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Azevedo LG, Sosa E, de Queiroz ATL, Barral A, Wheeler RJ, Nicolás MF, Farias LP, Do Porto DF, Ramos PIP. High-throughput prioritization of target proteins for development of new antileishmanial compounds. Int J Parasitol Drugs Drug Resist 2024; 25:100538. [PMID: 38669848 PMCID: PMC11068527 DOI: 10.1016/j.ijpddr.2024.100538] [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: 10/18/2023] [Revised: 03/11/2024] [Accepted: 04/04/2024] [Indexed: 04/28/2024]
Abstract
Leishmaniasis, a vector-borne disease, is caused by the infection of Leishmania spp., obligate intracellular protozoan parasites. Presently, human vaccines are unavailable, and the primary treatment relies heavily on systemic drugs, often presenting with suboptimal formulations and substantial toxicity, making new drugs a high priority for LMIC countries burdened by the disease, but a low priority in the agenda of most pharmaceutical companies due to unattractive profit margins. New ways to accelerate the discovery of new, or the repositioning of existing drugs, are needed. To address this challenge, our study aimed to identify potential protein targets shared among clinically-relevant Leishmania species. We employed a subtractive proteomics and comparative genomics approach, integrating high-throughput multi-omics data to classify these targets based on different druggability metrics. This effort resulted in the ranking of 6502 ortholog groups of protein targets across 14 pathogenic Leishmania species. Among the top 20 highly ranked groups, metabolic processes known to be attractive drug targets, including the ubiquitination pathway, aminoacyl-tRNA synthetases, and purine synthesis, were rediscovered. Additionally, we unveiled novel promising targets such as the nicotinate phosphoribosyltransferase enzyme and dihydrolipoamide succinyltransferases. These groups exhibited appealing druggability features, including less than 40% sequence identity to the human host proteome, predicted essentiality, structural classification as highly druggable or druggable, and expression levels above the 50th percentile in the amastigote form. The resources presented in this work also represent a comprehensive collection of integrated data regarding trypanosomatid biology.
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Affiliation(s)
- Lucas G Azevedo
- Center for Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz (Fiocruz Bahia), Salvador, Bahia, Brazil; Post-graduate Program in Biotechnology and Investigative Medicine, Instituto Gonçalo Moniz, Salvador, Bahia, Brazil.
| | - Ezequiel Sosa
- Universidad de Buenos Aires, Buenos Aires, Argentina.
| | - Artur T L de Queiroz
- Center for Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz (Fiocruz Bahia), Salvador, Bahia, Brazil; Post-graduate Program in Biotechnology and Investigative Medicine, Instituto Gonçalo Moniz, Salvador, Bahia, Brazil.
| | - Aldina Barral
- Laboratório de Medicina e Saúde Pública de Precisão (MeSP2), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz (Fiocruz Bahia), Salvador, Bahia, Brazil.
| | - Richard J Wheeler
- Peter Medawar Building for Pathogen Research, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom.
| | - Marisa F Nicolás
- Laboratório Nacional de Computação Científica, Petrópolis, Rio de Janeiro, Brazil.
| | - Leonardo P Farias
- Post-graduate Program in Biotechnology and Investigative Medicine, Instituto Gonçalo Moniz, Salvador, Bahia, Brazil; Laboratório de Medicina e Saúde Pública de Precisão (MeSP2), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz (Fiocruz Bahia), Salvador, Bahia, Brazil.
| | | | - Pablo Ivan P Ramos
- Center for Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz (Fiocruz Bahia), Salvador, Bahia, Brazil; Post-graduate Program in Biotechnology and Investigative Medicine, Instituto Gonçalo Moniz, Salvador, Bahia, Brazil.
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2
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Barakat A, Munro G, Heegaard AM. Finding new analgesics: Computational pharmacology faces drug discovery challenges. Biochem Pharmacol 2024; 222:116091. [PMID: 38412924 DOI: 10.1016/j.bcp.2024.116091] [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: 10/02/2023] [Revised: 01/10/2024] [Accepted: 02/23/2024] [Indexed: 02/29/2024]
Abstract
Despite the worldwide prevalence and huge burden of pain, pain is an undertreated phenomenon. Currently used analgesics have several limitations regarding their efficacy and safety. The discovery of analgesics possessing a novel mechanism of action has faced multiple challenges, including a limited understanding of biological processes underpinning pain and analgesia and poor animal-to-human translation. Computational pharmacology is currently employed to face these challenges. In this review, we discuss the theory, methods, and applications of computational pharmacology in pain research. Computational pharmacology encompasses a wide variety of theoretical concepts and practical methodological approaches, with the overall aim of gaining biological insight through data acquisition and analysis. Data are acquired from patients or animal models with pain or analgesic treatment, at different levels of biological organization (molecular, cellular, physiological, and behavioral). Distinct methodological algorithms can then be used to analyze and integrate data. This helps to facilitate the identification of biological molecules and processes associated with pain phenotype, build quantitative models of pain signaling, and extract translatable features between humans and animals. However, computational pharmacology has several limitations, and its predictions can provide false positive and negative findings. Therefore, computational predictions are required to be validated experimentally before drawing solid conclusions. In this review, we discuss several case study examples of combining and integrating computational tools with experimental pain research tools to meet drug discovery challenges.
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Affiliation(s)
- Ahmed Barakat
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Department of Pharmacology and Toxicology, Faculty of Pharmacy, Assiut University, Assiut, Egypt.
| | | | - Anne-Marie Heegaard
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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3
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Hu X, Gan L, Tang Z, Lin R, Liang Z, Li F, Zhu C, Han X, Zheng R, Shen J, Yu J, Luo N, Peng W, Tan J, Li X, Fan J, Wen Q, Wang X, Li J, Zheng X, Liu Q, Guo J, Shi G, Mao H, Chen W, Yin S, Zhou Y. A Natural Small Molecule Mitigates Kidney Fibrosis by Targeting Cdc42-mediated GSK-3β/β-catenin Signaling. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2307850. [PMID: 38240457 PMCID: PMC10987128 DOI: 10.1002/advs.202307850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 01/08/2024] [Indexed: 04/04/2024]
Abstract
Kidney fibrosis is a common fate of chronic kidney diseases (CKDs), eventually leading to renal dysfunction. Yet, no effective treatment for this pathological process has been achieved. During the bioassay-guided chemical investigation of the medicinal plant Wikstroemia chamaedaphne, a daphne diterpenoid, daphnepedunin A (DA), is characterized as a promising anti-renal fibrotic lead. DA shows significant anti-kidney fibrosis effects in cultured renal fibroblasts and unilateral ureteral obstructed mice, being more potent than the clinical trial drug pirfenidone. Leveraging the thermal proteome profiling strategy, cell division cycle 42 (Cdc42) is identified as the direct target of DA. Mechanistically, DA targets to reduce Cdc42 activity and down-regulates its downstream phospho-protein kinase Cζ(p-PKCζ)/phospho-glycogen synthase kinase-3β (p-GSK-3β), thereby promoting β-catenin Ser33/37/Thr41 phosphorylation and ubiquitin-dependent proteolysis to block classical pro-fibrotic β-catenin signaling. These findings suggest that Cdc42 is a promising therapeutic target for kidney fibrosis, and highlight DA as a potent Cdc42 inhibitor for combating CKDs.
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Affiliation(s)
- Xinrong Hu
- Department of NephrologyThe First Affiliated HospitalSun Yat‐sen UniversityNHC Key Laboratory of Clinical NephrologyGuangdong Provincial Key Laboratory of NephrologySun Yat‐Sen UniversityGuangzhou510080China
| | - Lu Gan
- School of Pharmaceutical SciencesSun Yat‐sen UniversityGuangzhou510006China
| | - Ziwen Tang
- Department of NephrologyThe First Affiliated HospitalSun Yat‐sen UniversityNHC Key Laboratory of Clinical NephrologyGuangdong Provincial Key Laboratory of NephrologySun Yat‐Sen UniversityGuangzhou510080China
| | - Ruoni Lin
- Department of NephrologyThe First Affiliated HospitalSun Yat‐sen UniversityNHC Key Laboratory of Clinical NephrologyGuangdong Provincial Key Laboratory of NephrologySun Yat‐Sen UniversityGuangzhou510080China
| | - Zhou Liang
- Department of NephrologyThe First Affiliated HospitalSun Yat‐sen UniversityNHC Key Laboratory of Clinical NephrologyGuangdong Provincial Key Laboratory of NephrologySun Yat‐Sen UniversityGuangzhou510080China
| | - Feng Li
- Department of NephrologyThe First Affiliated HospitalSun Yat‐sen UniversityNHC Key Laboratory of Clinical NephrologyGuangdong Provincial Key Laboratory of NephrologySun Yat‐Sen UniversityGuangzhou510080China
| | - Changjian Zhu
- Department of NephrologyThe First Affiliated HospitalSun Yat‐sen UniversityNHC Key Laboratory of Clinical NephrologyGuangdong Provincial Key Laboratory of NephrologySun Yat‐Sen UniversityGuangzhou510080China
| | - Xu Han
- Department of NephrologyThe First Affiliated HospitalSun Yat‐sen UniversityNHC Key Laboratory of Clinical NephrologyGuangdong Provincial Key Laboratory of NephrologySun Yat‐Sen UniversityGuangzhou510080China
| | - Ruilin Zheng
- Department of NephrologyThe First Affiliated HospitalSun Yat‐sen UniversityNHC Key Laboratory of Clinical NephrologyGuangdong Provincial Key Laboratory of NephrologySun Yat‐Sen UniversityGuangzhou510080China
| | - Jiani Shen
- Department of NephrologyThe First Affiliated HospitalSun Yat‐sen UniversityNHC Key Laboratory of Clinical NephrologyGuangdong Provincial Key Laboratory of NephrologySun Yat‐Sen UniversityGuangzhou510080China
| | - Jing Yu
- Department of NephrologyThe First Affiliated HospitalSun Yat‐sen UniversityNHC Key Laboratory of Clinical NephrologyGuangdong Provincial Key Laboratory of NephrologySun Yat‐Sen UniversityGuangzhou510080China
| | - Ning Luo
- Department of NephrologyThe First Affiliated HospitalSun Yat‐sen UniversityNHC Key Laboratory of Clinical NephrologyGuangdong Provincial Key Laboratory of NephrologySun Yat‐Sen UniversityGuangzhou510080China
| | - Wenxing Peng
- Department of NephrologyThe First Affiliated HospitalSun Yat‐sen UniversityNHC Key Laboratory of Clinical NephrologyGuangdong Provincial Key Laboratory of NephrologySun Yat‐Sen UniversityGuangzhou510080China
| | - Jiaqing Tan
- Department of NephrologyThe First Affiliated HospitalSun Yat‐sen UniversityNHC Key Laboratory of Clinical NephrologyGuangdong Provincial Key Laboratory of NephrologySun Yat‐Sen UniversityGuangzhou510080China
| | - Xiaoyan Li
- Department of NephrologyThe First Affiliated HospitalSun Yat‐sen UniversityNHC Key Laboratory of Clinical NephrologyGuangdong Provincial Key Laboratory of NephrologySun Yat‐Sen UniversityGuangzhou510080China
| | - Jinjin Fan
- Department of NephrologyThe First Affiliated HospitalSun Yat‐sen UniversityNHC Key Laboratory of Clinical NephrologyGuangdong Provincial Key Laboratory of NephrologySun Yat‐Sen UniversityGuangzhou510080China
| | - Qiong Wen
- Department of NephrologyThe First Affiliated HospitalSun Yat‐sen UniversityNHC Key Laboratory of Clinical NephrologyGuangdong Provincial Key Laboratory of NephrologySun Yat‐Sen UniversityGuangzhou510080China
| | - Xin Wang
- Department of NephrologyThe First Affiliated HospitalSun Yat‐sen UniversityNHC Key Laboratory of Clinical NephrologyGuangdong Provincial Key Laboratory of NephrologySun Yat‐Sen UniversityGuangzhou510080China
| | - Jianbo Li
- Department of NephrologyThe First Affiliated HospitalSun Yat‐sen UniversityNHC Key Laboratory of Clinical NephrologyGuangdong Provincial Key Laboratory of NephrologySun Yat‐Sen UniversityGuangzhou510080China
| | - Xunhua Zheng
- Department of NephrologyThe First Affiliated HospitalSun Yat‐sen UniversityNHC Key Laboratory of Clinical NephrologyGuangdong Provincial Key Laboratory of NephrologySun Yat‐Sen UniversityGuangzhou510080China
| | - Qinghua Liu
- Department of NephrologyThe First Affiliated HospitalSun Yat‐sen UniversityNHC Key Laboratory of Clinical NephrologyGuangdong Provincial Key Laboratory of NephrologySun Yat‐Sen UniversityGuangzhou510080China
| | - Jianping Guo
- Institute of Precision MedicineThe First Affiliated HospitalSun Yat‐sen UniversityGuangzhou510080China
| | - Guo‐Ping Shi
- Department of MedicineBrigham and Women's Hospital and Harvard Medical SchoolBostonMA02115USA
| | - Haiping Mao
- Department of NephrologyThe First Affiliated HospitalSun Yat‐sen UniversityNHC Key Laboratory of Clinical NephrologyGuangdong Provincial Key Laboratory of NephrologySun Yat‐Sen UniversityGuangzhou510080China
| | - Wei Chen
- Department of NephrologyThe First Affiliated HospitalSun Yat‐sen UniversityNHC Key Laboratory of Clinical NephrologyGuangdong Provincial Key Laboratory of NephrologySun Yat‐Sen UniversityGuangzhou510080China
| | - Sheng Yin
- School of Pharmaceutical SciencesSun Yat‐sen UniversityGuangzhou510006China
| | - Yi Zhou
- Department of NephrologyThe First Affiliated HospitalSun Yat‐sen UniversityNHC Key Laboratory of Clinical NephrologyGuangdong Provincial Key Laboratory of NephrologySun Yat‐Sen UniversityGuangzhou510080China
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Gervasoni S, Manelfi C, Adobati S, Talarico C, Biswas AD, Pedretti A, Vistoli G, Beccari AR. Target Prediction by Multiple Virtual Screenings: Analyzing the SARS-CoV-2 Phenotypic Screening by the Docking Simulations Submitted to the MEDIATE Initiative. Int J Mol Sci 2023; 25:450. [PMID: 38203621 PMCID: PMC10779154 DOI: 10.3390/ijms25010450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 12/15/2023] [Accepted: 12/21/2023] [Indexed: 01/12/2024] Open
Abstract
Phenotypic screenings are usually combined with deconvolution techniques to characterize the mechanism of action for the retrieved hits. These studies can be supported by various computational analyses, although docking simulations are rarely employed. The present study aims to assess if multiple docking calculations can prove successful in target prediction. In detail, the docking simulations submitted to the MEDIATE initiative are utilized to predict the viral targets involved in the hits retrieved by a recently published cytopathic screening. Multiple docking results are combined by the EFO approach to develop target-specific consensus models. The combination of multiple docking simulations enhances the performances of the developed consensus models (average increases in EF1% value of 40% and 25% when combining three and two docking runs, respectively). These models are able to propose reliable targets for about half of the retrieved hits (31 out of 59). Thus, the study emphasizes that docking simulations might be effective in target identification and provide a convincing validation for the collaborative strategies that inspire the MEDIATE initiative. Disappointingly, cross-target and cross-program correlations suggest that common scoring functions are not specific enough for the simulated target.
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Affiliation(s)
- Silvia Gervasoni
- Dipartimento di Scienze Farmaceutiche, Università Degli Studi di Milano, Via Mangiagalli, 25, I-20133 Milano, Italy; (S.G.); (S.A.); (A.P.)
- Department of Physics, Università di Cagliari, I-09042 Monserrato, Italy
| | - Candida Manelfi
- EXSCALATE, Dompé Farmaceutici S.p.A., Via Tommaso De Amicis, 95, I-80131 Napoli, Italy; (C.M.); (C.T.); (A.D.B.); (A.R.B.)
| | - Sara Adobati
- Dipartimento di Scienze Farmaceutiche, Università Degli Studi di Milano, Via Mangiagalli, 25, I-20133 Milano, Italy; (S.G.); (S.A.); (A.P.)
| | - Carmine Talarico
- EXSCALATE, Dompé Farmaceutici S.p.A., Via Tommaso De Amicis, 95, I-80131 Napoli, Italy; (C.M.); (C.T.); (A.D.B.); (A.R.B.)
| | - Akash Deep Biswas
- EXSCALATE, Dompé Farmaceutici S.p.A., Via Tommaso De Amicis, 95, I-80131 Napoli, Italy; (C.M.); (C.T.); (A.D.B.); (A.R.B.)
| | - Alessandro Pedretti
- Dipartimento di Scienze Farmaceutiche, Università Degli Studi di Milano, Via Mangiagalli, 25, I-20133 Milano, Italy; (S.G.); (S.A.); (A.P.)
| | - Giulio Vistoli
- Dipartimento di Scienze Farmaceutiche, Università Degli Studi di Milano, Via Mangiagalli, 25, I-20133 Milano, Italy; (S.G.); (S.A.); (A.P.)
| | - Andrea R. Beccari
- EXSCALATE, Dompé Farmaceutici S.p.A., Via Tommaso De Amicis, 95, I-80131 Napoli, Italy; (C.M.); (C.T.); (A.D.B.); (A.R.B.)
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5
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Swinney DC. Why medicines work. Pharmacol Ther 2022; 238:108175. [DOI: 10.1016/j.pharmthera.2022.108175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 03/20/2022] [Accepted: 03/22/2022] [Indexed: 11/27/2022]
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6
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Vandersluis S, Reid JC, Orlando L, Bhatia M. Evidence-based support for phenotypic drug discovery in acute myeloid leukemia. Drug Discov Today 2022; 27:103407. [DOI: 10.1016/j.drudis.2022.103407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Revised: 08/01/2022] [Accepted: 10/10/2022] [Indexed: 11/03/2022]
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7
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Bluhmki T, Traub S, Müller AK, Bitzer S, Schruf E, Bammert MT, Leist M, Gantner F, Garnett JP, Heilker R. Functional human iPSC-derived alveolar-like cells cultured in a miniaturized 96‑Transwell air-liquid interface model. Sci Rep 2021; 11:17028. [PMID: 34426605 PMCID: PMC8382767 DOI: 10.1038/s41598-021-96565-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 08/11/2021] [Indexed: 02/06/2023] Open
Abstract
In order to circumvent the limited access and donor variability of human primary alveolar cells, directed differentiation of human pluripotent stem cells (hiPSCs) into alveolar-like cells, provides a promising tool for respiratory disease modeling and drug discovery assays. In this work, a unique, miniaturized 96-Transwell microplate system is described where hiPSC-derived alveolar-like cells were cultured at an air-liquid interface (ALI). To this end, hiPSCs were differentiated into lung epithelial progenitor cells (LPCs) and subsequently matured into a functional alveolar type 2 (AT2)-like epithelium with monolayer-like morphology. AT2-like cells cultured at the physiological ALI conditions displayed characteristics of AT2 cells with classical alveolar surfactant protein expressions and lamellar-body like structures. The integrity of the epithelial barriers between the AT2-like cells was confirmed by applying a custom-made device for 96-parallelized transepithelial electric resistance (TEER) measurements. In order to generate an IPF disease-like phenotype in vitro, the functional AT2-like cells were stimulated with cytokines and growth factors present in the alveolar tissue of IPF patients. The cytokines stimulated the secretion of pro-fibrotic biomarker proteins both on the mRNA (messenger ribonucleic acid) and protein level. Thus, the hiPSC-derived and cellular model system enables the recapitulation of certain IPF hallmarks, while paving the route towards a miniaturized medium throughput approach of pharmaceutical drug discovery.
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Affiliation(s)
- Teresa Bluhmki
- Department of Drug Discovery Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, 88397, Biberach an der Riss, Germany.
| | - Stefanie Traub
- Trenzyme GmbH, Byk-Gulden-Str. 2, 78467, Constance, Germany
| | | | - Sarah Bitzer
- Department of Drug Discovery Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, 88397, Biberach an der Riss, Germany
| | - Eva Schruf
- Department of Immunology & Respiratory Diseases Research, Boehringer Ingelheim Pharma GmbH & Co. KG, 88397, Biberach an der Riss, Germany
| | - Marie-Therese Bammert
- Department of Immunology & Respiratory Diseases Research, Boehringer Ingelheim Pharma GmbH & Co. KG, 88397, Biberach an der Riss, Germany
| | - Marcel Leist
- In-vitro Toxicology and Biomedicine, University of Konstanz, 78457, Constance, Germany
| | - Florian Gantner
- Department of Translational Medicine and Clinical Pharmacology, C. H. Boehringer Sohn AG & Co. KG, 88397, Biberach an der Riss, Germany
| | - James P Garnett
- Department of Immunology & Respiratory Diseases Research, Boehringer Ingelheim Pharma GmbH & Co. KG, 88397, Biberach an der Riss, Germany
| | - Ralf Heilker
- Department of Drug Discovery Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, 88397, Biberach an der Riss, Germany
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Choi HJ, Wang C, Pan X, Jang J, Cao M, Brazzo JA, Bae Y, Lee K. Emerging machine learning approaches to phenotyping cellular motility and morphodynamics. Phys Biol 2021; 18:10.1088/1478-3975/abffbe. [PMID: 33971636 PMCID: PMC9131244 DOI: 10.1088/1478-3975/abffbe] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 05/10/2021] [Indexed: 12/22/2022]
Abstract
Cells respond heterogeneously to molecular and environmental perturbations. Phenotypic heterogeneity, wherein multiple phenotypes coexist in the same conditions, presents challenges when interpreting the observed heterogeneity. Advances in live cell microscopy allow researchers to acquire an unprecedented amount of live cell image data at high spatiotemporal resolutions. Phenotyping cellular dynamics, however, is a nontrivial task and requires machine learning (ML) approaches to discern phenotypic heterogeneity from live cell images. In recent years, ML has proven instrumental in biomedical research, allowing scientists to implement sophisticated computation in which computers learn and effectively perform specific analyses with minimal human instruction or intervention. In this review, we discuss how ML has been recently employed in the study of cell motility and morphodynamics to identify phenotypes from computer vision analysis. We focus on new approaches to extract and learn meaningful spatiotemporal features from complex live cell images for cellular and subcellular phenotyping.
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Affiliation(s)
- Hee June Choi
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA 01609, United States of America
- Vascular Biology Program and Department of Surgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States of America
| | - Chuangqi Wang
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA 01609, United States of America
- Present address. Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Xiang Pan
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA 01609, United States of America
- Vascular Biology Program and Department of Surgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States of America
| | - Junbong Jang
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA 01609, United States of America
- Vascular Biology Program and Department of Surgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States of America
| | - Mengzhi Cao
- Data Science Program, Worcester Polytechnic Institute, Worcester, MA 01609, United States of America
| | - Joseph A Brazzo
- Department of Pathology and Anatomical Sciences, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY 14203, United States of America
| | - Yongho Bae
- Department of Pathology and Anatomical Sciences, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY 14203, United States of America
| | - Kwonmoo Lee
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA 01609, United States of America
- Vascular Biology Program and Department of Surgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States of America
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Moreira-Filho JT, Silva AC, Dantas RF, Gomes BF, Souza Neto LR, Brandao-Neto J, Owens RJ, Furnham N, Neves BJ, Silva-Junior FP, Andrade CH. Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence. Front Immunol 2021; 12:642383. [PMID: 34135888 PMCID: PMC8203334 DOI: 10.3389/fimmu.2021.642383] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 04/30/2021] [Indexed: 12/20/2022] Open
Abstract
Schistosomiasis is a parasitic disease caused by trematode worms of the genus Schistosoma and affects over 200 million people worldwide. The control and treatment of this neglected tropical disease is based on a single drug, praziquantel, which raises concerns about the development of drug resistance. This, and the lack of efficacy of praziquantel against juvenile worms, highlights the urgency for new antischistosomal therapies. In this review we focus on innovative approaches to the identification of antischistosomal drug candidates, including the use of automated assays, fragment-based screening, computer-aided and artificial intelligence-based computational methods. We highlight the current developments that may contribute to optimizing research outputs and lead to more effective drugs for this highly prevalent disease, in a more cost-effective drug discovery endeavor.
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Affiliation(s)
- José T. Moreira-Filho
- LabMol – Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás – UFG, Goiânia, Brazil
| | - Arthur C. Silva
- LabMol – Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás – UFG, Goiânia, Brazil
| | - Rafael F. Dantas
- LaBECFar – Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Barbara F. Gomes
- LaBECFar – Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Lauro R. Souza Neto
- LaBECFar – Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Jose Brandao-Neto
- Diamond Light Source Ltd., Didcot, United Kingdom
- Research Complex at Harwell, Didcot, United Kingdom
| | - Raymond J. Owens
- The Rosalind Franklin Institute, Harwell, United Kingdom
- Division of Structural Biology, The Wellcome Centre for Human Genetic, University of Oxford, Oxford, United Kingdom
| | - Nicholas Furnham
- Department of Infection Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Bruno J. Neves
- LabMol – Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás – UFG, Goiânia, Brazil
| | - Floriano P. Silva-Junior
- LaBECFar – Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Carolina H. Andrade
- LabMol – Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás – UFG, Goiânia, Brazil
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10
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Lim XY, Chan JSW, Tan TYC, Teh BP, Mohd Abd Razak MR, Mohamad S, Syed Mohamed AF. Andrographis paniculata (Burm. F.) Wall. Ex Nees, Andrographolide, and Andrographolide Analogues as SARS-CoV-2 Antivirals? A Rapid Review. Nat Prod Commun 2021. [DOI: 10.1177/1934578x211016610] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Drug repurposing is commonly employed in the search for potential therapeutic agents. Andrographis paniculata, a medicinal plant commonly used for symptomatic relief of the common cold, and its phytoconstituent andrographolide, have been repeatedly identified as potential antivirals against SARS-CoV-2. In light of new evidence emerging since the onset of the COVID-19 pandemic, this rapid review was conducted to identify and evaluate the current SARS-CoV-2 antiviral evidence for A. paniculata, andrographolide, and andrographolide analogs. A systematic search and screen strategy of electronic databases and gray literature was undertaken to identify relevant primary articles. One target-based in vitro study reported the 3CLpro inhibitory activity of andrographolide as being no better than disulfiram. Another Vero cell-based study reported potential SARS-CoV-2 inhibitory activity for both andrographolide and A. paniculata extract. Eleven in silico studies predicted the binding of andrographolide and its analogs to several key antiviral targets of SARS-CoV-2 including the spike protein-ACE-2 receptor complex, spike protein, ACE-2 receptor, RdRp, 3CLpro, PLpro, and N-protein RNA-binding domain. In conclusion, in silico and in vitro studies collectively suggest multi-pathway targeting SARS-CoV-2 antiviral properties of andrographolide and its analogs, but in vivo data are needed to support these predictions.
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Affiliation(s)
- Xin Yi Lim
- Herbal Medicine Research Centre, Institute for Medical Research, National Institutes of Health, Ministry of Health Malaysia, Shah Alam, Malaysia
| | - Janice Sue Wen Chan
- Herbal Medicine Research Centre, Institute for Medical Research, National Institutes of Health, Ministry of Health Malaysia, Shah Alam, Malaysia
| | - Terence Yew Chin Tan
- Herbal Medicine Research Centre, Institute for Medical Research, National Institutes of Health, Ministry of Health Malaysia, Shah Alam, Malaysia
| | - Bee Ping Teh
- Herbal Medicine Research Centre, Institute for Medical Research, National Institutes of Health, Ministry of Health Malaysia, Shah Alam, Malaysia
| | - Mohd Ridzuan Mohd Abd Razak
- Herbal Medicine Research Centre, Institute for Medical Research, National Institutes of Health, Ministry of Health Malaysia, Shah Alam, Malaysia
| | - Saharuddin Mohamad
- Bioinformatics Programme, Faculty of Science, Institute of Biological Sciences, University of Malaya, Kuala Lumpur, Malaysia
- Centre of Research for Computational Sciences and Informatics for Biology, Bioindustry, Environment, Agriculture and Healthcare, University of Malaya, Kuala Lumpur, Malaysia
| | - Ami Fazlin Syed Mohamed
- Herbal Medicine Research Centre, Institute for Medical Research, National Institutes of Health, Ministry of Health Malaysia, Shah Alam, Malaysia
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11
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Veale CGL. Into the Fray! A Beginner's Guide to Medicinal Chemistry. ChemMedChem 2021; 16:1199-1225. [PMID: 33591595 DOI: 10.1002/cmdc.202000929] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Indexed: 12/31/2022]
Abstract
Modern medicinal chemistry is a complex, multidimensional discipline that operates at the interface of the chemical and biological sciences. The medicinal chemistry contribution to drug discovery is typically described in the context of the well-recited linear progression of the drug discovery pipeline. However, compound optimization is idiosyncratic to each project, and clear definitions of hit and lead molecules and the subsequent progress along the pipeline becomes easily blurred. In addition, this description lacks insight into the entangled relationship between chemical and pharmacological properties, and thus provides limited guidance on how innovative medicinal chemistry strategies can be applied to solve optimization problems, regardless of the stage in the pipeline. Through discussion and illustrative examples, this article seeks to provide insights into the finesse of medicinal chemistry and the subtlety of balancing chemical properties pharmacology. In so doing, it aims to serve as an accessible and simple-to-digest guide for anyone who wishes to learn about the underlying principles of medicinal chemistry, in a context that has been decoupled from the pipeline description.
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Affiliation(s)
- Clinton G L Veale
- School of Chemistry and Physics, Pietermaritzburg Campus, University of KwaZulu-Natal, Private Bag X01, Pietermaritzburg, Scottsville, 3209, South Africa
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12
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Malandraki-Miller S, Riley PR. Use of artificial intelligence to enhance phenotypic drug discovery. Drug Discov Today 2021; 26:887-901. [PMID: 33484947 DOI: 10.1016/j.drudis.2021.01.013] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 12/28/2020] [Accepted: 01/15/2021] [Indexed: 01/17/2023]
Abstract
Research and development (R&D) productivity across the pharmaceutical industry has received close scrutiny over the past two decades, especially taking into consideration reports of attrition rates and the colossal cost for drug development. The respective merits of the two main drug discovery approaches, phenotypic and target based, have divided opinion across the research community, because each hold different advantages for identifying novel molecular entities with a successful path to the market. Nevertheless, both have low translatability in the clinic. Artificial intelligence (AI) and adoption of machine learning (ML) tools offer the promise of revolutionising drug development, and overcoming obstacles in the drug discovery pipeline. Here, we assess the potential of target-driven and phenotypic-based approaches and offer a holistic description of the current state of the field, from both a scientific and industry perspective. With the emerging partnerships between AI/ML and pharma still in their relative infancy, we investigate the potential and current limitations with a particular focus on phenotypic drug discovery. Finally, we emphasise the value of public-private partnerships (PPPs) and cross-disciplinary collaborations to foster innovation and facilitate efficient drug discovery programmes.
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Affiliation(s)
| | - Paul R Riley
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK.
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13
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Bender A, Cortés-Ciriano I. Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 1: Ways to make an impact, and why we are not there yet. Drug Discov Today 2020; 26:511-524. [PMID: 33346134 DOI: 10.1016/j.drudis.2020.12.009] [Citation(s) in RCA: 102] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 09/07/2020] [Accepted: 12/11/2020] [Indexed: 12/30/2022]
Abstract
Although artificial intelligence (AI) has had a profound impact on areas such as image recognition, comparable advances in drug discovery are rare. This article quantifies the stages of drug discovery in which improvements in the time taken, success rate or affordability will have the most profound overall impact on bringing new drugs to market. Changes in clinical success rates will have the most profound impact on improving success in drug discovery; in other words, the quality of decisions regarding which compound to take forward (and how to conduct clinical trials) are more important than speed or cost. Although current advances in AI focus on how to make a given compound, the question of which compound to make, using clinical efficacy and safety-related end points, has received significantly less attention. As a consequence, current proxy measures and available data cannot fully utilize the potential of AI in drug discovery, in particular when it comes to drug efficacy and safety in vivo. Thus, addressing the questions of which data to generate and which end points to model will be key to improving clinically relevant decision-making in the future.
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Affiliation(s)
- Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road CB2 1EW, UK; Imaging and Data Analytics, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK.
| | - Isidro Cortés-Ciriano
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge CB10 1SD, UK.
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14
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Wang Y, Wang Y, Qian J, Pan X, Li X, Chen F, Hu J, Lü J. Single-cell infrared phenomics: phenotypic screening with infrared microspectroscopy. Chem Commun (Camb) 2020; 56:13237-13240. [PMID: 33030170 DOI: 10.1039/d0cc05721e] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
We conceptually demonstrate single-cell infrared phenomics as a novel strategy of phenotypic screening with infrared microspectroscopy. Based on this development, the cancer cell HepG2 glycocalyx was first identified as a potential target of protopanaxadiol, an herbal medicine. These findings provide a powerful tool to accurately evaluate the cell stress response and to largely expand the phenotypic screening toolkit for drug discovery.
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Affiliation(s)
- Yadi Wang
- College of Pharmacy, Binzhou Medical University, Yantai 264003, China
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15
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Lyu J, Ruan C, Zhang X, Wang Y, Li K, Ye M. Microparticle-Assisted Precipitation Screening Method for Robust Drug Target Identification. Anal Chem 2020; 92:13912-13921. [PMID: 32933243 DOI: 10.1021/acs.analchem.0c02756] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
While thermal proteome profiling (TPP) shines in the field of drug target screening by analyzing the soluble fraction of the proteome samples treated at high temperature, the counterpart, the insoluble precipitate, has been overlooked for a long time. The analysis of the precipitate is hampered by the inefficient sample processing procedure. Herein, we propose a novel method, termed microparticle-assisted precipitation screening (MAPS), for drug target identification. The MAPS method exploits the principle that drug-bound proteins will be more resistant to thermal unfolding similar to the classic TPP method, but the process of protein precipitation is assisted by microparticles. Upon heating, proteins unfold and aggregate on the surface of the microparticles. The introduction of a microparticle simplifies the whole sample preparation workflow. The proteins that precipitate on the microparticles are subjected to washing, alkylation, and digestion. The whole sample preparation is processed conveniently on the surface of the microparticles without any transfer. With the assistance of microparticles, sample loss is minimized. The MAPS method is compatible with minute amounts of initial proteins. MAPS was applied to screen the targets of several well-studied drugs and the known target proteins were successfully identified with high confidence and specificity. To investigate the specificity of the method, MAPS was applied to screen the targets of the pan-kinase inhibitor, staurosporine, and 32 protein kinases (specificity of 80%) were identified using only 20 μg of initial proteins of each sample. MAPS is an unbiased robust method for drug target screening, filling the vacancy of stability-based target screening using a precipitate.
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Affiliation(s)
- Jiawen Lyu
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, National Chromatographic R&A Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences (CAS), Dalian 116023, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Chengfei Ruan
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, National Chromatographic R&A Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences (CAS), Dalian 116023, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaolei Zhang
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, National Chromatographic R&A Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences (CAS), Dalian 116023, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yan Wang
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, National Chromatographic R&A Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences (CAS), Dalian 116023, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Kejia Li
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, National Chromatographic R&A Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences (CAS), Dalian 116023, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Mingliang Ye
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, National Chromatographic R&A Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences (CAS), Dalian 116023, China.,University of Chinese Academy of Sciences, Beijing 100049, China
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16
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Abstract
There is a great need for innovative new medicines to treat unmet medical needs. The discovery and development of innovative new medicines is extremely difficult, costly, and inefficient. In the last decade, phenotypic drug discovery (PDD) was reintroduced as a strategy to provide first-in-class medicines. PDD uses empirical, target-agnostic lead generation to identify pharmacologically active molecules and novel therapeutics which work through unprecedented drug mechanisms. The economic and scientific value of PDD is exemplified through game-changing medicines for hepatitis C virus, spinal muscular atrophy, and cystic fibrosis. In this short review, recent advances are noted for the implementation and de-risking of PDD (for compound library selection, biomarker development, mechanism identification, and safety studies) and the potential for artificial intelligence. A significant barrier in the decision to implement PDD is balancing the potential impact of a novel mechanism of drug action with an under-defined scientific path forward, with the desire to provide infrastructure and metrics to optimize return on investment, which a known mechanism provides. A means to address this knowledge gap in the future is to empower precompetitive research utilizing the empirical concepts of PDD to identify new mechanisms and pharmacologically active compounds.
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17
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Development of a miniaturized 96-Transwell air-liquid interface human small airway epithelial model. Sci Rep 2020; 10:13022. [PMID: 32747751 PMCID: PMC7400554 DOI: 10.1038/s41598-020-69948-2] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Accepted: 07/22/2020] [Indexed: 02/06/2023] Open
Abstract
In order to overcome the challenges associated with a limited number of airway epithelial cells that can be obtained from clinical sampling and their restrained capacity to divide ex vivo, miniaturization of respiratory drug discovery assays is of pivotal importance. Thus, a 96-well microplate system was developed where primary human small airway epithelial (hSAE) cells were cultured at an air–liquid interface (ALI). After four weeks of ALI culture, a pseudostratified epithelium containing basal, club, goblet and ciliated cells was produced. The 96-well ALI cultures displayed a cellular composition, ciliary beating frequency, and intercellular tight junctions similar to 24-well conditions. A novel custom-made device for 96-parallelized transepithelial electric resistance (TEER) measurements, together with dextran permeability measurements, confirmed that the 96-well culture developed a tight barrier function during ALI differentiation. 96-well hSAE cultures were responsive to transforming growth factor β1 (TGF-β1) and tumor necrosis factor α (TNF-α) in a concentration dependent manner. Thus, the miniaturized cellular model system enables the recapitulation of a physiologically responsive, differentiated small airway epithelium, and a robotic integration provides a medium throughput approach towards pharmaceutical drug discovery, for instance, in respect of fibrotic distal airway/lung diseases.
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18
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Turner RJ, Golz S, Wollnik C, Burkhardt N, Sternberger I, Andag U, Cornils H. A Whole Genome-Wide Arrayed CRISPR Screen in Primary Organ Fibroblasts to Identify Regulators of Kidney Fibrosis. SLAS DISCOVERY 2020; 25:591-604. [PMID: 32425084 PMCID: PMC7309357 DOI: 10.1177/2472555220915851] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Kidney fibrosis presents a hallmark of chronic kidney disease. With ever-increasing patient numbers and limited treatment options available, novel strategies for therapeutic intervention in kidney disease are warranted. Fibrosis commonly results from a wound healing response to repeated or chronic tissue damage, irrespective of the underlying etiology, and can occur in virtually any solid organ or tissue. In order to identify targets relevant for kidney fibrosis, we aimed to employ CRISPR screening in primary human kidney fibroblasts. We demonstrate that CRISPR technology can be applied in primary kidney fibroblasts and can furthermore be used to conduct arrayed CRISPR screening using a high-content imaging readout in a whole genome-wide manner. Hits coming out of this screen were validated using orthogonal approaches and present starting points for validation of novel targets relevant to kidney disease.
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Affiliation(s)
| | - Stefan Golz
- Lead Discovery, Bayer AG, Wuppertal, Germany
| | | | | | | | - Uwe Andag
- Metabolic Disease, Evotec International GmbH, Göttingen, Germany
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19
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Sebastian-Perez V, García-Rubia A, Seif El-Din SH, Sabra ANA, El-Lakkany NM, William S, Blundell TL, Maes L, Martinez A, Campillo NE, Botros SS, Gil C. Deciphering the enzymatic target of a new family of antischistosomal agents bearing a quinazoline scaffold using complementary computational tools. J Enzyme Inhib Med Chem 2020; 35:511-523. [PMID: 31939312 PMCID: PMC7717570 DOI: 10.1080/14756366.2020.1712595] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
A previous phenotypic screening campaign led to the identification of a quinazoline derivative with promising in vitro activity against Schistosoma mansoni. Follow-up studies of the antischistosomal potential of this candidate are presented here. The in vivo studies in a S. mansoni mouse model show a significant reduction of total worms and a complete disappearance of immature eggs when administered concomitantly with praziquantel in comparison with the administration of praziquantel alone. This fact is of utmost importance because eggs are responsible for the pathology and transmission of the disease. Subsequently, the chemical optimisation of the structure in order to improve the metabolic stability of the parent compound was carried out leading to derivatives with improved drug-like properties. Additionally, the putative target of this new class of antischistosomal compounds was envisaged by using computational tools and the binding mode to the target enzyme, aldose reductase, was proposed.
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Affiliation(s)
| | | | | | | | | | - Samia William
- Parasitology Department, Theodor Bilharz Research Institute, Giza, Egypt
| | - Tom L Blundell
- Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Louis Maes
- Laboratory for Microbiology, Parasitology and Hygiene (LMPH), University of Antwerp, Antwerp, Belgium
| | - Ana Martinez
- Centro de Investigaciones Biológicas (CIB-CSIC), Madrid, Spain
| | | | - Sanaa S Botros
- Pharmacology Department, Theodor Bilharz Research Institute, Giza, Egypt
| | - Carmen Gil
- Centro de Investigaciones Biológicas (CIB-CSIC), Madrid, Spain
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20
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21
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Huber RJ, Hughes SM, Liu W, Morgan A, Tuxworth RI, Russell C. The contribution of multicellular model organisms to neuronal ceroid lipofuscinosis research. Biochim Biophys Acta Mol Basis Dis 2019; 1866:165614. [PMID: 31783156 DOI: 10.1016/j.bbadis.2019.165614] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Revised: 11/14/2019] [Accepted: 11/15/2019] [Indexed: 02/07/2023]
Abstract
The NCLs (neuronal ceroid lipofuscinosis) are forms of neurodegenerative disease that affect people of all ages and ethnicities but are most prevalent in children. Commonly known as Batten disease, this debilitating neurological disorder is comprised of 13 different subtypes that are categorized based on the particular gene that is mutated (CLN1-8, CLN10-14). The pathological mechanisms underlying the NCLs are not well understood due to our poor understanding of the functions of NCL proteins. Only one specific treatment (enzyme replacement therapy) is approved, which is for the treating the brain in CLN2 disease. Hence there remains a desperate need for further research into disease-modifying treatments. In this review, we present and evaluate the genes, proteins and studies performed in the social amoeba, nematode, fruit fly, zebrafish, mouse and large animals pertinent to NCL. In particular, we highlight the use of multicellular model organisms to study NCL protein function, pathology and pathomechanisms. Their use in testing novel therapeutic approaches is also presented. With this information, we highlight how future research in these systems may be able to provide new insight into NCL protein functions in human cells and aid in the development of new therapies.
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Affiliation(s)
- Robert J Huber
- Department of Biology, Trent University, Peterborough, Ontario K9L 0G2, Canada
| | - Stephanie M Hughes
- Department of Biochemistry, School of Biomedical Sciences, Brain Health Research Centre and Genetics Otago, University of Otago, Dunedin, New Zealand
| | - Wenfei Liu
- School of Pharmacy, University College London, London, WC1N 1AX, UK
| | - Alan Morgan
- Department of Cellular and Molecular Physiology, Institute of Translational Medicine, University of Liverpool, Crown St., Liverpool L69 3BX, UK
| | - Richard I Tuxworth
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham B15 2TT, UK
| | - Claire Russell
- Dept. Comparative Biomedical Sciences, Royal Veterinary College, Royal College Street, London NW1 0TU, UK.
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23
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Sundaram JR, Wu Y, Lee IC, George SE, Hota M, Ghosh S, Kesavapany S, Ahmed M, Tan EK, Shenolikar S. PromISR-6, a Guanabenz Analogue, Improves Cellular Survival in an Experimental Model of Huntington's Disease. ACS Chem Neurosci 2019; 10:3575-3589. [PMID: 31313908 DOI: 10.1021/acschemneuro.9b00185] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Guanabenz (GBZ), an α2-adrenergic agonist, demonstrated off-target effects that restored protein homeostasis and ameliorated pathobiology in experimental models of neurodegenerative disease. However, GBZ did not directly activate the integrated stress response (ISR), and its proposed mode of action remains controversial. Utilizing an iterative in silico screen of over 10,000 GBZ analogues, we analyzed 432 representative compounds for cytotoxicity in Wild-type, PPP1R15A-/-, and PPP1R15B-/- mouse embryonic fibroblasts. Nine compounds clustering into three functional groups were studied in detail using cell biological and biochemical assays. Our studies demonstrated that PromISR-6 is a potent GBZ analogue that selectively activated ISR, eliciting sustained eIF2α phosphorylation. ISRIB, an ISR inhibitor, counteracted PromISR-6-mediated translational inhibition and reduction in intracellular mutant Huntingtin aggregates. Reduced protein synthesis combined with PromISR-6-stimulated autophagic clearance made PromISR-6 the most efficacious GBZ analogue to reduce Huntingtin aggregates and promote survival in a cellular model of Huntington's disease.
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Affiliation(s)
| | | | | | | | | | | | - Sashi Kesavapany
- GSK Neural Pathways Discovery Performance Unit, 11 Biopolis Way, Singapore 138667
| | - Mahmood Ahmed
- GSK Neural Pathways Discovery Performance Unit, 11 Biopolis Way, Singapore 138667
| | - Eng-King Tan
- National Neuroscience Institute of Singapore, 11 Jalan Tan Tock Seng, Singapore 308433
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24
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Precision medicine review: rare driver mutations and their biophysical classification. Biophys Rev 2019; 11:5-19. [PMID: 30610579 PMCID: PMC6381362 DOI: 10.1007/s12551-018-0496-2] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Accepted: 12/18/2018] [Indexed: 02/07/2023] Open
Abstract
How can biophysical principles help precision medicine identify rare driver mutations? A major tenet of pragmatic approaches to precision oncology and pharmacology is that driver mutations are very frequent. However, frequency is a statistical attribute, not a mechanistic one. Rare mutations can also act through the same mechanism, and as we discuss below, “latent driver” mutations may also follow the same route, with “helper” mutations. Here, we review how biophysics provides mechanistic guidelines that extend precision medicine. We outline principles and strategies, especially focusing on mutations that drive cancer. Biophysics has contributed profoundly to deciphering biological processes. However, driven by data science, precision medicine has skirted some of its major tenets. Data science embodies genomics, tissue- and cell-specific expression levels, making it capable of defining genome- and systems-wide molecular disease signatures. It classifies cancer driver genes/mutations and affected pathways, and its associated protein structural data guide drug discovery. Biophysics complements data science. It considers structures and their heterogeneous ensembles, explains how mutational variants can signal through distinct pathways, and how allo-network drugs can be harnessed. Biophysics clarifies how one mutation—frequent or rare—can affect multiple phenotypic traits by populating conformations that favor interactions with other network modules. It also suggests how to identify such mutations and their signaling consequences. Biophysics offers principles and strategies that can help precision medicine push the boundaries to transform our insight into biological processes and the practice of personalized medicine. By contrast, “phenotypic drug discovery,” which capitalizes on physiological cellular conditions and first-in-class drug discovery, may not capture the proper molecular variant. This is because variants of the same protein can express more than one phenotype, and a phenotype can be encoded by several variants.
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