Impact of pharmacodynamic biomarkers in immuno-oncology phase 1 clinical trials
Bibliographic record
Abstract
BACKGROUND: Phase 1 immuno-oncology (IO) trials frequently involve pharmacodynamic (PD) biomarker assessments involving tumour biopsies and/or blood collection, with increasing use of molecular imaging. PD biomarkers are set to play a fundamental role in early drug development of immuno-oncology (IO) agents. In the IO era, the impact of PD biomarkers for confirmation of biologic activity and their role in subsequent drug development have not been investigated. METHODS: Phase 1 studies published between January 2014 and December 2020 were reviewed. Studies that reported on-treatment PD biomarkers [tissue-derived (tissue-PD), blood-based (blood-PD) and imaging-based (imaging-PD)] were analysed. PD biomarker results and their correlation with clinical activity endpoints were evaluated. Authors' statements on the influence of PD biomarkers on further drug development decisions, and subsequent citations of PD biomarker study results were recorded. RESULTS: Among 386 trials, the most frequent IO agent classes evaluated were vaccines (32%) and PD-(L)1 inhibitors (25%). No PD biomarker assessments were reported in 100 trials (26%). Of the remaining 286, blood-PD, tissue-PD, and imaging-PD data were reported in 270 (94%), 94 (33%), and 12 (4%) trials, respectively. Assessments of more than one PD biomarker type were reported in 82 studies (29%). Similar proportions of blood-PD (9%), tissue-PD (7%), and imaging-PD studies (8%) had positive results that correlated with clinical activity. Results of 22 PD biomarker studies (8%) were referenced in subsequent clinical trials. CONCLUSIONS: Most phase 1 IO studies performed PD biomarker assessments. Overall, positive PD biomarker results were infrequently correlated with clinical activity or cited in subsequent trials, suggesting a limited impact on subsequent drug development. With emerging health regulatory emphasis on optimal dose selection based on PD activity, more informative and integrative multiplexed assays that capture the complexity of tumour-host immunity interactions are warranted to improve phase 1 IO trial methodology.
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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 | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".