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Record W3030936584 · doi:10.1111/hae.14015

World Federation of Hemophilia Gene Therapy Registry

2020· article· en· W3030936584 on OpenAlexaff
Barbara A. Konkle, Donna Coffin, Glenn F. Pierce, C. Clark, Lindsey A. George, Alfonso Iorio, Johnny Mahlangu, Mayss Naccache, Brian O’Mahony, Flora Peyvandi, S. W. PIPE, Adrian Quartel, Eileen K. Sawyer, Mark W. Skinner, Bartholomew J. Tortella, Crystal Watson, Ian Winburn

Bibliographic record

VenueHaemophilia · 2020
Typearticle
Languageen
FieldMedicine
TopicHemophilia Treatment and Research
Canadian institutionsMcMaster UniversityCanadian Hemophilia Society
FundersNational Heart, Lung, and Blood Institute
KeywordsMedicineHaemophiliaClinical trialGenetic enhancementHaemophilia AIntensive care medicinePediatricsInternal medicineGene

Abstract

fetched live from OpenAlex

We are at an exciting juncture in the treatment of haemophilia. The first gene therapy product for patients with haemophilia could receive regulatory approval as soon as August 2020, with several other products close on the horizon. Gene therapy carries the potential for a ‘functional cure of haemophilia’, with bleeding essentially eliminated in the majority of treated patients for the duration of multi-year follow-up reports to date.1-3 This is a dream that has been in the making since the cloning of the F8 and F9 genes in the early 1980s.4-9 Although the potential for these transformative gene therapies is huge, emerging technologies by definition have some unknowns in their safety and efficacy profiles.10 Such novel therapies are usually evaluated and approved based on a small number of treated individuals. Most phase I-II gene therapy clinical trials in haemophilia enroll less than 30 patients and current phase III trial protocols plan for enrolment of 40-134 patients.11 For a long lasting, potentially life long, therapy, mandated follow-up by the FDA is only 5 years, a relatively short time.12 This imposes a heavy reliance on the postmarketing surveillance to gather critical post-therapy evidence of safety and durable efficacy.10 Some data will come from open-label extensions of ongoing phase III and future phase IV clinical trials; however, much of the burden will be on registry studies to amass long-term data on a large cohort of patients. While clinical trial data provide reassurance of short-term safety and efficacy of specific gene therapy products, we are entering this new treatment era with limited experience of the longer-term impact of gene therapy. Ultimately, the accumulation of patient exposure captured in longitudinal registries is the most robust means of revealing unexpected or rare events associated with this new technology class. Detecting low incident or delayed safety events, particularly in small treatment cohorts of a rare disease, necessitates that each patient who receives gene therapy be followed over the long term, preferably their lifetime. Our current knowledge leaves many unanswered questions about the safety and long-term efficacy of gene therapy.10, 13-16 These gaps in evidence dictate that we must all contribute to strengthening the evidence base. This data collection/surveillance effort should be a shared responsibility. Healthcare providers and patients will need to work together to collect standardized data on patients who receive gene therapy, ensuring that their experiences are captured in a registry, over their entire lifetime. Such longer-term data will assist regulators and manufacturers who are also closely monitoring signals of potential safety events and may provide assistance to payers regarding the efficacy and potential safety milestones needed to inform their reimbursement strategies. Surveillance in rare diseases such as haemophilia necessitates a global reach, as patients who receive gene therapy will be dispersed throughout many countries and continents.17 A global strategy is required to ensure a large enough patient pool to allow robust evaluation and detection of low-incident events that may otherwise go undetected. If events are captured in disparate registries or databases, it would be more complex, laborious, technically challenging and ultimately slower to combine the data. Such delays should be avoided at all cost. As the overall field of gene therapy continues to make progress, a growing set of long-term safety and efficacy data will ultimately define the future of gene therapy in haemophilia. Integrating the collection of data into the clinical practice of physicians and the daily lives of patients requires a harmonious and uniform data collection methodology that will be accepted and used by all stakeholders. Only through cohesive efforts by all treating physicians, patients, regulatory agencies and manufacturers worldwide, will we be successful in ensuring gene therapy is safe and efficacious for our patients today, and in the future. Through a collaboration with the International Society of Thrombosis and Hemostasis (ISTH), the European Haemophilia Consortium (EHC), the US National Hemophilia Foundation (NHF), the American Thrombosis and Hemostasis Network (ATHN), industry gene therapy development partners and Regulatory liaisons, the WFH has formulated a world Gene Therapy Registry (WFH GTR), that aims to collect a standardized set of core data, developed with input from a multi-stakeholder steering committee. The aim of the WFH GTR project is to provide a robust, scientifically valid data collection avenue, available to all healthcare providers treating patients who receive gene therapy. The WFH GTR will collaborate with individual haemophilia treatment centres and existing gene therapy registries to leverage established data repositories. A patient mobile application will allow integrating patient-reported outcomes directly into the WFH GTR. The data stemming from the WFH GTR will provide for robust ongoing surveillance of safety and efficacy.8 We are now expanding our outreach for the WFH GTR to the provider and patient communities with implementation of the registry to begin later in 2020.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.714

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.076
GPT teacher head0.321
Teacher spread0.245 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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".

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Citations51
Published2020
Admission routes1
Has abstractyes

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