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Record W7033204183

Pharmacokinetic and economic implications when switching between hemophilia A treatments

2023· dissertation· en· W7033204183 on OpenAlexaboutno aff

Bibliographic record

VenueUWSpace (University of Waterloo) · 2023
Typedissertation
Languageen
FieldSocial Sciences
TopicReligious Tourism and Spaces
Canadian institutionsnot available
Fundersnot available
KeywordsDosingClotting factorPharmacokineticsLife expectancyCoagulationBleedRisk factorQuality of life (healthcare)
DOInot available

Abstract

fetched live from OpenAlex

Hemophilia is a bleeding disorder in which the blood is unable to form clots, and is also classified as severe (defined as having an endogenous factor VIII [FVIII] concentration < 1 IU/dL, or 1%), moderate (1-5%) or mild (5-50%). Those with severe hemophilia may spontaneously bleed without physical trauma. If not treated appropriately, people with hemophilia may develop hemophilic arthropathy, a joint disease commonly showing signs of bleeding in the knees, ankles, or elbows, which not only impairs movement, but significantly impacts life expectancy and quality of life. \nThe current treatment of hemophilia involves prophylactic administration of factor concentrates. Although prophylactic treatment has improved health outcomes compared to on-demand treatment, there are still some challenges regarding the use of clotting factor concentrates. Generally, clotting factor concentrates are dosed based on international units per weight, but this one size fits all dosing mechanism fails to account for pharmacokinetic variability within this population. Appropriate dosing regimens vary by patient and treatment with hemophilia A patients using FVIII concentrates should be individualized from a therapeutic and economic standpoint. \nPopulation pharmacokinetic (PopPK) models were used as the basis for individualizing dosing regimens for patients with hemophilia while on factor concentrates. PopPK models are built using PK data from multiple participants to quantify the relationships between covariates such as age and weight to PK parameters as well as define variability between and within participants for these same PK parameters. To determine individual PK using only a few clotting factor activity levels and patient covariates, PopPK models are leveraged for Bayesian forecasting. People with hemophilia who have few sampling points are still available to be assessed using prior knowledge available from the patient population. While PopPK models may be helpful in hemophilia, this poses a concern when switching between factor concentrates. Factor concentrate switches may be prompted by the availability of new, improved concentrates by termination of national contracts resulting in a discontinuation of drug coverage, hypersensitivity to their current drug formulation, or adverse drug reactions. The lack of PK-tailored guidance when switching from one product to another may result in a period of time where treatment may increase the risk of inappropriate dosing, leading to either an increased risk of bleeds or waste of expensive factor concentrate. The work presented in this dissertation uses knowledge of an individual’s PK on a prior factor concentrate to better predict an individual’s PK on a new factor concentrate using data available from the Web-Accessible Population Pharmacokinetic Service – Haemophilia (WAPPS-Hemo). \nEmicizumab is a bispecific, recombinant, monoclonal antibody that bridges activated factor IX and X, mimics and partially restores the function of clotting FVIII in people with hemophilia A without inhibitors and was approved as routine prophylaxis by Health Canada in 2019. While emicizumab has its advantages over factor concentrates, such as decreased frequency of administration, and subcutaneous route of administration versus intravenous injections, the drug label recommends that any unused solution from a vial must be discarded, thereby wasting expensive resources. The use of a PopPK model of emicizumab was used to explore the implications of dosing based on vial size. This dissertation concludes that administering the entire vial of emicizumab and reducing the frequency may result in a reduction of vials used annually and consequently potential cost-savings. \nWith high treatment costs and the approval of emicizumab, understanding the pharmacoeconomics of hemophilia is imperative for healthcare systems for reimbursement approval and contributing to commercial success and decision making as to whether emicizumab should be covered or not. Given the lifelong burden of the disease, the high cost of treatment in hemophilia, and the approval of emicizumab, a drug that may change the landscape of how hemophilia is treated, a cost-utility analysis studying the cost and quality-of-life of different prophylactic treatment regimens was presented in this dissertation, concluding that emicizumab is more effective and may be less costly than FVIII for patients with hemophilia A in Canada, conditional on drug cost assumptions. \nThe method for estimating individual PK using PopPK models developed described in this dissertation may have a high impact for patients with hemophilia taking factor concentrates, who benefit from a safer individualized dosing regimen when switching between factor concentrates, potentially reducing adverse events or medication wastage. For patients with hemophilia on emicizumab, the simulations conducted exploring the use of emicizumab dosed based on vial size may have significant economic implications in cost-savings and provide a more practical dosing regimen. Finally, the economic evaluation conducted in the Canadian healthcare landscape may provide the healthcare system insight regarding the health and economic effects of using emicizumab compared to factor concentrates.

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)0.009
metaresearch head score (Gemma)0.040
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.040
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0190.001

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.

Opus teacher head0.018
GPT teacher head0.267
Teacher spread0.249 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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