Sheriffdeen Hammed

Nigeria

Identification of Novel DYRK1A Inhibitors as Treatment Options for Alzheimer’s Disease through Comprehensive In Silico approaches

Ibrahim Akindeji Makinde1, Sheriffdeen Oladimeji Hammed2,3, Neeraj Kumar4, Najwa Ahmad Kuthi5, Haruna Isiyaku Umar3,6, Temitayo Abidat Hassan3,6, Idayat Oyinkansola Kehinde3, Ridwan Opeyemi Bello3,7, Abdullahi Tunde Aborode3,8, Tajudeen Ogundare Jimoh9, Al-Djazouli O Mahamat10, Ahmad Mohammad Salamatullah11

1 Department of Information Systems, School of Computing (SOC), Federal University of Technology Akure, P.M.B 704, Akure, Nigeria
2 Department of Biotechnology, Federal University of Technology, P. M. B. 704, Akure, Nigeria
3 Computer-Aided Therapeutic Discovery and Design Platform, Federal University of Technology, P. M. B. 704, Akure, Nigeria
4 Department of Pharmaceutical Chemistry, Bhupal Nobles’ College of Pharmacy, Udaipur, Rajasthan, India-313001
5 Department of Chemistry, Faculty of Science, Universiti Teknologi Malaysia (UTM), Johor Bahru, Johor, Malaysia
6 Department of Biochemistry, Federal University of Technology, P. M. B. 704, Akure, Ondo State, Nigeria
7 Faculty of Medicine, University of Queensland, Brisbane, Australia
8 Department of Chemistry, Mississippi State University, Jacksonville, USA
9 Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
10 Faculty of Exact Applied and Sciences, Department of Earth Sciences, N’Djamena University, Chad
11 Department of Food Science & Nutrition, College of Food and Agricultural Sciences, King Saud University, 11 P.O. Box 2460, Riyadh 11451, Saudi Arabia.

Abstract

Background

Alzheimer’s disease is one of the most severe conditions affecting older people’s brains and is rapidly growing in importance as a public health issue. Increased DYRK1A reactivity is associated with tau phosphorylation, a key process in Alzheimer’s disease (AD) pathology. This study aims to identify potential DYRK1A inhibitors from a curated database and utilize a Quantitative Structure-Activity Relationship (QSAR) model to predict the bioactivity of drug compounds in inhibiting the enzyme.

Methods

192 compounds were sourced from the SuperNatural 3.0 database and docked against DYRK1A using Maestro 12.5. The top five lead compounds and the reference drug Abemaciclib underwent ADMET profiling via the AI Drug Lab Server and a 200 nanosecond molecular dynamics simulation using Desmond. A machine learning-based QSAR analysis was then performed to predict their biological activity based on pIC50 values.

Results

The top five compounds, identified as 45934388, CNP0344929, CNP0360040, CNP0309850, and CNP0426983, demonstrated binding affinities of -13.337, -12.746, -11.712, -11.656, and -11.416 kcal/mol, respectively, outperforming Abemaciclib (-6.528 kcal/mol). None of the compounds violated Lipinski’s Rule of Five, and all exhibited favorable ADMET profiles, including optimal blood-brain barrier penetration and structural stability. The QSAR model successfully predicted the pIC50 values of the hit compounds (6.16, 5.758, 5.752, 6.003, 5.982), comparable to Abemaciclib (6.32).

Conclusions

This study utilized a computational approach to identify five promising compounds with strong binding affinities, favorable pharmacokinetic properties, and significant biological activity predictions. These compounds exhibit potential as novel DYRK1A inhibitors, which could contribute to the development of more effective therapeutic strategies for AD.