Clinical study design strategies to mitigate confounding effects of time‐dependent clearance on dose optimization of therapeutic antibodies
Bibliographic record
Abstract
Time-dependent pharmacokinetics (TDPK) is a frequent confounding factor that misleads exposure-response (ER) analysis of therapeutic antibodies, where a decline in clearance results in increased drug exposure over time in patients who respond to therapy, causing a false-positive ER finding. The object of our simulation study was to explore the influence of clinical trial designs on the frequency of false-positive ER findings. Two previously published population PK models representative of slow- (pembrolizumab) and fast-onset (rituximab) TDPK were used to simulate virtual patient cohorts with time-dependent clearance and the frequency of false-positive ER findings. The impact of varying the number of dose groups, dose range, and sample size was evaluated over time. Study designs with a single tested dose level showed a high probability of showing a false-positive ER finding. When TDPK has a slow onset, use of exposure measures from early timepoints in ER analysis significantly reduces the risk of a false-positive, while with fast onset it did not. Randomization of patients to two dose levels greatly reduced the risk, with a threefold or greater dose range offering the greatest benefit. The likelihood of false-positive increases with a larger sample size, where greater care should be taken to identify confounding factors. Clinical trial simulation supports that appropriate clinical study design and analysis with adequate dose exploration can reduce but cannot entirely eliminate the risk of misleading ER findings.
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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 | 0.154 | 0.213 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".