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Wei Y, Palazzolo L, Ben Mariem O, Bianchi D, Laurenzi T, Guerrini U, Eberini I. Investigation of in silico studies for cytochrome P450 isoforms specificity. Comput Struct Biotechnol J 2024; 23:3090-3103. [PMID: 39188968 PMCID: PMC11347072 DOI: 10.1016/j.csbj.2024.08.002] [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: 05/27/2024] [Revised: 07/26/2024] [Accepted: 08/01/2024] [Indexed: 08/28/2024] Open
Abstract
Cytochrome P450 (CYP450) enzymes comprise a highly diverse superfamily of heme-thiolate proteins that responsible for catalyzing over 90 % of enzymatic reactions associated with xenobiotic metabolism in humans. Accurately predicting whether chemicals are substrates or inhibitors of different CYP450 isoforms can aid in pre-selecting hit compounds for the drug discovery process, chemical toxicology studies, and patients treatment planning. In this work, we investigated in silico studies on CYP450s specificity over past twenty years, categorizing these studies into structure-based and ligand-based approaches. Subsequently, we utilized 100 of the most frequently prescribed drugs to test eleven machine learning-based prediction models which were published between 2015 and 2024. We analyzed various aspects of the evaluated models, such as their datasets, algorithms, and performance. This will give readers with a comprehensive overview of these prediction models and help them choose the most suitable one to do prediction. We also provide our insights for future research trend in both structure-based and ligand-based approaches in this field.
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Affiliation(s)
- Yao Wei
- Dipartimento di Scienze Farmacologiche e Biomolecolari “Rodolfo Paoletti”, Università degli Studi di Milano, Via Giuseppe Balzaretti 9, 20133 Milano, Italy
| | - Luca Palazzolo
- Dipartimento di Scienze Farmacologiche e Biomolecolari “Rodolfo Paoletti”, Università degli Studi di Milano, Via Giuseppe Balzaretti 9, 20133 Milano, Italy
| | - Omar Ben Mariem
- Dipartimento di Scienze Farmacologiche e Biomolecolari “Rodolfo Paoletti”, Università degli Studi di Milano, Via Giuseppe Balzaretti 9, 20133 Milano, Italy
| | - Davide Bianchi
- Dipartimento di Scienze Farmacologiche e Biomolecolari “Rodolfo Paoletti”, Università degli Studi di Milano, Via Giuseppe Balzaretti 9, 20133 Milano, Italy
| | - Tommaso Laurenzi
- Dipartimento di Scienze Farmacologiche e Biomolecolari “Rodolfo Paoletti”, Università degli Studi di Milano, Via Giuseppe Balzaretti 9, 20133 Milano, Italy
| | - Uliano Guerrini
- Dipartimento di Scienze Farmacologiche e Biomolecolari “Rodolfo Paoletti”, Università degli Studi di Milano, Via Giuseppe Balzaretti 9, 20133 Milano, Italy
| | - Ivano Eberini
- Dipartimento di Scienze Farmacologiche e Biomolecolari “Rodolfo Paoletti”, Università degli Studi di Milano, Via Giuseppe Balzaretti 9, 20133 Milano, Italy
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Chagaleti BK, B SK, G V A, Rajagopal R, Alfarhan A, Arockiaraj J, Muthu Kumaradoss K, Karthick Raja Namasivayam S. Targeting cyclin-dependent kinase 2 CDK2: Insights from molecular docking and dynamics simulation - A systematic computational approach to discover novel cancer therapeutics. Comput Biol Chem 2024; 112:108134. [PMID: 38964206 DOI: 10.1016/j.compbiolchem.2024.108134] [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: 04/18/2024] [Accepted: 06/20/2024] [Indexed: 07/06/2024]
Abstract
Global public health is confronted with significant challenges due to the prevalence of cancer and the emergence of treatment resistance. This work focuses on the identification of cyclin-dependent kinase 2 (CDK2) through a systematic computational approach to discover novel cancer therapeutics. A ligand-based pharmacophore model was initially developed using a training set of seven potent CDK2 inhibitors. The obtained most robust model was characterized by three features: one donor (|Don|) and two acceptors (|Acc|). Screening this model against the ZINC database resulted in identifying 108 hits, which underwent further molecular docking studies. The docking results indicated binding affinity, with energy values ranging from -6.59 kcal mol⁻¹ to -7.40 kcal mol⁻¹ compared to the standard Roscovitine. The top 10 compounds (Z1-Z10) selected from the docking data were further screened for ADMET profiling, ensuring their compliance with pharmacokinetic and toxicological criteria. The top 3 compounds (Z1-Z3) chosen from the docking were subjected to Density Functional Theory (DFT) studies. They revealed significant variations in electronic properties, providing insights into the reactivity, stability, and polarity of these compounds. Molecular dynamics simulations confirmed the stability of the ligand-protein complexes, with acceptable RMSD and RMSF values. Specifically, compound Z1 demonstrated stability, around 2.4 Å, and maintained throughout the 100 ns simulation period with minimal conformational changes, stable RMSD, and consistent protein-ligand interactions.
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Affiliation(s)
- Bharath Kumar Chagaleti
- Department of Pharmaceutical Chemistry, SRM College of Pharmacy, SRM Institute of Science and Technology, Chengalpattu District, Kattankulathur, Tamil Nadu 603203, India
| | - Shantha Kumar B
- Department of Pharmaceutical Chemistry, SRM College of Pharmacy, SRM Institute of Science and Technology, Chengalpattu District, Kattankulathur, Tamil Nadu 603203, India
| | - Anjana G V
- Department of Pharmaceutical Chemistry, SRM College of Pharmacy, SRM Institute of Science and Technology, Chengalpattu District, Kattankulathur, Tamil Nadu 603203, India
| | - Rajakrishnan Rajagopal
- Department of Botany and Microbiology, College of Science, King Saud University, P.O. Box No. 2455, Riyadh 11451, Saudi Arabia
| | - Ahmed Alfarhan
- Department of Botany and Microbiology, College of Science, King Saud University, P.O. Box No. 2455, Riyadh 11451, Saudi Arabia
| | - Jesu Arockiaraj
- Department of Biotechnology, Faculty of Science and Humanities, SRM Institute of Science and Technology, Chengalpattu District, Kattankulathur, Tamil Nadu 603203, India.
| | - Kathiravan Muthu Kumaradoss
- Dr. APJ Kalam Research Lab, Department of Pharmaceutical Chemistry, SRM College of Pharmacy, SRM Institute of Science and Technology, Chengalpattu District, Kattankulathur, Tamil Nadu 603203, India.
| | - S Karthick Raja Namasivayam
- Centre for Applied Research, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, Tamil Nadu 602105, India.
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Wang N, Li X, Xiao J, Liu S, Cao D. Data-driven toxicity prediction in drug discovery: Current status and future directions. Drug Discov Today 2024:104195. [PMID: 39357621 DOI: 10.1016/j.drudis.2024.104195] [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: 06/05/2024] [Revised: 09/13/2024] [Accepted: 09/26/2024] [Indexed: 10/04/2024]
Abstract
Early toxicity assessment plays a vital role in the drug discovery process on account of its significant influence on the attrition rate of candidates. Recently, constant upgrading of information technology has greatly promoted the continuous development of toxicity prediction. To give an overview of the current state of data-driven toxicity prediction, we reviewed relevant studies and summarize them in three main respects: the features and difficulties of toxicity prediction, the evolution of modeling approaches, and the available tools for toxicity prediction. For each approach, we expound the research status, existing challenges, and feasible solutions. Finally, several new directions and suggestions for toxicity prediction are also put forward.
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Affiliation(s)
- Ningning Wang
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008 Hunan, PR China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008 Hunan, PR China; The Hunan Institute of Pharmacy Practice and Clinical Research, Changsha 410008 Hunan, PR China
| | - Xinliang Li
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008 Hunan, PR China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008 Hunan, PR China; The Hunan Institute of Pharmacy Practice and Clinical Research, Changsha 410008 Hunan, PR China
| | - Jing Xiao
- Hunan Institute for Drug Control, Changsha 410001 Hunan, PR China
| | - Shao Liu
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008 Hunan, PR China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008 Hunan, PR China; The Hunan Institute of Pharmacy Practice and Clinical Research, Changsha 410008 Hunan, PR China.
| | - Dongsheng Cao
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008 Hunan, PR China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008 Hunan, PR China; Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, PR China.
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4
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Kar P, Oriola AO, Oyedeji AO. Toward Understanding the Anticancer Activity of the Phytocompounds from Eugenia uniflora Using Molecular Docking, in silico Toxicity and Dynamics Studies. Adv Appl Bioinform Chem 2024; 17:71-82. [PMID: 39318425 PMCID: PMC11421442 DOI: 10.2147/aabc.s473928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Accepted: 08/22/2024] [Indexed: 09/26/2024] Open
Abstract
Background The Surinam cherry, Eugenia uniflora belongs to the family Myrtaceae, an edible fruit-bearing medicinal plant with various biological properties. Several anticancer studies have been conducted on its essential oils while the non-essential oil compounds including phenolics, flavonoids, and carotenoids have not been fully investigated. Purpose Therefore, the study evaluated the in silico anticancer potentials of phenolic, flavonoid, and carotenoid compounds of E. uniflora against the MDM2 and Bcl-xL proteins, which are known to promote cancer cell growth and malignancy. The physicochemical parameters, validation, cytotoxicity, and mutagenicity of the polyphenols were determined using the SwissADME, pkCSM, ProTox-II, and vNN-ADMET online servers respectively. Lastly, the promising phytocompounds were validated using molecular dynamics (MD) simulation. Results An extensive literature search resulted in the compilation of forty-four (44) polyphenols from E. uniflora. Top-rank among the screened polyphenols is galloylastragalin, which exhibited a binding energy score of -8.7 and -8.5 kcal/mol with the hydrophobic interactions (Ala93, Val141) and (Leu54, Val93, Ile99), as well as hydrogen bond interactions (Tyr195) and (Gln72) of the proteins Bcl-xL and MDM2 respectively. A complete in silico toxicity assessment revealed that the compounds, galloylastragalin, followed by myricetin, resveratrol, p-Coumaroylquinic acid, and cyanidin-3-O-glucoside, were potentially non-mutagenic, non-carcinogenic, non-cytotoxic, and non-hepatotoxic. During the 120 ns MD simulations, the RMSF analysis of galloylastragalin- MDM2 (complex 1) and galloylastragalin- Bcl-xL (complex 2) showed the fewest fluctuations, indicating the conformational stability of the respective complexes. Conclusion This study has shown that polyphenol compounds of E. uniflora led by galloylastragalin, are potent inhibitors of the MDM2 and Bcl-xL cancer proteins. Thus, they may be considered as candidate polyphenols for further anticancer studies.
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Affiliation(s)
- Pallab Kar
- African Medicinal Flora and Fauna Research Niche, Walter Sisulu University, Mthatha, 5117, South Africa
- Department of Chemical and Physical Sciences, Walter Sisulu University, Mthatha, 5117, South Africa
| | - Ayodeji O Oriola
- Department of Chemical and Physical Sciences, Walter Sisulu University, Mthatha, 5117, South Africa
| | - Adebola O Oyedeji
- African Medicinal Flora and Fauna Research Niche, Walter Sisulu University, Mthatha, 5117, South Africa
- Department of Chemical and Physical Sciences, Walter Sisulu University, Mthatha, 5117, South Africa
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5
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Xu J, Wang Z, Niu Y, Tang Y, Wang Y, Huang J, Leung ELH. TRP Channels in Cancer: Therapeutic Opportunities and Research Strategies. Pharmacol Res 2024; 209:107412. [PMID: 39303771 DOI: 10.1016/j.phrs.2024.107412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2024] [Revised: 09/11/2024] [Accepted: 09/11/2024] [Indexed: 09/22/2024]
Abstract
The influence of gut microbiota on transient receptor potential (TRP) channels has been identified as an important element in developing gastrointestinal conditions, yet its involvement in cancer progression is not as thoroughly understood. This review explores the multifaceted roles of TRP channels in oncogenesis and emphasizes their significance in cancer progression and therapeutic outcomes. Critical focus was placed on the influence of traditional medicines, such as traditional Chinese medicine (TCM) related aromatic medicines, on TRP channel functions. Moreover, we explored the interplay between the gut microbiota and TRP channels in cancer signaling, highlighting the therapeutic potential of targeting this axis in cancer treatment. The impact of current therapies on TRP channel function was examined, highlighting the need for a comprehensive understanding of how different modalities affect TRP channels in cancer. Technological advancements, including artificial intelligence (AI) tools and computer-aided drug development (CADD), have been discussed in the context of leveraging TRP channels for innovative cancer therapies. Future directions emphasize the potential applications of TRP channel research in advancing cancer treatment and enhancing patient well-being.
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Affiliation(s)
- Jiahui Xu
- Cancer Center, Faculty of Health Sciences, University of Macau, Macau; MOE Frontiers Science Centre for Precision Oncology, University of Macau, Macau
| | - Ziming Wang
- Cancer Center, Faculty of Health Sciences, University of Macau, Macau; MOE Frontiers Science Centre for Precision Oncology, University of Macau, Macau
| | - Yuqing Niu
- Cancer Center, Faculty of Health Sciences, University of Macau, Macau; MOE Frontiers Science Centre for Precision Oncology, University of Macau, Macau
| | - Yuping Tang
- Key Laboratory of Shaanxi Administration of Traditional Chinese Medicine for TCM Compatibility, Shaanxi University of Chinese Medicine, Xianyang 712046, Shaanxi Province, China
| | - Yuwei Wang
- Key Laboratory of Shaanxi Administration of Traditional Chinese Medicine for TCM Compatibility, Shaanxi University of Chinese Medicine, Xianyang 712046, Shaanxi Province, China.
| | - Jumin Huang
- Cancer Center, Faculty of Health Sciences, University of Macau, Macau; MOE Frontiers Science Centre for Precision Oncology, University of Macau, Macau.
| | - Elaine Lai-Han Leung
- Cancer Center, Faculty of Health Sciences, University of Macau, Macau; MOE Frontiers Science Centre for Precision Oncology, University of Macau, Macau; State Key Laboratory of Quality Research in Chinese Medicine, University of Macau, Macau.
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6
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Shen C, Song J, Hsieh CY, Cao D, Kang Y, Ye W, Wu Z, Wang J, Zhang O, Zhang X, Zeng H, Cai H, Chen Y, Chen L, Luo H, Zhao X, Jian T, Chen T, Jiang D, Wang M, Ye Q, Wu J, Du H, Shi H, Deng Y, Hou T. DrugFlow: An AI-Driven One-Stop Platform for Innovative Drug Discovery. J Chem Inf Model 2024; 64:5381-5391. [PMID: 38920405 DOI: 10.1021/acs.jcim.4c00621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/27/2024]
Abstract
Artificial intelligence (AI)-aided drug design has demonstrated unprecedented effects on modern drug discovery, but there is still an urgent need for user-friendly interfaces that bridge the gap between these sophisticated tools and scientists, particularly those who are less computer savvy. Herein, we present DrugFlow, an AI-driven one-stop platform that offers a clean, convenient, and cloud-based interface to streamline early drug discovery workflows. By seamlessly integrating a range of innovative AI algorithms, covering molecular docking, quantitative structure-activity relationship modeling, molecular generation, ADMET (absorption, distribution, metabolism, excretion and toxicity) prediction, and virtual screening, DrugFlow can offer effective AI solutions for almost all crucial stages in early drug discovery, including hit identification and hit/lead optimization. We hope that the platform can provide sufficiently valuable guidance to aid real-word drug design and discovery. The platform is available at https://drugflow.com.
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Affiliation(s)
- Chao Shen
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Jianfei Song
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Chang-Yu Hsieh
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410004, Hunan, China
| | - Yu Kang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Wenling Ye
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Zhenxing Wu
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Jike Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Odin Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Xujun Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Hao Zeng
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Heng Cai
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Yu Chen
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Linkang Chen
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Hao Luo
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Xinda Zhao
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Tianye Jian
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Tong Chen
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Dejun Jiang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Mingyang Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Qing Ye
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Jialu Wu
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Hongyan Du
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Hui Shi
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Yafeng Deng
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
- Department of Automation, Tsinghua University, Beijing 100084, China
| | - Tingjun Hou
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
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Gu Y, Yu Z, Wang Y, Chen L, Lou C, Yang C, Li W, Liu G, Tang Y. admetSAR3.0: a comprehensive platform for exploration, prediction and optimization of chemical ADMET properties. Nucleic Acids Res 2024; 52:W432-W438. [PMID: 38647076 PMCID: PMC11223829 DOI: 10.1093/nar/gkae298] [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: 01/17/2024] [Revised: 04/03/2024] [Accepted: 04/05/2024] [Indexed: 04/25/2024] Open
Abstract
Absorption, distribution, metabolism, excretion and toxicity (ADMET) properties play a crucial role in drug discovery and chemical safety assessment. Built on the achievements of admetSAR and its successor, admetSAR2.0, this paper introduced the new version of the series, admetSAR3.0, as a comprehensive platform for chemical ADMET assessment, including search, prediction and optimization modules. In the search module, admetSAR3.0 hosted over 370 000 high-quality experimental ADMET data for 104 652 unique compounds, and supplemented chemical structure similarity search function to facilitate read-across. In the prediction module, we introduced comprehensive ADMET endpoints and two new sections for environmental and cosmetic risk assessments, empowering admetSAR3.0 to provide prediction for 119 endpoints, more than double numbers compared to the previous version. Furthermore, the advanced multi-task graph neural network framework offered robust and reliable support for ADMET prediction. In particular, a module named ADMETopt was added to automatically optimize the ADMET properties of query molecules through transformation rules or scaffold hopping. Finally, admetSAR3.0 provides user-friendly interfaces for multiple types of input data, such as SMILES string, chemical structure and batch molecule file, and supports various output types, including digital, chart displays and file downloads. In summary, admetSAR3.0 is anticipated to be a valuable and powerful tool in drug discovery and chemical safety assessment at http://lmmd.ecust.edu.cn/admetsar3/.
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Affiliation(s)
- Yaxin Gu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Zhuohang Yu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Yimeng Wang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Long Chen
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Chaofeng Lou
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Chen Yang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Weihua Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Yun Tang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
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8
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Swanson K, Walther P, Leitz J, Mukherjee S, Wu JC, Shivnaraine RV, Zou J. ADMET-AI: a machine learning ADMET platform for evaluation of large-scale chemical libraries. Bioinformatics 2024; 40:btae416. [PMID: 38913862 PMCID: PMC11226862 DOI: 10.1093/bioinformatics/btae416] [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: 12/13/2023] [Revised: 04/12/2024] [Accepted: 06/21/2024] [Indexed: 06/26/2024] Open
Abstract
MOTIVATION The emergence of large chemical repositories and combinatorial chemical spaces, coupled with high-throughput docking and generative AI, have greatly expanded the chemical diversity of small molecules for drug discovery. Selecting compounds for experimental validation requires filtering these molecules based on favourable druglike properties, such as Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET). RESULTS We developed ADMET-AI, a machine learning platform that provides fast and accurate ADMET predictions both as a website and as a Python package. ADMET-AI has the highest average rank on the TDC ADMET Leaderboard, and it is currently the fastest web-based ADMET predictor, with a 45% reduction in time compared to the next fastest public ADMET web server. ADMET-AI can also be run locally with predictions for one million molecules taking just 3.1 h. AVAILABILITY AND IMPLEMENTATION The ADMET-AI platform is freely available both as a web server at admet.ai.greenstonebio.com and as an open-source Python package for local batch prediction at github.com/swansonk14/admet_ai (also archived on Zenodo at doi.org/10.5281/zenodo.10372930). All data and models are archived on Zenodo at doi.org/10.5281/zenodo.10372418.
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Affiliation(s)
- Kyle Swanson
- Department of Computer Science, Stanford University, 353 Jane Stanford Way, Stanford, CA 94305, USA
- Greenstone Biosciences, 3160 Porter Drive, Suite 140, Palo Alto, CA 94304, USA
| | - Parker Walther
- Carleton College, One North College Street, Northfield, MN 55057, USA
| | - Jeremy Leitz
- Greenstone Biosciences, 3160 Porter Drive, Suite 140, Palo Alto, CA 94304, USA
| | - Souhrid Mukherjee
- Greenstone Biosciences, 3160 Porter Drive, Suite 140, Palo Alto, CA 94304, USA
| | - Joseph C Wu
- Stanford Cardiovascular Institute, Stanford University, 265 Campus Drive, Stanford, CA 94305, USA
| | | | - James Zou
- Department of Computer Science, Stanford University, 353 Jane Stanford Way, Stanford, CA 94305, USA
- Department of Biomedical Data Science, Stanford University, 1265 Welch Road, Stanford, CA 94305, USA
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9
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Raj AK, Lokhande KB, Khunteta K, Sarode SC, Sharma NK. Elevated N1-Acetylspermidine Levels in Doxorubicin-treated MCF-7 Cancer Cells: Histone Deacetylase 10 Inhibition with an N1-Acetylspermidine Mimetic. J Cancer Prev 2024; 29:32-44. [PMID: 38957589 PMCID: PMC11215339 DOI: 10.15430/jcp.24.002] [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: 03/31/2024] [Revised: 05/04/2024] [Accepted: 05/18/2024] [Indexed: 07/04/2024] Open
Abstract
Cancer drug resistance is associated with metabolic adaptation. Cancer cells have been shown to implicate acetylated polyamines in adaptations during cell death. However, exploring the mimetic of acetylated polyamines as a potential anticancer drug is lacking. We performed intracellular metabolite profiling of human breast cancer MCF-7 cells treated with doxorubicin (DOX), a well known anticancer drug. A novel and in-house vertical tube gel electrophoresis assisted procedure followed by LC-HRMS analysis was employed to detect acetylated polyamines such as N1-acetylspermidine. We designed a mimetic N1-acetylspermidine (MINAS) which is a known substrate of histone deacetylase 10 (HDAC10). Molecular docking and molecular dynamics (MDs) simulations were used to evaluate the inhibitory potential of MINAS against HDAC10. The inhibitory potential and the ADMET profile of MINAS were compared to a known HDAC10 inhibitor Tubastatin A. N1-acetylspermidine, an acetylated form of polyamine, was detected intracellularly in MCF-7 cells treated with DOX over DMSO-treated MCF-7 cells. We designed and curated MINAS (PubChem CID 162679241). Molecular docking and MD simulations suggested the strong and comparable inhibitory potential of MINAS (-8.2 kcal/mol) to Tubastatin A (-8.4 kcal/mol). MINAS and Tubastatin A share similar binding sites on HDAC10, including Ser138, Ser140, Tyr183, and Cys184. Additionally, MINAS has a better ADMET profile compared to Tubastatin A, with a high MRTD value and lower toxicity. In conclusion, the data show that N1-acetylspermidine levels rise during DOX-induced breast cancer cell death. Additionally, MINAS, an N1-acetylspermidine mimetic compound, could be investigated as a potential anticancer drug when combined with chemotherapy like DOX.
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Affiliation(s)
- Ajay Kumar Raj
- Cancer and Translational Research Lab, Dr. D.Y. Patil Biotechnology & Bioinformatics Institute, Dr. D.Y. Patil Vidyapeeth, India
| | - Kiran Bharat Lokhande
- Bioinformatics Research Laboratory, Dr. D. Y. Patil Biotechnology and Bioinformatics Institute, Dr. D. Y. Patil Vidyapeeth, India
| | - Kratika Khunteta
- Cancer and Translational Research Lab, Dr. D.Y. Patil Biotechnology & Bioinformatics Institute, Dr. D.Y. Patil Vidyapeeth, India
| | - Sachin Chakradhar Sarode
- Department of Oral Pathology and Microbiology, Dr. D. Y. Patil Dental College and Hospital, Dr. D.Y. Patil Vidyapeeth, Pune, India
| | - Nilesh Kumar Sharma
- Cancer and Translational Research Lab, Dr. D.Y. Patil Biotechnology & Bioinformatics Institute, Dr. D.Y. Patil Vidyapeeth, India
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10
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De Carlo A, Ronchi D, Piastra M, Tosca EM, Magni P. Predicting ADMET Properties from Molecule SMILE: A Bottom-Up Approach Using Attention-Based Graph Neural Networks. Pharmaceutics 2024; 16:776. [PMID: 38931898 PMCID: PMC11207804 DOI: 10.3390/pharmaceutics16060776] [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: 01/03/2024] [Revised: 05/08/2024] [Accepted: 05/30/2024] [Indexed: 06/28/2024] Open
Abstract
Understanding the pharmacokinetics, safety and efficacy of candidate drugs is crucial for their success. One key aspect is the characterization of absorption, distribution, metabolism, excretion and toxicity (ADMET) properties, which require early assessment in the drug discovery and development process. This study aims to present an innovative approach for predicting ADMET properties using attention-based graph neural networks (GNNs). The model utilizes a graph-based representation of molecules directly derived from Simplified Molecular Input Line Entry System (SMILE) notation. Information is processed sequentially, from substructures to the whole molecule, employing a bottom-up approach. The developed GNN is tested and compared with existing approaches using six benchmark datasets and by encompassing regression (lipophilicity and aqueous solubility) and classification (CYP2C9, CYP2C19, CYP2D6 and CYP3A4 inhibition) tasks. Results show the effectiveness of our model, which bypasses the computationally expensive retrieval and selection of molecular descriptors. This approach provides a valuable tool for high-throughput screening, facilitating early assessment of ADMET properties and enhancing the likelihood of drug success in the development pipeline.
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Affiliation(s)
| | | | | | | | - Paolo Magni
- Dipartimento di Ingegneria Industriale e dell’Informazione, Università degli Studi di Pavia, 27100 Pavia, Italy; (A.D.C.); (D.R.); (M.P.); (E.M.T.)
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11
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Majhi P, Sayyad S, Gaur M, Kedar G, Rathod S, Sahu R, Pradhan PK, Tripathy S, Ghosh G, Subudhi BB. Tinospora cordifolia Extract Enhances Dextromethorphan Bioavailability: Implications for Alzheimer's Disease. ACS OMEGA 2024; 9:23634-23648. [PMID: 38854540 PMCID: PMC11154920 DOI: 10.1021/acsomega.4c01219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 04/20/2024] [Accepted: 04/23/2024] [Indexed: 06/11/2024]
Abstract
Tinospora cordifolia (Willd.) Miers (Menispermaceae) is a traditional rejuvenator and a conventional medicine used to manage oxidative stress-related diseases, including those associated with the central nervous system. Decreased dextromethorphan (DEM) metabolism is necessary for high bioavailability and application against Alzheimer's disease (AD). Since T. cordifolia stem extract (TCE) can potentially inhibit several metabolic enzymes, it can also enhance dextromethorphan bioavailability. This study investigates the potential of TCE to improve DEM's bioavailability and efficacy for the management of AD. In silico analysis was carried out to find the inhibition potential of phytocomponents of T. cordifolia for CYP2D6 and CYP3A4. The LC-MS method was revalidated for the analysis of DEM and metabolite dextrorphan (DEX) in the presence of quinidine (QN). The ratio of DEM to DEX was estimated with varying doses of TCE following pharmacokinetic analysis. Network pharmacology analysis was carried out to understand the complementary potential of phytocomponents. This was further validated in the scopolamine-induced dementia model through behavioral and histopathological analyses. TCE (100 mg/kg) for 14 days increased the DEM to DEX ratio by 2.8-fold compared to QN treatment. While T max was comparable to that of QN treatment at this dose (100 mg/kg) of TCE, it increased significantly at the higher dose (400 mg/kg) of TCE pretreatment. All other pharmacokinetic parameters were also enhanced at this dose with a 4.7-fold increase in DEM/DEX compared with QN. Network pharmacology analysis indicated the ability of TCE to target multiple factors associated with AD. Furthermore, it improved spatial memory and reduced hyperactivity in rodents better than the combination of QN and DEM.
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Affiliation(s)
- Praful
Kumar Majhi
- Drug
Development and Analysis Laboratory, School of Pharmaceutical Sciences, Siksha ‘O’ Anusandhan (Deemed to be
University), Bhubaneswar, Odisha 751029, India
| | - Samir Sayyad
- Vitely
Bio LLP, Ahmedabad , Gujarat 380054, India
| | - Mahendra Gaur
- Drug
Development and Analysis Laboratory, School of Pharmaceutical Sciences, Siksha ‘O’ Anusandhan (Deemed to be
University), Bhubaneswar, Odisha 751029, India
| | | | | | - Rajanikant Sahu
- Drug
Development and Analysis Laboratory, School of Pharmaceutical Sciences, Siksha ‘O’ Anusandhan (Deemed to be
University), Bhubaneswar, Odisha 751029, India
| | | | - Shyamalendu Tripathy
- Drug
Development and Analysis Laboratory, School of Pharmaceutical Sciences, Siksha ‘O’ Anusandhan (Deemed to be
University), Bhubaneswar, Odisha 751029, India
| | - Goutam Ghosh
- Department
of Pharmaceutics, School of Pharmaceutical Sciences, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha 751029, India
| | - Bharat Bhusan Subudhi
- Drug
Development and Analysis Laboratory, School of Pharmaceutical Sciences, Siksha ‘O’ Anusandhan (Deemed to be
University), Bhubaneswar, Odisha 751029, India
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12
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Munir R, Zaib S, Zia-ur-Rehman M, Javed H, Roohi A, Zaheer M, Fatima N, Bhat MA, Khan I. Exploration of morpholine-thiophene hybrid thiosemicarbazones for the treatment of ureolytic bacterial infections via targeting urease enzyme: Synthesis, biochemical screening and computational analysis. Front Chem 2024; 12:1403127. [PMID: 38855062 PMCID: PMC11157103 DOI: 10.3389/fchem.2024.1403127] [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: 03/18/2024] [Accepted: 05/06/2024] [Indexed: 06/11/2024] Open
Abstract
An important component of the pathogenicity of potentially pathogenic bacteria in humans is the urease enzyme. In order to avoid the detrimental impact of ureolytic bacterial infections, the inhibition of urease enzyme appears to be an appealing approach. Therefore, in the current study, morpholine-thiophene hybrid thiosemicarbazone derivatives (5a-i) were designed, synthesized and characterized through FTIR, 1H NMR, 13C NMR spectroscopy and mass spectrometry. A range of substituents including electron-rich, electron-deficient and inductively electron-withdrawing groups on the thiophene ring was successfully tolerated. The synthesized derivatives were evaluated in vitro for their potential to inhibit urease enzyme using the indophenol method. The majority of compounds were noticeably more potent than the conventional inhibitor, thiourea. The lead inhibitor, 2-(1-(5-chlorothiophen-2-yl)ethylidene)-N-(2-morpholinoethyl)hydrazinecarbothioamide (5g) inhibited the urease in an uncompetitive manner with an IC50 value of 3.80 ± 1.9 µM. The findings of the docking studies demonstrated that compound 5g has a strong affinity for the urease active site. Significant docking scores and efficient binding free energies were displayed by the lead inhibitor. Finally, the ADME properties of lead inhibitor (5g) suggested the druglikeness behavior with zero violation.
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Affiliation(s)
- Rubina Munir
- Department of Chemistry, Kinnaird College for Women, Lahore, Pakistan
| | - Sumera Zaib
- Department of Basic and Applied Chemistry, Faculty of Science and Technology, University of Central Punjab, Lahore, Pakistan
| | | | - Hira Javed
- Department of Basic and Applied Chemistry, Faculty of Science and Technology, University of Central Punjab, Lahore, Pakistan
| | - Ayesha Roohi
- Department of Chemistry, Kinnaird College for Women, Lahore, Pakistan
| | - Muhammad Zaheer
- Applied Chemistry Research Centre, PCSIR Laboratories Complex, Lahore, Pakistan
| | - Nabiha Fatima
- Department of Chemistry, Kinnaird College for Women, Lahore, Pakistan
| | - Mashooq Ahmad Bhat
- Department of Pharmaceutical Chemistry, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - Imtiaz Khan
- Department of Chemistry and Manchester Institute of Biotechnology, The University of Manchester, Manchester, United Kingdom
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13
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Long TZ, Jiang DJ, Shi SH, Deng YC, Wang WX, Cao DS. Enhancing Multi-species Liver Microsomal Stability Prediction through Artificial Intelligence. J Chem Inf Model 2024; 64:3222-3236. [PMID: 38498003 DOI: 10.1021/acs.jcim.4c00159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Liver microsomal stability, a crucial aspect of metabolic stability, significantly impacts practical drug discovery. However, current models for predicting liver microsomal stability are based on limited molecular information from a single species. To address this limitation, we constructed the largest public database of compounds from three common species: human, rat, and mouse. Subsequently, we developed a series of classification models using both traditional descriptor-based and classic graph-based machine learning (ML) algorithms. Remarkably, the best-performing models for the three species achieved Matthews correlation coefficients (MCCs) of 0.616, 0.603, and 0.574, respectively, on the test set. Furthermore, through the construction of consensus models based on these individual models, we have demonstrated their superior predictive performance in comparison with the existing models of the same type. To explore the similarities and differences in the properties of liver microsomal stability among multispecies molecules, we conducted preliminary interpretative explorations using the Shapley additive explanations (SHAP) and atom heatmap approaches for the models and misclassified molecules. Additionally, we further investigated representative structural modifications and substructures that decrease the liver microsomal stability in different species using the matched molecule pair analysis (MMPA) method and substructure extraction techniques. The established prediction models, along with insightful interpretation information regarding liver microsomal stability, will significantly contribute to enhancing the efficiency of exploring practical drugs for development.
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Affiliation(s)
- Teng-Zhi Long
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - De-Jun Jiang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China
| | - Shao-Hua Shi
- Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Kowloon, Hong Kong SAR 999077, P. R. China
| | - You-Chao Deng
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - Wen-Xuan Wang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - Dong-Sheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
- Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Kowloon, Hong Kong SAR 999077, P. R. China
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, Hunan, P. R. China
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14
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Sari S, Yurtoğlu S, Zengin M, Marcinkowska M, Siwek A, Saraç S. Azoles display promising anticonvulsant effects through possible PPAR-α activation. Neurosci Lett 2024; 828:137750. [PMID: 38548219 DOI: 10.1016/j.neulet.2024.137750] [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: 02/28/2024] [Revised: 03/18/2024] [Accepted: 03/25/2024] [Indexed: 04/19/2024]
Abstract
Azoles such as nafimidone, denzimol and loreclezole are known for their clinical efficacy against epilepsy, and loreclezole acts by potentiating γ-aminobutyric acid (GABA)-ergic currents. In the current study, we report a series of azole derivatives in alcohol ester and oxime ester structure showing promising anticonvulsant effects in 6 Hz and maximal electro shock (MES) models with minimal toxicity. The most promising of the series, 5f, was active in both 6 Hz and MES tests with a median effective dose (ED50) of 118.92 mg/kg in 6 Hz test and a median toxic dose (TD50) twice as high in mice. The compounds were predicted druglike and blood-brain barrier (BBB) penetrant in silico. Contrary to what was expected, the compounds showed no in vitro affinity to GABAA receptors (GABAARs) in radioligand binding assays; however, they were found structurally similar to peroxisome proliferator-activated receptors alpha (PPAR-α) agonists and predicted to show high affinity and agonist-like binding to PPAR-α in molecular docking studies. As a result, 5f emerged as a safe azole anticonvulsant with a wide therapeutic window and possible action through PPAR-α activation.
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Affiliation(s)
- Suat Sari
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Hacettepe University, Ankara, Turkey.
| | - Sibel Yurtoğlu
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Hacettepe University, Ankara, Turkey
| | - Merve Zengin
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Hacettepe University, Ankara, Turkey
| | - Monika Marcinkowska
- Department of Medicinal Chemistry, Faculty of Pharmacy, Jagiellonian University Medical College, Krakow, Poland
| | - Agata Siwek
- Department of Medicinal Chemistry, Faculty of Pharmacy, Jagiellonian University Medical College, Krakow, Poland
| | - Selma Saraç
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Hacettepe University, Ankara, Turkey; Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Baskent University, Ankara, Turkey
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15
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Chivukula N, Ramesh K, Subbaroyan A, Sahoo AK, Dhanakoti GB, Ravichandran J, Samal A. ViCEKb: Vitiligo-linked Chemical Exposome Knowledgebase. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 913:169711. [PMID: 38160837 DOI: 10.1016/j.scitotenv.2023.169711] [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: 10/18/2023] [Revised: 12/23/2023] [Accepted: 12/25/2023] [Indexed: 01/03/2024]
Abstract
Vitiligo is a complex disease wherein the environmental factors, in conjunction with the underlying genetic predispositions, trigger the autoimmune destruction of melanocytes, ultimately leading to depigmented patches on the skin. While genetic factors have been extensively studied, the knowledge on environmental triggers remains sparse and less understood. To address this knowledge gap, we present the first comprehensive knowledgebase of vitiligo-triggering chemicals namely, Vitiligo-linked Chemical Exposome Knowledgebase (ViCEKb). ViCEKb involves an extensive and systematic manual effort in curation of published literature and subsequent compilation of 113 unique chemical triggers of vitiligo. ViCEKb standardizes various chemical information, and categorizes the chemicals based on their evidences and sources of exposure. Importantly, ViCEKb contains a wide range of metrics necessary for different toxicological evaluations. Notably, we observed that ViCEKb chemicals are present in a variety of consumer products. For instance, Propyl gallate is present as a fragrance substance in various household products, and Flutamide is used in medication to treat prostate cancer. These two chemicals have the highest level of evidence in ViCEKb, but are not regulated for their skin sensitizing effects. Furthermore, an extensive cheminformatics-based investigation revealed that ViCEKb chemical space is structurally diverse and comprises unique chemical scaffolds in comparison with skin specific regulatory lists. For example, Neomycin and 2,3,5-Triglycidyl-4-aminophenol have unique chemical scaffolds and the highest level of evidence in ViCEKb, but are not regulated for their skin sensitizing effects. Finally, a transcriptomics-based analysis of ViCEKb chemical perturbations in skin cell samples highlighted the commonality in their linked biological processes. Overall, we present the first comprehensive effort in compilation and exploration of various chemical triggers of vitiligo. We believe such a resource will enable in deciphering the complex etiology of vitiligo and aid in the characterization of human chemical exposome. ViCEKb is freely available for academic research at: https://cb.imsc.res.in/vicekb.
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Affiliation(s)
- Nikhil Chivukula
- The Institute of Mathematical Sciences (IMSc), Chennai, India; Homi Bhabha National Institute (HBNI), Mumbai, India
| | | | - Ajay Subbaroyan
- The Institute of Mathematical Sciences (IMSc), Chennai, India; Homi Bhabha National Institute (HBNI), Mumbai, India
| | - Ajaya Kumar Sahoo
- The Institute of Mathematical Sciences (IMSc), Chennai, India; Homi Bhabha National Institute (HBNI), Mumbai, India
| | | | - Janani Ravichandran
- The Institute of Mathematical Sciences (IMSc), Chennai, India; Homi Bhabha National Institute (HBNI), Mumbai, India
| | - Areejit Samal
- The Institute of Mathematical Sciences (IMSc), Chennai, India; Homi Bhabha National Institute (HBNI), Mumbai, India.
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16
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Qi X, Zhao Y, Qi Z, Hou S, Chen J. Machine Learning Empowering Drug Discovery: Applications, Opportunities and Challenges. Molecules 2024; 29:903. [PMID: 38398653 PMCID: PMC10892089 DOI: 10.3390/molecules29040903] [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: 01/15/2024] [Revised: 02/08/2024] [Accepted: 02/14/2024] [Indexed: 02/25/2024] Open
Abstract
Drug discovery plays a critical role in advancing human health by developing new medications and treatments to combat diseases. How to accelerate the pace and reduce the costs of new drug discovery has long been a key concern for the pharmaceutical industry. Fortunately, by leveraging advanced algorithms, computational power and biological big data, artificial intelligence (AI) technology, especially machine learning (ML), holds the promise of making the hunt for new drugs more efficient. Recently, the Transformer-based models that have achieved revolutionary breakthroughs in natural language processing have sparked a new era of their applications in drug discovery. Herein, we introduce the latest applications of ML in drug discovery, highlight the potential of advanced Transformer-based ML models, and discuss the future prospects and challenges in the field.
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Affiliation(s)
- Xin Qi
- School of Chemistry and Life Sciences, Suzhou University of Science and Technology, Suzhou 215011, China; (Y.Z.); (S.H.); (J.C.)
| | - Yuanchun Zhao
- School of Chemistry and Life Sciences, Suzhou University of Science and Technology, Suzhou 215011, China; (Y.Z.); (S.H.); (J.C.)
| | - Zhuang Qi
- School of Software, Shandong University, Jinan 250101, China;
| | - Siyu Hou
- School of Chemistry and Life Sciences, Suzhou University of Science and Technology, Suzhou 215011, China; (Y.Z.); (S.H.); (J.C.)
| | - Jiajia Chen
- School of Chemistry and Life Sciences, Suzhou University of Science and Technology, Suzhou 215011, China; (Y.Z.); (S.H.); (J.C.)
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17
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Nandangiri R, T N S, Raj AK, Lokhande KB, Khunteta K, Hebale A, Kothari H, Patel V, Sarode SC, Sharma NK. Secretion of Sphinganine by Drug-Induced Cancer Cells and Modified Mimetic Sphinganine (MMS) as c-Src Kinase Inhibitor. Asian Pac J Cancer Prev 2024; 25:433-446. [PMID: 38415528 PMCID: PMC11077104 DOI: 10.31557/apjcp.2024.25.2.433] [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: 05/20/2023] [Accepted: 02/18/2024] [Indexed: 02/29/2024] Open
Abstract
BACKGROUND Cancer cells exhibit selective metabolic reprogramming to promote proliferation, invasiveness, and metastasis. Sphingolipids such as sphingosine and sphinganine have been reported to modulate cell death processes in cancer cells. However, the potential of extracellular sphinganine and its mimetic compounds as inducers of cancer cell death has not been thoroughly investigated. METHODS We obtained extracellular conditioned medium from HCT-116 cells treated with the previously reported anticancer composition, goat urine DMSO fraction (GUDF). The extracellular metabolites were purified using a novel and in-house developed vertical tube gel electrophoresis (VTGE) technique and identified through LC-HRMS. Extracellular metabolites such as sphinganine, sphingosine, C16 sphinganine, and phytosphingosine were screened for their inhibitory role against intracellular kinases using molecular docking. Molecular dynamics (MD) simulations were performed to study the inhibitory potential of a novel designed modified mimetic sphinganine (MMS) (Pubchem CID: 162625115) upon c-Src kinase. Furthermore, inhibitory potential and ADME profile of MMS was compared with luteolin, a known c-Src kinase inhibitor. RESULTS Data showed accumulation of sphinganine and other sphingolipids such as C16 sphinganine, phytosphingosine, and ceramide (d18:1/14:0) in the extracellular compartment of GUDF-treated HCT-116 cells. Molecular docking projected c-Src kinase as an inhibitory target of sphinganine. MD simulations projected MMS with strong (-7.1 kcal/mol) and specific (MET341, ASP404) binding to the inhibitory pocket of c-Src kinase. The projected MMS showed comparable inhibitory role and acceptable ADME profile over known inhibitors. CONCLUSION In summary, our findings highlight the significance of extracellular sphinganine and other sphingolipids, including C16 sphinganine, phytosphingosine, and ceramide (d18:1/14:0), in the context of drug-induced cell death in HCT-116 cancer cells. Furthermore, we demonstrated the importance of extracellular sphinganine and its modified mimetic sphinganine (MMS) as a potential inhibitor of c-Src kinase. These findings suggest that MMS holds promise for future applications in targeted and combinatorial anticancer therapy.
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Affiliation(s)
- Rasika Nandangiri
- Cancer and Translational Research Lab, Dr. D.Y. Patil Biotechnology & Bioinformatics Institute, Dr. D.Y. Patil Vidyapeeth, Pimpri, Pune, Maharashtra, India.
| | - Seethamma T N
- Cancer and Translational Research Lab, Dr. D.Y. Patil Biotechnology & Bioinformatics Institute, Dr. D.Y. Patil Vidyapeeth, Pimpri, Pune, Maharashtra, India.
| | - Ajay Kumar Raj
- Cancer and Translational Research Lab, Dr. D.Y. Patil Biotechnology & Bioinformatics Institute, Dr. D.Y. Patil Vidyapeeth, Pimpri, Pune, Maharashtra, India.
| | - Kiran B. Lokhande
- Bioinformatics Research Laboratory, Dr. D. Y. Patil Biotechnology and Bioinformatics Institute, Dr. D. Y. Patil Vidyapeeth, Pimpri, Pune, India.
| | - Kratika Khunteta
- Cancer and Translational Research Lab, Dr. D.Y. Patil Biotechnology & Bioinformatics Institute, Dr. D.Y. Patil Vidyapeeth, Pimpri, Pune, Maharashtra, India.
| | - Ameya Hebale
- Cancer and Translational Research Lab, Dr. D.Y. Patil Biotechnology & Bioinformatics Institute, Dr. D.Y. Patil Vidyapeeth, Pimpri, Pune, Maharashtra, India.
| | - Haet Kothari
- Cancer and Translational Research Lab, Dr. D.Y. Patil Biotechnology & Bioinformatics Institute, Dr. D.Y. Patil Vidyapeeth, Pimpri, Pune, Maharashtra, India.
| | - Vaidehi Patel
- Cancer and Translational Research Lab, Dr. D.Y. Patil Biotechnology & Bioinformatics Institute, Dr. D.Y. Patil Vidyapeeth, Pimpri, Pune, Maharashtra, India.
| | - Sachin C Sarode
- Department of Oral Pathology and Microbiology, Dr. D. Y. Patil Dental College and Hospital, Dr. D.Y. Patil Vidyapeeth, Pimpri, Pune, Maharashtra, India.
| | - Nilesh Kumar Sharma
- Cancer and Translational Research Lab, Dr. D.Y. Patil Biotechnology & Bioinformatics Institute, Dr. D.Y. Patil Vidyapeeth, Pimpri, Pune, Maharashtra, India.
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18
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Arora S, Satija S, Mittal A, Solanki S, Mohanty SK, Srivastava V, Sengupta D, Rout D, Arul Murugan N, Borkar RM, Ahuja G. Unlocking The Mysteries of DNA Adducts with Artificial Intelligence. Chembiochem 2024; 25:e202300577. [PMID: 37874183 DOI: 10.1002/cbic.202300577] [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: 08/16/2023] [Revised: 10/18/2023] [Accepted: 10/23/2023] [Indexed: 10/25/2023]
Abstract
Cellular genome is considered a dynamic blueprint of a cell since it encodes genetic information that gets temporally altered due to various endogenous and exogenous insults. Largely, the extent of genomic dynamicity is controlled by the trade-off between DNA repair processes and the genotoxic potential of the causative agent (genotoxins or potential carcinogens). A subset of genotoxins form DNA adducts by covalently binding to the cellular DNA, triggering structural or functional changes that lead to significant alterations in cellular processes via genetic (e. g., mutations) or non-genetic (e. g., epigenome) routes. Identification, quantification, and characterization of DNA adducts are indispensable for their comprehensive understanding and could expedite the ongoing efforts in predicting carcinogenicity and their mode of action. In this review, we elaborate on using Artificial Intelligence (AI)-based modeling in adducts biology and present multiple computational strategies to gain advancements in decoding DNA adducts. The proposed AI-based strategies encompass predictive modeling for adduct formation via metabolic activation, novel adducts' identification, prediction of biochemical routes for adduct formation, adducts' half-life predictions within biological ecosystems, and, establishing methods to predict the link between adducts chemistry and its location within the genomic DNA. In summary, we discuss some futuristic AI-based approaches in DNA adduct biology.
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Affiliation(s)
- Sakshi Arora
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
| | - Shiva Satija
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
| | - Aayushi Mittal
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
| | - Saveena Solanki
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
| | - Sanjay Kumar Mohanty
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
| | - Vaibhav Srivastava
- Division of Glycoscience, Department of Chemistry CBH School, Royal Institute of Technology (KTH) AlbaNova University Center, 10691, Stockholm, Sweden
| | - Debarka Sengupta
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
| | - Diptiranjan Rout
- Department of Transfusion Medicine National Cancer Institute, AIIMS, New Delhi, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, 110608, India
| | - Natarajan Arul Murugan
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
| | - Roshan M Borkar
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER)-Guwahati, Sila Katamur Halugurisuk P.O.: Changsari, Dist, Guwahati, Assam, 781101, India
| | - Gaurav Ahuja
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
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Raj AK, Lokhande KB, Prasad TK, Nandangiri R, Choudhary S, Pal JK, Sharma NK. Intracellular Ellagic Acid Derived from Goat Urine DMSO Fraction (GUDF) Predicted as an Inhibitor of c-Raf Kinase. Curr Mol Med 2024; 24:264-279. [PMID: 36642883 DOI: 10.2174/1566524023666230113141032] [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: 07/02/2022] [Revised: 11/12/2022] [Accepted: 11/22/2022] [Indexed: 01/17/2023]
Abstract
BACKGROUND Dietary chemicals and their gut-metabolized products are explored for their anti-proliferative and pro-cell death effects. Dietary and metabolized chemicals are different from ruminants such as goats over humans. METHODS Loss of cell viability and induction of death due to goat urine DMSO fraction (GUDF) derived chemicals were assessed by routine in vitro assays upon MCF-7 breast cancer cells. Intracellular metabolite profiling of MCF-7 cells treated with goat urine DMSO fraction (GUDF) was performed using an in-house designed vertical tube gel electrophoresis (VTGE) assisted methodology, followed by LC-HRMS. Next, identified intracellular dietary chemicals such as ellagic acid were evaluated for their inhibitory effects against transducers of the c-Raf signaling pathway employing molecular docking and molecular dynamics (MD) simulation. RESULTS GUDF treatment upon MCF-7 cells displayed significant loss of cell viability and induction of cell death. A set of dietary and metabolized chemicals in the intracellular compartment of MCF-7 cells, such as ellagic acid, 2-hydroxymyristic acid, artelinic acid, 10-amino-decanoic acid, nervonic acid, 2,4-dimethyl-2-eicosenoic acid, 2,3,4'- Trihydroxy,4-Methoxybenzophenone and 9-amino-nonanoic acid were identified. Among intracellular dietary chemicals, ellagic acid displayed a strong inhibitory affinity (-8.7 kcal/mol) against c-Raf kinase. The inhibitory potential of ellagic acid was found to be significantly comparable with a known c-Raf kinase inhibitor sorafenib with overlapping inhibitory site residues (ARG450, GLU425, TRP423, VA403). CONCLUSION Intracellular dietary-derived chemicals such as ellagic acid are suggested for the induction of cell death in MCF-7 cells. Ellagic acid is predicted as an inhibitor of c-Raf kinase and could be explored as an anti-cancer drug.
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Affiliation(s)
- Ajay Kumar Raj
- Cancer and Translational Research Lab, Dr. D.Y. Patil Biotechnology & Bioinformatics Institute, Dr. D.Y. Patil Vidyapeeth, Pimpri, Pune, Maharashtra, 411033, India
| | - Kiran Bharat Lokhande
- Bioinformatics Research Laboratory, Dr. D. Y. Patil Biotechnology and Bioinformatics Institute, Dr. D. Y. Patil Vidyapeeth, Pimpri, Pune, Maharashtra, 411033, India
| | - Tanay Kondapally Prasad
- Cancer and Translational Research Lab, Dr. D.Y. Patil Biotechnology & Bioinformatics Institute, Dr. D.Y. Patil Vidyapeeth, Pimpri, Pune, Maharashtra, 411033, India
| | - Rasika Nandangiri
- Cancer and Translational Research Lab, Dr. D.Y. Patil Biotechnology & Bioinformatics Institute, Dr. D.Y. Patil Vidyapeeth, Pimpri, Pune, Maharashtra, 411033, India
| | - Sumitra Choudhary
- Cancer and Translational Research Lab, Dr. D.Y. Patil Biotechnology & Bioinformatics Institute, Dr. D.Y. Patil Vidyapeeth, Pimpri, Pune, Maharashtra, 411033, India
| | - Jayanta Kumar Pal
- Cancer and Translational Research Lab, Dr. D.Y. Patil Biotechnology & Bioinformatics Institute, Dr. D.Y. Patil Vidyapeeth, Pimpri, Pune, Maharashtra, 411033, India
| | - Nilesh Kumar Sharma
- Cancer and Translational Research Lab, Dr. D.Y. Patil Biotechnology & Bioinformatics Institute, Dr. D.Y. Patil Vidyapeeth, Pimpri, Pune, Maharashtra, 411033, India
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20
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Singh S, Singh PK, Sachan K, Kumar M, Bhardwaj P. Automation of Drug Discovery through Cutting-edge In-silico Research in Pharmaceuticals: Challenges and Future Scope. Curr Comput Aided Drug Des 2024; 20:723-735. [PMID: 37807412 DOI: 10.2174/0115734099260187230921073932] [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: 04/30/2023] [Revised: 08/05/2023] [Accepted: 08/18/2023] [Indexed: 10/10/2023]
Abstract
The rapidity and high-throughput nature of in silico technologies make them advantageous for predicting the properties of a large array of substances. In silico approaches can be used for compounds intended for synthesis at the beginning of drug development when there is either no or very little compound available. In silico approaches can be used for impurities or degradation products. Quantifying drugs and related substances (RS) with pharmaceutical drug analysis (PDA) can also improve drug discovery (DD) by providing additional avenues to pursue. Potential future applications of PDA include combining it with other methods to make insilico predictions about drugs and RS. One possible outcome of this is a determination of the drug potential of nontoxic RS. ADME estimation, QSAR research, molecular docking, bioactivity prediction, and toxicity testing all involve impurity profiling. Before committing to DD, RS with minimal toxicity can be utilised in silico. The efficacy of molecular docking in getting a medication to market is still debated despite its refinement and improvement. Biomedical labs and pharmaceutical companies were hesitant to adopt molecular docking algorithms for drug screening despite their decades of development and improvement. Despite the widespread use of "force fields" to represent the energy exerted within and between molecules, it has been impossible to reliably predict or compute the binding affinities between proteins and potential binding medications.
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Affiliation(s)
- Smita Singh
- Department of Pharmaceutics, SRM Modinagar College of Pharmacy, SRM Institute of Science and Technology, Delhi NCR Campus, Modinagar, Ghaziabad, India
| | - Pranjal Kumar Singh
- Department of Pharmaceutics, SRM Modinagar College of Pharmacy, SRM Institute of Science and Technology, Delhi NCR Campus, Modinagar, Ghaziabad, India
| | - Kapil Sachan
- KIET School of Pharmacy, KIET Group of Institutions, Ghaziabad, India
| | - Mukesh Kumar
- IIMT College of Medical Sciences, IIMT University, Ganga Nagar, Meerut, India
| | - Poonam Bhardwaj
- NKBR College of Pharmacy and Research Center, Phaphunda, Meerut, India
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21
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Adhikary T, Basak P. Optimizing the Extraction of Polyphenols from the Bark of Terminalia arjuna and an In-silico Investigation on its Activity in Colorectal Cancer. Curr Comput Aided Drug Des 2024; 20:653-665. [PMID: 37850546 DOI: 10.2174/0115734099264119230925054833] [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: 06/09/2023] [Revised: 08/09/2023] [Accepted: 08/18/2023] [Indexed: 10/19/2023]
Abstract
BACKGROUND The interconnection between different fields of research has gained interest due to its cutting-edge perspectives in solving scientific problems. Terminalia arjuna is indigenously used in India for curing several diseases, and its pharmacological activities are being revisited in recent drug-repurposing research. OBJECTIVES Efficient ultrasound-assisted extraction of phytochemicals from the bark of Terminalia arjuna is highlighted in this study. Following the optimization of the extraction process, the crude hydroethanolic extract is subjected to phytochemical profiling and an in-silico investigation of its anti-cancer properties. MATERIALS AND METHODS A three-level four-factor Box-Behnken design is exploited to optimize four operational parameters, namely extraction time, ultrasonic power, ethanol concentration (as the extracting solvent) and solute (in g): solvent (in mL) ratio. At the optimum parametric condition, the crude extract is obtained, and its GC-MS analysis is carried out. An analysis of network pharmacology (by constructing and visualizing biological networks using Cytoscape) combined with molecular docking reveals the potential antineoplastic targets of the crude extract. RESULTS The ANOVA table exhibits the significance, adequacy and reliability of the proposed second-order polynomial model with the R² value of 0.917 and adjusted R² of 0.865. Experimental results portray the significant antioxidant potential of the prepared extract in its crude form. The GC-MS analysis of the crude extract predicts the extracted phytochemicals, while the constructed biological networks highlight its multi-targeted activity in colorectal cancer. CONCLUSION The study identifies three phytochemicals viz. luteolin, β-sitosterol and arjunic acid as potent anti-cancer agents and can be extended with in-vitro and in-vivo experiments to validate the in-silico results, thus establishing lead phytochemicals in multi-targeted colorectal cancer therapies.
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Affiliation(s)
- Tathagata Adhikary
- School of Bioscience and Engineering, Jadavpur University, Kolkata, 700032, India
| | - Piyali Basak
- School of Bioscience and Engineering, Jadavpur University, Kolkata, 700032, India
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22
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Guéniche N, Lakehal Z, Habauzit D, Bruyère A, Fardel O, Le Hégarat L, Huguet A. Combined in silico and in vitro approaches to identify P-glycoprotein-inhibiting pesticides. J Biochem Mol Toxicol 2024; 38:e23588. [PMID: 37985955 DOI: 10.1002/jbt.23588] [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: 03/29/2023] [Revised: 10/04/2023] [Accepted: 11/10/2023] [Indexed: 11/22/2023]
Abstract
The P-glycoprotein (P-gp) efflux pump plays a major role in xenobiotic detoxification. The inhibition of its activity by environmental contaminants remains however rather little characterised. The present study was designed to develop a combination of different approaches to identify P-gp inhibitors among a large number of pesticides using in silico and in vitro models. First, the prediction performance of four web tools was evaluated alone or in combination using a set of recently marketed drugs. The best combination of web tools-AdmetSAR2.0/PgpRules/pkCSM-was next used to predict P-gp activity inhibition by 762 pesticides. Among the 187 pesticides predicted to be P-gp inhibitors, 11 were tested in vitro for their ability to inhibit the efflux of reference substrates (rhodamine 123 and Hoechst 33342) in P-gp overexpressing MCF7R cells and to inhibit the efflux of the reference substrate rhodamine 123 in the Caco-2 cell monolayer. In MCF7R cell assays, ivermectin B1a, emamectin B1 benzoate, spinosad, dimethomorph and tralkoxydim inhibited P-gp activity; ivermectin B1a, emamectin B1 benzoate and spinosad were determined to be stronger inhibitors (half-maximal inhibitory concentration [IC50 ] of 3 ± 1, 5 ± 1 and 7 ± 1 µM, respectively) than dimethomorph and tralkoxydim (IC50 of 102 ± 7 and 88 ± 7 µM, respectively). Ivermectin B1a, emamectin B1 benzoate, spinosad and dimethomorph also inhibited P-gp activity in Caco-2 cell monolayer assays, with dimethomorph being a weaker P-gp inhibitor. These combined approaches could be used to identify P-gp inhibitors among food contaminants, but need to be optimised and adapted for high-throughput screening.
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Affiliation(s)
- Nelly Guéniche
- Xenobiotics and Barriers team, Research Institut for Environmental and Occupational Health (IRSET), Rennes, France
- Fougères Laboratory, Toxicology of Contaminants Unit, French Agency for Food, Environmental and Occupational Health & Safety (ANSES), Fougères Cedex, France
| | - Zeineb Lakehal
- Fougères Laboratory, Toxicology of Contaminants Unit, French Agency for Food, Environmental and Occupational Health & Safety (ANSES), Fougères Cedex, France
| | - Denis Habauzit
- Fougères Laboratory, Toxicology of Contaminants Unit, French Agency for Food, Environmental and Occupational Health & Safety (ANSES), Fougères Cedex, France
| | - Arnaud Bruyère
- Xenobiotics and Barriers team, Research Institut for Environmental and Occupational Health (IRSET), Rennes, France
| | - Olivier Fardel
- University hospital center of Rennes, Xenobiotics and Barriers team, Research Institut for Environmental and Occupational Health (IRSET), Rennes, France
| | - Ludovic Le Hégarat
- Fougères Laboratory, Toxicology of Contaminants Unit, French Agency for Food, Environmental and Occupational Health & Safety (ANSES), Fougères Cedex, France
| | - Antoine Huguet
- Fougères Laboratory, Toxicology of Contaminants Unit, French Agency for Food, Environmental and Occupational Health & Safety (ANSES), Fougères Cedex, France
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Swanson K, Walther P, Leitz J, Mukherjee S, Wu JC, Shivnaraine RV, Zou J. ADMET-AI: A machine learning ADMET platform for evaluation of large-scale chemical libraries. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.28.573531. [PMID: 38234753 PMCID: PMC10793392 DOI: 10.1101/2023.12.28.573531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Summary The emergence of large chemical repositories and combinatorial chemical spaces, coupled with high-throughput docking and generative AI, have greatly expanded the chemical diversity of small molecules for drug discovery. Selecting compounds for experimental validation requires filtering these molecules based on favourable druglike properties, such as Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET). We developed ADMET-AI, a machine learning platform that provides fast and accurate ADMET predictions both as a website and as a Python package. ADMET-AI has the highest average rank on the TDC ADMET Benchmark Group leaderboard, and it is currently the fastest web-based ADMET predictor, with a 45% reduction in time compared to the next fastest ADMET web server. ADMET-AI can also be run locally with predictions for one million molecules taking just 3.1 hours. Availability and Implementation The ADMET-AI platform is freely available both as a web server at admet.ai.greenstonebio.com and as an open-source Python package for local batch prediction at github.com/swansonk14/admet_ai (also archived on Zenodo at doi.org/10.5281/zenodo.10372930 ). All data and models are archived on Zenodo at doi.org/10.5281/zenodo.10372418 .
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González RD, Simões S, Ferreira L, Carvalho ATP. Designing Cell Delivery Peptides and SARS-CoV-2-Targeting Small Interfering RNAs: A Comprehensive Bioinformatics Study with Generative Adversarial Network-Based Peptide Design and In Vitro Assays. Mol Pharm 2023; 20:6079-6089. [PMID: 37941379 DOI: 10.1021/acs.molpharmaceut.3c00444] [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] [Indexed: 11/10/2023]
Abstract
Nucleic acid technologies with designed intracellular delivery systems are some of the most promising therapies of the future. Small interfering (si)RNAs inhibit gene expression and protein synthesis and may complement current vaccines with faster design and production. Although successful delivery remains an issue, delivery peptides may help to fill this gap. Here, we address this issue by applying bioinformatic approaches to design new putative cell delivery peptides and siRNAs for COVID-19 variants and other related viral diseases. Of the 29,880 RNA sequences analyzed, 62 were identified in silico as able to target the virus mRNA sequence, and from the 9,984 peptide sequences analyzed, 10 were selected as delivery peptides. From the latter, we further performed in vitro studies of the two best-ranked peptides and compared them with the broadly used TAT delivery peptide. One of them, seq5, displayed better internalization results with about double intensity signal compared to TAT after a 1 h incubation time in GFP-HeLa cells. This peptide has, thus, the features of a delivery peptide and could be used for cargo intracellular delivery.
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Affiliation(s)
- Ricardo D González
- CNC-UC - Center for Neuroscience and Cell Biology, University of Coimbra, 3004-504 Coimbra, Portugal
- CIBB - Center for Innovative Biomedicine and Biotechnology, University of Coimbra, 3004-504 Coimbra, Portugal
- Institute for Interdisciplinary Research, Doctoral Programme in Experimental Biology and Biomedicine (PDBEB), University of Coimbra, 3030-789 Coimbra, Portugal
| | - Susana Simões
- CNC-UC - Center for Neuroscience and Cell Biology, University of Coimbra, 3004-504 Coimbra, Portugal
- CIBB - Center for Innovative Biomedicine and Biotechnology, University of Coimbra, 3004-504 Coimbra, Portugal
| | - Lino Ferreira
- CNC-UC - Center for Neuroscience and Cell Biology, University of Coimbra, 3004-504 Coimbra, Portugal
- CIBB - Center for Innovative Biomedicine and Biotechnology, University of Coimbra, 3004-504 Coimbra, Portugal
- Faculty of Medicine, University of Coimbra, 3000-370 Coimbra, Portugal
| | - Alexandra T P Carvalho
- CNC-UC - Center for Neuroscience and Cell Biology, University of Coimbra, 3004-504 Coimbra, Portugal
- CIBB - Center for Innovative Biomedicine and Biotechnology, University of Coimbra, 3004-504 Coimbra, Portugal
- Almac Sciences, Department of Biocatalysis and Isotope Chemistry, Almac House, 20 Seagoe Industrial Estate, Craigavon, Northern Ireland BT63 5QD, United Kingdom
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25
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Matevosyan M, Harutyunyan V, Abelyan N, Khachatryan H, Tirosyan I, Gabrielyan Y, Sahakyan V, Gevorgyan S, Arakelov V, Arakelov G, Zakaryan H. Design of new chemical entities targeting both native and H275Y mutant influenza a virus by deep reinforcement learning. J Biomol Struct Dyn 2023; 41:10798-10812. [PMID: 36541127 DOI: 10.1080/07391102.2022.2158936] [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: 08/02/2022] [Accepted: 12/10/2022] [Indexed: 12/24/2022]
Abstract
Influenza virus remains a major public health challenge due to its high morbidity and mortality and seasonal surge. Although antiviral drugs against the influenza virus are widely used as a first-line defense, the virus undergoes rapid genetic changes, resulting in the emergence of drug-resistant strains. Thus, new antiviral drugs that can outwit resistant strains are of significant importance. Herein, we used deep reinforcement learning (RL) algorithm to design new chemical entities (NCEs) that are able to bind to the native and H275Y mutant (oseltamivir-resistant) neuraminidases (NAs) of influenza A virus with better binding energy than oseltamivir. We generated more than 66211 NCEs, which were prioritized based on the filtering rules, structural alerts, and synthetic accessibility. Then, 18 NCEs with better MM/PBSA scores than oseltamivir were further analyzed in molecular dynamics (MD) simulations conducted for 100 ns. The MD experiments showed that 8 NCEs formed very stable complexes with the binding pocket of both native and H275Y mutant NAs of H1N1. Furthermore, most NCEs demonstrated much better binding affinity to group 2 (N2, N3, and N9) and influenza B virus NAs than oseltamivir. Although all 8 NCEs have non-sialic acid-like structures, they showed a similar binding mode as oseltamivir, indicating that it is possible to find new scaffolds with better binding and antiviral properties than sialic acid-like inhibitors. In conclusion, we have designed potential compounds as antiviral candidates for further synthesis and testing against wild and mutant influenza virus.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Vahram Arakelov
- Denovo Sciences Inc, Yerevan, Armenia
- Institute of Molecular Biology of National Academy of Sciences, Yerevan, Armenia
| | - Grigor Arakelov
- Denovo Sciences Inc, Yerevan, Armenia
- Institute of Molecular Biology of National Academy of Sciences, Yerevan, Armenia
| | - Hovakim Zakaryan
- Denovo Sciences Inc, Yerevan, Armenia
- Institute of Molecular Biology of National Academy of Sciences, Yerevan, Armenia
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Zhao Y, Yin J, Zhang L, Zhang Y, Chen X. Drug-drug interaction prediction: databases, web servers and computational models. Brief Bioinform 2023; 25:bbad445. [PMID: 38113076 PMCID: PMC10782925 DOI: 10.1093/bib/bbad445] [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: 07/21/2023] [Revised: 10/26/2023] [Accepted: 11/14/2023] [Indexed: 12/21/2023] Open
Abstract
In clinical treatment, two or more drugs (i.e. drug combination) are simultaneously or successively used for therapy with the purpose of primarily enhancing the therapeutic efficacy or reducing drug side effects. However, inappropriate drug combination may not only fail to improve efficacy, but even lead to adverse reactions. Therefore, according to the basic principle of improving the efficacy and/or reducing adverse reactions, we should study drug-drug interactions (DDIs) comprehensively and thoroughly so as to reasonably use drug combination. In this review, we first introduced the basic conception and classification of DDIs. Further, some important publicly available databases and web servers about experimentally verified or predicted DDIs were briefly described. As an effective auxiliary tool, computational models for predicting DDIs can not only save the cost of biological experiments, but also provide relevant guidance for combination therapy to some extent. Therefore, we summarized three types of prediction models (including traditional machine learning-based models, deep learning-based models and score function-based models) proposed during recent years and discussed the advantages as well as limitations of them. Besides, we pointed out the problems that need to be solved in the future research of DDIs prediction and provided corresponding suggestions.
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Affiliation(s)
- Yan Zhao
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Jun Yin
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Li Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Yong Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Xing Chen
- School of Science, Jiangnan University, Wuxi 214122, China
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27
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Chagaleti BK, Saravanan V, Vellapandian C, Kathiravan MK. Exploring cyclin-dependent kinase inhibitors: a comprehensive study in search of CDK-6 inhibitors using a pharmacophore modelling and dynamics approach. RSC Adv 2023; 13:33770-33785. [PMID: 38019988 PMCID: PMC10655667 DOI: 10.1039/d3ra05672d] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Accepted: 10/25/2023] [Indexed: 12/01/2023] Open
Abstract
Cancer prevalence and resistance issues in cancer treatment are a significant public health concern globally. Among the existing strategies in cancer therapy, targeting cyclin-dependent kinases (CDKs), especially CDK-6 is found to be one of the most promising targets, as this enzyme plays a pivotal role in cell cycle stages and cell proliferation. Cell proliferation is the characteristic feature of cancer giving rise to solid tumours. Our research focuses on creating novel compounds, specifically, pyrazolopyrimidine fused azetidinones, using a groundbreaking molecular hybridization approach to target CDK-6. Through computational investigations, ligand-based pharmacophore modelling, pharmacokinetic studies (ADMET), molecular docking, and dynamics simulations, we identified 18 promising compounds. The pharmacophore model featured one aromatic hydrophobic centre (F1: Aro/Hyd) and two H-bond acceptors (F2 and F3: Acc). Molecular docking results showed favourable binding energies (-6.5 to -8.0 kcal mol-1) and effective hydrogen bonds and hydrophobic interactions. The designed compounds demonstrated good ADMET profiles. Specifically, B6 and B18 showed low energy conformation (-7.8 kcal and -7.6 kcal), providing insights into target inhibition compared to the standard drug Palbociclib. Extensive molecular dynamics simulations confirmed the stability of these derivatives. Throughout the 100 ns simulation, the ligand-protein complexes maintained structural stability, with acceptable RMSD values. These compounds hold promise as potential leads in cancer therapy.
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Affiliation(s)
- Bharath Kumar Chagaleti
- Department of Pharmaceutical Chemistry, SRM College of Pharmacy, SRM Institute of Science and Technology Kattankulathur-603203 India
| | - Venkatesan Saravanan
- Department of Pharmaceutical Chemistry, SRM College of Pharmacy, SRM Institute of Science and Technology Kattankulathur-603203 India
| | - Chitra Vellapandian
- Department of Pharmacology, SRM College of Pharmacy SRMIST, Kattankulathur Chennai Tamil Nadu - 603 203 India
| | - Muthu K Kathiravan
- Department of Pharmaceutical Chemistry, SRM College of Pharmacy, SRM Institute of Science and Technology Kattankulathur-603203 India
- Dr A. P. J. Abdul Kalam Research Lab, Department of Pharmaceutical Chemistry, SRM College of Pharmacy SRMIST, Kattankulathur Chennai Tamil Nadu - 603 203 India
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28
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Zhang Y, Cui L, Chen W, Belviso BD, Yu B, Shen Y. Structure-based drug design of potential inhibitors of FBXW8, the substrate recognition component of Cullin-RING ligase 7. Mol Divers 2023; 27:2257-2271. [PMID: 36322340 DOI: 10.1007/s11030-022-10554-x] [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: 08/29/2022] [Accepted: 10/20/2022] [Indexed: 06/16/2023]
Abstract
FBXW8 plays an irreplaceable role in the substrate recognition of ubiquitin-dependent proteolysis, which further regulates cell cycle progression and signal transduction. However, the abnormal expression of FBXW8 triggers malignancy, inflammation, and autophagy irregulation. FBXW8 is considered as an effective therapeutic target for Cullin-RING ligase 7 (CRL7)-related cancers. Still, the lack of selective inhibitors hinders further therapeutic development and limits the exploration of its biological mechanism. This study constructed an integrated protocol that combines pharmacophore modeling, structure-based virtual screening, and Molecular Dynamic Simulation. It was then used as a screening query to identify hit compounds targeted at the substrate recognition site of FBXW8 from a large-scale compound database including 120 million compounds. Then, ten lead compounds were retrieved by using molecular docking analysis and ADMET prediction. Finally, MD simulations were performed to validate the binding stability of selected drug candidates. The result indicated that three newly obtained compounds, namely ZINC96179876, ZINC72174069, and ZINC97730272, might be potent FBXW8 inhibitors against CRL7-related cancers such as endometrial cancer.
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Affiliation(s)
- Yingying Zhang
- Department of Biotechnology, School of Biological Engineering, Henan University of Technology, Zhengzhou, 450001, Henan Province, People's Republic of China
| | - Liuqing Cui
- Department of Biotechnology, School of Biological Engineering, Henan University of Technology, Zhengzhou, 450001, Henan Province, People's Republic of China
| | - Wangji Chen
- Department of Biotechnology, School of International Education, Henan University of Technology, Zhengzhou, 450001, Henan Province, People's Republic of China
| | - Benny Danilo Belviso
- Institute of Crystallography, Consiglio Nazionale Delle Ricerche (CNR), 70126, Bari, Italy
| | - Bin Yu
- State Key Laboratory of Esophageal Cancer Prevention & Treatment, School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, 450052, Henan Province, People's Republic of China
| | - Yunpeng Shen
- Department of Biotechnology, School of Biological Engineering, Henan University of Technology, Zhengzhou, 450001, Henan Province, People's Republic of China.
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Nowak-Perlak M, Ziółkowski P, Woźniak M. A promising natural anthraquinones mediated by photodynamic therapy for anti-cancer therapy. PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2023; 119:155035. [PMID: 37603973 DOI: 10.1016/j.phymed.2023.155035] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 08/07/2023] [Accepted: 08/15/2023] [Indexed: 08/23/2023]
Abstract
BACKGROUND Experimental studies emphasize the therapeutic potential of plant-derived photosensitizers used in photodynamic therapy. Moreover, several in vitro and in vivo research present the promising roles of less-known anthraquinones that can selectively target cancer cells and eliminate them after light irradiation. This literature review summarizes the current knowledge of chosen plant-based-photosensitizers in PDT to show the results of emodin, aloe-emodin, parietin, rubiadin, hypericin, and soranjidiol in photodynamic therapy of cancer treatment and describe the comprehensive perspective of their role as natural photosensitizers. METHODS Literature searches of chosen anthraquinones were conducted on PubMed.gov with keywords: "emodin", "aloe-emodin", "hypericin", "parietin", "rubiadin", "soranjidiol" with "cancer" and "photodynamic therapy". RESULTS According to literature data, this review concentrated on all existing in vitro and in vivo studies of emodin, aloe-emodin, parietin, rubiadin, soranjidiol used as natural photosensitizers emphasizing their effectiveness and detailed mechanism of action in anticancer therapy. Moreover, comprehensive preclinical and clinical studies on hypericin reveal that the above-described substances may be included in the phototoxic treatment of different cancers. CONCLUSIONS Overall, this review presented less-known anthraquinones with their promising molecular mechanisms of action. It is expected that in the future they may be used as natural PSs in cancer treatment as well as hypericin.
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Affiliation(s)
- Martyna Nowak-Perlak
- Department of Clinical and Experimental Pathology, Division of General and Experimental Pathology, Wroclaw Medical University, Karola Marcinkowskiego 1 Street, 50-368, Wroclaw, Poland.
| | - Piotr Ziółkowski
- Department of Clinical and Experimental Pathology, Division of General and Experimental Pathology, Wroclaw Medical University, Karola Marcinkowskiego 1 Street, 50-368, Wroclaw, Poland
| | - Marta Woźniak
- Department of Clinical and Experimental Pathology, Division of General and Experimental Pathology, Wroclaw Medical University, Karola Marcinkowskiego 1 Street, 50-368, Wroclaw, Poland
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Vishwanath D, Shete-Aich A, Honnegowda MB, Anand MP, Chidambaram SB, Sapkal G, Basappa B, Yadav PD. Discovery of Hybrid Thiouracil-Coumarin Conjugates as Potential Novel Anti-SARS-CoV-2 Agents Targeting the Virus's Polymerase "RdRp" as a Confirmed Interacting Biomolecule. ACS OMEGA 2023; 8:27056-27066. [PMID: 37546653 PMCID: PMC10398856 DOI: 10.1021/acsomega.3c02079] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 06/15/2023] [Indexed: 08/08/2023]
Abstract
The coronavirus (COVID-19) pandemic, along with its various strains, has emerged as a global health crisis that has severely affected humankind and posed a great challenge to the public health system of affected countries. The replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mainly depends on RNA-dependent RNA polymerase (RdRp), a key enzyme that is involved in RNA synthesis. In this regard, we designed, synthesized, and characterized hybrid thiouracil and coumarin conjugates (HTCAs) by ether linkage, which were found to have anti-SARS-CoV-2 properties. Our in vitro real-time quantitative reverse transcription PCR (RT-qPCR) results confirmed that compounds such as 5d, 5e, 5f, and 5i inhibited the replication of SARS-CoV-2 with EC50 values of 14.3 ± 0.14, 6.59 ± 0.28, 86.3 ± 1.45, and 124 ± 2.38 μM, respectively. Also, compound 5d displayed significant antiviral activity against human coronavirus 229E (HCoV-229E). In addition, some of the HTCAs reduced the replication of SARS-CoV-2 variants such as D614G and B.617.2. In parallel, HTCAs in uninfected Vero CCL-81 cells indicated that no cytotoxicity was noticed. Furthermore, we compared the in silico interaction of lead compounds 5d and 5e toward the cocrystal structure of Suramin and RdRp polymerase with Remdesvir triphosphate, which showed that compounds 5d, 5e, and Remdesvir triphosphate (RTP) share a common catalytical site of RdRp but not Suramin. Additionally, the in silico ADMET properties predicted for the lead HTCAs and RTP showed that the maximum therapeutic doses recommended for compounds 5d and 5e were comparable to those of RTP. Concurrently, the pharmacokinetics of 5d was characterized in male Wistar Albino rats by administering a single oral gavage at a dose of 10 mg/kg, which gave a Cmax value of 0.22 μg/mL and a terminal elimination half-life period of 73.30 h. In conclusion, we established a new chemical entity that acts as a SARS-CoV-2 viral inhibitor with minimal or no toxicity to host cells in the rodent model, encouraging us to proceed with preclinical studies.
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Affiliation(s)
- Divakar Vishwanath
- Laboratory
of Chemical Biology, Department of Studies in Organic Chemistry, University of Mysore, Manasagangotri, Mysore 570006, India
| | - Anita Shete-Aich
- Indian
Council of Medical Research- National Institute of Virology (ICMR-NIV), Pune, Maharashtra411021, India
| | | | - Mahesh Padukudru Anand
- Department
of Respiratory Medicine, JSS Medical College, and Hospital, JSS Academy of Higher Education & Research, Mysore 570015, Karnataka, India
| | - Saravana Babu Chidambaram
- Department
of Pharmacology, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Mysore 570015, Karnataka, India
| | - Gajanan Sapkal
- Indian
Council of Medical Research- National Institute of Virology (ICMR-NIV), Pune, Maharashtra411021, India
| | - Basappa Basappa
- Laboratory
of Chemical Biology, Department of Studies in Organic Chemistry, University of Mysore, Manasagangotri, Mysore 570006, India
| | - Pragya D. Yadav
- Indian
Council of Medical Research- National Institute of Virology (ICMR-NIV), Pune, Maharashtra411021, India
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Kawashima H, Watanabe R, Esaki T, Kuroda M, Nagao C, Natsume-Kitatani Y, Ohashi R, Komura H, Mizuguchi K. DruMAP: A Novel Drug Metabolism and Pharmacokinetics Analysis Platform. J Med Chem 2023. [PMID: 37449459 PMCID: PMC10388294 DOI: 10.1021/acs.jmedchem.3c00481] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
We developed a novel drug metabolism and pharmacokinetics (DMPK) analysis platform named DruMAP. This platform consists of a database for DMPK parameters and programs that can predict many DMPK parameters based on the chemical structure of a compound. The DruMAP database includes curated DMPK parameters from public sources and in-house experimental data obtained under standardized conditions; it also stores predicted DMPK parameters produced by our prediction programs. Users can predict several DMPK parameters simultaneously for novel compounds not found in the database. Furthermore, the highly flexible search system enables users to search for compounds as they desire. The current version of DruMAP comprises more than 30,000 chemical compounds, about 40,000 activity values (collected from public databases and in-house data), and about 600,000 predicted values. Our platform provides a simple tool for searching and predicting DMPK parameters and is expected to contribute to the acceleration of new drug development. DruMAP can be freely accessed at: https://drumap.nibiohn.go.jp/.
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Affiliation(s)
- Hitoshi Kawashima
- Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, Settsu, Osaka 566-0002, Japan
| | - Reiko Watanabe
- Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, Settsu, Osaka 566-0002, Japan
- Laboratory for Computational Biology, Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan
| | - Tsuyoshi Esaki
- Data Science and AI Innovation Research Promotion Center, Shiga University, Hikone, Shiga 522-8522, Japan
| | - Masataka Kuroda
- Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, Settsu, Osaka 566-0002, Japan
- Discovery Technology Laboratories, Mitsubishi Tanabe Pharma Corporation, Yokohama, Kanagawa 227-0033, Japan
| | - Chioko Nagao
- Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, Settsu, Osaka 566-0002, Japan
- Laboratory for Computational Biology, Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan
| | - Yayoi Natsume-Kitatani
- Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, Settsu, Osaka 566-0002, Japan
- Institute of Advanced Medical Sciences, Tokushima University, Tokushima, Tokushima 770-8503, Japan
| | - Rikiya Ohashi
- Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, Settsu, Osaka 566-0002, Japan
| | - Hiroshi Komura
- University Research Administration Center, Osaka Metropolitan University, Osaka, Osaka 545-0051, Japan
| | - Kenji Mizuguchi
- Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, Settsu, Osaka 566-0002, Japan
- Laboratory for Computational Biology, Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan
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Grenier D, Audebert S, Preto J, Guichou JF, Krimm I. Linkers in fragment-based drug design: an overview of the literature. Expert Opin Drug Discov 2023; 18:987-1009. [PMID: 37466331 DOI: 10.1080/17460441.2023.2234285] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 07/05/2023] [Indexed: 07/20/2023]
Abstract
INTRODUCTION In fragment-based drug design, fragment linking is a popular strategy where two fragments binding to different sub-pockets of a target are linked together. This attractive method remains challenging especially due to the design of ideal linkers. AREAS COVERED The authors review the types of linkers and chemical reactions commonly used to the synthesis of linkers, including those utilized in protein-templated fragment self-assembly, where fragments are directly linked in the presence of the protein. Finally, they detail computational workflows and software including generative models that have been developed for fragment linking. EXPERT OPINION The authors believe that fragment linking offers key advantages for compound design, particularly for the design of bivalent inhibitors linking two distinct pockets of the same or different subunits. On the other hand, more studies are needed to increase the potential of protein-templated approaches in FBDD. Important computational tools such as structure-based de novo software are emerging to select suitable linkers. Fragment linking will undoubtedly benefit from developments in computational approaches and machine learning models.
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Affiliation(s)
- Dylan Grenier
- Team Small Molecules for Biological Targets, Centre de Recherche En Cancérologie (CRCL) - INSERM 1052 - CNRS 5286 - Centre Léon Bérard - Université Claude Bernard Lyon 1, Institut Convergence Plascan, Lyon, France
| | - Solène Audebert
- Centre de Biologie Structurale, CNRS, INSERM, Univ. Montpellier, Montpellier, France
| | - Jordane Preto
- Team Small Molecules for Biological Targets, Centre de Recherche En Cancérologie (CRCL) - INSERM 1052 - CNRS 5286 - Centre Léon Bérard - Université Claude Bernard Lyon 1, Institut Convergence Plascan, Lyon, France
| | - Jean-François Guichou
- Centre de Biologie Structurale, CNRS, INSERM, Univ. Montpellier, Montpellier, France
| | - Isabelle Krimm
- Team Small Molecules for Biological Targets, Centre de Recherche En Cancérologie (CRCL) - INSERM 1052 - CNRS 5286 - Centre Léon Bérard - Université Claude Bernard Lyon 1, Institut Convergence Plascan, Lyon, France
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AbdulHameed MDM, Liu R, Wallqvist A. Using a Graph Convolutional Neural Network Model to Identify Bile Salt Export Pump Inhibitors. ACS OMEGA 2023; 8:21853-21861. [PMID: 37360478 PMCID: PMC10286257 DOI: 10.1021/acsomega.3c01583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 05/19/2023] [Indexed: 06/28/2023]
Abstract
The bile salt export pump (BSEP) is a key transporter involved in the efflux of bile salts from hepatocytes to bile canaliculi. Inhibition of BSEP leads to the accumulation of bile salts within the hepatocytes, leading to possible cholestasis and drug-induced liver injury. Screening for and identification of chemicals that inhibit this transporter aid in understanding the safety liabilities of these chemicals. Moreover, computational approaches to identify BSEP inhibitors provide an alternative to the more resource-intensive, gold standard experimental approaches. Here, we used publicly available data to develop predictive machine learning models for the identification of potential BSEP inhibitors. Specifically, we analyzed the utility of a graph convolutional neural network (GCNN)-based approach in combination with multitask learning to identify BSEP inhibitors. Our analyses showed that the developed GCNN model performed better than the variable-nearest neighbor and Bayesian machine learning approaches, with a cross-validation receiver operating characteristic area under the curve of 0.86. In addition, we compared GCNN-based single-task and multitask models and evaluated their utility in addressing data limitation challenges commonly observed in bioactivity modeling. We found that multitask models performed better than single-task models and can be utilized to identify active molecules for targets with limited data availability. Overall, our developed multitask GCNN-based BSEP model provides a useful tool for prioritizing hits during early drug discovery and in risk assessment of chemicals.
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Affiliation(s)
- Mohamed Diwan M. AbdulHameed
- Department
of Defense Biotechnology High Performance Computing Software Applications
Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick 21702, Maryland, United States
- The
Henry M. Jackson Foundation for the Advancement of Military Medicine,
Inc., Bethesda 20817, Maryland, United States
| | - Ruifeng Liu
- Department
of Defense Biotechnology High Performance Computing Software Applications
Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick 21702, Maryland, United States
- The
Henry M. Jackson Foundation for the Advancement of Military Medicine,
Inc., Bethesda 20817, Maryland, United States
| | - Anders Wallqvist
- Department
of Defense Biotechnology High Performance Computing Software Applications
Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick 21702, Maryland, United States
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Pliushcheuskaya P, Künze G. Recent Advances in Computer-Aided Structure-Based Drug Design on Ion Channels. Int J Mol Sci 2023; 24:ijms24119226. [PMID: 37298178 DOI: 10.3390/ijms24119226] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 05/16/2023] [Accepted: 05/22/2023] [Indexed: 06/12/2023] Open
Abstract
Ion channels play important roles in fundamental biological processes, such as electric signaling in cells, muscle contraction, hormone secretion, and regulation of the immune response. Targeting ion channels with drugs represents a treatment option for neurological and cardiovascular diseases, muscular degradation disorders, and pathologies related to disturbed pain sensation. While there are more than 300 different ion channels in the human organism, drugs have been developed only for some of them and currently available drugs lack selectivity. Computational approaches are an indispensable tool for drug discovery and can speed up, especially, the early development stages of lead identification and optimization. The number of molecular structures of ion channels has considerably increased over the last ten years, providing new opportunities for structure-based drug development. This review summarizes important knowledge about ion channel classification, structure, mechanisms, and pathology with the main focus on recent developments in the field of computer-aided, structure-based drug design on ion channels. We highlight studies that link structural data with modeling and chemoinformatic approaches for the identification and characterization of new molecules targeting ion channels. These approaches hold great potential to advance research on ion channel drugs in the future.
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Affiliation(s)
- Palina Pliushcheuskaya
- Institute for Drug Discovery, Medical Faculty, University of Leipzig, Brüderstr. 34, D-04103 Leipzig, Germany
| | - Georg Künze
- Institute for Drug Discovery, Medical Faculty, University of Leipzig, Brüderstr. 34, D-04103 Leipzig, Germany
- Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstr. 16-18, D-04107 Leipzig, Germany
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Chen XH, Ruan Y, Liu YG, Duan XY, Jiang F, Tang H, Zhang HY, Zhang QY. Transporter proteins knowledge graph construction and its application in drug development. Comput Struct Biotechnol J 2023; 21:2973-2984. [PMID: 37235186 PMCID: PMC10206172 DOI: 10.1016/j.csbj.2023.05.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 04/17/2023] [Accepted: 05/02/2023] [Indexed: 05/28/2023] Open
Abstract
Transporters are the main determinant for pharmacokinetics characteristics of drugs, such as absorption, distribution, and excretion of drugs in humans. However, it is difficult to perform drug transporter validation and structure analysis of membrane transporter proteins by experimental methods. Many studies have demonstrated that knowledge graphs (KG) could effectively excavate potential association information between different entities. To improve the effectiveness of drug discovery, a transporter-related KG was constructed in this study. Meanwhile, a predictive frame (AutoInt_KG) and a generative frame (MolGPT_KG) were established based on the heterogeneity information obtained from the transporter-related KG by the RESCAL model. Natural product Luteolin with known transporters was selected to verify the reliability of the AutoInt_KG frame, its ROC-AUC (1:1), ROC-AUC (1:10), PR-AUC (1:1), PR-AUC (1:10) are 0.91, 0.94, 0.91 and 0.78, respectively. Subsequently, the MolGPT_KG frame was constructed to implement efficient drug design based on transporter structure. The evaluation results showed that the MolGPT_KG could generate novel and valid molecules and that these molecules were further confirmed by molecular docking analysis. The docking results showed that they could bind to important amino acids at the active site of the target transporter. Our findings will provide rich information resources and guidance for the further development of the transporter-related drugs.
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Lemée P, Fessard V, Habauzit D. Prioritization of mycotoxins based on mutagenicity and carcinogenicity evaluation using combined in silico QSAR methods. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 323:121284. [PMID: 36804886 DOI: 10.1016/j.envpol.2023.121284] [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: 09/26/2022] [Revised: 02/01/2023] [Accepted: 02/12/2023] [Indexed: 06/18/2023]
Abstract
Mycotoxins and their metabolites are a family of compounds that contains a great diversity of both structure and biological properties. Information on their toxicity is spread within several databases and in scientific literature. Due to the number of molecules and their structure diversity, the cost and time required for hazard evaluation of each compound is unrealistic. In that purpose, new approach methodologies (NAMs) can be applied to evaluate such large set of molecules. Among them, quantitative structure-activity relationship (QSAR) in silico models could be useful to predict the mutagenic and carcinogenic properties of mycotoxins. First, a complete list of 904 mycotoxins and metabolites was built. Then, some known mycotoxins were used to determine the best QSAR tools for mutagenicity and carcinogenicity predictions. The best tool was further applied to the whole list of 904 mycotoxins. At the end, 95 mycotoxins were identified as both mutagen and carcinogen and should be prioritized for further evaluation.
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Affiliation(s)
- Pierre Lemée
- ANSES (French Agency for Food, Environmental and Occupational Health & Safety), Toxicology of Contaminants Unit, Fougères, France
| | - Valérie Fessard
- ANSES (French Agency for Food, Environmental and Occupational Health & Safety), Toxicology of Contaminants Unit, Fougères, France
| | - Denis Habauzit
- ANSES (French Agency for Food, Environmental and Occupational Health & Safety), Toxicology of Contaminants Unit, Fougères, France.
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Raza A, Chohan TA, Sarfraz M, Chohan TA, Imran Sajid M, Tiwari RK, Ansari SA, Alkahtani HM, Yasmeen Ansari S, Khurshid U, Saleem H. Molecular modeling of pyrrolo-pyrimidine based analogs as potential FGFR1 inhibitors: a scientific approach for therapeutic drugs. J Biomol Struct Dyn 2023; 41:14358-14371. [PMID: 36898855 DOI: 10.1080/07391102.2023.2187638] [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: 08/30/2022] [Accepted: 02/10/2023] [Indexed: 03/12/2023]
Abstract
Fibroblast growth factor receptors 1 (FGFR1) is an emerging target for the development of anticancer drugs. Uncontrolled expression of FGFR1 is strongly associated with a number of different types of cancers. Apart from a few FGFR inhibitors, the FGFR family members have not been thoroughly studied to produce clinically effective anticancer drugs. The application of proper computational techniques may aid in understanding the mechanism of protein-ligand complex formation, which may provide a better notion for developing potent FGFR1 inhibitors. In this study, a variety of computational techniques, including 3D-QSAR, flexible docking and MD simulation followed by MMGB/PBSA, H-bonds and distance analysis, have been performed to systematically explore the binding mechanism of pyrrolo-pyrimidine derivatives against FGFR1. The 3D-QSAR model was generated to deduce the structural determinants of FGFR1 inhibition. The high q2 and r2 values for the CoMFA and CoMSIA models indicated that the created 3D-QSAR models could reliably predict the bioactivities of FGFR1 inhibitors. The computed binding free energies (MMGB/PBSA) for the selected compounds were consistent with the ranking of their experimental binding affinities against FGFR1. Furthermore, per-residue energy decomposition analysis revealed that the residues Lys514 in catalytic region, Asn568, Glu571 in solvent accessible portion and Asp641 in DFG motif exhibited a strong tendency to mediate ligand-protein interactions through the hydrogen bonding and Van Der Waals interactions. These findings may benefit researchers in gaining better knowledge of FGFR1 inhibition and may serve as a guideline for the development of novel and highly effective FGFR1 inhibitors.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Ali Raza
- College of Pharmacy, The University of Lahore, Lahore, Pakistan
| | - Tahir Ali Chohan
- Institute of Pharmaceutical Sciences (IPS), University of Veterinary and Animal Sciences (UVAS), Lahore, Pakistan
| | - Muhammad Sarfraz
- College of Pharmacy, Al Ain University, Al Ain, United Arab Emirates
| | - Talha Ali Chohan
- Institute of Molecular Biology and Biotechnology, The University of Lahore, Lahore, Pakistan
| | - Muhammad Imran Sajid
- Center for Targeted Drug Delivery, Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Harry and Diane Rinker Health Science Campus, Irvine, CA, USA
| | - Rakesh Kumar Tiwari
- Center for Targeted Drug Delivery, Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Harry and Diane Rinker Health Science Campus, Irvine, CA, USA
| | - Siddique Akber Ansari
- Department of Pharmaceutical Chemistry, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - Hamad M Alkahtani
- Department of Pharmaceutical Chemistry, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - Shabana Yasmeen Ansari
- Pharmaceutical Unit, Department of Electronics, Chemistry and Industrial Engineering, University of Messina, Messina, Italy
| | - Umair Khurshid
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, The Islamia University of Bahawalpur, Punjab, Pakistan
| | - Hammad Saleem
- Institute of Pharmaceutical Sciences (IPS), University of Veterinary and Animal Sciences (UVAS), Lahore, Pakistan
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Wang G, Xu L, Chen H, Liu Y, Pan P, Hou T. Recent advances in computational studies on voltage‐gated sodium channels: Drug design and mechanism studies. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2023. [DOI: 10.1002/wcms.1641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Affiliation(s)
- Gaoang Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University College of Pharmaceutical Sciences, Zhejiang University Hangzhou Zhejiang China
| | - Lei Xu
- Institute of Bioinformatics and Medical Engineering School of Electrical and Information Engineering, Jiangsu University of Technology Changzhou Jiangsu China
| | - Haiyi Chen
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University College of Pharmaceutical Sciences, Zhejiang University Hangzhou Zhejiang China
| | - Yifei Liu
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University College of Pharmaceutical Sciences, Zhejiang University Hangzhou Zhejiang China
| | - Peichen Pan
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University College of Pharmaceutical Sciences, Zhejiang University Hangzhou Zhejiang China
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University College of Pharmaceutical Sciences, Zhejiang University Hangzhou Zhejiang China
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Tran TTV, Tayara H, Chong KT. Recent Studies of Artificial Intelligence on In Silico Drug Distribution Prediction. Int J Mol Sci 2023; 24:1815. [PMID: 36768139 PMCID: PMC9915725 DOI: 10.3390/ijms24031815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 01/11/2023] [Accepted: 01/13/2023] [Indexed: 01/19/2023] Open
Abstract
Drug distribution is an important process in pharmacokinetics because it has the potential to influence both the amount of medicine reaching the active sites and the effectiveness as well as safety of the drug. The main causes of 90% of drug failures in clinical development are lack of efficacy and uncontrolled toxicity. In recent years, several advances and promising developments in drug distribution property prediction have been achieved, especially in silico, which helped to drastically reduce the time and expense of screening undesired drug candidates. In this study, we provide comprehensive knowledge of drug distribution background, influencing factors, and artificial intelligence-based distribution property prediction models from 2019 to the present. Additionally, we gathered and analyzed public databases and datasets commonly utilized by the scientific community for distribution prediction. The distribution property prediction performance of five large ADMET prediction tools is mentioned as a benchmark for future research. On this basis, we also offer future challenges in drug distribution prediction and research directions. We hope that this review will provide researchers with helpful insight into distribution prediction, thus facilitating the development of innovative approaches for drug discovery.
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Affiliation(s)
- Thi Tuyet Van Tran
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea
- Department of Information Technology, An Giang University, Long Xuyen 880000, Vietnam
- Vietnam National University–Ho Chi Minh City, Ho Chi Minh 700000, Vietnam
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju 54896, Republic of Korea
| | - Kil To Chong
- Advances Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, Republic of Korea
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Dulsat J, López-Nieto B, Estrada-Tejedor R, Borrell JI. Evaluation of Free Online ADMET Tools for Academic or Small Biotech Environments. MOLECULES (BASEL, SWITZERLAND) 2023; 28:molecules28020776. [PMID: 36677832 PMCID: PMC9864198 DOI: 10.3390/molecules28020776] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 12/27/2022] [Accepted: 01/10/2023] [Indexed: 01/15/2023]
Abstract
For a new molecular entity (NME) to become a drug, it is not only essential to have the right biological activity also be safe and efficient, but it is also required to have a favorable pharmacokinetic profile including toxicity (ADMET). Consequently, there is a need to predict, during the early stages of development, the ADMET properties to increase the success rate of compounds reaching the lead optimization process. Since Lipinski's rule of five, the prediction of pharmacokinetic parameters has evolved towards the current in silico tools based on empirical approaches or molecular modeling. The commercial specialized software for performing such predictions, which is usually costly, is, in many cases, not among the possibilities for research laboratories in academia or at small biotech companies. Nevertheless, in recent years, many free online tools have become available, allowing, more or less accurately, for the prediction of the most relevant pharmacokinetic parameters. This paper studies 18 free web servers capable of predicting ADMET properties and analyzed their advantages and disadvantages, their model-based calculations, and their degree of accuracy by considering the experimental data reported for a set of 24 FDA-approved tyrosine kinase inhibitors (TKIs) as a model of a research project.
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41
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Qazi S, Khanna K, Raza K. Dihydroquercetin (DHQ) has the potential to promote apoptosis in ovarian cancer cells: An in silico and in vitro study. J Mol Struct 2023. [DOI: 10.1016/j.molstruc.2022.134093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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42
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Rathod SB, Prajapati PB, Pal R, Mansuri MF. AMPA GluA2 subunit competitive inhibitors for PICK1 PDZ domain: Pharmacophore-based virtual screening, molecular docking, molecular dynamics simulation, and ADME studies. J Biomol Struct Dyn 2023; 41:336-351. [PMID: 34809533 DOI: 10.1080/07391102.2021.2006086] [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: 01/03/2023]
Abstract
PICK1 (Protein interacting with C kinase-1) plays a key role in the regulation of intracellular trafficking of AMPA GluA2 subunit that is linked with synaptic plasticity. PICK1 is a scaffolding protein and binds numerous proteins through its PDZ domain. Research showed that synaptic plasticity is altered upon disrupting the GluA2-PDZ interactions. Inhibiting PDZ and GluA2 binding lead to beneficial effects in the cure of neurological diseases thus, targeting PDZ domain is proposed as a novel therapeutic target in such diseases. For this, various classes of synthetic peptides were tested. Though small organic molecules have been utilized to prevent these interactions, the number of such molecules is inadequate. Hence, in this study, ten molecular libraries containing large number of molecules were screened against the PDZ domain using pharmacophore-based virtual screening to find the best hits for the PDZ domain. Molecular docking and molecular dynamics simulation studies revealed that Hit_II is a potent inhibitor for the PDZ domain and confirm the allosteric nature of Hit_III. Additionally, ADME analysis suggests the drug-likeness of both Hit_II and Hit_III. This study suggests that tested hits may have potency against the PDZ domain and can be considered effective to treat neurological disorders.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Shravan B Rathod
- Department of Chemistry, Smt. S. M. Panchal Science College, Talod, Gujarat, India
| | - Pravin B Prajapati
- Department of Chemistry, Sheth M. N. Science College, Patan, Gujarat, India
| | - Ranjan Pal
- Department of Medical Genetics, Sanjay Gandhi Post-Graduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India
| | - Mohmedyasin F Mansuri
- Department of Microbiology, Smt. S. M. Panchal Science College, Talod, Gujarat, India
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43
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Yadav PK, Jaiswal A, Singh RK. In silico study on spice-derived antiviral phytochemicals against SARS-CoV-2 TMPRSS2 target. J Biomol Struct Dyn 2022; 40:11874-11884. [PMID: 34427179 DOI: 10.1080/07391102.2021.1965658] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Corona Virus Disease (COVID-19) caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a pandemic that has claimed so far over half a million human life across the globe. Researchers all over the world are exploring various molecules including phytochemicals to get a potential anti-COVID-19 drug. Certain phytochemicals present in some spices are claimed to possess antiviral, anti-bacterial, and anti-fungal properties. Hence, an in-silico study was done by selecting eighteen well reported antiviral phytochemicals from some spices commonly used in Indian kitchen viz. Curcuma longa (Turmeric), Nigella sativa (Black cumin), Piper nigrum (Black pepper), Trachyspermum ammi (Carom) and Zingiber officinale (Ginger) to find out whether they can prevent SARS-CoV-2 infection. Firstly, we predicted the structure of TMPRSS2 (transmembrane protease serine 2), a host protein that truncates spike protein of SARS-CoV-2 thereby facilitating its endocytosis, and then docked against its catalytic domain the selected phytochemicals and camostat (a well-known synthetic inhibitor of TMPRSS2). Thereafter, stability of seven best docked phytochemicals and camostat were scrutinized by Molecular Dynamic Simulation (MDS). MDS analysis indicated bisdemethoxycurcumin (BDMC), carvacrol and thymol as better inhibitors than the camostat due to their stable binding with TMPRSS2 in its oxyanion hole and inducing subtle modification in the spatial arrangement of the catalytic triad residues. Among these three phytochemicals, carvacrol appeared to be the best inhibitor, followed by BDMC, whereas thymol was least effective.
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Affiliation(s)
- Pradeep Kumar Yadav
- Bioinformatics Centre, Faculty of Life Sciences, Rajiv Gandhi University, Doimukh, Arunachal Pradesh, India
| | - Amit Jaiswal
- Bioinformatics Centre, Faculty of Life Sciences, Rajiv Gandhi University, Doimukh, Arunachal Pradesh, India
| | - Rajiv Kumar Singh
- Bioinformatics Centre, Faculty of Life Sciences, Rajiv Gandhi University, Doimukh, Arunachal Pradesh, India
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Tian H, Ketkar R, Tao P. ADMETboost: a web server for accurate ADMET prediction. J Mol Model 2022; 28:408. [PMID: 36454321 PMCID: PMC9903341 DOI: 10.1007/s00894-022-05373-8] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 10/31/2022] [Indexed: 12/03/2022]
Abstract
The absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties are important in drug discovery as they define efficacy and safety. In this work, we applied an ensemble of features, including fingerprints and descriptors, and a tree-based machine learning model, extreme gradient boosting, for accurate ADMET prediction. Our model performs well in the Therapeutics Data Commons ADMET benchmark group. For 22 tasks, our model is ranked first in 18 tasks and top 3 in 21 tasks. The trained machine learning models are integrated in ADMETboost, a web server that is publicly available at https://ai-druglab.smu.edu/admet .
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Affiliation(s)
- Hao Tian
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, 75205, TX, USA
| | | | - Peng Tao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, 75205, TX, USA.
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Singh R, Bhardwaj VK, Purohit R. Inhibition of nonstructural protein 15 of SARS-CoV-2 by golden spice: A computational insight. Cell Biochem Funct 2022; 40:926-934. [PMID: 36203381 PMCID: PMC9874790 DOI: 10.1002/cbf.3753] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 08/16/2022] [Accepted: 09/15/2022] [Indexed: 01/27/2023]
Abstract
The quick widespread of the coronavirus and speedy upsurge in the tally of cases demand the fast development of effective drugs. The uridine-directed endoribonuclease activity of nonstructural protein 15 (Nsp15) of the coronavirus is responsible for the invasion of the host immune system. Therefore, developing potential inhibitors against Nsp15 is a promising strategy. In this concern, the in silico approach can play a significant role, as it is fast and cost-effective in comparison to the trial and error approaches of experimental investigations. In this study, six turmeric derivatives (curcuminoids) were chosen for in silico analysis. The molecular interactions, pharmacokinetics, and drug-likeness of all the curcuminoids were measured. Further, the stability of Nsp15-curcuminoids complexes was appraised by employing molecular dynamics (MD) simulations and MM-PBSA approaches. All the molecules were affirmed to have strong interactions and pharmacokinetic profile. The MD simulations data stated that the Nsp15-curcuminoids complexes were stable during simulations. All the curcuminoids showed stable and high binding affinity, and these curcuminoids could be admitted as potential modulators for Nsp15 inhibition.
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Affiliation(s)
- Rahul Singh
- Structural Bioinformatics Lab, CSIR‐Institute of Himalayan Bioresource Technology (CSIR‐IHBT)The Himalayan Centre for High‐throughput Computational Biology (HiCHiCoB, A BIC Supported by DBT)PalampurIndia,Biotechnology divisionCSIR‐IHBTPalampurIndia,Academy of Scientific & Innovative Research (AcSIR)GhaziabadIndia
| | - Vijay K. Bhardwaj
- Structural Bioinformatics Lab, CSIR‐Institute of Himalayan Bioresource Technology (CSIR‐IHBT)The Himalayan Centre for High‐throughput Computational Biology (HiCHiCoB, A BIC Supported by DBT)PalampurIndia,Biotechnology divisionCSIR‐IHBTPalampurIndia,Academy of Scientific & Innovative Research (AcSIR)GhaziabadIndia
| | - Rituraj Purohit
- Structural Bioinformatics Lab, CSIR‐Institute of Himalayan Bioresource Technology (CSIR‐IHBT)The Himalayan Centre for High‐throughput Computational Biology (HiCHiCoB, A BIC Supported by DBT)PalampurIndia,Biotechnology divisionCSIR‐IHBTPalampurIndia,Academy of Scientific & Innovative Research (AcSIR)GhaziabadIndia
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Sangeet S, Pawar S, Nawani N, Junnarkar M, Gaikwad S. Computational approach to attenuate virulence of Pseudomonas aeruginosa through bioinspired silver nanoparticles. 3 Biotech 2022; 12:317. [PMID: 36276439 PMCID: PMC9547761 DOI: 10.1007/s13205-022-03367-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 09/17/2022] [Indexed: 11/24/2022] Open
Abstract
In this study we aim to investigate the computational docking approach of biofabricated silver nanoparticles against P. aeruginosa virulent exoenzymes, such as ExoS and ExoY. Therefore, the synthesis and characterization of biofabricated silver nanoparticles using Piper betle leaves (Pb-AgNPs) were carried out. The surface topology and functional group attachment on the surface of Pb-AgNPs were analyzed using UV-visible spectroscopy, Scanning Electron Microscopy, Fourier Transformed Infrared Spectroscopy (FTIR), and X-Ray Diffraction. The FTIR analysis revealed that the synthesized silver nanoparticles were capped with P. betle phytochemicals importantly Eugenol and Hydroxychavicol. These are the major bioactive compounds present in P. betle leaves; therefore, computational docking of Eugenol-conjugated AgNPs (PbEu-AgNPs) and Hydroxychavicol-conjugated AgNPs (PbHy-AgNPs) against ExoS and ExoY was performed. The active residues of PbEu-AgNPs and PbHy-AgNPs interacted with the active site of ExoS and ExoY exoenzymes. Biofabricated AgNP-mediated inhibition of these virulent exoenzymes blocked the adverse effect of P. aeruginosa on the host cell. The computational analysis provides new approach into the design of biofabricated AgNPs as promising anti-infective nanomedicine agents. Supplementary Information The online version contains supplementary material available at 10.1007/s13205-022-03367-0.
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Affiliation(s)
- Satyam Sangeet
- Microbial Diversity Research Center, Dr. D.Y. Patil Biotechnology and Bioinformatics Institute, Dr. D. Y. Patil Vidyapeeth, Pimpri, Pune, Maharashtra India
- Present Address: Department of Chemical Sciences, Indian Institute of Science Education and Research (IISER), Kolkata, 741246 India
| | - Sarika Pawar
- Microbial Diversity Research Center, Dr. D.Y. Patil Biotechnology and Bioinformatics Institute, Dr. D. Y. Patil Vidyapeeth, Pimpri, Pune, Maharashtra India
| | - Neelu Nawani
- Microbial Diversity Research Center, Dr. D.Y. Patil Biotechnology and Bioinformatics Institute, Dr. D. Y. Patil Vidyapeeth, Pimpri, Pune, Maharashtra India
| | - Manisha Junnarkar
- Microbial Diversity Research Center, Dr. D.Y. Patil Biotechnology and Bioinformatics Institute, Dr. D. Y. Patil Vidyapeeth, Pimpri, Pune, Maharashtra India
| | - Swapnil Gaikwad
- Microbial Diversity Research Center, Dr. D.Y. Patil Biotechnology and Bioinformatics Institute, Dr. D. Y. Patil Vidyapeeth, Pimpri, Pune, Maharashtra India
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Iftkhar S, de Sá AGC, Velloso JPL, Aljarf R, Pires DEV, Ascher DB. cardioToxCSM: A Web Server for Predicting Cardiotoxicity of Small Molecules. J Chem Inf Model 2022; 62:4827-4836. [PMID: 36219164 DOI: 10.1021/acs.jcim.2c00822] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The design of novel, safe, and effective drugs to treat human diseases is a challenging venture, with toxicity being one of the main sources of attrition at later stages of development. Failure due to toxicity incurs a significant increase in costs and time to market, with multiple drugs being withdrawn from the market due to their adverse effects. Cardiotoxicity, for instance, was responsible for the failure of drugs such as fenspiride, propoxyphene, and valdecoxib. While significant effort has been dedicated to mitigate this issue by developing computational approaches that aim to identify molecules likely to be toxic, including quantitative structure-activity relationship models and machine learning methods, current approaches present limited performance and interpretability. To overcome these, we propose a new web-based computational method, cardioToxCSM, which can predict six types of cardiac toxicity outcomes, including arrhythmia, cardiac failure, heart block, hERG toxicity, hypertension, and myocardial infarction, efficiently and accurately. cardioToxCSM was developed using the concept of graph-based signatures, molecular descriptors, toxicophore matchings, and molecular fingerprints, leveraging explainable machine learning, and was validated internally via different cross validation schemes and externally via low-redundancy blind sets. The models presented robust performances with areas under ROC curves of up to 0.898 on 5-fold cross-validation, consistent with metrics on blind tests. Additionally, our models provide interpretation of the predictions by identifying whether substructures that are commonly enriched in toxic compounds were present. We believe cardioToxCSM will provide valuable insight into the potential cardiotoxicity of small molecules early on drug screening efforts. The method is made freely available as a web server at https://biosig.lab.uq.edu.au/cardiotoxcsm.
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Affiliation(s)
- Saba Iftkhar
- School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia 4072, Queensland, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville 3052, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, Victoria, Australia
| | - Alex G C de Sá
- School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia 4072, Queensland, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville 3052, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, Victoria, Australia.,Baker Department of Cardiometabolic Health, Melbourne Medical School, University of Melbourne, Parkville 3010, Victoria, Australia
| | - João P L Velloso
- School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia 4072, Queensland, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville 3052, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, Victoria, Australia
| | - Raghad Aljarf
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville 3052, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, Victoria, Australia.,Baker Department of Cardiometabolic Health, Melbourne Medical School, University of Melbourne, Parkville 3010, Victoria, Australia
| | - Douglas E V Pires
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville 3052, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, Victoria, Australia.,School of Computing and Information Systems, University of Melbourne, Parkville 3052, Victoria, Australia
| | - David B Ascher
- School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia 4072, Queensland, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville 3052, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, Victoria, Australia.,Baker Department of Cardiometabolic Health, Melbourne Medical School, University of Melbourne, Parkville 3010, Victoria, Australia
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Kolligundla LP, Gupta S, Lata S, Mulukala SKN, Killaka P, Akif M, Pasupulati AK. Identification of Novel GTP Analogs as Potent and Specific Reversible Inhibitors for Transglutaminase 2. MOLECULAR SIMULATION 2022. [DOI: 10.1080/08927022.2022.2123917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Affiliation(s)
- Lakshmi P. Kolligundla
- Department of Biochemistry, School of Life Sciences, University of Hyderabad, Hyderabad, India
| | - Samriddhi Gupta
- Department of Biochemistry, School of Life Sciences, University of Hyderabad, Hyderabad, India
| | - Surabhi Lata
- Department of Biochemistry, School of Life Sciences, University of Hyderabad, Hyderabad, India
| | - Sandeep K. N. Mulukala
- Department of Biochemistry, School of Life Sciences, University of Hyderabad, Hyderabad, India
| | - Praneeth Killaka
- Department of Biochemistry, School of Life Sciences, University of Hyderabad, Hyderabad, India
| | - Mohd Akif
- Department of Biochemistry, School of Life Sciences, University of Hyderabad, Hyderabad, India
| | - Anil K. Pasupulati
- Department of Biochemistry, School of Life Sciences, University of Hyderabad, Hyderabad, India
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Computational Prediction of Inhibitors and Inducers of the Major Isoforms of Cytochrome P450. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27185875. [PMID: 36144612 PMCID: PMC9503090 DOI: 10.3390/molecules27185875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 09/06/2022] [Accepted: 09/08/2022] [Indexed: 11/29/2022]
Abstract
Human cytochrome P450 enzymes (CYPs) are heme-containing monooxygenases. This superfamily of drug-metabolizing enzymes is responsible for the metabolism of most drugs and other xenobiotics. The inhibition of CYPs may lead to drug–drug interactions and impair the biotransformation of drugs. CYP inducers may decrease the bioavailability and increase the clearance of drugs. Based on the freely available databases ChEMBL and PubChem, we have collected over 70,000 records containing the structures of inhibitors and inducers together with the IC50 values for the inhibitors of the five major human CYPs: 1A2, 3A4, 2D6, 2C9, and 2C19. Based on the collected data, we developed (Q)SAR models for predicting inhibitors and inducers of these CYPs using GUSAR and PASS software. The developed (Q)SAR models could be applied for assessment of the interaction of novel drug-like substances with the major human CYPs. The created (Q)SAR models demonstrated reasonable accuracy of prediction. They have been implemented in the web application P450-Analyzer that is freely available via the Internet.
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50
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Microwave-Assisted Synthesis, Characterization, Docking Studies and Molecular Dynamic of Some Novel Phenyl Thiazole Analogs as Xanthine Oxidase Inhibitor. JOURNAL OF THE IRANIAN CHEMICAL SOCIETY 2022. [DOI: 10.1007/s13738-022-02574-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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