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Ma T, Richard D, Yang YB, Kashlak AB, Anton C. Functional non-parametric mixed effects models for cytotoxicity assessment and clustering. Sci Rep 2023; 13:4075. [PMID: 36906619 PMCID: PMC10008646 DOI: 10.1038/s41598-023-31011-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 03/06/2023] [Indexed: 03/13/2023] Open
Abstract
A multitude of natural and synthetic chemicals are present in our environment.Through the study of a compound's cytotoxicity, researchers can carefully set regulations regarding how much of a certain chemical in the ambient environment is tolerable. In the past, research has focused on point measurements such as the LD50. Instead, we consider entire time-dependent cellular response curves through the application of functional mixed effects models. We identify differences in such curves corresponding to the chemical's mode of action-i.e. how the compound attacks human cells. Through such analysis, we identify curve features to be used for cluster analysis via application of both k-means and self organizing maps. The data is analyzed by making use of functional principal components as a data driven basis and separately by considering B-splines for identifying local-time features. Our analysis can be used to drastically speed up future cytotoxicity research.
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Affiliation(s)
- Tiantian Ma
- Mathematical and Statistical Sciences, University of Alberta, Edmonton, Canada
| | - Dan Richard
- Mathematical and Statistical Sciences, University of Alberta, Edmonton, Canada.,Mathematics and Statistics, Grant MacEwan University, Edmonton, Canada
| | - Yongqing Betty Yang
- Mathematical and Statistical Sciences, University of Alberta, Edmonton, Canada
| | - Adam B Kashlak
- Mathematical and Statistical Sciences, University of Alberta, Edmonton, Canada.
| | - Cristina Anton
- Mathematics and Statistics, Grant MacEwan University, Edmonton, Canada
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2
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Scott-Fordsmand JJ, Amorim MJB. Using Machine Learning to make nanomaterials sustainable. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 859:160303. [PMID: 36410486 DOI: 10.1016/j.scitotenv.2022.160303] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 11/06/2022] [Accepted: 11/15/2022] [Indexed: 06/16/2023]
Abstract
Sustainable development is a key challenge for contemporary human societies; failure to achieve sustainability could threaten human survival. In this review article, we illustrate how Machine Learning (ML) could support more sustainable development, covering the basics of data gathering through each step of the Environmental Risk Assessment (ERA). The literature provides several examples showing how ML can be employed in most steps of a typical ERA.A key observation is that there are currently no clear guidance for using such autonomous technologies in ERAs or which standards/checks are required. Steering thus seems to be the most important task for supporting the use of ML in the ERA of nano- and smart-materials. Resources should be devoted to developing a strategy for implementing ML in ERA with a strong emphasis on data foundations, methodologies, and the related sensitivities/uncertainties. We should recognise historical errors and biases (e.g., in data) to avoid embedding them during ML programming.
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Affiliation(s)
| | - Mónica J B Amorim
- Department of Biology & CESAM, University of Aveiro, 3810-193 Aveiro, Portugal.
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3
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Androulakis IP. Towards a comprehensive assessment of QSP models: what would it take? J Pharmacokinet Pharmacodyn 2022:10.1007/s10928-022-09820-0. [PMID: 35962928 PMCID: PMC9922790 DOI: 10.1007/s10928-022-09820-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 07/15/2022] [Indexed: 10/15/2022]
Abstract
Quantitative Systems Pharmacology (QSP) has emerged as a powerful ensemble of approaches aiming at developing integrated mathematical and computational models elucidating the complex interactions between pharmacology, physiology, and disease. As the field grows and matures its applications expand beyond the boundaries of research and development and slowly enter the decision making and regulatory arenas. However, widespread acceptance and eventual adoption of a new modeling approach requires assessment criteria and quantifiable metrics that establish credibility and increase confidence in model predictions. QSP aims to provide an integrated understanding of pathology in the context of therapeutic interventions. Because of its ambitious nature and the fact that QSP emerged in an uncoordinated manner as a result of activities distributed across organizations and academic institutions, high entropy characterizes the tools, methods, and computational methodologies and approaches used. The eventual acceptance of QSP model predictions as supporting material for an application to a regulatory agency will require that two key aspects are considered: (1) increase confidence in the QSP framework, which drives standardization and assessment; and (2) careful articulation of the expectations. Both rely heavily on our ability to rigorously and consistently assess QSP models. In this manuscript, we wish to discuss the meaning and purpose of such an assessment in the context of QSP model development and elaborate on the differentiating features of QSP that render such an endeavor challenging. We argue that QSP establishes a conceptual, integrative framework rather than a specific and well-defined computational methodology. QSP elicits the use of a wide variety of modeling and computational methodologies optimized with respect to specific applications and available data modalities, which exceed the data structures employed by chemometrics and PK/PD models. While the range of options fosters creativity and promises to substantially advance our ability to design pharmaceutical interventions rationally and optimally, our expectations of QSP models need to be clearly articulated and agreed on, with assessment emphasizing the scope of QSP studies rather than the methods used. Nevertheless, QSP should not be considered an independent approach, rather one of many in the broader continuum of computational models.
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Affiliation(s)
- Ioannis P Androulakis
- Biomedical Engineering Department and Chemical & Biochemical Engineering Department, Rutgers, The State University of New Jersey, New Brunswick, USA.
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4
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Davies G, Vincent J, Packer MJ, Murray D. Grouping concentration response curves by features of their shape to aid rapid and consistent analysis of large data sets in high throughput screens. SLAS DISCOVERY : ADVANCING LIFE SCIENCES R & D 2022; 27:272-277. [PMID: 35058182 DOI: 10.1016/j.slasd.2021.11.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Affiliation(s)
- Gareth Davies
- Hit Discovery, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Alderley Park, UK.
| | - John Vincent
- Hit Discovery, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Alderley Park, UK; Discovery Science & Technology, Medicines Discovery Catapult, Alderley Park, UK
| | | | - David Murray
- Hit Discovery, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Alderley Park, UK; Lighthouse Laboratory, Medicines Discovery Catapult, Alderley Park, UK
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5
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van Heerden A, van Wyk R, Birkholtz LM. Machine Learning Uses Chemo-Transcriptomic Profiles to Stratify Antimalarial Compounds With Similar Mode of Action. Front Cell Infect Microbiol 2021; 11:688256. [PMID: 34268139 PMCID: PMC8277430 DOI: 10.3389/fcimb.2021.688256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 06/04/2021] [Indexed: 11/26/2022] Open
Abstract
The rapid development of antimalarial resistance motivates the continued search for novel compounds with a mode of action (MoA) different to current antimalarials. Phenotypic screening has delivered thousands of promising hit compounds without prior knowledge of the compounds’ exact target or MoA. Whilst the latter is not initially required to progress a compound in a medicinal chemistry program, identifying the MoA early can accelerate hit prioritization, hit-to-lead optimization and preclinical combination studies in malaria research. The effects of drug treatment on a cell can be observed on systems level in changes in the transcriptome, proteome and metabolome. Machine learning (ML) algorithms are powerful tools able to deconvolute such complex chemically-induced transcriptional signatures to identify pathways on which a compound act and in this manner provide an indication of the MoA of a compound. In this study, we assessed different ML approaches for their ability to stratify antimalarial compounds based on varied chemically-induced transcriptional responses. We developed a rational gene selection approach that could identify predictive features for MoA to train and generate ML models. The best performing model could stratify compounds with similar MoA with a classification accuracy of 76.6 ± 6.4%. Moreover, only a limited set of 50 biomarkers was required to stratify compounds with similar MoA and define chemo-transcriptomic fingerprints for each compound. These fingerprints were unique for each compound and compounds with similar targets/MoA clustered together. The ML model was specific and sensitive enough to group new compounds into MoAs associated with their predicted target and was robust enough to be extended to also generate chemo-transcriptomic fingerprints for additional life cycle stages like immature gametocytes. This work therefore contributes a new strategy to rapidly, specifically and sensitively indicate the MoA of compounds based on chemo-transcriptomic fingerprints and holds promise to accelerate antimalarial drug discovery programs.
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Affiliation(s)
- Ashleigh van Heerden
- Department of Biochemistry, Genetics and Microbiology, University of Pretoria, Hatfield, South Africa.,University of Pretoria Institute for Sustainable Malaria Control, University of Pretoria, Hatfield, South Africa
| | - Roelof van Wyk
- Department of Biochemistry, Genetics and Microbiology, University of Pretoria, Hatfield, South Africa.,University of Pretoria Institute for Sustainable Malaria Control, University of Pretoria, Hatfield, South Africa
| | - Lyn-Marie Birkholtz
- Department of Biochemistry, Genetics and Microbiology, University of Pretoria, Hatfield, South Africa.,University of Pretoria Institute for Sustainable Malaria Control, University of Pretoria, Hatfield, South Africa
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Franke R, Hinkelmann B, Fetz V, Stradal T, Sasse F, Klawonn F, Brönstrup M. xCELLanalyzer: A Framework for the Analysis of Cellular Impedance Measurements for Mode of Action Discovery. SLAS DISCOVERY 2019; 24:213-223. [PMID: 30681906 DOI: 10.1177/2472555218819459] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Mode of action (MoA) identification of bioactive compounds is very often a challenging and time-consuming task. We used a label-free kinetic profiling method based on an impedance readout to monitor the time-dependent cellular response profiles for the interaction of bioactive natural products and other small molecules with mammalian cells. Such approaches have been rarely used so far due to the lack of data mining tools to properly capture the characteristics of the impedance curves. We developed a data analysis pipeline for the xCELLigence Real-Time Cell Analysis detection platform to process the data, assess and score their reproducibility, and provide rank-based MoA predictions for a reference set of 60 bioactive compounds. The method can reveal additional, previously unknown targets, as exemplified by the identification of tubulin-destabilizing activities of the RNA synthesis inhibitor actinomycin D and the effects on DNA replication of vioprolide A. The data analysis pipeline is based on the statistical programming language R and is available to the scientific community through a GitHub repository.
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Affiliation(s)
- Raimo Franke
- 1 Department of Chemical Biology, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Bettina Hinkelmann
- 1 Department of Chemical Biology, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Verena Fetz
- 1 Department of Chemical Biology, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Theresia Stradal
- 2 Department of Cell Biology, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Florenz Sasse
- 1 Department of Chemical Biology, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Frank Klawonn
- 3 Biostatistics Group, Helmholtz Centre for Infection Research, Braunschweig, Germany.,4 Department of Computer Science, Ostfalia University, Wolfenbuettel, Germany
| | - Mark Brönstrup
- 1 Department of Chemical Biology, Helmholtz Centre for Infection Research, Braunschweig, Germany.,5 Center of Biomolecular Drug Research (BMWZ), Institute of Organic Chemistry, Leibniz Universität, Hannover, Germany
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7
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Ouchi K, Lindvall C, Chai PR, Boyer EW. Machine Learning to Predict, Detect, and Intervene Older Adults Vulnerable for Adverse Drug Events in the Emergency Department. J Med Toxicol 2018; 14:248-252. [PMID: 29858745 PMCID: PMC6097964 DOI: 10.1007/s13181-018-0667-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2018] [Revised: 05/15/2018] [Accepted: 05/21/2018] [Indexed: 02/08/2023] Open
Abstract
Adverse drug events (ADEs) are common and have serious consequences in older adults. ED visits are opportunities to identify and alter the course of such vulnerable patients. Current practice, however, is limited by inaccurate reporting of medication list, time-consuming medication reconciliation, and poor ADE assessment. This manuscript describes a novel approach to predict, detect, and intervene vulnerable older adults at risk of ADE using machine learning. Toxicologists' expertise in ADE is essential to creating the machine learning algorithm. Leveraging the existing electronic health records to better capture older adults at risk of ADE in the ED may improve their care.
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Affiliation(s)
- Kei Ouchi
- Department of Emergency Medicine, Brigham and Women's Hospital, 75 Francis St, Neville 200, Boston, MA, 02125, USA.
- Department of Emergency Medicine, Harvard Medical School, Boston, MA, USA.
- Serious Illness Care Program, Ariadne Labs, Boston, MA, USA.
- Department of Psychosocial Oncology and Palliative Care, Dana Farber Cancer Institute, Boston, MA, USA.
| | - Charlotta Lindvall
- Department of Psychosocial Oncology and Palliative Care, Dana Farber Cancer Institute, Boston, MA, USA
- Division of Palliative Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Peter R Chai
- Department of Emergency Medicine, Brigham and Women's Hospital, 75 Francis St, Neville 200, Boston, MA, 02125, USA
- Department of Emergency Medicine, Harvard Medical School, Boston, MA, USA
- The Fenway Institute, Boston, MA, USA
| | - Edward W Boyer
- Department of Emergency Medicine, Brigham and Women's Hospital, 75 Francis St, Neville 200, Boston, MA, 02125, USA
- Department of Emergency Medicine, Harvard Medical School, Boston, MA, USA
- The Fenway Institute, Boston, MA, USA
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