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Jarzynska K, Gajewicz-Skretna A, Ciura K, Puzyn T. Predicting zeta potential of liposomes from their structure: A nano-QSPR model for DOPE, DC-Chol, DOTAP, and EPC formulations. Comput Struct Biotechnol J 2024; 25:3-8. [PMID: 38328349 PMCID: PMC10848030 DOI: 10.1016/j.csbj.2024.01.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 01/17/2024] [Accepted: 01/19/2024] [Indexed: 02/09/2024] Open
Abstract
Liposomes, nanoscale spherical structures composed of amphiphilic lipids, hold great promise for various pharmaceutical applications, especially as nanocarriers in targeted drug delivery, due to their biocompatibility, biodegradability, and low immunogenicity. Understanding the factors influencing their physicochemical properties is crucial for designing and optimizing liposomes. In this study, we have presented the kernel-weighted local polynomial regression (KwLPR) nano-quantitative structure-property relationships (nano-QSPR) model to predict the zeta potential (ZP) based on the structure of 12 liposome formulations, including 1,2-dioleoyl-sn-glycero-3-phosphoethanolamine (DOPE), 3ß-[N-(N',N'-dimethylaminoethane)-carbamoyl]cholesterol (DC-Chol), 1,2-dioleoyl-3-trimethylammonium-propane (DOTAP), and L-α-phosphatidylcholine (EPC). The developed model is well-fitted (R 2 = 0.96, RMSE C = 5.76), flexible (Q CVloo 2 = 0.83, RMSE CVloo = 10.77), and reliable (Q Ext 2 = 0.89 RMSE Ext = 5.17). Furthermore, we have established the formula for computing molecular nanodescriptors for liposomes, based on constituent lipids' molar fractions. Through the correlation matrix and principal component analysis (PCA), we have identified two key structural features affecting liposomes' zeta potential: hydrophilic-lipophilic balance (HLB) and enthalpy of formation. Lower HLB values, indicating a more lipophilic nature, are associated with a higher zeta potential, and thus stability. Higher enthalpy of formation reflects reduced zeta potential and decreased stability of liposomes. We have demonstrated that the nano-QSPR approach allows for a better understanding of how the composition and molecular structure of liposomes affect their zeta potential, filling a gap in ZP nano-QSPR modeling methodologies for nanomaterials (NMs). The proposed proof-of-concept study is the first step in developing a comprehensive and computationally based system for predicting the physicochemical properties of liposomes as one of the most important drug nano-vehicles.
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Affiliation(s)
- Kamila Jarzynska
- Laboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - Agnieszka Gajewicz-Skretna
- Laboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - Krzesimir Ciura
- Laboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
- Department of Physical Chemistry, Faculty of Pharmacy, Medical University of Gdansk, Gen. Hallera 107, 80-416 Gdańsk, Poland
| | - Tomasz Puzyn
- Laboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
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2
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Banerjee A, Roy K. How to correctly develop q-RASAR models for predictive cheminformatics. Expert Opin Drug Discov 2024; 19:1017-1022. [PMID: 38966910 DOI: 10.1080/17460441.2024.2376651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2024] [Accepted: 07/02/2024] [Indexed: 07/06/2024]
Affiliation(s)
- Arkaprava Banerjee
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
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3
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Alharthy RD, Khalid S, Fatima S, Ullah S, Khan A, Mali SN, Jawarkar RD, Dhabarde SS, Kashtoh H, Taslimi P, Al-Harrasi A, Shafiq Z, Boshta NM. Synthesis of the chromone-thiosemicarbazone scaffold as promising α-glucosidase inhibitors: An in vitro and in silico approach toward antidiabetic drug design. Arch Pharm (Weinheim) 2024; 357:e2400140. [PMID: 38687119 DOI: 10.1002/ardp.202400140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 04/03/2024] [Accepted: 04/05/2024] [Indexed: 05/02/2024]
Abstract
Diabetes is a serious metabolic disorder affecting individuals of all age groups and prevails globally due to the failure of previous treatments. This study aims to address the most prevalent form of type 2 diabetes mellitus (T2DM) by reporting on the design, synthesis, and in vitro as well as in silico evaluation of chromone-based thiosemicarbazones as potential α-glucosidase inhibitors. In vitro experiments showed that the tested compounds were significantly more potent than the standard acarbose, with the lead compound 3n exhibiting an IC50 value of 0.40 ± 0.02 μM, ~2183-fold higher than acarbose having an IC50 of 873.34 ± 1.67 μM. A kinetic mechanism analysis demonstrated that compound 3n exhibited reversible inhibition of α-glucosidase. To gain deeper insights, in silico molecular docking, pharmacokinetics, and molecular dynamics simulations were conducted for the investigation of the interactions, orientation, stability, and conformation of the synthesized compounds within the active pocket of α-glucosidase.
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Affiliation(s)
- Rima D Alharthy
- Department of Chemistry, Science & Arts College, Rabigh Branch, King Abdulaziz University, Rabigh, Saudi Arabia
| | - Sana Khalid
- Institute of Chemical Sciences, Bahauddin Zakariya University, Multan, Pakistan
| | - Shamool Fatima
- Department of Chemistry, Quaid-i-Azam University, Islamabad, Pakistan
| | - Saeed Ullah
- Natural and Medical Sciences Research Centre, University of Nizwa, Nizwa, Sultanate of Oman
| | - Ajmal Khan
- Natural and Medical Sciences Research Centre, University of Nizwa, Nizwa, Sultanate of Oman
| | - Suraj N Mali
- Department of Pharmaceutical Science and Technology, Birla Institute of Technology, Mesra, India
| | - Rahul D Jawarkar
- Department of Medicinal Chemistry and Drug Discovery, Dr. Rajendra Gode Institute of Pharmacy, Amravati, India
| | | | - Hamdy Kashtoh
- Department of Biotechnology, Yeungnam University, Gyeongsan, Gyeongbuk, Republic of Korea
| | - Parham Taslimi
- Department of Biotechnology, Faculty of Science, Bartin University, Bartin, Turkey
| | - Ahmed Al-Harrasi
- Natural and Medical Sciences Research Centre, University of Nizwa, Nizwa, Sultanate of Oman
| | - Zahid Shafiq
- Institute of Chemical Sciences, Bahauddin Zakariya University, Multan, Pakistan
| | - Nader M Boshta
- Chemistry Department, Faculty of Science, Menoufia University, Shebin El-Koam, Egypt
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Naseem S, Oneto A, Ullah S, Fatima S, Mali SN, Jawarkar RD, Khan A, Alharthy RD, Kashtoh H, Al-Harrasi A, Shafiq Z, Boshta NM. Synthesis, biological evaluation, and molecular modelling of substituted thiazolyl thiourea derivatives: A new class of prolyl oligopeptidase inhibitors. Int J Biol Macromol 2024; 275:133571. [PMID: 38960243 DOI: 10.1016/j.ijbiomac.2024.133571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2024] [Revised: 06/20/2024] [Accepted: 06/28/2024] [Indexed: 07/05/2024]
Abstract
Prolyl oligopeptidase (POP) is a compelling therapeutic target associated with aging and neurodegenerative disorders due to its pivotal role in neuropeptide processing. Despite initial promise demonstrated by early-stage POP inhibitors, their progress in clinical trials has been halted at Phase I or II. This impediment has prompted the pursuit of novel inhibitors. The current study seeks to contribute to the identification of efficacious POP inhibitors through the design, synthesis, and comprehensive evaluation (both in vitro and in silico) of thiazolyl thiourea derivatives (5a-r). In vitro experimentation exhibited that the compounds displayed significant higher potency as POP inhibitors. Compound 5e demonstrated an IC50 value of 16.47 ± 0.54 μM, representing a remarkable potency. A meticulous examination of the structure-activity relationship indicated that halogen and methoxy substituents were the most efficacious. In silico investigations delved into induced fit docking, pharmacokinetics, and molecular dynamics simulations to elucidate the intricate interactions, orientation, and conformational changes of these compounds within the active site of the enzyme. Moreover, our pharmacokinetic assessments confirmed that the majority of the synthesized compounds possess attributes conducive to potential drug development.
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Affiliation(s)
- Saira Naseem
- Institute of Chemical Sciences, Bahauddin Zakariya University, Multan 60800, Pakistan
| | - Angelo Oneto
- Department of Pharmaceutical & Medicinal Chemistry, An der Immenburg 4, D-53121 Bonn, Germany
| | - Saeed Ullah
- Natural and Medical Sciences Research Centre, University of Nizwa, P.O. Box 33, PC 616, Birkat Al Mauz, Nizwa, Sultanate of Oman
| | - Shamool Fatima
- Department of Chemistry, Quaid-i-Azam University, Islamabad, Pakistan
| | - Suraj N Mali
- Department of Pharmaceutical Science and Technology, Birla Institute of Technology, Mesra 835215, India; School of Pharmacy, D.Y. Patil University (Deemed to be University), Sector 7, Nerul 400706, Navi Mumbai, India
| | - Rahul D Jawarkar
- Department of Medicinal Chemistry and Drug Discovery, Dr. Rajendra Gode Institute of Pharmacy, University Mardi Road, Amravati 444603, India
| | - Ajmal Khan
- Natural and Medical Sciences Research Centre, University of Nizwa, P.O. Box 33, PC 616, Birkat Al Mauz, Nizwa, Sultanate of Oman
| | - Rima D Alharthy
- Department of Chemistry, Science & Arts College, Rabigh Branch, King Abdulaziz University, Rabigh 21911, Saudi Arabia
| | - Hamdy Kashtoh
- Department of Biotechnology, Yeungnam University, Gyeongsan 38541, Gyeongbuk, Republic of Korea
| | - Ahmed Al-Harrasi
- Natural and Medical Sciences Research Centre, University of Nizwa, P.O. Box 33, PC 616, Birkat Al Mauz, Nizwa, Sultanate of Oman.
| | - Zahid Shafiq
- Institute of Chemical Sciences, Bahauddin Zakariya University, Multan 60800, Pakistan; Department of Pharmaceutical & Medicinal Chemistry, An der Immenburg 4, D-53121 Bonn, Germany.
| | - Nader M Boshta
- Chemistry Department, Faculty of Science, Menoufia University, Shebin El-Koam 32511, Egypt
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Keshavarz MH, Shirazi Z, Jafari M, Oliaeei A. Toxicity of individual and mixture of organic compounds to P. Phosphoreum and S. Capricornutum using interpretable simple structural parameters. CHEMOSPHERE 2024; 357:142046. [PMID: 38636913 DOI: 10.1016/j.chemosphere.2024.142046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 04/01/2024] [Accepted: 04/12/2024] [Indexed: 04/20/2024]
Abstract
Human and environmental ecosystem beings are exposed to multicomponent compound mixtures but the toxicity nature of compound mixtures is not alike to the individual chemicals. This work introduces four models for the prediction of the negative logarithm of median effective concentration (pEC50) of individual chemicals to marine bacteria Photobacterium Phosphoreum (P. Phosphoreum) and algal test species Selenastrum Capricornutum (S. Capricornutum) as well as their mixtures to P. Phosphoreum, and S. Capricornutum. These models provide the simplest approaches for the forecast of pEC50 of some classes of organic compounds from their interpretable structural parameters. Due to the lack of adequate toxicity data for chemical mixtures, the largest available experimental data of individual chemicals (55 data) and their mixtures (99 data) are used to derive the new correlations. The models of individual chemicals are based on two simple structural parameters but chemical mixture models require further interaction terms. The new model's results are compared with the outputs of the best accessible quantitative structure-activity relationships (QSARs) models. Various statistical parameters are done on the new and comparative complex QSAR models, which confirm the higher reliability and simplicity of the new correlations.
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Affiliation(s)
| | - Zeinab Shirazi
- Faculty of Applied Sciences, Malek Ashtar University of Technology, Iran
| | - Mohammad Jafari
- Faculty of Applied Sciences, Malek Ashtar University of Technology, Iran
| | - Ahmadreza Oliaeei
- Faculty of Applied Sciences, Malek Ashtar University of Technology, Iran
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6
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Jiang JR, Cai WX, Chen ZF, Liao XL, Cai Z. Prediction of acute toxicity for Chlorella vulgaris caused by tire wear particle-derived compounds using quantitative structure-activity relationship models. WATER RESEARCH 2024; 256:121643. [PMID: 38663211 DOI: 10.1016/j.watres.2024.121643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 04/16/2024] [Accepted: 04/17/2024] [Indexed: 05/12/2024]
Abstract
Tire wear particles (TWPs) enter aquatic ecosystems through various pathways, such as rainwater and urban runoff. Additives in TWPs can harm aquatic organisms in these ecosystems. Therefore, it is essential to investigate their toxicity to aquatic organisms. In our study, we initially recorded the median effective concentrations of 21 TWP-derived compounds on Chlorella vulgaris growth, ranging from 0.04 to 8.60 mg/L. Subsequently, through an extensive review of the literature, we incorporated 112 compounds with specific toxicity endpoints to construct the QSAR model using genetic algorithm and multiple linear regression techniques, followed by the construction of the consensus model and the quantitative read-across structure-activity relationship (q-RASAR) model. Meanwhile, we employed rigorous internal and external validation measures to assess the performance of the model. The results indicated that the developed q-RASAR model exhibited strong adaptation, robustness, and reliable prediction, with q-RASAR indicators of Q2LOO = 0.7673, R2tr = 0.8079, R2test = 0.8610, Q2Fn = 0.8285-0.8614, and CCCtest = 0.9222. Based on an external dataset containing 128 emerging TWP-derived compounds, the model's applicability domain coverage was 90.6 %. The q-RASAR model predicted that the structure of diphenylamine was associated with higher toxicity, possibly liked to the SpMax2_Bhm and LogBCF descriptors. The established model reliably provides prediction and fills a critical data gap. These findings highlight the potential risks posed by emerging TWP-derived compounds to aquatic organisms.
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Affiliation(s)
- Jie-Ru Jiang
- Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China
| | - Wen-Xi Cai
- Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China
| | - Zhi-Feng Chen
- Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China.
| | - Xiao-Liang Liao
- Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China
| | - Zongwei Cai
- Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China; State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong 999077, China.
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7
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Masand VH, Al-Hussain SA, Alzahrani AY, Al-Mutairi AA, Hussien RA, Samad A, Zaki MEA. Estrogen Receptor Alpha Binders for Hormone-Dependent Forms of Breast Cancer: e-QSAR and Molecular Docking Supported by X-ray Resolved Structures. ACS OMEGA 2024; 9:16759-16774. [PMID: 38617692 PMCID: PMC11007693 DOI: 10.1021/acsomega.4c00906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Revised: 03/16/2024] [Accepted: 03/19/2024] [Indexed: 04/16/2024]
Abstract
Cancer, a life-disturbing and lethal disease with a high global impact, causes significant economic, social, and health challenges. Breast cancer refers to the abnormal growth of cells originating from breast tissues. Hormone-dependent forms of breast cancer, such as those influenced by estrogen, prompt the exploration of estrogen receptors as targets for potential therapeutic interventions. In this study, we conducted e-QSAR molecular docking and molecular dynamics analyses on a diverse set of inhibitors targeting estrogen receptor alpha (ER-α). The e-QSAR model is based on a genetic algorithm combined with multilinear regression analysis. The newly developed model possesses a balance between predictive accuracy and mechanistic insights adhering to the OECD guidelines. The e-QSAR model pointed out that sp2-hybridized carbon and nitrogen atoms are important atoms governing binding profiles. In addition, a specific combination of H-bond donors and acceptors with carbon, nitrogen, and ring sulfur atoms also plays a crucial role. The results are supported by molecular docking, MD simulations, and X-ray-resolved structures. The novel results could be useful for future drug development for ER-α.
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Affiliation(s)
- Vijay H Masand
- Department of Chemistry, Vidya Bharati Mahavidyalaya, Amravati 444 602, Maharashtra, India
| | - Sami A Al-Hussain
- Department of Chemistry, College of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh 11623, Saudi Arabia
| | - Abdullah Y Alzahrani
- Department of Chemistry, Faculty of Science and Arts, King Khalid University, Mohail 61421, Saudi Arabia
| | - Aamal A Al-Mutairi
- Department of Chemistry, College of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh 11623, Saudi Arabia
| | - Rania A Hussien
- Department of Chemistry, Faculty of Science, Al-Baha University, Al-Baha 65799, Kingdom of Saudi Arabia
| | - Abdul Samad
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Tishk International University, Erbil 44001, Iraq
| | - Magdi E A Zaki
- Department of Chemistry, College of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh 11623, Saudi Arabia
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Zhuravskyi Y, Iduoku K, Erickson ME, Karuth A, Usmanov D, Casanola-Martin G, Sayfiyev MN, Ziyaev DA, Smanova Z, Mikolajczyk A, Rasulev B. Quantitative Structure-Permittivity Relationship Study of a Series of Polymers. ACS MATERIALS AU 2024; 4:195-203. [PMID: 38496050 PMCID: PMC10941280 DOI: 10.1021/acsmaterialsau.3c00079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 12/01/2023] [Accepted: 12/13/2023] [Indexed: 03/19/2024]
Abstract
Dielectric constant is an important property which is widely utilized in many scientific fields and characterizes the degree of polarization of substances under the external electric field. In this work, a structure-property relationship of the dielectric constants (ε) for a diverse set of polymers was investigated. A transparent mechanistic model was developed with the application of a machine learning approach that combines genetic algorithm and multiple linear regression analysis, to obtain a mechanistically explainable and transparent model. Based on the evaluation conducted using various validation criteria, four- and eight-variable models were proposed. The best model showed a high predictive performance for training and test sets, with R2 values of 0.905 and 0.812, respectively. Obtained statistical performance results and selected descriptors in the best models were analyzed and discussed. With the validation procedures applied, the models were proven to have a good predictive ability and robustness for further applications in polymer permittivity prediction.
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Affiliation(s)
- Yevhenii Zhuravskyi
- Department of Technology of Organic Products, Lviv Polytechnic National University, Lviv 79013, Ukraine
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
| | - Kweeni Iduoku
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
| | - Meade E Erickson
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
| | - Anas Karuth
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
| | - Durbek Usmanov
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
- Institute of the Chemistry of Plant Substances AS RUz, Tashkent 100170, Uzbekistan
| | - Gerardo Casanola-Martin
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
| | - Maqsud N Sayfiyev
- Department of Chemistry, National University of Uzbekistan, Tashkent 100174, Uzbekistan
| | - Dilshod A Ziyaev
- Department of Chemistry, National University of Uzbekistan, Tashkent 100174, Uzbekistan
| | - Zulayho Smanova
- Department of Chemistry, National University of Uzbekistan, Tashkent 100174, Uzbekistan
| | - Alicja Mikolajczyk
- Laboratory of Environmental Chemometrics, Institute for Environmental and Human Health Protection, Faculty of Chemistry, University of Gdansk, Gdansk 80-308, Poland
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
- Department of Chemistry, National University of Uzbekistan, Tashkent 100174, Uzbekistan
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9
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Naseem S, Fatima S, Ullah S, Khan A, Mali SN, Jawarkar RD, Syed A, Elgorban AM, Al-Harrasi A, Shafiq Z. Carbonylbis(hydrazine-1-carbothioamide) derivatives as a new class of α-glucosidase inhibitors and their mechanistic insights via molecular docking and dynamic simulations. Arch Pharm (Weinheim) 2024; 357:e2300604. [PMID: 38148299 DOI: 10.1002/ardp.202300604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 12/03/2023] [Accepted: 12/05/2023] [Indexed: 12/28/2023]
Abstract
In the past, efforts have been made to find a cure for diabetes, mainly evaluating new classes of compounds to explore their potency. In this study, we present the synthesis and evaluation of carbonylbis(hydrazine-1-carbothioamide) derivatives as potential α-glucosidase inhibitors, employing both in vivo and in silico investigations. The in vitro experiments revealed that all tested compounds were significantly potent for α-glucosidase inhibition, with the lead compound 3a displaying approximately 80 times higher activity than acarbose. To delve deeper, in silico induced fit docking, pharmacokinetics, and molecular dynamics studies were conducted. Significantly, compound 3a exhibited a docking score of -7.87 kcal/mol, surpassing acarbose, which had a docking score of -6.59 kcal/mol. The in silico ADMET indicated that most of the synthesized compounds have properties conducive to drug development. Molecular dynamics analysis demonstrated that, when the ligand 3a was coupled with the target 3TOP, Cα-RMSD backbone RMSD values below 2.4 Å and "Lig_fit_Prot" values below 2.7 Å were observed. QSAR analysis demonstrates that the "fOC8A" descriptor positively correlates with α-glucosidase inhibition activity, while "lipoplus_AbSA" positively contributes and "notringC_notringO_8B" negatively contributes to this activity.
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Affiliation(s)
- Saira Naseem
- Institute of Chemical Sciences, Bahauddin Zakariya University, Multan, Pakistan
| | - Shamool Fatima
- Department of Chemistry, Quaid-i-Azam University, Islamabad, Pakistan
| | - Saeed Ullah
- Natural and Medical Sciences Research Centre, University of Nizwa, Birkat Al Mauz, Nizwa, Oman
| | - Ajmal Khan
- Natural and Medical Sciences Research Centre, University of Nizwa, Birkat Al Mauz, Nizwa, Oman
| | - Suraj N Mali
- Department of Pharmaceutical Science and Technology, Birla Institute of Technology, Mesra, India
| | - Rahul D Jawarkar
- Department of Medicinal Chemistry and Drug Discovery, Dr. Rajendra Gode Institute of Pharmacy, Amravati, India
| | - Asad Syed
- Department of Botany and Microbiology, King Saud University, Riyadh, Saudi Arabia
| | - Abdallah M Elgorban
- Department of Botany and Microbiology, King Saud University, Riyadh, Saudi Arabia
| | - Ahmed Al-Harrasi
- Natural and Medical Sciences Research Centre, University of Nizwa, Birkat Al Mauz, Nizwa, Oman
| | - Zahid Shafiq
- Department of Pharmaceutical & Medicinal Chemistry, Bonn, Germany
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Jawarkar RD, Zaki MEA, Al-Hussain SA, Al-Mutairi AA, Samad A, Mukerjee N, Ghosh A, Masand VH, Ming LC, Rashid S. QSAR modeling approaches to identify a novel ACE2 inhibitor that selectively bind with the C and N terminals of the ectodomain. J Biomol Struct Dyn 2024; 42:2550-2569. [PMID: 37144753 DOI: 10.1080/07391102.2023.2205948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 04/17/2023] [Indexed: 05/06/2023]
Abstract
Due to the high rates of drug development failure and the massive expenses associated with drug discovery, repurposing existing drugs has become more popular. As a result, we have used QSAR modelling on a large and varied dataset of 657 compounds in an effort to discover both explicit and subtle structural features requisite for ACE2 inhibitory activity, with the goal of identifying novel hit molecules. The QSAR modelling yielded a statistically robust QSAR model with high predictivity (R2tr=0.84, R2ex=0.79), previously undisclosed features, and novel mechanistic interpretations. The developed QSAR model predicted the ACE2 inhibitory activity (PIC50) of 1615 ZINC FDA compounds. This led to the detection of a PIC50 of 8.604 M for the hit molecule (ZINC000027990463). The hit molecule's docking score is -9.67 kcal/mol (RMSD 1.4). The hit molecule revealed 25 interactions with the residue ASP40, which defines the N and C termini of the ectodomain of ACE2. The HIT molecule conducted more than thirty contacts with water molecules and exhibited polar interaction with the ARG522 residue coupled with the second chloride ion, which is 10.4 nm away from the zinc ion. Both molecular docking and QSAR produced comparable findings. Moreover, MD simulation and MMGBSA studies verified docking analysis. The MD simulation showed that the hit molecule-ACE2 receptor complex is stable for 400 ns, suggesting that repurposed hit molecule 3 is a viable ACE2 inhibitor.
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Affiliation(s)
- Rahul D Jawarkar
- Department of Medicinal Chemistry and Drug Discovery, Dr Rajendra Gode Institute of Pharmacy, Amravati, Maharashtra, India
| | - Magdi E A Zaki
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - Sami A Al-Hussain
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - Aamal A Al-Mutairi
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - Abdul Samad
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Tishk International University, Erbil, Kurdistan Region, Iraq
| | - Nobendu Mukerjee
- Department of Microbiology, Ramakrishna Mission Vivekananda Centenary College, Kolkata, India
| | - Arabinda Ghosh
- Microbiology Division, Department of Botany, Gauhati University, Guwahati, India
| | - Vijay H Masand
- Department of Chemistry, Vidyabharati Mahavidyalalya, Amravati, Maharashtra, India
| | - Long Chiau Ming
- School of Medical and Life Sciences, Sunway University, Sunway City, Malaysia
| | - Summya Rashid
- Department of Pharmacology & Toxicology, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
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Akinola LK, Uzairu A, Shallangwa GA, Abechi SE, Umar AB. Identification of estrogen receptor agonists among hydroxylated polychlorinated biphenyls using classification-based quantitative structure-activity relationship models. Curr Res Toxicol 2024; 6:100158. [PMID: 38435023 PMCID: PMC10907392 DOI: 10.1016/j.crtox.2024.100158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 02/22/2024] [Accepted: 02/22/2024] [Indexed: 03/05/2024] Open
Abstract
Identification of estrogen receptor (ER) agonists among environmental toxicants is essential for assessing the potential impact of toxicants on human health. Using 2D autocorrelation descriptors as predictor variables, two binary logistic regression models were developed to identify active ER agonists among hydroxylated polychlorinated biphenyls (OH-PCBs). The classifications made by the two models on the training set compounds resulted in accuracy, sensitivity and specificity of 95.9 %, 93.9 % and 97.6 % for ERα dataset and 91.9 %, 90.9 % and 92.7 % for ERβ dataset. The areas under the ROC curves, constructed with the training set data, were found to be 0.985 and 0.987 for the two models. Predictions made by models I and II correctly classified 84.0 % and 88.0 % of the test set compounds and 89.8 % and 85.8% of the cross-validation set compounds respectively. The two classification-based QSAR models proposed in this paper are considered robust and reliable for rapid identification of ERα and ERβ agonists among OH-PCB congeners.
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Affiliation(s)
- Lukman K. Akinola
- Department of Chemistry, Ahmadu Bello University, Zaria, Nigeria
- Department of Chemistry, Bauchi State University, Gadau, Nigeria
| | - Adamu Uzairu
- Department of Chemistry, Ahmadu Bello University, Zaria, Nigeria
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12
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Jawarkar RD, Zaki MEA, Al-Hussain SA, Al-Mutairi AA, Samad A, Masand V, Humane V, Mali S, Alzahrani AYA, Rashid S, Elossaily GM. Mechanistic QSAR modeling derived virtual screening, drug repurposing, ADMET and in- vitro evaluation to identify anticancer lead as lysine-specific demethylase 5a inhibitor. J Biomol Struct Dyn 2024:1-31. [PMID: 38385447 DOI: 10.1080/07391102.2024.2319104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 02/11/2024] [Indexed: 02/23/2024]
Abstract
A lysine-specific demethylase is an enzyme that selectively eliminates methyl groups from lysine residues. KDM5A, also known as JARID1A or RBP2, belongs to the KDM5 Jumonji histone demethylase subfamily. To identify novel molecules that interact with the LSD5A receptor, we created a quantitative structure-activity relationship (QSAR) model. A group of 435 compounds was used in a study of the quantitative relationship between structure and activity to guess the IC50 values for blocking LASD5A. We used a genetic algorithm-multilinear regression-based quantitative structure-activity connection model to forecast the bioactivity (PIC50) of 1615 food and drug administration pharmaceuticals from the zinc database with the goal of repurposing clinically used medications. We used molecular docking, molecular dynamic simulation modelling, and molecular mechanics generalised surface area analysis to investigate the molecule's binding mechanism. A genetic algorithm and multi-linear regression method were used to make six variable-based quantitative structure-activity relationship models that worked well (R2 = 0.8521, Q2LOO = 0.8438, and Q2LMO = 0.8414). ZINC000000538621 was found to be a new hit against LSD5A after a quantitative structure-activity relationship-based virtual screening of 1615 zinc food and drug administration compounds. The docking analysis revealed that the hit molecule 11 in the KDM5A binding pocket adopted a conformation similar to the pdb-6bh1 ligand (docking score: -8.61 kcal/mol). The results from molecular docking and the quantitative structure-activity relationship were complementary and consistent. The most active lead molecule 11, which has shown encouraging results, has good absorption, distribution, metabolism, and excretion (ADME) properties, and its toxicity has been shown to be minimal. In addition, the MTT assay of ZINC000000538621 with MCF-7 cell lines backs up the in silico studies. We used molecular mechanics generalise borne surface area analysis and a 200-ns molecular dynamics simulation to find structural motifs for KDM5A enzyme interactions. Thus, our strategy will likely expand food and drug administration molecule repurposing research to find better anticancer drugs and therapies.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Rahul D Jawarkar
- Department of Medicinal Chemistry and Drug discovery, Dr. Rajendra Gode Institute of Pharmacy, Amravati, Maharashtra, India
| | - Magdi E A Zaki
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - Sami A Al-Hussain
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - Aamal A Al-Mutairi
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - Abdul Samad
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Tishk International University, Erbil, Kurdistan Region, Iraq
| | - Vijay Masand
- Department of Chemistry, Amravati, Maharashtra, India
| | - Vivek Humane
- Department of Chemistry, Shri R. R. Lahoti Science college, Morshi District: Amravati, Maharashtra, India
| | - Suraj Mali
- School of Pharmacy, D.Y. Patil University (Deemed to be University), Nerul, Navi Mumbai, India
| | | | - Summya Rashid
- Department of Pharmacology & Toxicology, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Gehan M Elossaily
- Department of Basic Medical Sciences, College of Medicine, AlMaarefa University, Riyadh, Saudi Arabia
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13
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Banjare P, Singh R, Pandey NK, Matore BW, Murmu A, Singh J, Roy PP. In silico soil degradation and ecotoxicity analysis of veterinary pharmaceuticals on terrestrial species: first report. Toxicol Res (Camb) 2024; 13:tfae020. [PMID: 38496320 PMCID: PMC10939401 DOI: 10.1093/toxres/tfae020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 02/01/2024] [Accepted: 02/02/2024] [Indexed: 03/19/2024] Open
Abstract
With the aim of persistence property analysis and ecotoxicological impact of veterinary pharmaceuticals on different terrestrial species, different classes of veterinary pharmaceuticals (n = 37) with soil degradation property (DT50) were gathered and subjected to QSAR and q-RASAR model development. The models were developed from 2D descriptors under organization for economic cooperation and development guidelines with the application of multiple linear regressions along with genetic algorithm. All developed QSAR and q-RASAR were statistically significant (Internal = R2adj: 0.721-0.861, Q2LOO: 0.609-0.757, and external = Q2Fn = 0.597-0.933, MAEext = 0.174-0.260). Further, the leverage approach of applicability domain assured the model's reliability. The veterinary pharmaceuticals with no experimental values were classified based on their persistence level. Further, the terrestrial toxicity analysis of persistent veterinary pharmaceuticals was done using toxicity prediction by computer assisted technology and in-house built quantitative structure toxicity relationship models to prioritize the toxic and persistent veterinary pharmaceuticals. This study will be helpful in estimation of persistence and toxicity of existing and upcoming veterinary pharmaceuticals.
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Affiliation(s)
- Purusottam Banjare
- Department of Pharmacy, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur 495009, Chhattisgarh, India
- Department of Pharmaceutical Chemistry, Apollo College of Pharmacy, Anjora, Durg 491001, Chhattisgarh, India
| | - Rekha Singh
- Department of Pharmacy, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur 495009, Chhattisgarh, India
| | - Nilesh Kumar Pandey
- Department of Pharmacy, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur 495009, Chhattisgarh, India
| | - Balaji Wamanrao Matore
- Department of Pharmacy, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur 495009, Chhattisgarh, India
| | - Anjali Murmu
- Department of Pharmacy, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur 495009, Chhattisgarh, India
| | - Jagadish Singh
- Department of Pharmacy, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur 495009, Chhattisgarh, India
| | - Partha Pratim Roy
- Department of Pharmacy, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur 495009, Chhattisgarh, India
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14
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Pandey NK, Murmu A, Banjare P, Matore BW, Singh J, Roy PP. Integrated predictive QSAR, Read Across, and q-RASAR analysis for diverse agrochemical phytotoxicity in oat and corn: A consensus-based approach for risk assessment and prioritization. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:12371-12386. [PMID: 38228952 DOI: 10.1007/s11356-024-31872-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 01/02/2024] [Indexed: 01/18/2024]
Abstract
In the modern fast-paced lifestyle, time-efficient and nutritionally rich foods like corn and oat have gained popularity for their amino acids and antioxidant contents. The increasing demand for these cereals necessitates higher production which leads to dependency on agrochemicals, which can pose health risks through residual present in the plant products. To first report the phytotoxicity for corn and oat, our study employs QSAR, quantitative Read-Across and quantitative RASAR (q-RASAR). All developed QSAR and q-RASAR models were equally robust (R2 = 0.680-0.762, Q2Loo = 0.593-0.693, Q2F1 = 0.680-0.860) and find their superiority in either oat or corn model, respectively, based on MAE criteria. AD and PRI had been performed which confirm the reliability and predictability of the models. The mechanistic interpretation reveals that the symmetrical arrangement of electronegative atoms and polar groups directly influences the toxicity of compounds. The final phytotoxicity and prioritization are performed by the consensus approach which results into selection of 15 most toxic compounds for both species.
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Affiliation(s)
- Nilesh Kumar Pandey
- Department of Pharmacy, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur, 495009, India
| | - Anjali Murmu
- Department of Pharmacy, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur, 495009, India
| | | | - Balaji Wamanrao Matore
- Department of Pharmacy, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur, 495009, India
| | - Jagadish Singh
- Department of Pharmacy, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur, 495009, India
| | - Partha Pratim Roy
- Department of Pharmacy, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur, 495009, India.
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15
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Zaki MEA, AL-Hussain SA, Al-Mutairi AA, Samad A, Masand VH, Ingle RG, Rathod VD, Gaikwad NM, Rashid S, Khatale PN, Burakale PV, Jawarkar RD. Application of in-silico drug discovery techniques to discover a novel hit for target-specific inhibition of SARS-CoV-2 Mpro's revealed allosteric binding with MAO-B receptor: A theoretical study to find a cure for post-covid neurological disorder. PLoS One 2024; 19:e0286848. [PMID: 38227609 PMCID: PMC10790994 DOI: 10.1371/journal.pone.0286848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 05/24/2023] [Indexed: 01/18/2024] Open
Abstract
Several studies have revealed that SARS-CoV-2 damages brain function and produces significant neurological disability. The SARS-CoV-2 coronavirus, which causes COVID-19, may infect the heart, kidneys, and brain. Recent research suggests that monoamine oxidase B (MAO-B) may be involved in metabolomics variations in delirium-prone individuals and severe SARS-CoV-2 infection. In light of this situation, we have employed a variety of computational to develop suitable QSAR model using PyDescriptor and genetic algorithm-multilinear regression (GA-MLR) models (R2 = 0.800-793, Q2LOO = 0.734-0.727, and so on) on the data set of 106 molecules whose anti-SARS-CoV-2 activity was empirically determined. QSAR models generated follow OECD standards and are predictive. QSAR model descriptors were also observed in x-ray-resolved structures. After developing a QSAR model, we did a QSAR-based virtual screening on an in-house database of 200 compounds and found a potential hit molecule. The new hit's docking score (-8.208 kcal/mol) and PIC50 (7.85 M) demonstrated a significant affinity for SARS-CoV-2's main protease. Based on post-covid neurodegenerative episodes in Alzheimer's and Parkinson's-like disorders and MAO-B's role in neurodegeneration, the initially disclosed hit for the SARS-CoV-2 main protease was repurposed against the MAO-B receptor using receptor-based molecular docking, which yielded a docking score of -12.0 kcal/mol. This shows that the compound that inhibits SARS-CoV-2's primary protease may bind allosterically to the MAO-B receptor. We then did molecular dynamic simulations and MMGBSA tests to confirm molecular docking analyses and quantify binding free energy. The drug-receptor complex was stable during the 150-ns MD simulation. The first computational effort to show in-silico inhibition of SARS-CoV-2 Mpro and allosteric interaction of novel inhibitors with MAO-B in post-covid neurodegenerative symptoms and other disorders. The current study seeks a novel compound that inhibits SAR's COV-2 Mpro and perhaps binds MAO-B allosterically. Thus, this study will enable scientists design a new SARS-CoV-2 Mpro that inhibits the MAO-B receptor to treat post-covid neurological illness.
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Affiliation(s)
- Magdi E. A. Zaki
- Faculty of Science, Department of Chemistry, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - Sami A. AL-Hussain
- Faculty of Science, Department of Chemistry, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - Aamal A. Al-Mutairi
- Faculty of Science, Department of Chemistry, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - Abdul Samad
- Faculty of Pharmacy, Department of Pharmaceutical Chemistry, Tishk International University, Erbil, Kurdistan Region, Iraq
| | - Vijay H. Masand
- Department of Chemistry, Vidya Bharti Mahavidyalaya, Amravati, Maharashtra, India
| | - Rahul G. Ingle
- Datta Meghe College of Pharmacy, DMIHER Deemed University, Wardha, India
| | - Vivek Digamber Rathod
- Department of Chemical Technology, Dr Babasaheb Ambedkar Marathwada University, Aurangabad, India
| | | | - Summya Rashid
- Department of Pharmacology & Toxicology, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Pravin N. Khatale
- Department of Medicinal Chemistry and Drug Discovery, Dr Rajendra Gode Institute of Pharmacy, University Mardi Road, Amravati, Maharashtra, India
| | - Pramod V. Burakale
- Department of Medicinal Chemistry and Drug Discovery, Dr Rajendra Gode Institute of Pharmacy, University Mardi Road, Amravati, Maharashtra, India
| | - Rahul D. Jawarkar
- Department of Medicinal Chemistry and Drug Discovery, Dr Rajendra Gode Institute of Pharmacy, University Mardi Road, Amravati, Maharashtra, India
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16
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Neal WM, Pandey P, Khan SI, Khan IA, Chittiboyina AG. Machine learning and traditional QSAR modeling methods: a case study of known PXR activators. J Biomol Struct Dyn 2024; 42:903-917. [PMID: 37059719 DOI: 10.1080/07391102.2023.2196701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 03/22/2023] [Indexed: 04/16/2023]
Abstract
Pregnane X receptor (PXR), extensively expressed in human tissues related to digestion and metabolism, is responsible for recognizing and detoxifying diverse xenobiotics encountered by humans. To comprehend the promiscuous nature of PXR and its ability to bind a variety of ligands, computational approaches, viz., quantitative structure-activity relationship (QSAR) models, aid in the rapid dereplication of potential toxicological agents and mitigate the number of animals used to establish a meaningful regulatory decision. Recent advancements in machine learning techniques accommodating larger datasets are expected to aid in developing effective predictive models for complex mixtures (viz., dietary supplements) before undertaking in-depth experiments. Five hundred structurally diverse PXR ligands were used to develop traditional two-dimensional (2D) QSAR, machine-learning-based 2D-QSAR, field-based three-dimensional (3D) QSAR, and machine-learning-based 3D-QSAR models to establish the utility of predictive machine learning methods. Additionally, the applicability domain of the agonists was established to ensure the generation of robust QSAR models. A prediction set of dietary PXR agonists was used to externally-validate generated QSAR models. QSAR data analysis revealed that machine-learning 3D-QSAR techniques were more accurate in predicting the activity of external terpenes with an external validation squared correlation coefficient (R2) of 0.70 versus an R2 of 0.52 in machine-learning 2D-QSAR. Additionally, a visual summary of the binding pocket of PXR was assembled from the field 3D-QSAR models. By developing multiple QSAR models in this study, a robust groundwork for assessing PXR agonism from various chemical backbones has been established in anticipation of the identification of potential causative agents in complex mixtures.
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Affiliation(s)
- William M Neal
- Division of Pharmacognosy, Department of BioMolecular Sciences, School of Pharmacy, The University of Mississippi, University, MS, USA
| | - Pankaj Pandey
- National Center for Natural Products Research, Research Institute of Pharmaceutical Sciences, School of Pharmacy, The University of Mississippi, University, MS, USA
| | - Shabana I Khan
- Division of Pharmacognosy, Department of BioMolecular Sciences, School of Pharmacy, The University of Mississippi, University, MS, USA
- National Center for Natural Products Research, Research Institute of Pharmaceutical Sciences, School of Pharmacy, The University of Mississippi, University, MS, USA
| | - Ikhlas A Khan
- Division of Pharmacognosy, Department of BioMolecular Sciences, School of Pharmacy, The University of Mississippi, University, MS, USA
- National Center for Natural Products Research, Research Institute of Pharmaceutical Sciences, School of Pharmacy, The University of Mississippi, University, MS, USA
| | - Amar G Chittiboyina
- National Center for Natural Products Research, Research Institute of Pharmaceutical Sciences, School of Pharmacy, The University of Mississippi, University, MS, USA
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17
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Wang F, Wen M, Zhou B. Exploring details about structure requirements based on antioxidant tripeptide derived from β-Lactoglobulin by in silico approaches. Amino Acids 2023; 55:1909-1922. [PMID: 37917178 DOI: 10.1007/s00726-023-03350-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 10/12/2023] [Indexed: 11/04/2023]
Abstract
β-Lactoglobulin is one of the proteins in milk possessing antioxidant activity. The peptides derived from β-Lactoglobulin exhibit higher antioxidant activities than the most commonly used antioxidant. Furthermore, the detailed structure-activity relationship of these antioxidant peptides has not been elucidated. Therefore, in the present work, two-dimensional quantitative structure-activity relationship (2D-QSAR) and three-dimensional quantitative structure-activity relationship (3D-QSAR) models were constructed to investigate the structural factors affecting activities and gave information for the rational design of novel antioxidant peptides. After calculation and screening of molecular descriptors, linear and nonlinear models were developed by multiple linear regression (MLR), partial least squares regression (PLSR) and support vector machines (SVM) approaches. The statistical parameters are as follows: R2 = 0.643, Q2 = 0.553/MLR, R2 = 0.612, Q2 = 0.5278/PLSR, R2 = 0.7085, Q2 = 0.6887/SVM, indicating that the SVM model is superior to the MLR and PLSR models. In addition, in the 3D-QSAR models, the Dragon-CoMFA (R2cv = 0.537, R2pred = 0.5201) and Dragon-CoMSIA (R2cv = 0.665, R2pred = 0.6489) methods were conducted to predict the antioxidant activities. Comparison of statistical parameters illustrates that the suitability of Dragon-CoMSIA is superior to the Dragon-CoMFA model. The results show the robustness and excellent prediction of the proposed models, and would be applied for modifying and designing novel and potent antioxidant peptides.
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Affiliation(s)
- Fangfang Wang
- School of Life Science, Linyi University, Linyi, 276000, China.
| | - Menghao Wen
- School of Life Science, Linyi University, Linyi, 276000, China
| | - Bo Zhou
- State Key Laboratory of Functions and Applications of Medicinal Plants, College of Basic Medical, Guizhou Medical University, Guizhou, 550004, China
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18
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Celsie AKD, Parnis JM, Brown TN. Metrics for estimating vapour pressure deviation from ideality in binary mixtures. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2023:1-19. [PMID: 37982180 DOI: 10.1080/1062936x.2023.2280634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Accepted: 10/30/2023] [Indexed: 11/21/2023]
Abstract
A novel method is introduced for estimating the degree of interactions occurring between two different compounds in a binary mixture resulting in deviations from ideality as predicted by Raoult's law. Metrics of chemical similarity between binary mixture components were used as descriptors and correlated with the Root-Mean Square Error (RMSE) associated with Raoult's law calculations of total vapour pressure prediction, including Abraham descriptors, sigma moments, and several chemical properties. The best correlation was for a quantitative structure-activity relationship (QSAR) equation using differences in Abraham parameters as descriptors (r2 = 0.7585), followed by a QSAR using differences in COSMO-RS sigma moment descriptors (r2 = 0.7461), and third by a QSAR using differences in the chemical properties of log KAW, melting point, and molecular weight as descriptors (r2 = 0.6878). Of these chemical properties, Δlog KAW had the strongest correlation with deviation from Raoult's law (RMSE) and this property alone resulted in an r2 of 0.6630. These correlations are useful for assessing the expected deviation in Raoult's law estimations of vapour pressures, a key property for estimating inhalation exposure.
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Affiliation(s)
- A K D Celsie
- Department of Chemistry and Canadian Environmental Modelling Centre, Trent University, Peterborough, ON, Canada
| | - J M Parnis
- Department of Chemistry and Canadian Environmental Modelling Centre, Trent University, Peterborough, ON, Canada
| | - T N Brown
- Arnot Research and Consulting, Inc. (ARC), Toronto, ON, Canada
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19
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Basri R, Ullah S, Khan A, Mali SN, Abchir O, Chtita S, El-Gokha A, Taslimi P, Binsaleh AY, El-Kott AF, Al-Harrasi A, Shafiq Z. Synthesis, biological evaluation and molecular modelling of 3-Formyl-6-isopropylchromone derived thiosemicarbazones as α-glucosidase inhibitors. Bioorg Chem 2023; 139:106739. [PMID: 37478545 DOI: 10.1016/j.bioorg.2023.106739] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 07/11/2023] [Accepted: 07/15/2023] [Indexed: 07/23/2023]
Abstract
Type-2 Diabetes Mellitus (T2DM) is one of the most common metabolic disorders in the world and over the past three decades its incidence has increased drastically. α-Glucosidase inhibitors are used to control the hyperglycemic affect of T2DM. Herein, we report the synthesis, α-glucosidase inhibition, structure activity relationship, pharmacokinetics and docking analysis of various novel chromone based thiosemicarbazones 3(a-r). The derivatives displayed potent activity against α-glucosidase with IC50 in range of 0.11 ± 0.01-79.37 ± 0.71 µM. Among all the synthesized compounds, 3a (IC50 = 0.17 ± 0.026 µM), 3 g (IC50 = 0.11 ± 0.01 µM), 3n (IC50 = 0.55 ± 0.02 µM), and 3p (IC50 = 0.43 ± 0.025 µM) displayed higher inhibitory activity as compared to the standard, acarbose. Moreover, we have developed a statistically significant 2D-QSAR model (R2tr:0.9693; F: 50.4647 and Q2LOO:0.9190), which can be used in future to further design potent thiosemicarbazones as inhibitors of α-glucosidase.
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Affiliation(s)
- Rabia Basri
- Institute of Chemical Sciences, Bahauddin Zakariya University, Multan 60800, Pakistan
| | - Saeed Ullah
- Natural and Medical Sciences Research Centre, University of Nizwa, P.O. Box 33, PC 616, Birkat Al Mauz, Nizwa, Sultanate of Oman
| | - Ajmal Khan
- Natural and Medical Sciences Research Centre, University of Nizwa, P.O. Box 33, PC 616, Birkat Al Mauz, Nizwa, Sultanate of Oman
| | - Suraj N Mali
- Department of Pharmaceutical Science and Technology, Birla Institute of Technology, Mesra 835215, India
| | - Oussama Abchir
- Laboratory of Analytical and Molecular Chemistry, Faculty of Sciences Ben M'Sik, Hassan II University of Casablanca, Casablanca B.P 7955, Morocco
| | - Samir Chtita
- Laboratory of Analytical and Molecular Chemistry, Faculty of Sciences Ben M'Sik, Hassan II University of Casablanca, Casablanca B.P 7955, Morocco
| | - Ahmed El-Gokha
- Chemistry Department, Faculty of Science, Menoufia University Menoufia, Egypt
| | - Parham Taslimi
- Department of Biotechnology, Faculty of Science, Bartin University, 74100 Bartin, Turkey
| | - Ammena Y Binsaleh
- Department of Pharmacy Practice, College of Pharmacy, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Attalla F El-Kott
- Department of Biology, College of Science, King Khalid University, Abha 61421, Saudi Arabia; Department of Zoology, College of Science, Damanhour University, Damanhour 22511, Egypt
| | - Ahmed Al-Harrasi
- Natural and Medical Sciences Research Centre, University of Nizwa, P.O. Box 33, PC 616, Birkat Al Mauz, Nizwa, Sultanate of Oman.
| | - Zahid Shafiq
- Institute of Chemical Sciences, Bahauddin Zakariya University, Multan 60800, Pakistan; Department of Pharmaceutical & Medicinal Chemistry, An der Immenburg 4, D-53121 Bonn, Germany.
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20
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Alcázar JJ, Misad Saide AC, Campodónico PR. Reliable and accurate prediction of basic pK[Formula: see text] values in nitrogen compounds: the pK[Formula: see text] shift in supramolecular systems as a case study. J Cheminform 2023; 15:90. [PMID: 37770903 PMCID: PMC10540475 DOI: 10.1186/s13321-023-00763-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 09/20/2023] [Indexed: 09/30/2023] Open
Abstract
This article presents a quantitative structure-activity relationship (QSAR) approach for predicting the acid dissociation constant (pK[Formula: see text]) of nitrogenous compounds, including those within supramolecular complexes based on cucurbiturils. The model combines low-cost quantum mechanical calculations with QSAR methodology and linear regressions to achieve accurate predictions for a broad range of nitrogen-containing compounds. The model was developed using a diverse dataset of 130 nitrogenous compounds and exhibits excellent predictive performance, with a high coefficient of determination (R[Formula: see text]) of 0.9905, low standard error (s) of 0.3066, and high Fisher statistic (F) of 2142. The model outperforms existing methods, such as Chemaxon software and previous studies, in terms of accuracy and its ability to handle heterogeneous datasets. External validation on pharmaceutical ingredients, dyes, and supramolecular complexes based on cucurbiturils confirms the reliability of the model. To enhance usability, a script-like tool has been developed, providing a streamlined process for users to access the model. This study represents a significant advancement in pK[Formula: see text] prediction, offering valuable insights for drug design and supramolecular system optimization.
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Affiliation(s)
- Jackson J. Alcázar
- Centro de Química Médica, Universidad del Desarrollo, Av.Plaza 680, 7780272 Santiago, RM Chile
| | | | - Paola R. Campodónico
- Centro de Química Médica, Universidad del Desarrollo, Av.Plaza 680, 7780272 Santiago, RM Chile
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21
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Patan A, Aanandhi M V, P G. Molecular dynamics simulation approach of hybrid chalcone-thiazole complex derivatives for DNA gyrase B inhibition: lead generation. RSC Adv 2023; 13:24291-24308. [PMID: 37583661 PMCID: PMC10424056 DOI: 10.1039/d3ra00732d] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 07/31/2023] [Indexed: 08/17/2023] Open
Abstract
Compounds bearing thiazole and chalcone groups have been reported to be excellent leads for antibacterial, antitubercular and anticancer activities. In view of this, we performed quantitative structure-activity relationship studies using QSARINS for dataset preparation and for developing validated QSAR models that can predict novel series of thiazole-chalcone hybrids and further evaluate them for bioactivities. The molecular descriptors AATS8i, AVP-1, MoRSEE17 and GATSe7 were found to be active in predicting the structure-activity relationship. Molecular docking and dynamics simulation studies of the developed leads have shown insights into structural analysis. Furthermore, computational studies using AutoDock and Desmond predicted the key binding interactions responsible for the activity and the SwissADME tool computed the in silico drug likeliness properties. The lead compound 178 generated through this study creates a route for the optimization and development of novel drugs against tuberculosis infections. RMSD, RMSF, RoG, H-bond and SASA analysis confirmed the stable binding of compound 178 with the 6J90 structure. In addition, MM-PBSA and MM-GBSA also confirm the docking results. We propose the designed compound 178 as the best theoretical lead, which may further be experimentally studied for selective inhibition.
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Affiliation(s)
- Afroz Patan
- Department of Pharmaceutical Chemistry, School of Pharmaceutical Sciences, VISTAS Chennai Tamil Nadu India
| | - Vijey Aanandhi M
- Department of Pharmaceutical Chemistry, School of Pharmaceutical Sciences, VISTAS Chennai Tamil Nadu India
| | - Gopinath P
- Department of Pharmaceutical Chemistry, GITAM School of Pharmacy, GITAM University Hyderabad Telangana India
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22
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Furxhi I, Kalapus M, Costa A, Puzyn T. Artificial augmented dataset for the enhancement of nano-QSARs models. A methodology based on topological projections. Nanotoxicology 2023; 17:529-544. [PMID: 37885250 DOI: 10.1080/17435390.2023.2268163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Accepted: 10/02/2023] [Indexed: 10/28/2023]
Abstract
Nanoinformatics demands accurate predictive models to assess the potential hazards of nanomaterials (NMs). However, limited data availability and the diverse nature of NMs physicochemical properties and their interaction with biological media, hinder the development of robust nano-Quantitative Structure-Activity Relationship (QSAR) models. This article proposes an approach that combines artificially data generation techniques and topological projections to address the challenges of insufficient dataset sizes and their limited representativeness of the chemical space. By leveraging the rich information embedded in the topological features, this methodology enhances the representation of the chemical space, enabling a more an exploration of the structure-activity relationships. We demonstrate the efficacy of our approach through extensive experiments, employing various machine learning regression algorithms to validate the methodology. Finally, we compare two different resampling approaches based on different modeling scenarios. The results showcase a significant improved predictive performance of QSAR models demonstrating a promising strategy to overcome the limitations of small datasets in the field of nanoinformatics. The proposed approach offers noteworthy potential for advancing nanoinformatics research within the nanosafety domain by enabling the development of more accurate predictive models for assessing the potential hazards associated with NMs.
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Affiliation(s)
- Irini Furxhi
- Dept. of Accounting and Finance, Kemmy Business School, University of Limerick, Ireland
- Transgero Limited, Cullinagh, Newcastle West, Co. Limerick, Limerick, Ireland
| | - Michal Kalapus
- Laboratory of Environmental Chemoinformatics, Department of Environmental Chemistry and Radiochemistry, Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | - Anna Costa
- CNR-ISSMC Istituto di Scienza, Tecnologia e Sostenibilità per lo Sviluppo dei Materiali Ceramici, Faenza, Italy
| | - Tomasz Puzyn
- Laboratory of Environmental Chemoinformatics, Department of Environmental Chemistry and Radiochemistry, Faculty of Chemistry, University of Gdansk, Gdansk, Poland
- QSAR Lab Ltd, Gdansk, Poland
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23
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Moulishankar A, Thirugnanasambandam S. Quantitative structure activity relationship (QSAR) modeling study of some novel thiazolidine 4-one derivatives as potent anti-tubercular agents. J Recept Signal Transduct Res 2023; 43:83-92. [PMID: 37990804 DOI: 10.1080/10799893.2023.2281671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 09/03/2023] [Indexed: 11/23/2023]
Abstract
This study aims to develop a QSAR model for Antitubercular activity. The quantitative structure-activity relationship (QSAR) approach predicted the thiazolidine-4-ones derivative's Antitubercular activity. For the QSAR study, 53 molecules with Antitubercular activity on H37Rv were collected from the literature. Compound structures were drawn by ACD/Labs ChemSketch. The energy minimization of the 2D structure was done using the MM2 force field in Chem3D pro. PaDEL Descriptor software was used to construct the molecular descriptors. QSARINS software was used in this work to develop the 2D QSAR model. A series of thiazolidine 4-one with MIC data were taken from the literature to develop the QSAR model. These compounds were split into a training set (43 compounds) and a test set (10 compounds). The PaDEL software calculated 2300 descriptors for this series of thiazolidine 4-one derivatives. The best predictive Model 4, which has R2 of 0.9092, R2adj of 0.8950 and LOF parameter of 0.0289 identify a preferred fit. The QSAR study resulted in a stable, predictive, and robust model representing the original dataset. In the QSAR equation, the molecular descriptor of MLFER_S, GATSe2, Shal, and EstateVSA 6 positively correlated with Antitubercular activity. While the SpMAD_Dzs 6 is negatively correlated with Antitubercular activity. The high polarizability, Electronegativities, Surface area contributions and number of Halogen atoms in the thiazolidine 4-one derivatives will increase the Antitubercular activity.
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Affiliation(s)
- Anguraj Moulishankar
- Department of Pharmaceutical Chemistry, SRM College of Pharmacy, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamil Nadu 603203, India
| | - Sundarrajan Thirugnanasambandam
- Department of Pharmaceutical Chemistry, SRM College of Pharmacy, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamil Nadu 603203, India
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24
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Lowe CN, Charest N, Ramsland C, Chang DT, Martin TM, Williams AJ. Transparency in Modeling through Careful Application of OECD's QSAR/QSPR Principles via a Curated Water Solubility Data Set. Chem Res Toxicol 2023; 36:465-478. [PMID: 36877669 PMCID: PMC10357388 DOI: 10.1021/acs.chemrestox.2c00379] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2023]
Abstract
The need for careful assembly, training, and validation of quantitative structure-activity/property models (QSAR/QSPR) is more significant than ever as data sets become larger and sophisticated machine learning tools become increasingly ubiquitous and accessible to the scientific community. Regulatory agencies such as the United States Environmental Protection Agency must carefully scrutinize each aspect of a resulting QSAR/QSPR model to determine its potential use in environmental exposure and hazard assessment. Herein, we revisit the goals of the Organisation for Economic Cooperation and Development (OECD) in our application and discuss the validation principles for structure-activity models. We apply these principles to a model for predicting water solubility of organic compounds derived using random forest regression, a common machine learning approach in the QSA/PR literature. Using public sources, we carefully assembled and curated a data set consisting of 10,200 unique chemical structures with associated water solubility measurements. This data set was then used as a focal narrative to methodically consider the OECD's QSA/PR principles and how they can be applied to random forests. Despite some expert, mechanistically informed supervision of descriptor selection to enhance model interpretability, we achieved a model of water solubility with comparable performance to previously published models (5-fold cross validated performance 0.81 R2 and 0.98 RMSE). We hope this work will catalyze a necessary conversation around the importance of cautiously modernizing and explicitly leveraging OECD principles while pursuing state-of-the-art machine learning approaches to derive QSA/PR models suitable for regulatory consideration.
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Affiliation(s)
- Charles N. Lowe
- Center for Computational Toxicology and Exposure, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina 27711, United States
| | - Nathaniel Charest
- ORAU Student Services Contractor to Center for Computational Toxicology and Exposure, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina 27711, United States
| | - Christian Ramsland
- ORAU Student Services Contractor to Center for Computational Toxicology and Exposure, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina 27711, United States
| | - Daniel T. Chang
- Center for Computational Toxicology and Exposure, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina 27711, United States
| | - Todd M. Martin
- Center for Computational Toxicology and Exposure, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina 27711, United States
| | - Antony J. Williams
- Center for Computational Toxicology and Exposure, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina 27711, United States
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25
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Tomic A, Kovacic M, Kusic H, Karamanis P, Rasulev B, Loncaric Bozic A. Structural Features Promoting Photocatalytic Degradation of Contaminants of Emerging Concern: Insights into Degradation Mechanism Employing QSA/PR Modeling. Molecules 2023; 28:molecules28062443. [PMID: 36985414 PMCID: PMC10057466 DOI: 10.3390/molecules28062443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 03/01/2023] [Accepted: 03/03/2023] [Indexed: 03/10/2023] Open
Abstract
Although heterogeneous photocatalysis has shown promising results in degradation of contaminants of emerging concern (CECs), the mechanistic implications related to structural diversity of chemicals, affecting oxidative (by HO•) or reductive (by O2•−) degradation pathways are still scarce. In this study, the degradation extents and rates of selected organics in the absence and presence of common scavengers for reactive oxygen species (ROS) generated during photocatalytic treatment were determined. The obtained values were then brought into correlation as K coefficients (MHO•/MO2•−), denoting the ratio of organics degraded by two occurring mechanisms: oxidation and reduction via HO• and O2•−. The compounds possessing K >> 1 favor oxidative degradation over HO•, and vice versa for reductive degradation (i.e., if K << 1 compounds undergo reductive reactions driven by O2•−). Such empirical values were brought into correlation with structural features of CECs, represented by molecular descriptors, employing a quantitative structure activity/property relationship (QSA/PR) modeling. The functional stability and predictive power of the resulting QSA/PR model was confirmed by internal and external cross-validation. The most influential descriptors were found to be the size of the molecule and presence/absence of particular molecular fragments such as C − O and C − Cl bonds; the latter favors HO•-driven reaction, while the former the reductive pathway. The developed QSA/PR models can be considered robust predictive tools for evaluating distribution between degradation mechanisms occurring in photocatalytic treatment.
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Affiliation(s)
- Antonija Tomic
- Faculty of Chemical Engineering and Technology, University of Zagreb, Marulicev Trg 19, 10000 Zagreb, Croatia
| | - Marin Kovacic
- Faculty of Chemical Engineering and Technology, University of Zagreb, Marulicev Trg 19, 10000 Zagreb, Croatia
| | - Hrvoje Kusic
- Faculty of Chemical Engineering and Technology, University of Zagreb, Marulicev Trg 19, 10000 Zagreb, Croatia
- Department for Packaging, Recycling and Environmental Protection, University North, Trg dr. Žarka Dolinara 1, 48000 Koprivnica, Croatia
- Correspondence: ; Tel.: +385-1-4597-160
| | - Panaghiotis Karamanis
- E2S UPPA, CNRS, IPREM, Université de Pau et des Pays de l’Adour, Hélioparc Pau Pyrénées, 2 Rue de President Angot, 64053 Pau, France
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102, USA
| | - Ana Loncaric Bozic
- Faculty of Chemical Engineering and Technology, University of Zagreb, Marulicev Trg 19, 10000 Zagreb, Croatia
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26
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Singh A, Kumar S, Kapoor A, Kumar P, Kumar A. Development of reliable quantitative structure-toxicity relationship models for toxicity prediction of benzene derivatives using semiempirical descriptors. Toxicol Mech Methods 2023; 33:222-232. [PMID: 36042574 DOI: 10.1080/15376516.2022.2118092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Abstract
The Health and environmental hazards of benzene and nitrobenzene (NB) derivatives have remained a topic of interest of researchers. In silico methods for prediction of toxicity of chemicals have proved their worth in accurate forecast of environmental as well as health toxicity and are strongly recommended by regulatory authorities. Two quantitative structure-toxicity relationship (QSTR) models explaining Scenedesmus obliquus toxicity trends among 39 benzene derivatives and Tetrahymena pyriformis toxicity of 103 NB and 392 benzene derivatives are developed using semiempirical quantum chemical parameters. The best constructed QSTR models have good fitting ability (R2 = 0.8053, 0.7591, and 0.8283) and robustness (Q2LOO = 0.7507, 0.7227, and 0.8194; Q2LMO = 0.7338, 0.7153, and 0.8172). The external predictivity of all the models are quite good (R2EXT = 0.8256, 0.9349, and 0.8698). Electronegativity, Cosmo volume, total energy, and molecular weight are responsible for the increase and decrease of toxicity of benzene derivatives against S. obliquus while electronegativity, electrophilicity index, the heat of formation, total energy, hydrophobicity, and cosmo volume are responsible for modulation of toxicity of NB and benzene derivatives toward T. pyriformis. These models fulfill the requirements of all the five OECD principles.
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Affiliation(s)
- Ayushi Singh
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and Technology, Hisar, India
| | - Sunil Kumar
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and Technology, Hisar, India
| | - Archana Kapoor
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and Technology, Hisar, India
| | - Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - Ashwani Kumar
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and Technology, Hisar, India
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27
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Zothantluanga JH, Chetia D, Rajkhowa S, Umar AK. Unsupervised machine learning, QSAR modelling and web tool development for streamlining the lead identification process of antimalarial flavonoids. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2023; 34:117-146. [PMID: 36744427 DOI: 10.1080/1062936x.2023.2169347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 01/10/2023] [Indexed: 06/18/2023]
Abstract
Identification of lead compounds with the traditional laboratory approach is expensive and time-consuming. Nowadays, in silico techniques have emerged as a promising approach for lead identification. In this study, we aim to develop robust and predictive 2D-QSAR models to identify lead flavonoids by predicting the IC50 against Plasmodium falciparum. We applied machine learning algorithms (Principal component analysis followed by K-means clustering) and Pearson correlation analysis to select 9 molecular descriptors (MDs) for model building. We selected and validated the three best QSAR models after execution of multiple linear regression (MLR) 100 times with different combinations of MDs. The developed models have fulfilled the five principles for QSAR models as specified by the Organization for Economic Co-operation and Development. The outcome of the study is a reliable and sustainable in silico method of IC50 (Mean ± SD) prediction that will positively impact the antimalarial drug development process by reducing the money and time required to identify potential antimalarial lead compounds from the class of flavonoids. We also developed a web tool (JazQSAR, https://etflin.com/news/4) to offer an easily accessible platform for the developed QSAR models.
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Affiliation(s)
- J H Zothantluanga
- Department of Pharmaceutical Sciences, Faculty of Science and Engineering, Dibrugarh University, Dibrugarh, India
| | - D Chetia
- Department of Pharmaceutical Sciences, Faculty of Science and Engineering, Dibrugarh University, Dibrugarh, India
| | - S Rajkhowa
- Centre for Biotechnology and Bioinformatics, Faculty of Biological Sciences, Dibrugarh University, Dibrugarh, India
| | - A K Umar
- Department of Pharmaceutics and Pharmaceutical Technology, Faculty of Pharmacy, Universitas Padjadjaran, Sumedang, Indonesia
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28
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Banjare P, Singh J, Papa E, Roy PP. Aquatic toxicity prediction of diverse pesticides on two algal species using QSTR modeling approach. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:10599-10612. [PMID: 36083366 DOI: 10.1007/s11356-022-22635-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Accepted: 08/17/2022] [Indexed: 06/15/2023]
Abstract
With the aim of identification of toxic nature of the diverse pesticides on the aquatic compartment, a large dataset of pesticides (n = 325) with experimental toxicity data on two algal test species (Pseudokirchneriella subcapitata (PS) (synonym: Raphidocelis subcapitata, Selenastrum capricornutum) and Scenedemus subspicatus (SS)) was gathered and subjected to quantitative structure toxicity relationship (QSTR) analysis to predict aquatic toxicity of pesticides. The QSTR models were developed by multiple linear regressions (MLRs), and the genetic algorithm (GA) was used for the variable selection. The developed GA-MLR models were statistically robust enough internally (Q2LOO = 0.620-0.663) and externally (Q2Fn = 0.693-0.868, CCCext = 0.843-0.877). The leverage approach of applicability domain (AD) and prediction reliability indicator assured the reliability of the developed models. The mechanistic interpretation highlighted that the presence of SO2, F and aromatic rings influenced the toxicity of pesticides towards PS species while the presence of alkyl, alkyl halide, aromatic rings and carbonyl was responsible for the toxicity of pesticides towards SS species. Additionally, we have reported the application of developed models to pesticides without experimental value and the cumulative toxicity of pesticides on the aquatic environment by using principal component analysis (PCA). The reliable prediction and prioritization of toxic compounds from the developed models will be useful in the aquatic toxicity assessment of pesticides.
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Affiliation(s)
- Purusottam Banjare
- Department of Pharmacy, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur, 495009, India
| | - Jagadish Singh
- Department of Pharmacy, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur, 495009, India
| | - Ester Papa
- Department of Theoretical and Applied Sciences (DiSTA), University of Insubria, Via J.H. Dunant 3, 21100, Varese, Italy
| | - Partha Pratim Roy
- Department of Pharmacy, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur, 495009, India.
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29
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Zaki MEA, Al-Hussain SA, Al-Mutairi AA, Samad A, Ghosh A, Chaudhari S, Khatale PN, Ajmire P, Jawarkar RD. In-silico studies to recognize repurposing therapeutics toward arginase-I inhibitors as a potential onco-immunomodulators. Front Pharmacol 2023; 14:1129997. [PMID: 37144217 PMCID: PMC10151555 DOI: 10.3389/fphar.2023.1129997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 02/27/2023] [Indexed: 05/06/2023] Open
Abstract
Rudolf Virchow was the first person to point out the important link between immune function and cancer. He did this by noticing that leukocytes were often found in tumors. Overexpression of arginase 1 (ARG1) and inducible nitric oxide synthase (iNOS) in myeloid-derived suppressor cells (MDSCs) and tumour-associated macrophages (TAMs) depletes both intracellular and extracellular arginine. TCR signalling is slowed as a result, and the same types of cells produce reactive oxygen and nitrogen species (ROS and RNS), which aggravates the situation. Human arginase I is a double-stranded manganese metalloenzyme that helps L-arginine break down into L-ornithine and urea. Thus, a quantitative structure-activity relationship (QSAR) analysis was performed to unearth the unrecognised structural aspects crucial for arginase-I inhibition. In this work, a balanced QSAR model with good prediction performance and clear mechanistic interpretation was developed using a dataset of 149 molecules encompassing a broad range of structural scaffolds and compositions. The model was made to meet OECD standards, and all of its validation parameters have values that are higher than the minimum requirements (R2 tr = 0.89, Q2 LMO = 0.86, and R2 ex = 0.85). The present QSAR study linked structural factors to arginase-I inhibitory action, including the proximity of lipophilic atoms to the molecule's centre of mass (within 3A), the position of the donor to the ring nitrogen (exactly 3 bonds away), and the surface area ratio. As OAT-1746 and two others are the only arginase-I inhibitors in development at the time, we have performed a QSAR-based virtual screening with 1650 FDA compounds taken from the zinc database. In this screening, 112 potential hit compounds were found to have a PIC50 value of less than 10 nm against the arginase-I receptor. The created QSAR model's application domain was evaluated in relation to the most active hit molecules identified using QSAR-based virtual screening, utilising a training set of 149 compounds and a prediction set of 112 hit molecules. As shown in the Williams plot, the top hit molecule, ZINC000252286875, has a low leverage value of HAT i/i h* = 0.140, placing it towards the boundary of the usable range. Furthermore, one of 112 hit molecules with a docking score of -10.891 kcal/mol (PIC50 = 10.023 M) was isolated from a study of arginase-I using molecular docking. Protonated ZINC000252286875-linked arginase-1 showed 2.9 RMSD, whereas non-protonated had 1.8. RMSD plots illustrate protein stability in protonated and non-protonated ZINC000252286875-bound states. Protonated-ZINC000252286875-bound proteins contain 25 Rg. The non-protonated protein-ligand combination exhibits a 25.2-Rg, indicating compactness. Protonated and non-protonated ZINC000252286875 stabilised protein targets in binding cavities posthumously. Significant root mean square fluctuations (RMSF) were seen in the arginase-1 protein at a small number of residues for a time function of 500 ns in both the protonated and unprotonated states. Protonated and non-protonated ligands interacted with proteins throughout the simulation. ZINC000252286875 bound Lys64, Asp124, Ala171, Arg222, Asp232, and Gly250. Aspartic acid residue 232 exhibited 200% ionic contact. 500-ns simulations-maintained ions. Salt bridges for ZINC000252286875 aided docking. ZINC000252286875 created six ionic bonds with Lys68, Asp117, His126, Ala171, Lys224, and Asp232 residues. Asp117, His126, and Lys224 showed 200% ionic interactions. In protonated and deprotonated states, GbindvdW, GbindLipo, and GbindCoulomb energies played crucial role. Moreover, ZINC000252286875 meets all of the ADMET standards to serve as a drug. As a result, the current analyses were successful in locating a novel and potent hit molecule that inhibits arginase-I effectively at nanomolar concentrations. The results of this investigation can be used to develop brand-new arginase I inhibitors as an alternative immune-modulating cancer therapy.
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Affiliation(s)
- Magdi E. A. Zaki
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
- *Correspondence: Magdi E. A. Zaki, ; Rahul D. Jawarkar,
| | - Sami A. Al-Hussain
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - Aamal A. Al-Mutairi
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - Abdul Samad
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Tishk International University, Erbil, Kurdistan Region, Iraq
| | - Arabinda Ghosh
- Microbiology Division, Department of Botany, Gauhati University, Guwahati, India
| | - Somdatta Chaudhari
- Department of Pharmaceutical Chemistry, Progressive Education Society’s Modern College of Pharmacy, Pune, India
| | - Pravin N. Khatale
- Department of Medicinal Chemistry, Dr Rajendra Gode Institute of Pharmacy, Amravati, Maharashtra, India
| | - Prashant Ajmire
- Department of Medicinal Chemistry, Dr Rajendra Gode Institute of Pharmacy, Amravati, Maharashtra, India
| | - Rahul D. Jawarkar
- Department of Medicinal Chemistry, Dr Rajendra Gode Institute of Pharmacy, Amravati, Maharashtra, India
- *Correspondence: Magdi E. A. Zaki, ; Rahul D. Jawarkar,
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30
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Fernandes PDO, Martins JPA, de Melo EB, de Oliveira RB, Kronenberger T, Maltarollo VG. Quantitative structure-activity relationship and machine learning studies of 2-thiazolylhydrazone derivatives with anti- Cryptococcus neoformans activity. J Biomol Struct Dyn 2022; 40:9789-9800. [PMID: 34121616 DOI: 10.1080/07391102.2021.1935321] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Cryptococcus neoformans is a fungus responsible for infections in humans with a significant number of cases in immunosuppressed patients, mainly in underdeveloped countries. In this context, the thiazolylhydrazones are a promising class of compounds with activity against C. neoformans. The understanding of the structure-activity relationship of these derivatives could lead to the design of robust compounds that could be promising drug candidates for fungal infections. Specifically, modern techniques such as 4D-QSAR and machine learning methods were employed in this work to generate two QSAR models (one 2D and one 4D) with high predictive power (r2 for the test set equals to 0.934 and 0.831, respectively), and one random forest classification model was reported with Matthews correlation coefficient equals to 1 and 0.62 for internal and external validations, respectively. The physicochemical interpretation of selected models, indicated the importance of aliphatic substituents at the hydrazone moiety to antifungal activity, corroborating experimental data.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Philipe de Oliveira Fernandes
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - João Paulo A Martins
- Departamento de Química, Instituto de Ciências Exatas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Eduardo B de Melo
- Laboratório de Química Medicinal e Ambiental Teórica, Universidade Estadual do Oeste do Paraná, Cascavel, Paraná, Brazil
| | - Renata Barbosa de Oliveira
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Thales Kronenberger
- Department of Pneumonology and Oncology, Internal Medicine VIII, University Hospital of Tübingen, Tübingen, Baden-Württemberg, Germany
| | - Vinícius Gonçalves Maltarollo
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
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31
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Tomic A, Cvetnic M, Kovacic M, Kusic H, Karamanis P, Bozic AL. Structural features promoting adsorption of contaminants of emerging concern onto TiO 2 P25: experimental and computational approaches. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:87628-87644. [PMID: 35819674 DOI: 10.1007/s11356-022-21891-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 07/02/2022] [Indexed: 06/15/2023]
Abstract
The study of the structural features affecting the adsorption of organics, especially contaminants of emerging concern (CECs), onto TiO2 P25 in aqueous medium has far-reaching implications for the understanding and modification of TiO2 P25 in the roles such as an adsorbent and photocatalyst. The effect of pH and γ(TiO2 P25) as variables on the extent of removal of organics by adsorption on TiO2 P25 was investigated by response surface methodology (RSM) and quantitative structure-property relationship (QSPR) modeling. Experimentally determined coefficients of adsorption were used as responses in RSM, yielding a quadratic polynomial equation (QPE) for each of the studied organics. Furthermore, coefficients (A, B, C, D, E, and F) obtained from QPEs were used as responses in QSPR modeling to establish their dependence on the structural features of the studied organics. The functional stability and predictive power of the resulting QSPR models were confirmed with internal and external cross validation. The influence of structural features of organics on the adsorption process is explained by molecular descriptors included in the derived QSPR models. The most influential descriptors on the adsorption of organics on TiO2 P25 are found to be those correlated with ionization potential, molecular mass, and volume, then molecular fragments (e.g., -CH =) and particular topological features such as C and N atoms, or two heteroatoms (e.g., N and N or O and Cl) at certain distance. Derived QSPR models can be considered as robust predictive tools for evaluating efficiency of adsorption processes onto TiO2 P25, providing insights into influential structural features facilitating adsorption process.
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Affiliation(s)
- Antonija Tomic
- Faculty of Chemical Engineering and Technology, University of Zagreb, Marulicev trg 19, 10000, Zagreb, Croatia
| | - Matija Cvetnic
- Faculty of Chemical Engineering and Technology, University of Zagreb, Marulicev trg 19, 10000, Zagreb, Croatia
| | - Marin Kovacic
- Faculty of Chemical Engineering and Technology, University of Zagreb, Marulicev trg 19, 10000, Zagreb, Croatia
| | - Hrvoje Kusic
- Faculty of Chemical Engineering and Technology, University of Zagreb, Marulicev trg 19, 10000, Zagreb, Croatia.
| | - Panagiotis Karamanis
- Institute of Analytical Sciences and Physico-Chemistry for Environment and Materials, French National Centre for Scientific Research, Avenue de l'Université BP 576, 64012, Pau, France
| | - Ana Loncaric Bozic
- Faculty of Chemical Engineering and Technology, University of Zagreb, Marulicev trg 19, 10000, Zagreb, Croatia
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Masand VH, Al-Hussain SA, Rathore MM, Thakur SD, Akasapu S, Samad A, Al-Mutairi AA, Zaki MEA. Pharmacophore Synergism in Diverse Scaffold Clinches in Aurora Kinase B. Int J Mol Sci 2022; 23:ijms232314527. [PMID: 36498857 PMCID: PMC9739353 DOI: 10.3390/ijms232314527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 11/01/2022] [Accepted: 11/11/2022] [Indexed: 11/23/2022] Open
Abstract
Aurora kinase B (AKB) is a crucial signaling kinase with an important role in cell division. Therefore, inhibition of AKB is an attractive approach to the treatment of cancer. In the present work, extensive quantitative structure-activity relationships (QSAR) analysis has been performed using a set of 561 structurally diverse aurora kinase B inhibitors. The Organization for Economic Cooperation and Development (OECD) guidelines were used to develop a QSAR model that has high statistical performance (R2tr = 0.815, Q2LMO = 0.808, R2ex = 0.814, CCCex = 0.899). The seven-variable-based newly developed QSAR model has an excellent balance of external predictive ability (Predictive QSAR) and mechanistic interpretation (Mechanistic QSAR). The QSAR analysis successfully identifies not only the visible pharmacophoric features but also the hidden features. The analysis indicates that the lipophilic and polar groups-especially the H-bond capable groups-must be present at a specific distance from each other. Moreover, the ring nitrogen and ring carbon atoms play important roles in determining the inhibitory activity for AKB. The analysis effectively captures reported as well as unreported pharmacophoric features. The results of the present analysis are also supported by the reported crystal structures of inhibitors bound to AKB.
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Affiliation(s)
- Vijay H. Masand
- Department of Chemistry, Vidya Bharati Mahavidyalaya, Amravati 444602, Maharashtra, India
- Correspondence: (V.H.M.); (M.E.A.Z.)
| | - Sami A. Al-Hussain
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh 11623, Saudi Arabia
| | - Mithilesh M. Rathore
- Department of Chemistry, Vidya Bharati Mahavidyalaya, Amravati 444602, Maharashtra, India
| | - Sumer D. Thakur
- Department of Chemistry, RDIK and NKD College, Badnera, Amravati 444701, Maharashtra, India
| | | | - Abdul Samad
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Tishk International University, Erbil 44001, Iraq
| | - Aamal A. Al-Mutairi
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh 11623, Saudi Arabia
| | - Magdi E. A. Zaki
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh 11623, Saudi Arabia
- Correspondence: (V.H.M.); (M.E.A.Z.)
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Király P, Kiss R, Kovács D, Ballaj A, Tóth G. The Relevance of Goodness-of-fit, Robustness and Prediction Validation Categories of OECD-QSAR Principles with Respect to Sample Size and Model Type. Mol Inform 2022; 41:e2200072. [PMID: 35773201 PMCID: PMC9787734 DOI: 10.1002/minf.202200072] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 06/30/2022] [Indexed: 12/30/2022]
Abstract
We investigated the relevance of the validation principles on the Quantitative Structure Activity Relationship models issued by Organization for Economic and Co-operation and Development. We checked the goodness-of-fit, robustness and predictivity categories in linear and nonlinear models using benchmark datasets. Most of our conclusions are drawn using the sample size dependence of the different validation parameters. We found that the goodness-of-fit parameters misleadingly overestimate the models on small samples. In the case of neural network and support vector models, the feasibility of the goodness-of-fit parameters often might be questioned. We propose to use the simplest y-scrambling method to estimate chance correlation. We found that the leave-one-out and leave-many-out cross-validation parameters can be rescaled to each other in all models and the computationally feasible method should be chosen depending on the model type. We assessed the interdependence of the validation parameters by calculating their rank correlations. Goodness of fit and robustness correlate quite well over a sample size for linear models and one of the approaches might be redundant. In the rank correlation between internal and external validation parameters, we found that the assignment of good and bad modellable data to the training or the test causes negative correlations.
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Affiliation(s)
- Péter Király
- Institute of ChemistryLoránd Eötvös UniversityPázmány S.1/A1117BudapestHungary
| | - Ramóna Kiss
- Institute of ChemistryLoránd Eötvös UniversityPázmány S.1/A1117BudapestHungary
| | - Dániel Kovács
- Institute of ChemistryLoránd Eötvös UniversityPázmány S.1/A1117BudapestHungary
| | - Amine Ballaj
- Institute of ChemistryLoránd Eötvös UniversityPázmány S.1/A1117BudapestHungary
| | - Gergely Tóth
- Institute of ChemistryLoránd Eötvös UniversityPázmány S.1/A1117BudapestHungary
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Computational modelling of some phenolic diterpenoids compounds as anti-influenza A virus agents. SCIENTIFIC AFRICAN 2022. [DOI: 10.1016/j.sciaf.2022.e01462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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Mali SN, Pandey A, Bhandare RR, Shaik AB. Identification of hydantoin based Decaprenylphosphoryl-β-D-Ribose Oxidase (DprE1) inhibitors as antimycobacterial agents using computational tools. Sci Rep 2022; 12:16368. [PMID: 36180452 PMCID: PMC9525719 DOI: 10.1038/s41598-022-20325-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 09/12/2022] [Indexed: 11/08/2022] Open
Abstract
Tuberculosis (TB) is one of the emerging infectious diseases in the world. DprE1 (Decaprenylphosphoryl-β-D-ribose 2'-epimerase), an enzyme accountable for mycobacterial cell wall synthesis was the first drug gable target based on discoveries of inhibitors via HTS (high throughput screening). Since then, many literature reports have been published so far enlightening varieties of chemical scaffolds acting as inhibitors of DprE1. Herein, in our present study, we have developed statistically robust GA-MLR (genetic algorithm multiple linear regression), atom-based as well as field based-3D-QSAR models. Both atom-based as well as field based-3D-QSAR models (internally as well as externally validated) were obtained with robust Training set, R2 > 0.69 and Test set, Q2 > 0.50. We have also developed top ranked 5 point hypothesis AAAHR_1 among 14 CPHs (common pharmacophore hypotheses). We found that our dataset molecule had more docking score (XP mode = - 9.068 kcal/mol) than the standards isoniazid and ethambutol; when docked into binding pockets of enzyme 4P8C with Glide module. We further queried our best docked dataset molecule 151 for ligand based virtual screening using "SwissSimilarity" platform. Among 9 identified hits, we found ZINC12196803 had best binding energies and docking score (docking score = - 9.437 kcal/mol, MMGBSA dgBind = - 70.508 kcal/mol). Finally, our molecular dynamics studies for 1.2-100 ns depicts that these complexes are stable. We have also carried out in-silico ADMET predictions, Cardiac toxicity, 'SwissTargetPredictions' and Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) binding energy calculations for further explorations of dataset as well as hit molecules. Our current studies showed that the hit molecule ZINC12196803 may enlighten the path for future developments of DprE1 inhibitors.
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Affiliation(s)
- Suraj N Mali
- Department of Pharmaceutical Sciences and Technology, Birla Institute of Technology, Mesra, 835215, India.
| | - Anima Pandey
- Department of Pharmaceutical Sciences and Technology, Birla Institute of Technology, Mesra, 835215, India
| | - Richie R Bhandare
- Department of Pharmaceutical Sciences, College of Pharmacy and Health Sciences, Ajman University, P O Box 346, Ajman, United Arab Emirates.
- Center of Medical and Bio-allied Health Sciences Research, Ajman University, P O Box 346, Ajman, United Arab Emirates.
| | - Afzal B Shaik
- St. Mary's College of Pharmacy, St. Mary's Group of Institutions Guntur, Affiliated to Jawaharlal Nehru Technological University Kakinada, Chebrolu, Guntur, Andhra Pradesh, 522212, India.
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Mukerjee N, Das A, Jawarkar RD, Maitra S, Das P, Castrosanto MA, Paul S, Samad A, Zaki MEA, Al-Hussain SA, Masand VH, Hasan MM, Bukhari SNA, Perveen A, Alghamdi BS, Alexiou A, Kamal MA, Dey A, Malik S, Bakal RL, Abuzenadah AM, Ghosh A, Md Ashraf G. Repurposing food molecules as a potential BACE1 inhibitor for Alzheimer's disease. Front Aging Neurosci 2022; 14:878276. [PMID: 36072483 PMCID: PMC9443073 DOI: 10.3389/fnagi.2022.878276] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 07/07/2022] [Indexed: 11/13/2022] Open
Abstract
Alzheimer's disease (AD) is a severe neurodegenerative disorder of the brain that manifests as dementia, disorientation, difficulty in speech, and progressive cognitive and behavioral impairment. The emerging therapeutic approach to AD management is the inhibition of β-site APP cleaving enzyme-1 (BACE1), known to be one of the two aspartyl proteases that cleave β-amyloid precursor protein (APP). Studies confirmed the association of high BACE1 activity with the proficiency in the formation of β-amyloid-containing neurotic plaques, the characteristics of AD. Only a few FDA-approved BACE1 inhibitors are available in the market, but their adverse off-target effects limit their usage. In this paper, we have used both ligand-based and target-based approaches for drug design. The QSAR study entails creating a multivariate GA-MLR (Genetic Algorithm-Multilinear Regression) model using 552 molecules with acceptable statistical performance (R 2 = 0.82, Q 2 loo = 0.81). According to the QSAR study, the activity has a strong link with various atoms such as aromatic carbons and ring Sulfur, acceptor atoms, sp2-hybridized oxygen, etc. Following that, a database of 26,467 food compounds was primarily used for QSAR-based virtual screening accompanied by the application of the Lipinski rule of five; the elimination of duplicates, salts, and metal derivatives resulted in a truncated dataset of 8,453 molecules. The molecular descriptor was calculated and a well-validated 6-parametric version of the QSAR model was used to predict the bioactivity of the 8,453 food compounds. Following this, the food compounds whose predicted activity (pKi) was observed above 7.0 M were further docked into the BACE1 receptor which gave rise to the Identification of 4-(3,4-Dihydroxyphenyl)-2-hydroxy-1H-phenalen-1-one (PubChem I.D: 4468; Food I.D: FDB017657) as a hit molecule (Binding Affinity = -8.9 kcal/mol, pKi = 7.97 nM, Ki = 10.715 M). Furthermore, molecular dynamics simulation for 150 ns and molecular mechanics generalized born and surface area (MMGBSA) study aided in identifying structural motifs involved in interactions with the BACE1 enzyme. Molecular docking and QSAR yielded complementary and congruent results. The validated analyses can be used to improve a drug/lead candidate's inhibitory efficacy against the BACE1. Thus, our approach is expected to widen the field of study of repurposing nutraceuticals into neuroprotective as well as anti-cancer and anti-viral therapeutic interventions.
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Affiliation(s)
- Nobendu Mukerjee
- Department of Microbiology, Ramakrishna Mission Vivekananda Centenary College, Khardaha, India
- Department of Health Sciences, Novel Global Community Educational Foundation, Hebersham, NSW, Australia
| | - Anubhab Das
- Institute of Health Sciences, Presidency University, Kolkata, India
| | - Rahul D. Jawarkar
- Department of Medicinal Chemistry, Dr. Rajendra Gode Institute of Pharmacy, Amravati, India
| | - Swastika Maitra
- Department of Microbiology, Adamas University, Kolkata, India
| | | | - Melvin A. Castrosanto
- Institute of Chemistry, University of the Philippines Los Baños, Los Baños, Philippines
| | - Soumyadip Paul
- Department of Microbiology, Ramakrishna Mission Vivekananda Centenary College, Khardaha, India
| | - Abdul Samad
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Tishk International University, Erbil, Iraq
| | - Magdi E. A. Zaki
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - Sami A. Al-Hussain
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - Vijay H. Masand
- Department of Chemistry, Vidya Bharati Mahavidyalaya, Amravati, India
| | - Mohammad Mehedi Hasan
- Department of Biochemistry and Molecular Biology, Faculty of Life Sciences, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
| | - Syed Nasir Abbas Bukhari
- Department of Pharmaceutical Chemistry, College of Pharmacy, Jouf University, Sakaka, Saudi Arabia
| | - Asma Perveen
- Glocal School of Life Sciences, Glocal University, Saharanpur, India
| | - Badrah S. Alghamdi
- Department of Physiology, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
- Pre-Clinical Research Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia
- The Neuroscience Research Unit, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Athanasios Alexiou
- Department of Science and Engineering, Novel Global Community Educational Foundation, Hebersham, NSW, Australia
- AFNP Med, Vienna, Austria
| | - Mohammad Amjad Kamal
- Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
- King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka, Bangladesh
- Enzymoics, Novel Global Community Educational Foundation, Hebersham, NSW, Australia
| | - Abhijit Dey
- Department of Life Sciences, Presidency University, Kolkata, India
| | - Sumira Malik
- Amity Institute of Biotechnology, Amity University, Jharkhand, Ranchi, India
| | - Ravindra L. Bakal
- Department of Medicinal Chemistry, Dr. Rajendra Gode Institute of Pharmacy, Amravati, India
| | - Adel Mohammad Abuzenadah
- King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Arabinda Ghosh
- Microbiology Division, Department of Botany, Gauhati University, Guwahati, India
| | - Ghulam Md Ashraf
- Pre-Clinical Research Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
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Jawarkar RD, Sharma P, Jain N, Gandhi A, Mukerjee N, Al-Mutairi AA, Zaki MEA, Al-Hussain SA, Samad A, Masand VH, Ghosh A, Bakal RL. QSAR, Molecular Docking, MD Simulation and MMGBSA Calculations Approaches to Recognize Concealed Pharmacophoric Features Requisite for the Optimization of ALK Tyrosine Kinase Inhibitors as Anticancer Leads. Molecules 2022; 27:molecules27154951. [PMID: 35956900 PMCID: PMC9370430 DOI: 10.3390/molecules27154951] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 07/15/2022] [Accepted: 07/22/2022] [Indexed: 12/04/2022] Open
Abstract
ALK tyrosine kinase ALK TK is an important target in the development of anticancer drugs. In the present work, we have performed a QSAR analysis on a dataset of 224 molecules in order to quickly predict anticancer activity on query compounds. Double cross validation assigns an upward plunge to the genetic algorithm−multi linear regression (GA-MLR) based on robust univariate and multivariate QSAR models with high statistical performance reflected in various parameters like, fitting parameters; R2 = 0.69−0.87, F = 403.46−292.11, etc., internal validation parameters; Q2LOO = 0.69−0.86, Q2LMO = 0.69−0.86, CCCcv = 0.82−0.93, etc., or external validation parameters Q2F1 = 0.64−0.82, Q2F2 = 0.63−0.82, Q2F3 = 0.65−0.81, R2ext = 0.65−0.83 including RMSEtr < RMSEcv. The present QSAR evaluation successfully identified certain distinct structural features responsible for ALK TK inhibitory potency, such as planar Nitrogen within four bonds from the Nitrogen atom, Fluorine atom within five bonds beside the non-ring Oxygen atom, lipophilic atoms within two bonds from the ring Carbon atoms. Molecular docking, MD simulation, and MMGBSA computation results are in consensus with and complementary to the QSAR evaluations. As a result, the current study assists medicinal chemists in prioritizing compounds for experimental detection of anticancer activity, as well as their optimization towards more potent ALK tyrosine kinase inhibitor.
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Affiliation(s)
- Rahul D. Jawarkar
- Faculty of Pharmacy, Oriental University, Indore 453555, Madhya Pradesh, India; (P.S.); (N.J.)
- Correspondence: (R.D.J.); (M.E.A.Z.); Tel.: +91-7385178762 (R.D.J.)
| | - Praveen Sharma
- Faculty of Pharmacy, Oriental University, Indore 453555, Madhya Pradesh, India; (P.S.); (N.J.)
| | - Neetesh Jain
- Faculty of Pharmacy, Oriental University, Indore 453555, Madhya Pradesh, India; (P.S.); (N.J.)
| | - Ajaykumar Gandhi
- Department of Chemistry, Government College of Arts and Science, Aurangabad 431004, Maharashtra, India;
| | - Nobendu Mukerjee
- Department of Microbiology, Ramakrishna Mission Vivekananda Centenary College, Kolkata 700118, West Bengal, India;
| | - Aamal A. Al-Mutairi
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh 13318, Saudi Arabia; (A.A.A.-M.); (S.A.A.-H.)
| | - Magdi E. A. Zaki
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh 13318, Saudi Arabia; (A.A.A.-M.); (S.A.A.-H.)
- Correspondence: (R.D.J.); (M.E.A.Z.); Tel.: +91-7385178762 (R.D.J.)
| | - Sami A. Al-Hussain
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh 13318, Saudi Arabia; (A.A.A.-M.); (S.A.A.-H.)
| | - Abdul Samad
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Tishk International University, Erbil 44001, Kurdistan Region, Iraq;
| | - Vijay H. Masand
- Department of Chemistry, Vidyabharati Mahavidyalalya, Camp Road, Amravati 444602, Maharashtra, India;
| | - Arabinda Ghosh
- Microbiology Division, Department of Botany, Gauhati University, Guwahati 781014, Assam, India;
| | - Ravindra L. Bakal
- Department of Medicinal Chemistry, Dr. Rajendra Gode Institute of Pharmacy, University-Mardi Road, Amravati 444603, Maharashtra, India;
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Keshavarz MH, Shirazi Z, Sayehvand F. A novel approach for assessment of antitrypanosomal activity of sesquiterpene lactones through additive and non-additive molecular structure parameters. Mol Divers 2022:10.1007/s11030-022-10495-5. [DOI: 10.1007/s11030-022-10495-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 07/06/2022] [Indexed: 11/30/2022]
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Bukhari SNA, Elsherif MA, Junaid K, Ejaz H, Alam P, Samad A, Jawarkar RD, Masand VH. Perceiving the Concealed and Unreported Pharmacophoric Features of the 5-Hydroxytryptamine Receptor Using Balanced QSAR Analysis. Pharmaceuticals (Basel) 2022; 15:ph15070834. [PMID: 35890133 PMCID: PMC9316833 DOI: 10.3390/ph15070834] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 06/12/2022] [Accepted: 06/25/2022] [Indexed: 02/04/2023] Open
Abstract
The 5-hydroxytryptamine receptor 6 (5-HT6) has gained attention as a target for developing therapeutics for Alzheimer’s disease, schizophrenia, cognitive dysfunctions, anxiety, and depression, to list a few. In the present analysis, a larger and diverse dataset of 1278 molecules covering a broad chemical and activity space was used to identify visual and concealed structural features associated with binding affinity for 5-HT6. For this, quantitative structure–activity relationships (QSAR) and molecular docking analyses were executed. This led to the development of a statistically robust QSAR model with a balance of excellent predictivity (R2tr = 0.78, R2ex = 0.77), the identification of unreported aspects of known features, and also novel mechanistic interpretations. Molecular docking and QSAR provided similar as well as complementary results. The present analysis indicates that the partial charges on ring carbons present within four bonds from a sulfur atom, the occurrence of sp3-hybridized carbon atoms bonded with donor atoms, and a conditional occurrence of lipophilic atoms/groups from nitrogen atoms, which are prominent but unreported pharmacophores that should be considered while optimizing a molecule for 5-HT6. Thus, the present analysis led to identification of some novel unreported structural features that govern the binding affinity of a molecule. The results could be beneficial in optimizing the molecules for 5-HT6.
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Affiliation(s)
- Syed Nasir Abbas Bukhari
- Department of Pharmaceutical Chemistry, College of Pharmacy, Jouf University, Sakaka 72388, Saudi Arabia
| | | | - Kashaf Junaid
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Jouf University, Sakaka 72388, Saudi Arabia
| | - Hasan Ejaz
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Jouf University, Sakaka 72388, Saudi Arabia
| | - Pravej Alam
- Department of Biology, College of Science and Humanities, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
| | - Abdul Samad
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Tishk International University, Erbil 44001, Iraq
| | - Rahul D Jawarkar
- Department of Medicinal Chemistry, Dr. Rajendra Gode Institute of Pharmacy, University-Mardi Road, Amravati 444603, Maharashtra, India
| | - Vijay H Masand
- Department of Chemistry, Vidya Bharati Mahavidyalaya, Amravati 444602, Maharashtra, India
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Azevedo L, Serafim MSM, Maltarollo VG, Grabrucker AM, Granato D. Atherosclerosis fate in the era of tailored functional foods: Evidence-based guidelines elicited from structure- and ligand-based approaches. Trends Food Sci Technol 2022. [DOI: 10.1016/j.tifs.2022.07.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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41
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Pirintsos S, Panagiotopoulos A, Bariotakis M, Daskalakis V, Lionis C, Sourvinos G, Karakasiliotis I, Kampa M, Castanas E. From Traditional Ethnopharmacology to Modern Natural Drug Discovery: A Methodology Discussion and Specific Examples. Molecules 2022; 27:4060. [PMID: 35807306 PMCID: PMC9268545 DOI: 10.3390/molecules27134060] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/19/2022] [Accepted: 06/22/2022] [Indexed: 12/04/2022] Open
Abstract
Ethnopharmacology, through the description of the beneficial effects of plants, has provided an early framework for the therapeutic use of natural compounds. Natural products, either in their native form or after crude extraction of their active ingredients, have long been used by different populations and explored as invaluable sources for drug design. The transition from traditional ethnopharmacology to drug discovery has followed a straightforward path, assisted by the evolution of isolation and characterization methods, the increase in computational power, and the development of specific chemoinformatic methods. The deriving extensive exploitation of the natural product chemical space has led to the discovery of novel compounds with pharmaceutical properties, although this was not followed by an analogous increase in novel drugs. In this work, we discuss the evolution of ideas and methods, from traditional ethnopharmacology to in silico drug discovery, applied to natural products. We point out that, in the past, the starting point was the plant itself, identified by sustained ethnopharmacological research, with the active compound deriving after extensive analysis and testing. In contrast, in recent years, the active substance has been pinpointed by computational methods (in silico docking and molecular dynamics, network pharmacology), followed by the identification of the plant(s) containing the active ingredient, identified by existing or putative ethnopharmacological information. We further stress the potential pitfalls of recent in silico methods and discuss the absolute need for in vitro and in vivo validation as an absolute requirement. Finally, we present our contribution to natural products' drug discovery by discussing specific examples, applying the whole continuum of this rapidly evolving field. In detail, we report the isolation of novel antiviral compounds, based on natural products active against influenza and SARS-CoV-2 and novel substances active on a specific GPCR, OXER1.
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Affiliation(s)
- Stergios Pirintsos
- Department of Biology, School of Sciences and Technology, University of Crete, 71409 Heraklion, Greece;
- Botanical Garden, University of Crete, 74100 Rethymnon, Greece
- Nature Crete Pharmaceuticals, 71305 Heraklion, Greece; (C.L.); (G.S.); (M.K.)
| | - Athanasios Panagiotopoulos
- Laboratory of Experimental Endocrinology, School of Medicine, University of Crete, 71409 Heraklion, Greece;
| | - Michalis Bariotakis
- Department of Biology, School of Sciences and Technology, University of Crete, 71409 Heraklion, Greece;
| | - Vangelis Daskalakis
- Department of Chemical Engineering, Cyprus University of Technology, Limassol 3603, Cyprus;
| | - Christos Lionis
- Nature Crete Pharmaceuticals, 71305 Heraklion, Greece; (C.L.); (G.S.); (M.K.)
- Clinic of Social and Family Medicine, School of Medicine, University of Crete, 71409 Heraklion, Greece
| | - George Sourvinos
- Nature Crete Pharmaceuticals, 71305 Heraklion, Greece; (C.L.); (G.S.); (M.K.)
- Laboratory of Clinical Virology, School of Medicine, University of Crete, 71409 Heraklion, Greece
| | - Ioannis Karakasiliotis
- Laboratory of Biology, School of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece;
| | - Marilena Kampa
- Nature Crete Pharmaceuticals, 71305 Heraklion, Greece; (C.L.); (G.S.); (M.K.)
- Laboratory of Experimental Endocrinology, School of Medicine, University of Crete, 71409 Heraklion, Greece;
| | - Elias Castanas
- Nature Crete Pharmaceuticals, 71305 Heraklion, Greece; (C.L.); (G.S.); (M.K.)
- Laboratory of Experimental Endocrinology, School of Medicine, University of Crete, 71409 Heraklion, Greece;
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Integration of Ligand-Based and Structure-Based Methods for the Design of Small-Molecule TLR7 Antagonists. Molecules 2022; 27:molecules27134026. [PMID: 35807273 PMCID: PMC9268101 DOI: 10.3390/molecules27134026] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 05/31/2022] [Accepted: 06/01/2022] [Indexed: 12/30/2022] Open
Abstract
Toll-like receptor 7 (TLR7) is activated in response to the binding of single-stranded RNA. Its over-activation has been implicated in several autoimmune disorders, and thus, it is an established therapeutic target in such circumstances. TLR7 small-molecule antagonists are not yet available for therapeutic use. We conducted a ligand-based drug design of new TLR7 antagonists through a concerted effort encompassing 2D-QSAR, 3D-QSAR, and pharmacophore modelling of 54 reported TLR7 antagonists. The developed 2D-QSAR model depicted an excellent correlation coefficient (R2training: 0.86 and R2test: 0.78) between the experimental and estimated activities. The ligand-based drug design approach utilizing the 3D-QSAR model (R2training: 0.95 and R2test: 0.84) demonstrated a significant contribution of electrostatic potential and steric fields towards the TLR7 antagonism. This consolidated approach, along with a pharmacophore model with high correlation (Rtraining: 0.94 and Rtest: 0.92), was used to design quinazoline-core-based hTLR7 antagonists. Subsequently, the newly designed molecules were subjected to molecular docking onto the previously proposed binding model and a molecular dynamics study for a better understanding of their binding pattern. The toxicity profiles and drug-likeness characteristics of the designed compounds were evaluated with in silico ADMET predictions. This ligand-based study contributes towards a better understanding of lead optimization and the future development of potent TLR7 antagonists.
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Modeling of Anticancer Sulfonamide Derivatives Lipophilicity by Chemometric and Quantitative Structure-Retention Relationships Approaches. Molecules 2022; 27:molecules27133965. [PMID: 35807212 PMCID: PMC9268166 DOI: 10.3390/molecules27133965] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 06/13/2022] [Accepted: 06/15/2022] [Indexed: 02/01/2023] Open
Abstract
Sulfonamides are a classic group of chemotherapeutic drugs with a broad spectrum of pharmacological action, including anticancer activity. In this work, reversed-phase high-performance liquid chromatography and biomimetic chromatography were applied to characterize the lipophilicity of sulfonamide derivatives with proven anticancer activities against human colon cancer. Chromatographically determined lipophilicity parameters were compared with obtained logP, employing various computational approaches. Similarities and dissimilarities between experimental and computational logP were studied using principal component analysis, cluster analysis, and the sum of ranking differences. Furthermore, quantitative structure–retention relationship modeling was applied to understand the influences of sulfonamide’s molecular properties on lipophilicity and affinity to phospholipids.
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Mechanistic Analysis of Chemically Diverse Bromodomain-4 Inhibitors Using Balanced QSAR Analysis and Supported by X-ray Resolved Crystal Structures. Pharmaceuticals (Basel) 2022; 15:ph15060745. [PMID: 35745664 PMCID: PMC9231298 DOI: 10.3390/ph15060745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 06/07/2022] [Accepted: 06/07/2022] [Indexed: 11/17/2022] Open
Abstract
Bromodomain-4 (BRD-4) is a key enzyme in post-translational modifications, transcriptional activation, and many other cellular processes. Its inhibitors find their therapeutic usage in cancer, acute heart failure, and inflammation to name a few. In the present study, a dataset of 980 molecules with a significant diversity of structural scaffolds and composition was selected to develop a balanced QSAR model possessing high predictive capability and mechanistic interpretation. The model was built as per the OECD (Organisation for Economic Co-operation and Development) guidelines and fulfills the endorsed threshold values for different validation parameters (R2tr = 0.76, Q2LMO = 0.76, and R2ex = 0.76). The present QSAR analysis identified that anti-BRD-4 activity is associated with structural characters such as the presence of saturated carbocyclic rings, the occurrence of carbon atoms near the center of mass of a molecule, and a specific combination of planer or aromatic nitrogen with ring carbon, donor, and acceptor atoms. The outcomes of the present analysis are also supported by X-ray-resolved crystal structures of compounds with BRD-4. Thus, the QSAR model effectively captured salient as well as unreported hidden pharmacophoric features. Therefore, the present study successfully identified valuable novel pharmacophoric features, which could be beneficial for the future optimization of lead/hit compounds for anti-BRD-4 activity.
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Kar S, Pathakoti K, Leszczynska D, Tchounwou PB, Leszczynski J. In vitro and in silico study of mixtures cytotoxicity of metal oxide nanoparticles to Escherichia coli: a mechanistic approach. Nanotoxicology 2022; 16:566-579. [PMID: 36149909 PMCID: PMC10266837 DOI: 10.1080/17435390.2022.2123750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 09/06/2022] [Accepted: 09/07/2022] [Indexed: 01/04/2023]
Abstract
Metal oxide nanoparticles (MONPs) are commonly found in the aquatic and terrestrial systems as chemical mixtures. Assessment of cytotoxicity associated with single and combination of MONPs can truly identify the concerned environmental risk. Thus, using Escherichia coli as a test model, in vitro cytotoxicity of 6 single MONPs, 15 binary and 20 tertiary mixtures with equitoxic ratios was evaluated following standard bioassay protocols. Assessment of oxidative stress suggested that the production of reactive oxygen species (ROS) was negligible, and the release of metal zinc ions played an important role in the toxicity of MONP mixtures. From our experimental data points, seven quantitative structure-activity relationships (QSARs) models were developed to model the cytotoxicity of these MONPs, based on our created periodic table-based descriptors and experimentally analyzed Zeta-potential. Two strategic approaches i.e. pharmacological and mathematical hypotheses were considered to identify the mixture descriptors pool for modeling purposes. The stringent validation criteria suggested that the model (Model M4) developed with mixture descriptors generated by square-root mole contribution outperformed the other six models considering validation criteria. While considering the pharmacological approach, the 'independent action' generated descriptor pool offered the best model (Model M2), which firmly confirmed that each MONP in the mixture acts through 'independent action' to induce cytotoxicity to E. coli instead of fostering an additive, antagonistic or synergistic effect among MONPs. The total metal electronegativity in a specific metal oxide relative to the number of oxygen atoms and metal valence was associated with a positive contribution to cytotoxicity. At the same time, the core count, which gives a measure of molecular bulk and Zeta potential, had a negative contribution to cytotoxicity.
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Affiliation(s)
- Supratik Kar
- Chemometrics and Molecular Modeling Laboratory, Department of Chemistry, Kean University, 1000 Morris Avenue, Union, NJ 07083, USA
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS-39217, USA
| | - Kavitha Pathakoti
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS-39217, USA
- RCMI Center for Environmental Health, Department of Biology, Jackson State University, Jackson, MS-39217, USA
| | - Danuta Leszczynska
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS-39217, USA
- Department of Civil and Environmental Engineering, Jackson State University, Jackson, MS-39217, USA
| | - Paul B. Tchounwou
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS-39217, USA
- RCMI Center for Environmental Health, Department of Biology, Jackson State University, Jackson, MS-39217, USA
| | - Jerzy Leszczynski
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS-39217, USA
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Chatterjee M, Roy K. Application of cross-validation strategies to avoid overestimation of performance of 2D-QSAR models for the prediction of aquatic toxicity of chemical mixtures. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2022; 33:463-484. [PMID: 35638563 DOI: 10.1080/1062936x.2022.2081255] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 05/19/2022] [Indexed: 06/15/2023]
Abstract
The quantitative structure-activity relationship (QSAR) modelling of mixtures is not as simple as that for individual chemicals, and it needs additional care to avoid overestimation of the performance. In this research, we have developed a 2D-QSAR model using only 2D interpretable and reproducible descriptors to predict the aquatic toxicity of mixtures of polar and non-polar narcotic substances present in the environment. Partial least squares (PLS) regression has been used to model the response variable (log 1/EC50 against Photobacterium phosphoreum) and the structural features of 84 binary mixtures of polar and nonpolar narcotic toxicants complying with the Organization of Economic Co-operation and Development (OECD) protocols. The model was cross-validated by mixtures-out and compounds-out cross-validation to nullify the developmental bias. The reliability of prediction of the model has been judged by the Prediction Reliability Indicator (PRI) tool using a newly designed set. The new model is robust, reproducible, extremely predictive, easily interpretable, and can be used for reliable prediction of aquatic toxicity of any untested chemical mixtures within the applicability domain. We have additionally used a machine learning-based chemical read-across algorithm in this study to improve the quality of predictions for the toxicity of the mixtures with the modelled descriptors.
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Affiliation(s)
- M Chatterjee
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - K Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
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Keshavarz MH, Shirazi Z, Barghahi A, Mousaviazar A, Zali A. A novel model for prediction of stability constants of the thiosemicarbazone ligands with different types of toxic heavy metal ions using structural parameters and multivariate linear regression method. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:37084-37095. [PMID: 35031996 DOI: 10.1007/s11356-021-17714-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 11/19/2021] [Indexed: 06/14/2023]
Abstract
A novel model is presented for reliable estimation of the stability constants of the thiosemicarbazone ligands with different types of toxic heavy metal ions (log β11) in an aqueous solution, which has wide usage in environmental safety and ecotoxicology applications. The biggest reported data of log β11 for 120 metalthiosemicarbazone complexes are used for deriving and testing the novel model. In contrast to available methods where they need the two-dimensional (2D) and three-dimensional (3D) complex molecular descriptors as well as expert users and computer codes, the novel correlation uses four additive and two non-additive structural parameters of thiosemicarbazone ligands. The calculated results of the novel correlation are compared with the outputs of the genetic algorithm with multivariate linear regression method (GA-MLR) as one of the best existing methods, which requires seven complex descriptors. The estimated results for 78 of training as well as 42 of two different test sets were established by external and internal validations. The values of statistical parameters comprising average deviation, average absolute deviation, average absolute relative deviation, absolute maximum deviation, and the coefficient of determination for 73 data of training set of New model/GA-MLR are 0.04/ - 0.25, 1.06/1.31, 14.4/18.7, 3.18/7.92, and 0.830/0.652, respectively. Thus, the predicted results of the new model are worthy as compared to the complex GA-MLR model. Moreover, assessments of various statistical parameters confirm that the new model provides great reliability, goodness-of-fit, accuracy, and precision.
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Affiliation(s)
| | - Zeinab Shirazi
- Faculty of Applied Sciences, Malek Ashtar University of Technology, Shahin Shahr, Iran
| | - Asileh Barghahi
- Faculty of Applied Sciences, Malek Ashtar University of Technology, Shahin Shahr, Iran
| | - Ali Mousaviazar
- Faculty of Applied Sciences, Malek Ashtar University of Technology, Shahin Shahr, Iran
| | - Abbas Zali
- Faculty of Applied Sciences, Malek Ashtar University of Technology, Shahin Shahr, Iran
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An insilico study of KLK-14 protein and its inhibition with curcumin and its derivatives. CHEMICAL PAPERS 2022. [DOI: 10.1007/s11696-022-02209-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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49
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Spiegel M. Current Trends in Computational Quantum Chemistry Studies on Antioxidant Radical Scavenging Activity. J Chem Inf Model 2022; 62:2639-2658. [PMID: 35436117 PMCID: PMC9198981 DOI: 10.1021/acs.jcim.2c00104] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
![]()
The antioxidative
nature of chemicals is now routinely studied
using computational quantum chemistry. Scientists are constantly proposing
new approaches to investigate those methods, and the subject is evolving
at a rapid pace. The goal of this review is to collect, consolidate,
and present current trends in a clear, methodical, and reference-rich
manner. This paper is divided into several sections, each of which
corresponds to a different stage of elaborations: preliminary concerns,
electronic structure analysis, and general reactivity (thermochemistry
and kinetics). The sections are further subdivided based on methodologies
used. Concluding remarks and future perspectives are presented based
on the remaining elements.
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Affiliation(s)
- Maciej Spiegel
- Department of Pharmacognosy and Herbal Medicines, Wroclaw Medical University, Borowska 211A, 50-556 Wroclaw, Poland
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Bakal R, Jawarkar R, Manwar J, Jaiswal M, Ghosh A, Gandhi A, Zaki ME, Al-Hussain S, Samad A, Masand V, Mukerjee N, Nasir Abbas Bukhari S, Sharma P, Lewaa I. Identification of Potent Aldose Reductase Inhibitors as Antidiabetic (Anti-hyperglycemic) agents using QSAR Based Virtual Screening, Molecular Docking, MD Simulation and MMGBSA Approaches. Saudi Pharm J 2022; 30:693-710. [PMID: 35812153 PMCID: PMC9257878 DOI: 10.1016/j.jsps.2022.04.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Accepted: 04/01/2022] [Indexed: 11/16/2022] Open
Abstract
The aldose reductase (AR) enzyme is an important target enzyme in the development of therapeutics against hyperglycaemia induced health complications such as retinopathy, etc. In the present study, a quantitative structure activity relationship (QSAR) evaluation of a dataset of 226 reported AR inhibitor (ARi) molecules is performed using a genetic algorithm – multi linear regression (GA-MLR) technique. Multi-criteria decision making (MCDM) analysis furnished two five variables based QSAR models with acceptably high performance reflected in various statistical parameters such as, R2 = 0.79–0.80, Q2LOO = 0.78–0.79, Q2LMO = 0.78–0.79. The QSAR model analysis revealed some of the molecular features that play crucial role in deciding inhibitory potency of the molecule against AR such as; hydrophobic Nitrogen within 2 Å of the center of mass of the molecule, non-ring Carbon separated by three and four bonds from hydrogen bond donor atoms, number of sp2 hybridized Oxygen separated by four bonds from sp2 hybridized Carbon atoms, etc. 14 in silico generated hits, using a compound 18 (a most potent ARi from present dataset with pIC50 = 8.04 M) as a template, on QSAR based virtual screening (QSAR-VS) furnished a scaffold 5 with better ARi activity (pIC50 = 8.05 M) than template compound 18. Furthermore, molecular docking of compound 18 (Docking Score = –7.91 kcal/mol) and scaffold 5 (Docking Score = –8.08 kcal/mol) against AR, divulged that they both occupy the specific pocket(s) in AR receptor binding sites through hydrogen bonding and hydrophobic interactions. Molecular dynamic simulation (MDS) and MMGBSA studies right back the docking results by revealing the fact that binding site residues interact with scaffold 5 and compound 18 to produce a stable complex similar to co-crystallized ligand's conformation. The QSAR analysis, molecular docking, and MDS results are all in agreement and complementary. QSAR-VS successfully identified a more potent novel ARi and can be used in the development of therapeutic agents to treat diabetes.
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