Identification of pyrrolo[2,3-d]pyrimidine-based dual MERTK and FLT3 inhibitor: Hit-to-lead, machine learning,...

Area of Science:

• Medicinal Chemistry
• Drug Discovery
• Hematological Malignancies

Background:

• Overexpression of MERTK and FLT3 kinases is implicated in human hematological malignancies.
• These kinases represent promising therapeutic targets for acute myeloid leukemia (AML).

Purpose of the Study:

• To design and synthesize novel MERTK/FLT3 dual inhibitors using machine learning and bioisosterism.
• To identify potent and selective drug candidates for AML treatment.

Main Methods:

• Hybridized machine learning (ML) with bioisosterism for rational drug design.
• Synthesized and characterized a series of pyrrolo[2,3-d] pyridine derivatives.
• Conducted structure-activity relationship (SAR) studies to optimize inhibitors.

Main Results:

• Identified lead compound 31l as a highly potent and selective MERTK/FLT3 dual inhibitor.
• Compound 31l demonstrated nanomolar IC50 values against MERTK (2.58 nM) and FLT3 (0.86 nM).
• Showcased significant anti-proliferative effects on MOLM-13 AML cells (IC50 = 7.50 nM) with good metabolic stability and oral bioavailability (56%).

Conclusions:

• Compound 31l is a promising preclinical candidate for MERTK/FLT3 dual inhibitor-based AML therapy.
• The developed pyrrolo[2,3-d] pyridine scaffold represents a valuable starting point for further AML drug development.
• This study highlights the successful application of ML-guided drug design in identifying novel kinase inhibitors.