Screening for Potential Compounds using Drug-Repurposing and Virtual Screening of N-Methyl-S-Aspartate (NMDA) Receptor for Autism Spectrum Disorder (ASD)
Main Article Content
Abstract
In Malaysia, the study on autism spectrum disorders (ASD) is limited. Most studies only focus on gene neuroligin 3 (NLGN3), NLGN4X, neurexin 1 (NRXN1) and SH3. This study focuses on the N-methyl-D-aspartate (NMDA) that was believed to have a significant effect on ASD. In this study, potential compounds and drugs that can restore receptor function in autistic patients were analysed. This research used an effective in silico method known as drug-repurposing to discover and rediscover drugs and analyse the binding of potential compounds or drugs to the NMDA receptor. AMPA and DOCK4 were used as controls in this study. Using a trusted server, Drug ReposER, 13 potential compounds or drugs that bind to NMDAR were identified. Then, proceed to the docking of potential compounds or drugs that bind to the NMDA receptor using Autodock Vina, Autodock, Hdock and CB dock and three drugs were selected that have the best binding score to NMDA, AMPA and DOCK4. The drugs were alitretinoin, salicylic acid and indinavir, respectively. Next, molecular dynamics simulations were performed with all selected compounds to study drug-protein binding, with detailed analysis of bond stability using root-mean-square fluctuation (RMSF) oscillations. Finally, ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) predictions identify 4-androstenedione, tryptophan, carbocisteine and vitamin A as having minimal toxic effects. This study showed that alitretinoin, which was known to treat skin lesions from Kaposi’s sarcoma, might have the ability to reverse the effect in ASD, particularly in NMDA receptors, potentially making a significant impact on the field of neurology and psychiatry.
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Bobo W V, Cooper W O, Stein C M, Olfson M, Graham D, Daugherty J, Fuchs D C and Ray W A. (2013). Antipsychotics and the risk of type 2 Diabetes mellitus in children and youth. JAMA Psychiatry 70(10): 1067. https://doi.org/10.1001/jamapsychiatry.2013.2053
Chanda S, Aoto J, Lee S -J, Wernig M and Südhof T C. (2016). Pathogenic mechanism of an autism-associated neuroligin mutation involves altered AMPA-receptor trafficking. Molecular Psychiatry 21(2): 169–177. https://doi.org/10.1038/mp.2015.20
Che Azmi N, Farhan Mohd Hajad M, Faruqhy Zaffuan Khalid M, Luqman Hakeem Sazali M, Amani Md Daud H, Azmi L, Aini Ahmad Zaini A and Abdul Wahab A. (2022). Knowledge and attitude towards autism among the general public in Malaysia. Proceeding of International Conference on Science, Health, and Technology, LPPM Universitas Duta Bangsa Surakarta, Indonesia, September, 317–324.
https://doi.org/10.47701/icohetech.v3i1.2163
Coleman D M, Adams J B, Anderson A L and Frye R E. (2019). Rating of the effectiveness of 26 psychiatric and seizure medications for Autism Spectrum Disorder: Results of a national survey. Journal of Child and Adolescent Psychopharmacology 29(2): 107–123. https://doi.org/10.1089/cap.2018.0121
Correll C U. (2009). Cardiometabolic risk of second-generation antipsychotic medications during first-time use in children and adolescents. JAMA 302(16): 1765. https://doi.org/10.1001/jama.2009.1549
Dym O, Eisenberg D and Yeates T O. (2012). Detection of errors in protein models. In International tables for crystallography. Wiley, 677–683. https://doi.org/10.1107/97809553602060000881
Eow S Y, Gan W Y, Lim P Y, Awang H and Mohd Shariff Z. (2020). Factors associated with autism severity among Malaysian children with Autism Spectrum Disorder. Research in Developmental Disabilities 100: 103632. https://doi.org/10.1016/j.ridd.2020.103632
Fusar-Poli L, Brondino N, Rocchetti M, Petrosino B, Arillotta D, Damiani S, Provenzani U, Petrosino C, Aguglia E and Politi P. (2019). Prevalence and predictors of psychotropic medication use in adolescents and adults with autism spectrum disorder in Italy: A cross-sectional study. Psychiatry Research 276: 203–209. https://doi.org/10.1016/j.psychres.2019.04.013
Guo D, Peng Y, Wang L, Sun X, Wang X, Liang C, Yang X, Li S, Xu J, Ye W -C, Jiang B and Shi L. (2021). Autism-like social deficit generated by Dock4 deficiency is rescued by restoration of Rac1 activity and NMDA receptor function. Molecular Psychiatry 26(5): 1505–1519. https://doi.org/10.1038/s41380-019-0472-7
Kim J -W, Park K, Kang R J, Gonzales E L T, Kim D G, Oh H A, Seung H, Ko M J, Kwon K J, Kim K C, Lee S H, Chung C and Shin C Y. (2019). Pharmacological modulation of AMPA receptor rescues social impairments in animal models of autism. Neuropsychopharmacology 44(2): 314–323. https://doi.org/10.1038/s41386-018-0098-5
Kumar A, Kini S G and Rathi E. (2021). A recent appraisal of artificial intelligence and in silico ADMET prediction in the early stages of drug discovery. Mini-Reviews in Medicinal Chemistry 21(18): 2788–2800. https://doi.org/10.2174/1389557521666210401091147
LeClerc S and Easley D. (2015). Pharmacological therapies for autism spectrum disorder: A review. P & T: A Peer-Reviewed Journal for Formulary Management 40(6): 389–397.
Liu Y, Yang X, Gan J, Chen S, Xiao Z -X and Cao Y. (2022). CB-Dock2: Improved protein–ligand blind docking by integrating cavity detection, docking and homologous template fitting. Nucleic Acids Research 50(W1): W159–W164. https://doi.org/10.1093/nar/gkac394
Lord C, Elsabbagh M, Baird G and Veenstra-Vanderweele J. (2018). Autism spectrum disorder. The Lancet 392(10146): 508–520. https://doi.org/10.1016/S0140-6736(18)31129-2
Madden J M, Lakoma M D, Lynch F L, Rusinak D, Owen-Smith A A, Coleman K J, Quinn V P, Yau V M, Qian Y X and Croen L A. (2017). Psychotropic medication use among insured children with autism spectrum disorder. Journal of Autism and Developmental Disorders 47(1): 144–154. https://doi.org/10.1007/s10803-016-2946-7
Morris G M, Huey R, Lindstrom W, Sanner M F, Belew R K, Goodsell D S and Olson A J. (2009). AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. Journal of Computational Chemistry 30(16): 2785–2791. https://doi.org/10.1002/jcc.21256
Niescier R F and Lin Y -C. (2021). The potential role of AMPA receptor trafficking in autism and other neurodevelopmental conditions. Neuroscience 479: 180–191. https://doi.org/10.1016/j.neuroscience.2021.09.013
Pawlowski M, Gajda M J, Matlak R and Bujnicki J M. (2008). MetaMQAP: A meta-server for the quality assessment of protein models. BMC Bioinformatics 9(1): 403. https://doi.org/10.1186/1471-2105-9-403
Schyman P, Liu R, Desai V and Wallqvist A. (2017). vNN Web Server for ADMET predictions. Frontiers in Pharmacology 8: 889. https://doi.org/10.3389/fphar.2017.00889
Trott O and Olson A J. (2010). AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry 31(2): 455–461. https://doi.org/10.1002/jcc.21334
Williams K, Brignell A, Randall M, Silove N and Hazell P. (2013). Selective serotonin reuptake inhibitors (SSRIs) for autism spectrum disorders (ASD). Cochrane Database of Systematic Reviews 8: CD004677. https://doi.org/10.1002/14651858.CD004677.pub3
Yan Y, Zhang D, Zhou P, Li B and Huang S -Y. (2017). HDOCK: A web server for protein–protein and protein–DNA/RNA docking based on a hybrid strategy. Nucleic Acids Research 45(W1): W365–W373. https://doi.org/10.1093/nar/gkx407