Zoatier, Bayan; Yildiztekin, Gizem; Alagoz, Mehmet Abdullah; Hepokur, Ceylan; Dilek, Esra; Algul, Oztekin

Alzheimer's disease (AD), the most prevalent form of dementia, leads to progressive cognitive decline due to pathological hallmarks including amyloid plaques, neurofibrillary tangles, synaptic loss, neuroinflammation, and neuronal cell death, highlighting the urgent need for multitarget therapeutic strategies. The p38α mitogen-activated protein kinase (p38α MAPK) pathway is a key regulator of neuroinflammation and has been implicated in AD pathogenesis. Additionally, dysregulation of p38α MAPK is associated with tumorigenesis, making it a promising target for both neurodegenerative and proliferative diseases. In this article, a series of benzoxazole derivatives is designed and synthesized to evaluate their dual inhibitory potential against p38α MAPK and acetylcholinesterase (AChE), aiming for a multifaceted therapeutic approach to AD. A total of 31 compounds are synthesized and assessed for their antiproliferative activity, p38α MAPK inhibition, and AChE inhibitory effects. In vitro assays demonstrate that several compounds exhibit potent dual inhibition of p38α MAPK and AChE, while molecular docking studies provide insights into their binding interactions within the active sites. These findings suggest that benzoxazole-based scaffolds offer a promising framework for the development of dual-acting inhibitors targeting both neuroinflammation and tumorigenesis. Further in vivo and mechanistic studies are warranted to explore their therapeutic potential.

Yucel, Mehmet Ali; Adal, Ercan; Aktekin, Mine Buga; Hepokur, Ceylan; Gambacorta, Nicola; Nicolotti, Orazio; Algul, Oztekin

Vascular endothelial growth factor receptor 2 (VEGFR-2) stands as a prominent therapeutic target in oncology, playing a critical role in angiogenesis, tumor growth, and metastasis. FDA-approved VEGFR-2 inhibitors are associated with diverse side effects. Thus, finding novel and more effective inhibitors is of utmost importance. In this study, a deep learning (DL) classification model was first developed and then employed to select putative active VEGFR-2 inhibitors from an in-house chemical library including 187 druglike compounds. A pool of 18 promising candidates was shortlisted and screened against VEGFR-2 by using molecular docking. Finally, two compounds, RHE-334 and EA-11, were prioritized as promising VEGFR-2 inhibitors by employing PLATO, our target fishing and bioactivity prediction platform. Based on this rationale, we prepared RHE-334 and EA-11 and successfully tested their anti-proliferative potential against MCF-7 human breast cancer cells with IC50 values of 26.78±4.02 and 38.73±3.84 μM, respectively. Their toxicities were instead challenged against the WI-38. Interestingly, expression studies indicated that, in the presence of RHE-334, VEGFR-2 was equal to 0.52±0.03, thus comparable to imatinib equal to 0.63±0.03. In conclusion, this workflow based on theoretical and experimental approaches demonstrates effective in identifying VEGFR-2 inhibitors and can be easily adapted to other medicinal chemistry goals.