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Stefan SM, Rafehi M. Medicinal polypharmacology: Exploration and exploitation of the polypharmacolome in modern drug development. Drug Dev Res 2024; 85:e22125. [PMID: 37920929 DOI: 10.1002/ddr.22125] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 09/23/2023] [Accepted: 10/12/2023] [Indexed: 11/04/2023]
Abstract
At the core of complex and multifactorial human diseases, such as cancer, metabolic syndrome, or neurodegeneration, are multiple players that cross-talk in robust biological networks which are intrinsically resilient to alterations. These multifactorial diseases are characterized by sophisticated feedback mechanisms which manifest cellular imbalance and resistance to drug therapy. By adhering to the specificity paradigm ("one target-one drug concept"), research focused for many years on drugs with very narrow mechanisms of action. This narrow focus promoted therapy ineffectiveness and resistance. However, modern drug discovery has evolved over the last years, increasingly emphasizing integral strategies for the development of clinically effective drugs. These integral strategies include the controlled engagement of multiple targets to overcome therapy resistance. Apart from the additive or even synergistic effects in therapy, multitarget drugs harbor molecular-structural attributes to explore orphan targets of which intrinsic substrates/physiological role(s) and/or modulators are unknown for future therapy purposes. We designated this multidisciplinary and translational research field between medicinal chemistry, chemical biology, and molecular pharmacology as 'medicinal polypharmacology'. Medicinal polypharmacology emerged as alternative approach to common single-targeted pharmacology stretching from basic drug and target identification processes to clinical evaluation of multitarget drugs, and the exploration and exploitation of the 'polypharmacolome' is at the forefront of modern drug development research.
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Affiliation(s)
- Sven Marcel Stefan
- Drug Development and Chemical Biology, Lübeck Institute of Experimental Dermatology (LIED), University of Lübeck and University Medical Center Schleswig-Holstein, Lübeck, Germany
- Translational Neurodegeneration Research and Neuropathology Lab, Department of Pathology, Section of Neuropathology and Oslo University Hospital, University of Oslo, Oslo, Norway
- School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Camperdown, New South Wales, Australia
| | - Muhammad Rafehi
- Department of Medical Education, Augsburg University Medicine, Augsburg, Germany
- Institute of Clinical Pharmacology, University Medical Center Göttingen, Göttingen, Germany
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2
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Pitsillou E, Beh RC, Liang JJ, Tang TS, Zhou X, Siow YY, Ma Y, Hu Z, Wu Z, Hung A, Karagiannis TC. EpiMed Coronabank Chemical Collection: Compound selection, ADMET analysis, and utilisation in the context of potential SARS-CoV-2 antivirals. J Mol Graph Model 2023; 125:108602. [PMID: 37597309 DOI: 10.1016/j.jmgm.2023.108602] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 08/08/2023] [Accepted: 08/13/2023] [Indexed: 08/21/2023]
Abstract
Antiviral drugs are important for the coronavirus disease 2019 (COVID-19) response, as vaccines and antibodies may have reduced efficacy against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants. Antiviral drugs that have been made available for use, albeit with questionable efficacy, include remdesivir (Veklury®), nirmatrelvir-ritonavir (Paxlovid™), and molnupiravir (Lagevrio®). To expand the options available for COVID-19 and prepare for future pandemics, there is a need to investigate new uses for existing drugs and design novel compounds. To support these efforts, we have created a comprehensive library of 750 molecules that have been sourced from in vitro, in vivo, and in silico studies. It is publicly available at our dedicated website (https://epimedlab.org/crl/). The EpiMed Coronabank Chemical Collection consists of compounds that have been divided into 10 main classes based on antiviral properties, as well as the potential to be used for the management, prevention, or treatment of COVID-19 related complications. A detailed description of each compound is provided, along with the molecular formula, canonical SMILES, and U.S. Food and Drug Administration approval status. The chemical structures have been obtained and are available for download. Moreover, the pharmacokinetic properties of the ligands have been characterised. To demonstrate an application of the EpiMed Coronabank Chemical Collection, molecular docking was used to evaluate the binding characteristics of ligands against SARS-CoV-2 nonstructural and accessory proteins. Overall, our database can be used to aid the drug repositioning process, and for gaining further insight into the molecular mechanisms of action of potential compounds of interest.
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Affiliation(s)
- Eleni Pitsillou
- Epigenomic Medicine Laboratory at prospED, Carlton, VIC, 3053, Australia; School of Science, STEM College, RMIT University, VIC, 3001, Australia
| | - Raymond C Beh
- Epigenomic Medicine Laboratory at prospED, Carlton, VIC, 3053, Australia; School of Science, STEM College, RMIT University, VIC, 3001, Australia
| | - Julia J Liang
- Epigenomic Medicine Laboratory at prospED, Carlton, VIC, 3053, Australia; School of Science, STEM College, RMIT University, VIC, 3001, Australia
| | - Thinh Sieu Tang
- Department of Microbiology and Immunology, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Xun Zhou
- Department of Clinical Pathology, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Ya Yun Siow
- Department of Microbiology and Immunology, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Yinghao Ma
- Department of Microbiology and Immunology, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Zifang Hu
- Department of Microbiology and Immunology, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Zifei Wu
- Department of Microbiology and Immunology, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Andrew Hung
- School of Science, STEM College, RMIT University, VIC, 3001, Australia
| | - Tom C Karagiannis
- Epigenomic Medicine Laboratory at prospED, Carlton, VIC, 3053, Australia; Department of Microbiology and Immunology, The University of Melbourne, Parkville, VIC, 3010, Australia; Department of Clinical Pathology, The University of Melbourne, Parkville, VIC, 3010, Australia.
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3
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Rao M, McDuffie E, Sachs C. Artificial Intelligence/Machine Learning-Driven Small Molecule Repurposing via Off-Target Prediction and Transcriptomics. TOXICS 2023; 11:875. [PMID: 37888725 PMCID: PMC10611213 DOI: 10.3390/toxics11100875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 10/12/2023] [Accepted: 10/20/2023] [Indexed: 10/28/2023]
Abstract
The process of discovering small molecule drugs involves screening numerous compounds and optimizing the most promising ones, both in vitro and in vivo. However, approximately 90% of these optimized candidates fail during trials due to unexpected toxicity or insufficient efficacy. Current concepts with respect to drug-protein interactions suggest that each small molecule interacts with an average of 6-11 targets. This implies that approved drugs and even discontinued compounds could be repurposed by leveraging their interactions with unintended targets. Therefore, we developed a computational repurposing framework for small molecules, which combines artificial intelligence/machine learning (AI/ML)-based and chemical similarity-based target prediction methods with cross-species transcriptomics information. This repurposing methodology incorporates eight distinct target prediction methods, including three machine learning methods. By using multiple orthogonal methods for a "dataset" composed of 2766 FDA-approved drugs targeting multiple therapeutic target classes, we identified 27,371 off-target interactions involving 2013 protein targets (i.e., an average of around 10 interactions per drug). Relative to the drugs in the dataset, we identified 150,620 structurally similar compounds. The highest number of predicted interactions were for drugs targeting G protein-coupled receptors (GPCRs), enzymes, and kinases with 10,648, 4081, and 3678 interactions, respectively. Notably, 17,283 (63%) of the off-target interactions have been confirmed in vitro. Approximately 4000 interactions had an IC50 of <100 nM for 1105 FDA-approved drugs and 1661 interactions had an IC50 of <10 nM for 696 FDA-approved drugs. Together, the confirmation of numerous predicted interactions and the exploration of tissue-specific expression patterns in human and animal tissues offer insights into potential drug repurposing for new therapeutic applications.
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Affiliation(s)
- Mohan Rao
- Neurocrine Biosciences, Inc., Nonclinical Toxicology, San Diego, CA 92130, USA; (E.M.); (C.S.)
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4
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Lightfoot HL, Smith GF. Targeting RNA with small molecules-A safety perspective. Br J Pharmacol 2023. [PMID: 36631428 DOI: 10.1111/bph.16027] [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: 02/24/2022] [Revised: 06/30/2022] [Accepted: 12/20/2022] [Indexed: 01/13/2023] Open
Abstract
RNA is a major player in cellular function, and consequently can drive a number of disease pathologies. Over the past several years, small molecule-RNA targeting (smRNA targeting) has developed into a promising drug discovery approach. Numerous techniques, tools, and assays have been developed to support this field, and significant investments have been made by pharmaceutical and biotechnology companies. To date, the focus has been on identifying disease validated primary targets for smRNA drug development, yet RNA as a secondary (off) target for all small molecule drug programs largely has been unexplored. In this perspective, we discuss structure, target, and mechanism-driven safety aspects of smRNAs and highlight how these parameters can be evaluated in drug discovery programs to produce potentially safer drugs.
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Affiliation(s)
- Helen L Lightfoot
- Safety and Mechanistic Pharmacology, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK
| | - Graham F Smith
- Data Science and AI, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK
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5
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Accurate in silico simulation of the rabbit Purkinje fiber electrophysiological assay to facilitate early pharmaceutical cardiosafety assessment: Dream or reality? J Pharmacol Toxicol Methods 2022; 115:107172. [DOI: 10.1016/j.vascn.2022.107172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 03/31/2022] [Accepted: 04/08/2022] [Indexed: 11/24/2022]
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6
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Chen SJ, Bi YH, Zhang LH. Systematic analysis of the potential off-target activities of osimertinib by computational target fishing. Anticancer Drugs 2022; 33:e434-e443. [PMID: 34459459 DOI: 10.1097/cad.0000000000001229] [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: 11/26/2022]
Abstract
Osimertinib is a third-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor used to treat non-small cell lung cancer. However, its off-targets are obscure, and systematic analysis of off-target activities remains to be performed. Here, we identified the off-targets of osimertinib using PharmMapper and DRAR-CPI and analyzed the intersected targets using the GeneMANIA and DAVID servers. A drug-target-pathway network was constructed to visualize the associations. The results showed that osimertinib is associated with 31 off-targets, 40 Kyoto Encyclopedia of Genes and Genomes pathways, and 9 diseases. Network analysis revealed that the targets were involved in cancer and other physiological processes. In addition to EGFR, molecular docking analysis showed that seven proteins, namely Janus kinase 3, peroxisome proliferator-activated receptor alpha, renin, mitogen-activated protein kinases, lymphocyte-specific protein tyrosine kinase, cell division protein kinase 2 and proto-oncogene tyrosine-protein kinase Src, could also be potential targets of osimertinib. In conclusion, osimertinib is predicted to target multiple proteins and pathways, resulting in the formation of an action network via which it exerts systematic pharmacological effects.
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Affiliation(s)
- Shao-Jun Chen
- Department of Traditional Chinese Medicine, Zhejiang Pharmaceutical College, Ningbo
| | - Yan-Hua Bi
- The Children's Hospital, Zhejiang University School of Medicine, National clinical research center for child health, Hangzhou
| | - Li-Hua Zhang
- Department of Food Science, Faculty of Food Science, Zhejiang Pharmaceutical College, Ningbo, China
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7
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Halland N, Schmidt F, Weiss T, Li Z, Czech J, Saas J, Ding-Pfennigdorff D, Dreyer MK, Strübing C, Nazare M. Rational Design of Highly Potent, Selective, and Bioavailable SGK1 Protein Kinase Inhibitors for the Treatment of Osteoarthritis. J Med Chem 2021; 65:1567-1584. [PMID: 34931844 DOI: 10.1021/acs.jmedchem.1c01601] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The serine/threonine kinase SGK1 is an activator of the β-catenin pathway and a powerful stimulator of cartilage degradation that is found to be upregulated under genomic control in diseased osteoarthritic cartilage. Today, no oral disease-modifying treatments are available and chronic treatment in this indication sets high requirements for the drug selectivity, pharmacokinetic, and safety profile. We describe the identification of a highly selective druglike 1H-pyrazolo[3,4-d]pyrimidine SGK1 inhibitor 17a that matches both safety and pharmacokinetic requirements for oral dosing. Rational compound design was facilitated by a novel hSGK1 co-crystal structure, and multiple ligand-based computer models were applied to guide the chemical optimization of the compound ADMET and selectivity profiles. Compounds were selected for subchronic proof of mechanism studies in the mouse femoral head cartilage explant model, and compound 17a emerged as a druglike SGK1 inhibitor, with a highly optimized profile suitable for oral dosing as a novel, potentially disease-modifying agent for osteoarthritis.
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Affiliation(s)
- Nis Halland
- Integrated Drug Discovery, Sanofi R&D, Industriepark Höchst, D-65926 Frankfurt am Main, Germany
| | - Friedemann Schmidt
- Integrated Drug Discovery, Sanofi R&D, Industriepark Höchst, D-65926 Frankfurt am Main, Germany
| | - Tilo Weiss
- Integrated Drug Discovery, Sanofi R&D, Industriepark Höchst, D-65926 Frankfurt am Main, Germany
| | - Ziyu Li
- Integrated Drug Discovery, Sanofi R&D, Industriepark Höchst, D-65926 Frankfurt am Main, Germany
| | - Jörg Czech
- Integrated Drug Discovery, Sanofi R&D, Industriepark Höchst, D-65926 Frankfurt am Main, Germany
| | - Joachim Saas
- Integrated Drug Discovery, Sanofi R&D, Industriepark Höchst, D-65926 Frankfurt am Main, Germany
| | | | - Matthias K Dreyer
- Integrated Drug Discovery, Sanofi R&D, Industriepark Höchst, D-65926 Frankfurt am Main, Germany
| | - Carsten Strübing
- Integrated Drug Discovery, Sanofi R&D, Industriepark Höchst, D-65926 Frankfurt am Main, Germany
| | - Marc Nazare
- Leibniz-Institut für Molekulare Pharmakologie (FMP), Robert-Rössle-Straße 10, 13125 Berlin-Buch, Germany
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8
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Cichero E, Calautti A, Francesconi V, Tonelli M, Schenone S, Fossa P. Probing In Silico the Benzimidazole Privileged Scaffold for the Development of Drug-like Anti-RSV Agents. Pharmaceuticals (Basel) 2021; 14:ph14121307. [PMID: 34959708 PMCID: PMC8707824 DOI: 10.3390/ph14121307] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 12/06/2021] [Accepted: 12/10/2021] [Indexed: 12/16/2022] Open
Abstract
Targeting the fusion (F) protein has been recognized as a fruitful strategy for the development of anti-RSV agents. Despite the considerable efforts so far put into the development of RSV F protein inhibitors, the discovery of adequate therapeutics for the treatment of RSV infections is still awaiting a positive breakthrough. Several benzimidazole-containing derivatives have been discovered and evaluated in clinical trials, with only some of them being endowed with a promising pharmacokinetic profile. In this context, we applied a computational study based on a careful analysis of a number of X-ray crystallographic data of the RSV F protein, in the presence of different clinical candidates. A deepen comparison of the related electrostatic features and H-bonding motifs allowed us to pave the way for the following molecular dynamic simulation of JNJ-53718678 and then to perform docking studies of the in-house library of potent benzimidazole-containing anti-RSV agents. The results revealed not only the deep flexibility of the biological target but also the most relevant and recurring key contacts supporting the benzimidazole F protein inhibitor ability. Among them, several hydrophobic interactions and π-π stacking involving F140 and F488 proved to be mandatory, as well as H-bonding to D486. Specific requirements turning in RSV F protein binding ability were also explored thanks to structure-based pharmacophore analysis. Along with this, in silico prediction of absorption, distribution, metabolism, excretion (ADME) properties, and also of possible off-target events was performed. The results highlighted once more that the benzimidazole ring represents a privileged scaffold whose properties deserve to be further investigated for the rational design of novel and orally bioavailable anti-RSV agents.
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Affiliation(s)
- Elena Cichero
- Correspondence: (E.C.); (M.T.); Tel.: +39-010-353-8350 (E.C.); +39-010-353-8378 (M.T.)
| | | | | | - Michele Tonelli
- Correspondence: (E.C.); (M.T.); Tel.: +39-010-353-8350 (E.C.); +39-010-353-8378 (M.T.)
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9
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Loskill P, Hardwick RN, Roth A. Challenging the pipeline. Stem Cell Reports 2021; 16:2033-2037. [PMID: 34525380 PMCID: PMC8452595 DOI: 10.1016/j.stemcr.2021.08.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 08/05/2021] [Accepted: 08/06/2021] [Indexed: 12/24/2022] Open
Abstract
This commentary presents a thought experiment seeking to answer the key question: "If you were to put aside all the traditional drug discovery processes and start a new drug discovery program that places the highest priority on human and disease-relevant models throughout the entire process, how could it be done?"
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Affiliation(s)
- Peter Loskill
- Department of Biomedical Engineering, Faculty of Medicine, Eberhard Karls University Tübingen, Tübingen, Germany; NMI Natural and Medical Sciences Institute at the University of Tübingen, Reutlingen, Germany; 3R-Center for In vitro Models and Alternatives to Animal Testing, Eberhard Karls University Tübingen, Tübingen, Germany.
| | - Rhiannon N Hardwick
- Translational Safety Sciences, Theravance Biopharma US, Inc., South San Francisco, CA, USA
| | - Adrian Roth
- Personalized Healthcare Safety Interface, Product Development Safety, Roche Innovation Centre Basel, Basel, Switzerland
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10
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Cerqueira APM, Santana IB, Araújo JSC, Lima HG, Batatinha MJM, Branco A, Santos Junior MCD, Botura MB. Homology modeling, docking, molecular dynamics and in vitro studies to identify Rhipicephalus microplus acetylcholinesterase inhibitors. J Biomol Struct Dyn 2021; 40:6787-6797. [PMID: 33645442 DOI: 10.1080/07391102.2021.1889666] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Rhipicephalus microplus is an important ectoparasite of cattle, causing considerable economical losses. Resistance to chemical acaricides has stimulated the search for new antiparasitic drugs, including natural products as an eco-friendly alternative of control. Flavonoids represent a class of natural compounds with many biological activities, such as enzyme inhibitors. Acetylcholinesterase is an essential enzyme for tick survival that stands out as an important target for the development of acaricides. This work aimed to predict this 3D structure by homology modeling and use the model to identify compound with inhibitory activity. The model of R. microplus AChE1 (RmAChE1) was constructed using MODELLER program. The optimization and molecular dynamic investigation were performed in GROMACS program. The model developed was used, by molecular docking, to evaluate the anticholinesterase activity of flavonoids (quercetin, rutin, diosmin, naringin and hesperidin) and an acaricide synthetic (eserine). Additionally, in vitro inhibition of AChE and larval immersion tests were performed. The model of RmAChE1 showed to be sterically and energetically acceptable. In molecular dynamics simulations, the 3D structure remains stable with Root Mean Square Deviation = 3.58 Å and Root Mean Square Fluctuation = 1.43 Å. In molecular docking analyses, only eserine and quercetin show affinity energy to the RmAChE (Gridscore: -52.17 and -39.44 kcal/mol, respectively). Among the flavonoids, quercetin exhibited the best in vitro inhibition of AChE activity (15.8%) and mortality of larvae tick (30.2%). The use of in silico and in vitro techniques has shown that quercetin showed promising anti-tick activity and structural requirements to interact with RmAChE1. Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Amanda Ponce Morais Cerqueira
- Departamento de Biologia, Programa de Pós-Graduação em Biotecnologia, Universidade Estadual de Feira de Santana, Feira de Santana, BA, Brazil
| | - Isis Bugia Santana
- Departamento de Biologia, Programa de Pós-Graduação em Biotecnologia, Universidade Estadual de Feira de Santana, Feira de Santana, BA, Brazil
| | - Janay Stefany Carneiro Araújo
- Departamento de Biologia, Programa de Pós-Graduação em Biotecnologia, Universidade Estadual de Feira de Santana, Feira de Santana, BA, Brazil
| | - Hélimar Gonçalves Lima
- Laboratório de Toxicologia, Hospital de Medicina Veterinária, Universidade Federal da Bahia, Ondina, Salvador, BA, Brazil
| | - Maria José Moreira Batatinha
- Laboratório de Toxicologia, Hospital de Medicina Veterinária, Universidade Federal da Bahia, Ondina, Salvador, BA, Brazil
| | - Alexsandro Branco
- Departamento de Saúde, Universidade Estadual de Feira de Santana, Feira de Santana, BA, Brazil
| | | | - Mariana Borges Botura
- Departamento de Saúde, Universidade Estadual de Feira de Santana, Feira de Santana, BA, Brazil
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11
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Schmidt F. Computational Toxicology. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11534-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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12
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Jenkinson S, Schmidt F, Rosenbrier Ribeiro L, Delaunois A, Valentin JP. A practical guide to secondary pharmacology in drug discovery. J Pharmacol Toxicol Methods 2020; 105:106869. [PMID: 32302774 DOI: 10.1016/j.vascn.2020.106869] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 03/21/2020] [Accepted: 04/03/2020] [Indexed: 01/29/2023]
Abstract
Secondary pharmacological profiling is increasingly applied in pharmaceutical drug discovery to address unwanted pharmacological side effects of drug candidates before entering the clinic. Regulators, drug makers and patients share a demand for deep characterization of secondary pharmacology effects of novel drugs and their metabolites. The scope of such profiling has therefore expanded substantially in the past two decades, leading to the implementation of broad in silico profiling methods and focused in vitro off-target screening panels, to identify liabilities, but also opportunities, as early as possible. The pharmaceutical industry applies such panels at all stages of drug discovery routinely up to early development. Nevertheless, target composition, screening technologies, assay formats, interpretation and scheduling of panels can vary significantly between companies in the absence of dedicated guidelines. To contribute towards best practices in secondary pharmacology profiling, this review aims to summarize the state-of-the art in this field. Considerations are discussed with respect to panel design, screening strategy, implementation and interpretation of the data, including regulatory perspectives. The cascaded, or integrated, use of in silico and off-target profiling allows to exploit synergies for comprehensive safety assessment of drug candidates.
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Affiliation(s)
- Stephen Jenkinson
- Drug Safety Research and Development, Pfizer Inc., La Jolla, CA 92121, United States of America.
| | - Friedemann Schmidt
- Sanofi, R&D Preclinical Safety, Industriepark Höchst, 65926 Frankfurt/Main, Germany
| | - Lyn Rosenbrier Ribeiro
- Medicines Discovery Catapult, Block 35, Mereside, Alderley Park, Alderley Edge, SK10 4TG, United Kingdom
| | - Annie Delaunois
- UCB BioPharma SRL, Early Solutions, Development Science, Non-Clinical Safety, 1420 Braine L'Alleud, Walloon Region, Belgium
| | - Jean-Pierre Valentin
- UCB BioPharma SRL, Early Solutions, Development Science, Non-Clinical Safety, 1420 Braine L'Alleud, Walloon Region, Belgium
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13
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Garrido A, Lepailleur A, Mignani SM, Dallemagne P, Rochais C. hERG toxicity assessment: Useful guidelines for drug design. Eur J Med Chem 2020; 195:112290. [PMID: 32283295 DOI: 10.1016/j.ejmech.2020.112290] [Citation(s) in RCA: 109] [Impact Index Per Article: 27.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 03/27/2020] [Accepted: 03/27/2020] [Indexed: 02/06/2023]
Abstract
All along the drug development process, one of the most frequent adverse side effects, leading to the failure of drugs, is the cardiac arrhythmias. Such failure is mostly related to the capacity of the drug to inhibit the human ether-à-go-go-related gene (hERG) cardiac potassium channel. The early identification of hERG inhibition properties of biological active compounds has focused most of attention over the years. In order to prevent the cardiac side effects, a great number of in silico, in vitro and in vivo assays have been performed. The main goal of these studies is to understand the reasons of these effects, and then to give information or instructions to scientists involved in drug development to avoid the cardiac side effects. To evaluate anticipated cardiovascular effects, early evaluation of hERG toxicity has been strongly recommended for instance by the regulatory agencies such as U.S. Food and Drug Administration (FDA) and European Medicines Agency (EMA). Thus, following an initial screening of a collection of compounds to find hits, a great number of pharmacomodulation studies on the novel identified chemical series need to be performed including activity evaluation towards hERG. We provide in this concise review clear guidelines, based on described examples, illustrating successful optimization process to avoid hERG interactions as cases studies and to spur scientists to develop safe drugs.
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Affiliation(s)
- Amanda Garrido
- Normandie Univ, UNICAEN, Centre d'Etudes et de Recherche sur le Médicament de Normandie (CERMN), Caen, France
| | - Alban Lepailleur
- Normandie Univ, UNICAEN, Centre d'Etudes et de Recherche sur le Médicament de Normandie (CERMN), Caen, France
| | - Serge M Mignani
- UMR 860, Laboratoire de Chimie et de Biochimie Pharmacologiques et Toxicologique, Université Paris Descartes, PRES Sorbonne Paris Cité, CNRS, 45 rue des Saints Pères, 75006, Paris, France; CQM - Centro de Química da Madeira, MMRG, Universidade da Madeira, Campus da Penteada, 9020-105, Funchal, Portugal
| | - Patrick Dallemagne
- Normandie Univ, UNICAEN, Centre d'Etudes et de Recherche sur le Médicament de Normandie (CERMN), Caen, France
| | - Christophe Rochais
- Normandie Univ, UNICAEN, Centre d'Etudes et de Recherche sur le Médicament de Normandie (CERMN), Caen, France.
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15
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Bendels S, Bissantz C, Fasching B, Gerebtzoff G, Guba W, Kansy M, Migeon J, Mohr S, Peters JU, Tillier F, Wyler R, Lerner C, Kramer C, Richter H, Roberts S. Safety screening in early drug discovery: An optimized assay panel. J Pharmacol Toxicol Methods 2019; 99:106609. [DOI: 10.1016/j.vascn.2019.106609] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Revised: 06/29/2019] [Accepted: 07/01/2019] [Indexed: 12/20/2022]
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Rao MS, Gupta R, Liguori MJ, Hu M, Huang X, Mantena SR, Mittelstadt SW, Blomme EAG, Van Vleet TR. Novel Computational Approach to Predict Off-Target Interactions for Small Molecules. Front Big Data 2019; 2:25. [PMID: 33693348 PMCID: PMC7931946 DOI: 10.3389/fdata.2019.00025] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Accepted: 06/26/2019] [Indexed: 12/01/2022] Open
Abstract
Most small molecule drugs interact with unintended, often unknown, biological targets and these off-target interactions may lead to both preclinical and clinical toxic events. Undesired off-target interactions are often not detected using current drug discovery assays, such as experimental polypharmacological screens. Thus, improvement in the early identification of off-target interactions represents an opportunity to reduce safety-related attrition rates during preclinical and clinical development. In order to better identify potential off-target interactions that could be linked to predictable safety issues, a novel computational approach to predict safety-relevant interactions currently not covered was designed and evaluated. These analyses, termed Off-Target Safety Assessment (OTSA), cover more than 7,000 targets (~35% of the proteome) and > 2,46,704 preclinical and clinical alerts (as of January 20, 2019). The approach described herein exploits a highly curated training set of >1 million compounds (tracking >20 million compound-structure activity relationship/SAR data points) with known in vitro activities derived from patents, journals, and publicly available databases. This computational process was used to predict both the primary and secondary pharmacological activities for a selection of 857 diverse small molecule drugs for which extensive secondary pharmacology data are readily available (456 discontinued and 401 FDA approved). The OTSA process predicted a total of 7,990 interactions for these 857 molecules. Of these, 3,923 and 4,067 possible high-scoring interactions were predicted for the discontinued and approved drugs, respectively, translating to an average of 9.3 interactions per drug. The OTSA process correctly identified the known pharmacological targets for >70% of these drugs, but also predicted a significant number of off-targets that may provide additional insight into observed in vivo effects. About 51.5% (2,025) and 22% (900) of these predicted high-scoring interactions have not previously been reported for the discontinued and approved drugs, respectively, and these may have a potential for repurposing efforts. Moreover, for both drug categories, higher promiscuity was observed for compounds with a MW range of 300 to 500, TPSA of ~200, and clogP ≥7. This computation also revealed significantly lower promiscuity (i.e., number of confirmed off-targets) for compounds with MW > 700 and MW<200 for both categories. In addition, 15 internal small molecules with known off-target interactions were evaluated. For these compounds, the OTSA framework not only captured about 56.8% of in vitro confirmed off-target interactions, but also identified the right pharmacological targets for 14 compounds as one of the top scoring targets. In conclusion, the OTSA process demonstrates good predictive performance characteristics and represents an additional tool with utility during the lead optimization stage of the drug discovery process. Additionally, the computed physiochemical properties such as clogP (i.e., lipophilicity), molecular weight, pKa and logS (i.e., solubility) were found to be statistically different between the approved and discontinued drugs, but the internal compounds were close to the approved drugs space in most part.
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Affiliation(s)
- Mohan S Rao
- Global Preclinical Safety, Abbvie, North Chicago, IL, United States
| | - Rishi Gupta
- Information Research, Abbvie, North Chicago, IL, United States
| | | | - Mufeng Hu
- Discovery and Early Pipeline Statistics, Abbvie, North Chicago, IL, United States
| | - Xin Huang
- Discovery and Early Pipeline Statistics, Abbvie, North Chicago, IL, United States
| | | | | | - Eric A G Blomme
- Global Preclinical Safety, Abbvie, North Chicago, IL, United States
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17
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Novel quinazolin-4-one derivatives as potentiating agents of doxorubicin cytotoxicity. Bioorg Chem 2019; 82:204-210. [DOI: 10.1016/j.bioorg.2018.10.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Revised: 10/01/2018] [Accepted: 10/02/2018] [Indexed: 12/14/2022]
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18
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Abstract
Pharmacological science is trying to establish the link between chemicals, targets, and disease-related phenotypes. A plethora of chemical proteomics and structural data have been generated, thanks to the target-based approach that has dominated drug discovery at the turn of the century. There is an invaluable source of information for in silico target profiling. Prediction is based on the principle of chemical similarity (similar drugs bind similar targets) or on first principles from the biophysics of molecular interactions. In the first case, compound comparison is made through ligand-based chemical similarity search or through classifier-based machine learning approach. The 3D techniques are based on 3D structural descriptors or energy-based scoring scheme to infer a binding affinity of a compound with its putative target. More recently, a new approach based on compound set metric has been proposed in which a query compound is compared with a whole of compounds associated with a target or a family of targets. This chapter reviews the different techniques of in silico target profiling and their main applications such as inference of unwanted targets, drug repurposing, or compound prioritization after phenotypic-based screening campaigns.
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Hou TY, Weng CF, Leong MK. Insight Analysis of Promiscuous Estrogen Receptor α-Ligand Binding by a Novel Machine Learning Scheme. Chem Res Toxicol 2018; 31:799-813. [PMID: 30019586 DOI: 10.1021/acs.chemrestox.8b00130] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Estrogen receptor α (ERα) plays a significant role in occurrence of breast cancer and may cause various adverse side-effects when ERα is an off-target protein. A theoretical model was derived to predict the binding affinity of ERα using the pharmacophore ensemble/support vector machine (PhE/SVM) scheme to consider the promiscuous characteristic of ERα. The estimations by PhE/SVM were discovered to be in good agreement with the observed values for those training molecules ( n = 31, r2 = 0.80, qCV2 = 0.77, RMSE = 0.57, s = 0.58), test molecules ( n = 179, q2 = 0.91-0.96, RMSE = 0.33, s = 0.26) and outliers ( n = 15, q2 = 0.80-0.86, RMSE = 0.56, s = 0.49). When subjected to various statistical validations, the PhE/SVM model consistently fulfilled the strictest criteria. A mock test also asserted its predictivity. When compared with crystal structures, the calculated results are consistent with the reported ERα-ligand co-complex structure, and the plasticity nature of ERα is also disclosed. Consequently, this precise, fast, and robust model can be adopted to predict ERα-ligand binding affinities and to design safer non-ERα-targeted pharmaceuticals in the process of drug discovery and development.
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Mishra V, Pathak C. Structural insights into pharmacophore-assisted in silico identification of protein-protein interaction inhibitors for inhibition of human toll-like receptor 4 - myeloid differentiation factor-2 (hTLR4-MD-2) complex. J Biomol Struct Dyn 2018; 37:1968-1991. [PMID: 29842849 DOI: 10.1080/07391102.2018.1474804] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
Abstract
Toll-like receptor 4 (TLR4) is a member of Toll-Like Receptors (TLRs) family that serves as a receptor for bacterial lipopolysaccharide (LPS). TLR4 alone cannot recognize LPS without aid of co-receptor myeloid differentiation factor-2 (MD-2). Binding of LPS with TLR4 forms a LPS-TLR4-MD-2 complex and directs downstream signaling for activation of immune response, inflammation and NF-κB activation. Activation of TLR4 signaling is associated with various pathophysiological consequences. Therefore, targeting protein-protein interaction (PPI) in TLR4-MD-2 complex formation could be an attractive therapeutic approach for targeting inflammatory disorders. The aim of present study was directed to identify small molecule PPI inhibitors (SMPPIIs) using pharmacophore mapping-based approach of computational drug discovery. Here, we had retrieved the information about the hot spot residues and their pharmacophoric features at both primary (TLR4-MD-2) and dimerization (MD-2-TLR4*) protein-protein interaction interfaces in TLR4-MD-2 homo-dimer complex using in silico methods. Promising candidates were identified after virtual screening, which may restrict TLR4-MD-2 protein-protein interaction. In silico off-target profiling over the virtually screened compounds revealed other possible molecular targets. Two of the virtually screened compounds (C11 and C15) were predicted to have an inhibitory concentration in μM range after HYDE assessment. Molecular dynamics simulation study performed for these two compounds in complex with target protein confirms the stability of the complex. After virtual high throughput screening we found selective hTLR4-MD-2 inhibitors, which may have therapeutic potential to target chronic inflammatory diseases.
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Affiliation(s)
- Vinita Mishra
- a Department of Cell Biology, School of Biological Sciences & Biotechnology , Indian Institute of Advanced Research, Koba Institutional Area , Gandhinagar , India
| | - Chandramani Pathak
- a Department of Cell Biology, School of Biological Sciences & Biotechnology , Indian Institute of Advanced Research, Koba Institutional Area , Gandhinagar , India
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21
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Abstract
The use of computational toxicology methods within drug discovery began in the early 2000s with applications such as predicting bacterial mutagenicity and hERG inhibition. The field has been continuously expanding ever since and the tasks at hand have become more complex. These approaches are now strategically integrated into the risk assessment process, as a complement to in vitro and in vivo methods. Today, computational toxicology can be used in every phase of drug discovery and development, from profiling large libraries early on, to predicting off-target effects in the mid-discovery phase, to assessing potential mutagenic impurities in development and degradants as part of life-cycle management. This chapter provides an overview of the field and describes the application of computational toxicology throughout the entire discovery and development process.
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Affiliation(s)
- Catrin Hasselgren
- PureInfo Discovery Inc., Albuquerque, NM, USA.
- Leadscope Inc., Columbus, OH, USA.
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Mulliner D, Schmidt F, Stolte M, Spirkl HP, Czich A, Amberg A. Computational Models for Human and Animal Hepatotoxicity with a Global Application Scope. Chem Res Toxicol 2016; 29:757-67. [PMID: 26914516 DOI: 10.1021/acs.chemrestox.5b00465] [Citation(s) in RCA: 76] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Hepatic toxicity is a key concern for novel pharmaceutical drugs since it is difficult to anticipate in preclinical models, and it can originate from pharmacologically unrelated drug effects, such as pathway interference, metabolism, and drug accumulation. Because liver toxicity still ranks among the top reasons for drug attrition, the reliable prediction of adverse hepatic effects is a substantial challenge in drug discovery and development. To this end, more effort needs to be focused on the development of improved predictive in-vitro and in-silico approaches. Current computational models often lack applicability to novel pharmaceutical candidates, typically due to insufficient coverage of the chemical space of interest, which is either imposed by size or diversity of the training data. Hence, there is an urgent need for better computational models to allow for the identification of safe drug candidates and to support experimental design. In this context, a large data set comprising 3712 compounds with liver related toxicity findings in humans and animals was collected from various sources. The complex pathology was clustered into 21 preclinical and human hepatotoxicity endpoints, which were organized into three levels of detail. Support vector machine models were trained for each endpoint, using optimized descriptor sets from chemometrics software. The optimized global human hepatotoxicity model has high sensitivity (68%) and excellent specificity (95%) in an internal validation set of 221 compounds. Models for preclinical endpoints performed similarly. To allow for reliable prediction of "truly external" novel compounds, all predictions are tagged with confidence parameters. These parameters are derived from a statistical analysis of the predictive probability densities. The whole approach was validated for an external validation set of 269 proprietary compounds. The models are fully integrated into our early safety in-silico workflow.
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Affiliation(s)
- Denis Mulliner
- R&D DSAR/Preclinical Safety FF, Sanofi-Aventis Deutschland GmbH , Industriepark Hoechst, Building H831, D-65926 Frankfurt am Main, Germany
| | - Friedemann Schmidt
- R&D DSAR/Preclinical Safety FF, Sanofi-Aventis Deutschland GmbH , Industriepark Hoechst, Building H831, D-65926 Frankfurt am Main, Germany
| | - Manuela Stolte
- R&D DSAR/Preclinical Safety FF, Sanofi-Aventis Deutschland GmbH , Industriepark Hoechst, Building H831, D-65926 Frankfurt am Main, Germany
| | - Hans-Peter Spirkl
- R&D DSAR/Preclinical Safety FF, Sanofi-Aventis Deutschland GmbH , Industriepark Hoechst, Building H831, D-65926 Frankfurt am Main, Germany
| | - Andreas Czich
- R&D DSAR/Preclinical Safety FF, Sanofi-Aventis Deutschland GmbH , Industriepark Hoechst, Building H831, D-65926 Frankfurt am Main, Germany
| | - Alexander Amberg
- R&D DSAR/Preclinical Safety FF, Sanofi-Aventis Deutschland GmbH , Industriepark Hoechst, Building H831, D-65926 Frankfurt am Main, Germany
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Kazakiewicz D, Karr JR, Langner KM, Plewczynski D. A combined systems and structural modeling approach repositions antibiotics for Mycoplasma genitalium. Comput Biol Chem 2015; 59 Pt B:91-7. [DOI: 10.1016/j.compbiolchem.2015.07.007] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2015] [Revised: 05/05/2015] [Accepted: 07/24/2015] [Indexed: 12/13/2022]
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Garcia-Serna R, Vidal D, Remez N, Mestres J. Large-Scale Predictive Drug Safety: From Structural Alerts to Biological Mechanisms. Chem Res Toxicol 2015; 28:1875-87. [PMID: 26360911 DOI: 10.1021/acs.chemrestox.5b00260] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The recent explosion of data linking drugs, proteins, and pathways with safety events has promoted the development of integrative systems approaches to large-scale predictive drug safety. The added value of such approaches is that, beyond the traditional identification of potentially labile chemical fragments for selected toxicity end points, they have the potential to provide mechanistic insights for a much larger and diverse set of safety events in a statistically sound nonsupervised manner, based on the similarity to drug classes, the interaction with secondary targets, and the interference with biological pathways. The combined identification of chemical and biological hazards enhances our ability to assess the safety risk of bioactive small molecules with higher confidence than that using structural alerts only. We are still a very long way from reliably predicting drug safety, but advances toward gaining a better understanding of the mechanisms leading to adverse outcomes represent a step forward in this direction.
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Affiliation(s)
- Ricard Garcia-Serna
- Chemotargets SL , Parc Científic de Barcelona, Baldiri Reixac 4 (TI-05A7), 08028 Barcelona, Catalonia, Spain
| | - David Vidal
- Chemotargets SL , Parc Científic de Barcelona, Baldiri Reixac 4 (TI-05A7), 08028 Barcelona, Catalonia, Spain
| | - Nikita Remez
- Chemotargets SL , Parc Científic de Barcelona, Baldiri Reixac 4 (TI-05A7), 08028 Barcelona, Catalonia, Spain.,Systems Pharmacology, Research Program on Biomedical Informatics (GRIB), IMIM Hospital del Mar Medical Research Institute and University Pompeu Fabra , Parc de Recerca Biomèdica, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain
| | - Jordi Mestres
- Chemotargets SL , Parc Científic de Barcelona, Baldiri Reixac 4 (TI-05A7), 08028 Barcelona, Catalonia, Spain.,Systems Pharmacology, Research Program on Biomedical Informatics (GRIB), IMIM Hospital del Mar Medical Research Institute and University Pompeu Fabra , Parc de Recerca Biomèdica, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain
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25
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In silico assessment of adverse drug reactions and associated mechanisms. Drug Discov Today 2015; 21:58-71. [PMID: 26272036 DOI: 10.1016/j.drudis.2015.07.018] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2015] [Revised: 07/15/2015] [Accepted: 07/31/2015] [Indexed: 12/31/2022]
Abstract
During recent years, various in silico approaches have been developed to estimate chemical and biological drug features, for example chemical fragments, protein targets, pathways, among others, that correlate with adverse drug reactions (ADRs) and explain the associated mechanisms. These features have also been used for the creation of predictive models that enable estimation of ADRs during the early stages of drug development. In this review, we discuss various in silico approaches to predict these features for a certain drug, estimate correlations with ADRs, establish causal relationships between selected features and ADR mechanisms and create corresponding predictive models.
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26
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Halland N, Schmidt F, Weiss T, Saas J, Li Z, Czech J, Dreyer M, Hofmeister A, Mertsch K, Dietz U, Strübing C, Nazare M. Discovery of N-[4-(1H-Pyrazolo[3,4-b]pyrazin-6-yl)-phenyl]-sulfonamides as Highly Active and Selective SGK1 Inhibitors. ACS Med Chem Lett 2015; 6:73-8. [PMID: 25589934 DOI: 10.1021/ml5003376] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2014] [Accepted: 10/17/2014] [Indexed: 12/11/2022] Open
Abstract
From a virtual screening starting point, inhibitors of the serum and glucocorticoid regulated kinase 1 were developed through a combination of classical medicinal chemistry and library approaches. This resulted in highly active small molecules with nanomolar activity and a good overall in vitro and ADME profile. Furthermore, the compounds exhibited unusually high kinase and off-target selectivity due to their rigid structure.
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Affiliation(s)
- Nis Halland
- Sanofi R&D, Industriepark Höchst Building G838, D-65926 Frankfurt am Main, Germany
| | - Friedemann Schmidt
- Sanofi R&D, Industriepark Höchst Building G838, D-65926 Frankfurt am Main, Germany
| | - Tilo Weiss
- Sanofi R&D, Industriepark Höchst Building G838, D-65926 Frankfurt am Main, Germany
| | - Joachim Saas
- Sanofi R&D, Industriepark Höchst Building G838, D-65926 Frankfurt am Main, Germany
| | - Ziyu Li
- Sanofi R&D, Industriepark Höchst Building G838, D-65926 Frankfurt am Main, Germany
| | - Jörg Czech
- Sanofi R&D, Industriepark Höchst Building G838, D-65926 Frankfurt am Main, Germany
| | - Matthias Dreyer
- Sanofi R&D, Industriepark Höchst Building G838, D-65926 Frankfurt am Main, Germany
| | - Armin Hofmeister
- Sanofi R&D, Industriepark Höchst Building G838, D-65926 Frankfurt am Main, Germany
| | - Katharina Mertsch
- Sanofi R&D, Industriepark Höchst Building G838, D-65926 Frankfurt am Main, Germany
| | - Uwe Dietz
- Sanofi R&D, Industriepark Höchst Building G838, D-65926 Frankfurt am Main, Germany
| | - Carsten Strübing
- Sanofi R&D, Industriepark Höchst Building G838, D-65926 Frankfurt am Main, Germany
| | - Marc Nazare
- Leibniz-Institut für Molekulare Pharmakologie (FMP), Robert-Rössle-Straße 10, 13125 Berlin-Buch, Germany
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