Use of a Target‐Mediated Drug Disposition Model to Predict the Human Pharmacokinetics and Target Occupancy of <scp>GC</scp>1118, an Anti‐epidermal Growth Factor Receptor Antibody
Bibliographic record
Abstract
Abstract GC1118 is an anti‐epidermal growth factor receptor (EGFR) monoclonal antibody that is currently under clinical development. In this study, the pharmacokinetics (PK) of GC1118 were modelled in monkeys to predict human PK and receptor occupancy (RO) profiles. The serum concentrations of GC1118 and its comparator (cetuximab) were assessed in monkeys with a non‐compartmental analysis and a target‐mediated drug disposition (TMDD) model after intravenous infusion (3–25 mg/kg) of these drugs. The scaling exponent of the EGFR synthesis rate was determined using a sensitivity analysis. The human cetuximab exposures were simulated by applying different exponents (0.7–1.0) for the EGFR synthesis rate in the allometric monkey PK model. Simulated Cmax and area under the curve values therein were compared with those previously reported in the literature to find the best exponent for the EGFR synthesis rate in human beings. The TMDD model appropriately described the monkey PK profile, which showed a decrease in clearance (CL; 1.2–0.4 ml/hr/kg) as the dose increased. The exponents for CL (0.75) and volume of distribution (Vd; 1.0) were used for the allometric scaling to predict human PK. The allometric coefficient for the EGFR synthesis rate chosen by the sensitivity analysis was 0.85, and the RO profiles that could not be measured experimentally were estimated based on the predicted concentrations of the total target and the drug–target complex. Our monkey TMDD model successfully predicts human PK and RO profiles of GC1118 and can be used to determine the appropriate dose for a first‐in‐human study investigating this drug.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".