From reactive to predictive: Advancing biologic dosing in dermatology
Bibliographic record
Abstract
Biologic therapies are targeted immunomodulators that have revolutionised the treatment of chronic inflammatory dermatoses, including psoriasis, hidradenitis suppurativa and atopic dermatitis, offering high efficacy and safety for moderate-to-severe disease. However, standardised dosing regimens fail to account for inter-individual heterogeneity, leading to non-optimal drug exposure in certain patients. This may, in part, explain why primary non-response, secondary loss of response or adverse effects are observed in real-world practice. While empirical off-label adjustments are commonly employed, data-driven dosing optimisation may enable better disease control and reduce the burden of lifelong biologic treatment by avoiding excessive dosing. Therapeutic drug monitoring (TDM) involves adjusting doses based on serum drug concentrations to maintain therapeutic ranges, relying on established dose-exposure-response correlations. Although TDM provides reactive or proactive optimisation approaches, its threshold-based adjustments are limited by an inability to predict individual pharmacokinetic variability or future clinical outcomes. Model-informed precision dosing (MIPD) represents an evolution beyond conventional TDM by integrating population pharmacokinetic-pharmacodynamic models, patient-specific characteristics and Bayesian forecasting to deliver personalised, adaptive dosing recommendations. This approach captures inter-individual variability while linking serum drug concentrations to clinical response, enabling proactive dose adjustments using sparse or non-steady-state data. Emerging digital tools, point-of-care testing and integration of pharmacogenomics and biomarker profiling are expected to enhance MIPD's precision and accessibility. Once implemented in routine care, MIPD has the potential to transform biologic therapy dosing using a dynamic, patient-centric system that maximises clinical response, minimises overtreatment and supports sustainable healthcare delivery.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".