Impact of pharmacodynamic biomarkers in immuno-oncology (IO) phase 1 clinical trials.
Bibliographic record
Abstract
2653 Background: Pharmacodynamic biomarkers (PD) are considered fundamental for go/no-go decisions in phase 1 trials. Despite an increase in the availability of blood-based biomarker assays, the requirement of invasive non-diagnostic research tumor biopsies for trial eligibility remains common. In the immuno-oncology (IO) era, the impact of PD analysis for the confirmation of biologic activity and recommended phase 2 dose (RP2D) has not been investigated. Methods: Phase 1 studies from 01/2014 to 12/2018 were reviewed. Among 12053 abstracts screened, a total of 143 phase I-IO trials were identified. Characteristics of studies that included on-treatment PD biomarkers (tissue-derived, blood-based and radiomic) were extracted and analyzed. Outcomes from the biomarker data in terms of proof of mechanism/biologic activity and statistically significant correlation with clinical benefit (objective response or survival) were collected. Authors’ statements on the influence of PD results on RP2D were also noted. Results: Out of 143 phase 1 IO trials, 107 (75%) were monotherapy. The most frequent IO evaluated were vaccines (41%), cell therapy (16%), immunomodulators (13%) and cytokines (7%). Of the 36 combination studies, 20 (61%) included a second IO drug while 16 (39%) included molecular-targeted agents. Only 18 of 143 studies (12%) did not report any PD data. Of the remaining 125 studies, tissue-derived PD (t-PD) biomarkers alone, blood-based PD (b-PD) biomarkers alone, both t-PD and b-PD biomarkers, and imaging biomarkers were tested in 3 (2%), 97 (78%), 25 (20%), and 7 (6%), respectively. Demonstration of proof of mechanism/biologic activity only were reported in 16/28 (57%), 80/122 (66%) and 4/7 (57%) of the t-PD, b-PD and imaging biomarker studies, respectively. Significant correlation with clinical benefit was reported in 2/28 (7%), 7/122 (6%) and 0/7 (0%) of the t-PD, b-PD and imaging biomarker studies, respectively; these involved 4 vaccines (1 in combination with PD1 blockade), 1 cell therapy and 1 oncolytic virus (in combination with CTLA4 blockade). Among 35 b-PD studies with negative results, 5 also performed t-PD biomarkers, all with negative results. Notably, 3 out of 10 t-PD studies with negative results reported concurrent positive b-PD results. Based on the published reports, authors stated that biomarker results helped with RP2D determination in 16/28 (57%) of t-PD and 78/122 (64%) of b-PD studies. Conclusions: Our results suggest that in the IO era, most studies perform PD analysis, with similar proportions of t-PD and b-PD showing proof of mechanism/biologic activity. IO PD biomarkers have limited correlation with clinical benefit. Many authors considered IO PD biomarkers to be relevant in RP2D decisions, but this needs confirmation by other measures of impact. With continued technological developments utilizing circulating biomarkers, b-PD may ultimately replace many t-PD tests in future IO studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.105 | 0.209 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".