Review article: Optimisation of biologic (monoclonal antibody) therapeutic response in inflammatory bowel disease
Bibliographic record
Abstract
BACKGROUND: There are a plethora of therapeutic options for the management of inflammatory bowel disease (IBD). Despite this, clinical outcomes with standard dosing often fall short of established targets. While efforts centre on developing novel therapies, there is an ongoing need to optimise the use of existing agents. AIMS: To focus on strategies to optimise response to biologic (monoclonal antibody) therapies in IBD, including use of therapeutic drug monitoring (TDM). METHODS: An extensive review of the published literature. RESULTS: TDM is a strategy aimed at enhancing the effectiveness of drugs with variable exposure-response relationships by measuring serum concentrations of biologic therapies and detecting neutralising antibodies. Reactive TDM is performed when therapeutic goals have not been achieved. Tumour necrosis factor alpha (TNF) inhibitors are the treatment class most frequently associated with immunogenicity and loss of response. Immunogenicity can be reduced through avoidance of low serum drug concentrations by dose optimisation or use of concomitant immunomodulator therapy. Subtherapeutic dosing in the absence of antidrug antibodies is best managed by dose escalation or dose interval reduction. Persistent neutralising drug antibodies necessitate switching to an alternative therapy. Proactively ensuring adequate serum trough levels might help sustain treatment durability and prevent loss of response. Newer non-TNF inhibitors demonstrate less robust exposure-response relationships, and TDM may not prove as beneficial. CONCLUSIONS: In the treat-to-target paradigm of IBD treatment, optimising treatment effect with dose optimisation, which may involve strategies including TDM, increases the likelihood of achieving clinical remission and may accomplish deeper levels of remission beyond symptom control.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".