Clinical modeling of motor function to predict treatment efficacy and enable in silico treatment comparisons in infantile‐onset Pompe disease
Bibliographic record
Abstract
Infantile-onset Pompe disease (IOPD) is a rare, deadly, quickly-progressing degenerative disease. Even with life-sustaining treatment (e.g., alglucosidase alfa [ALGLU]), many patients experience continued motor impairment. The Mini-COMET trial evaluated avalglucosidase alfa (AVAL) versus ALGLU on motor and other outcomes in IOPD. However, treatment groups were imbalanced at baseline and the trial was not powered to directly compare treatments. To supplement this limited data, we developed a modeling and simulation approach to compare AVAL versus ALGLU head-to-head in in silico (i.e., computer-simulated) trials. We first developed a longitudinal clinical model to establish the relationship between changes in motor function and changes in urinary hexose tetrasaccharide (uHex4), an established biomarker in IOPD. This model was based on pooled data from Mini-COMET (n = 21) and COMET trials (n = 100 patients with late-onset Pompe disease, LOPD). We then conducted in silico trials mimicking Mini-COMET. Simulated trials were informed by motor data generated from the clinical model and uHex4 profiles simulated in a quantitative systems pharmacology model. The virtual IOPD population was based on observed Mini-COMET baseline characteristics but engineered to have well-balanced baseline characteristics across treatment cohorts. In silico trials showed that patients with IOPD would have the greatest improvements in motor function with AVAL 40 mg/kg every other week (Q2W), suboptimal improvement with ALGLU 40 mg/kg Q2W, and no improvement with ALGLU 20 mg/kg Q2W. This study provides information on the relative efficacy of IOPD treatments and mitigates the confounding effects of imbalanced treatment cohorts. Our approach could also be applied in other rare diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".