Data-Driven Construction of Antitumor Agents with Controlled Polypharmacology

Area of Science:

• Biochemistry
• Medicinal Chemistry
• Computational Biology

Background:

• Selective drug targeting is crucial for effective cancer therapy.
• The TYRO3, AXL, and MERTK (TAM) family of tyrosine kinases are implicated in various cancers.
• Achieving restricted polypharmacology is a challenge in drug design.

Purpose of the Study:

• To develop a rational, data-driven strategy for designing selective antitumor agents.
• To target the TAM family of tyrosine kinases with high specificity.
• To demonstrate the efficacy of designed inhibitors in preclinical models.

Main Methods:

• Utilized a computational approach based on fragments in structural environments (FRASE).
• Distilled chemical information from structural and chemogenomic databases.
• Assembled three-dimensional inhibitor structures directly within protein pockets.

Main Results:

• Designed inhibitors demonstrated selective engagement with TAM tyrosine kinases.
• Inhibitors disrupted oncogenic phenotypes in enzymatic assays and cancer cell lines (ALL/AML, NSCLC).
• X-ray crystallography confirmed the structural rationale; lead compound showed potent inhibition in vivo.

Conclusions:

• The FRASE approach enables rational design of selective kinase inhibitors.
• Targeted inhibition of TAM kinases is a viable strategy for antitumor therapy.
• This method holds promise for developing novel cancer therapeutics.