Nashrieh Shimi va Mohandesi Shimi Iran

Nashrieh Shimi va Mohandesi Shimi Iran

Three-Dimensional Quantitative Structure-Activity Relationship Studies Using CoMFA and CoMSIA Methods on a Series of mGlu1 Positive Allosteric Regulating Compounds as Anti-Schizophrenic Agents

Document Type : Research Article

Authors
Eslam Pourbasheer 1
Reza Mahmoudzadeh Laki 1
Soraya Dashti 2
1 Department of Chemistry, Faculty of Basic Sciences, Mohaghegh Ardabili University, Ardabil, I.R. IRAN
2 Department of Chemistry, Faculty of Basic Sciences, Payame Noor University of Ardabil, Ardabil, I.R. IRAN
Abstract
The aim of the present study is to develop a three-dimensional quantitative structure-activity relationship (3D-QSAR) model with high predictive capability for a set of positive allosteric modulators of mGlu1 receptors, which act as anti-schizophrenic compounds. Computational modeling was based on comparative molecular field analysis (CoMFA), CoMFA-focused, and comparative molecular similarity indices analysis (CoMSIA) methods. The dataset consisting of 91 molecules was divided into training and test sets, and they were aligned based on the most active compound. The constructed and optimized models using the partial least squares (PLS) approach yielded satisfactory results. External predictability of the models was assessed by Leave-One-Out or Cross-Validation techniques, resulting in q2 values of 0.631, 0.653, and 0.594 for CoMFA, CoMFA- focused, and CoMSIA models, respectively. The statistical parameters obtained from the constructed models indicate the reliability of the models. Additionally, the 3D contours derived from the modeling process serve as a useful guide for designing more active compounds. Using the results of CoMFA- focused modeling, six new compounds were designed, and their pEC50 values were predicted. The designed compounds exhibited pEC50 values in the range of 8.28 to 8.58, indicating an increase in their biological activity compared to the reference compound.
Keywords
Keywords: Three-dimensional quantitative structure-activity relationships
Positive allosteric regulatory compounds
mGlu1
Schizophrenia
CoMFA
CoMSIA

Subjects

[1] Wout M., Aleman A., Kessels R.P., Laroi F., Kahn R.S., Emotional Processing in a Non-Clinical Psychosis-Prone Sample. Schizophrenia research, 68(2-3): 271-281 (2004).
[2] Altschuler E., Shakespeare Knew the Layered Clothing Sign of Schizophrenia. BMJ, 319(7208): 520 (1999).
[3] Meehl P.E., Schizotaxia, Schizotypy, Schizophrenia, in Schizophrenia. Routledge, 13(2): 21-46 (2017).
[4] Mathews J.R., Barch D.M., Episodic Memory for Emotional and Non-Emotional Words in Individuals with Anhedonia. Psychiatry research, 143(2-3): 121-133 (2006).
[5] Inwanna S., Duangchan C., Matthews A.K., Effectiveness of Interventions to Promote Medication Adherence in Schizophrenic Populations in Thailand: A Systematic Review. International Journal of Environmental Research and Public Health, 19(5): 2887 (2022).
[6] Alpert M., Rosenberg S.D., Pouget E.R., Shaw R.J., Prosody and Lexical Accuracy in Flat Affect Schizophrenia. Psychiatry research, 97(2-3): 107-118 (2000).
[7] Guardascione A., Selfhood and Alterity: Schizophrenic Experience Between Blankenburg and Tatossian. Frontiers in Psychology, 14(9): 861(2023).
[8] Katritzky A.R., Karelson M., Lobanov V.S., QSPR as a Means of Predicting and Understanding Chemical and Physical Properties in Terms of Structure. Pure and Applied Chemistry, 69(2): 245-248 (1997).
[9] McClelland H.E., Jurs P.C., Quantitative Structure− Property Relationships for the Prediction of Vapor Pressures of Organic Compounds from Molecular Structures. Journal of Chemical Information and Computer Sciences, 40(4): 967-975 (2000).
[10] Thaimattam R., Daga P., Rajjak S.A., Banerjee R., Iqbal J., 3D-QSAR CoMFA, CoMSIA Studies on Substituted Ureas as Raf-1 Kinase Inhibitors and Its Confirmation with Structure-Based Studies. Bioorganic & medicinal chemistry, 12(24): 6415-6425 (2004).
[11] Akamatsu M., Current State and Perspectives of 3D-QSAR. Current topics in medicinal chemistry, 2(12): 1381-1394 (2002).
[ 12] پوربشیر، اسلام؛ مهاجری اول، ژیلا؛ نکوئی، مهدی؛ حمیدوند، سمیه؛ مطالعه ارتباط کمّی ساختار- فعالیت برای پیش بینی فعالیت مهارکنندگی PIM مشتق‌های تری آزولوپیریدین با استفاده از الگوریتم ژنتیک ـ برازش خطی چندگانه، نشریه شیمی و مهندسی شیمی ایران،  (2)37: 137 تا 148 (1397).
[13] آل‌کثیر، الهام؛ عباسی‌تبار، فاطمه؛ توسعه مدل‌های رابطه های کمی ساختار - ویژگی برای پیش‌بینی حد اشتعال‌پذیری بالای ترکیب‌های آلی، نشریه شیمی و مهندسی شیمی ایران،  (1)41: 139 تا 151 (1401).
[14] Bultinck P., De Winter H., Langenaeker W., Tollenare J.P., "Computational Medicinal Chemistry for Drug Discovery", CRC Press (2003).
[15] Kim J.-H. Jeong J.-H., Structure-Activity Relationship Studies Based on 3D-QSAR CoMFA/CoMSIA for Thieno-Pyrimidine Derivatives as Triple Negative Breast Cancer Inhibitors. Molecules, 27(22): 7974 (2022).
[16] Meng, L., Ou-Yang Y., Lv F., Song J., Yao J.. Research on the Anti-Tumor Activity of a Novel Aminopeptidase Inhibitor Based on 3D QSAR Model. Letters in Drug Design & Discovery, 19(9): 811-822 (2022).
[17] Pourbasheer E. Amanlou M., 3D-QSAR Analysis of Anti-Cancer Agents by CoMFA and CoMSIA. Medicinal Chemistry Research, 23(1): 800-809 (2014).
[18] Pourbasheer E. Aalizadeh R., 3D-QSAR and Molecular Docking Study of LRRK2 Kinase Inhibitors by CoMFA and CoMSIA Methods. SAR and QSAR in Environmental Research, 27(5): 385-407 (2016).
[19] رحمان ستایش، شهربانو؛ طریک، عبدالحلیم؛ زبیدی، رحمان؛ پیش بینی دمای ذوب مایع‌های یونی بر پایه آنیون بیس(تری‌فلورومتیل‌سولفونیل) ایمید با رویکرد QSPR، نشریه شیمی و مهندسی شیمی ایران،  (1)39: 149 تا 158 (1399).
[20] Zhang C., Li Q., Meng L., Ren Y., Design of Novel Dopamine D2 and Serotonin 5-HT2A Receptors dual Antagonists Toward Schizophrenia: An Integrated Study with QSAR, Molecular Docking, Virtual Screening and Molecular Dynamics Simulations. Journal of Biomolecular Structure and Dynamics, 38(3): 860-885 (2020).
[21] El Fadili M., Er-Rajy M., Kara M., Assouguem A., Belhassan A., Alotaibi A., Mrabti N.N., Fidan H., Ullah R., Ercisli S., QSAR, ADMET In Silico Pharmacokinetics, Molecular Docking and Molecular Dynamics Studies of Novel Bicyclo (Aryl Methyl) Benzamides as Potent GlyT1 Inhibitors for the Treatment of Schizophrenia. Pharmaceuticals, 15(6): 670-694 (2022).
[22] Marunnan S.M., Pulikkal B.P., Jabamalairaj A., Bandaru S., Yadav M., Nayarisseri A., Doss V.A., Development of MLR and SVM Aided QSAR Models to Identify Common SAR of GABA Uptake Herbal Inhibitors used in the Treatment of Schizophrenia. Current neuropharmacology, 15(8): 1085-1092 (2017).
[23] Primi M.C., Maltarollo V.G., Magalhaes J.G., Malta de Sá M., Rangel-Yagui C.O., Trossini G.H.G., Convergent QSAR Studies on a Series of NK3 Receptor Antagonists for Schizophrenia Treatment. Journal of Enzyme Inhibition and Medicinal Chemistry, 31(2): 283-294 (2016).
[24] Mondal C., Halder A.K., Adhikari N., Jha T., Structural Findings of Cinnolines as Anti-Schizophrenic PDE10A Inhibitors Through Comparative Chemometric Modeling. Molecular diversity, 18(1): 655-671 (2014).
[25] Garcia-Barrantes P.M., Cho H.P., Blobaum A.L., Niswender C.M., Conn P.J., Lindsley C.W., Lead Optimization of the VU0486321 Series of mGlu1 PAMs. Part 1: SAR of Modifications to the Central Aryl Core. Bioorganic & medicinal chemistry letters, 25(22): 5107-5110 (2015).
[26] Garcia-Barrantes P.M., Cho H.P., Metts A.M., Blobaum A.L., Niswender C.M., Conn P.J., Lindsley C.W., Lead Optimization of the VU0486321 Series of mGlu1 PAMs. Part 2: SAR of Alternative 3-Methyl Heterocycles and Progress Towards an in Vivo Tool. Bioorganic & medicinal chemistry letters, 26(3): 751-756 (2016).
[27] Garcia-Barrantes P.M., Cho H.P., Blobaum A.L., Niswender C.M., Conn P.J., Lindsley C.W., Lead Optimization of the VU0486321 Series of mGlu1 PAMs. Part 3. Engineering Plasma Stability by Discovery and Optimization of Isoindolinone Analogs. Bioorganic & medicinal chemistry letters, 26(8): 1869-1872 (2016).
[28] Davis D.C., Bungard J.D., Chang S., Rodriguez A.L., Blobaum A.L., Boutaud O., Melancon B.J., Niswender C.M., Conn P.J., Lindsley C.W., Lead Optimization of the VU0486321 Series of mGlu1 PAMs. Part 4: SAR Reveals Positive Cooperativity Across Multiple mGlu Receptor Subtypes Leading to Subtype Unselective PAMs. Bioorganic & Medicinal Chemistry Letters, 32(1): 127724 (2021).
[29] Sebaugh J., Guidelines for Accurate EC50/IC50 Estimation. Pharmaceutical statistics, 10(2): 128-134 (2011).
[30] Louis R., "SYBYL-X Tripos International", St, USA (2010).
[31] Brown T., ChemDraw. The Science Teacher, 81(2): 67 (2014).
[32] Clark M. Cramer III R.D., The Probability of Chance Correlation using Partial Least Squares (PLS). Quantitative Structure‐Activity Relationships, 12(2): 137-145 (1993).
[33] Cramer III R.D., Bunce J.D., Patterson D.E., Frank I.E., Crossvalidation, Bootstrapping, and Partial Least Squares Compared with Multiple Regression in Conventional QSAR Studies. Quantitative Structure‐Activity Relationships, 7(1): 18-25 (1988).
[34] Pourbasheer E., Aalizadeh R., Ebadi A., Ganjali M.R., 3D-QSAR Analysis of MCD Inhibitors by CoMFA and CoMSIA. Combinatorial Chemistry & High Throughput Screening, 18(8): 751-766 (2015).
[35] Abdel-Ilah L., Veljović E., Gurbeta L., Badnjević A., Applications of QSAR Study in Drug Design. International Journal of Engineering Research & Technology (IJERT), 6(06): 128-149 (2017).