PD118 Company-Led Submissions For Cancer Medicines: The Singapore Experience
Bibliographic record
Abstract
Introduction The Agency for Care Effectiveness (ACE) conducts health technology assessments (HTAs) to inform funding decisions by the Ministry of Health (MOH) Drug Advisory Committee (DAC) in Singapore. In 2021 ACE introduced the company-led submission (CLS) process for cancer medicines, which allows pharmaceutical companies to request evaluations alongside regulatory reviews. This review reports key findings from the first year of its implementation. Methods A total of 10 CLS topics from the first year of implementation were included. We reviewed the status and outcomes of the DAC recommendations. We also used descriptive statistical methods to evaluate the time from HTA submission to first HTA recommendation and from regulatory approval to first HTA recommendation. The timelines were further analyzed by whether submissions were parallel submissions (i.e., HTA submission in tandem with regulatory review) or sequential submissions (i.e., HTA submission after regulatory approval). These statistics were compared with overseas reference jurisdictions (Australia, Canada, and the UK). Results At the time of review, three topics were pending discussion. Of the remaining seven topics, three (43%) received positive recommendations for inclusion on the MOH Cancer Drug List and three (43%) received negative recommendations. The DAC was unable to make a recommendation on one topic. The median time from HTA submission or regulatory approval to first HTA recommendation was 172 days (range 169 to 263 days) and 279 days (range 53 to 374 days), respectively. Notably, parallel submissions (75 days; n=2) had considerably shorter timelines from regulatory approval to first HTA recommendation than sequential submissions (328 days; n=4). These timelines were within the range of the overseas reference countries. Conclusions Parallel CLS allows HTA processes to be conducted in tandem with regulatory reviews, moving HTA recommendations upstream and expediting patient access to clinically effective and cost-effective medicines. Efforts will be made to further evolve the CLS process to achieve timely reimbursement reviews from regulatory approval and to expand this process to noncancer medicines.
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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 | 0.046 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".