Target Identification and Prioritization: A Kidney Cancer Use Case
Dive into groundbreaking research on Renal Cell Cancer, the most common kidney cancer in adults. Our blog explores innovative strategies for target identification, prioritization, and how these contribute to preventing and tackling this disease.
The Kidney Cancer Landscape
Kidney cancer, also called renal cancer, accounts for around 2% of all cancers.¹ In 2020 alone, the global medical community reported nearly half a million new cases, highlighting the significance of the disease.¹ In 2023, projections indicate that over 80,000 adults in the United States will be diagnosed with kidney cancer.² The most common type of kidney cancer in adults is Renal Cell Cancer (RCC),³ typically affecting individuals in their in 60s and 70s. However, recent data reveals rising incidences of in young adults.⁴
Although remarkable progress has been made in treating RCC over the years, finding a definitive cure for advanced or metastatic RCC remains challenging. Current treatment options often have undesirable side effects that can significantly impact patients’ quality of life. For example, sunitinib, a commonly used drug for RCC, has been associated with anorexia, skin toxicity, and hypertension, according to Causaly data. Moreover, the emergence of drug resistance further compromises the effectiveness of current treatments, highlighting the urgent need for innovative drugs with alternative mechanisms of action. By identifying drug targets quickly, researchers can accelerate the drug discovery process to address the limitations of existing therapies and to enhance patient outcomes.
Target Identification and Prioritization: Renal Cell Cancer (RCC)
By leveraging the power of Causaly, researchers can explore biological cause-and-effect relationships and accelerate target discovery through interactive visualizations. This allows scientists to assess and qualify scientific evidence based on key findings, which have been demonstrated here for renal cell cancer (RCC).
Using advanced machine-reading technology, Causaly dramatically reduces the time taken to search the biomedical literature for promising drug targets, enabling researchers to view available evidence with complete transparency and minimal bias.
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