Clinical development of onercept, a tumor necrosis factor binding protein, in psoriasis
Bibliographic record
Abstract
BACKGROUND: Tumor necrosis factor (TNF)-α plays a critical role in psoriasis pathogenesis, and several anti-TNF agents have been developed as therapeutic drugs in this indication. SCOPE: To present the preclinical rationale and clinical data for onercept, a novel anti-TNF agent developed for the treatment of moderate-to-severe psoriasis, and to critically evaluate the onercept clinical development program. FINDINGS: Onercept was shown in preclinical studies to inhibit TNF-α and suppress clinical signs in several inflammatory conditions. In phase II studies onercept demonstrated a therapeutic benefit in psoriasis and psoriatic arthritis and no safety issues were identified. Based on these results, a phase III program comprising three multicenter, randomized, double-blind, placebo-controlled studies examining onercept in moderate-to-severe plaque psoriasis was initiated. Following the occurrence of two cases of systemic inflammatory response syndrome (SIRS) and lower than expected efficacy results, an independent Data Safety Monitoring Board (DSMB) determined that the risk-benefit ratio was not sufficiently favorable to justify continued development, and all clinical studies were promptly terminated. Although not initially diagnosed as such by the investigators, two further SIRS events were reported, one after study discontinuation. Although an increased incidence of infection and sepsis-like events has been associated with other anti-TNF therapies, an increased risk of infection was not observed with onercept treatment. Moreover, no infectious etiology was determined in the SIRS cases. The data suggest that the SIRS reactions were due to a systemic inflammatory response. CONCLUSIONS: Despite promising early clinical results, onercept showed many of the expected risks associated with other anti-TNF agents and proved not to have an exceptional efficacy and safety profile. The clinical development of onercept highlights the critical importance of DSMBs and closely monitoring patient safety and evaluating risk-benefit profiles in large clinical programs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".