Quantitative Systems Pharmacology‐Based Digital Twins Approach Supplements Clinical Trial Data for Enzyme Replacement Therapies in Pompe Disease

医学 人口 酶替代疗法 药效学 疾病 临床试验 生物标志物 队列 糖原贮积病 内科学 药理学 药代动力学 生物 遗传学 环境卫生
作者
Chanchala Kaddi,Mengdi Tao,Silke Bergeler,Kelly George,Hugo Geerts,Piet H. van der Graaf,Julie L. Batista,Meredith C. Foster,Catherine Ortemann‐Renon,Atef Zaher,Kristina An Haack,Susana Zaph
出处
期刊:Clinical Pharmacology & Therapeutics [Wiley]
卷期号:117 (2): 579-588 被引量:9
标识
DOI:10.1002/cpt.3498
摘要

Pompe disease is a rare, progressive neuromuscular disease caused by deficient lysosomal glycogen degradation, and includes both late-onset (LOPD) and severe infantile-onset (IOPD) phenotypes. Due to very small patient numbers in IOPD and the high phenotypic heterogeneity observed in this population, a quantitative systems pharmacology (QSP)-based "digital twin" approach was developed to perform an in silico comparison of the efficacy of avalglucosidase alfa vs. the standard of care, in a virtual population of IOPD patients. A QSP model was developed that represents key elements of Pompe disease pathophysiology, including tissue glycogen accumulation and the elevation of the biomarker urine Hex4 in both LOPD and IOPD patients. In this approach, the QSP model was used to generate digital twins of each IOPD patient enrolled in the avalglucosidase alfa clinical program, considering their respective disease burden, demographics, and treatment history. This virtual cohort supplemented clinical observations by simulating and comparing tissue glycogen and urine Hex4 following avalglucosidase alfa treatment vs. the standard of care. The digital twin analysis supports the interpretation that the enhanced reduction in urine Hex4 observed following avalglucosidase alfa treatment is attributable to greater tissue glycogen clearance. Overall, this study provides mechanism-based insight into avalglucosidase alfa efficacy across the phenotypic spectrum of Pompe disease and demonstrates the value of applying a QSP-based digital twin analysis to support rare disease drug development.
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