Molecular Pharmacodynamics-Guided Scheduling of Biologically Effective Doses: A Drug Development Paradigm Applied to MET Tyrosine Kinase Inhibitors
Bibliographic record
Abstract
Abstract The development of molecularly targeted agents has benefited from use of pharmacodynamic markers to identify “biologically effective doses” (BED) below MTDs, yet this knowledge remains underutilized in selecting dosage regimens and in comparing the effectiveness of targeted agents within a class. We sought to establish preclinical proof-of-concept for such pharmacodynamics-based BED regimens and effectiveness comparisons using MET kinase small-molecule inhibitors. Utilizing pharmacodynamic biomarker measurements of MET signaling (tumor pY1234/1235MET/total MET ratio) in a phase 0–like preclinical setting, we developed optimal dosage regimens for several MET kinase inhibitors and compared their antitumor efficacy in a MET-amplified gastric cancer xenograft model (SNU-5). Reductions in tumor pY1234/1235MET/total MET of 95%–99% were achievable with tolerable doses of EMD1214063/MSC2156119J (tepotinib), XL184 (cabozantinib), and XL880/GSK1363089 (foretinib), but not ARQ197 (tivantinib), which did not alter the pharmacodynamic biomarker. Duration of kinase suppression and rate of kinase recovery were specific to each agent, emphasizing the importance of developing customized dosage regimens to achieve continuous suppression of the pharmacodynamic biomarker at the required level (here, ≥90% MET kinase suppression). The customized dosage regimen of each inhibitor yielded substantial and sustained tumor regression; the equivalent effectiveness of customized dosage regimens that achieve the same level of continuous molecular target control represents preclinical proof-of-concept and illustrates the importance of proper scheduling of targeted agent BEDs. Pharmacodynamics-guided biologically effective dosage regimens (PD-BEDR) potentially offer a superior alternative to pharmacokinetic guidance (e.g., drug concentrations in surrogate tissues) for developing and making head-to-head comparisons of targeted agents. Mol Cancer Ther; 17(3); 698–709. ©2018 AACR.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".