2024-ASCO-Mutational analysis of cfDNA to identify predictive biomarkers in previously untreated patients with metastatic pancreatic cancer receiving the GSK-3 inhibitor elraglusib (9-ING-41) in combination with gemcitabine/nab-paclitaxel in the 1801 phase 2 study
Wednesday, May 29, 2024 BiopharmaWatch Research 1 min read
Key Takeaway: The 1801 Phase 2 study is investigating the use of elraglusib (9-ING-41) combined with gemcitabine and nab-paclitaxel in previously untreated patients with metastatic pancreatic cancer. The study aims to analyze cfDNA to identify predictive biomarkers that could enhance treatment outcomes. This research may provide valuable insights into personalized cancer therapy.
Market Sentiment Analysis
POSITIVE FACTORS
Focus on identifying predictive biomarkers for treatment efficacy.
Combination therapy shows potential for previously untreated patients.
Elraglusib (9-ING-41) is being evaluated in a significant Phase 2 study.
BiopharmaWatch Analysis
From our catalyst data and publicly available data · not financial advice
Best trade, last catalyst
+9%
120-day peak, hindsight
Typical move
9.4%
average across 4 past catalysts
Cash runway
~3 mo
High dilution risk
Lead asset
9-ING-41
Phase 2 · Cancer
Full Press Release Details
2024-ASCO-Mutational analysis of cfDNA to identify predictive biomarkers in previously untreated patients with metastatic pancreatic cancer receiving the GSK-3 inhibitor elraglusib (9-ING-41) in combination with gemcitabine/nab-paclitaxel in the 1801 phase 2 study Download PDF View Resource
Frequently Asked Questions
What is the focus of the 1801 Phase 2 study?
The study focuses on identifying predictive biomarkers in pancreatic cancer patients.
What treatment is being tested in the study?
The study tests elraglusib (9-ING-41) combined with gemcitabine and nab-paclitaxel.
Who are the participants in this study?
Participants are previously untreated patients with metastatic pancreatic cancer.
What is cfDNA's role in this research?
cfDNA is analyzed to identify biomarkers that may predict treatment efficacy.