Quantitative Systems Pharmacology‐Based Digital Twins Approach Supplements Clinical Trial Data for Enzyme Replacement Therapies in Pompe Disease
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".