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Maulana AF, Maksum IP, Sriwidodo S, Rukayadi Y. Proposed molecular mechanism of non-competitive inhibition using molecular dynamics simulations between α-glucosidase enzyme and mangostin compound as antidiabetic. J Mol Model 2024; 30:136. [PMID: 38634946 DOI: 10.1007/s00894-024-05934-z] [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: 01/09/2024] [Accepted: 04/09/2024] [Indexed: 04/19/2024]
Abstract
CONTEXT Further understanding of the molecular mechanisms is necessary since it is important for designing new drugs. This study aimed to understand the molecular mechanisms involved in the design of drugs that are inhibitors of the α-glucosidase enzyme. This research aims to gain further understanding of the molecular mechanisms underlying antidiabetic drug design. The molecular docking process yielded 4 compounds with the best affinity energy, including γ-Mangostin, 1,6-dimethyl-ester-3-isomangostin, 1,3,6-trimethyl-ester-α-mangostin, and 3,6,7-trimethyl-ester-γ-mangostin. Free energy calculation with molecular mechanics with generalized born and surface area solvation indicated that the 3,6,7-trimethyl-γ-mangostin had a better free energy value compared to acarbose and simulated maltose together with 3,6,7-trimethyl-γ-mangostin compound. Based on the analysis of electrostatic, van der Waals, and intermolecular hydrogen interactions, 3,6,7-trimethyl-γ-mangostin adopts a noncompetitive inhibition mechanism, whereas acarbose adopts a competitive inhibition mechanism. Consequently, 3,6,7-trimethyl-ester-γ-mangostin, which is a derivative of γ-mangostin, can provide better activity in silico with molecular docking approaches and molecular dynamics simulations. METHOD This research commenced with retrieving protein structures from the RCSB database, generating the formation of ligands using the ChemDraw Professional software, conducting molecular docking with the Autodock Vina software, and performing molecular dynamics simulations using the Amber software, along with the evaluation of RMSD values and intermolecular hydrogen bonds. Free energy, electrostatic interactions, and Van der Waals interaction were calculated using MM/GBSA. Acarbose, used as a positive control, and maltose are simulated together with test compound that has the best free energy. The forcefields used for molecular dynamics simulations are ff19SB, gaff2, and tip3p.
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Affiliation(s)
- Ahmad Fariz Maulana
- Department of Chemistry, Faculty of Mathematics and Natural Sciences, Padjadjaran University, Jatinangor, Sumedang, 45363, Indonesia
| | - Iman Permana Maksum
- Department of Chemistry, Faculty of Mathematics and Natural Sciences, Padjadjaran University, Jatinangor, Sumedang, 45363, Indonesia.
| | - Sriwidodo Sriwidodo
- Department of Pharmacy and Pharmaceutical Technology, Faculty of Pharmacy, Padjadjaran University, Jatinangor, Sumedang, 45363, Indonesia
| | - Yaya Rukayadi
- Department of Food Science, Faculty of Food Science and Technology, Universiti Putra Malaysia, 43400, Serdang, Selangor, Malaysia
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Djuris J, Cvijic S, Djekic L. Model-Informed Drug Development: In Silico Assessment of Drug Bioperformance following Oral and Percutaneous Administration. Pharmaceuticals (Basel) 2024; 17:177. [PMID: 38399392 PMCID: PMC10892858 DOI: 10.3390/ph17020177] [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: 11/03/2023] [Revised: 12/23/2023] [Accepted: 12/29/2023] [Indexed: 02/25/2024] Open
Abstract
The pharmaceutical industry has faced significant changes in recent years, primarily influenced by regulatory standards, market competition, and the need to accelerate drug development. Model-informed drug development (MIDD) leverages quantitative computational models to facilitate decision-making processes. This approach sheds light on the complex interplay between the influence of a drug's performance and the resulting clinical outcomes. This comprehensive review aims to explain the mechanisms that control the dissolution and/or release of drugs and their subsequent permeation through biological membranes. Furthermore, the importance of simulating these processes through a variety of in silico models is emphasized. Advanced compartmental absorption models provide an analytical framework to understand the kinetics of transit, dissolution, and absorption associated with orally administered drugs. In contrast, for topical and transdermal drug delivery systems, the prediction of drug permeation is predominantly based on quantitative structure-permeation relationships and molecular dynamics simulations. This review describes a variety of modeling strategies, ranging from mechanistic to empirical equations, and highlights the growing importance of state-of-the-art tools such as artificial intelligence, as well as advanced imaging and spectroscopic techniques.
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Affiliation(s)
- Jelena Djuris
- Department of Pharmaceutical Technology and Cosmetology, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11221 Belgrade, Serbia; (S.C.); (L.D.)
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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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In Silico Study of Mangostin Compounds and Its Derivatives as Inhibitors of α-Glucosidase Enzymes for Anti-Diabetic Studies. BIOLOGY 2022; 11:biology11121837. [PMID: 36552346 PMCID: PMC9775444 DOI: 10.3390/biology11121837] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 12/02/2022] [Accepted: 12/12/2022] [Indexed: 12/24/2022]
Abstract
Diabetes is a chronic disease with a high mortality rate worldwide and can cause other diseases such as kidney damage, narrowing of blood vessels, and heart disease. The concomitant use of drugs such as metformin, sulfonylurea, miglitol, and acarbose may cause side effects with long-term administration. Therefore, natural ingredients are the best choice, considering that their long-term side effects are not significant. One of the compounds that can be used as a candidate antidiabetic is mangostin; however, information on the molecular mechanism needs to be further analyzed through molecular docking, simulating molecular dynamics, and testing the in silico antidiabetic potential. This study focused on modeling the protein structure, molecular docking, and molecular dynamics simulations and analyses. This process produces RMSD values, free energies, and intermolecular hydrogen bonding. Based on the analysis results, all molecular dynamics simulations can occur under physiological conditions, and γ-mangostin is the best among the test compounds.
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Chemical and Biological Insights on Phaulopsis falcisepala: A Source of Bioactive Compounds with Multifunctional Anticancer Potentials. CHEMISTRY AFRICA 2022. [DOI: 10.1007/s42250-022-00553-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Artificial Intelligence: A Shifting Paradigm in Cardio-Cerebrovascular Medicine. J Clin Med 2021; 10:jcm10235710. [PMID: 34884412 PMCID: PMC8658222 DOI: 10.3390/jcm10235710] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 12/02/2021] [Indexed: 12/21/2022] Open
Abstract
The future of healthcare is an organic blend of technology, innovation, and human connection. As artificial intelligence (AI) is gradually becoming a go-to technology in healthcare to improve efficiency and outcomes, we must understand our limitations. We should realize that our goal is not only to provide faster and more efficient care, but also to deliver an integrated solution to ensure that the care is fair and not biased to a group of sub-population. In this context, the field of cardio-cerebrovascular diseases, which encompasses a wide range of conditions-from heart failure to stroke-has made some advances to provide assistive tools to care providers. This article aimed to provide an overall thematic review of recent development focusing on various AI applications in cardio-cerebrovascular diseases to identify gaps and potential areas of improvement. If well designed, technological engines have the potential to improve healthcare access and equitability while reducing overall costs, diagnostic errors, and disparity in a system that affects patients and providers and strives for efficiency.
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Baghban R, Farajnia S, Ghasemi Y, Mortazavi M, Ghasemali S, Zakariazadeh M, Zarghami N, Samadi N. Engineering of Ocriplasmin Variants by Bioinformatics Methods for the Reduction of Proteolytic and Autolytic Activities. IRANIAN JOURNAL OF MEDICAL SCIENCES 2021; 46:454-467. [PMID: 34840386 PMCID: PMC8611222 DOI: 10.30476/ijms.2020.86984.1705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2020] [Revised: 09/05/2020] [Accepted: 09/06/2020] [Indexed: 06/13/2023]
Abstract
BACKGROUND Ocriplasmin has been developed for the induction of posterior vitreous detachment in patients with vitreomacular adhesion. At physiological pH, ocriplasmin is susceptible to autolytic and proteolytic degradation, limiting its activity duration. These undesirable properties of ocriplasmin can be reduced by site-directed mutagenesis, so that its enzymatic activities can be augmented. This study aimed to design ocriplasmin variants with improved biological/physicochemical characteristics via bioinformatics tools. METHODS This study was performed in Tabriz University of Medical Sciences, Tabriz, Iran, 2019. Through site-directed mutagenesis, three ocriplasmin variants were designed. Structural analysis was performed on the wild-type variant and the mutant variants using the Protein Interactions Calculator (PIC) server. The interactions between the S-2403 substrate and the ocriplasmin variants were studied by molecular docking simulations, and binding capability was evaluated by the calculation of free binding energy. The conformational features of protein-substrate complex systems for all the variants were evaluated using molecular dynamic simulations at 100 nanoseconds. RESULTS The structural analysis of ocriplasmin revealed that the substitution of threonine for alanine 59 significantly reduced proteolytic activity, while the substitution of glutamic acid for lysine 156 influenced autolytic function. The molecular docking simulation results indicated the appropriate binding of the substrate to the ocriplasmin variants with high-to-low affinities. The binding affinity of the wild-type variant for the substrate was higher than that between the mutant variants and the substrate. Simulation analyses, consisting of the root-mean-square deviation, the root-mean-square fluctuation, and the center-of-mass average distance showed a higher affinity of the substrate for the wild type than for the mutant variants. CONCLUSION The mutational analysis of ocriplasmin revealed that A59T and K156E mutagenesis could be used for the development of a new variant with higher therapeutic efficacy.
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Affiliation(s)
- Roghayyeh Baghban
- Department of Medical Biotechnology, School of Advanced Medical Science, Tabriz University of Medical Sciences,Tabriz, Iran
- Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran
- Poostchi Ophthalmology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Safar Farajnia
- Drug Applied Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Younes Ghasemi
- Pharmaceutical Sciences Research Center, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran
- Department of Pharmaceutical Biotechnology, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mojtaba Mortazavi
- Department of Biotechnology, Institute of Science and High Technology and Environmental Sciences, Graduate University of Advanced Technology, Kerman, Iran
| | - Samaneh Ghasemali
- Department of Medical Biotechnology, School of Advanced Medical Science, Tabriz University of Medical Sciences,Tabriz, Iran
| | | | - Nosratollah Zarghami
- Department of Medical Biotechnology, School of Advanced Medical Science, Tabriz University of Medical Sciences,Tabriz, Iran
| | - Nasser Samadi
- Department of Medical Biotechnology, School of Advanced Medical Science, Tabriz University of Medical Sciences,Tabriz, Iran
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Bhat V, Chatterjee J. The Use of In Silico Tools for the Toxicity Prediction of Potential Inhibitors of SARS-CoV-2. Altern Lab Anim 2021; 49:22-32. [PMID: 33845649 PMCID: PMC8047515 DOI: 10.1177/02611929211008196] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
The current strategy for treating the Covid-19 coronavirus disease involves the repurposing of existing drugs or the use of convalescent plasma therapy, as no specific therapeutic intervention has yet received regulatory approval. However, severe adverse effects have been reported for some of these repurposed drugs. Recently, several in silico studies have identified compounds that are potential inhibitors of the main protease (3-chymotrypsin-like cysteine protease) and the nucleocapsid protein of SARS-CoV-2. An essential step of drug development is the careful evaluation of toxicity, which has a range of associated financial, temporal and ethical limitations. In this study, a number of in silico tools were used to predict the toxicity of 19 experimental compounds. A range of web-based servers and applications were used to predict hepatotoxicity, mutagenicity, acute oral toxicity, carcinogenicity, cardiotoxicity, and other potential adverse effects. The compounds were assessed based on the consensus of results, and were labelled as positive or negative for a particular toxicity endpoint. The compounds were then categorised into three classes, according to their predicted toxicity. Ten compounds (52.6%) were predicted to be non-mutagenic and non-hERG inhibitors, and exhibited zero or low level hepatotoxicity and carcinogenicity. Furthermore, from the consensus of results, all 19 compounds were predicted to be non-mutagenic and negative for acute oral toxicity. Overall, most of the compounds displayed encouraging toxicity profiles. These results can assist further lead optimisation studies and drug development efforts to combat Covid-19.
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Affiliation(s)
- Varsha Bhat
- Department of Biotechnology, 209468PES University, Bangalore, India
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Pande P, Giambalvo M, Huang Z. Complementing preclinical safety assessments through genomic analyses. CURRENT OPINION IN TOXICOLOGY 2018. [DOI: 10.1016/j.cotox.2019.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Affiliation(s)
- Saeed Alqahtani
- Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
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Lampa S, Alvarsson J, Spjuth O. Towards agile large-scale predictive modelling in drug discovery with flow-based programming design principles. J Cheminform 2016; 8:67. [PMID: 27942268 PMCID: PMC5123367 DOI: 10.1186/s13321-016-0179-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2016] [Accepted: 11/16/2016] [Indexed: 11/28/2022] Open
Abstract
Predictive modelling in drug discovery is challenging to automate as it often contains multiple analysis steps and might involve cross-validation and parameter tuning that create complex dependencies between tasks. With large-scale data or when using computationally demanding modelling methods, e-infrastructures such as high-performance or cloud computing are required, adding to the existing challenges of fault-tolerant automation. Workflow management systems can aid in many of these challenges, but the currently available systems are lacking in the functionality needed to enable agile and flexible predictive modelling. We here present an approach inspired by elements of the flow-based programming paradigm, implemented as an extension of the Luigi system which we name SciLuigi. We also discuss the experiences from using the approach when modelling a large set of biochemical interactions using a shared computer cluster.. ![]()
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Affiliation(s)
- Samuel Lampa
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24 Uppsala, Sweden
| | - Jonathan Alvarsson
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24 Uppsala, Sweden
| | - Ola Spjuth
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24 Uppsala, Sweden ; Science for Life Laboratory, Uppsala University, Box 3037, 750 03 Uppsala, Sweden
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Pellegatti M. Dogs and monkeys in preclinical drug development: the challenge of reducing and replacing. Expert Opin Drug Metab Toxicol 2013; 9:1171-80. [PMID: 23705836 DOI: 10.1517/17425255.2013.804061] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
INTRODUCTION Animal experimentation is a very contentious issue affecting reputation of drug industry. There are several reasons to forecast an increase in the number of dogs and monkeys used in safety and pharmacokinetic studies. This increase may trigger a strong reaction of the public opinion. There have been many proposals and initiatives to change the present approach to safety and metabolic studies. Tests based on new technologies, in vitro cell assays, stem cells, imaging, and computational systems, have the potential to anticipate effects in humans. Unfortunately, all these efforts and ideas have not changed standard approaches and regulatory expectations. AREAS COVERED This review looks at opportunities to reduce the number of dogs and monkeys currently used in pharmaceutical research. It also discusses present efforts and approaches, their strengths and potentials and the reasons why they may not fulfill expectations. EXPERT OPINION Unless the pharmaceutical industry gets more involved, an alternative paradigm of preclinical drug development is unlikely to be established in the foreseeable future. One can imagine a scenario where the political pressure against the use of dogs and monkeys in biomedical research becomes irresistible while alternative methods are not yet established. To avoid this situation, the pharmaceutical industry should take the lead and preclinical scientists at all levels need to influence decision makers and help develop new innovative approaches in drug safety evaluation.
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Abstract
Use of predictive technologies is an important aspect of many efforts in today's research, development, and regulatory landscapes. Computational methods as predictive tools for supporting drug safety assessments is of widespread interest as the field of in silico assessments rapidly changes with emerging technologies and the large amount of existing data available for modeling. There are challenges associated with application of in silico analyses for drug toxicity predictions and need for strategies and harmonization to enable an acceptable in silico evaluation for prediction of specific toxicity assay outcomes. This chapter will provide an overview focused on computational tools using structure-activity relationships and will highlight initiatives for use of computational assessments and realistic applications for predictive modeling in evaluating potential toxicities of drug-related molecules.
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Valerio LG, Choudhuri S. Chemoinformatics and chemical genomics: potential utility of in silico methods. J Appl Toxicol 2012; 32:880-9. [PMID: 22886396 DOI: 10.1002/jat.2804] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2012] [Revised: 06/26/2012] [Accepted: 06/27/2012] [Indexed: 12/24/2022]
Abstract
Computational life sciences and informatics are inseparably intertwined and they lie at the heart of modern biology, predictive quantitative modeling and high-performance computing. Two of the applied biological disciplines that are poised to benefit from such progress are pharmacology and toxicology. This review will describe in silico chemoinformatics methods such as (quantitative) structure-activity relationship modeling and will overview how chemoinformatic technologies are considered in applied regulatory research. Given the post-genomics era and large-scale repositories of omics data that are available, this review will also address potential applications of in silico techniques in chemical genomics. Chemical genomics utilizes small molecules to explore the complex biological phenomena that may not be not amenable to straightforward genetic approach. The reader will gain the understanding that chemoinformatics stands at the interface of chemistry and biology with enabling systems for mapping, statistical modeling, pattern recognition, imaging and database tools. The great potential of these technologies to help address complex issues in the toxicological sciences is appreciated with the applied goal of the protection of public health.
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Affiliation(s)
- Luis G Valerio
- Science and Research Staff, Office of Pharmaceutical Science, Center for Drug Evaluation and Research, US Food and Drug Administration, White Oak 51, Room 4128, 10903 New Hampshire Avenue, Silver Spring, MD 20993-0002, USA.
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