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Record W4400519828 · doi:10.1016/s1470-2045(24)00286-9

Clinical benefit, reimbursement outcomes, and prices of FDA-approved cancer drugs reviewed through Project Orbis in the USA, Canada, England, and Scotland: a retrospective, comparative analysis

2024· article· en· W4400519828 on OpenAlexfundaboutno aff
Kristina Jenei, Arianna Gentilini, Alyson Haslam, Vinay Prasad

Bibliographic record

VenueThe Lancet Oncology · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchArnold Ventures
KeywordsReimbursementMedicineCancer drugsFamily medicinePolitical sciencePharmacologyDrugLawHealth care

Abstract

fetched live from OpenAlex

BACKGROUND: Project Orbis is a global initiative that aims to streamline regulatory review processes across international regulators in the USA, Canada, Australia, UK, Israel, Brazil, Singapore, and Switzerland to bring promising cancer drugs to patients earlier. We explored the clinical benefit, time to regulatory approval and health technology assessment recommendations, reimbursement outcomes, and monthly treatment prices of cancer drugs reviewed through this initiative. METHODS: For this retrospective, comparative analysis, we identified cancer drug approvals reviewed through Project Orbis in the USA, Canada, and the UK between May 1, 2019, and Nov 1, 2023. Approvals of cancer drugs reviewed Project Orbis were extracted from the Food and Drug Administration (FDA) Oncology Centre of Excellence and all other FDA approvals from the Drugs@FDA database. The co-primary outcomes were time of regulatory review, time from regulatory approval to health technology assessment recommendation (England, Scotland, and Canada), reimbursement outcomes, clinical benefit (defined as median gains in progression-free survival and overall survival) between cancer drug approvals reviewed by Project Orbis and other FDA approval processes, and monthly treatment prices. The Wilcoxon rank-sum and Fisher's Exact tests were used to examine statistical significance between approvals reviewed through Project Orbis and other FDA approvals during the same period. FINDINGS: Between May 1, 2019 and Nov 1, 2023, 81 (33%) of 244 cancer drugs approved by the FDA were reviewed through Project Orbis. The median overall survival gains were 4·1 months (IQR 3·3-5·1) compared with 2·7 months (2·1-3·9) for other FDA approvals. Similarly, progression-free survival gains were 2·6 months (IQR 1·7-4·9) for Project Orbis compared with 2·6 months (0·6-5·1) for other FDA approvals. Neither overall survival (p=0·11) nor progression-free survival (p=0·44) gains were significantly different between the two cohorts of approvals. Of the 14 UK Medicines and Healthcare products Regulatory Agency (MHRA) approvals reviewed by the Scottish Medicines Consortium (SMC), the agency gave positive recommendations for all 14 (100%). Of the 15 MHRA approvals reviewed by the National Institute for Health and Care Excellence (NICE), the agency gave positive recommendations for six (40%). Of the 49 approvals reviewed by the Canadian Agency for Drugs and Technologies in Health (CADTH), the agency conditionally recommended 44 (90%). The time between regulatory approval to NICE recommendation increased from a median of 137 days (IQR 102-172) in 2021 to 302 days (184-483) in 2023, SMC recommendation increased from 185 days (in 2021 for one drug only) to 368 days (IQR 313-476) in 2023, and CADTH decision increased from 97 days (in 2020 for one drug only) to 202 days (IQR 153-304) in 2023. The median monthly price of approvals reviewed through Project Orbis was US$20 000 per month (IQR 13 000-37 000). INTERPRETATION: Clinical outcomes of Project Orbis were no different than other FDA approvals during the same time, and access, after a successful health technology assessment, was considerably delayed or absent, raising questions about whether Project Orbis participation translates into faster patient access to medicines with high clinical benefit and sustainable costs. Although future challenges might benefit from regulatory harmonisation, the advantages are currently unclear. FUNDING: None.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.698
Threshold uncertainty score0.601

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0090.014
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.450
GPT teacher head0.533
Teacher spread0.083 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2024
Admission routes2
Has abstractyes

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