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Gallo FN, Marquez AB, Fidalgo DM, Dana A, Dellarole M, García CC, Bollini M. Antiviral drug discovery: Pyrimidine entry inhibitors for Zika and dengue viruses. Eur J Med Chem 2024; 272:116465. [PMID: 38718623 DOI: 10.1016/j.ejmech.2024.116465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 04/26/2024] [Accepted: 04/28/2024] [Indexed: 05/27/2024]
Abstract
Vector-borne diseases, constituting over 17 % of infectious diseases, are caused by parasites, viruses, and bacteria, and their prevalence is shaped by environmental and social factors. Dengue virus (DENV) and Zika virus (ZIKV), some of the most prevalent infectious agents of this type of diseases, are transmitted by mosquitoes belonging to the genus Aedes. The highest prevalence is observed in tropical regions, inhabited by around 3 billion people. DENV infects millions of people annually and constitutes an additional sanitary challenge due to the circulation of four serotypes, which has complicated vaccine development. ZIKV causes large outbreaks globally and its infection is known to lead to severe neurological diseases, including microcephaly in newborns. Besides, not only mosquito control programs have proved to be not totally effective, but also, no antiviral drugs have been developed so far. The envelope protein (E) is a major component of DENV and ZIKV virion surface. This protein plays a key role during the virus cell entry, constituting an attractive target for the development of antiviral drugs. Our previous studies have identified two pyrimidine analogs (3e and 3h) as inhibitors; however, their activity was found to be hindered by their low water solubility. In this study, we performed a low-throughput antiviral screening, revealing compound 16a as a potent DENV-2 and ZIKV inhibitor (EC50 = 1.4 μM and 2.4 μM, respectively). This work was aimed at designing molecules with improved selectivity and pharmacokinetic properties, thus advancing the antiviral efficacy of compounds for potential therapeutic use.
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Affiliation(s)
- Facundo N Gallo
- Centro de Investigaciones en Bionanociencias (CIBION) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina; Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Química Biológica, Laboratorio de Estrategias Antivirales, CONICET, Instituto de Química Biológica (IQUIBICEN), Buenos Aires, Argentina
| | - Agostina B Marquez
- Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Química Biológica, Laboratorio de Estrategias Antivirales, CONICET, Instituto de Química Biológica (IQUIBICEN), Buenos Aires, Argentina
| | - Daniela M Fidalgo
- Centro de Investigaciones en Bionanociencias (CIBION) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
| | - Alejandro Dana
- Centro de Investigaciones en Bionanociencias (CIBION) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina; Meton AI, Inc, Wilmington, DE, 19801, USA
| | - Mariano Dellarole
- Centro de Investigaciones en Bionanociencias (CIBION) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
| | - Cybele C García
- Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Química Biológica, Laboratorio de Estrategias Antivirales, CONICET, Instituto de Química Biológica (IQUIBICEN), Buenos Aires, Argentina.
| | - Mariela Bollini
- Centro de Investigaciones en Bionanociencias (CIBION) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina.
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2
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García-Sosa AT. Benford's Law and distributions for better drug design. Expert Opin Drug Discov 2024; 19:131-137. [PMID: 37921672 DOI: 10.1080/17460441.2023.2277342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Accepted: 10/26/2023] [Indexed: 11/04/2023]
Abstract
INTRODUCTION Modern drug discovery incorporates various tools and data, heralding the beginning of the data-driven drug design (DD) era. The distributions of chemical and physical data used for Artificial Intelligence (AI)/Machine Learning (ML) and to drive DD have thus become highly important to be understood and used effectively. AREAS COVERED The authors perform a comprehensive exploration of the statistical distributions driving the data-intensive era of drug discovery, including Benford's Law in AI/ML-based DD. EXPERT OPINION As the relevance of data-driven discovery escalates, we anticipate meticulous scrutiny of datasets utilizing principles like Benford's Law to enhance data integrity and guide efficient resource allocation and experimental planning. In this data-driven era of the pharmaceutical and medical industries, addressing critical aspects such as bias mitigation, algorithm effectiveness, data stewardship, effects, and fraud prevention are essential. Harnessing Benford's Law and other distributions and statistical tests in DD provides a potent strategy to detect data anomalies, fill data gaps, and enhance dataset quality. Benford's Law is a fast method for data integrity and quality of datasets, the backbone of AI/ML and other modeling approaches, proving very useful in the design process.
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Affiliation(s)
- Alfonso T García-Sosa
- Chair of Molecular Technology, Institute of Chemistry, University of Tartu, Tartu, Estonia
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3
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Szél V, Zsidó BZ, Jeszenői N, Hetényi C. Target-ligand binding affinity from single point enthalpy calculation and elemental composition. Phys Chem Chem Phys 2023; 25:31714-31725. [PMID: 37964670 DOI: 10.1039/d3cp04483a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2023]
Abstract
Reliable target-ligand binding thermodynamics data are essential for successful drug design and molecular engineering projects. Besides experimental methods, a number of theoretical approaches have been introduced for the generation of binding thermodynamics data. However, available approaches often neglect electronic effects or explicit water molecules influencing target-ligand interactions. To handle electronic effects within a reasonable time frame, we introduce a fast calculator QMH-L using a single target-ligand complex structure pre-optimized at the molecular mechanics level. QMH-L is composed of the semi-empirical quantum mechanics calculation of binding enthalpy with predicted explicit water molecules at the complex interface, and a simple descriptor based on the elemental composition of the ligand. QMH-L estimates the target-ligand binding free energy with a root mean square error (RMSE) of 0.94 kcal mol-1. The calculations also provide binding enthalpy values and they were compared with experimental binding thermodynamics data collected from the most reliable isothermal titration calorimetry studies of systems including various protein targets and challenging, large peptide ligands with a molecular weight of up to 2-3 thousand. The single point enthalpy calculations of QMH-L require modest computational resources and are based on short runs with open source and/or free software like Gromacs, Mopac, MobyWat, and Fragmenter. QMH-L can be applied for fast, automated scoring of drug candidates during a virtual screen, enthalpic engineering of new ligands or thermodynamic explanation of complex interactions.
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Affiliation(s)
- Viktor Szél
- Pharmacoinformatics Unit, Department of Pharmacology and Pharmacotherapy, Medical School, University of Pécs, Szigeti út 12, 7624 Pécs, Hungary.
| | - Balázs Zoltán Zsidó
- Pharmacoinformatics Unit, Department of Pharmacology and Pharmacotherapy, Medical School, University of Pécs, Szigeti út 12, 7624 Pécs, Hungary.
| | - Norbert Jeszenői
- Pharmacoinformatics Unit, Department of Pharmacology and Pharmacotherapy, Medical School, University of Pécs, Szigeti út 12, 7624 Pécs, Hungary.
| | - Csaba Hetényi
- Pharmacoinformatics Unit, Department of Pharmacology and Pharmacotherapy, Medical School, University of Pécs, Szigeti út 12, 7624 Pécs, Hungary.
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Kralj S, Jukič M, Bren U. Commercial SARS-CoV-2 Targeted, Protease Inhibitor Focused and Protein-Protein Interaction Inhibitor Focused Molecular Libraries for Virtual Screening and Drug Design. Int J Mol Sci 2021; 23:393. [PMID: 35008818 PMCID: PMC8745317 DOI: 10.3390/ijms23010393] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 12/23/2021] [Accepted: 12/28/2021] [Indexed: 01/08/2023] Open
Abstract
Since December 2019, the new SARS-CoV-2-related COVID-19 disease has caused a global pandemic and shut down the public life worldwide. Several proteins have emerged as potential therapeutic targets for drug development, and we sought out to review the commercially available and marketed SARS-CoV-2-targeted libraries ready for high-throughput virtual screening (HTVS). We evaluated the SARS-CoV-2-targeted, protease-inhibitor-focused and protein-protein-interaction-inhibitor-focused libraries to gain a better understanding of how these libraries were designed. The most common were ligand- and structure-based approaches, along with various filtering steps, using molecular descriptors. Often, these methods were combined to obtain the final library. We recognized the abundance of targeted libraries offered and complimented by the inclusion of analytical data; however, serious concerns had to be raised. Namely, vendors lack the information on the library design and the references to the primary literature. Few references to active compounds were also provided when using the ligand-based design and usually only protein classes or a general panel of targets were listed, along with a general reference to the methods, such as molecular docking for the structure-based design. No receptor data, docking protocols or even references to the applied molecular docking software (or other HTVS software), and no pharmacophore or filter design details were given. No detailed functional group or chemical space analyses were reported, and no specific orientation of the libraries toward the design of covalent or noncovalent inhibitors could be observed. All libraries contained pan-assay interference compounds (PAINS), rapid elimination of swill compounds (REOS) and aggregators, as well as focused on the drug-like model, with the majority of compounds possessing their molecular mass around 500 g/mol. These facts do not bode well for the use of the reviewed libraries in drug design and lend themselves to commercial drug companies to focus on and improve.
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Affiliation(s)
- Sebastjan Kralj
- Laboratory of Physical Chemistry and Chemical Thermodynamics, Faculty of Chemistry and Chemical Engineering, University of Maribor, Smetanova 17, SI-2000 Maribor, Slovenia; (S.K.); (M.J.)
| | - Marko Jukič
- Laboratory of Physical Chemistry and Chemical Thermodynamics, Faculty of Chemistry and Chemical Engineering, University of Maribor, Smetanova 17, SI-2000 Maribor, Slovenia; (S.K.); (M.J.)
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, SI-6000 Koper, Slovenia
| | - Urban Bren
- Laboratory of Physical Chemistry and Chemical Thermodynamics, Faculty of Chemistry and Chemical Engineering, University of Maribor, Smetanova 17, SI-2000 Maribor, Slovenia; (S.K.); (M.J.)
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, SI-6000 Koper, Slovenia
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In Silico Assessment and Molecular Docking Studies of Some Phyto-Triterpenoid for Potential Disruption of Mortalin-p53 Interaction. Processes (Basel) 2021. [DOI: 10.3390/pr9111983] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Human hepatocellular carcinoma (HCC), the most common type of liver cancer, represents the second most common cause of death from cancer worldwide. The high toxicity and side effects of some cancer chemotherapy drugs increase the demand for new anti-cancer drugs from natural products. Mortalin/mtHsp70, a stress response protein, has been reported to contribute to the process of carcinogenesis in several ways, including the inhibition of the transcriptional activation of p53. This study conducted a molecular docking study of 41 phyto triterpenes originated from Vietnamese plants for potential Mortalin inhibition activity. Nine compounds were considered as promising inhibitors based on the analysis of binding affinity and drug-like and pharmacokinetic properties.
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6
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Azad I, Khan T, Maurya AK, Irfan Azad M, Mishra N, Alanazi AM. Identification of Severe Acute Respiratory Syndrome Coronavirus-2 inhibitors through in silico structure-based virtual screening and molecular interaction studies. J Mol Recognit 2021; 34:e2918. [PMID: 34132436 PMCID: PMC8420533 DOI: 10.1002/jmr.2918] [Citation(s) in RCA: 7] [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: 04/22/2021] [Revised: 05/11/2021] [Accepted: 05/20/2021] [Indexed: 01/10/2023]
Abstract
The novel coronavirus Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) or COVID-19 has caused a worldwide pandemic. The fatal virus has affected the health of human beings as well as the socio-economic situation all over the world. To date, no concrete medicinal solution has been proposed to combat the viral infection, calling for an urgent, strategic, and cost-effective drug development approach that may be achievable by applying targeted computational and virtual screening protocols. Immunity is the body's natural defense against disease-causing pathogens, which can be boosted by consuming plant-based or natural food products. Active constituents derived from natural sources also scavenge the free radicals and have anti-inflammatory activities. Herbs and spices have been used for various medicinal purposes. In this study, 2,96 365 natural and synthetic derivatives (ligands) belonging to 102 classes of compounds were obtained from PubChem and assessed on Lipinski's parameters for their potential bioavailability. Out of all the derivatives, 3254 obeyed Lipinski's rule and were virtually screened. The 115 top derivatives were docked against SARS-CoV-2, SARS-CoV, MERS-CoV, and HCoV-HKV1 main proteases (Mpro s) as receptors using AutoDock Vina, AutoDock, and iGEMDOCK 2.1. The lowest binding energy was exhibited by ligands 2 and 6 against all the four Mpro s. The molecular dynamic simulation was also performed with ligand 6 using the GROMACS package. Good bioactivity scores, absorption, distribution, metabolism, excretion, and toxicity profile and drug-like pharmacokinetic parameters were also obtained. Hydroxychloroquine was used as the control drug.
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Affiliation(s)
- Iqbal Azad
- Department of ChemistryIntegral UniversityLucknowIndia
| | - Tahmeena Khan
- Department of ChemistryIntegral UniversityLucknowIndia
| | - Akhilesh Kumar Maurya
- Department of Applied SciencesIndian Institute of Information Technology AllahabadPrayagrajIndia
| | | | - Nidhi Mishra
- Department of Applied SciencesIndian Institute of Information Technology AllahabadPrayagrajIndia
| | - Amer M. Alanazi
- Department of Pharmaceutical ChemistryCollege of Pharmacy, King Saud UniversityRiyadhSaudi Arabia
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Characterisation of twelve newly synthesised N-(substituted phenyl)-2-chloroacetamides with QSAR analysis and antimicrobial activity tests. Arh Hig Rada Toksikol 2021; 72:70-79. [PMID: 33787186 PMCID: PMC8191425 DOI: 10.2478/aiht-2021-72-3483] [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: 09/01/2020] [Accepted: 02/01/2021] [Indexed: 11/20/2022] Open
Abstract
In this study we screened twelve newly synthesised N-(substituted phenyl)-2-chloroacetamides for antimicrobial potential relying on quantitative structure-activity relationship (QSAR) analysis based on the available cheminformatics prediction models (Molinspiration, SwissADME, PreADMET, and PkcSM) and verified it through standard antimicrobial testing against Escherichia coli, Staphylococcus aureus, methicillin-resistant S. aureus (MRSA), and Candida albicans. Our compounds met all the screening criteria of Lipinski’s rule of five (Ro5) as well as Veber’s and Egan’s methods for predicting biological activity. In antimicrobial activity tests, all chloroacetamides were effective against Gram-positive S. aureus and MRSA, less effective against the Gram-negative E. coli, and moderately effective against the yeast C. albicans. Our study confirmed that the biological activity of chloroacetamides varied with the position of substituents bound to the phenyl ring, which explains why some molecules were more effective against Gram-negative than Gram-positive bacteria or C. albicans. Bearing the halogenated p-substituted phenyl ring, N-(4-chlorophenyl), N-(4-fluorophenyl), and N-(3-bromophenyl) chloroacetamides were among the most active thanks to high lipophilicity, which allows them to pass rapidly through the phospholipid bilayer of the cell membrane. They are the most promising compounds for further investigation, particularly against Gram-positive bacteria and pathogenic yeasts.
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Swain SS, Paidesetty SK, Padhy RN. Phytochemical conjugation as a potential semisynthetic approach toward reactive and reuse of obsolete sulfonamides against pathogenic bacteria. Drug Dev Res 2020; 82:149-166. [PMID: 33025605 DOI: 10.1002/ddr.21746] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 09/16/2020] [Accepted: 09/20/2020] [Indexed: 12/20/2022]
Abstract
The emergence and reemergence of multidrug-resistant (MDR) bacteria and mycobacteria in community and hospital periphery have directly enhanced the hospitalization costs, morbidity and mortality, globally. The appearance of MDR pathogens, the currently used antibiotics, remains insufficient, and the development of potent antibacterial(s) is merely slow. Thus, the development of active antibacterials is the call of the day. The sulfonamides class of antibacterials was the most successful synthesized drug in the 19th century. Mechanically, sulfonamides were targeting bacterial folic acid biosynthesis and today, those are obsolete or clinically inactive. Nevertheless, the magic sulfonamide pharmacophore has been used continuously in several mainstream antibacterial, antidiabetic, antiviral drugs. Concomitantly, thousands of phytochemicals with antimicrobial potencies have been recorded and were commanded as alternate antibacterials toward control of MDR pathogens. However, none/very few isolated phytochemicals have gone up to the pure-drug stage due to the lack of the desired drug-likeness values and the required pharmacokinetic properties. Thus, chemical modification of parent drug remains as the versatile approach in antibacterial drug development. Improvement of clinically inactive sulfa drugs with suitable phytochemicals to develop active, low-toxic drug molecules followed by medicinal chemistry could be prudent. This review highlights such "sulfonamide-phytochemical" hybrid drug development research works for utilizing inactive sulfonamides and phytochemicals; the ingenious cost-effective and resource-saving hybrid drug concept could be a new trend in current antibacterial drug discovery to reactive the obsolete antibacterials.
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Affiliation(s)
- Shasank S Swain
- Central Research Laboratory, Institute of Medical Sciences and Sum Hospital, Siksha 'O' Anusandhan Deemed to be University, Bhubaneswar, Odisha, India
| | - Sudhir K Paidesetty
- Department of Pharmaceutical Chemistry, School of Pharmaceutical Sciences, Siksha 'O' Anusandhan Deemed to be University, Bhubaneswar, Odisha, India
| | - Rabindra N Padhy
- Central Research Laboratory, Institute of Medical Sciences and Sum Hospital, Siksha 'O' Anusandhan Deemed to be University, Bhubaneswar, Odisha, India
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9
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Brzuzan P, Mazur-Marzec H, Florczyk M, Stefaniak F, Fidor A, Konkel R, Woźny M. Luciferase reporter assay for small-molecule inhibitors of MIR92b-3p function: Screening cyanopeptolins produced by Nostoc from the Baltic Sea. Toxicol In Vitro 2020; 68:104951. [PMID: 32721573 DOI: 10.1016/j.tiv.2020.104951] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 07/16/2020] [Accepted: 07/22/2020] [Indexed: 12/16/2022]
Abstract
We developed a cell sensor that detects the liver cancer-specific microRNA MIR92b-3p, involved in hepatocellular carcinoma development and hepatitis C virus infection. To validate our small-molecule screen that employs a Huh7 human hepatoma cell line stably transfected with a pmirGLO vector containing dual luciferase reporters, we used i) a MIR92b-3p antisense or a MIR92b-3p mimicking agent (concentrations from 0.1 pM to 100 nM), ii) expression of XIST, a long non-coding RNA that is a cellular target of MIR92b, and iii) ectopic expression of Luc2 luciferase. This reporter system was used to test four cyanopeptolins from a de novo library of peptides that were isolated from the Baltic Sea cyanobacteria Nostoc edaphicum strain CCNP1411. Exposure of the Huh7-pmirGLO-MIR92b-3p cells to increasing concentrations (from 10 nM to 100 μM) of the cyanopeptolins and microcystin-LR (MC-LR; a treatment control) did not lead to a dose-dependent restoration of the luciferase signal. Instead, when the reporter cells were treated with MC-LR, the luciferase signal decreased markedly, most likely due to non-target, toxic effects of MC-LR on the cells. Although the first use of this reporter system to screen selected Nostoc peptides did not identify inhibitors of MIR92b, this method provides a means to identify functional miRNA regulators and could be readily extended to other compounds.
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Affiliation(s)
- Paweł Brzuzan
- Department of Environmental Biotechnology, Faculty of Geoengineering, University of Warmia and Mazury in Olsztyn, Poland.
| | | | - Maciej Florczyk
- Department of Environmental Biotechnology, Faculty of Geoengineering, University of Warmia and Mazury in Olsztyn, Poland
| | - Filip Stefaniak
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Anna Fidor
- Division of Marine Biotechnology, University of Gdańsk, Gdańsk, Poland
| | - Robert Konkel
- Division of Marine Biotechnology, University of Gdańsk, Gdańsk, Poland
| | - Maciej Woźny
- Department of Environmental Biotechnology, Faculty of Geoengineering, University of Warmia and Mazury in Olsztyn, Poland
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Abstract
Aim: The explosion of data based technology has accelerated pattern mining. However, it is clear that quality and bias of data impacts all machine learning and modeling. Results & methodology: A technique is presented for using the distribution of first significant digits of medicinal chemistry features: logP, logS, and pKa. experimental and predicted, to assess their following of Benford's law as seen in many natural phenomena. Conclusion: Quality of data depends on the dataset sizes, diversity, and magnitudes. Profiling based on drugs may be too small or narrow; using larger sets of experimentally determined or predicted values recovers the distribution seen in other natural phenomena. This technique may be used to improve profiling, machine learning, large dataset assessment and other data based methods for better (automated) data generation and designing compounds.
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11
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Yosipof A, Guedes RC, García-Sosa AT. Data Mining and Machine Learning Models for Predicting Drug Likeness and Their Disease or Organ Category. Front Chem 2018; 6:162. [PMID: 29868564 PMCID: PMC5954128 DOI: 10.3389/fchem.2018.00162] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Accepted: 04/20/2018] [Indexed: 12/11/2022] Open
Abstract
Data mining approaches can uncover underlying patterns in chemical and pharmacological property space decisive for drug discovery and development. Two of the most common approaches are visualization and machine learning methods. Visualization methods use dimensionality reduction techniques in order to reduce multi-dimension data into 2D or 3D representations with a minimal loss of information. Machine learning attempts to find correlations between specific activities or classifications for a set of compounds and their features by means of recurring mathematical models. Both models take advantage of the different and deep relationships that can exist between features of compounds, and helpfully provide classification of compounds based on such features or in case of visualization methods uncover underlying patterns in the feature space. Drug-likeness has been studied from several viewpoints, but here we provide the first implementation in chemoinformatics of the t-Distributed Stochastic Neighbor Embedding (t-SNE) method for the visualization and the representation of chemical space, and the use of different machine learning methods separately and together to form a new ensemble learning method called AL Boost. The models obtained from AL Boost synergistically combine decision tree, random forests (RF), support vector machine (SVM), artificial neural network (ANN), k nearest neighbors (kNN), and logistic regression models. In this work, we show that together they form a predictive model that not only improves the predictive force but also decreases bias. This resulted in a corrected classification rate of over 0.81, as well as higher sensitivity and specificity rates for the models. In addition, separation and good models were also achieved for disease categories such as antineoplastic compounds and nervous system diseases, among others. Such models can be used to guide decision on the feature landscape of compounds and their likeness to either drugs or other characteristics, such as specific or multiple disease-category(ies) or organ(s) of action of a molecule.
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Affiliation(s)
- Abraham Yosipof
- Department of Information Systems and Department of Business Administration, College of Law & Business, Ramat-Gan, Israel
| | - Rita C Guedes
- Department of Medicinal Chemistry, Faculty of Pharmacy, Research Institute for Medicines (iMed.ULisboa), Universidade de Lisboa, Lisbon, Portugal
| | - Alfonso T García-Sosa
- Department of Molecular Technology, Institute of Chemistry, University of Tartu, Tartu, Estonia
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12
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Sosnina EA, Osolodkin DI, Radchenko EV, Sosnin S, Palyulin VA. Influence of Descriptor Implementation on Compound Ranking Based on Multiparameter Assessment. J Chem Inf Model 2018; 58:1083-1093. [PMID: 29689160 DOI: 10.1021/acs.jcim.7b00734] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Most of the common molecular descriptors have numerous different implementations. This can influence the results of compound prioritization based on the multiparameter assessment (MPA) approach that allows a medicinal chemist to simultaneously analyze and achieve the desired balance of the diverse and often conflicting molecular and pharmacological properties. In this study, we analyzed the feasibility of using different implementations of common descriptors (logP, logS, TPSA, logBB, hERG, nHBA) interchangeably in predesigned sets of requirements in the course of multiparameter compound optimization. The influence of methods of descriptor calculation, continuity or discreteness of their values, their applicability domains, as well as of the nature of desirability functions in an MPA profile were examined in terms of the stability of MPA compound ranking. It was shown that the interchangeable use of different methods of descriptor calculation is reliably acceptable only for continuously distributed parameters transformed by a smooth desirability function. If a descriptor in an MPA scheme is discretely distributed, only the implementation that was used for building the scoring profile may be used for assessment. An inconsistency of assessment due to different applicability domains of descriptors was also demonstrated.
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Affiliation(s)
- Ekaterina A Sosnina
- Department of Chemistry , Lomonosov Moscow State University , Moscow 119991 , Russia.,Center for Computational and Data-Intensive Science and Engineering , Skolkovo Institute of Science and Technology , Moscow 143026 , Russia.,Institute of Physiologically Active Compounds RAS , Chernogolovka 142432 , Russia
| | - Dmitry I Osolodkin
- Department of Chemistry , Lomonosov Moscow State University , Moscow 119991 , Russia.,Chumakov Institute of Poliomyelitis and Viral Encephalitides, Chumakov FSC R&D IBP RAS , Moscow 108819 , Russia.,Sechenov First Moscow State Medical University , Moscow 119991 , Russia
| | - Eugene V Radchenko
- Department of Chemistry , Lomonosov Moscow State University , Moscow 119991 , Russia.,Institute of Physiologically Active Compounds RAS , Chernogolovka 142432 , Russia
| | - Sergey Sosnin
- Center for Computational and Data-Intensive Science and Engineering , Skolkovo Institute of Science and Technology , Moscow 143026 , Russia.,Institute of Physiologically Active Compounds RAS , Chernogolovka 142432 , Russia
| | - Vladimir A Palyulin
- Department of Chemistry , Lomonosov Moscow State University , Moscow 119991 , Russia.,Institute of Physiologically Active Compounds RAS , Chernogolovka 142432 , Russia
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13
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Walker MA. Improvement in aqueous solubility achieved via small molecular changes. Bioorg Med Chem Lett 2017; 27:5100-5108. [PMID: 29100802 DOI: 10.1016/j.bmcl.2017.09.041] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2017] [Revised: 09/14/2017] [Accepted: 09/17/2017] [Indexed: 10/18/2022]
Abstract
Overcoming poor solubility is a significant issue in drug discovery. The most common solution is to replace carbon atoms with polar heteroatoms such as N and O or by attaching a solubilizing appendage. This approach can lead to other issues such as poor activity and PK or the increased risk for toxicity. However, there are more subtle structural changes which can be employed that lead to an increase in solubility. These include, excising hydrophobic groups which do not efficiently contribute to binding, modifying stereo- and regiochemistry, increasing or decreasing the degree of unsaturation or adding small hydrophobic groups such as fluorine or methyl.
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Affiliation(s)
- Michael A Walker
- Dart Neuroscience, 12278 Scripps Summit Dr., San Diego, CA 92131, USA.
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Zeidan M, Rayan M, Zeidan N, Falah M, Rayan A. Indexing Natural Products for Their Potential Anti-Diabetic Activity: Filtering and Mapping Discriminative Physicochemical Properties. Molecules 2017; 22:molecules22091563. [PMID: 28926980 PMCID: PMC6151781 DOI: 10.3390/molecules22091563] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2017] [Revised: 09/14/2017] [Accepted: 09/14/2017] [Indexed: 12/12/2022] Open
Abstract
Diabetes mellitus (DM) poses a major health problem, for which there is an unmet need to develop novel drugs. The application of in silico techniques and optimization algorithms is instrumental to achieving this goal. A set of 97 approved anti-diabetic drugs, representing the active domain, and a set of 2892 natural products, representing the inactive domain, were used to construct predictive models and to index anti-diabetic bioactivity. Our recently-developed approach of ‘iterative stochastic elimination’ was utilized. This article describes a highly discriminative and robust model, with an area under the curve above 0.96. Using the indexing model and a mix ratio of 1:1000 (active/inactive), 65% of the anti-diabetic drugs in the sample were captured in the top 1% of the screened compounds, compared to 1% in the random model. Some of the natural products that scored highly as potential anti-diabetic drug candidates are disclosed. One of those natural products is caffeine, which is noted in the scientific literature as having the capability to decrease blood glucose levels. The other nine phytochemicals await evaluation in a wet lab for their anti-diabetic activity. The indexing model proposed herein is useful for the virtual screening of large chemical databases and for the construction of anti-diabetes focused libraries.
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Affiliation(s)
- Mouhammad Zeidan
- Molecular Genetics and Virology Laboratory, QRC-Qasemi Research Center, Al-Qasemi Academic College, P.O. Box 124, Baka EL-Garbiah 30100, Israel.
| | - Mahmoud Rayan
- Institute of Applied Research-Galilee Society, P.O. Box 437, Shefa-Amr 20200, Israel.
| | - Nuha Zeidan
- Clalit Health Service, Diet and Nutrition Unit, P.O. Box 789, Arara 30026, Israel.
| | - Mizied Falah
- Eliachar Research Laboratory, Galilee Medical Center, P.O. Box 21, Nahariya 22100, Israel.
- Faculty of Medicine in the Galilee, Bar-Ilan University, Ramat Gan 52900, Israel.
| | - Anwar Rayan
- Institute of Applied Research-Galilee Society, P.O. Box 437, Shefa-Amr 20200, Israel.
- Drug Discovery Informatics Laboratory, QRC-Qasemi Research Center, Al-Qasemi Academic College, P.O. Box 124, Baka EL-Garbiah 30100, Israel.
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15
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Cavalluzzi MM, Mangiatordi GF, Nicolotti O, Lentini G. Ligand efficiency metrics in drug discovery: the pros and cons from a practical perspective. Expert Opin Drug Discov 2017; 12:1087-1104. [PMID: 28814111 DOI: 10.1080/17460441.2017.1365056] [Citation(s) in RCA: 68] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
INTRODUCTION Ligand efficiency metrics are almost universally accepted as a valuable indicator of compound quality and an aid to reduce attrition. Areas covered: In this review, the authors describe ligand efficiency metrics giving a balanced overview on their merits and points of weakness in order to enable the readers to gain an informed opinion. Relevant theoretical breakthroughs and drug-like properties are also illustrated. Several recent exemplary case studies are discussed in order to illustrate the main fields of application of ligand efficiency metrics. Expert opinion: As a medicinal chemist guide, ligand efficiency metrics perform in a context- and chemotype-dependent manner; thus, they should not be used as a magic box. Since the 'big bang' of efficiency metrics occurred more or less ten years ago and the average time to develop a new drug is over the same period, the next few years will give a clearer outlook on the increased rate of success, if any, gained by means of these new intriguing tools.
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Affiliation(s)
| | | | - Orazio Nicolotti
- a Department of Pharmacy - Drug Sciences , University of Bari Aldo Moro , Bari , Italy
| | - Giovanni Lentini
- a Department of Pharmacy - Drug Sciences , University of Bari Aldo Moro , Bari , Italy
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16
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Sánchez-Rodríguez A, Pérez-Castillo Y, Schürer SC, Nicolotti O, Mangiatordi GF, Borges F, Cordeiro MNDS, Tejera E, Medina-Franco JL, Cruz-Monteagudo M. From flamingo dance to (desirable) drug discovery: a nature-inspired approach. Drug Discov Today 2017. [PMID: 28624633 DOI: 10.1016/j.drudis.2017.05.008] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The therapeutic effects of drugs are well known to result from their interaction with multiple intracellular targets. Accordingly, the pharma industry is currently moving from a reductionist approach based on a 'one-target fixation' to a holistic multitarget approach. However, many drug discovery practices are still procedural abstractions resulting from the attempt to understand and address the action of biologically active compounds while preventing adverse effects. Here, we discuss how drug discovery can benefit from the principles of evolutionary biology and report two real-life case studies. We do so by focusing on the desirability principle, and its many features and applications, such as machine learning-based multicriteria virtual screening.
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Affiliation(s)
- Aminael Sánchez-Rodríguez
- Departamento de Ciencias Naturales, Universidad Técnica Particular de Loja, Calle París S/N, EC1101608 Loja, Ecuador
| | | | - Stephan C Schürer
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine and Center for Computational Science, University of Miami, Miami, FL 33136, USA
| | - Orazio Nicolotti
- Dipartimento di Farmacia - Scienze del Farmaco, Università di Bari Aldo Moro, Bari 072006, Italy
| | | | - Fernanda Borges
- CIQUP/Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Porto 4169-007, Portugal
| | - M Natalia D S Cordeiro
- REQUIMTE/Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Porto 4169-007, Portugal
| | - Eduardo Tejera
- Facultad de Medicina, Universidad de Las Américas, 170513 Quito, Ecuador
| | - José L Medina-Franco
- Universidad Nacional Autónoma de México, Departamento de Farmacia, Facultad de Química, Avenida Universidad 3000, Mexico City 04510, Mexico
| | - Maykel Cruz-Monteagudo
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine and Center for Computational Science, University of Miami, Miami, FL 33136, USA; CIQUP/Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Porto 4169-007, Portugal; REQUIMTE/Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Porto 4169-007, Portugal; Department of General Education, West Coast University-Miami Campus, Doral, FL 33178, USA.
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17
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Ogungbe IV, Setzer WN. The Potential of Secondary Metabolites from Plants as Drugs or Leads against Protozoan Neglected Diseases-Part III: In-Silico Molecular Docking Investigations. Molecules 2016; 21:E1389. [PMID: 27775577 PMCID: PMC6274513 DOI: 10.3390/molecules21101389] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2016] [Revised: 10/06/2016] [Accepted: 10/12/2016] [Indexed: 12/11/2022] Open
Abstract
Malaria, leishmaniasis, Chagas disease, and human African trypanosomiasis continue to cause considerable suffering and death in developing countries. Current treatment options for these parasitic protozoal diseases generally have severe side effects, may be ineffective or unavailable, and resistance is emerging. There is a constant need to discover new chemotherapeutic agents for these parasitic infections, and natural products continue to serve as a potential source. This review presents molecular docking studies of potential phytochemicals that target key protein targets in Leishmania spp., Trypanosoma spp., and Plasmodium spp.
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Affiliation(s)
- Ifedayo Victor Ogungbe
- Department of Chemistry and Biochemistry, Jackson State University, Jackson, MS 39217, USA.
| | - William N Setzer
- Department of Chemistry, University of Alabama in Huntsville, Huntsville, AL 35899, USA.
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18
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Byler KG, Collins JT, Ogungbe IV, Setzer WN. Alphavirus protease inhibitors from natural sources: A homology modeling and molecular docking investigation. Comput Biol Chem 2016; 64:163-184. [DOI: 10.1016/j.compbiolchem.2016.06.005] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2016] [Revised: 05/08/2016] [Accepted: 06/20/2016] [Indexed: 12/11/2022]
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19
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Viira B, Selyutina A, García-Sosa AT, Karonen M, Sinkkonen J, Merits A, Maran U. Design, discovery, modelling, synthesis, and biological evaluation of novel and small, low toxicity s-triazine derivatives as HIV-1 non-nucleoside reverse transcriptase inhibitors. Bioorg Med Chem 2016; 24:2519-2529. [PMID: 27108399 DOI: 10.1016/j.bmc.2016.04.018] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2015] [Revised: 03/10/2016] [Accepted: 04/08/2016] [Indexed: 11/15/2022]
Abstract
A set of top-ranked compounds from a multi-objective in silico screen was experimentally tested for toxicity and the ability to inhibit the activity of HIV-1 reverse transcriptase (RT) in cell-free assay and in cell-based assay using HIV-1 based virus-like particles. Detailed analysis of a commercial sample that indicated specific inhibition of HIV-1 reverse transcription revealed that a minor component that was structurally similar to that of the main compound was responsible for the strongest inhibition. As a result, novel s-triazine derivatives were proposed, modelled, discovered, and synthesised, and their antiviral activity and cellular toxicity were tested. Compounds 18a and 18b were found to be efficient HIV-1 RT inhibitors, with an IC50 of 5.6±1.1μM and 0.16±0.05μM in a cell-based assay using infectious HIV-1, respectively. Compound 18b also had no detectable toxicity for different human cell lines. Their binding mode and interactions with the RT suggest that there was strong and adaptable binding in a tight (NNRTI) hydrophobic pocket. In summary, this iterative study produced structural clues and led to a group of non-toxic, novel compounds to inhibit HIV-RT with up to nanomolar potency.
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Affiliation(s)
- Birgit Viira
- Institute of Chemistry, University of Tartu, Tartu 50411, Estonia
| | | | | | - Maarit Karonen
- Department of Chemistry, University of Turku, FI-20014 Turku, Finland
| | - Jari Sinkkonen
- Department of Chemistry, University of Turku, FI-20014 Turku, Finland
| | - Andres Merits
- Institute of Technology, University of Tartu, Tartu 50411, Estonia.
| | - Uko Maran
- Institute of Chemistry, University of Tartu, Tartu 50411, Estonia.
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20
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Cummins DJ, Bell MA. Integrating Everything: The Molecule Selection Toolkit, a System for Compound Prioritization in Drug Discovery. J Med Chem 2016; 59:6999-7010. [DOI: 10.1021/acs.jmedchem.5b01338] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- David J. Cummins
- Eli Lilly and Company, 893 South
Delaware Street, Indianapolis, Indiana 46285, United States
| | - Michael A. Bell
- Eli Lilly and Company, 893 South
Delaware Street, Indianapolis, Indiana 46285, United States
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21
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O'Hagan S, Kell DB. Understanding the foundations of the structural similarities between marketed drugs and endogenous human metabolites. Front Pharmacol 2015; 6:105. [PMID: 26029108 PMCID: PMC4429554 DOI: 10.3389/fphar.2015.00105] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2015] [Accepted: 04/29/2015] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND A recent comparison showed the extensive similarities between the structural properties of metabolites in the reconstructed human metabolic network ("endogenites") and those of successful, marketed drugs ("drugs"). RESULTS Clustering indicated the related but differential population of chemical space by endogenites and drugs. Differences between the drug-endogenite similarities resulting from various encodings and judged by Tanimoto similarity could be related simply to the fraction of the bitstrings set to 1. By extracting drug/endogenite substructures, we develop a novel family of fingerprints, the Drug Endogenite Substructure (DES) encodings, based on the ranked frequency of the various substructures. These provide a natural assessment of drug-endogenite likeness, and may be used as descriptors with which to derive quantitative structure-activity relationships (QSARs). CONCLUSIONS "Drug-endogenite likeness" seems to have utility, and leads to a simple, novel and interpretable substructure-based molecular encoding for cheminformatics.
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Affiliation(s)
- Steve O'Hagan
- School of Chemistry, The University of Manchester Manchester, UK ; The Manchester Institute of Biotechnology, The University of Manchester Manchester, UK
| | - Douglas B Kell
- School of Chemistry, The University of Manchester Manchester, UK ; The Manchester Institute of Biotechnology, The University of Manchester Manchester, UK
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22
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Abstract
A number of alternative variables have appeared in the medicinal chemistry literature trying to provide a more rigorous formulation of the guidelines proposed by Lipinski to exclude chemical entities with poor pharmacokinetic properties early in the discovery process. Typically, these variables combine the affinity towards the target with physicochemical properties of the ligand and are named efficiencies or ligand efficiencies. Several formulations have been defined and used by different laboratories with different degrees of success. A unified formulation, ligand efficiency indices, was proposed that included efficiency in two complementary variables (i.e., size and polarity) to map and monitor the drug-discovery process (AtlasCBS). The use of this formulation in combination with an extended multiparameter optimization is presented, with examples, as a promising methodology to optimize the drug-discovery process in the future. Future perspectives and challenges for this approach are also discussed.
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23
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Di Domizio A, Vitriolo A, Vistoli G, Pedretti A. SPILLO-PBSS: detecting hidden binding sites within protein 3D-structures through a flexible structure-based approach. J Comput Chem 2014; 35:2005-17. [PMID: 25179993 DOI: 10.1002/jcc.23714] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2014] [Revised: 07/30/2014] [Accepted: 08/03/2014] [Indexed: 11/11/2022]
Abstract
The study reports a flexible structure-based approach aimed at identifying binding sites within target proteins starting from a well-defined reference binding site. The method, named SPILLO potential binding sites searcher (SPILLO-PBSS), includes a suitably designed tolerance which allows an efficient recognition of the potential binding sites regardless of both involved residues and protein conformation. Hence, the proposed method overcomes the rigidity which affects the available approaches and which prevents a proper analysis of distorted binding sites. We apply SPILLO-PBSS to several test cases, including the search for the guanosine diphosphate binding site in distorted H-Ras proteins and the identification of acetylcholine binding proteins from among a library of heterogeneous resolved proteins. Tests are also performed to compare SPILLO-PBSS with other related and available methods. The encouraging results confirm the notable potentialities of this approach and lay the foundation for its use to analyze and predict target proteins on a proteome-wide scale.
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Affiliation(s)
- Alessandro Di Domizio
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza della Scienza, 2, 20126, Milan, Italy; Department of Pharmaceutical Sciences, University of Milan, Via Mangiagalli, 25, 20133, Milan, Italy
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24
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Chen FC, Liao YC, Huang JM, Lin CH, Chen YY, Dou HY, Hsiung CA. Pros and cons of the tuberculosis drugome approach--an empirical analysis. PLoS One 2014; 9:e100829. [PMID: 24971632 PMCID: PMC4074101 DOI: 10.1371/journal.pone.0100829] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2014] [Accepted: 05/27/2014] [Indexed: 01/20/2023] Open
Abstract
Drug-resistant Mycobacterium tuberculosis (MTB), the causative pathogen of tuberculosis (TB), has become a serious threat to global public health. Yet the development of novel drugs against MTB has been lagging. One potentially powerful approach to drug development is computation-aided repositioning of current drugs. However, the effectiveness of this approach has rarely been examined. Here we select the "TB drugome" approach--a protein structure-based method for drug repositioning for tuberculosis treatment--to (1) experimentally validate the efficacy of the identified drug candidates for inhibiting MTB growth, and (2) computationally examine how consistently drug candidates are prioritized, considering changes in input data. Twenty three drugs in the TB drugome were tested. Of them, only two drugs (tamoxifen and 4-hydroxytamoxifen) effectively suppressed MTB growth at relatively high concentrations. Both drugs significantly enhanced the inhibitory effects of three first-line anti-TB drugs (rifampin, isoniazid, and ethambutol). However, tamoxifen is not a top-listed drug in the TB drugome, and 4-hydroxytamoxifen is not approved for use in humans. Computational re-examination of the TB drugome indicated that the rankings were subject to technical and data-related biases. Thus, although our results support the effectiveness of the TB drugome approach for identifying drugs that can potentially be repositioned for stand-alone applications or for combination treatments for TB, the approach requires further refinements via incorporation of additional biological information. Our findings can also be extended to other structure-based drug repositioning methods.
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Affiliation(s)
- Feng-Chi Chen
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Miaoli County, Taiwan
- Department of Life Sciences, National Chiao-Tung University, Hsinchu, Taiwan
- Department of Dentistry, China Medical University, Taichung, Taiwan
| | - Yu-Chieh Liao
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Miaoli County, Taiwan
| | - Jie-Mao Huang
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Miaoli County, Taiwan
| | - Chieh-Hua Lin
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Miaoli County, Taiwan
- Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, Taiwan
| | - Yih-Yuan Chen
- National Institute of Infectious Diseases and Vaccinology, National Health Research Institutes, Zhunan, Miaoli County, Taiwan
| | - Horng-Yunn Dou
- National Institute of Infectious Diseases and Vaccinology, National Health Research Institutes, Zhunan, Miaoli County, Taiwan
| | - Chao Agnes Hsiung
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Miaoli County, Taiwan
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Abstract
INTRODUCTION A high-quality drug must achieve a balance of physicochemical and absorption, distribution, metabolism and elimination properties, safety and potency against its therapeutic target(s). Multiparameter optimization (MPO) methods guide the simultaneous optimization of multiple factors to quickly target compounds with the highest chance of downstream success. MPO can be combined with 'de novo design' methods to automatically generate and assess a large number of diverse structures and identify strategies to optimize a compound's overall balance of properties. AREAS COVERED The article provides a review of MPO methods and recent developments in the methods and opinions in the field. It also provides a description of advances in de novo design that improve the relevance of automatically generated compound structures and integrate MPO. Finally, the article provides discussion of a recent case study of the automatic design of ligands to polypharmacological profiles. EXPERT OPINION Recent developments have reduced the generation of chemically infeasible structures and improved the quality of compounds generated by de novo design methods. There are concerns about the ability of simple drug-like properties and ligand efficiency indices to effectively guide the detailed optimization of compounds. De novo design methods cannot identify a perfect compound for synthesis, but it can identify high-quality ideas for detailed consideration by an expert scientist.
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Affiliation(s)
- Matthew Segall
- Optibrium Ltd , 7221 Cambridge Research Park, Beach Drive, Cambridge, CB25 9TL , UK +44 1223 815902 ; +44 1223 815907 ;
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26
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García-Sosa AT. Hydration Properties of Ligands and Drugs in Protein Binding Sites: Tightly-Bound, Bridging Water Molecules and Their Effects and Consequences on Molecular Design Strategies. J Chem Inf Model 2013; 53:1388-405. [DOI: 10.1021/ci3005786] [Citation(s) in RCA: 54] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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García-Sosa AT, Maran U. Drugs, non-drugs, and disease category specificity: organ effects by ligand pharmacology. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2013; 24:319-331. [PMID: 23534612 DOI: 10.1080/1062936x.2013.773373] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Important understanding can be gained from using molecular biology-based and chemistry-based techniques together. Bayesian classifiers have thus been developed in the present work using several statistically significant molecular properties of compiled datasets of drugs and non-drugs, including their disease category or organ. The results show they provide a useful classification and simplicity of several different ligand efficiencies and molecular properties. Early recall of drugs among non-drugs using the classifiers as a ranking tool is also provided. As the chemical space of compounds is addressed together with their anatomical characterization, chemical libraries can be improved to select for specific organ or disease. Eventually, by including even finer detail, the method may help in designing libraries with specific pharmacological or toxicological target chemical space. Alternatively, a lack of statistically significant differences in property density distributions may help in further describing compounds with possibility of activity on several organs or disease groups, and given their very similar or considerably overlapping chemical space, therefore wanted or unwanted side-effects. The overlaps between densities for several properties of organs or disease categories were calculated by integrating the area under the curves where they intersect. The naïve Bayesian classifiers are readily built, fast to score, and easily interpretable.
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Affiliation(s)
- A T García-Sosa
- Institute of Chemistry, University of Tartu, Tartu, Estonia.
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García-Sosa AT, Oja M, Hetényi C, Maran U. DrugLogit: logistic discrimination between drugs and nondrugs including disease-specificity by assigning probabilities based on molecular properties. J Chem Inf Model 2012; 52:2165-80. [PMID: 22830445 DOI: 10.1021/ci200587h] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The increasing knowledge of both structure and activity of compounds provides a good basis for enhancing the pharmacological characterization of chemical libraries. In addition, pharmacology can be seen as incorporating both advances from molecular biology as well as chemical sciences, with innovative insight provided from studying target-ligand data from a ligand molecular point of view. Predictions and profiling of libraries of drug candidates have previously focused mainly on certain cases of oral bioavailability. Inclusion of other administration routes and disease-specificity would improve the precision of drug profiling. In this work, recent data are extended, and a probability-based approach is introduced for quantitative and gradual classification of compounds into categories of drugs/nondrugs, as well as for disease- or organ-specificity. Using experimental data of over 1067 compounds and multivariate logistic regressions, the classification shows good performance in training and independent test cases. The regressions have high statistical significance in terms of the robustness of coefficients and 95% confidence intervals provided by a 1000-fold bootstrapping resampling. Besides their good predictive power, the classification functions remain chemically interpretable, containing only one to five variables in total, and the physicochemical terms involved can be easily calculated. The present approach is useful for an improved description and filtering of compound libraries. It can also be applied sequentially or in combinations of filters, as well as adapted to particular use cases. The scores and equations may be able to suggest possible routes for compound or library modification. The data is made available for reuse by others, and the equations are freely accessible at http://hermes.chem.ut.ee/~alfx/druglogit.html.
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García-Sosa AT, Oja M, Hetényi C, Maran U. Disease-Specific Differentiation Between Drugs and Non-Drugs Using Principal Component Analysis of Their Molecular Descriptor Space. Mol Inform 2012; 31:369-83. [DOI: 10.1002/minf.201100094] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2011] [Accepted: 01/25/2012] [Indexed: 01/04/2023]
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