Chasing the target: reports from the Advances in Targeted Therapies meeting, 2024
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".