Translational Impact of Nanoparticle–Drug Conjugate CRLX101 with or without Bevacizumab in Advanced Ovarian Cancer
Bibliographic record
Abstract
PURPOSE: Increased tumor hypoxia and hence elevated hypoxia-inducible factor-1α (HIF1α) is thought to limit the efficacy of vascular endothelial growth factor (VEGF) pathway-targeting drugs by upregulating adaptive resistance genes. One strategy to counteract this is to combine antiangiogenic drugs with agents able to suppress HIF1α. One such possibility is the investigational drug CRLX101, a nanoparticle-drug conjugate (NDC) containing the payload camptothecin, a known topoisomerase-I poison. EXPERIMENTAL DESIGN: CRLX101 was evaluated both as a monotherapy and combination with bevacizumab in a preclinical mouse model of advanced metastatic ovarian cancer. These preclinical studies contributed to the rationale for undertaking a phase II clinical study to evaluate CRLX101 monotherapy in patients with advanced platinum-resistant ovarian cancer. RESULTS: Preclinically, CRLX101 is highly efficacious as a monotherapy when administered at maximum-tolerated doses. Furthermore, chronic low-dose CRLX101 with bevacizumab reduced bevacizumab-induced HIF1α upregulation and resulted in synergistic efficacy, with minimal toxicity in mice. In parallel, initial data reported here from an ongoing phase II clinical study of CRLX101 monotherapy shows measurable tumor reductions in 74% of patients and a 16% RECIST response rate to date. CONCLUSIONS: Given these preclinical and initial clinical results, further clinical studies are currently evaluating CRLX101 in combination with bevacizumab in ovarian cancer and warrant the evaluation of this therapy combination in other cancer types where HIF1α is implicated in pathogenesis, as it may potentially be able to improve the efficacy of antiangiogenic drugs.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".