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Hemedan AA, Schneider R, Ostaszewski M. Applications of Boolean modeling to study the dynamics of a complex disease and therapeutics responses. FRONTIERS IN BIOINFORMATICS 2023; 3:1189723. [PMID: 37325771 PMCID: PMC10267406 DOI: 10.3389/fbinf.2023.1189723] [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: 03/19/2023] [Accepted: 05/18/2023] [Indexed: 06/17/2023] Open
Abstract
Computational modeling has emerged as a critical tool in investigating the complex molecular processes involved in biological systems and diseases. In this study, we apply Boolean modeling to uncover the molecular mechanisms underlying Parkinson's disease (PD), one of the most prevalent neurodegenerative disorders. Our approach is based on the PD-map, a comprehensive molecular interaction diagram that captures the key mechanisms involved in the initiation and progression of PD. Using Boolean modeling, we aim to gain a deeper understanding of the disease dynamics, identify potential drug targets, and simulate the response to treatments. Our analysis demonstrates the effectiveness of this approach in uncovering the intricacies of PD. Our results confirm existing knowledge about the disease and provide valuable insights into the underlying mechanisms, ultimately suggesting potential targets for therapeutic intervention. Moreover, our approach allows us to parametrize the models based on omics data for further disease stratification. Our study highlights the value of computational modeling in advancing our understanding of complex biological systems and diseases, emphasizing the importance of continued research in this field. Furthermore, our findings have potential implications for the development of novel therapies for PD, which is a pressing public health concern. Overall, this study represents a significant step forward in the application of computational modeling to the investigation of neurodegenerative diseases, and underscores the power of interdisciplinary approaches in tackling challenging biomedical problems.
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Singh AV, Varma M, Laux P, Choudhary S, Datusalia AK, Gupta N, Luch A, Gandhi A, Kulkarni P, Nath B. Artificial intelligence and machine learning disciplines with the potential to improve the nanotoxicology and nanomedicine fields: a comprehensive review. Arch Toxicol 2023; 97:963-979. [PMID: 36878992 PMCID: PMC10025217 DOI: 10.1007/s00204-023-03471-x] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 02/20/2023] [Indexed: 03/08/2023]
Abstract
The use of nanomaterials in medicine depends largely on nanotoxicological evaluation in order to ensure safe application on living organisms. Artificial intelligence (AI) and machine learning (MI) can be used to analyze and interpret large amounts of data in the field of toxicology, such as data from toxicological databases and high-content image-based screening data. Physiologically based pharmacokinetic (PBPK) models and nano-quantitative structure-activity relationship (QSAR) models can be used to predict the behavior and toxic effects of nanomaterials, respectively. PBPK and Nano-QSAR are prominent ML tool for harmful event analysis that is used to understand the mechanisms by which chemical compounds can cause toxic effects, while toxicogenomics is the study of the genetic basis of toxic responses in living organisms. Despite the potential of these methods, there are still many challenges and uncertainties that need to be addressed in the field. In this review, we provide an overview of artificial intelligence (AI) and machine learning (ML) techniques in nanomedicine and nanotoxicology to better understand the potential toxic effects of these materials at the nanoscale.
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Affiliation(s)
- Ajay Vikram Singh
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Max-Dohrn-Straße 8-10, 10589, Berlin, Germany.
| | - Mansi Varma
- Department of Regulatory Toxicology, National Institute of Pharmaceutical Education and Research (NIPER-Raebareli), Lucknow, 229001, India
| | - Peter Laux
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Max-Dohrn-Straße 8-10, 10589, Berlin, Germany
| | - Sunil Choudhary
- Department of Radiotherapy and Radiation Medicine, Institute of Medical Sciences, Banaras Hindu University, Varanasi, 221005, India
| | - Ashok Kumar Datusalia
- Department of Regulatory Toxicology, National Institute of Pharmaceutical Education and Research (NIPER-Raebareli), Lucknow, 229001, India
| | - Neha Gupta
- Department of Radiation Oncology, Apex Hospital, Varanasi, 221005, India
| | - Andreas Luch
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Max-Dohrn-Straße 8-10, 10589, Berlin, Germany
| | - Anusha Gandhi
- Elisabeth-Selbert-Gymnasium, Tübinger Str. 71, 70794, Filderstadt, Germany
| | - Pranav Kulkarni
- Seeta Nursing Home, Shivaji Nagar, Nashik, Maharashtra, 422002, India
| | - Banashree Nath
- Department of Obstetrics and Gynaecology, All India Institute of Medical Sciences, Raebareli, Uttar Pradesh, 229405, India
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Prescription Drugs and Mitochondrial Metabolism. Biosci Rep 2022; 42:231068. [PMID: 35315490 PMCID: PMC9016406 DOI: 10.1042/bsr20211813] [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: 01/02/2022] [Revised: 03/17/2022] [Accepted: 03/21/2022] [Indexed: 11/17/2022] Open
Abstract
Mitochondria are central to the physiology and survival of nearly all eukaryotic cells and house diverse metabolic processes including oxidative phosphorylation, reactive oxygen species buffering, metabolite synthesis/exchange, and Ca2+ sequestration. Mitochondria are phenotypically heterogeneous and this variation is essential to the complexity of physiological function among cells, tissues, and organ systems. As a consequence of mitochondrial integration with so many physiological processes, small molecules that modulate mitochondrial metabolism induce complex systemic effects. In the case of many common prescribed drugs, these interactions may contribute to drug therapeutic mechanisms, induce adverse drug reactions, or both. The purpose of this article is to review historical and recent advances in the understanding of the effects of prescription drugs on mitochondrial metabolism. Specific 'modes' of xenobiotic-mitochondria interactions are discussed to provide a set of qualitative models that aid in conceptualizing how the mitochondrial energy transduction system may be affected. Findings of recent in vitro high-throughput screening studies are reviewed, and a few candidate drug classes are chosen for additional brief discussion (i.e. antihyperglycemics, antidepressants, antibiotics, and antihyperlipidemics). Finally, recent improvements in pharmacokinetic models that aid in quantifying systemic effects of drug-mitochondria interactions are briefly considered.
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Hemedan AA, Niarakis A, Schneider R, Ostaszewski M. Boolean modelling as a logic-based dynamic approach in systems medicine. Comput Struct Biotechnol J 2022; 20:3161-3172. [PMID: 35782730 PMCID: PMC9234349 DOI: 10.1016/j.csbj.2022.06.035] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 06/14/2022] [Accepted: 06/14/2022] [Indexed: 11/17/2022] Open
Abstract
Molecular mechanisms of health and disease are often represented as systems biology diagrams, and the coverage of such representation constantly increases. These static diagrams can be transformed into dynamic models, allowing for in silico simulations and predictions. Boolean modelling is an approach based on an abstract representation of the system. It emphasises the qualitative modelling of biological systems in which each biomolecule can take two possible values: zero for absent or inactive, one for present or active. Because of this approximation, Boolean modelling is applicable to large diagrams, allowing to capture their dynamic properties. We review Boolean models of disease mechanisms and compare a range of methods and tools used for analysis processes. We explain the methodology of Boolean analysis focusing on its application in disease modelling. Finally, we discuss its practical application in analysing signal transduction and gene regulatory pathways in health and disease.
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Affiliation(s)
- Ahmed Abdelmonem Hemedan
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Anna Niarakis
- Université Paris-Saclay, Laboratoire Européen de Recherche pour la Polyarthrite rhumatoïde – Genhotel, Univ Evry, Evry, France
- Lifeware Group, Inria, Saclay-île de France, 91120 Palaiseau, France
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Corresponding author at: Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, L-4367 Belvaux, Luxembourg.
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Sahu A, Blätke MA, Szymański JJ, Töpfer N. Advances in flux balance analysis by integrating machine learning and mechanism-based models. Comput Struct Biotechnol J 2021; 19:4626-4640. [PMID: 34471504 PMCID: PMC8382995 DOI: 10.1016/j.csbj.2021.08.004] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Revised: 08/03/2021] [Accepted: 08/03/2021] [Indexed: 02/08/2023] Open
Abstract
The availability of multi-omics data sets and genome-scale metabolic models for various organisms provide a platform for modeling and analyzing genotype-to-phenotype relationships. Flux balance analysis is the main tool for predicting flux distributions in genome-scale metabolic models and various data-integrative approaches enable modeling context-specific network behavior. Due to its linear nature, this optimization framework is readily scalable to multi-tissue or -organ and even multi-organism models. However, both data and model size can hamper a straightforward biological interpretation of the estimated fluxes. Moreover, flux balance analysis simulates metabolism at steady-state and thus, in its most basic form, does not consider kinetics or regulatory events. The integration of flux balance analysis with complementary data analysis and modeling techniques offers the potential to overcome these challenges. In particular machine learning approaches have emerged as the tool of choice for data reduction and selection of most important variables in big data sets. Kinetic models and formal languages can be used to simulate dynamic behavior. This review article provides an overview of integrative studies that combine flux balance analysis with machine learning approaches, kinetic models, such as physiology-based pharmacokinetic models, and formal graphical modeling languages, such as Petri nets. We discuss the mathematical aspects and biological applications of these integrated approaches and outline challenges and future perspectives.
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Affiliation(s)
- Ankur Sahu
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, 06466 Gatersleben, Germany
| | - Mary-Ann Blätke
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, 06466 Gatersleben, Germany
| | - Jędrzej Jakub Szymański
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, 06466 Gatersleben, Germany
| | - Nadine Töpfer
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, 06466 Gatersleben, Germany
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Ruiz P, Emond C, McLanahan ED, Joshi-Barr S, Mumtaz M. Exploring Mechanistic Toxicity of Mixtures Using PBPK Modeling and Computational Systems Biology. Toxicol Sci 2021; 174:38-50. [PMID: 31851354 DOI: 10.1093/toxsci/kfz243] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022] Open
Abstract
Mixtures risk assessment needs an efficient integration of in vivo, in vitro, and in silico data with epidemiology and human studies data. This involves several approaches, some in current use and others under development. This work extends the Agency for Toxic Substances and Disease Registry physiologically based pharmacokinetic (PBPK) toolkit, available for risk assessors, to include a mixture PBPK model of benzene, toluene, ethylbenzene, and xylenes. The recoded model was evaluated and applied to exposure scenarios to evaluate the validity of dose additivity for mixtures. In the second part of this work, we studied toluene, ethylbenzene, and xylene (TEX)-gene-disease associations using Comparative Toxicogenomics Database, pathway analysis and published microarray data from human gene expression changes in blood samples after short- and long-term exposures. Collectively, this information was used to establish hypotheses on potential linkages between TEX exposures and human health. The results show that 236 genes expressed were common between the short- and long-term exposures. These genes could be central for the interconnecting biological pathways potentially stimulated by TEX exposure, likely related to respiratory and neuro diseases. Using publicly available data we propose a conceptual framework to study pathway perturbations leading to toxicity of chemical mixtures. This proposed methodology lends mechanistic insights of the toxicity of mixtures and when experimentally validated will allow data gaps filling for mixtures' toxicity assessment. This work proposes an approach using current knowledge, available multiple stream data and applying computational methods to advance mixtures risk assessment.
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Affiliation(s)
- Patricia Ruiz
- Division of Toxicology and Human Health Sciences, Agency for Toxic Substances and Disease Registry, Atlanta, Georgia
| | - Claude Emond
- BioSimulation Consulting, Inc., Newark, Delaware
| | - Evad D McLanahan
- Division of Community Health Investigations, Agency for Toxic Substances and Disease Registry, Atlanta, Georgia
| | | | - Moiz Mumtaz
- Division of Toxicology and Human Health Sciences, Agency for Toxic Substances and Disease Registry, Atlanta, Georgia
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Singh AV, Ansari MHD, Rosenkranz D, Maharjan RS, Kriegel FL, Gandhi K, Kanase A, Singh R, Laux P, Luch A. Artificial Intelligence and Machine Learning in Computational Nanotoxicology: Unlocking and Empowering Nanomedicine. Adv Healthc Mater 2020; 9:e1901862. [PMID: 32627972 DOI: 10.1002/adhm.201901862] [Citation(s) in RCA: 119] [Impact Index Per Article: 29.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Revised: 04/17/2020] [Indexed: 12/22/2022]
Abstract
Advances in nanomedicine, coupled with novel methods of creating advanced materials at the nanoscale, have opened new perspectives for the development of healthcare and medical products. Special attention must be paid toward safe design approaches for nanomaterial-based products. Recently, artificial intelligence (AI) and machine learning (ML) gifted the computational tool for enhancing and improving the simulation and modeling process for nanotoxicology and nanotherapeutics. In particular, the correlation of in vitro generated pharmacokinetics and pharmacodynamics to in vivo application scenarios is an important step toward the development of safe nanomedicinal products. This review portrays how in vitro and in vivo datasets are used in in silico models to unlock and empower nanomedicine. Physiologically based pharmacokinetic (PBPK) modeling and absorption, distribution, metabolism, and excretion (ADME)-based in silico methods along with dosimetry models as a focus area for nanomedicine are mainly described. The computational OMICS, colloidal particle determination, and algorithms to establish dosimetry for inhalation toxicology, and quantitative structure-activity relationships at nanoscale (nano-QSAR) are revisited. The challenges and opportunities facing the blind spots in nanotoxicology in this computationally dominated era are highlighted as the future to accelerate nanomedicine clinical translation.
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Affiliation(s)
- Ajay Vikram Singh
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Max-Dohrn-Strasse 8-10, Berlin, 10589, Germany
| | - Mohammad Hasan Dad Ansari
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Via Rinaldo Piaggio 34, Pontedera, 56025, Italy
- Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Via Rinaldo Piaggio 34, Pontedera, 56025, Italy
| | - Daniel Rosenkranz
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Max-Dohrn-Strasse 8-10, Berlin, 10589, Germany
| | - Romi Singh Maharjan
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Max-Dohrn-Strasse 8-10, Berlin, 10589, Germany
| | - Fabian L Kriegel
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Max-Dohrn-Strasse 8-10, Berlin, 10589, Germany
| | - Kaustubh Gandhi
- Bosch Sensortec GmbH, Gerhard-Kindler-Straße 9, Reutlingen, 72770, Germany
| | - Anurag Kanase
- Department of Bioengineering, Northeastern University, Boston, MA, 02215, USA
| | - Rishabh Singh
- Rajarshi Shahu College of Engineering, Pune, Maharashtra, 411033, India
| | - Peter Laux
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Max-Dohrn-Strasse 8-10, Berlin, 10589, Germany
| | - Andreas Luch
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Max-Dohrn-Strasse 8-10, Berlin, 10589, Germany
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Graepel R, Ter Braak B, Escher S, Fisher C, Gardner I, Kamp H, Kroese D, Leist M, Moné M, Pastor M, van de Water B. Paradigm shift in safety assessment using new approach methods: The EU-ToxRisk strategy. CURRENT OPINION IN TOXICOLOGY 2019. [DOI: 10.1016/j.cotox.2019.03.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
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Pastor M, Quintana J, Sanz F. Development of an Infrastructure for the Prediction of Biological Endpoints in Industrial Environments. Lessons Learned at the eTOX Project. Front Pharmacol 2018; 9:1147. [PMID: 30364191 PMCID: PMC6193068 DOI: 10.3389/fphar.2018.01147] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Accepted: 09/21/2018] [Indexed: 11/13/2022] Open
Abstract
In silico methods are increasingly being used for assessing the chemical safety of substances, as a part of integrated approaches involving in vitro and in vivo experiments. A paradigmatic example of these strategies is the eTOX project http://www.etoxproject.eu, funded by the European Innovative Medicines Initiative (IMI), which aimed at producing high quality predictions of in vivo toxicity of drug candidates and resulted in generating about 200 models for diverse endpoints of toxicological interest. In an industry-oriented project like eTOX, apart from the predictive quality, the models need to meet other quality parameters related to the procedures for their generation and their intended use. For example, when the models are used for predicting the properties of drug candidates, the prediction system must guarantee the complete confidentiality of the compound structures. The interface of the system must be designed to provide non-expert users all the information required to choose the models and appropriately interpret the results. Moreover, procedures like installation, maintenance, documentation, validation and versioning, which are common in software development, must be also implemented for the models and for the prediction platform in which they are implemented. In this article we describe our experience in the eTOX project and the lessons learned after 7 years of close collaboration between industrial and academic partners. We believe that some of the solutions found and the tools developed could be useful for supporting similar initiatives in the future.
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Affiliation(s)
| | | | - Ferran Sanz
- *Correspondence: Manuel Pastor, Ferran Sanz,
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10
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Piñero J, Furlong LI, Sanz F. In silico models in drug development: where we are. Curr Opin Pharmacol 2018; 42:111-121. [PMID: 30205360 DOI: 10.1016/j.coph.2018.08.007] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2018] [Revised: 07/30/2018] [Accepted: 08/13/2018] [Indexed: 02/07/2023]
Abstract
The use and utility of computational models in drug development has significantly grown in the last decades, fostered by the availability of high throughput datasets and new data analysis strategies. These in silico approaches are demonstrating their ability to generate reliable predictions as well as new knowledge on the mode of action of drugs and the mechanisms underlying their side effects, altogether helping to reduce the costs of drug development. The aim of this review is to provide a panorama of developments in the field in the last two years.
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Affiliation(s)
- Janet Piñero
- Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences (DCEXS), Hospital del Mar Medical Research Institute (IMIM), Universitat Pompeu Fabra (UPF), Carrer Dr. Aiguader 88, 08003 Barcelona, Spain
| | - Laura I Furlong
- Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences (DCEXS), Hospital del Mar Medical Research Institute (IMIM), Universitat Pompeu Fabra (UPF), Carrer Dr. Aiguader 88, 08003 Barcelona, Spain
| | - Ferran Sanz
- Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences (DCEXS), Hospital del Mar Medical Research Institute (IMIM), Universitat Pompeu Fabra (UPF), Carrer Dr. Aiguader 88, 08003 Barcelona, Spain.
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11
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Maldonado EM, Fisher CP, Mazzatti DJ, Barber AL, Tindall MJ, Plant NJ, Kierzek AM, Moore JB. Multi-scale, whole-system models of liver metabolic adaptation to fat and sugar in non-alcoholic fatty liver disease. NPJ Syst Biol Appl 2018; 4:33. [PMID: 30131870 PMCID: PMC6102210 DOI: 10.1038/s41540-018-0070-3] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2017] [Revised: 07/30/2018] [Accepted: 07/31/2018] [Indexed: 12/11/2022] Open
Abstract
Non-alcoholic fatty liver disease (NAFLD) is a serious public health issue associated with high fat, high sugar diets. However, the molecular mechanisms mediating NAFLD pathogenesis are only partially understood. Here we adopt an iterative multi-scale, systems biology approach coupled to in vitro experimentation to investigate the roles of sugar and fat metabolism in NAFLD pathogenesis. The use of fructose as a sweetening agent is controversial; to explore this, we developed a predictive model of human monosaccharide transport, signalling and metabolism. The resulting quantitative model comprising a kinetic model describing monosaccharide transport and insulin signalling integrated with a hepatocyte-specific genome-scale metabolic network (GSMN). Differential kinetics for the utilisation of glucose and fructose were predicted, but the resultant triacylglycerol production was predicted to be similar for monosaccharides; these predictions were verified by in vitro data. The role of physiological adaptation to lipid overload was explored through the comprehensive reconstruction of the peroxisome proliferator activated receptor alpha (PPARα) regulome integrated with a hepatocyte-specific GSMN. The resulting qualitative model reproduced metabolic responses to increased fatty acid levels and mimicked lipid loading in vitro. The model predicted that activation of PPARα by lipids produces a biphasic response, which initially exacerbates steatosis. Our data support the evidence that it is the quantity of sugar rather than the type that is critical in driving the steatotic response. Furthermore, we predict PPARα-mediated adaptations to hepatic lipid overload, shedding light on potential challenges for the use of PPARα agonists to treat NAFLD.
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Affiliation(s)
- Elaina M. Maldonado
- School of Biosciences and Medicine, University of Surrey, Guildford, Surrey, GU2 7XH UK
| | - Ciarán P. Fisher
- Certara UK Limited, Simcyp Division, Level 2-Acero, 1 Concourse Way, Sheffield, S1 2BJ UK
| | | | - Amy L. Barber
- School of Biosciences and Medicine, University of Surrey, Guildford, Surrey, GU2 7XH UK
| | - Marcus J. Tindall
- Department of Mathematics and Statistics, University of Reading, Berkshire, RG6 6AX UK
- Institute of Cardiovascular and Metabolic Research, University of Reading, Berkshire, RG6 6UR UK
| | - Nicholas J. Plant
- School of Biosciences and Medicine, University of Surrey, Guildford, Surrey, GU2 7XH UK
- Faculty of Biological Sciences, University of Leeds, Leeds, West Yorkshire, LS2 9JT UK
| | - Andrzej M. Kierzek
- School of Biosciences and Medicine, University of Surrey, Guildford, Surrey, GU2 7XH UK
- Certara UK Limited, Simcyp Division, Level 2-Acero, 1 Concourse Way, Sheffield, S1 2BJ UK
| | - J. Bernadette Moore
- School of Biosciences and Medicine, University of Surrey, Guildford, Surrey, GU2 7XH UK
- School of Food Science & Nutrition, University of Leeds, Leeds, West Yorkshire, LS2 9JT UK
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12
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Cheng YH, Riviere JE, Monteiro-Riviere NA, Lin Z. Probabilistic risk assessment of gold nanoparticles after intravenous administration by integrating in vitro and in vivo toxicity with physiologically based pharmacokinetic modeling. Nanotoxicology 2018; 12:453-469. [PMID: 29658401 DOI: 10.1080/17435390.2018.1459922] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
This study aimed to conduct an integrated and probabilistic risk assessment of gold nanoparticles (AuNPs) based on recently published in vitro and in vivo toxicity studies coupled to a physiologically based pharmacokinetic (PBPK) model. Dose-response relationships were characterized based on cell viability assays in various human cell types. A previously well-validated human PBPK model for AuNPs was applied to quantify internal concentrations in liver, kidney, skin, and venous plasma. By applying a Bayesian-based probabilistic risk assessment approach incorporating Monte Carlo simulation, probable human cell death fractions were characterized. Additionally, we implemented in vitro to in vivo and animal-to-human extrapolation approaches to independently estimate external exposure levels of AuNPs that cause minimal toxicity. Our results suggest that under the highest dosing level employed in existing animal studies (worst-case scenario), AuNPs coated with branched polyethylenimine (BPEI) would likely induce ∼90-100% cellular death, implying high cytotoxicity compared to <10% cell death induced by low-to-medium animal dosing levels, which are commonly used in animal studies. The estimated human equivalent doses associated with 5% cell death in liver and kidney were around 1 and 3 mg/kg, respectively. Based on points of departure reported in animal studies, the human equivalent dose estimates associated with gene expression changes and tissue cell apoptosis in liver were 0.005 and 0.5 mg/kg, respectively. Our analyzes provide insights into safety evaluation, risk prediction, and point of departure estimation of AuNP exposure for humans and illustrate an approach that could be applied to other NPs when sufficient data are available.
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Affiliation(s)
- Yi-Hsien Cheng
- a Institute of Computational Comparative Medicine (ICCM), Department of Anatomy and Physiology, College of Veterinary Medicine , Kansas State University , Manhattan , KS , USA
| | - Jim E Riviere
- a Institute of Computational Comparative Medicine (ICCM), Department of Anatomy and Physiology, College of Veterinary Medicine , Kansas State University , Manhattan , KS , USA
| | - Nancy A Monteiro-Riviere
- b Nanotechnology Innovation Center of Kansas State (NICKS), Department of Anatomy and Physiology, College of Veterinary Medicine , Kansas State University , Manhattan , KS , USA
| | - Zhoumeng Lin
- a Institute of Computational Comparative Medicine (ICCM), Department of Anatomy and Physiology, College of Veterinary Medicine , Kansas State University , Manhattan , KS , USA
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