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Tsiliki G, Ag Seleci D, Zabeo A, Basei G, Hristozov D, Jeliazkova N, Boyles M, Murphy F, Peijnenburg W, Wohlleben W, Stone V. Bayesian based similarity assessment of nanomaterials to inform grouping. NANOIMPACT 2022; 25:100389. [PMID: 35559895 DOI: 10.1016/j.impact.2022.100389] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 12/29/2021] [Accepted: 02/02/2022] [Indexed: 06/15/2023]
Abstract
Nanoforms can be manufactured in plenty of variants by differing their physicochemical properties and toxicokinetic behaviour which can affect their hazard potential. To avoid testing of each single nanomaterial and nanoform variation and subsequently save resources, grouping and read-across strategies are used to estimate groups of substances, based on carefully selected evidence, that could potentially have similar human health and environmental hazard impact. A novel computational similarity method is presented aiming to compare dose-response curves and identify sets of similar nanoforms. The suggested method estimates the statistical model that best fits the data by leveraging pairwise Bayes Factor analysis to compare pairs of curves and evaluate whether each of the nanoforms is sufficiently similar to all other nanoforms. Pairwise comparisons to benchmark materials are used to define threshold similarity values and set the criteria for identifying groups of nanoforms with comparatively similar toxicity. Applications to use case data are shown to demonstrate that the method can support grouping hypotheses linked to a certain hazard endpoint and route of exposure.
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Affiliation(s)
- Georgia Tsiliki
- Institute for the Management of Information Systems, Athena Research Center, Marousi, Greece.
| | - Didem Ag Seleci
- Advanced Materials Research, Dept. of Material Physics and Analytics and Dept. of Experimental Toxicology and Ecology, BASF SE, Ludwigshafen, Germany
| | | | | | | | | | - Matthew Boyles
- Institute of Occupational Medicine, Edinburgh, United Kingdom
| | - Fiona Murphy
- NanoSafety Group, Heriot-Watt University, Edinburgh, United Kingdom
| | - Willie Peijnenburg
- National Institute of Public Health and the Environment (RIVM), Center for Safety of Substances and Products, Bilthoven, the Netherlands; Leiden University, Institute of Environmental Sciences (CML), Leiden, the Netherlands
| | - Wendel Wohlleben
- Advanced Materials Research, Dept. of Material Physics and Analytics and Dept. of Experimental Toxicology and Ecology, BASF SE, Ludwigshafen, Germany
| | - Vicki Stone
- NanoSafety Group, Heriot-Watt University, Edinburgh, United Kingdom
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ElHarouni D, Berker Y, Peterziel H, Gopisetty A, Turunen L, Kreth S, Stainczyk SA, Oehme I, Pietiäinen V, Jäger N, Witt O, Schlesner M, Oppermann S. iTReX: Interactive exploration of mono- and combination therapy dose response profiling data. Pharmacol Res 2021; 175:105996. [PMID: 34848323 DOI: 10.1016/j.phrs.2021.105996] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 11/18/2021] [Accepted: 11/19/2021] [Indexed: 12/11/2022]
Abstract
High throughput screening methods, measuring the sensitivity and resistance of tumor cells to drug treatments have been rapidly evolving. Not only do these screens allow correlating response profiles to tumor genomic features for developing novel predictors of treatment response, but they can also add evidence for therapy decision making in precision oncology. Recent analysis methods developed for either assessing single agents or combination drug efficacies enable quantification of dose-response curves with restricted symmetric fit settings. Here, we introduce iTReX, a user-friendly and interactive Shiny/R application, for both the analysis of mono- and combination therapy responses. The application features an extended version of the drug sensitivity score (DSS) based on the integral of an advanced five-parameter dose-response curve model and a differential DSS for combination therapy profiling. Additionally, iTReX includes modules that visualize drug target interaction networks and support the detection of matches between top therapy hits and the sample omics features to enable the identification of druggable targets and biomarkers. iTReX enables the analysis of various quantitative drug or therapy response readouts (e.g. luminescence, fluorescence microscopy) and multiple treatment strategies (drug treatments, radiation). Using iTReX we validate a cost-effective drug combination screening approach and reveal the application's ability to identify potential sample-specific biomarkers based on drug target interaction networks. The iTReX web application is accessible at https://itrex.kitz-heidelberg.de.
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Affiliation(s)
- Dina ElHarouni
- Bioinformatics and Omics Data Analytics, German Cancer Research Center (DKFZ), Heidelberg, Germany; Hopp Children's Cancer Center (KiTZ), Heidelberg, Germany; Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), Heidelberg, Germany; Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
| | - Yannick Berker
- Hopp Children's Cancer Center (KiTZ), Heidelberg, Germany; Clinical Cooperation Unit Pediatric Oncology, German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Heike Peterziel
- Hopp Children's Cancer Center (KiTZ), Heidelberg, Germany; Clinical Cooperation Unit Pediatric Oncology, German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Apurva Gopisetty
- Hopp Children's Cancer Center (KiTZ), Heidelberg, Germany; Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Laura Turunen
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
| | - Sina Kreth
- Hopp Children's Cancer Center (KiTZ), Heidelberg, Germany; Division of Neuroblastoma Genomics, German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Sabine A Stainczyk
- Hopp Children's Cancer Center (KiTZ), Heidelberg, Germany; Division of Neuroblastoma Genomics, German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Ina Oehme
- Hopp Children's Cancer Center (KiTZ), Heidelberg, Germany; Clinical Cooperation Unit Pediatric Oncology, German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Vilja Pietiäinen
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
| | - Natalie Jäger
- Hopp Children's Cancer Center (KiTZ), Heidelberg, Germany; Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Olaf Witt
- Hopp Children's Cancer Center (KiTZ), Heidelberg, Germany; Clinical Cooperation Unit Pediatric Oncology, German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), Heidelberg, Germany; Department of Pediatric Oncology, Hematology, Immunology and Pulmonology Heidelberg University Hospital, Heidelberg, Germany
| | - Matthias Schlesner
- Bioinformatics and Omics Data Analytics, German Cancer Research Center (DKFZ), Heidelberg, Germany; Biomedical Informatics, Data Mining and Data Analytics, Faculty of Applied Computer Science and Medical Faculty, University of Augsburg, Augsburg, Germany
| | - Sina Oppermann
- Hopp Children's Cancer Center (KiTZ), Heidelberg, Germany; Clinical Cooperation Unit Pediatric Oncology, German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), Heidelberg, Germany
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Agathokleous E, Barceló D, Calabrese EJ. US EPA: Is there room to open a new window for evaluating potential sub-threshold effects and ecological risks? ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 284:117372. [PMID: 34087668 DOI: 10.1016/j.envpol.2021.117372] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 04/08/2021] [Accepted: 05/04/2021] [Indexed: 05/17/2023]
Abstract
With a rule published on 6 January 2021, the US Environmental Protection Agency (EPA) considers for the first time sub-threshold responses, abandoning the use of default dose-response models. This may affect worldwide scientific research, in terms of research design and methodology, and regulatory actions in China and other countries.
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Affiliation(s)
- Evgenios Agathokleous
- Key Laboratory of Agrometeorology of Jiangsu Province, Department of Ecology, School of Applied Meteorology, Nanjing University of Information Science & Technology (NUIST), Nanjing, 210044, China.
| | - Damià Barceló
- Institute of Environmental Assessment and Water Research, IDAEA-CSIC, C/ Jordi Girona 18-26, 08034, Barcelona, Spain; Catalan Institute for Water Research, ICRA-CERCA, Emili Grahit 101, 17003, Girona, Spain
| | - Edward J Calabrese
- Department of Environmental Health Sciences, Morrill I, N344, University of Massachusetts, Amherst, MA, 01003, USA
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Green AJ, Anchang B, Akhtari FS, Reif DM, Motsinger-Reif A. Extending the lymphoblastoid cell line model for drug combination pharmacogenomics. Pharmacogenomics 2021; 22:543-551. [PMID: 34044623 DOI: 10.2217/pgs-2020-0160] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Combination drug therapies have become an integral part of precision oncology, and while evidence of clinical effectiveness continues to grow, the underlying mechanisms supporting synergy are poorly understood. Immortalized human lymphoblastoid cell lines (LCLs) have been proven as a particularly useful, scalable and low-cost model in pharmacogenetics research, and are suitable for elucidating the molecular mechanisms of synergistic combination therapies. In this review, we cover the advantages of LCLs in synergy pharmacogenomics and consider recent studies providing initial evidence of the utility of LCLs in synergy research. We also discuss several opportunities for LCL-based systems to address gaps in the research through the expansion of testing regimens, assessment of new drug classes and higher-order combinations, and utilization of integrated omics technologies.
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Affiliation(s)
- Adrian J Green
- Department of Biological Sciences & the Bioinformatics Research Center, NC State University, Raleigh, NC, USA
| | - Benedict Anchang
- Biostatistics & Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Farida S Akhtari
- Biostatistics & Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - David M Reif
- Department of Biological Sciences & the Bioinformatics Research Center, NC State University, Raleigh, NC, USA
| | - Alison Motsinger-Reif
- Biostatistics & Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, NC, USA
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