effective against tumors with mutations in genes like BRCA1 and BRCA2. We present here the preclinical PK/PD/Efficacy modeling work used to understand the anti-tumor mechanism of AZD9750 in combination with AKT and PARP inhibitors. We developed a novel mechanistic mathematical model applied to in vivo preclinical hormone sensitive, ARwt prostate PDX models C901 and MR041. C901 has homologous deletion of BRCA2 and MR041 is PTEN null, making them appropriate candidates for combination with PARP and AKT inhibitors, respectively. The PK module of the model describes the compound exposure in monotherapy and combination. The PD module describes the AR, AKT, GSK3beta and S6 total and phosphorylated levels measured by Western Blotting and PARylation levels measured by ELISA. In the efficacy module, the levels of AR, pS6 and PARylation were linked to tumor growth inhibition while pGSK3beta and PARylation levels were linked to induction of apoptosis; subsequently, these parameters determine the tumor size. All model parameters were derived from internal studies; some were estimated using Non-Linear Mixed Effect modeling of individual longitudinal PK, PD biomarkers and tumor size data taken from several studies. The model describes well the relationship between plasma concentration of the different compounds and PD biomarkers modulation both in monotherapy and in combination. Furthermore, the mathematical model is capable of explaining the enhanced anti-tumor efficacy in combination as a function of the different biomarkers' modulation. This study provides quantitative mechanistic insights into the AZD9750 combination with AKT and PARP inhibitors. The study enriches our understanding of biomarkers relevant to AR-PROTACs, PARP inhibitors, and AKT inhibitors, informing the selection of biomarkers for monitoring in clinical trials. Additionally, it quantifies the extent of biomarker modulation required to achieve maximal antitumor activity and supports rational combination strategies, as well as dose and schedule optimization for clinical development.
利益披露 Disclosure
A. Quiroga, AstraZeneca Employment, Stock, Stock Option. P. Morentin Gutierrez, AstraZeneca Employment, Stock, Stock Option. A. Ramos-Montoya, AstraZeneca Employment, Stock, Stock Option. C. Michaloglou, AstraZeneca Employment, Stock, Stock Option. N. Galeano-Dalmau, AstraZeneca Employment, Stock, Stock Option. C. Crafter, AstraZeneca Employment, Stock, Stock Option. A. Smith, AstraZeneca Employment, Stock, Stock Option. J. Scott, AstraZeneca Employment, Stock, Stock Option. M. Niedbala, AstraZeneca Employment, Stock, Stock Option.

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