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da Rocha MN, da Fonseca AM, Dantas ANM, Dos Santos HS, Marinho ES, Marinho GS. In Silico Study in MPO and Molecular Docking of the Synthetic Drynaran Analogues Against the Chronic Tinnitus: Modulation of the M1 Muscarinic Acetylcholine Receptor. Mol Biotechnol 2024; 66:254-269. [PMID: 37079267 DOI: 10.1007/s12033-023-00748-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 04/03/2023] [Indexed: 04/21/2023]
Abstract
Tinnitus is a syndrome that affects the human auditory system and is characterized by a perception of sounds in the absence of acoustic stimuli, or in total silence. Research indicates that muscarinic acetylcholine receptors (mAChRs), especially the M1 type, have a fundamental role in the alterations of auditory perceptions of tinnitus. Here, a series of computer-aided tools were used, from molecular surface analysis software to services available on the web for estimating pharmacokinetics and pharmacodynamics. The results infer that the low lipophilicity ligands, that is, the 1a-d alkyl furans, present the best pharmacokinetic profile, as compounds with an optimal alignment between permeability and clearance. However, only ligands 1a and 1b have properties that are safe for the central nervous system, the site of cholinergic modulation. These ligands showed similarity with compounds deposited in the European Molecular Biology Laboratory chemical (ChEMBL) database acting on the mAChRs M1 type, the target selected for the molecular docking test. The simulations suggest that the 1 g ligand can form the ligand-receptor complex with the best affinity energy order and that, together with the 1b ligand, they are competitive agonists in relation to the antagonist Tiotropium, in addition to acting in synergism with the drug Bromazepam in the treatment of chronic tinnitus.
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Affiliation(s)
- Matheus Nunes da Rocha
- Graduate Program in Natural Sciences, Center for Science and Technology, State University of Ceará, Fortaleza, CE, Brazil.
| | - Aluísio Marques da Fonseca
- Institute of Engineering and Sustainable Development, Academic Master in Sociobiodiversity and Sustainable Technologies, University of International Integration of Afro-Brazilian Lusofonia, Acarape, CE, Brazil
| | | | | | - Emmanuel Silva Marinho
- Graduate Program in Natural Sciences, Center for Science and Technology, State University of Ceará, Fortaleza, CE, Brazil
- Group of Theoretical Chemistry and Electrochemistry, State University of Ceará, Limoeiro Do Norte, CE, Brazil
| | - Gabrielle Silva Marinho
- Group of Theoretical Chemistry and Electrochemistry, State University of Ceará, Limoeiro Do Norte, CE, Brazil
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2
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Canham SM, Wang Y, Cornett A, Auld DS, Baeschlin DK, Patoor M, Skaanderup PR, Honda A, Llamas L, Wendel G, Mapa FA, Aspesi P, Labbé-Giguère N, Gamber GG, Palacios DS, Schuffenhauer A, Deng Z, Nigsch F, Frederiksen M, Bushell SM, Rothman D, Jain RK, Hemmerle H, Briner K, Porter JA, Tallarico JA, Jenkins JL. Systematic Chemogenetic Library Assembly. Cell Chem Biol 2020; 27:1124-1129. [PMID: 32707038 DOI: 10.1016/j.chembiol.2020.07.004] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 06/03/2020] [Accepted: 07/02/2020] [Indexed: 12/22/2022]
Abstract
Chemogenetic libraries, collections of well-defined chemical probes, provide tremendous value to biomedical research but require substantial effort to ensure diversity as well as quality of the contents. We have assembled a chemogenetic library by data mining and crowdsourcing institutional expertise. We are sharing our approach, lessons learned, and disclosing our current collection of 4,185 compounds with their primary annotated gene targets (https://github.com/Novartis/MoaBox). This physical collection is regularly updated and used broadly both within Novartis and in collaboration with external partners.
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Affiliation(s)
- Stephen M Canham
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA.
| | - Yuan Wang
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Allen Cornett
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Douglas S Auld
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA.
| | - Daniel K Baeschlin
- Novartis Institute for BioMedical Research, Novartis Pharma AG, Forum 1 Novartis Campus, 4056 Basel, Switzerland
| | - Maude Patoor
- Novartis Institute for BioMedical Research, Novartis Pharma AG, Forum 1 Novartis Campus, 4056 Basel, Switzerland
| | - Philip R Skaanderup
- Novartis Institute for BioMedical Research, Novartis Pharma AG, Forum 1 Novartis Campus, 4056 Basel, Switzerland
| | - Ayako Honda
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Luis Llamas
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Greg Wendel
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Felipa A Mapa
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Peter Aspesi
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Nancy Labbé-Giguère
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Gabriel G Gamber
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Daniel S Palacios
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Ansgar Schuffenhauer
- Novartis Institute for BioMedical Research, Novartis Pharma AG, Forum 1 Novartis Campus, 4056 Basel, Switzerland
| | - Zhan Deng
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Florian Nigsch
- Novartis Institute for BioMedical Research, Novartis Pharma AG, Forum 1 Novartis Campus, 4056 Basel, Switzerland
| | - Mathias Frederiksen
- Novartis Institute for BioMedical Research, Novartis Pharma AG, Forum 1 Novartis Campus, 4056 Basel, Switzerland
| | - Simon M Bushell
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Deborah Rothman
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Rishi K Jain
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Horst Hemmerle
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Karin Briner
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Jeffery A Porter
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - John A Tallarico
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Jeremy L Jenkins
- Novartis Institute for BioMedical Research, 181 Massachusetts Avenue, Cambridge, MA 02139, USA.
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3
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D’Souza S, Prema K, Balaji S. Machine learning models for drug–target interactions: current knowledge and future directions. Drug Discov Today 2020; 25:748-756. [DOI: 10.1016/j.drudis.2020.03.003] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2019] [Revised: 02/28/2020] [Accepted: 03/05/2020] [Indexed: 12/22/2022]
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Insights into the EGFR SAR of N-phenylquinazolin-4-amine-derivatives using quantum mechanical pairwise-interaction energies. J Comput Aided Mol Des 2019; 33:745-757. [PMID: 31494804 DOI: 10.1007/s10822-019-00221-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2019] [Accepted: 08/21/2019] [Indexed: 10/26/2022]
Abstract
Protein kinases are an important class of enzymes that play an essential role in virtually all major disease areas. In addition, they account for approximately 50% of the current targets pursued in drug discovery research. In this work, we explore the generation of structure-based quantum mechanical (QM) quantitative structure-activity relationship models (QSAR) as a means to facilitate structure-guided optimization of protein kinase inhibitors. We explore whether more accurate, interpretable QSAR models can be generated for a series of 76 N-phenylquinazolin-4-amine inhibitors of epidermal growth factor receptor (EGFR) kinase by comparing and contrasting them to other standard QSAR methodologies. The QM-based method involved molecular docking of inhibitors followed by their QM optimization within a ~ 300 atom cluster model of the EGFR active site at the M062X/6-31G(d,p) level. Pairwise computations of the interaction energies with each active site residue were performed. QSAR models were generated by splitting the datasets 75:25 into a training and test set followed by modelling using partial least squares (PLS). Additional QSAR models were generated using alignment dependent CoMFA and CoMSIA methods as well as alignment independent physicochemical, e-state indices and fingerprint descriptors. The structure-based QM-QSAR model displayed good performance on the training and test sets (r2 ~ 0.7) and was demonstrably more predictive than the QSAR models built using other methods. The descriptor coefficients from the QM-QSAR models allowed for a detailed rationalization of the active site SAR, which has implications for subsequent design iterations.
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Abstract
Following the elucidation of the human genome, chemogenomics emerged in the beginning of the twenty-first century as an interdisciplinary research field with the aim to accelerate target and drug discovery by making best usage of the genomic data and the data linkable to it. What started as a systematization approach within protein target families now encompasses all types of chemical compounds and gene products. A key objective of chemogenomics is the establishment, extension, analysis, and prediction of a comprehensive SAR matrix which by application will enable further systematization in drug discovery. Herein we outline future perspectives of chemogenomics including the extension to new molecular modalities, or the potential extension beyond the pharma to the agro and nutrition sectors, and the importance for environmental protection. The focus is on computational sciences with potential applications for compound library design, virtual screening, hit assessment, analysis of phenotypic screens, lead finding and optimization, and systems biology-based prediction of toxicology and translational research.
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Affiliation(s)
- Edgar Jacoby
- Janssen Research & Development, Beerse, Belgium.
| | - J B Brown
- Life Science Informatics Research Unit, Laboratory of Molecular Biosciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
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Chiddarwar RK, Rohrer SG, Wolf A, Tresch S, Wollenhaupt S, Bender A. In silico target prediction for elucidating the mode of action of herbicides including prospective validation. J Mol Graph Model 2016; 71:70-79. [PMID: 27846423 DOI: 10.1016/j.jmgm.2016.10.021] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2016] [Accepted: 10/25/2016] [Indexed: 01/04/2023]
Abstract
The rapid emergence of pesticide resistance has given rise to a demand for herbicides with new mode of action (MoA). In the agrochemical sector, with the availability of experimental high throughput screening (HTS) data, it is now possible to utilize in silico target prediction methods in the early discovery phase to suggest the MoA of a compound via data mining of bioactivity data. While having been established in the pharmaceutical context, in the agrochemical area this approach poses rather different challenges, as we have found in this work, partially due to different chemistry, but even more so due to different (usually smaller) amounts of data, and different ways of conducting HTS. With the aim to apply computational methods for facilitating herbicide target identification, 48,000 bioactivity data against 16 herbicide targets were processed to train Laplacian modified Naïve Bayesian (NB) classification models. The herbicide target prediction model ("HerbiMod") is an ensemble of 16 binary classification models which are evaluated by internal, external and prospective validation sets. In addition to the experimental inactives, 10,000 random agrochemical inactives were included in the training process, which showed to improve the overall balanced accuracy of our models up to 40%. For all the models, performance in terms of balanced accuracy of≥80% was achieved in five-fold cross validation. Ranking target predictions was addressed by means of z-scores which improved predictivity over using raw scores alone. An external testset of 247 compounds from ChEMBL and a prospective testset of 394 compounds from BASF SE tested against five well studied herbicide targets (ACC, ALS, HPPD, PDS and PROTOX) were used for further validation. Only 4% of the compounds in the external testset lied in the applicability domain and extrapolation (and correct prediction) was hence impossible, which on one hand was surprising, and on the other hand illustrated the utilization of using applicability domains in the first place. However, performance better than 60% in balanced accuracy was achieved on the prospective testset, where all the compounds fell within the applicability domain, and which hence underlines the possibility of using target prediction also in the area of agrochemicals.
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Affiliation(s)
- Rucha K Chiddarwar
- Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - Sebastian G Rohrer
- Global Research Crop Protection, BASF SE, Speyerer Strasse 2, 67177 Limburgerhof, Germany
| | - Antje Wolf
- Computational Chemistry and Biology, BASF SE, Carl-Bosch-Strasse 38, 67056 Ludwigshafen, Germany
| | - Stefan Tresch
- Global Research Crop Protection, BASF SE, Speyerer Strasse 2, 67177 Limburgerhof, Germany
| | - Sabrina Wollenhaupt
- Computational Chemistry and Biology, BASF SE, Carl-Bosch-Strasse 38, 67056 Ludwigshafen, Germany
| | - Andreas Bender
- Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom.
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Schirle M, Jenkins JL. Identifying compound efficacy targets in phenotypic drug discovery. Drug Discov Today 2015; 21:82-89. [PMID: 26272035 DOI: 10.1016/j.drudis.2015.08.001] [Citation(s) in RCA: 102] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2015] [Revised: 07/10/2015] [Accepted: 08/03/2015] [Indexed: 12/30/2022]
Abstract
The identification of the efficacy target(s) for hits from phenotypic compound screens remains a key step to progress compounds into drug development. In addition to efficacy targets, the characterization of epistatic proteins influencing compound activity often facilitates the elucidation of the underlying mechanism of action; and, further, early determination of off-targets that cause potentially unwanted secondary phenotypes helps in assessing potential liabilities. This short review discusses the most important technologies currently available for characterizing the direct and indirect target space of bioactive compounds following phenotypic screening. We present a comprehensive strategy employing complementary approaches to balance individual technology strengths and weaknesses.
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Affiliation(s)
- Markus Schirle
- Developmental & Molecular Pathways, Novartis Institutes for BioMedical Research, Cambridge, MA 02139, USA.
| | - Jeremy L Jenkins
- Developmental & Molecular Pathways, Novartis Institutes for BioMedical Research, Cambridge, MA 02139, USA.
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Liggi S, Drakakis G, Hendry AE, Hanson KM, Brewerton SC, Wheeler GN, Bodkin MJ, Evans DA, Bender A. Extensions to In Silico Bioactivity Predictions Using Pathway Annotations and Differential Pharmacology Analysis: Application toXenopus laevisPhenotypic Readouts. Mol Inform 2013; 32:1009-24. [DOI: 10.1002/minf.201300102] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2013] [Accepted: 08/06/2013] [Indexed: 12/20/2022]
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9
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Medina-Franco JL, Giulianotti MA, Welmaker GS, Houghten RA. Shifting from the single to the multitarget paradigm in drug discovery. Drug Discov Today 2013; 18:495-501. [PMID: 23340113 PMCID: PMC3642214 DOI: 10.1016/j.drudis.2013.01.008] [Citation(s) in RCA: 294] [Impact Index Per Article: 26.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2012] [Revised: 12/03/2012] [Accepted: 01/14/2013] [Indexed: 01/30/2023]
Abstract
Increasing evidence that several drug compounds exert their effects through interactions with multiple targets is boosting the development of research fields that challenge the data reductionism approach. In this article, we review and discuss the concepts of drug repurposing, polypharmacology, chemogenomics, phenotypic screening and high-throughput in vivo testing of mixture-based libraries in an integrated manner. These research fields offer alternatives to the current paradigm of drug discovery, from a one target-one drug model to a multiple-target approach. Furthermore, the goals of lead identification are being expanded accordingly to identify not only 'key' compounds that fit with a single-target 'lock', but also 'master key' compounds that favorably interact with multiple targets (i.e. operate a set of desired locks to gain access to the expected clinical effects).
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Affiliation(s)
- José L Medina-Franco
- Instituto de Química, Universidad Nacional Autónoma de México, Circuito Exterior, Ciudad Universitaria, México, D.F. 04510, Mexico.
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