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Singh G. In silico Prediction and Pharmacokinetic Studies on Glucosinolates as a Potential Drug and Key Inhibitor Molecule for Lanosterol-14α- demethylase: A Fungal Membrane Biosynthesis Enzyme. Curr Drug Discov Technol 2022; 19:e150622206033. [PMID: 35708080 DOI: 10.2174/1570163819666220615142933] [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: 02/21/2022] [Revised: 05/01/2022] [Accepted: 05/19/2022] [Indexed: 01/27/2023]
Abstract
BACKGROUND Glucosinolates (β-thioglucoside-N-hydroxysulfates) are a water-soluble organic anion with sulfur- and nitrogen-containing glycosides which are found in abundance in Cruciferous plants. Ergosterol (ERG13) lanosterol-14α-demethylase protein has been targeted for inhibition studies as a key regulator enzyme of fungal membrane biosynthesis. OBJECTIVES To understand the molecular mechanism of inhibition of Ergosterol (ERG13) lanosterol- 14α-demethylase by various phytochemicals from brassicales, i.e., glucosinolates and their potential role as putative drug molecules. METHODS In this study, in silico analyses were performed to predict the molecular basis of various glucosinolates as a potential inhibitor of lanosterol-14α-demethylase protein, which is a key regulator of fungal membrane biosynthesis and its pharmacodynamics and toxicity profile. 3d structures of various glucosinolates were retrieved from PubChem, and the target protein, lanosterol-14α-demethylase (Pdb ID- 4lxj), was retrieved from the RCSB protein data bank. Molecular docking and interactions were carried out using the PyRx software using the AutoDOCK toolbar with default parameters. Dru- LiTo, ORISIS web servers were used to predict various drug likeliness predictions and Lipinski's Rule of 5, whereas admetSAR was used for prediction of toxicity, and PASS Program was used to study the antifungal and antimicrobial properties of these compounds. RESULTS This study shows that among the different compounds screened, gluconasturtiin, Glucotropaeolin, and Indolylmethyl-Glucosinolate showed the highest binding energies of -8.7 kcal/mol, -8.5 kcal/mol, and -8.3 kcal/mol with the lanosterol-14α-demethylase, respectively. Further all the compounds follow the Lipinski's rule as well as they are found to be non-carcinogenic and non-cytotoxic in nature. These compounds also show antifungal properties. CONCLUSION This study thus reveals that various glucosinolates interact with the ERG13 enzyme at various amino acid positions, which behaves as a catalytic site, thus indicates the probable mechanism of inactivation, and subsequently, these can be used as potential drug molecules. In vitro studies can be taken to further examine the utility of these compounds as antifungal agents.
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Affiliation(s)
- Gurpreet Singh
- Department of Biotechnology, Lyallpur Khalsa College, Jalandhar, India
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In-Silico Drug Toxicity and Interaction Prediction for Plant Complexes Based on Virtual Screening and Text Mining. Int J Mol Sci 2022; 23:ijms231710056. [PMID: 36077464 PMCID: PMC9456415 DOI: 10.3390/ijms231710056] [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: 08/19/2022] [Revised: 08/30/2022] [Accepted: 09/01/2022] [Indexed: 11/16/2022] Open
Abstract
Potential drug toxicities and drug interactions of redundant compounds of plant complexes may cause unexpected clinical responses or even severe adverse events. On the other hand, super-additivity of drug interactions between natural products and synthetic drugs may be utilized to gain better performance in disease management. Although without enough datasets for prediction model training, based on the SwissSimilarity and PubChem platforms, for the first time, a feasible workflow of prediction of both toxicity and drug interaction of plant complexes was built in this study. The optimal similarity score threshold for toxicity prediction of this system is 0.6171, based on an analysis of 20 different herbal medicines. From the PubChem database, 31 different sections of toxicity information such as "Acute Effects", "NIOSH Toxicity Data", "Interactions", "Hepatotoxicity", "Carcinogenicity", "Symptoms", and "Human Toxicity Values" sections have been retrieved, with dozens of active compounds predicted to exert potential toxicities. In Spatholobus suberectus Dunn (SSD), there are 9 out of 24 active compounds predicted to play synergistic effects on cancer management with various drugs or factors. The synergism between SSD, luteolin and docetaxel in the management of triple-negative breast cancer was proved by the combination index assay, synergy score detection assay, and xenograft model.
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Mosallanejad S, Mahmoodi M, Tavakkoli H, Khosravi A, Salarkia E, Keyhani A, Dabiri S, Gozashti MH, Pardakhty A, Khodabandehloo H, Pourghadamyari H. Empagliflozin induces apoptotic-signaling pathway in embryonic vasculature: In vivo and in silico approaches via chick’s yolk sac membrane model. Front Pharmacol 2022; 13:970402. [PMID: 36120349 PMCID: PMC9474685 DOI: 10.3389/fphar.2022.970402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 08/12/2022] [Indexed: 11/18/2022] Open
Abstract
The present investigation was conducted to evaluate the vascular-toxicity of empagliflozin (EMP) in embryonic vasculature. Firstly, the vascular-toxicity of the drug as well as its interaction with apoptotic regulator proteins was predicted via in silico approach. In the next step, the apoptotic-signaling pathway in embryonic vasculature was evaluated using a chick’s YSM model. In silico simulation confirmed vascular-toxicity of EMP. There was also an accurate affinity between EMP, Bax and Bcl-2 (−7.9 kcal/mol). Molecular dynamics assay revealed complex stability in the human body conditions. Furthermore, EMP is suggested to alter Bcl-2 more than BAX. Morphometric quantification of the vessels showed that the apoptotic activity of EMP in embryonic vasculature was related to a marked reduction in vessel area, vessel diameter and mean capillary area. Based on the qPCR and immunohistochemistry assays, enhanced expression level of BAX and reduced expression level of Bcl-2 confirmed apoptotic responses in the vessels of the YSM. We observed that induction of an apoptotic signal can cause the embryonic defect of the vascular system following EMP treatment. The acquired data also raised suspicions that alteration in apoptotic genes and proteins in the vasculature are two critical pathways in vascular-toxicity of EMP.
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Affiliation(s)
- Saeedeh Mosallanejad
- Department of Clinical Biochemistry, Afzalipour School of Medicine, Kerman University of Medical Sciences, Kerman, Iran
| | - Mehdi Mahmoodi
- Department of Clinical Biochemistry, Afzalipour School of Medicine, Kerman University of Medical Sciences, Kerman, Iran
- *Correspondence: Mehdi Mahmoodi, ; Hossein Pourghadamyari,
| | - Hadi Tavakkoli
- Department of Clinical Sciences, School of Veterinary Medicine, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Ahmad Khosravi
- Leishmaniasis Research Center, Kerman University of Medical Sciences, Kerman, Iran
| | - Ehsan Salarkia
- Leishmaniasis Research Center, Kerman University of Medical Sciences, Kerman, Iran
| | - Alireza Keyhani
- Leishmaniasis Research Center, Kerman University of Medical Sciences, Kerman, Iran
| | - Shahriar Dabiri
- Afzalipour School of Medicine, Pathology and Stem Cell Research Center, Kerman University of Medical Science, Kerman, Iran
| | - Mohammad Hossein Gozashti
- Endocrinology and Metabolism Research Center, Institute of Basic and Clinical Physiology Sciences, Kerman University of Medical Sciences, Kerman, Iran
| | - Abbas Pardakhty
- Pharmaceutics Research Center, Kerman University of Medical Sciences, Kerman, Iran
| | - Hadi Khodabandehloo
- Department of Clinical Biochemistry, School of Medicine Zanjan University of Medical Sciences, Zanjan, Iran
| | - Hossein Pourghadamyari
- Department of Clinical Biochemistry, Afzalipour School of Medicine, Kerman University of Medical Sciences, Kerman, Iran
- Gastroenterology and Hepatology Research Center, Institute of Basic and Clinical Physiology Sciences, Kerman University of Medical Sciences, Kerman, Iran
- *Correspondence: Mehdi Mahmoodi, ; Hossein Pourghadamyari,
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Shams Ul Hassan S, Abbas SQ, Hassan M, Jin HZ. Computational Exploration of Anti-Cancer Potential of Guaiane Dimers from Xylopia vielana by Targeting B-Raf Kinase Using Chemo-Informatics, Molecular Docking and MD Simulation Studies. Anticancer Agents Med Chem 2021; 22:731-746. [PMID: 34645380 DOI: 10.2174/1871520621666211013115500] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 02/10/2021] [Accepted: 02/22/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND Natural products from herbs are prolific to display robust anticancer activities. OBJECTIVES In the current study, B-Raf kinase protein (PDB: 3OG7), a potent target for melanoma, was tested against two guaiane-type sesquiterpene dimers, xylopin E-F, obtained from Xylopia vielana. METHODS In this work, a systematic in silico study using ADMET analysis, bioactivity score forecasts, molecular docking, and its simulations were conducted to understand compounds' pharmacological properties. RESULTS During ADMET predictions of both the compounds, Xylopin E-F has displayed a safer profile in hepatotoxicity, cytochrome inhibition, and only xylopin F displayed as non-cardiotoxic compared to FDA approved drug vemurafenib. Both the compounds were proceeded to molecular docking experiments using Autodock docking software and both the compounds Xylopin E-F have displayed higher binding potential with -11.5Kcal/mol energy compared to control vemurafenib -10.2 Kcal/mol. All the compounds were further evaluated for their MD simulations and their molecular interactions with the B-Raf kinase complex displayed precise interactions with the active gorge of the enzyme by hydrogen bonding. CONCLUSIONS Overall, xylopin F had a better profile relative to xylopin E and vemurafenib, and these findings indicated that this bio-molecule could be used as an anti-melanoma agent and as a possible anticancer drug in the future. Therefore, this is a systematic optimized in silico approach to creating an anticancer pathway for guaiane dimers against the backdrop of its potential for future drug development.
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Affiliation(s)
- Syed Shams Ul Hassan
- Shanghai Key Laboratory for Molecular Engineering of Chiral Drugs, School of Pharmacy, Shanghai Jiao Tong University, Shanghai, 200240. China
| | - Syed Qamar Abbas
- Department of Pharmacy, Sarhad University of Science and Technology, Peshawar. Pakistan
| | - Mubashir Hassan
- Institute of Molecular Biology and Biotechnology, The University of Lahore. Pakistan
| | - Hui-Zi Jin
- Shanghai Key Laboratory for Molecular Engineering of Chiral Drugs, School of Pharmacy, Shanghai Jiao Tong University, Shanghai, 200240. China
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Data-science based analysis of perceptual spaces of odors in olfactory loss. Sci Rep 2021; 11:10595. [PMID: 34012047 PMCID: PMC8134481 DOI: 10.1038/s41598-021-89969-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Accepted: 04/26/2021] [Indexed: 11/26/2022] Open
Abstract
Diminished sense of smell impairs the quality of life but olfactorily disabled people are hardly considered in measures of disability inclusion. We aimed to stratify perceptual characteristics and odors according to the extent to which they are perceived differently with reduced sense of smell, as a possible basis for creating olfactory experiences that are enjoyed in a similar way by subjects with normal or impaired olfactory function. In 146 subjects with normal or reduced olfactory function, perceptual characteristics (edibility, intensity, irritation, temperature, familiarity, hedonics, painfulness) were tested for four sets of 10 different odors each. Data were analyzed with (i) a projection based on principal component analysis and (ii) the training of a machine-learning algorithm in a 1000-fold cross-validated setting to distinguish between olfactory diagnosis based on odor property ratings. Both analytical approaches identified perceived intensity and familiarity with the odor as discriminating characteristics between olfactory diagnoses, while evoked pain sensation and perceived temperature were not discriminating, followed by edibility. Two disjoint sets of odors were identified, i.e., d = 4 “discriminating odors” with respect to olfactory diagnosis, including cis-3-hexenol, methyl salicylate, 1-butanol and cineole, and d = 7 “non-discriminating odors”, including benzyl acetate, heptanal, 4-ethyl-octanoic acid, methional, isobutyric acid, 4-decanolide and p-cresol. Different weightings of the perceptual properties of odors with normal or reduced sense of smell indicate possibilities to create sensory experiences such as food, meals or scents that by emphasizing trigeminal perceptions can be enjoyed by both normosmic and hyposmic individuals.
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Hughes RE, Elliott RJR, Dawson JC, Carragher NO. High-content phenotypic and pathway profiling to advance drug discovery in diseases of unmet need. Cell Chem Biol 2021; 28:338-355. [PMID: 33740435 DOI: 10.1016/j.chembiol.2021.02.015] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 12/10/2020] [Accepted: 02/18/2021] [Indexed: 02/07/2023]
Abstract
Conventional thinking in modern drug discovery postulates that the design of highly selective molecules which act on a single disease-associated target will yield safer and more effective drugs. However, high clinical attrition rates and the lack of progress in developing new effective treatments for many important diseases of unmet therapeutic need challenge this hypothesis. This assumption also impinges upon the efficiency of target agnostic phenotypic drug discovery strategies, where early target deconvolution is seen as a critical step to progress phenotypic hits. In this review we provide an overview of how emerging phenotypic and pathway-profiling technologies integrate to deconvolute the mechanism-of-action of phenotypic hits. We propose that such in-depth mechanistic profiling may support more efficient phenotypic drug discovery strategies that are designed to more appropriately address complex heterogeneous diseases of unmet need.
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Affiliation(s)
- Rebecca E Hughes
- Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XR, UK
| | - Richard J R Elliott
- Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XR, UK
| | - John C Dawson
- Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XR, UK
| | - Neil O Carragher
- Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XR, UK.
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Patel H, Ahmad I, Jadhav H, Pawara R, Lokwani D, Surana S. Investigating the Impact of Different Acrylamide (Electrophilic Warhead) on Osimertinib’s Pharmacological Spectrum by Molecular Mechanic and Quantum Mechanic Approach. Comb Chem High Throughput Screen 2020; 25:149-166. [DOI: 10.2174/1386207323666201204125524] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 10/14/2020] [Accepted: 10/24/2020] [Indexed: 11/22/2022]
Abstract
Background:
Lung cancer has become the prominent cause of the cancer-related deaths globally. More than 80
% of all lung cancers have been diagnosed with Non- Small Cell Lung Cancer (NSCLC). The USFDA approved osimertinib
to treat patients with metastatic T790M EGFR NSCLC on a regular basis in March 2017. Recently, C797S mutation to
osimertinib has been reported, which indicates the need for structural modification to overcome the problem of mutation.
Objective:
In this bioinformatics study, we have evaluated the impact of various acrylamide as an electrophilic warhead on
the activity and selectivity of osimertinib.
Result:
Osimertinib analouge 48, 50, 60, 61, 67, 75, 80, 86, 89, 92, 93, 116 and 124 were the most active and selective
compounds against T790M EGFR mutants compared to Osimertinib.
Conclusion:
These compounds also showed less inclination towards WT-EGFR.
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Affiliation(s)
- Harun Patel
- Department of Pharmaceutical Chemistry, R. C. Patel Institute of Pharmaceutical Education and Research, Shirpur, India
| | - Iqrar Ahmad
- Department of Pharmaceutical Chemistry, R. C. Patel Institute of Pharmaceutical Education and Research, Shirpur, India
| | - Harsha Jadhav
- Department of Pharmaceutical Chemistry, R. C. Patel Institute of Pharmaceutical Education and Research, Shirpur, India
| | - Rahul Pawara
- Department of Pharmaceutical Chemistry, R. C. Patel Institute of Pharmaceutical Education and Research, Shirpur, India
| | - Deepak Lokwani
- Department of Pharmaceutical Chemistry, R. C. Patel Institute of Pharmaceutical Education and Research, Shirpur, India
| | - Sanjay Surana
- Department of Pharmaceutical Chemistry, R. C. Patel Institute of Pharmaceutical Education and Research, Shirpur, India
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Roman DL, Isvoran A, Filip M, Ostafe V, Zinn M. In silico Assessment of Pharmacological Profile of Low Molecular Weight Oligo-Hydroxyalkanoates. Front Bioeng Biotechnol 2020; 8:584010. [PMID: 33324621 PMCID: PMC7726197 DOI: 10.3389/fbioe.2020.584010] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Accepted: 11/02/2020] [Indexed: 12/23/2022] Open
Abstract
Polyhydroxyalkanoates (PHAs) are a large class of polyesters that are biosynthesized by microorganisms at large molecular weights (Mw > 80 kDa) and have a great potential for medical applications because of their recognized biocompatibility. Among PHAs, poly(3-hydroxybutyrate), poly(4-hydroxybutyrate), poly(3-hydroxyvalerate), poly(4-hydroxyvalerate), and their copolymers are proposed to be used in biomedicine, but only poly(4-hydroxybutyrate) has been certified for medical application. Along with the hydrolysis of these polymers, low molecular weight oligomers are released typically. In this study, we have used a computational approach to assess the absorption, distribution, metabolism, and excretion (ADME)-Tox profiles of low molecular weight oligomers (≤32 units) consisting of 3-hydroxybutyrate, 4-hydroxybutyrate, 3-hydroxyvalerate, 4-hydroxyvalerate, 3-hydroxybutyrate-co-3-hydroxyvalerate, and the hypothetical PHA consisting of 4-hydroxybutyrate-co-4-hydroxyvalerate. According to our simulations, these oligomers do not show cardiotoxicity, hepatotoxicity, carcinogenicity or mutagenicity, and are neither substrates nor inhibitors of the cytochromes involved in the xenobiotic's metabolism. They also do not affect the human organic cation transporter 2 (OCT2). However, they are considered to be inhibitors of the organic anion transporters OATP1B1, and OATP1B3. In addition, they may produce eye irritation, and corrosion, skin irritation and have a low antagonistic effect on the androgen receptor.
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Affiliation(s)
- Diana Larisa Roman
- Advanced Environmental Research Laboratories, Department of Biology-Chemistry, Faculty of Chemistry, Biology, Geography, West University of Timisoara, Timisoara, Romania
| | - Adriana Isvoran
- Advanced Environmental Research Laboratories, Department of Biology-Chemistry, Faculty of Chemistry, Biology, Geography, West University of Timisoara, Timisoara, Romania
| | - Mǎdǎlina Filip
- Advanced Environmental Research Laboratories, Department of Biology-Chemistry, Faculty of Chemistry, Biology, Geography, West University of Timisoara, Timisoara, Romania
| | - Vasile Ostafe
- Advanced Environmental Research Laboratories, Department of Biology-Chemistry, Faculty of Chemistry, Biology, Geography, West University of Timisoara, Timisoara, Romania
| | - Manfred Zinn
- Institute of Life Technologies, University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis), Delémont, Switzerland
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Reyes-Chaparro A, Verdín-Betancourt FA, Sierra-Santoyo A. Human Biotransformation Pathway of Temephos Using an In Silico Approach. Chem Res Toxicol 2020; 33:2765-2774. [PMID: 33112607 DOI: 10.1021/acs.chemrestox.0c00105] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Temephos is an organophosphorothioate (OPT) larvicide used for controlling vectors of diseases such as dengue, chikungunya, and Zika. OPTs require a metabolic activation mediated by cytochrome P540 (CYP) to cause toxic effects, such as acetylcholinesterase (AChE) activity inhibition. There is no information about temephos biotransformation in humans, and it is considered to have low toxicity in mammals. Recent studies have reported that temephos-oxidized derivatives cause AChE inhibition. The aim of this study was to propose the human biotransformation pathway of temephos using in silico tools. The metabolic pathway was proposed using the MetaUltra program of MultiCase software as well as the Way2Drug and Xenosite web servers. The results show the following three essential reactions of phase I metabolism: (1) S-oxidation, (2) oxidative desulfurization, and (3) dephosphorylation, as well as the formation of 19 possible intermediary metabolites. Temephos dephosphorylation is the most likely reaction, and it enables phase II metabolism for glucuronidation to be excreted. However, the CYP-dependent metabolism showed that temephos oxon can be formed, which could lead to toxic effects in mammals. CYP2B6, 2C9, and 2C19 are the main isoforms involved in temephos metabolism, and CYP3A4 and 2D6 have minor contributions. According to computational predictions, the highest probability of temephos metabolism is dephosphorylation and phase II reactions that do not produce cholinergic toxic effects; nonetheless, the participation of CYPs is highly possible if the primary reaction is depleted.
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Affiliation(s)
- Andrés Reyes-Chaparro
- Departamento de Toxicologı́a, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (Cinvestav-IPN), Av. IPN 2508, Col. Zacatenco, G. A. Madero, Mexico City 07360, Mexico
| | - Francisco Alberto Verdín-Betancourt
- Departamento de Toxicologı́a, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (Cinvestav-IPN), Av. IPN 2508, Col. Zacatenco, G. A. Madero, Mexico City 07360, Mexico
| | - Adolfo Sierra-Santoyo
- Departamento de Toxicologı́a, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (Cinvestav-IPN), Av. IPN 2508, Col. Zacatenco, G. A. Madero, Mexico City 07360, Mexico
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Poroikov VV. Computer-Aided Drug Design: from Discovery of Novel Pharmaceutical Agents to Systems Pharmacology. BIOCHEMISTRY (MOSCOW), SUPPLEMENT SERIES B: BIOMEDICAL CHEMISTRY 2020. [DOI: 10.1134/s1990750820030117] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
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Cortés-Ciriano I, Škuta C, Bender A, Svozil D. QSAR-derived affinity fingerprints (part 2): modeling performance for potency prediction. J Cheminform 2020; 12:41. [PMID: 33431016 PMCID: PMC7339533 DOI: 10.1186/s13321-020-00444-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Accepted: 05/16/2020] [Indexed: 01/22/2023] Open
Abstract
Affinity fingerprints report the activity of small molecules across a set of assays, and thus permit to gather information about the bioactivities of structurally dissimilar compounds, where models based on chemical structure alone are often limited, and model complex biological endpoints, such as human toxicity and in vitro cancer cell line sensitivity. Here, we propose to model in vitro compound activity using computationally predicted bioactivity profiles as compound descriptors. To this aim, we apply and validate a framework for the calculation of QSAR-derived affinity fingerprints (QAFFP) using a set of 1360 QSAR models generated using Ki, Kd, IC50 and EC50 data from ChEMBL database. QAFFP thus represent a method to encode and relate compounds on the basis of their similarity in bioactivity space. To benchmark the predictive power of QAFFP we assembled IC50 data from ChEMBL database for 18 diverse cancer cell lines widely used in preclinical drug discovery, and 25 diverse protein target data sets. This study complements part 1 where the performance of QAFFP in similarity searching, scaffold hopping, and bioactivity classification is evaluated. Despite being inherently noisy, we show that using QAFFP as descriptors leads to errors in prediction on the test set in the ~ 0.65-0.95 pIC50 units range, which are comparable to the estimated uncertainty of bioactivity data in ChEMBL (0.76-1.00 pIC50 units). We find that the predictive power of QAFFP is slightly worse than that of Morgan2 fingerprints and 1D and 2D physicochemical descriptors, with an effect size in the 0.02-0.08 pIC50 units range. Including QSAR models with low predictive power in the generation of QAFFP does not lead to improved predictive power. Given that the QSAR models we used to compute the QAFFP were selected on the basis of data availability alone, we anticipate better modeling results for QAFFP generated using more diverse and biologically meaningful targets. Data sets and Python code are publicly available at https://github.com/isidroc/QAFFP_regression .
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Affiliation(s)
- Isidro Cortés-Ciriano
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK. .,European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, CB10 1SD, UK.
| | - Ctibor Škuta
- CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Institute of Molecular Genetics of the ASCR, v. v. i., Vídeňská 1083, 142 20, Prague, Czech Republic
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
| | - Daniel Svozil
- CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Institute of Molecular Genetics of the ASCR, v. v. i., Vídeňská 1083, 142 20, Prague, Czech Republic.,CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Department of Informatics and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology Prague, Technická 5, 166 28, Prague, Czech Republic
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12
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Škuta C, Cortés-Ciriano I, Dehaen W, Kříž P, van Westen GJP, Tetko IV, Bender A, Svozil D. QSAR-derived affinity fingerprints (part 1): fingerprint construction and modeling performance for similarity searching, bioactivity classification and scaffold hopping. J Cheminform 2020; 12:39. [PMID: 33431038 PMCID: PMC7260783 DOI: 10.1186/s13321-020-00443-6] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Accepted: 05/16/2020] [Indexed: 02/11/2023] Open
Abstract
An affinity fingerprint is the vector consisting of compound’s affinity or potency against the reference panel of protein targets. Here, we present the QAFFP fingerprint, 440 elements long in silico QSAR-based affinity fingerprint, components of which are predicted by Random Forest regression models trained on bioactivity data from the ChEMBL database. Both real-valued (rv-QAFFP) and binary (b-QAFFP) versions of the QAFFP fingerprint were implemented and their performance in similarity searching, biological activity classification and scaffold hopping was assessed and compared to that of the 1024 bits long Morgan2 fingerprint (the RDKit implementation of the ECFP4 fingerprint). In both similarity searching and biological activity classification, the QAFFP fingerprint yields retrieval rates, measured by AUC (~ 0.65 and ~ 0.70 for similarity searching depending on data sets, and ~ 0.85 for classification) and EF5 (~ 4.67 and ~ 5.82 for similarity searching depending on data sets, and ~ 2.10 for classification), comparable to that of the Morgan2 fingerprint (similarity searching AUC of ~ 0.57 and ~ 0.66, and EF5 of ~ 4.09 and ~ 6.41, depending on data sets, classification AUC of ~ 0.87, and EF5 of ~ 2.16). However, the QAFFP fingerprint outperforms the Morgan2 fingerprint in scaffold hopping as it is able to retrieve 1146 out of existing 1749 scaffolds, while the Morgan2 fingerprint reveals only 864 scaffolds.![]()
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Affiliation(s)
- C Škuta
- CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Institute of Molecular Genetics of the ASCR, v. v. i., Vídeňská 1083, 142 20, Prague 4, Czech Republic
| | - I Cortés-Ciriano
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
| | - W Dehaen
- CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Institute of Molecular Genetics of the ASCR, v. v. i., Vídeňská 1083, 142 20, Prague 4, Czech Republic.,CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Department of Informatics and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology Prague, Technická 5, 166 28, Prague, Czech Republic
| | - P Kříž
- Department of Mathematics, Faculty of Chemical Engineering, University of Chemistry and Technology Prague, Technická 5, 166 28, Prague, Czech Republic
| | - G J P van Westen
- Computational Drug Discovery, Drug Discovery and Safety, LACDR, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands
| | - I V Tetko
- Helmholtz Zentrum Muenchen - German Research Center for Environmental Health (GmbH) and BIGCHEM GmbH, Ingolstaedter Landstrasse 1, 85764, Neuherberg, Germany
| | - A Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
| | - D Svozil
- CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Institute of Molecular Genetics of the ASCR, v. v. i., Vídeňská 1083, 142 20, Prague 4, Czech Republic. .,CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Department of Informatics and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology Prague, Technická 5, 166 28, Prague, Czech Republic.
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Poroikov VV. [Computer-aided drug design: from discovery of novel pharmaceutical agents to systems pharmacology]. BIOMEDIT︠S︡INSKAI︠A︡ KHIMII︠A︡ 2020; 66:30-41. [PMID: 32116224 DOI: 10.18097/pbmc20206601030] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
New drug discovery is based on the analysis of public information about the mechanisms of the disease, molecular targets, and ligands, which interaction with the target could lead to the normalization of the pathological process. The available data on diseases, drugs, pharmacological effects, molecular targets, and drug-like substances, taking into account the combinatorics of the associative relations between them, correspond to the Big Data. To analyze such data, the application of computer-aided drug design methods is necessary. An overview of the studies in this area performed by the Laboratory for Structure-Function Based Drug Design of IBMC is presented. We have developed the approaches to identifying promising pharmacological targets, predicting several thousand types of biological activity based on the structural formula of the compound, analyzing protein-ligand interactions based on assessing local similarity of amino acid sequences, identifying likely molecular mechanisms of side effects of drugs, calculating the integral toxicity of drugs taking into account their metabolism, have been developed in the human body, predicting sustainable and sensitive options strains and evaluating the effectiveness of combinations of antiretroviral drugs in patients, taking into account the molecular genetic characteristics of the clinical isolates of HIV-1. Our computer programs are implemented as the web-services freely available on the Internet, which are used by thousands of researchers from many countries of the world to select the most promising substances for the synthesis and determine the priority areas for experimental testing of their biological activity.
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Affiliation(s)
- V V Poroikov
- Institute of Biomedical Chemistry, Moscow, Russia
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14
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Macalino SJY, Billones JB, Organo VG, Carrillo MCO. In Silico Strategies in Tuberculosis Drug Discovery. Molecules 2020; 25:E665. [PMID: 32033144 PMCID: PMC7037728 DOI: 10.3390/molecules25030665] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 12/15/2019] [Accepted: 12/17/2019] [Indexed: 12/16/2022] Open
Abstract
Tuberculosis (TB) remains a serious threat to global public health, responsible for an estimated 1.5 million mortalities in 2018. While there are available therapeutics for this infection, slow-acting drugs, poor patient compliance, drug toxicity, and drug resistance require the discovery of novel TB drugs. Discovering new and more potent antibiotics that target novel TB protein targets is an attractive strategy towards controlling the global TB epidemic. In silico strategies can be applied at multiple stages of the drug discovery paradigm to expedite the identification of novel anti-TB therapeutics. In this paper, we discuss the current TB treatment, emergence of drug resistance, and the effective application of computational tools to the different stages of TB drug discovery when combined with traditional biochemical methods. We will also highlight the strengths and points of improvement in in silico TB drug discovery research, as well as possible future perspectives in this field.
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Affiliation(s)
- Stephani Joy Y. Macalino
- Chemistry Department, De La Salle University, 2401 Taft Avenue, Manila 0992, Philippines;
- OVPAA-EIDR Program, “Computer-Aided Discovery of Compounds for the Treatment of Tuberculosis in the Philippines”, Department of Physical Sciences and Mathematics, College of Arts and Sciences, University of the Philippines Manila, Manila 1000, Philippines; (V.G.O.); (M.C.O.C.)
| | - Junie B. Billones
- OVPAA-EIDR Program, “Computer-Aided Discovery of Compounds for the Treatment of Tuberculosis in the Philippines”, Department of Physical Sciences and Mathematics, College of Arts and Sciences, University of the Philippines Manila, Manila 1000, Philippines; (V.G.O.); (M.C.O.C.)
| | - Voltaire G. Organo
- OVPAA-EIDR Program, “Computer-Aided Discovery of Compounds for the Treatment of Tuberculosis in the Philippines”, Department of Physical Sciences and Mathematics, College of Arts and Sciences, University of the Philippines Manila, Manila 1000, Philippines; (V.G.O.); (M.C.O.C.)
| | - Maria Constancia O. Carrillo
- OVPAA-EIDR Program, “Computer-Aided Discovery of Compounds for the Treatment of Tuberculosis in the Philippines”, Department of Physical Sciences and Mathematics, College of Arts and Sciences, University of the Philippines Manila, Manila 1000, Philippines; (V.G.O.); (M.C.O.C.)
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15
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Tarasova O, Biziukova N, Kireev D, Lagunin A, Ivanov S, Filimonov D, Poroikov V. A Computational Approach for the Prediction of Treatment History and the Effectiveness or Failure of Antiretroviral Therapy. Int J Mol Sci 2020; 21:ijms21030748. [PMID: 31979356 PMCID: PMC7037494 DOI: 10.3390/ijms21030748] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 01/20/2020] [Accepted: 01/21/2020] [Indexed: 02/01/2023] Open
Abstract
Human Immunodeficiency Virus Type 1 (HIV-1) infection is associated with high mortality if no therapy is provided. Currently, the treatment of an HIV-1 positive patient requires that several drugs should be taken simultaneously. The resistance of the virus to an antiretroviral drug may lead to treatment failure. Our approach focuses on predicting the exposure of a particular viral variant to an antiretroviral drug or drug combination. It also aims at the prediction of drug treatment success or failure. We utilized nucleotide sequences of HIV-1 encoding protease and reverse transcriptase to perform such types of prediction. The PASS (Prediction of Activity Spectra for Substances) algorithm based on the naive Bayesian classifier was used to make a prediction. We calculated the probability of whether a sequence belonged (P1) or did not belong (P0) to the class associated with exposure of the viral sequence to the set of drugs that can be associated with resistance to the set of drugs. The accuracy calculated as the average Area Under the ROC (Receiver Operating Characteristic) Curve (AUC/ROC) for classifying exposure of the sequence to the HIV-1 protease inhibitors was 0.81 (±0.07), and for HIV-1 reverse transcriptase, it was 0.83 (±0.07). To predict cases of treatment effectiveness or failure, we used P1 and P0 values, obtained in PASS, along with the binary vector constructed based on short nucleotide descriptors and the applied random forest classifier. Average AUC/ROC prediction accuracy for the prediction of treatment effectiveness or failure for the combinations of HIV-1 protease inhibitors was 0.82 (±0.06) and of HIV-1 reverse transcriptase was 0.76 (±0.09).
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Affiliation(s)
- Olga Tarasova
- Department of Bioinformatics, Institute of Biomedical Chemistry, 119121 Moscow, Russia; (N.B.); (A.L.); (S.I.); (D.F.); (V.P.)
- Correspondence:
| | - Nadezhda Biziukova
- Department of Bioinformatics, Institute of Biomedical Chemistry, 119121 Moscow, Russia; (N.B.); (A.L.); (S.I.); (D.F.); (V.P.)
| | - Dmitry Kireev
- Central Research Institute of Epidemiology, 111123 Moscow, Russia;
| | - Alexey Lagunin
- Department of Bioinformatics, Institute of Biomedical Chemistry, 119121 Moscow, Russia; (N.B.); (A.L.); (S.I.); (D.F.); (V.P.)
- Department of Bioinformatics, Pirogov Russian National Research Medical University, 117997 Moscow, Russia
| | - Sergey Ivanov
- Department of Bioinformatics, Institute of Biomedical Chemistry, 119121 Moscow, Russia; (N.B.); (A.L.); (S.I.); (D.F.); (V.P.)
- Department of Bioinformatics, Pirogov Russian National Research Medical University, 117997 Moscow, Russia
| | - Dmitry Filimonov
- Department of Bioinformatics, Institute of Biomedical Chemistry, 119121 Moscow, Russia; (N.B.); (A.L.); (S.I.); (D.F.); (V.P.)
| | - Vladimir Poroikov
- Department of Bioinformatics, Institute of Biomedical Chemistry, 119121 Moscow, Russia; (N.B.); (A.L.); (S.I.); (D.F.); (V.P.)
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A Toolbox for the Identification of Modes of Action of Natural Products. PROGRESS IN THE CHEMISTRY OF ORGANIC NATURAL PRODUCTS 110 2019; 110:73-97. [DOI: 10.1007/978-3-030-14632-0_3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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17
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Abstract
Abstract
The biological pre-validation of natural products (NPs) and their underlying frameworks ensures an unrivaled source of inspiration for chemical probe and drug design. However, the poor knowledge of their drug target counterparts critically hinders the broader exploration of NPs in chemical biology and molecular medicine. Cutting-edge algorithms now provide powerful means for the target deconvolution of phenotypic screen hits and generate motivated research hypotheses. Herein, we present recent progress in artificial intelligence applied to target identification that may accelerate future NP-inspired molecular medicine.
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Salgueiro AC, Folmer V, Bassante FE, Cardoso MH, da Rosa HS, Puntel GO. Predictive antidiabetic activities of plants used by persons with Diabetes mellitus. Complement Ther Med 2018; 41:1-9. [DOI: 10.1016/j.ctim.2018.08.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2018] [Revised: 06/15/2018] [Accepted: 08/20/2018] [Indexed: 11/29/2022] Open
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19
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Olotu FA, Munsamy G, Soliman MES. Does Size Really Matter? Probing the Efficacy of Structural Reduction in the Optimization of Bioderived Compounds - A Computational "Proof-of-Concept". Comput Struct Biotechnol J 2018; 16:573-586. [PMID: 30546858 PMCID: PMC6280605 DOI: 10.1016/j.csbj.2018.11.005] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Revised: 11/14/2018] [Accepted: 11/18/2018] [Indexed: 02/07/2023] Open
Abstract
Over the years, numerous synthetic approaches have been utilized in drug design to improve the pharmacological properties of naturally derived compounds and most importantly, minimize toxic effects associated with their transition to drugs. The reduction of complex bioderived compounds to simpler bioactive fragments has been identified as a viable strategy to develop lead compounds with improved activities and minimal toxicities. Although this ‘reductive’ strategy has been widely exemplified, underlying biological events remain unresolved, hence the unanswered question remains how does the fragmentation of a natural compound improve its bioactivity and reduce toxicities? Herein, using a combinatorial approach, we initialize a computational “proof-of- concept” to expound the differential pharmacological and antagonistic activities of a natural compound, Anguinomycin D, and its synthetic fragment, SB640 towards Exportin Chromosome Region Maintenance 1 (CRM1). Interestingly, our findings revealed that in comparison with the parent compound, SB640 exhibited improved pharmacological attributes, while toxicities and off-target activities were relatively minimal. Moreover, we observed that the reduced size of SB640 allowed ‘deep access’ at the Nuclear Export Signals (NES) binding groove of CRM1, which favored optimal and proximal positioning towards crucial residues while the presence of the long polyketide tail in Anguinomycin D constrained its burial at the hydrophobic groove. Furthermore, with regards to their antagonistic functions, structural inactivation (rigidity) was more pronounced in CRM1 when bound by SB640 as compared to Anguinomycin D. These findings provide essential insights that portray synthetic fragmentation of natural compounds as a feasible approach towards the discovery of potential leads in disease treatment.
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Affiliation(s)
- Fisayo A Olotu
- Molecular Bio-computation and Drug Design Laboratory, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban 4001, South Africa
| | - Geraldene Munsamy
- Molecular Bio-computation and Drug Design Laboratory, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban 4001, South Africa
| | - Mahmoud E S Soliman
- Molecular Bio-computation and Drug Design Laboratory, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban 4001, South Africa
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20
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Lagunin AA, Romanova MA, Zadorozhny AD, Kurilenko NS, Shilov BV, Pogodin PV, Ivanov SM, Filimonov DA, Poroikov VV. Comparison of Quantitative and Qualitative (Q)SAR Models Created for the Prediction of K i and IC 50 Values of Antitarget Inhibitors. Front Pharmacol 2018; 9:1136. [PMID: 30364128 PMCID: PMC6192375 DOI: 10.3389/fphar.2018.01136] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Accepted: 09/18/2018] [Indexed: 12/20/2022] Open
Abstract
Estimation of interaction of drug-like compounds with antitargets is important for the assessment of possible toxic effects during drug development. Publicly available online databases provide data on the experimental results of chemical interactions with antitargets, which can be used for the creation of (Q)SAR models. The structures and experimental Ki and IC50 values for compounds tested on the inhibition of 30 antitargets from the ChEMBL 20 database were used. Data sets with Ki and IC50 values including more than 100 compounds were created for each antitarget. The (Q)SAR models were created by GUSAR software using quantitative neighborhoods of atoms (QNA), multilevel neighborhoods of atoms (MNA) descriptors, and self-consistent regression. The accuracy of (Q)SAR models was validated by the fivefold cross-validation procedure. The balanced accuracy was higher for qualitative SAR models (0.80 and 0.81 for Ki and IC50 values, respectively) than for quantitative QSAR models (0.73 and 0.76 for Ki and IC50 values, respectively). In most cases, sensitivity was higher for SAR models than for QSAR models, but specificity was higher for QSAR models. The mean R 2 and RMSE were 0.64 and 0.77 for Ki values and 0.59 and 0.73 for IC50 values, respectively. The number of compounds falling within the applicability domain was higher for SAR models than for the test sets.
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Affiliation(s)
- Alexey A. Lagunin
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
- Department of Bioinformatics, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Maria A. Romanova
- Department of Bioinformatics, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Anton D. Zadorozhny
- Department of Bioinformatics, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Natalia S. Kurilenko
- Department of Bioinformatics, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Boris V. Shilov
- Department of Bioinformatics, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Pavel V. Pogodin
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
| | - Sergey M. Ivanov
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
- Department of Bioinformatics, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Dmitry A. Filimonov
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
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Van Vleet TR, Liguori MJ, Lynch JJ, Rao M, Warder S. Screening Strategies and Methods for Better Off-Target Liability Prediction and Identification of Small-Molecule Pharmaceuticals. SLAS DISCOVERY 2018; 24:1-24. [PMID: 30196745 DOI: 10.1177/2472555218799713] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Pharmaceutical discovery and development is a long and expensive process that, unfortunately, still results in a low success rate, with drug safety continuing to be a major impedance. Improved safety screening strategies and methods are needed to more effectively fill this critical gap. Recent advances in informatics are now making it possible to manage bigger data sets and integrate multiple sources of screening data in a manner that can potentially improve the selection of higher-quality drug candidates. Integrated screening paradigms have become the norm in Pharma, both in discovery screening and in the identification of off-target toxicity mechanisms during later-stage development. Furthermore, advances in computational methods are making in silico screens more relevant and suggest that they may represent a feasible option for augmenting the current screening paradigm. This paper outlines several fundamental methods of the current drug screening processes across Pharma and emerging techniques/technologies that promise to improve molecule selection. In addition, the authors discuss integrated screening strategies and provide examples of advanced screening paradigms.
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Affiliation(s)
- Terry R Van Vleet
- 1 Department of Investigative Toxicology and Pathology, AbbVie, N Chicago, IL, USA
| | - Michael J Liguori
- 1 Department of Investigative Toxicology and Pathology, AbbVie, N Chicago, IL, USA
| | - James J Lynch
- 2 Department of Integrated Science and Technology, AbbVie, N Chicago, IL, USA
| | - Mohan Rao
- 1 Department of Investigative Toxicology and Pathology, AbbVie, N Chicago, IL, USA
| | - Scott Warder
- 3 Department of Target Enabling Science and Technology, AbbVie, N Chicago, IL, USA
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22
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Roman M, Roman DL, Ostafe V, Ciorsac A, Isvoran A. Computational Assessment of Pharmacokinetics and Biological Effects of Some Anabolic and Androgen Steroids. Pharm Res 2018; 35:41. [PMID: 29404794 DOI: 10.1007/s11095-018-2353-1] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Accepted: 01/19/2018] [Indexed: 12/26/2022]
Abstract
PURPOSE The aim of this study is to use computational approaches to predict the ADME-Tox profiles, pharmacokinetics, molecular targets, biological activity spectra and side/toxic effects of 31 anabolic and androgen steroids in humans. METHODS The following computational tools are used: (i) FAFDrugs4, SwissADME and admetSARfor obtaining the ADME-Tox profiles and for predicting pharmacokinetics;(ii) SwissTargetPrediction and PASS online for predicting the molecular targets and biological activities; (iii) PASS online, Toxtree, admetSAR and Endocrine Disruptomefor envisaging the specific toxicities; (iv) SwissDock to assess the interactions of investigated steroids with cytochromes involved in drugs metabolism. RESULTS Investigated steroids usually reveal a high gastrointestinal absorption and a good oral bioavailability, may inhibit someof the human cytochromes involved in the metabolism of xenobiotics (CYP2C9 being the most affected) and reflect a good capacity for skin penetration. There are predicted numerous side effects of investigated steroids in humans: genotoxic carcinogenicity, hepatotoxicity, cardiovascular, hematotoxic and genitourinary effects, dermal irritations, endocrine disruption and reproductive dysfunction. CONCLUSIONS These results are important to be known as an occupational exposure to anabolic and androgenic steroids at workplaces may occur and because there also is a deliberate human exposure to steroids for their performance enhancement and anti-aging properties.
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Affiliation(s)
- Marin Roman
- Department of Biology-Chemistry and Advanced Environmental Research Laboratories, West University of Timisoara, 16 Pestalozzi, 300115, Timisoara, Romania
| | - Diana Larisa Roman
- Department of Biology-Chemistry and Advanced Environmental Research Laboratories, West University of Timisoara, 16 Pestalozzi, 300115, Timisoara, Romania
| | - Vasile Ostafe
- Department of Biology-Chemistry and Advanced Environmental Research Laboratories, West University of Timisoara, 16 Pestalozzi, 300115, Timisoara, Romania
| | - Alecu Ciorsac
- Department of Physical Education and Sport, Politehnica University of Timisoara, Timisoara, Romania
| | - Adriana Isvoran
- Department of Biology-Chemistry and Advanced Environmental Research Laboratories, West University of Timisoara, 16 Pestalozzi, 300115, Timisoara, Romania.
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In search of selective 11β-HSD type 1 inhibitors without nephrotoxicity: An approach to resolve the metabolic syndrome by virtual based screening. ARAB J CHEM 2018. [DOI: 10.1016/j.arabjc.2015.08.003] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
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24
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Ndagi U, Mhlongo NN, Soliman ME. Re-emergence of an orphan therapeutic target for the treatment of resistant prostate cancer - a thorough conformational and binding analysis for ROR-γ protein. J Biomol Struct Dyn 2018; 36:335-350. [PMID: 28027708 DOI: 10.1080/07391102.2016.1277555] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2016] [Accepted: 12/20/2016] [Indexed: 01/14/2023]
Abstract
Recent studies have linked a deadly form of prostate cancer known as metastatic castration-resistant prostate cancer to retinoic acid-related orphan-receptor gamma (ROR-γ). Most of these studies continued to place ROR-γ as orphan because of unidentifiable inhibitor. Recently identified inhibitors of ROR-γ and their therapeutic potential were evaluated, among which inhibitor XY018 was the potent. However, molecular understanding of the conformational features of XY018-ROR-γ complex is still elusive. Herein, molecular dynamics simulations were conducted on HC9-ROR-γ and XY018-ROR-γ complexes to understand their conformational features at molecular level and the influence of XY018 binding on the dynamics of ROR-γ with the aid of post-dynamic analytical tools. These include; principal component analysis, radius of gyration, binding free energy calculation (MM/GBSA), per-residue fluctuation and hydrogen bond occupancy. Findings from this study revealed that (1) hydrophobic packing contributes significantly to binding free energy, (2) Ile136 and Leu60 exhibited high hydrogen-bond occupancy in XY018-ROR-γ and HC9-ROR-γ, respectively, (3) XY018-ROR-γ displayed a relatively high loop region residue fluctuation compared to HC9-ROR-γ, (4) electrostatic interactions are a potential binding force in XY018-ROR-γ complex compared to HC9-ROR-γ, (5) XY018-ROR-γ assumes a rigid conformation which is highlighted by a decrease in residual fluctuation, (6) XY018 could potentially induce pseudoporphyria, nephritis and interstitial nephritis but potentially safe in renal failure. This study could serve as a base line for the design of new potential ROR-γ inhibitors.
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Affiliation(s)
- Umar Ndagi
- a Molecular Modelling and Drug Design Research Group, School of Health Sciences, University of KwaZulu-Natal , Westville , Durban 4000 , South Africa
| | - Ndumiso N Mhlongo
- a Molecular Modelling and Drug Design Research Group, School of Health Sciences, University of KwaZulu-Natal , Westville , Durban 4000 , South Africa
| | - Mahmoud E Soliman
- a Molecular Modelling and Drug Design Research Group, School of Health Sciences, University of KwaZulu-Natal , Westville , Durban 4000 , South Africa
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Malhotra S, Mugumbate G, Blundell TL, Higueruelo AP. TIBLE: a web-based, freely accessible resource for small-molecule binding data for mycobacterial species. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2018; 2017:3866794. [PMID: 29220433 PMCID: PMC5502366 DOI: 10.1093/database/bax041] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2017] [Accepted: 04/25/2017] [Indexed: 02/03/2023]
Abstract
Database URL http://www-cryst.bioc.cam.ac.uk/tible/.
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Affiliation(s)
- Sony Malhotra
- Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge CB2 1GA, UK
| | - Grace Mugumbate
- Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge CB2 1GA, UK
| | - Tom L Blundell
- Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge CB2 1GA, UK
| | - Alicia P Higueruelo
- Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge CB2 1GA, UK
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26
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Mohd Fauzi F, John CM, Karunanidhi A, Mussa HY, Ramasamy R, Adam A, Bender A. Understanding the mode-of-action of Cassia auriculata via in silico and in vivo studies towards validating it as a long term therapy for type II diabetes. JOURNAL OF ETHNOPHARMACOLOGY 2017; 197:61-72. [PMID: 27452659 DOI: 10.1016/j.jep.2016.07.058] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2016] [Revised: 07/13/2016] [Accepted: 07/21/2016] [Indexed: 05/23/2023]
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE Cassia auriculata (CA) is used as an antidiabetic therapy in Ayurvedic and Siddha practice. This study aimed to understand the mode-of-action of CA via combined cheminformatics and in vivo biological analysis. In particular, the effect of 10 polyphenolic constituents of CA in modulating insulin and immunoprotective pathways were studied. MATERIALS AND METHODS In silico target prediction was first employed to predict the probability of the polyphenols interacting with key protein targets related to insulin signalling, based on a model trained on known bioactivity data and chemical similarity considerations. Next, CA was investigated in in vivo studies where induced type 2 diabetic rats were treated with CA for 28 days and the expression levels of genes regulating insulin signalling pathway, glucose transporters of hepatic (GLUT2) and muscular (GLUT4) tissue, insulin receptor substrate (IRS), phosphorylated insulin receptor (AKT), gluconeogenesis (G6PC and PCK-1), along with inflammatory mediators genes (NF-κB, IL-6, IFN-γ and TNF-α) and peroxisome proliferators-activated receptor gamma (PPAR-γ) were determined by qPCR. RESULTS In silico analysis shows that several of the top 20 enriched targets predicted for the constituents of CA are involved in insulin signalling pathways e.g. PTPN1, PCK-α, AKT2, PI3K-γ. Some of the predictions were supported by scientific literature such as the prediction of PI3K for epigallocatechin gallate. Based on the in silico and in vivo findings, we hypothesized that CA may enhance glucose uptake and glucose transporter expressions via the IRS signalling pathway. This is based on AKT2 and PI3K-γ being listed in the top 20 enriched targets. In vivo analysis shows significant increase in the expression of IRS, AKT, GLUT2 and GLUT4. CA may also affect the PPAR-γ signalling pathway. This is based on the CA-treated groups showing significant activation of PPAR-γ in the liver compared to control. PPAR-γ was predicted by the in silico target prediction with high normalisation rate although it was not in the top 20 most enriched targets. CA may also be involved in the gluconeogenesis and glycogenolysis in the liver based on the downregulation of G6PC and PCK-1 genes seen in CA-treated groups. In addition, CA-treated groups also showed decreased cholesterol, triglyceride, glucose, CRP and Hb1Ac levels, and increased insulin and C-peptide levels. These findings demonstrate the insulin secretagogue and sensitizer effect of CA. CONCLUSION Based on both an in silico and in vivo analysis, we propose here that CA mediates glucose/lipid metabolism via the PI3K signalling pathway, and influence AKT thereby causing insulin secretion and insulin sensitivity in peripheral tissues. CA enhances glucose uptake and expression of glucose transporters in particular via the upregulation of GLUT2 and GLUT4. Thus, based on its ability to modulate immunometabolic pathways, CA appears as an attractive long term therapy for T2DM even at relatively low doses.
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Affiliation(s)
- Fazlin Mohd Fauzi
- Department of Pharmacology and Chemistry, Faculty of Pharmacy, Universiti Teknologi MARA, 42300 Bandar Puncak Alam, Selangor Darul Ehsan, Malaysia; Center for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, CB2 1EW Cambridge, United Kingdom.
| | - Cini Mathew John
- Department of Pharmacology and Chemistry, Faculty of Pharmacy, Universiti Teknologi MARA, 42300 Bandar Puncak Alam, Selangor Darul Ehsan, Malaysia; Department of Physiology and Pharmacology, Faculty of Medicine, University of Calgary, 3330 Hospital Dr. NW, Calgary, AB, Canada T2N 4N1
| | - Arunkumar Karunanidhi
- Department of Medical Microbiology and Parasitology, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor Darul Ehsan, Malaysia
| | - Hamse Y Mussa
- Center for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, CB2 1EW Cambridge, United Kingdom
| | - Rajesh Ramasamy
- Immunology Unit, Department of Pathology, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor Darul Ehsan, Malaysia
| | - Aishah Adam
- Department of Pharmacology and Chemistry, Faculty of Pharmacy, Universiti Teknologi MARA, 42300 Bandar Puncak Alam, Selangor Darul Ehsan, Malaysia
| | - Andreas Bender
- Center for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, CB2 1EW Cambridge, United Kingdom
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Liu X, Baarsma H, Thiam C, Montrone C, Brauner B, Fobo G, Heier JS, Duscha S, Königshoff M, Angeli V, Ruepp A, Campillos M. Systematic Identification of Pharmacological Targets from Small-Molecule Phenotypic Screens. Cell Chem Biol 2016; 23:1302-1313. [DOI: 10.1016/j.chembiol.2016.08.011] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2016] [Revised: 06/10/2016] [Accepted: 08/05/2016] [Indexed: 01/29/2023]
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Synthesis, PASS-Predication and in Vitro Antimicrobial Activity of Benzyl 4-O-benzoyl-α-l-rhamnopyranoside Derivatives. Int J Mol Sci 2016; 17:ijms17091412. [PMID: 27618893 PMCID: PMC5037692 DOI: 10.3390/ijms17091412] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2016] [Revised: 08/03/2016] [Accepted: 08/16/2016] [Indexed: 11/17/2022] Open
Abstract
Benzyl α-l-rhamnopyranoside 4, obtained by both conventional and microwave assisted glycosidation techniques, was subjected to 2,3-O-isopropylidene protection to yield compound 5 which on benzoylation and subsequent deprotection of isopropylidene group gave the desired 4-O-benzoylrhamnopyranoside 7 in reasonable yield. Di-O-acetyl derivative of benzoate 7 was prepared to get newer rhamnopyranoside. The structure activity relationship (SAR) of the designed compounds was performed along with the prediction of activity spectra for substances (PASS) training set. Experimental studies based on antimicrobial activities verified the predictions obtained by the PASS software. Protected rhamnopyranosides 5 and 6 exhibited slight distortion from regular 1C4 conformation, probably due to the fusion of pyranose and isopropylidene ring. Synthesized rhamnopyranosides 4–8 were employed as test chemicals for in vitro antimicrobial evaluation against eight human pathogenic bacteria and two fungi. Antimicrobial and SAR study showed that the rhamnopyranosides were prone against fungal organisms as compared to that of the bacterial pathogens. Interestingly, PASS prediction of the rhamnopyranoside derivatives 4–8 were 0.49 < Pa < 0.60 (where Pa is probability ‘to be active’) as antibacterial and 0.65 < Pa < 0.73 as antifungal activities, which showed significant agreement with experimental data, suggesting rhamnopyranoside derivatives 4–8 were more active against pathogenic fungi as compared to human pathogenic bacteria thus, there is a more than 50% chance that the rhamnopyranoside derivative structures 4–8 have not been reported with antimicrobial activity, making it a possible valuable lead compound.
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Yehye WA, Abdul Rahman N, Saad O, Ariffin A, Abd Hamid SB, Alhadi AA, Kadir FA, Yaeghoobi M, Matlob AA. Rational Design and Synthesis of New, High Efficiency, Multipotent Schiff Base-1,2,4-triazole Antioxidants Bearing Butylated Hydroxytoluene Moieties. Molecules 2016; 21:E847. [PMID: 27367658 PMCID: PMC6273539 DOI: 10.3390/molecules21070847] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2016] [Revised: 06/22/2016] [Accepted: 06/24/2016] [Indexed: 11/16/2022] Open
Abstract
A new series of multipotent antioxidants (MPAOs), namely Schiff base-1,2,4-triazoles attached to the oxygen-derived free radical scavenging moiety butylated hydroxytoluene (BHT) were designed and subsequently synthesized. The structure-activity relationship (SAR) of the designed antioxidants was established alongside the prediction of activity spectra for substances (PASS). The antioxidant activities of the synthesized compounds 4-10 were tested by the DPPH bioassay. The synthesized compounds 4-10 inhibited stable DPPH free radicals at a level that is 10(-4) M more than the well-known standard antioxidant BHT. Compounds 8-10 with para-substituents were less active than compounds 4 and 5 with trimethoxy substituents compared to those with a second BHT moiety (compounds 6 and 7). With an IC50 of 46.13 ± 0.31 µM, compound 6 exhibited the most promising in vitro inhibition at 89%. Therefore, novel MPAOs containing active triazole rings, thioethers, Schiff bases, and BHT moieties are suggested as potential antioxidants for inhibiting oxidative stress processes and scavenging free radicals, hence, this combination of functions is anticipated to play a vital role in repairing cellular damage, preventing various human diseases and in medical therapeutic applications.
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Affiliation(s)
- Wageeh A Yehye
- Nanotechnology & Catalysis Research Centre (NANOCAT), University of Malaya, Block 3A, Institute of Postgraduate Studies Building, Kuala Lumpur 50603, Malaysia.
| | - Noorsaadah Abdul Rahman
- Department of Chemistry, Faculty of Science, University of Malaya, Kuala Lumpur 50603, Malaysia.
- Drug Design and Development Research Group, Department of Chemistry, Faculty of Science, University of Malaya, Kuala Lumpur 50603, Malaysia.
| | - Omar Saad
- Department of Pharmacy, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia.
| | - Azhar Ariffin
- Department of Chemistry, Faculty of Science, University of Malaya, Kuala Lumpur 50603, Malaysia.
| | - Sharifah Bee Abd Hamid
- Nanotechnology & Catalysis Research Centre (NANOCAT), University of Malaya, Block 3A, Institute of Postgraduate Studies Building, Kuala Lumpur 50603, Malaysia.
| | - Abeer A Alhadi
- Department of Chemistry, Faculty of Science, University of Malaya, Kuala Lumpur 50603, Malaysia.
- Drug Design and Development Research Group, Department of Chemistry, Faculty of Science, University of Malaya, Kuala Lumpur 50603, Malaysia.
| | - Farkaad A Kadir
- Division of Human Biology, School of Medicine, International Medical University, Kuala Lumpur 57000, Malaysia.
| | - Marzieh Yaeghoobi
- Department of Chemistry, Faculty of Science, University of Malaya, Kuala Lumpur 50603, Malaysia.
- Drug Design and Development Research Group, Department of Chemistry, Faculty of Science, University of Malaya, Kuala Lumpur 50603, Malaysia.
| | - Abdulsalam A Matlob
- Department of Environmental Technology, College of Environment, Mosul University, Mosul 41001, Iraq.
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Global Mapping of Traditional Chinese Medicine into Bioactivity Space and Pathways Annotation Improves Mechanistic Understanding and Discovers Relationships between Therapeutic Action (Sub)classes. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2016; 2016:2106465. [PMID: 26989424 PMCID: PMC4775820 DOI: 10.1155/2016/2106465] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2015] [Accepted: 12/03/2015] [Indexed: 02/08/2023]
Abstract
Traditional Chinese medicine (TCM) still needs more scientific rationale to be proven for it to be accepted further in the West. We are now in the position to propose computational hypotheses for the mode-of-actions (MOAs) of 45 TCM therapeutic action (sub)classes from in silico target prediction algorithms, whose target was later annotated with Kyoto Encyclopedia of Genes and Genomes pathway, and to discover the relationship between them by generating a hierarchical clustering. The results of 10,749 TCM compounds showed 183 enriched targets and 99 enriched pathways from Estimation Score ≤ 0 and ≥ 5% of compounds/targets in a (sub)class. The MOA of a (sub)class was established from supporting literature. Overall, the most frequent top three enriched targets/pathways were immune-related targets such as tyrosine-protein phosphatase nonreceptor type 2 (PTPN2) and digestive system such as mineral absorption. We found two major protein families, G-protein coupled receptor (GPCR), and protein kinase family contributed to the diversity of the bioactivity space, while digestive system was consistently annotated pathway motif, which agreed with the important treatment principle of TCM, “the foundation of acquired constitution” that includes spleen and stomach. In short, the TCM (sub)classes, in many cases share similar targets/pathways despite having different indications.
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Patel HM, Noolvi MN, Shirkhedkar AA, Kulkarni AD, Pardeshi CV, Surana SJ. Anti-convulsant potential of quinazolinones. RSC Adv 2016. [DOI: 10.1039/c6ra01284a] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
A series of novel quinazoline derivatives were virtually screened through different filters and evaluated for their anticonvulsant activity against electrically and chemically induced seizures.
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Affiliation(s)
- Harun M. Patel
- Department of Pharmaceutical Chemistry
- R.C. Patel Institute of Pharmaceutical Education and Research
- Dhule 425405
- India
| | - Malleshappa N. Noolvi
- Department of Pharmaceutical Chemistry
- Shree Dhanvantary Pharmacy College
- Kim (Surat)-3941110
- India
| | - Atul A. Shirkhedkar
- Department of Pharmaceutical Chemistry
- R.C. Patel Institute of Pharmaceutical Education and Research
- Dhule 425405
- India
| | - Abhijeet D. Kulkarni
- Department of Pharmaceutical Chemistry
- R.C. Patel Institute of Pharmaceutical Education and Research
- Dhule 425405
- India
| | - Chandrakantsing V. Pardeshi
- Department of Pharmaceutical Chemistry
- R.C. Patel Institute of Pharmaceutical Education and Research
- Dhule 425405
- India
| | - Sanjay J. Surana
- Department of Pharmaceutical Chemistry
- R.C. Patel Institute of Pharmaceutical Education and Research
- Dhule 425405
- India
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Devillers J, Bro E, Millot F. Prediction of the endocrine disruption profile of pesticides. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2015; 26:831-852. [PMID: 26548639 DOI: 10.1080/1062936x.2015.1104809] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Numerous manmade chemicals released into the environment can interfere with normal, hormonally regulated biological processes to adversely affect the development and reproductive functions of living species. Various in vivo and in vitro tests have been designed for detecting endocrine disruptors, but the number of chemicals to test is so high that to save time and money, (quantitative) structure-activity relationship ((Q)SAR) models are increasingly used as a surrogate for these laboratory assays. However, most of them focus only on a specific target (e.g. estrogenic or androgenic receptor) while, to be more efficient, endocrine disruption modelling should preferentially consider profiles of activities to better gauge this complex phenomenon. In this context, an attempt was made to evaluate the endocrine disruption profile of 220 structurally diverse pesticides using the Endocrine Disruptome simulation (EDS) tool, which simultaneously predicts the probability of binding of chemicals on 12 nuclear receptors. In a first step, the EDS web-based system was successfully applied to 16 pharmaceutical compounds known to target at least one of the studied receptors. About 13% of the studied pesticides were estimated to be potential disruptors of the endocrine system due to their high predicted affinity for at least one receptor. In contrast, about 55% of them were unlikely to be endocrine disruptors. The simulation results are discussed and some comments on the use of the EDS tool are made.
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Affiliation(s)
| | - E Bro
- b Research Department , National Game and Wildlife Institute (ONCFS) , Le Perray en Yvelines , France
| | - F Millot
- b Research Department , National Game and Wildlife Institute (ONCFS) , Le Perray en Yvelines , France
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Polishchuk PG, Samoylenko GV, Khristova TM, Krysko OL, Kabanova TA, Kabanov VM, Kornylov AY, Klimchuk O, Langer T, Andronati SA, Kuz'min VE, Krysko AA, Varnek A. Design, Virtual Screening, and Synthesis of Antagonists of αIIbβ3 as Antiplatelet Agents. J Med Chem 2015; 58:7681-94. [PMID: 26367138 DOI: 10.1021/acs.jmedchem.5b00865] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
This article describes design, virtual screening, synthesis, and biological tests of novel αIIbβ3 antagonists, which inhibit platelet aggregation. Two types of αIIbβ3 antagonists were developed: those binding either closed or open form of the protein. At the first step, available experimental data were used to build QSAR models and ligand- and structure-based pharmacophore models and to select the most appropriate tool for ligand-to-protein docking. Virtual screening of publicly available databases (BioinfoDB, ZINC, Enamine data sets) with developed models resulted in no hits. Therefore, small focused libraries for two types of ligands were prepared on the basis of pharmacophore models. Their screening resulted in four potential ligands for open form of αIIbβ3 and four ligands for its closed form followed by their synthesis and in vitro tests. Experimental measurements of affinity for αIIbβ3 and ability to inhibit ADP-induced platelet aggregation (IC50) showed that two designed ligands for the open form 4c and 4d (IC50 = 6.2 nM and 25 nM, respectively) and one for the closed form 12b (IC50 = 11 nM) were more potent than commercial antithrombotic Tirofiban (IC50 = 32 nM).
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Affiliation(s)
- Pavel G Polishchuk
- A.V. Bogatsky Physico-Chemical Institute of National Academy of Sciences of Ukraine , Lustdorfskaya doroga 86, Odessa 65080, Ukraine
| | - Georgiy V Samoylenko
- A.V. Bogatsky Physico-Chemical Institute of National Academy of Sciences of Ukraine , Lustdorfskaya doroga 86, Odessa 65080, Ukraine
| | - Tetiana M Khristova
- A.V. Bogatsky Physico-Chemical Institute of National Academy of Sciences of Ukraine , Lustdorfskaya doroga 86, Odessa 65080, Ukraine.,Laboratory of Chemoinformatics (UMR 7140 CNRS/UniStra), University of Strasbourg , 1, rue B. Pascal, Strasbourg 67000, France
| | - Olga L Krysko
- A.V. Bogatsky Physico-Chemical Institute of National Academy of Sciences of Ukraine , Lustdorfskaya doroga 86, Odessa 65080, Ukraine
| | - Tatyana A Kabanova
- A.V. Bogatsky Physico-Chemical Institute of National Academy of Sciences of Ukraine , Lustdorfskaya doroga 86, Odessa 65080, Ukraine
| | - Vladimir M Kabanov
- A.V. Bogatsky Physico-Chemical Institute of National Academy of Sciences of Ukraine , Lustdorfskaya doroga 86, Odessa 65080, Ukraine
| | - Alexander Yu Kornylov
- A.V. Bogatsky Physico-Chemical Institute of National Academy of Sciences of Ukraine , Lustdorfskaya doroga 86, Odessa 65080, Ukraine
| | - Olga Klimchuk
- Laboratory of Chemoinformatics (UMR 7140 CNRS/UniStra), University of Strasbourg , 1, rue B. Pascal, Strasbourg 67000, France
| | - Thierry Langer
- Department of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna , Althanstraße 14, 1090 Vienna, Austria
| | - Sergei A Andronati
- A.V. Bogatsky Physico-Chemical Institute of National Academy of Sciences of Ukraine , Lustdorfskaya doroga 86, Odessa 65080, Ukraine
| | - Victor E Kuz'min
- A.V. Bogatsky Physico-Chemical Institute of National Academy of Sciences of Ukraine , Lustdorfskaya doroga 86, Odessa 65080, Ukraine
| | - Andrei A Krysko
- A.V. Bogatsky Physico-Chemical Institute of National Academy of Sciences of Ukraine , Lustdorfskaya doroga 86, Odessa 65080, Ukraine
| | - Alexandre Varnek
- Laboratory of Chemoinformatics (UMR 7140 CNRS/UniStra), University of Strasbourg , 1, rue B. Pascal, Strasbourg 67000, France
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Vuorinen A, Odermatt A, Schuster D. Reprint of "In silico methods in the discovery of endocrine disrupting chemicals". J Steroid Biochem Mol Biol 2015; 153:93-101. [PMID: 26291836 DOI: 10.1016/j.jsbmb.2015.08.015] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2012] [Revised: 04/03/2013] [Accepted: 04/07/2013] [Indexed: 12/18/2022]
Abstract
The prevalence of sex hormone-dependent cancers, reproductive problems, obesity, and cardiovascular complications has risen especially in the Western world. It has been suggested, that the exposure to various endocrine disrupting chemicals (EDCs) contributes to the development and progression of these diseases. EDCs can interfere with various proteins: nuclear steroid hormone receptors, such as estrogen-, androgen-, glucocorticoid- and mineralocorticoid receptors (ER, AR, GR, MR), and enzymes that are involved in steroid hormone synthesis and metabolism, for example hydroxysteroid dehydrogenases (HSDs). Numerous chemicals are known as endocrine disruptors. However, the mechanism of action for most of these EDCs is still unknown. It is exhaustive and time consuming to test in vitro all chemicals - potential EDCs - used in industry, agriculture or as food preservatives against their effects on the endocrine system. Computational methods, such as virtual screening, quantitative structure activity relationships and docking, are already well recognized and used in drug development. The same methods could also aid the research on EDCs. So far, the computational methods in the search of EDCs have been retrospective. There are, however, some prospective studies reporting the use of in silico methods: five studies reporting the identification of previously unknown 17β-HSD3 inhibitors, MR agonists, and ER antagonists/agonists. This review provides an overview of case studies and in silico methods that are used in the search of EDCs. This article is part of a Special Issue entitled 'CSR 2013'.
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Affiliation(s)
- Anna Vuorinen
- Institute of Pharmacy/Pharmaceutical Chemistry and Center for Molecular Biosciences Innsbruck - CMBI, University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria
| | - Alex Odermatt
- Swiss Center for Applied Human Toxicology and Division of Molecular and Systems Toxicology, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, 4056 Basel, Switzerland
| | - Daniela Schuster
- Institute of Pharmacy/Pharmaceutical Chemistry and Center for Molecular Biosciences Innsbruck - CMBI, University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria.
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Metri R, Hariharaputran S, Ramakrishnan G, Anand P, Raghavender US, Ochoa-Montaño B, Higueruelo AP, Sowdhamini R, Chandra NR, Blundell TL, Srinivasan N. SInCRe-structural interactome computational resource for Mycobacterium tuberculosis. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2015; 2015:bav060. [PMID: 26130660 PMCID: PMC4485431 DOI: 10.1093/database/bav060] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2015] [Accepted: 05/26/2015] [Indexed: 11/20/2022]
Abstract
We have developed an integrated database for Mycobacterium tuberculosis H37Rv (Mtb) that collates information on protein sequences, domain assignments, functional annotation and 3D structural information along with protein–protein and protein–small molecule interactions. SInCRe (Structural Interactome Computational Resource) is developed out of CamBan (Cambridge and Bangalore) collaboration. The motivation for development of this database is to provide an integrated platform to allow easily access and interpretation of data and results obtained by all the groups in CamBan in the field of Mtb informatics. In-house algorithms and databases developed independently by various academic groups in CamBan are used to generate Mtb-specific datasets and are integrated in this database to provide a structural dimension to studies on tuberculosis. The SInCRe database readily provides information on identification of functional domains, genome-scale modelling of structures of Mtb proteins and characterization of the small-molecule binding sites within Mtb. The resource also provides structure-based function annotation, information on small-molecule binders including FDA (Food and Drug Administration)-approved drugs, protein–protein interactions (PPIs) and natural compounds that bind to pathogen proteins potentially and result in weakening or elimination of host–pathogen protein–protein interactions. Together they provide prerequisites for identification of off-target binding. Database URL:http://proline.biochem.iisc.ernet.in/sincre
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Affiliation(s)
- Rahul Metri
- Department of Biochemistry and Indian Institute of Science Mathematics Initiative, Indian Institute of Science, Bangalore, India
| | - Sridhar Hariharaputran
- Department of Biochemistry and National Centre for Biological Sciences, TIFR, UAS-GKVK Campus, Bellary Road, Bangalore, India
| | - Gayatri Ramakrishnan
- Indian Institute of Science Mathematics Initiative, Indian Institute of Science, Bangalore, India, Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India, and
| | | | | | | | - Alicia P Higueruelo
- Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge, UK
| | - Ramanathan Sowdhamini
- National Centre for Biological Sciences, TIFR, UAS-GKVK Campus, Bellary Road, Bangalore, India
| | | | - Tom L Blundell
- Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge, UK
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Tarasova OA, Urusova AF, Filimonov DA, Nicklaus MC, Zakharov AV, Poroikov VV. QSAR Modeling Using Large-Scale Databases: Case Study for HIV-1 Reverse Transcriptase Inhibitors. J Chem Inf Model 2015; 55:1388-99. [PMID: 26046311 DOI: 10.1021/acs.jcim.5b00019] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Large-scale databases are important sources of training sets for various QSAR modeling approaches. Generally, these databases contain information extracted from different sources. This variety of sources can produce inconsistency in the data, defined as sometimes widely diverging activity results for the same compound against the same target. Because such inconsistency can reduce the accuracy of predictive models built from these data, we are addressing the question of how best to use data from publicly and commercially accessible databases to create accurate and predictive QSAR models. We investigate the suitability of commercially and publicly available databases to QSAR modeling of antiviral activity (HIV-1 reverse transcriptase (RT) inhibition). We present several methods for the creation of modeling (i.e., training and test) sets from two, either commercially or freely available, databases: Thomson Reuters Integrity and ChEMBL. We found that the typical predictivities of QSAR models obtained using these different modeling set compilation methods differ significantly from each other. The best results were obtained using training sets compiled for compounds tested using only one method and material (i.e., a specific type of biological assay). Compound sets aggregated by target only typically yielded poorly predictive models. We discuss the possibility of "mix-and-matching" assay data across aggregating databases such as ChEMBL and Integrity and their current severe limitations for this purpose. One of them is the general lack of complete and semantic/computer-parsable descriptions of assay methodology carried by these databases that would allow one to determine mix-and-matchability of result sets at the assay level.
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Affiliation(s)
- Olga A Tarasova
- †Institute of Biochemical Chemistry, 10-8, Pogodinskaya St., 119121, Moscow, Russia
| | - Aleksandra F Urusova
- †Institute of Biochemical Chemistry, 10-8, Pogodinskaya St., 119121, Moscow, Russia
| | - Dmitry A Filimonov
- †Institute of Biochemical Chemistry, 10-8, Pogodinskaya St., 119121, Moscow, Russia
| | - Marc C Nicklaus
- ‡CADD Group, Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, DHHS, NCI-Frederick, 376 Boyles St., Frederick, Maryland 21702, United States
| | - Alexey V Zakharov
- ‡CADD Group, Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, DHHS, NCI-Frederick, 376 Boyles St., Frederick, Maryland 21702, United States
| | - Vladimir V Poroikov
- †Institute of Biochemical Chemistry, 10-8, Pogodinskaya St., 119121, Moscow, Russia
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Afzal AM, Mussa HY, Turner RE, Bender A, Glen RC. A multi-label approach to target prediction taking ligand promiscuity into account. J Cheminform 2015; 7:24. [PMID: 26064191 PMCID: PMC4461803 DOI: 10.1186/s13321-015-0071-9] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2015] [Accepted: 04/27/2015] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND According to Cobanoglu et al., it is now widely acknowledged that the single target paradigm (one protein/target, one disease, one drug) that has been the dominant premise in drug development in the recent past is untenable. More often than not, a drug-like compound (ligand) can be promiscuous - it can interact with more than one target protein. In recent years, in in silico target prediction methods the promiscuity issue has generally been approached computationally in three main ways: ligand-based methods; target-protein-based methods; and integrative schemes. In this study we confine attention to ligand-based target prediction machine learning approaches, commonly referred to as target-fishing. The target-fishing approaches that are currently ubiquitous in cheminformatics literature can be essentially viewed as single-label multi-classification schemes; these approaches inherently bank on the single target paradigm assumption that a ligand can zero in on one single target. In order to address the ligand promiscuity issue, one might be able to cast target-fishing as a multi-label multi-class classification problem. For illustrative and comparison purposes, single-label and multi-label Naïve Bayes classification models (denoted here by SMM and MMM, respectively) for target-fishing were implemented. The models were constructed and tested on 65,587 compounds/ligands and 308 targets retrieved from the ChEMBL17 database. RESULTS On classifying 3,332 test multi-label (promiscuous) compounds, SMM and MMM performed differently. At the 0.05 significance level, a Wilcoxon signed rank test performed on the paired target predictions yielded by SMM and MMM for the test ligands gave a p-value < 5.1 × 10(-94) and test statistics value of 6.8 × 10(5), in favour of MMM. The two models performed differently when tested on four datasets comprising single-label (non-promiscuous) compounds; McNemar's test yielded χ (2) values of 15.657, 16.500 and 16.405 (with corresponding p-values of 7.594 × 10(-05), 4.865 × 10(-05) and 5.115 × 10(-05)), respectively, for three test sets, in favour of MMM. The models performed similarly on the fourth set. CONCLUSIONS The target prediction results obtained in this study indicate that multi-label multi-class approaches are more apt than the ubiquitous single-label multi-class schemes when it comes to the application of ligand-based classifiers to target-fishing.
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Affiliation(s)
- Avid M Afzal
- Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW UK
| | - Hamse Y Mussa
- Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW UK
| | - Richard E Turner
- Department of Engineering, Computational and Biological Learning Lab, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ UK
| | - Andreas Bender
- Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW UK
| | - Robert C Glen
- Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW UK
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Paricharak S, Cortés-Ciriano I, IJzerman AP, Malliavin TE, Bender A. Proteochemometric modelling coupled to in silico target prediction: an integrated approach for the simultaneous prediction of polypharmacology and binding affinity/potency of small molecules. J Cheminform 2015; 7:15. [PMID: 25926892 PMCID: PMC4413554 DOI: 10.1186/s13321-015-0063-9] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2014] [Accepted: 03/17/2015] [Indexed: 11/10/2022] Open
Abstract
The rampant increase of public bioactivity databases has fostered the development of computational chemogenomics methodologies to evaluate potential ligand-target interactions (polypharmacology) both in a qualitative and quantitative way. Bayesian target prediction algorithms predict the probability of an interaction between a compound and a panel of targets, thus assessing compound polypharmacology qualitatively, whereas structure-activity relationship techniques are able to provide quantitative bioactivity predictions. We propose an integrated drug discovery pipeline combining in silico target prediction and proteochemometric modelling (PCM) for the respective prediction of compound polypharmacology and potency/affinity. The proposed pipeline was evaluated on the retrospective discovery of Plasmodium falciparum DHFR inhibitors. The qualitative in silico target prediction model comprised 553,084 ligand-target associations (a total of 262,174 compounds), covering 3,481 protein targets and used protein domain annotations to extrapolate predictions across species. The prediction of bioactivities for plasmodial DHFR led to a recall value of 79% and a precision of 100%, where the latter high value arises from the structural similarity of plasmodial DHFR inhibitors and T. gondii DHFR inhibitors in the training set. Quantitative PCM models were then trained on a dataset comprising 20 eukaryotic, protozoan and bacterial DHFR sequences, and 1,505 distinct compounds (in total 3,099 data points). The most predictive PCM model exhibited R20test and RMSEtest values of 0.79 and 0.59 pIC50 units respectively, which was shown to outperform models based exclusively on compound (R20test/RMSEtest = 0.63/0.78) and target information (R20test/RMSEtest = 0.09/1.22), as well as inductive transfer knowledge between targets, with respective R20test and RMSEtest values of 0.76 and 0.63 pIC50 units. Finally, both methods were integrated to predict the protein targets and the potency on plasmodial DHFR for the GSK TCAMS dataset, which comprises 13,533 compounds displaying strong anti-malarial activity. 534 of those compounds were identified as DHFR inhibitors by the target prediction algorithm, while the PCM algorithm identified 25 compounds, and 23 compounds (predicted pIC50 > 7) were identified by both methods. Overall, this integrated approach simultaneously provides target and potency/affinity predictions for small molecules. Proteochemometric modelling coupled to in silico target prediction. ![]()
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Affiliation(s)
- Shardul Paricharak
- Department of Chemistry, Centre for Molecular Science Informatics, University of Cambridge, Lensfield Road, CB2 1EW Cambridge, UK.,Division of Medicinal Chemistry, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, , 2300 RA Leiden, The Netherlands
| | - Isidro Cortés-Ciriano
- Unité de Bioinformatique Structurale, Institut Pasteur and CNRS UMR 3825, Structural Biology and Chemistry Department, 25-28, rue du Dr. Roux, 75 724 Paris, France
| | - Adriaan P IJzerman
- Division of Medicinal Chemistry, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, , 2300 RA Leiden, The Netherlands
| | - Thérèse E Malliavin
- Unité de Bioinformatique Structurale, Institut Pasteur and CNRS UMR 3825, Structural Biology and Chemistry Department, 25-28, rue du Dr. Roux, 75 724 Paris, France
| | - Andreas Bender
- Department of Chemistry, Centre for Molecular Science Informatics, University of Cambridge, Lensfield Road, CB2 1EW Cambridge, UK
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Ariffin A, Rahman NA, Yehye WA, Alhadi AA, Kadir FA. PASS-assisted design, synthesis and antioxidant evaluation of new butylated hydroxytoluene derivatives. Eur J Med Chem 2014; 87:564-77. [DOI: 10.1016/j.ejmech.2014.10.001] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2014] [Revised: 09/02/2014] [Accepted: 10/01/2014] [Indexed: 01/22/2023]
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Wang Y, Ge H, Li Y, Xie Y, He Y, Xu M, Gu Q, Xu J. Predicting dual-targeting anti-influenza agents using multi-models. Mol Divers 2014; 19:123-34. [PMID: 25273562 DOI: 10.1007/s11030-014-9552-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2014] [Accepted: 08/26/2014] [Indexed: 12/11/2022]
Abstract
Influenza is an acute respiratory infectious disease caused by influenza viruses. Its subtype can be distinguished based on the antigenicity of two surface glycoproteins, hemagglutinin (HA) and neuraminidase (NA). One of the main challenges in anti-influenza drug development is the quick evolution of drug resistance due to virus mutations. One solution to this problem is to develop dual-targeting anti-influenza agents. In this paper, a new rationally designed virtual screening protocol that combines structure-based approaches (molecular docking and molecular dynamic simulations) and ligand-based approaches (support vector machines and 3D shape & electrostatic similarity algorithms) is reported for the virtual screening of dual-targeting agents against HA and NA. The final hits came from the consensus of the ligand- and receptor-based knowledge of HA and NA and were tested using ADMET predictions. Evidence from the binding energy calculations and binding mode analyses suggested that several of the hits are promising as dual-targeting anti-influenza agents. The virtual screening protocol may also lead to the identification of innovative drugs in other fields.
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Affiliation(s)
- Yu Wang
- School of Pharmaceutical Sciences & Institute of Human Virology, Sun Yat-Sen University, 132 East Circle Road at University City, Guangzhou, 510006, China
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41
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Filimonov DA, Lagunin AA, Gloriozova TA, Rudik AV, Druzhilovskii DS, Pogodin PV, Poroikov VV. Prediction of the Biological Activity Spectra of Organic Compounds Using the Pass Online Web Resource. Chem Heterocycl Compd (N Y) 2014. [DOI: 10.1007/s10593-014-1496-1] [Citation(s) in RCA: 208] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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42
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Lötsch J, Schneider G, Reker D, Parnham MJ, Schneider P, Geisslinger G, Doehring A. Common non-epigenetic drugs as epigenetic modulators. Trends Mol Med 2013; 19:742-53. [PMID: 24054876 DOI: 10.1016/j.molmed.2013.08.006] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2013] [Revised: 08/16/2013] [Accepted: 08/19/2013] [Indexed: 12/15/2022]
Abstract
Epigenetic effects are exerted by a variety of factors and evidence increases that common drugs such as opioids, cannabinoids, valproic acid, or cytostatics may induce alterations in DNA methylation patterns or histone conformations. These effects occur via chemical structural interactions with epigenetic enzymes, through interactions with DNA repair mechanisms. Computational predictions indicate that one-twentieth of all drugs might potentially interact with human histone deacetylase, which was prospectively experimentally verified for the compound with the highest predicted interaction probability. These epigenetic effects add to wanted and unwanted drug effects, contributing to mechanisms of drug resistance or disease-related and unrelated phenotypes. Because epigenetic changes might be transmitted to offspring, the need for reliable and cost-effective epigenetic screening tools becomes acute.
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Affiliation(s)
- Jörn Lötsch
- Institute of Clinical Pharmacology, Goethe University, Theodor-Stern-Kai 7, D-60590 Frankfurt am Main, Germany; Fraunhofer Institute of Molecular Biology and Applied Ecology - Project Group Translational Medicine and Pharmacology (IME-TMP), Theodor-Stern-Kai 7, D-60590 Frankfurt am Main, Germany.
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Vuorinen A, Odermatt A, Schuster D. In silico methods in the discovery of endocrine disrupting chemicals. J Steroid Biochem Mol Biol 2013; 137:18-26. [PMID: 23688835 DOI: 10.1016/j.jsbmb.2013.04.009] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2012] [Revised: 04/03/2013] [Accepted: 04/07/2013] [Indexed: 11/27/2022]
Abstract
The prevalence of sex hormone-dependent cancers, reproductive problems, obesity, and cardiovascular complications has risen especially in the Western world. It has been suggested, that the exposure to various endocrine disrupting chemicals (EDCs) contributes to the development and progression of these diseases. EDCs can interfere with various proteins: nuclear steroid hormone receptors, such as estrogen-, androgen-, glucocorticoid- and mineralocorticoid receptors (ER, AR, GR, MR), and enzymes that are involved in steroid hormone synthesis and metabolism, for example hydroxysteroid dehydrogenases (HSDs). Numerous chemicals are known as endocrine disruptors. However, the mechanism of action for most of these EDCs is still unknown. It is exhaustive and time consuming to test in vitro all chemicals - potential EDCs - used in industry, agriculture or as food preservatives against their effects on the endocrine system. Computational methods, such as virtual screening, quantitative structure activity relationships and docking, are already well recognized and used in drug development. The same methods could also aid the research on EDCs. So far, the computational methods in the search of EDCs have been retrospective. There are, however, some prospective studies reporting the use of in silico methods: five studies reporting the identification of previously unknown 17β-HSD3 inhibitors, MR agonists, and ER antagonists/agonists. This review provides an overview of case studies and in silico methods that are used in the search of EDCs. This article is part of a Special Issue entitled 'CSR 2013'.
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Affiliation(s)
- Anna Vuorinen
- Institute of Pharmacy/Pharmaceutical Chemistry and Center for Molecular Biosciences Innsbruck - CMBI, University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria
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Yao Z, Lin Z, Wang T, Tian D, Zou X, Gao Y, Yin D. Using molecular docking-based binding energy to predict toxicity of binary mixture with different binding sites. CHEMOSPHERE 2013; 92:1169-1176. [PMID: 23484458 DOI: 10.1016/j.chemosphere.2013.01.081] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2012] [Revised: 01/14/2013] [Accepted: 01/20/2013] [Indexed: 06/01/2023]
Abstract
The flood of chemical substances in the environment result in the complexity of chemical mixtures, and one of the reasons for complexity is that their individual chemicals bind to different binding sites on different (or same) target proteins within the organism. A general approaches therefore are proposed in this study to predict the toxicity of chemical mixtures with different binding sites by using molecular docking-based binding energy (Ebinding). Aldehydes and cyanogenic toxicants were selected as the example of chemical mixtures with same binding site. Triazines and urea herbicide were selected as the example of chemical mixtures with different binding sites but on same target protein. Sulfonamides and trimethoprim toxicants were selected as the example of chemical mixtures with different target proteins. Although these chemical mixtures bind to their binding sites by different ways, there is a general relationship between their binary mixture toxicity (EC50M) and their corresponding Ebinding of individual chemicals and logKow(mix). By using the Ebinding to describe how the individual chemicals work in the different binding sites, the approach may provide a general and simply model to predict mixture toxicity to microorganism.
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Affiliation(s)
- Zhifeng Yao
- State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
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Reutlinger M, Koch CP, Reker D, Todoroff N, Schneider P, Rodrigues T, Schneider G. Chemically Advanced Template Search (CATS) for Scaffold-Hopping and Prospective Target Prediction for 'Orphan' Molecules. Mol Inform 2013; 32:133-138. [PMID: 23956801 PMCID: PMC3743170 DOI: 10.1002/minf.201200141] [Citation(s) in RCA: 120] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2012] [Accepted: 01/18/2013] [Indexed: 02/04/2023]
Affiliation(s)
- Michael Reutlinger
- ETH, Department of Chemistry and Applied Biosciences, Institute of Pharmaceutical Sciences Wolfgang-Pauli-Str. 10, CH-8093 Zurich, Switzerland fax: +41 44 633 13 79, tel: +41 44 633 74 38
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46
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Peragovics Á, Simon Z, Tombor L, Jelinek B, Hári P, Czobor P, Málnási-Csizmadia A. Virtual affinity fingerprints for target fishing: a new application of Drug Profile Matching. J Chem Inf Model 2012; 53:103-13. [PMID: 23215025 DOI: 10.1021/ci3004489] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
We recently introduced Drug Profile Matching (DPM), a novel virtual affinity fingerprinting bioactivity prediction method. DPM is based on the docking profiles of ca. 1200 FDA-approved small-molecule drugs against a set of nontarget proteins and creates bioactivity predictions based on this pattern. The effectiveness of this approach was previously demonstrated for therapeutic effect prediction of drug molecules. In the current work, we investigated the applicability of DPM for target fishing, i.e. for the prediction of biological targets for compounds. Predictions were made for 77 targets, and their accuracy was measured by Receiver Operating Characteristic (ROC) analysis. Robustness was tested by a rigorous 10-fold cross-validation procedure. This procedure identified targets (N = 45) with high reliability based on DPM performance. These 45 categories were used in a subsequent study which aimed at predicting the off-target profiles of currently approved FDA drugs. In this data set, 79% of the known drug-target interactions were correctly predicted by DPM, and additionally 1074 new drug-target interactions were suggested. We focused our further investigation on the suggested interactions of antipsychotic molecules and confirmed several interactions by a review of the literature.
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Affiliation(s)
- Ágnes Peragovics
- Department of Biochemistry, Institute of Biology, Eötvös Loránd University, Pázmány Péter sétány 1/C, H-1117 Budapest, Hungary
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Worth A, Fuart‐Gatnik M, Lapenna S, Serafimova R. Applicability of QSAR analysis in the evaluation of developmental and neurotoxicity effects for the assessment of the toxicological relevance of metabolites and degradates of pesticide active substances for dietary risk assessment. ACTA ACUST UNITED AC 2011. [DOI: 10.2903/sp.efsa.2011.en-169] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Andrew Worth
- European Commission Joint Research Centre, Institute for Health & Consumer Protection Italy
| | - Mojca Fuart‐Gatnik
- European Commission Joint Research Centre, Institute for Health & Consumer Protection Italy
| | - Silvia Lapenna
- European Commission Joint Research Centre, Institute for Health & Consumer Protection Italy
| | - Rositsa Serafimova
- European Commission Joint Research Centre, Institute for Health & Consumer Protection Italy
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Koutsoukas A, Simms B, Kirchmair J, Bond PJ, Whitmore AV, Zimmer S, Young MP, Jenkins JL, Glick M, Glen RC, Bender A. From in silico target prediction to multi-target drug design: current databases, methods and applications. J Proteomics 2011; 74:2554-74. [PMID: 21621023 DOI: 10.1016/j.jprot.2011.05.011] [Citation(s) in RCA: 186] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2011] [Revised: 04/10/2011] [Accepted: 05/06/2011] [Indexed: 01/31/2023]
Abstract
Given the tremendous growth of bioactivity databases, the use of computational tools to predict protein targets of small molecules has been gaining importance in recent years. Applications span a wide range, from the 'designed polypharmacology' of compounds to mode-of-action analysis. In this review, we firstly survey databases that can be used for ligand-based target prediction and which have grown tremendously in size in the past. We furthermore outline methods for target prediction that exist, both based on the knowledge of bioactivities from the ligand side and methods that can be applied in situations when a protein structure is known. Applications of successful in silico target identification attempts are discussed in detail, which were based partly or in whole on computational target predictions in the first instance. This includes the authors' own experience using target prediction tools, in this case considering phenotypic antibacterial screens and the analysis of high-throughput screening data. Finally, we will conclude with the prospective application of databases to not only predict, retrospectively, the protein targets of a small molecule, but also how to design ligands with desired polypharmacology in a prospective manner.
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Affiliation(s)
- Alexios Koutsoukas
- Unilever Centre for Molecular Sciences Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
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Lagunin A, Zakharov A, Filimonov D, Poroikov V. QSAR Modelling of Rat Acute Toxicity on the Basis of PASS Prediction. Mol Inform 2011; 30:241-50. [PMID: 27466777 DOI: 10.1002/minf.201000151] [Citation(s) in RCA: 187] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2010] [Accepted: 02/23/2011] [Indexed: 11/07/2022]
Abstract
The method for QSAR modelling of rat acute toxicity based on the combination of QNA (Quantitative Neighbourhoods of Atoms) descriptors, PASS (Prediction of Activity Spectra for Substances) predictions and self-consistent regression (SCR) is presented. PASS predicted biological activity profiles are used as independent input variables for QSAR modelling with SCR. QSAR models were developed using LD50 values for compounds tested on rats with four types of administration (oral, intravenous, intraperitoneal, subcutaneous). The proposed method was evaluated on the set of compounds tested for acute rat toxicity with oral administration (7286 compounds) used for testing the known QSAR methods in T.E.S.T. 3.0 program (U.S. EPA). The several other sets of compounds tested for acute rat toxicity by different routes of administration selected from SYMYX MDL Toxicity Database were used too. The method was compared with the results of prediction of acute rodent toxicity for noncongeneric sets obtained by ACD/Labs Inc. The test sets were predicted with regards to the applicability domain. Comparison of accuracy for QSAR models obtained separately using QNA descriptors, PASS predictions, nearest neighbours' assessment with consensus models clearly demonstrated the benefits of consensus prediction. Free available web-service for prediction of LD50 values of rat acute toxicity was developed: http://www.pharmaexpert.ru/GUSAR/AcuToxPredict/.
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Affiliation(s)
- Alexey Lagunin
- Department for Bioinformatics, Institute of Biomedical Chemistry of the Russian Academy of Medical Sciences, Pogodinskaya Str., 10, Moscow, 119121, Russia phone/fax: +7 499 2553029/+7499 2450857.
| | - Alexey Zakharov
- Department for Bioinformatics, Institute of Biomedical Chemistry of the Russian Academy of Medical Sciences, Pogodinskaya Str., 10, Moscow, 119121, Russia phone/fax: +7 499 2553029/+7499 2450857
| | - Dmitry Filimonov
- Department for Bioinformatics, Institute of Biomedical Chemistry of the Russian Academy of Medical Sciences, Pogodinskaya Str., 10, Moscow, 119121, Russia phone/fax: +7 499 2553029/+7499 2450857
| | - Vladimir Poroikov
- Department for Bioinformatics, Institute of Biomedical Chemistry of the Russian Academy of Medical Sciences, Pogodinskaya Str., 10, Moscow, 119121, Russia phone/fax: +7 499 2553029/+7499 2450857
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Abstract
IMPORTANCE OF THE FIELD: PubChem is a public molecular information repository, a scientific showcase of the NIH Roadmap Initiative. The PubChem database holds over 27 million records of unique chemical structures of compounds (CID) derived from nearly 70 million substance depositions (SID), and contains more than 449,000 bioassay records with over thousands of in vitro biochemical and cell-based screening bioassays established, with targeting more than 7000 proteins and genes linking to over 1.8 million of substances. AREAS COVERED IN THIS REVIEW: This review builds on recent PubChem-related computational chemistry research reported by other authors while providing readers with an overview of the PubChem database, focusing on its increasing role in cheminformatics, virtual screening and toxicity prediction modeling. WHAT THE READER WILL GAIN: These publicly available datasets in PubChem provide great opportunities for scientists to perform cheminformatics and virtual screening research for computer-aided drug design. However, the high volume and complexity of the datasets, in particular the bioassay-associated false positives/negatives and highly imbalanced datasets in PubChem, also creates major challenges. Several approaches regarding the modeling of PubChem datasets and development of virtual screening models for bioactivity and toxicity predictions are also reviewed. TAKE HOME MESSAGE: Novel data-mining cheminformatics tools and virtual screening algorithms are being developed and used to retrieve, annotate and analyze the large-scale and highly complex PubChem biological screening data for drug design.
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Affiliation(s)
- Xiang-Qun Xie
- Department of Pharmaceutical Sciences, School of Pharmacy; Drug Discovery Institute/Pittsburgh Molecular Library Screening Center (PMLSC); Pittsburgh Chemical Methodologies & Library Development (PCMLD) Center; Departments of Computational Biology and Structural Biology; University of Pittsburgh, Pittsburgh, PA 15260, USA
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