Forecasting asparaginase quantity required to treat pediatric ALL in LMICs using ACCESS FORxECAST.
Bibliographic record
Abstract
10031 Background: Asparaginase (ASN) is a crucial component of pediatric acute lymphoblastic leukemia (ALL) protocols. ASN is available in three enzyme formulations: native from Escherichia Coli ( E. coli), PEGylated from E. coli (PEG), and native erwinia from Erwinia chrysanthemi (Erwinase). PEG is typically preferred in high-income countries, while E. coli is more accessible in low and middle income countries (LMICs). Erwinase is reserved for patients who develop hypersensitivity. Short shelf lives, high prices, intermittent availability, and concern for substandard formulations in LMICs have created a need for proactive ASN demand estimates, particularly in LMICs. Methods: We modified FORxECAST, a publicly available tool that forecasts pediatric cancer drug quantity and cost, to estimate ASN quantity required to treat pediatric ALL in 2021 across all LMICs. Incidence data is based on the Global Childhood Cancer microsimulation model, which extrapolates country registries to estimate diagnosed pediatric ALL patients. We forecast ASN quantity for both a base regimen (BR), recommended by the International Pediatric Oncology Society (SIOP), and a more aggressive regimen (AR) used in some LMICs with more advanced supportive care capacity. For both BR and AR, we estimate ASN quantity across four scenarios, outlining how quantity would vary based on formulation and ability to switch in cases of hypersensitivity. Results: The estimated quantity of ASN required to treat all children diagnosed with ALL in LMICs in 2021, across scenarios and regimens, is provided (Table). If E. coli were used to treat all diagnosed pediatric ALL patients across LMICs, required quantity would range from 1,198 M IU (BR) to 1,661 M IU (AR) (Scenario 1). If PEG were used, required quantity would range 150 M IU (BR) to 473 M IU (AR) (Scenario 2). Accounting for hypersensitivity would require 77 M IU (BR) to 137 M IU (AR) Erwinase (Scenarios 3 and 4). Conclusions: We adapted FORxECAST to be ASN-specific and estimated demand in LMICs for a range of scenarios, including for second line Erwinase; accounting for hypersensitivity is particularly important because discontinuation typically results in lower cure rates. We also estimated how quantity of ASN required would increase with treatment intensity. These results provide the first quantification of ASN need for pediatric ALL in LMICs, creating a demand estimate that can inform private and public efforts to produce a reliable supply of high quality ASN for all children with ALL.[Table: see text]
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".