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Thorne D, McHugh D, Simms L, Lee KM, Fujimoto H, Moses S, Gaca M. Applying new approach methodologies to assess next-generation tobacco and nicotine products. FRONTIERS IN TOXICOLOGY 2024; 6:1376118. [PMID: 38938663 PMCID: PMC11208635 DOI: 10.3389/ftox.2024.1376118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 04/30/2024] [Indexed: 06/29/2024] Open
Abstract
In vitro toxicology research has accelerated with the use of in silico, computational approaches and human in vitro tissue systems, facilitating major improvements evaluating the safety and health risks of novel consumer products. Innovation in molecular and cellular biology has shifted testing paradigms, with less reliance on low-throughput animal data and greater use of medium- and high-throughput in vitro cellular screening approaches. These new approach methodologies (NAMs) are being implemented in other industry sectors for chemical testing, screening candidate drugs and prototype consumer products, driven by the need for reliable, human-relevant approaches. Routine toxicological methods are largely unchanged since development over 50 years ago, using high-doses and often employing in vivo testing. Several disadvantages are encountered conducting or extrapolating data from animal studies due to differences in metabolism or exposure. The last decade saw considerable advancement in the development of in vitro tools and capabilities, and the challenges of the next decade will be integrating these platforms into applied product testing and acceptance by regulatory bodies. Governmental and validation agencies have launched and applied frameworks and "roadmaps" to support agile validation and acceptance of NAMs. Next-generation tobacco and nicotine products (NGPs) have the potential to offer reduced risks to smokers compared to cigarettes. These include heated tobacco products (HTPs) that heat but do not burn tobacco; vapor products also termed electronic nicotine delivery systems (ENDS), that heat an e-liquid to produce an inhalable aerosol; oral smokeless tobacco products (e.g., Swedish-style snus) and tobacco-free oral nicotine pouches. With the increased availability of NGPs and the requirement of scientific studies to support regulatory approval, NAMs approaches can supplement the assessment of NGPs. This review explores how NAMs can be applied to assess NGPs, highlighting key considerations, including the use of appropriate in vitro model systems, deploying screening approaches for hazard identification, and the importance of test article characterization. The importance and opportunity for fit-for-purpose testing and method standardization are discussed, highlighting the value of industry and cross-industry collaborations. Supporting the development of methods that are accepted by regulatory bodies could lead to the implementation of NAMs for tobacco and nicotine NGP testing.
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Affiliation(s)
- David Thorne
- BAT (Investments) Ltd., Southampton, Hampshire, United Kingdom
| | - Damian McHugh
- PMI R&D Philip Morris Products S. A., Neuchâtel, Switzerland
| | - Liam Simms
- Imperial Brands, Bristol, United Kingdom
| | - K. Monica Lee
- Altria Client Services LLC, Richmond, VA, United States
| | | | | | - Marianna Gaca
- BAT (Investments) Ltd., Southampton, Hampshire, United Kingdom
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2
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To KKW, Chow JCH, Cheung KM, Cho WCS. Circumvention of Gefitinib Resistance by Repurposing Flunarizine via Histone Deacetylase Inhibition. ACS Pharmacol Transl Sci 2023; 6:1531-1543. [PMID: 37854628 PMCID: PMC10580381 DOI: 10.1021/acsptsci.3c00202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Indexed: 10/20/2023]
Abstract
Gefitinib is an epidermal growth factor receptor tyrosine kinase inhibitor (EGFR TKI) for treating advanced non-small cell lung cancer (NSCLC). However, drug resistance seriously impedes the clinical efficacy of gefitinib. This study investigated the repositioning of the non-oncology drug capable of inhibiting histone deacetylases (HDACs) to overcome gefitinib resistance. A few drug candidates were identified using the in silico repurposing tool "DRUGSURV" and tested for HDAC inhibition. Flunarizine, originally indicated for migraine prophylaxis and vertigo treatment, was selected for detailed investigation in NSCLC cell lines harboring a range of different gefitinib resistance mechanisms (EGFR T790M, KRAS G12S, MET amplification, or PTEN loss). The circumvention of gefitinib resistance by flunarizine was further demonstrated in an EGFR TKI (erlotinib)-refractory patient-derived tumor xenograft (PDX) model in vivo. The acetylation level of cellular histone protein was increased by flunarizine in a concentration- and time-dependent manner. Among the NSCLC cell lines evaluated, the extent of gefitinib resistance circumvention by flunarizine was found to be the most pronounced in EGFR T790M-bearing H1975 cells. The gefitinib-flunarizine combination was shown to induce the apoptotic protein Bim but reduce the antiapoptotic protein Bcl-2, which apparently circumvented gefitinib resistance. The induction of Bim by flunarizine was accompanied by an increase in the histone acetylation and E2F1 interaction with the BIM gene promoter. Flunarizine was also found to upregulate E-cadherin but downregulate the vimentin expression, which subsequently inhibited cancer cell migration and invasion. Importantly, flunarizine was also shown to significantly potentiate the tumor growth suppressive effect of gefitinib in EGFR TKI-refractory PDX in vivo. The findings advocate for the translational application of flunarizine to circumvent gefitinib resistance in the clinic.
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Affiliation(s)
- Kenneth K. W. To
- School
of Pharmacy, Faculty of Medicine, The Chinese
University of Hong Kong, Hong Kong, SAR, China
| | - James C. H. Chow
- Department
of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong, SAR, China
| | - Ka-Man Cheung
- Department
of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong, SAR, China
| | - William C. S. Cho
- Department
of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong, SAR, China
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3
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To KKW, Cheung KM, Cho WCS. Repurposing of triamterene as a histone deacetylase inhibitor to overcome cisplatin resistance in lung cancer treatment. J Cancer Res Clin Oncol 2023; 149:7217-7234. [PMID: 36905422 DOI: 10.1007/s00432-023-04641-1] [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: 11/22/2022] [Accepted: 02/07/2023] [Indexed: 03/12/2023]
Abstract
PURPOSE Cisplatin is the core chemotherapeutic drug used for first-line treatment of advanced non-small cell lung cancer (NSCLC). However, drug resistance is severely hindering its clinical efficacy. This study investigated the circumvention of cisplatin resistance by repurposing non-oncology drugs with putative histone deacetylase (HDAC) inhibitory effect. METHODS A few clinically approved drugs were identified by a computational drug repurposing tool called "DRUGSURV" and evaluated for HDAC inhibition. Triamterene, originally indicated as a diuretic, was chosen for further investigation in pairs of parental and cisplatin-resistant NSCLC cell lines. Sulforhodamine B assay was used to evaluate cell proliferation. Western blot analysis was performed to examine histone acetylation. Flow cytometry was used to examine apoptosis and cell cycle effects. Chromatin immunoprecipitation was conducted to investigate the interaction of transcription factors to the promoter of genes regulating cisplatin uptake and cell cycle progression. The circumvention of cisplatin resistance by triamterene was further verified in a patient-derived tumor xenograft (PDX) from a cisplatin-refractory NSCLC patient. RESULTS Triamterene was found to inhibit HDACs. It was shown to enhance cellular cisplatin accumulation and potentiate cisplatin-induced cell cycle arrest, DNA damage, and apoptosis. Mechanistically, triamterene was found to induce histone acetylation in chromatin, thereby reducing the association of HDAC1 but promoting the interaction of Sp1 with the gene promoter of hCTR1 and p21. Triamterene was further shown to potentiate the anti-cancer effect of cisplatin in cisplatin-resistant PDX in vivo. CONCLUSION The findings advocate further clinical evaluation of the repurposing use of triamterene to overcome cisplatin resistance.
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Affiliation(s)
- Kenneth K W To
- School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, Room 801N, Lo Kwee-Seong Integrated Biomedical Sciences Building, Area 39, Shatin, New Territories, Hong Kong SAR, China.
| | - Ka M Cheung
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong SAR, China
| | - William C S Cho
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong SAR, China
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4
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Huang L, Zhang Z, Xing H, Luo Y, Yang J, Sui X, Wang Y. Risk assessment based on dose-responsive and time-responsive genes to build PLS-DA models for exogenously induced lung injury. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 256:114891. [PMID: 37054470 DOI: 10.1016/j.ecoenv.2023.114891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Revised: 02/28/2023] [Accepted: 04/08/2023] [Indexed: 06/19/2023]
Abstract
Xenobiotics can easily harm human lungs owing to the openness of the respiratory system. Identifying pulmonary toxicity remains challenging owing to several reasons: 1) no biomarkers for pulmonary toxicity are available that might help to detect lung injury; 2) traditional animal experiments are time-consuming; 3) traditional detection methods solely focus on poisoning accidents; 4) analytical chemistry methods hardly achieve universal detection. An in vitro testing system able to identify the pulmonary toxicity of contaminants from food, the environment, and drugs is urgently needed. Compounds are virtually infinite, whereas toxicological mechanisms are countable. Therefore, universal methods to identify and predict the risks of contaminants can be designed based on these well-known toxicity mechanisms. In this study, we established a dataset based on transcriptome sequencing of A549 cells upon treatment with different compounds. The representativeness of our dataset was analyzed using bioinformatics methods. Artificial intelligence methods, namely partial least squares discriminant analysis (PLS-DA) models, were employed for toxicity prediction and toxicant identification. The developed model predicted the pulmonary toxicity of compounds with a 92 % accuracy. These models were submitted to an external validation using highly heterogeneous compounds, which supported the accuracy and robustness of our developed methodology. This assay exhibits universal potential applications for water quality monitoring, crop pollution detection, food and drug safety evaluation, as well as chemical warfare agent detection.
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Affiliation(s)
- Lijuan Huang
- State Key Laboratory of Toxicology and Medical Countermeasures, Institute of Pharmacology and Toxicology, Academy of Military Medical Sciences, Beijing, China
| | - Zinan Zhang
- State Key Laboratory of Toxicology and Medical Countermeasures, Institute of Pharmacology and Toxicology, Academy of Military Medical Sciences, Beijing, China
| | - Huanchun Xing
- State Key Laboratory of Toxicology and Medical Countermeasures, Institute of Pharmacology and Toxicology, Academy of Military Medical Sciences, Beijing, China
| | - Yuan Luo
- State Key Laboratory of Toxicology and Medical Countermeasures, Institute of Pharmacology and Toxicology, Academy of Military Medical Sciences, Beijing, China
| | - Jun Yang
- State Key Laboratory of Toxicology and Medical Countermeasures, Institute of Pharmacology and Toxicology, Academy of Military Medical Sciences, Beijing, China
| | - Xin Sui
- State Key Laboratory of Toxicology and Medical Countermeasures, Institute of Pharmacology and Toxicology, Academy of Military Medical Sciences, Beijing, China.
| | - Yongan Wang
- State Key Laboratory of Toxicology and Medical Countermeasures, Institute of Pharmacology and Toxicology, Academy of Military Medical Sciences, Beijing, China.
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5
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Dahlin JL, Hua BK, Zucconi BE, Nelson SD, Singh S, Carpenter AE, Shrimp JH, Lima-Fernandes E, Wawer MJ, Chung LPW, Agrawal A, O'Reilly M, Barsyte-Lovejoy D, Szewczyk M, Li F, Lak P, Cuellar M, Cole PA, Meier JL, Thomas T, Baell JB, Brown PJ, Walters MA, Clemons PA, Schreiber SL, Wagner BK. Reference compounds for characterizing cellular injury in high-content cellular morphology assays. Nat Commun 2023; 14:1364. [PMID: 36914634 PMCID: PMC10011410 DOI: 10.1038/s41467-023-36829-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 02/20/2023] [Indexed: 03/16/2023] Open
Abstract
Robust, generalizable approaches to identify compounds efficiently with undesirable mechanisms of action in complex cellular assays remain elusive. Such a process would be useful for hit triage during high-throughput screening and, ultimately, predictive toxicology during drug development. Here we generate cell painting and cellular health profiles for 218 prototypical cytotoxic and nuisance compounds in U-2 OS cells in a concentration-response format. A diversity of compounds that cause cellular damage produces bioactive cell painting morphologies, including cytoskeletal poisons, genotoxins, nonspecific electrophiles, and redox-active compounds. Further, we show that lower quality lysine acetyltransferase inhibitors and nonspecific electrophiles can be distinguished from more selective counterparts. We propose that the purposeful inclusion of cytotoxic and nuisance reference compounds such as those profiled in this resource will help with assay optimization and compound prioritization in complex cellular assays like cell painting.
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Grants
- R35 GM127045 NIGMS NIH HHS
- U01 CA272612 NCI NIH HHS
- T32 HL007627 NHLBI NIH HHS
- R37 GM062437 NIGMS NIH HHS
- S10 OD026839 NIH HHS
- R35 GM122481 NIGMS NIH HHS
- U01 DK123717 NIDDK NIH HHS
- Wellcome Trust
- R35 GM122547 NIGMS NIH HHS
- U01 CA217848 NCI NIH HHS
- K99 GM124357 NIGMS NIH HHS
- R35 GM149229 NIGMS NIH HHS
- This study was supported by the Ono Pharma Breakthrough Science Initiative Award (to BKW). Authors acknowledge the following financial support: JLD (NIH NHLBI, T32-HL007627); BKH (National Science Foundation, DGE1144152 and DGE1745303); BEZ (NIH NIGMS, K99-GM124357); SDN (Harvard University’s Graduate Prize Fellowship, Eli Lilly Graduate Fellowship in Chemistry); PA Cole (NIH NIGMS, R37-GM62437); SLS (NIGMS, R35-GM127045); BKW (Ono Pharma Foundation; NIH NIDDK, U01-DK123717); SS (NIH NIGMS, R35-GM122547). The authors gratefully acknowledge the use of the Opera Phenix High-Content/High-Throughput imaging system at the Broad Institute, funded by the NIH S10 grant OD026839. This research was supported in part by the Intramural/Extramural research program of the NCATS, NIH.
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Affiliation(s)
- Jayme L Dahlin
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD, USA.
- Chemical Biology and Therapeutics Science Program, Broad Institute, Cambridge, MA, USA.
| | - Bruce K Hua
- Chemical Biology and Therapeutics Science Program, Broad Institute, Cambridge, MA, USA
| | - Beth E Zucconi
- Division of Genetics, Departments of Medicine and Biological Chemistry and Molecular Pharmacology, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
| | | | | | | | - Jonathan H Shrimp
- Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, USA
| | | | - Mathias J Wawer
- Chemical Biology and Therapeutics Science Program, Broad Institute, Cambridge, MA, USA
| | - Lawrence P W Chung
- Chemical Biology and Therapeutics Science Program, Broad Institute, Cambridge, MA, USA
| | - Ayushi Agrawal
- Chemical Biology and Therapeutics Science Program, Broad Institute, Cambridge, MA, USA
| | | | | | - Magdalena Szewczyk
- Structural Genomics Consortium, University of Toronto, Toronto, ON, Canada
| | - Fengling Li
- Structural Genomics Consortium, University of Toronto, Toronto, ON, Canada
| | - Parnian Lak
- Department of Pharmaceutical Chemistry and Quantitative Biology Institute, University of California San Francisco, San Francisco, CA, USA
| | - Matthew Cuellar
- Institute for Therapeutics Discovery and Development, University of Minnesota, Minneapolis, MN, USA
| | - Philip A Cole
- Division of Genetics, Departments of Medicine and Biological Chemistry and Molecular Pharmacology, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
| | - Jordan L Meier
- Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, USA
| | - Tim Thomas
- Department of Medical Biology, University of Melbourne, Parkville, VIC, Australia
| | - Jonathan B Baell
- Medicinal Chemistry Theme, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, Australia
| | - Peter J Brown
- Structural Genomics Consortium, University of Toronto, Toronto, ON, Canada
| | - Michael A Walters
- Institute for Therapeutics Discovery and Development, University of Minnesota, Minneapolis, MN, USA
| | - Paul A Clemons
- Chemical Biology and Therapeutics Science Program, Broad Institute, Cambridge, MA, USA
| | - Stuart L Schreiber
- Chemical Biology and Therapeutics Science Program, Broad Institute, Cambridge, MA, USA
| | - Bridget K Wagner
- Chemical Biology and Therapeutics Science Program, Broad Institute, Cambridge, MA, USA.
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6
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Cerisier N, Dafniet B, Badel A, Taboureau O. Linking chemicals, genes and morphological perturbations to diseases. Toxicol Appl Pharmacol 2023; 461:116407. [PMID: 36736439 DOI: 10.1016/j.taap.2023.116407] [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: 10/27/2022] [Revised: 01/13/2023] [Accepted: 01/28/2023] [Indexed: 02/05/2023]
Abstract
The progress in image-based high-content screening technology has facilitated high-throughput phenotypic profiling notably the quantification of cell morphology perturbation by chemicals. However, understanding the mechanism of action of a chemical and linking it to cell morphology and phenotypes remains a challenge in drug discovery. In this study, we intended to integrate molecules that induced transcriptomic perturbations and cellular morphological changes into a biological network in order to assess chemical-phenotypic relationships in humans. Such a network was enriched with existing disease information to suggest molecular and cellular profiles leading to phenotypes. Two datasets were used for this study. Firstly, we used the "Cell Painting morphological profiling assay" dataset, composed of 30,000 compounds tested on human osteosarcoma cells (named U2OS). Secondly, we used the "L1000 mRNA profiling assay" dataset, a collection of transcriptional expression data from cultured human cells treated with approximately 20,000 bioactive small molecules from the Library of Integrated Network-based Cellular Signatures (LINCS). Furthermore, pathways, gene ontology terms and disease enrichments were performed on the transcriptomics data. Overall, our study makes it possible to develop a biological network combining chemical-gene-pathway-morphological perturbation and disease relationships. It contains an ensemble of 9989 chemicals, 732 significant morphological features and 12,328 genes. Through diverse examples, we demonstrated that some drugs shared similar genes, pathways and morphological profiles that, taken together, could help in deciphering chemical-phenotype observations.
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Affiliation(s)
- Natacha Cerisier
- Université Paris Cité, INSERM U1133, CNRS UMR 8251, 75006 Paris, France
| | - Bryan Dafniet
- Université Paris Cité, INSERM U1133, CNRS UMR 8251, 75006 Paris, France
| | - Anne Badel
- Université Paris Cité, INSERM U1133, CNRS UMR 8251, 75006 Paris, France
| | - Olivier Taboureau
- Université Paris Cité, INSERM U1133, CNRS UMR 8251, 75006 Paris, France.
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7
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Baldovinos Y, Archer A, Salamanca J, Strongin RM, Sayes CM. Chemical Interactions and Cytotoxicity of Terpene and Diluent Vaping Ingredients. Chem Res Toxicol 2022; 36:589-597. [PMID: 36279315 PMCID: PMC10114068 DOI: 10.1021/acs.chemrestox.2c00218] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Vaping devices have risen in popularity since their inception in 2007. The practice involves using a variety of commercially available devices. Internal heating systems in devices aerosolize e-liquid formulations of complex mixtures including an active ingredient (e.g., THC, CBD, and nicotine), diluents (or cutting agents), solvents, and flavoring agents (e.g., terpenes and aldehydes). The vaping toxicology literature consists of cytotoxicity studies of individual chemicals and commercial formulas. Because of the variation of e-liquid composition, there is a limited understanding of the toxicity of ingredient combinations. This study analyzed the cytotoxic effects after exposure to individual and binary mixtures of a representative terpene (+-R-limonene) and diluent (triethyl citrate) on human lung cell models. Data were analyzed to determine the effects of 97:3 and 80:20% v/v (triethyl citrate/limonene) binary mixtures. BEAS-2B cells, a bronchial epithelial cell, and A549 cells, a type II alveolar epithelial cell, served as models for comparison. LC50 values were calculated and isobolograms were used to assess chemical interactions. Results show that limonene was more cytotoxic than triethyl citrate. Isobolographic analyses confirmed that the 97:3% v/v mixture resulted in an antagonistic chemical interaction. The 80:20% v/v mixture resulted in a similar result. Further testing of different ratios of binary mixtures is needed for chemical interaction screening to inform safety assessments.
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Affiliation(s)
- Yanira Baldovinos
- Department of Environmental Science, Baylor University, Waco, Texas76706, United States
| | - Alexandra Archer
- Department of Chemistry, Portland State University, Portland, Oregon97201, United States
| | - James Salamanca
- Department of Chemistry, Portland State University, Portland, Oregon97201, United States
| | - Robert M Strongin
- Department of Chemistry, Portland State University, Portland, Oregon97201, United States
| | - Christie M Sayes
- Department of Environmental Science, Baylor University, Waco, Texas76706, United States
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8
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El-Masri H, Paul Friedman K, Isaacs K, Wetmore BA. Advances in computational methods along the exposure to toxicological response paradigm. Toxicol Appl Pharmacol 2022; 450:116141. [PMID: 35777528 PMCID: PMC9619339 DOI: 10.1016/j.taap.2022.116141] [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: 04/12/2022] [Revised: 05/27/2022] [Accepted: 06/23/2022] [Indexed: 10/17/2022]
Abstract
Human health risk assessment is a function of chemical toxicity, bioavailability to reach target biological tissues, and potential environmental exposure. These factors are complicated by many physiological, biochemical, physical and lifestyle factors. Furthermore, chemical health risk assessment is challenging in view of the large, and continually increasing, number of chemicals found in the environment. These challenges highlight the need to prioritize resources for the efficient and timely assessment of those environmental chemicals that pose greatest health risks. Computational methods, either predictive or investigative, are designed to assist in this prioritization in view of the lack of cost prohibitive in vivo experimental data. Computational methods provide specific and focused toxicity information using in vitro high throughput screening (HTS) assays. Information from the HTS assays can be converted to in vivo estimates of chemical levels in blood or target tissue, which in turn are converted to in vivo dose estimates that can be compared to exposure levels of the screened chemicals. This manuscript provides a review for the landscape of computational methods developed and used at the U.S. Environmental Protection Agency (EPA) highlighting their potentials and challenges.
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Affiliation(s)
- Hisham El-Masri
- Center for Computational Toxicology and Exposure, Office of Research and Development, U. S. Environmental Protection Agency, Research Triangle Park, NC, USA.
| | - Katie Paul Friedman
- Center for Computational Toxicology and Exposure, Office of Research and Development, U. S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Kristin Isaacs
- Center for Computational Toxicology and Exposure, Office of Research and Development, U. S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Barbara A Wetmore
- Center for Computational Toxicology and Exposure, Office of Research and Development, U. S. Environmental Protection Agency, Research Triangle Park, NC, USA
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9
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Kong JW, Lam Z, Chan KH, Ganguly R, Joey Lee JY, Loo LH, Webster RD, Wong ZX, Leong WK. Group VIII Metal Carbonyl Cluster-Boronic Acid Conjugates: Cytotoxicity and Mode of Action Studies. ACS OMEGA 2021; 6:29045-29053. [PMID: 34746593 PMCID: PMC8567370 DOI: 10.1021/acsomega.1c04116] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 10/08/2021] [Indexed: 06/13/2023]
Abstract
A set of metal carbonyl cluster-boronic acid conjugates of the group VIII metals (Fe, Ru, and Os) were synthesized and their antiproliferative effects measured against two breast cancer cell lines (MCF-7 and MDA-MB-231) and a noncancerous breast epithelial (MCF-10A) cell line. The cytotoxicity followed the order Ru > Os > Fe for the MDA-MB-231 cells, although the latter two exhibited similar cytotoxicity against MCF-7 and MCF-10A cells. The osmium species {Os3(CO)10(μ-H)[μ-SC6H4-p-B(OH)2]} (2) could be chemically oxidized to its hydroxy analogue [Os3(CO)10(μ-H)(μ-SC6H4 -p-OH)] (2-OH), which showed comparable cytotoxicity. Mode of action studies pointed to an apoptotic pathway for cell death.
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Affiliation(s)
- Jia Wen Kong
- Division
of Chemistry and Biological Chemistry, School of Mathematical and
Physical Sciences, Nanyang Technological
University, Singapore 637371, Singapore
| | - Zhiyong Lam
- Division
of Chemistry and Biological Chemistry, School of Mathematical and
Physical Sciences, Nanyang Technological
University, Singapore 637371, Singapore
| | - Kiat Hwa Chan
- Yale-NUS
College, 16 College Avenue West, Singapore 138527, Singapore
| | - Rakesh Ganguly
- Division
of Chemistry and Biological Chemistry, School of Mathematical and
Physical Sciences, Nanyang Technological
University, Singapore 637371, Singapore
| | - Jia-Ying Joey Lee
- Agency
for Science, Technology, and Research (A*STAR), Bioinformatics Institute (BII), Singapore 138671, Singapore
| | - Lit-Hsin Loo
- Agency
for Science, Technology, and Research (A*STAR), Bioinformatics Institute (BII), Singapore 138671, Singapore
- Department
of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119077, Singapore
| | - Richard D. Webster
- Division
of Chemistry and Biological Chemistry, School of Mathematical and
Physical Sciences, Nanyang Technological
University, Singapore 637371, Singapore
| | - Zhen Xuan Wong
- Division
of Chemistry and Biological Chemistry, School of Mathematical and
Physical Sciences, Nanyang Technological
University, Singapore 637371, Singapore
| | - Weng Kee Leong
- Division
of Chemistry and Biological Chemistry, School of Mathematical and
Physical Sciences, Nanyang Technological
University, Singapore 637371, Singapore
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10
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Pérez Santín E, Rodríguez Solana R, González García M, García Suárez MDM, Blanco Díaz GD, Cima Cabal MD, Moreno Rojas JM, López Sánchez JI. Toxicity prediction based on artificial intelligence: A multidisciplinary overview. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2021. [DOI: 10.1002/wcms.1516] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Efrén Pérez Santín
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - Raquel Rodríguez Solana
- Department of Food Science and Health Andalusian Institute of Agricultural and Fisheries Research and Training (IFAPA), Alameda del Obispo Avda Córdoba, Andalucía Spain
| | - Mariano González García
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - María Del Mar García Suárez
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - Gerardo David Blanco Díaz
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - María Dolores Cima Cabal
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - José Manuel Moreno Rojas
- Department of Food Science and Health Andalusian Institute of Agricultural and Fisheries Research and Training (IFAPA), Alameda del Obispo Avda Córdoba, Andalucía Spain
| | - José Ignacio López Sánchez
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
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11
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Baum ZJ, Yu X, Ayala PY, Zhao Y, Watkins SP, Zhou Q. Artificial Intelligence in Chemistry: Current Trends and Future Directions. J Chem Inf Model 2021; 61:3197-3212. [PMID: 34264069 DOI: 10.1021/acs.jcim.1c00619] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The application of artificial intelligence (AI) to chemistry has grown tremendously in recent years. In this Review, we studied the growth and distribution of AI-related chemistry publications in the last two decades using the CAS Content Collection. The volume of both journal and patent publications have increased dramatically, especially since 2015. Study of the distribution of publications over various chemistry research areas revealed that analytical chemistry and biochemistry are integrating AI to the greatest extent and with the highest growth rates. We also investigated trends in interdisciplinary research and identified frequently occurring combinations of research areas in publications. Furthermore, topic analyses were conducted for journal and patent publications to illustrate emerging associations of AI with certain chemistry research topics. Notable publications in various chemistry disciplines were then evaluated and presented to highlight emerging use cases. Finally, the occurrence of different classes of substances and their roles in AI-related chemistry research were quantified, further detailing the popularity of AI adoption in the life sciences and analytical chemistry. In summary, this Review offers a broad overview of how AI has progressed in various fields of chemistry and aims to provide an understanding of its future directions.
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Affiliation(s)
- Zachary J Baum
- Chemical Abstracts Service, 2540 Olentangy River Road, Columbus, Ohio 43210, United States
| | - Xiang Yu
- Chemical Abstracts Service, 2540 Olentangy River Road, Columbus, Ohio 43210, United States
| | - Philippe Y Ayala
- Chemical Abstracts Service, 2540 Olentangy River Road, Columbus, Ohio 43210, United States
| | - Yanan Zhao
- Chemical Abstracts Service, 2540 Olentangy River Road, Columbus, Ohio 43210, United States
| | - Steven P Watkins
- Chemical Abstracts Service, 2540 Olentangy River Road, Columbus, Ohio 43210, United States
| | - Qiongqiong Zhou
- Chemical Abstracts Service, 2540 Olentangy River Road, Columbus, Ohio 43210, United States
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Structure-based virtual screening of CYP1A1 inhibitors: towards rapid tier-one assessment of potential developmental toxicants. Arch Toxicol 2021; 95:3031-3048. [PMID: 34181028 PMCID: PMC8380238 DOI: 10.1007/s00204-021-03111-2] [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] [Received: 05/25/2021] [Accepted: 06/17/2021] [Indexed: 10/26/2022]
Abstract
Cytochrome P450 1A1 (CYP1A1) metabolizes estrogens, melatonin, and other key endogenous signaling molecules critical for embryonic/fetal development. The enzyme has increasing expression during pregnancy, and its inhibition or knockout increases embryonic/fetal lethality and/or developmental problems. Here, we present a virtual screening model for CYP1A1 inhibitors based on the orthosteric and predicted allosteric sites of the enzyme. Using 1001 reference compounds with CYP1A1 activity data, we optimized the decision thresholds of our model and classified the training compounds with 68.3% balanced accuracy (91.0% sensitivity and 45.7% specificity). We applied our final model to 11 known CYP1A1 orthosteric binders and related compounds, and found that our ranking of the known orthosteric binders generally agrees with the relative activity of CYP1A1 in metabolizing these compounds. We also applied the model to 22 new test compounds with unknown/unclear CYP1A1 inhibitory activity, and predicted 16 of them are CYP1A1 inhibitors. The CYP1A1 potency and modes of inhibition of these 22 compounds were experimentally determined. We confirmed that most predicted inhibitors, including drugs contraindicated during pregnancy (amiodarone, bicalutamide, cyproterone acetate, ketoconazole, and tamoxifen) and environmental agents suspected to be endocrine disruptors (bisphenol A, diethyl and dibutyl phthalates, and zearalenone), are indeed potent inhibitors of CYP1A1. Our results suggest that virtual screening may be used as a rapid tier-one method to screen for potential CYP1A1 inhibitors, and flag them out for further experimental evaluations.
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Paul Friedman K, Gagne M, Loo LH, Karamertzanis P, Netzeva T, Sobanski T, Franzosa JA, Richard AM, Lougee RR, Gissi A, Lee JYJ, Angrish M, Dorne JL, Foster S, Raffaele K, Bahadori T, Gwinn MR, Lambert J, Whelan M, Rasenberg M, Barton-Maclaren T, Thomas RS. Utility of In Vitro Bioactivity as a Lower Bound Estimate of In Vivo Adverse Effect Levels and in Risk-Based Prioritization. Toxicol Sci 2021; 173:202-225. [PMID: 31532525 DOI: 10.1093/toxsci/kfz201] [Citation(s) in RCA: 131] [Impact Index Per Article: 43.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Use of high-throughput, in vitro bioactivity data in setting a point-of-departure (POD) has the potential to accelerate the pace of human health safety evaluation by informing screening-level assessments. The primary objective of this work was to compare PODs based on high-throughput predictions of bioactivity, exposure predictions, and traditional hazard information for 448 chemicals. PODs derived from new approach methodologies (NAMs) were obtained for this comparison using the 50th (PODNAM, 50) and the 95th (PODNAM, 95) percentile credible interval estimates for the steady-state plasma concentration used in in vitro to in vivo extrapolation of administered equivalent doses. Of the 448 substances, 89% had a PODNAM, 95 that was less than the traditional POD (PODtraditional) value. For the 48 substances for which PODtraditional < PODNAM, 95, the PODNAM and PODtraditional were typically within a factor of 10 of each other, and there was an enrichment of chemical structural features associated with organophosphate and carbamate insecticides. When PODtraditional < PODNAM, 95, it did not appear to result from an enrichment of PODtraditional based on a particular study type (eg, developmental, reproductive, and chronic studies). Bioactivity:exposure ratios, useful for identification of substances with potential priority, demonstrated that high-throughput exposure predictions were greater than the PODNAM, 95 for 11 substances. When compared with threshold of toxicological concern (TTC) values, the PODNAM, 95 was greater than the corresponding TTC value 90% of the time. This work demonstrates the feasibility, and continuing challenges, of using in vitro bioactivity as a protective estimate of POD in screening-level assessments via a case study.
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Affiliation(s)
- Katie Paul Friedman
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, 27711
| | - Matthew Gagne
- Healthy Environments and Consumer Safety Branch, Health Canada, Government of Canada, Ottawa, Ontario, Canada, K1A0K9
| | - Lit-Hsin Loo
- Innovations in Food and Chemical Safety Programme and Bioinformatics Institute, Agency for Science, Technology and Research, Singapore, 138671, Singapore
| | - Panagiotis Karamertzanis
- Computational Assessment Unit, European Chemicals Agency, European Chemicals Agency Annankatu 18, P.O. Box 400, FI-00121 Helsinki, Uusimaa, Finland
| | - Tatiana Netzeva
- Computational Assessment Unit, European Chemicals Agency, European Chemicals Agency Annankatu 18, P.O. Box 400, FI-00121 Helsinki, Uusimaa, Finland
| | - Tomasz Sobanski
- Computational Assessment Unit, European Chemicals Agency, European Chemicals Agency Annankatu 18, P.O. Box 400, FI-00121 Helsinki, Uusimaa, Finland
| | - Jill A Franzosa
- National Health and Environmental Effects Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, 27711
| | - Ann M Richard
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, 27711
| | - Ryan R Lougee
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, 27711.,Oak Ridge Institute for Science and Education, U.S. Department of Energy, Oak Ridge, TN 37831, USA
| | - Andrea Gissi
- Computational Assessment Unit, European Chemicals Agency, European Chemicals Agency Annankatu 18, P.O. Box 400, FI-00121 Helsinki, Uusimaa, Finland
| | - Jia-Ying Joey Lee
- Innovations in Food and Chemical Safety Programme and Bioinformatics Institute, Agency for Science, Technology and Research, Singapore, 138671, Singapore
| | - Michelle Angrish
- National Center for Environmental Assessment, Office of Research and Development, US Environmental Protection Agency, Washington, DC, 20004 and Research Triangle Park, NC 27711
| | - Jean Lou Dorne
- Scientific Committee and Emerging Risks Unit Department of Risk Assessment and Scientific Assistance, Via Carlo Magno 1A, 43126 Parma, Italy
| | - Stiven Foster
- Office of Land and Emergency Management, U.S. Environmental Protection Agency, Washington, DC, 20004
| | - Kathleen Raffaele
- Office of Land and Emergency Management, U.S. Environmental Protection Agency, Washington, DC, 20004
| | - Tina Bahadori
- Oak Ridge Institute for Science and Education, U.S. Department of Energy, Oak Ridge, TN 37831, USA
| | - Maureen R Gwinn
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, 27711
| | - Jason Lambert
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, 27711
| | - Maurice Whelan
- European Commission, Joint Research Centre (JRC), Via Enrico Fermi, 2749, I - 21027 Ispra, Italy
| | - Mike Rasenberg
- Computational Assessment Unit, European Chemicals Agency, European Chemicals Agency Annankatu 18, P.O. Box 400, FI-00121 Helsinki, Uusimaa, Finland
| | - Tara Barton-Maclaren
- Healthy Environments and Consumer Safety Branch, Health Canada, Government of Canada, Ottawa, Ontario, Canada, K1A0K9
| | - Russell S Thomas
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, 27711
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Chandrasekaran SN, Ceulemans H, Boyd JD, Carpenter AE. Image-based profiling for drug discovery: due for a machine-learning upgrade? Nat Rev Drug Discov 2021; 20:145-159. [PMID: 33353986 PMCID: PMC7754181 DOI: 10.1038/s41573-020-00117-w] [Citation(s) in RCA: 147] [Impact Index Per Article: 49.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/13/2020] [Indexed: 12/20/2022]
Abstract
Image-based profiling is a maturing strategy by which the rich information present in biological images is reduced to a multidimensional profile, a collection of extracted image-based features. These profiles can be mined for relevant patterns, revealing unexpected biological activity that is useful for many steps in the drug discovery process. Such applications include identifying disease-associated screenable phenotypes, understanding disease mechanisms and predicting a drug's activity, toxicity or mechanism of action. Several of these applications have been recently validated and have moved into production mode within academia and the pharmaceutical industry. Some of these have yielded disappointing results in practice but are now of renewed interest due to improved machine-learning strategies that better leverage image-based information. Although challenges remain, novel computational technologies such as deep learning and single-cell methods that better capture the biological information in images hold promise for accelerating drug discovery.
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Affiliation(s)
| | - Hugo Ceulemans
- Discovery Data Sciences, Janssen Pharmaceutica NV, Beerse, Belgium
| | - Justin D Boyd
- High Content Imaging Technology Center, Internal Medicine Research Unit, Pfizer Inc., Cambridge, MA, USA
| | - Anne E Carpenter
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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15
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Arora NB, von Salm JL. Fall 2020 Proceedings of the Cannabis Chemistry Subdivision. ACS CHEMICAL HEALTH & SAFETY 2021. [DOI: 10.1021/acs.chas.0c00119] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Nigam B. Arora
- Cannabis Chemistry Subdivision, Washington, D.C. 20036, United States
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16
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Jaladanki CK, He Y, Zhao LN, Maurer-Stroh S, Loo LH, Song H, Fan H. Virtual screening of potentially endocrine-disrupting chemicals against nuclear receptors and its application to identify PPARγ-bound fatty acids. Arch Toxicol 2020; 95:355-374. [PMID: 32909075 PMCID: PMC7811525 DOI: 10.1007/s00204-020-02897-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 08/27/2020] [Indexed: 12/17/2022]
Abstract
Nuclear receptors (NRs) are key regulators of energy homeostasis, body development, and sexual reproduction. Xenobiotics binding to NRs may disrupt natural hormonal systems and induce undesired adverse effects in the body. However, many chemicals of concerns have limited or no experimental data on their potential or lack-of-potential endocrine-disrupting effects. Here, we propose a virtual screening method based on molecular docking for predicting potential endocrine-disrupting chemicals (EDCs) that bind to NRs. For 12 NRs, we systematically analyzed how multiple crystal structures can be used to distinguish actives and inactives found in previous high-throughput experiments. Our method is based on (i) consensus docking scores from multiple structures at a single functional state (agonist-bound or antagonist-bound), (ii) multiple functional states (agonist-bound and antagonist-bound), and (iii) multiple pockets (orthosteric site and alternative sites) of these NRs. We found that the consensus enrichment from multiple structures is better than or comparable to the best enrichment from a single structure. The discriminating power of this consensus strategy was further enhanced by a chemical similarity-weighted scoring scheme, yielding better or comparable enrichment for all studied NRs. Applying this optimized method, we screened 252 fatty acids against peroxisome proliferator-activated receptor gamma (PPARγ) and successfully identified 3 previously unknown fatty acids with Kd = 100-250 μM including two furan fatty acids: furannonanoic acid (FNA) and furanundecanoic acid (FUA), and one cyclopropane fatty acid: phytomonic acid (PTA). These results suggested that the proposed method can be used to rapidly screen and prioritize potential EDCs for further experimental evaluations.
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Affiliation(s)
- Chaitanya K Jaladanki
- Bioinformatics Institute (BII), Agency for Science, Technology, and Research (A*STAR), 30 Biopolis Street, Matrix No. 07-01, Singapore, 138671, Singapore
- Toxicity Mode-of-Action Discovery (ToxMAD) Platform, Innovations in Food and Chemical Safety Programme, Agency for Science, Technology, and Research (A*STAR), Singapore, 138671, Singapore
| | - Yang He
- Institute of Molecular and Cell Biology, 61 Biopolis Drive, Singapore, 138673, Singapore
| | - Li Na Zhao
- Bioinformatics Institute (BII), Agency for Science, Technology, and Research (A*STAR), 30 Biopolis Street, Matrix No. 07-01, Singapore, 138671, Singapore
| | - Sebastian Maurer-Stroh
- Bioinformatics Institute (BII), Agency for Science, Technology, and Research (A*STAR), 30 Biopolis Street, Matrix No. 07-01, Singapore, 138671, Singapore
- Toxicity Mode-of-Action Discovery (ToxMAD) Platform, Innovations in Food and Chemical Safety Programme, Agency for Science, Technology, and Research (A*STAR), Singapore, 138671, Singapore
| | - Lit-Hsin Loo
- Bioinformatics Institute (BII), Agency for Science, Technology, and Research (A*STAR), 30 Biopolis Street, Matrix No. 07-01, Singapore, 138671, Singapore
- Toxicity Mode-of-Action Discovery (ToxMAD) Platform, Innovations in Food and Chemical Safety Programme, Agency for Science, Technology, and Research (A*STAR), Singapore, 138671, Singapore
| | - Haiwei Song
- Institute of Molecular and Cell Biology, 61 Biopolis Drive, Singapore, 138673, Singapore.
| | - Hao Fan
- Bioinformatics Institute (BII), Agency for Science, Technology, and Research (A*STAR), 30 Biopolis Street, Matrix No. 07-01, Singapore, 138671, Singapore.
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17
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Optimum concentration-response curve metrics for supervised selection of discriminative cellular phenotypic endpoints for chemical hazard assessment. Arch Toxicol 2020; 94:2951-2964. [PMID: 32601827 DOI: 10.1007/s00204-020-02813-3] [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: 03/01/2020] [Accepted: 06/15/2020] [Indexed: 10/24/2022]
Abstract
High-content imaging (HCI) provides quantitative and information-rich measurements of chemical effects on human in vitro cell models. Identification of discriminative phenotypic endpoints from cellular features obtained from HCI is required for accurate assessments of potential chemical hazards. However, the use of suboptimal metrics to quantify the concentration-response curves (CRC) of chemicals based on these features may obscure discriminative features, and lead to non-predictive endpoints and poor chemical classifications or hazard assessments. Here, we present a systematic and data-driven study on the performances of different CRC metrics in identifying image-based phenotypic features that can accurately classify the effects of reference chemicals with known in vivo toxicities. We studied four previous HCI in vitro nephro- or pulmono-toxicity datasets, which contain phenotypic feature measurements from different cell and feature types. Within a feature type, we found that efficacy metrics at higher chemical concentrations tend to give higher classification accuracy, whereas potency metrics do not have obvious trends across different response levels. Across different cell and feature types, efficacy metrics generally gave higher classification accuracy than potency metrics and area under the curve (AUC). Our results suggest that efficacy metrics, especially at higher concentrations, are more likely to help us to identify discriminative phenotypic endpoints. Therefore, HCI experiments for toxicological applications should include measurements at sufficiently high chemical concentrations, and efficacy metrics should always be analyzed. The identified features may be used as specific toxicity endpoints for further chemical hazard assessment.
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Hussain F, Basu S, Heng JJH, Loo LH, Zink D. Predicting direct hepatocyte toxicity in humans by combining high-throughput imaging of HepaRG cells and machine learning-based phenotypic profiling. Arch Toxicol 2020; 94:2749-2767. [PMID: 32533217 DOI: 10.1007/s00204-020-02778-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Accepted: 05/05/2020] [Indexed: 02/07/2023]
Abstract
Accurate prediction of drug- and chemical-induced hepatotoxicity remains to be a problem for pharmaceutical companies as well as other industries and regulators. The goal of the current study was to develop an in vitro/in silico method for the rapid and accurate prediction of drug- and chemical-induced hepatocyte injury in humans. HepaRG cells were employed for high-throughput imaging in combination with phenotypic profiling. A reference set of 69 drugs and chemicals was screened at a range of 7 concentrations, and the cellular response values were used for training a supervised classifier and for determining assay performance by using tenfold cross-validation. The results showed that the best performing phenotypic features were related to nuclear translocation of RELA (RELA proto-oncogene, NF-kB subunit; also known as NF-kappa B p65), DNA organization, and the F-actin cytoskeleton. Using a subset of 30 phenotypic features, direct hepatocyte toxicity in humans could be predicted with a test sensitivity, specificity and balanced accuracy of 73%, 92%, and 83%, respectively. The method was applied to another set of 26 drugs and chemicals with unclear annotation and their hepatocyte toxicity in humans was predicted. The results also revealed that the identified discriminative phenotypic changes were related to cell death and cellular senescence. Whereas cell death-related endpoints are widely applied in in vitro toxicology, cellular senescence-related endpoints are not, although cellular senescence can be induced by various drugs and other small molecule compounds and plays an important role in liver injury and disease. These findings show how phenotypic profiling can reveal unexpected chemical-induced mechanisms in toxicology.
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Affiliation(s)
- Faezah Hussain
- NanoBio Lab and Institute of Bioengineering and Nanotechnology (IBN), 31 Biopolis Way, The Nanos, Singapore, 138669, Singapore
| | - Sreetama Basu
- Bioinformatics Institute, 30 Biopolis Street, #07-01 Matrix, Singapore, 138671, Singapore
| | - Javen Jun Hao Heng
- NanoBio Lab and Institute of Bioengineering and Nanotechnology (IBN), 31 Biopolis Way, The Nanos, Singapore, 138669, Singapore
| | - Lit-Hsin Loo
- Bioinformatics Institute, 30 Biopolis Street, #07-01 Matrix, Singapore, 138671, Singapore. .,Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, 16 Medical Drive, Singapore, 117600, Singapore.
| | - Daniele Zink
- NanoBio Lab and Institute of Bioengineering and Nanotechnology (IBN), 31 Biopolis Way, The Nanos, Singapore, 138669, Singapore.
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Chen Z, Liu Y, Wright FA, Chiu WA, Rusyn I. Rapid hazard characterization of environmental chemicals using a compendium of human cell lines from different organs. ALTEX 2020; 37:623-638. [PMID: 32521033 PMCID: PMC7941183 DOI: 10.14573/altex.2002291] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Accepted: 06/08/2020] [Indexed: 02/07/2023]
Abstract
The lack of adequate toxicity data for the vast majority of chemicals in the environment has spurred the development of new approach methodologies (NAMs). This study aimed to develop a practical high-throughput in vitro model for rapidly evaluating potential hazards of chemicals using a small number of human cells. Forty-two compounds were tested using human induced pluripotent stem cell (iPSC)-derived cells (hepatocytes, neurons, cardiomyocytes and endothelial cells), and a primary endothelial cell line. Both functional and cytotoxicity endpoints were evaluated using high-content imaging. Concentration-response was used to derive points-of-departure (POD). PODs were integrated with ToxPi and used as surrogate NAM-based PODs for risk characterization. We found chemical class-specific similarity among the chemicals tested; metal salts exhibited the highest overall bioactivity. We also observed cell type-specific patterns among classes of chemicals, indicating the ability of the proposed in vitro model to recognize effects on different cell types. Compared to available NAM datasets, such as ToxCast/Tox21 and chemical structure-based descriptors, we found that the data from the five-cell-type model was as good or even better in assigning compounds to chemical classes. Additionally, the PODs from this model performed well as a conservative surrogate for regulatory in vivo PODs and were less likely to underestimate in vivo potency and potential risk compared to other NAM-based PODs. In summary, we demonstrate the potential of this in vitro screening model to inform rapid risk-based decision-making through ranking, clustering, and assessment of both hazard and risks of diverse environmental chemicals.
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Affiliation(s)
- Zunwei Chen
- Interdisciplinary Faculty of Toxicology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA
- Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA
| | - Yizhong Liu
- Interdisciplinary Faculty of Toxicology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA
- Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA
| | - Fred A. Wright
- Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
- Departments of Statistics and Biological Sciences, North Carolina State University, Raleigh, NC, USA
| | - Weihsueh A. Chiu
- Interdisciplinary Faculty of Toxicology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA
- Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA
| | - Ivan Rusyn
- Interdisciplinary Faculty of Toxicology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA
- Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA
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Zink D, Chuah JKC, Ying JY. Assessing Toxicity with Human Cell-Based In Vitro Methods. Trends Mol Med 2020; 26:570-582. [PMID: 32470384 DOI: 10.1016/j.molmed.2020.01.008] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Revised: 01/02/2020] [Accepted: 01/21/2020] [Indexed: 01/01/2023]
Abstract
In toxicology, there is a strong push towards replacing animal experiments with alternative methods, which include cell-based in vitro methods for the assessment of adverse health effects in humans. High-throughput methods are of central interest due to the large and steadily growing numbers of compounds that require assessment. Tremendous progress has been made during the last decade in developing and applying such methods. Innovative technologies for addressing complex biological interactions include induced pluripotent stem cell- and organoid-based approaches, organotypic coculture systems, and microfluidic 'multiorgan' chips. Combining in vitro methods with bioinformatics and in silico modeling generates new powerful tools for toxicity assessment, and the rapid progress in the field is expected to continue.
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Affiliation(s)
- Daniele Zink
- NanoBio Lab, Agency for Science, Technology and Research (A*STAR), 31 Biopolis Way, The Nanos, #09-01, Singapore 138669, Singapore; Innovations in Food and Chemical Safety Programme, A*STAR, Singapore.
| | - Jacqueline Kai Chin Chuah
- NanoBio Lab, Agency for Science, Technology and Research (A*STAR), 31 Biopolis Way, The Nanos, #09-01, Singapore 138669, Singapore; Cellbae Pte Ltd, 31 Biopolis Way, The Nanos, Singapore 138669, Singapore
| | - Jackie Y Ying
- NanoBio Lab, Agency for Science, Technology and Research (A*STAR), 31 Biopolis Way, The Nanos, #09-01, Singapore 138669, Singapore.
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van der Ven LTM, Rorije E, Sprong RC, Zink D, Derr R, Hendriks G, Loo LH, Luijten M. A Case Study with Triazole Fungicides to Explore Practical Application of Next-Generation Hazard Assessment Methods for Human Health. Chem Res Toxicol 2020; 33:834-848. [PMID: 32041405 DOI: 10.1021/acs.chemrestox.9b00484] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
The ongoing developments in chemical risk assessment have led to new concepts building on integration of sophisticated nonanimal models for hazard characterization. Here we explore a pragmatic approach for implementing such concepts, using a case study of three triazole fungicides, namely, flusilazole, propiconazole, and cyproconazole. The strategy applied starts with evaluating the overall level of concern by comparing exposure estimates to toxicological potential, followed by a combination of in silico tools and literature-derived high-throughput screening assays and computational elaborations to obtain insight into potential toxicological mechanisms and targets in the organism. Additionally, some targeted in vitro tests were evaluated for their utility to confirm suspected mechanisms of toxicity and to generate points of departure. Toxicological mechanisms instead of the current "end point-by-end point" approach should guide the selection of methods and assays that constitute a toolbox for next-generation risk assessment. Comparison of the obtained in silico and in vitro results with data from traditional in vivo testing revealed that, overall, nonanimal methods for hazard identification can produce adequate qualitative hazard information for risk assessment. Follow-up studies are needed to further refine the proposed approach, including the composition of the toolbox, toxicokinetics models, and models for exposure assessment.
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