Repurposing of the Syk inhibitor fostamatinib using a machine learning algorithm

TAM (TYRO3, AXL, MERTK) receptor tyrosine kinases (RTKs) have intrinsic roles in tumor cell proliferation, migration, chemoresistance, and suppression of antitumor immunity. The overexpression of TAM RTKs is associated with poor prognosis in various types of cancer. Single-target agents of TAM RTKs have limited efficacy because of an adaptive feedback mechanism resulting from the cooperation of TAM family members. This suggests that multiple targeting of members has the potential for a more potent anticancer effect. The present study used a deep-learning based drug-target interaction (DTI) prediction model called molecule transformer-DTI (MT-DTI) to identify commercially available drugs that may inhibit the three members of TAM RTKs. The results showed that fostamatinib, a spleen tyrosine kinase (Syk) inhibitor, could inhibit the three receptor kinases of the TAM family with an IC₅₀ <1 µM. Notably, no other Syk inhibitors were predicted by the MT-DTI model. To verify this result, this study performed in vitro studies with various types of cancer cell lines. Consistent with the DTI results, this study observed that fostamatinib suppressed cell proliferation by inhibiting TAM RTKs, while other Syk inhibitors showed no inhibitory activity. These results suggest that fostamatinib could exhibit anticancer activity as a pan-TAM inhibitor. Taken together, these findings demonstrated that this artificial intelligence model could be effectively used for drug repurposing and repositioning. Furthermore, by identifying its novel mechanism of action, this study confirmed the potential for fostamatinib to expand its indications as a TAM inhibitor.