Retrospective Assessment of Translational Pharmacokinetic–Pharmacodynamic Modeling Performance: A Case Study with Apitolisib, a Dual PI3K/mTOR Inhibitor
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Despite significant progress in biomedical research, the rate of success in oncology drug development remains inferior to that of other therapeutic fields. Mechanistic models provide comprehensive understanding of the therapeutic effects of drugs, which is crucial for designing effective clinical trials. This study was performed to acquire a better understanding of PI3K-AKT-TOR pathway modulation and preclinical to clinical translational bridging for a specific compound, apitolisib (PI3K/mTOR inhibitor), by developing integrated mechanistic models. METHODS: Integrated pharmacokinetic (PK)-pharmacodynamic (PD)-efficacy models were developed for xenografts bearing human renal cell adenocarcinoma and for patients with solid tumors (phase 1 studies) to characterize relationships between exposure of apitolisib, modulation of the phosphorylated Akt (pAkt) biomarker triggered by inhibition of the PI3K-AKT-mTOR pathway, and tumor response. RESULTS: Both clinical and preclinical integrated models show a steep sigmoid curve linking pAkt inhibition to tumor growth inhibition and quantified that a minimum of 35-45% pAkt modulation is required for tumor shrinkage in patients, based on platelet-rich plasma surrogate matrix and in xenografts based on tumor tissue matrix. Based on this relationship between targeted pAkt modulation and tumor shrinkage rate, it appeared that a constant pAkt inhibition of 61% and 65%, respectively, would be necessary to achieve tumor stasis in xenografts and patients. CONCLUSIONS: These results help when it comes to evaluating the translatability of the preclinical analysis to the clinical target, and provide information that will enhance the value of future preclinical translational dose-finding and dose-optimization studies to accelerate clinical drug development. TRIAL REGISTRY: ClinicalTrials.gov NCT00854152 and NCT00854126.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".