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Emerging Science, Emerging Ethical Issues: Who Should Fund Innate Alloimmunity-suppressing Drugs ?

2008· article· en· W2418639309 on OpenAlexaff
W. Land, Th. Gutmann, Abdallah S. Daar

Bibliographic record

VenueActa chirurgica Belgica · 2008
Typearticle
Languageen
FieldMedicine
TopicScience, Research, and Medicine
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineAlloimmunityEngineering ethicsImmunologyAntibody

Abstract

fetched live from OpenAlex

An emerging body of evidence suggests that the innate immune system plays a critical role in allograft rejection. Any injury to the donor organ, e.g. the reperfusion injury, induces an inflammatory milieu in the allograft which appears to be the initial event for activation of the innate immune system. Injury-induced intragraft damage- associated molecular patterns (DAMPs) are recognized by donor-derived and recipient-derived, TLR4/2-bearing immature dendritic cells (iDCs). After recognition, these cells mature and initiate allorecognition/alloactivation in the lymphoid system of the recipient. Indeed, the key "innate" event, leading to activation of the adaptive alloimmune response, is the injury-induced, TLR4-triggered, and NFkappaB-mediated maturation of DCs ("innate alloimmunity"). Time-restricted treatment of innate immune events would include 1) treatment of the donor during organ removal, 2) in-situ/ex-vivo treatment of the donor organs alone, and 3) treatment of the recipient during allograft reperfusion and immediately postoperatively. Treatment modalities would include 1) minimization of the oxidative allograft injury with the use of antioxidants; 2) prevention of the TLR4-triggered maturation of DCs with the use of TLR4-antagonists; 3) inhibition of complement activation with the use of complement inhibiting agents. According to data from clinical and experimental studies it can be assumed that successful suppression of innate alloimmune events results in either subsequent significant reduction in, or even complete avoidance of the currently applied adaptive alloimmunity-suppressing drugs. However, in view of the time-restricted period of treatment, and the fear to potentially destroy its own business with currently applied alloimmunity-suppressing drugs, the pharmaceutical industry is still, but quite legitimately, reluctant to invest in the high cost of clinical development of those drugs for transplant patients because there are no marketing interests. On the other hand, clinical development of innate alloimmunity-suppressing drugs is urgently warranted. But: Who should fund? In this article, three options are explored which may contribute to a solution of the problem: 1) provision of incentives to companies for drug development; 2) conduction of clinical trials in developing countries; and 3) creation of a public-private professional partnership in analogy to the "European Rare Diseases Therapeutic Initiative" (ERDITI). We suggest and recommend the creation of such a partnership which may be called: "The European Initiative for the Suppression of Innate Alloimmunity" ("EISIA"). In analogy to ERDITI, the main goals of this organization should be:--to provide a streamlined facilitated process of collaboration between Academic Teams/Transplant Centres, Study Groups, and Pharma Companies to develop innate alloimmunity-suppressing drugs;--to give Academic Teams/Transplant Centres facilitated access to a large variety of compounds, developed by companies for other indications, which can be evaluated pre-clinically and, if warranted, clinically;--to guarantee the continuity all the way from research to development and commercialisation of the drug. If preclinical studies uncover the potential of a compound for suppressing innate alloimmune events, the Pharma Partner who has rights to this compound will either develop himself the drug for organ transplantation indication or allow its development by the academic team or a third party if he has no intentions of developing himself.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.812
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0030.003
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.003
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 head0.079
GPT teacher head0.393
Teacher spread0.315 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations3
Published2008
Admission routes1
Has abstractyes

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