In the context of
blood cancers, the evasion of programmed cell death is a key factor in the development and progression of
tumors, which is often due to the overproduction of proteins that prevent cell death, such as
BCL-2 and
BCL-XL.
Venetoclax, a drug that targets BCL-2, has been approved by the FDA for certain types of
leukemia. However, the development of BCL-XL inhibitors has been challenging due to their potential to cause
thrombocytopenia, a condition characterized by a low platelet count, because platelets also rely on BCL-XL for survival.
To address this issue, researchers have proposed a new strategy involving BCL-XL degraders, which aim to selectively break down the BCL-XL protein in cancer cells while sparing platelets, which have low levels of the protein responsible for degrading BCL-XL. An early version of such a degrader, DT2216, showed promise but still resulted in platelet toxicity. This was hypothesized to be due to the strong BCL-XL inhibitory properties of the degrader's 'warhead' component, which could also affect platelets.
Using an AI-driven platform, researchers have designed a new candidate molecule, NXD02, which binds weakly to BCL-XL but can still facilitate its degradation. In preclinical studies, NXD02 demonstrated a significantly weaker binding affinity to BCL-XL compared to
DT2216 but was more potent in degrading the protein and inducing apoptosis in certain cells. It also showed a strong anti-proliferative effect without affecting platelet viability in vitro and had a higher drug exposure and comparable platelet toxicity profile in vivo to DT2216.
Furthermore, NXD02 displayed stronger anti-tumor activity in a mouse model and supported the potential for weekly dosing without significant body weight loss. The molecule has also undergone initial in vitro toxicity testing with no concerns raised, and ongoing in vivo safety assessments are positive, indicating NXD02 as a promising candidate for further development for the treatment of certain cancers.
How to Use Synapse Database to Search and Analyze Translational Medicine Data?
The transational medicine section of the Synapse database supports searches based on fields such as drug, target, and indication, covering the T0-T3 stages of translation. Additionally, it offers a historical conference search function as well as filtering options, view modes, translation services, and highlights summaries, providing you with a unique search experience.

Taking obesity as an example, select "obesity" under the indication category and click search to enter the Translational Medicine results list page. By clicking on the title, you can directly navigate to the original page.

By clicking the analysis button, you can observe that GLP-1R treatment for obesity has gained significant attention over the past three years, with preclinical research still ongoing in 2023. Additionally, there are emerging potential targets, such as GDF15, among others.

Click on the image below to go directly to the Translational Medicine search interface.
