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Abstract PR09: Synergistic activity of CRLX101, a nanopharmaceutical in Phase II clinical trials, with antiangiogenic therapies mediated through HIF-1alpha inhibition: A translational research program.

2013· article· en· W2036143910 on OpenAlexaff
Scott Eliasof, Sarah J. Conley, Stephen J. O’Keefe, Robert S. Kerbel, Carolyn Krasner, Douglas Lazarus, Christian Peters, Elizabeth Pham, Max S. Wicha, Edward G. Garmey

Bibliographic record

VenueMolecular Cancer Therapeutics · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineCancer researchBevacizumabCamptothecinAngiogenesisMetastasisCancerOvarian cancerTumor hypoxiaGemcitabineOncologyPharmacologyInternal medicineChemotherapyBiologyRadiation therapy

Abstract

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Abstract Background: Antiangiogenic drugs reduce blood flow to tumors and thereby inhibit tumor growth by starving tumors of oxygen and nutrients. However, antiangiogenic drugs have achieved limited success as monotherapies, in part because of their induction of hypoxia and the concomitant up-regulation of hypoxia-inducible factor 1α (HIF-1α), now well implicated in the promotion of tumor angiogenesis, invasion, metastasis, and cancer stem cell formation. We describe here a translational research program to investigate whether the efficacy of antiangiogenic drugs can be improved through combination with CRLX101, a camptothecin (CPT) containing nanopharmaceutical that inhibits both topoisomerase-1 and HIF-1α. Material and methods: We will present preclinical and clinical projects conducted across several major research institutions intended to demonstrate the anti-HIF-1α activity of CRLX101, the capacity of this drug to block the epithelial-mesenchymal transition (EMT) and the formation of cancer stem cells, and the synergistic activity of CRLX101 given in combination with antiangiogenic drugs. We will further describe two ongoing clinical trials evaluating these hypotheses, one at the University of Pennsylvania in advanced renal cell carcinoma (RCC) and one at the Massachusetts General Hospital in relapsed ovarian cancer following progression through prior platinum-containing chemotherapy. Results: A single dose of CRLX101 durably inhibits HIF-1α protein levels across multiple tumor types. Evaluation of CRLX101 in combination with bevacizumab, aflibercept or pazopanib in the A2780 ovarian xenograft tumor model demonstrates synergistic inhibition of tumor growth inhibition as well as increases in the rate of long-term survivorship. While all three antiangiogenic drugs alone increased HIF-1α protein levels, levels were inhibited in response to combination with CRLX101. In clinical evaluations, a CRLX101-bevacizumab combination appears safe and well tolerated with no dose limiting toxicities observed to date. Notable tumor decreases and long periods of progression free survival have been noted among patients treated with CRLX101-based mono and combination therapy. Conclusions: Results generated through this translational research program suggest that CRLX101 can overcome HIF-1α-mediated acquired resistance to antiangiogenic drugs, supporting the use of CRLX101 in combination with antiangiogenic drugs as an exciting new paradigm for the treatment of cancer. Citation Information: Mol Cancer Ther 2013;12(11 Suppl):PR09. Citation Format: Scott Eliasof, Sarah Conley, Stephen M. Keefe, Robert Kerbel, Carolyn N. Krasner, Douglas Lazarus, Christian Peters, Elizabeth Pham, Max S. Wicha, Edward G. Garmey. Synergistic activity of CRLX101, a nanopharmaceutical in Phase II clinical trials, with antiangiogenic therapies mediated through HIF-1alpha inhibition: A translational research program. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr PR09.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.135
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.164
GPT teacher head0.472
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2013
Admission routes1
Has abstractyes

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