Model-Informed Drug Approach to Recommend Therapeutic Burosumab Dosing Regimens for Pediatric and Adult Patients with Tumor-Induced Osteomalacia
Bibliographic record
Abstract
Aim: Burosumab is approved for the treatment of X-linked hypophosphatemia (XLH) and hypophosphatemia secondary to persistent tumor-induced osteomalacia (TIO). The relationship between burosumab and fasting serum phosphate levels were previously described in patients with XLH. This work evaluated burosumab pharmacokinetic (PK) and PK/pharmacodynamic (PD) in the TIO population to support TIO dosing. Methods: Previously developed PK and PK/PD models in XLH were fitted to the combined dataset of patients with XLH and TIO to understand PK and PK/PD characteristics and covariates specific to TIO. Simulations of PK and PK/PD profiles were performed using the final models to support dosing recommendations for adult and pediatric patients with TIO. Results: Burosumab PK and PK/PD models were similar to those previously described in XLH, with additional covariates identified in TIO: baseline FGF23 on PK/PD parameters, and steeped PK/PD curve in TIO. Simulations demonstrated that in pediatric patients starting doses of burosumab 0.3 mg/kg and 0.4 mg/kg Q2W at steady state would achieve normal serum phosphate levels in at least 30% of patients with relatively low risk of hyperphosphatemia (<3% of patients), while in adults, burosumab 0.3 mg/kg and 0.5 mg/kg Q4W achieves similar percentages of responders and relative low risk of hyperphosphatemia (<7%) Titration based burosumab dosing increased the probability of achieving normal serum phosphate levels. Conclusions: This analysis identified the predictors and quantified the relationship between burosumab serum concentrations and fasting serum phosphate levels amongst patients with TIO. The models supported titration based burosumab dosing, guided by monitoring fasting serum phosphate levels.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".