LBPO.BCS02 · 生物信息与计算 · Late-Breaking
全身PBPK/PD建模以支持GenSci140(一种潜在同类最佳FRalpha双互补位ADC)在癌症患者中的首次人体剂量选择
Whole body PBPK/PD modeling to support first-in-human dose selection for GenSci140, a potential best-in-class FRalpha biparatopic ADC, in patients with cancer
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摘要 Abstract
中文摘要
背景:GenSci140是一种新型FRalpha靶向双互补位ADC,其特点是通过可裂解连接子偶联拓扑异构酶I抑制剂作为有效载荷。FRalpha在正常组织中表达有限,但在上皮性癌症(如卵巢癌、非小细胞肺癌和子宫内膜癌)中过表达。临床前研究已显示其在多种异种移植模型中具有广谱抗肿瘤活性。为支持首次人体(FIH)研究(NCT07251166)的剂量选择,我们开发了一个多尺度全身生理药代动力学/药效学(PBPK/PD)模型,以预测晚期实体瘤患者的有效剂量范围。
方法:该多尺度PBPK/PD模型使用PK-Sim®、MoBi®和R软件构建,整合了ADC、其有效载荷和裸抗体(nAb)的系统药代动力学(PK)以及关键步骤,包括FcRn介导的再循环、双互补位FRalpha靶点结合、交联内化、细胞内转运、有效载荷释放及随后的肿瘤生长抑制(TGI)。该模型还整合了FRalpha的合成与降解,以及ADC与nAb在肿瘤和各种健康组织中对FRalpha的竞争性结合动力学。利用生理参数实现跨物种转化。构建了GenSci140的猴PBPK模型和Elahere(靶向FRalpha的单特异性ADC)的临床PBPK模型,以估计人类各组织中FRalpha的表达。使用ADC、总抗体(Tab)和有效载荷的血浆PK数据,以及来自卵巢癌(OVCAR-3)和肺癌(NCI-H441)细胞来源异种移植(CDX)模型的TGI数据,开发了GenSci140的小鼠PBPK/PD模型,以推断人类TGI参数。这些多尺度参数被整合到一个针对癌症患者的全面全身PBPK/PD模型中。随后进行虚拟临床试验模拟,以预测GenSci140的PK和疗效结果。
结果:猴PBPK模型准确描述了GenSci140 ADC、Tab和有效载荷在各测试剂量下的血浆PK。Elahere的临床PBPK模型成功模拟了患者在多个剂量水平下观察到的血浆PK。GenSci140的小鼠PBPK/PD模型有效模拟了CDX模型中的血浆PK和TGI数据。使用所提出的人类PBPK/PD模型,预测了患者的有效剂量范围,疗效定义为相对于基线肿瘤大小达到100% TGI的剂量。
结论:全身PBPK/PD模型从机制上阐明了GenSci140的作用模式,并建立了稳健的PK/TGI关系。它为确定临床有效剂量提供了定量依据,并为FIH剂量选择提供了信息。随着试验数据的积累,该模型将被纳入更广泛的疾病平台,以指导剂量扩展和持续的临床优化。
查看英文原文 English abstract
Background: GenSci140 is a novel FRalpha-directed biparatopic ADC by featuring a cleavable linker conjugated to a topoisomerase I inhibitor as payload. FRalpha displays limited expression in normal tissues but is overexpressed in epithelial cancers (e.g., ovarian, non-small cell lung, and endometrial carcinomas). Preclinical studies have showed its broad-spectrum antitumor activity across multiple xenograft models. To support dose selection for first-in-human (FIH) study (NCT07251166), a multiscale whole body physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) model was developed to predict the effective dose range in patients with advanced solid tumors.
Methods: The multiscale PBPK/PD model was constructed using PK-Sim®, MoBi®, and R softwares, integrating systemic pharmacokinetics (PK) of the ADC, its payload and naked antibody (nAb) with the key steps, including FcRn-mediated recycling, biparatopic FRalpha target binding, crosslinking internalization, intracellular trafficking, payload release, and the subsequent tumor growth inhibition (TGI). The model also integrated the synthesis and degradation of FRalpha, and the competitive binding dynamics between the ADC and nAb for FRalpha across tumor and various healthy tissues. Physiological parameters were utilized to enable cross-species translation. Monkey PBPK model for GenSci140 and clinical PBPK model for Elahere (monospecific ADC targeting FRalpha) were constructed to estimate human FRalpha expression across tissues. Mouse PBPK/PD models of GenSci140 were developed using plasma PK of ADC, total antibody (Tab) and payload, along with TGI data from ovarian cancer (OVCAR-3) and lung cancer (NCI-H441) cell-derived xenograft (CDX) models, to inform human TGI parameters. These multiscale parameters were integrated into a comprehensive whole-body PBPK/PD model for cancer patients. Virtual clinical trial simulations were subsequently performed to predict GenSci140 PK and efficacy result.
Results: The monkey PBPK model accurately described the plasma PK of GenSci140 ADC, Tab and payload across tested doses. Clinical PBPK models for Elahere successfully simulate observed plasma PK in patients at multiple dose levels. The mouse PBPK/PD model for GenSci140 effectively simulated plasma PK and TGI data in CDX models. Using proposed human PBPK/PD model, the efficacious does range for patients was projected, with efficacy defined as the dose achieving 100% TGI relative to baseline tumor size.
Conclusions: The whole body PBPK/PD model mechanistically elucidated the mode of action of GenSci140 and established a robust PK/TGI relationship. It provided a quantitative basis for identifying clinically effective doses and informed FIH dose selection. This model will be incorporated into a broader disease platform to guide dose expansion and ongoing clinical optimization as trial data accrue.
利益披露 Disclosure
F. Zhang,
Changchun GeneScience Pharmaceutical Co., Ltd. Employment.
Y. Xie,
Changchun GeneScience Pharmaceutical Co., Ltd. Employment.
G. Ma,
Changchun GeneScience Pharmaceutical Co., Ltd. Employment.
X. Liang,
Changchun GeneScience Pharmaceutical Co., Ltd. Employment.
X. Zhao,
Changchun GeneScience Pharmaceutical Co., Ltd. Employment.
Y. Lin,
Changchun GeneScience Pharmaceutical Co., Ltd. Employment.
X. Ye,
Changchun GeneScience Pharmaceutical Co., Ltd. Employment.
P. Qi,
Changchun GeneScience Pharmaceutical Co., Ltd. Employment.
X. Wang,
Changchun GeneScience Pharmaceutical Co., Ltd. Employment.
H. Zhang,
Changchun GeneScience Pharmaceutical Co., Ltd. Employment.
S. Zhang,
Changchun GeneScience Pharmaceutical Co., Ltd. Employment.
S. Wang,
Changchun GeneScience Pharmaceutical Co., Ltd. Employment.
L. Jin,
Changchun GeneScience Pharmaceutical Co., Ltd. Employment.