LUPUS RESEARCH ALLIANCE 2024-2028 RESEARCH STRATEGIC PLAN: ACCELERATING PRECISION MEDICINE FOR PEOPLE LIVING WITH LUPUS
Notice bibliographique
Résumé
PV140 / #234 Poster Topic: AS17 - Miscellaneous Background/Purpose Lupus is a systemic autoimmune disease characterized by complicated etiopathogenesis, heterogeneous clinical manifestations and a range of immunological abnormalities that affects millions of people worldwide. At present, therapy for lupus is mostly empiric and involves largely nonspecific anti-inflammatory and immunosuppressive agents. The Lupus Research Alliance (LRA), the world’s largest private funder of lupus research, has invested over US$270M in more than 600 individual research programs since 1999. Recently, the LRA successfully completed its previous 5-year research strategic plan, resulting in the establishment of robust research governing bodies, a tripling of the number of grant programs, and the establishment of critical translational and clinical research infrastructure to include a Public-Private Partnership with the FDA, Lupus ABC, a lupus registry, biorepository, and data exchange platform, Lupus Nexus, and has significantly increased the number of clinical studies the organization supports through the clinical affiliate, Lupus Therapeutics. To build upon this success, the LRA set out to create a new 5-year Research Strategic Plan as part of an organization-wide goal to directly impact individuals living with lupus by enabling research that advances safer and accessible treatment options. Methods More than 30 interviews with lupus patients and US and international lupus/related fields researchers were conducted, as well as a survey of the research and clinical landscape and a detailed analysis of the clinical trial landscape using clinicaltrials.gov and other sources. Additionally, an in-depth evaluation of LRA’s research portfolio and broader funding landscape analyses were performed to inform the evolution of LRA’s grant programs and ensure they continue to synergize with external research funding. A planning committee of academic and industry experts, and patients advised the development of the strategy by analyzing the data and engaging in discussions about emerging gaps and opportunities. Results With recent developments in understanding lupus pathogenesis and heterogeneity, impactful technological advancements, and the emergence of engineered cell therapy as a novel treatment paradigm for lupus, the LRA is uniquely positioned to address critical gaps and opportunities to accelerate precision medicine for people living with lupus. The clinical trial landscape analysis highlighted that while the number of lupus clinical trials has increased, there is a high failure rate for primary outcomes, demonstrating the need for better trial design and population characterization, as well as mechanistic understanding of therapies entering trials and for diagnostics/prognostics to stratify patients and quantify outcomes. Importantly, the analysis showed that the LRA clinical affiliate, Lupus Therapeutics, participated in ~25-30% of all lupus trials. The new strategic plan was built on its unique capabilities and the LRA’s robust infrastructure and research programs and includes three 5-year research goals, each with corresponding objectives and intended patient impact: 1) Improve understanding of patient heterogeneity as a basis for individual therapeutic choices; 2) Increase the number of molecular stratification, prognostic/diagnostic tools, and biomarkers; 3) Accelerate development of treatments that reprogram the immune system. The new Plan calls for the LRA to become an effective driver of clinical development, for substantive changes to LRA’s funding portfolio to focus on the new priorities, and the establishment of new partnerships and patient-centric research initiatives. Conclusions By implementing this plan, the LRA envisions a future where patients are promptly diagnosed, clinically and molecularly profiled, and offered safer and more effective personalized treatments and possible cures.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,039 | 0,040 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,010 | 0,007 |
| Science ouverte | 0,004 | 0,010 |
| Intégrité de la recherche | 0,009 | 0,011 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,121 | 0,087 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».