Discovery and Anticancer Activity of Novel 1,3,4-Thiadiazole- and Aziridine-Based Indolin-2-ones via In Silico Design Followed by Supramolecular Green Synthesis

Three crucial anticancer scaffolds, namely indolin-2-one, 1,3,4-thiadiazole, and aziridine, are explored to synthesize virtually screened target molecules based on the c-KIT kinase protein. The stem cell factor receptor c-KIT was selected as target because most U.S. FDA-approved receptor tyrosine kinase inhibitors bearing the indolin-2-one scaffold profoundly inhibit c-KIT. Molecular hybrids of indolin-2-one with 1,3,4-thiadiazole (IIIa-m) and aziridine (VIa and VIc) were afforded through a modified Schiff base green synthesis using β-cyclodextrin-SO₃H in water as a recyclable proton-donor catalyst. A computational study found that indolin-2,3-dione forms a supramolecular inclusion complex with β-cyclodextrin-SO₃H through noncovalent interactions. A molecular docking study of all the synthesized compounds was executed on the c-KIT kinase domain, and most compounds displayed binding affinities similar to that of Sunitinib. On the basis of the pharmacokinetic significance of the aryl thioether linkage in small molecules, 1,3,4-thiadiazole hybrids (IIIa-m) were extended to a new series of 3-((5-(phenylthio)-1,3,4-thiadiazol-2-yl)imino)indolin-2-ones (IVa-m) via thioetherification using bis(triphenylphosphine)palladium(II)dichloride as the catalyst for C-S bond formation. Target compounds were tested against NCI-60 human cancer cell lines for a single-dose concentration. Among all three series of indolin-2-ones, the majority of compounds demonstrated broad-spectrum activity toward various cancer cell lines. Compounds IVc and VIc were further evaluated for a five-dose anticancer study. Compound IVc showed a potent activity of IC₅₀ = 1.47 μM against a panel of breast cancer cell lines, whereas compound VIc exhibited the highest inhibition for a panel of colon cancer cell lines at IC₅₀ = 1.40 μM. In silico ADME property descriptors of all the target molecules are in an acceptable range. Machine learning algorithms were used to examine the metabolites and phase I and II regioselectivities of compounds IVc and VIc, and the results suggested that these two compounds could be potential leads for the treatment of cancer.