Chasing the target: reports from the Advances in Targeted Therapies meeting, 2024
Notice bibliographique
Résumé
OBJECTIVES: The Advances in Targeted Therapies annual meeting brings together experts within the field of rheumatology and immunology to highlight and discuss the latest scientific developments and needs in the field. The objective is to highlight unmet scientific needs in the field of rheumatology. METHODS: The 24th annual Advances in Targeted Therapies meeting convened with more than 100 international clinicians and scientific researchers in rheumatology, immunology, and other specialities relating to all aspects of immune-mediated inflammatory diseases. During the meeting, we held 5 rheumatologic disease-specific discussion sections consisting of experts in each field. These groups included rheumatoid arthritis (RA), psoriatic arthritis (PsA), axial spondyloarthritis (axSpA), osteoarthritis (OA), and systemic lupus erythematosus (SLE). In each group, experts were asked to identify the top 2 to 3 most important overarching and disease-specific scientific unmet needs to be addressed in the next 5 years. RESULTS: The overarching themes across disciplines included the need for precision medicine, improved classification of disease states, and the further identification of targets and associated therapies, including the potential role of chimeric antigen receptor (CAR) T cell therapies. Within RA, the group highlighted the lack of precision medicine and the need for better biomarkers. Further, the lack of targeted therapies against fibroblasts in RA was discussed, with the potential impact of targeting fibroblasts early in the disease as an unmet need. For PsA, there is a continued need for a better definition of disease endotypes and for the categorisation of those with complex and difficult-to-treat (D2T) diseases. The development of bispecific molecules and combination therapeutic approaches remain a high priority. For axSpA, the disease-modifying characteristics of nonsteroid anti-inflammatory drugs need further evaluation, as does the treatment of residual pain and fatigue frequently in the disease. In OA, new therapeutic targets remain an unmet need, and the discussion group prioritised potential experimental strategies that could lead to innovative therapeutic targets. Elucidating the specific signalling and target cells responsible for, or inhibiting, repair will be essential for developing targeted therapies. SLE experts emphasised the need to identify the most predictive biological contributions to disease progression in patients with early clinical precursors of SLE. The role of CAR T cell therapy must be further investigated, along with ancillary biologic studies (eg, immune system profiling) that provide critical insights into disease pathogenesis. Further, there is a need to determine the relationship of patient-relevant symptoms to the pathophysiology of SLE and identify new therapeutic targets for these symptoms. CONCLUSIONS: There remain many unmet needs on the road to precision medicine with regard to identifying disease endotypes and biomarkers for disease progression or therapeutic response. For most diseases discussed, a strong unmet need remains with regard to identifying new targets and therapies for those with refractory or D2T disease. The ability to prevent or cure rheumatic disease remains the ultimate unmet need in rheumatology.
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 | 0,007 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,005 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,004 |
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 ».