1
|
Cronin MTD, Belfield SJ, Briggs KA, Enoch SJ, Firman JW, Frericks M, Garrard C, Maccallum PH, Madden JC, Pastor M, Sanz F, Soininen I, Sousoni D. Making in silico predictive models for toxicology FAIR. Regul Toxicol Pharmacol 2023; 140:105385. [PMID: 37037390 DOI: 10.1016/j.yrtph.2023.105385] [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: 01/03/2023] [Revised: 02/18/2023] [Accepted: 04/07/2023] [Indexed: 04/12/2023]
Abstract
In silico predictive models for toxicology include quantitative structure-activity relationship (QSAR) and physiologically based kinetic (PBK) approaches to predict physico-chemical and ADME properties, toxicological effects and internal exposure. Such models are used to fill data gaps as part of chemical risk assessment. There is a growing need to ensure in silico predictive models for toxicology are available for use and reproducible. This paper describes how the FAIR (Findable, Accessible, Interoperable, Reusable) principles, developed for data sharing, have been applied to in silico predictive models. In particular, this investigation has focussed on how the FAIR principles could be applied to improved regulatory acceptance of predictions from such models. Eighteen principles have been developed that cover all aspects of FAIR. It is intended that FAIRification of in silico predictive models for toxicology will increase their use and acceptance.
Collapse
Affiliation(s)
- Mark T D Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK.
| | - Samuel J Belfield
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK
| | - Katharine A Briggs
- Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Holbeck, Leeds, LS11 5PS, UK
| | - Steven J Enoch
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK
| | - James W Firman
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK
| | - Markus Frericks
- BASF SE, APD/ET - Li 444, Speyerer St 2, 67117, Limburgerhof, Germany
| | - Clare Garrard
- ELIXIR, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Peter H Maccallum
- ELIXIR, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Judith C Madden
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK
| | - Manuel Pastor
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Dept. of Medicine and Life Sciences (MELIS), Universitat Pompeu Fabra, Carrer Dr. Aiguader 88, 08003, Barcelona, Spain
| | - Ferran Sanz
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Dept. of Medicine and Life Sciences (MELIS), Universitat Pompeu Fabra, Carrer Dr. Aiguader 88, 08003, Barcelona, Spain
| | - Inari Soininen
- Synapse Research Management Partners SL, Calle Velazquez 94, planta 1, 28006, Madrid, Spain
| | - Despoina Sousoni
- ELIXIR, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| |
Collapse
|
2
|
Firman JW, Ebbrell DJ, Bauer FJ, Sapounidou M, Hodges G, Campos B, Roberts J, Gutsell S, Thomas PC, Bonnell M, Cronin MTD. Construction of an In Silico Structural Profiling Tool Facilitating Mechanistically Grounded Classification of Aquatic Toxicants. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:17805-17814. [PMID: 36445296 PMCID: PMC9775196 DOI: 10.1021/acs.est.2c03736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 11/14/2022] [Accepted: 11/16/2022] [Indexed: 06/16/2023]
Abstract
The performance of chemical safety assessment within the domain of environmental toxicology is often impeded by a shortfall of appropriate experimental data describing potential hazards across the many compounds in regular industrial use. In silico schemes for assigning aquatic-relevant modes or mechanisms of toxic action to substances, based solely on consideration of chemical structure, have seen widespread employment─including those of Verhaar, Russom, and later Bauer (MechoA). Recently, development of a further system was reported by Sapounidou, which, in common with MechoA, seeks to ground its classifications in understanding and appreciation of molecular initiating events. Until now, this Sapounidou scheme has not seen implementation as a tool for practical screening use. Accordingly, the primary purpose of this study was to create such a resource─in the form of a computational workflow. This exercise was facilitated through the formulation of 183 structural alerts/rules describing molecular features associated with narcosis, chemical reactivity, and specific mechanisms of action. Output was subsequently compared relative to that of the three aforementioned alternative systems to identify strengths and shortcomings as regards coverage of chemical space.
Collapse
Affiliation(s)
- James W. Firman
- School
of Pharmacy and Biomolecular Sciences, Liverpool
John Moores University, Byrom Street, Liverpool L3 3AF, U.K.
| | - David J. Ebbrell
- School
of Pharmacy and Biomolecular Sciences, Liverpool
John Moores University, Byrom Street, Liverpool L3 3AF, U.K.
| | - Franklin J. Bauer
- KREATiS
SAS, 23 rue du Creuzat, ZAC de St-Hubert 38080, L′Isle d′Abeau, France
| | - Maria Sapounidou
- School
of Pharmacy and Biomolecular Sciences, Liverpool
John Moores University, Byrom Street, Liverpool L3 3AF, U.K.
| | - Geoff Hodges
- Safety
and Environmental Assurance Centre, Unilever, Colworth Science Park, Sharnbrook, Bedford MK44 1LQ, Bedfordshire, U.K.
| | - Bruno Campos
- Safety
and Environmental Assurance Centre, Unilever, Colworth Science Park, Sharnbrook, Bedford MK44 1LQ, Bedfordshire, U.K.
| | - Jayne Roberts
- Safety
and Environmental Assurance Centre, Unilever, Colworth Science Park, Sharnbrook, Bedford MK44 1LQ, Bedfordshire, U.K.
| | - Steve Gutsell
- Safety
and Environmental Assurance Centre, Unilever, Colworth Science Park, Sharnbrook, Bedford MK44 1LQ, Bedfordshire, U.K.
| | - Paul C. Thomas
- KREATiS
SAS, 23 rue du Creuzat, ZAC de St-Hubert 38080, L′Isle d′Abeau, France
| | - Mark Bonnell
- Science
and Risk Assessment Directorate, Environment
& Climate Change Canada, 351 St. Joseph Blvd, Gatineau, Quebec K1A 0H3, Canada
| | - Mark T. D. Cronin
- School
of Pharmacy and Biomolecular Sciences, Liverpool
John Moores University, Byrom Street, Liverpool L3 3AF, U.K.
| |
Collapse
|
3
|
Computational modelling and simulation for immunotoxicity prediction induced by skin sensitisers. Comput Struct Biotechnol J 2022; 20:6172-6181. [DOI: 10.1016/j.csbj.2022.10.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 10/21/2022] [Accepted: 10/21/2022] [Indexed: 11/23/2022] Open
|
4
|
Cronin MTD, Bauer FJ, Bonnell M, Campos B, Ebbrell DJ, Firman JW, Gutsell S, Hodges G, Patlewicz G, Sapounidou M, Spînu N, Thomas PC, Worth AP. A scheme to evaluate structural alerts to predict toxicity - Assessing confidence by characterising uncertainties. Regul Toxicol Pharmacol 2022; 135:105249. [PMID: 36041585 PMCID: PMC9585125 DOI: 10.1016/j.yrtph.2022.105249] [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: 03/18/2022] [Revised: 07/12/2022] [Accepted: 08/17/2022] [Indexed: 11/26/2022]
Abstract
Structure-activity relationships (SARs) in toxicology have enabled the formation of structural rules which, when coded as structural alerts, are essential tools in in silico toxicology. Whilst other in silico methods have approaches for their evaluation, there is no formal process to assess the confidence that may be associated with a structural alert. This investigation proposes twelve criteria to assess the uncertainty associated with structural alerts, allowing for an assessment of confidence. The criteria are based around the stated purpose, description of the chemistry, toxicology and mechanism, performance and coverage, as well as corroborating and supporting evidence of the alert. Alerts can be given a confidence assessment and score, enabling the identification of areas where more information may be beneficial. The scheme to evaluate structural alerts was placed in the context of various use cases for industrial and regulatory applications. The analysis of alerts, and consideration of the evaluation scheme, identifies the different characteristics an alert may have, such as being highly specific or generic. These characteristics may determine when an alert can be used for specific uses such as identification of analogues for read-across or hazard identification. Structural alerts are useful tools for predictive toxicology. 12 criteria to evaluate structural alerts have been identified. A strategy to determine confidence of structural alerts is presented. Different use cases require different characteristics of structural alerts. A Scheme to Evaluate Structural Alerts to Predict Toxicity – Assessing Confidence By Characterising Uncertainties.
Collapse
Affiliation(s)
- Mark T D Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK
| | - Franklin J Bauer
- KREATiS SAS, 23 rue du Creuzat, ZAC de St-Hubert, 38080, L'Isle d'Abeau, France
| | - Mark Bonnell
- Science and Risk Assessment Directorate, Environment & Climate Change Canada, 351 St. Joseph Blvd, Gatineau, Quebec, K1A 0H3, Canada
| | - Bruno Campos
- Safety and Environmental Assurance Centre, Unilever, Colworth Science Park, Bedfordshire, MK44 1LQ, UK
| | - David J Ebbrell
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK
| | - James W Firman
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK
| | - Steve Gutsell
- Safety and Environmental Assurance Centre, Unilever, Colworth Science Park, Bedfordshire, MK44 1LQ, UK
| | - Geoff Hodges
- Safety and Environmental Assurance Centre, Unilever, Colworth Science Park, Bedfordshire, MK44 1LQ, UK
| | - Grace Patlewicz
- Center for Computational Toxicology and Exposure (CCTE), US Environmental Protection Agency, 109 TW Alexander Dr, RTP, NC, 27709, USA
| | - Maria Sapounidou
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK
| | - Nicoleta Spînu
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK
| | - Paul C Thomas
- KREATiS SAS, 23 rue du Creuzat, ZAC de St-Hubert, 38080, L'Isle d'Abeau, France
| | - Andrew P Worth
- European Commission, Joint Research Centre (JRC), Ispra, Italy.
| |
Collapse
|
5
|
Shroff T, Aina K, Maass C, Cipriano M, Lambrecht J, Tacke F, Mosig A, Loskill P. Studying metabolism with multi-organ chips: new tools for disease modelling, pharmacokinetics and pharmacodynamics. Open Biol 2022; 12:210333. [PMID: 35232251 PMCID: PMC8889168 DOI: 10.1098/rsob.210333] [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] [Indexed: 01/07/2023] Open
Abstract
Non-clinical models to study metabolism including animal models and cell assays are often limited in terms of species translatability and predictability of human biology. This field urgently requires a push towards more physiologically accurate recapitulations of drug interactions and disease progression in the body. Organ-on-chip systems, specifically multi-organ chips (MOCs), are an emerging technology that is well suited to providing a species-specific platform to study the various types of metabolism (glucose, lipid, protein and drug) by recreating organ-level function. This review provides a resource for scientists aiming to study human metabolism by providing an overview of MOCs recapitulating aspects of metabolism, by addressing the technical aspects of MOC development and by providing guidelines for correlation with in silico models. The current state and challenges are presented for two application areas: (i) disease modelling and (ii) pharmacokinetics/pharmacodynamics. Additionally, the guidelines to integrate the MOC data into in silico models could strengthen the predictive power of the technology. Finally, the translational aspects of metabolizing MOCs are addressed, including adoption for personalized medicine and prospects for the clinic. Predictive MOCs could enable a significantly reduced dependence on animal models and open doors towards economical non-clinical testing and understanding of disease mechanisms.
Collapse
Affiliation(s)
- Tanvi Shroff
- NMI Natural and Medical Sciences Institute at the University of Tübingen, Reutlingen, Germany,Department for Microphysiological Systems, Institute for Biomedical Engineering, Faculty of Medicine, Eberhard Karls University Tübingen, Österbergstraße 3, 72074 Tübingen, Germany
| | - Kehinde Aina
- Institute of Biochemistry II, Center for Sepsis Control and Care, Jena University Hospital, Jena, Germany
| | | | - Madalena Cipriano
- Department for Microphysiological Systems, Institute for Biomedical Engineering, Faculty of Medicine, Eberhard Karls University Tübingen, Österbergstraße 3, 72074 Tübingen, Germany
| | - Joeri Lambrecht
- Department of Hepatology and Gastroenterology, Charité University Medicine Berlin, Campus Virchow Klinikum and Campus Charité Mitte, Berlin, Germany
| | - Frank Tacke
- Department of Hepatology and Gastroenterology, Charité University Medicine Berlin, Campus Virchow Klinikum and Campus Charité Mitte, Berlin, Germany
| | - Alexander Mosig
- Institute of Biochemistry II, Center for Sepsis Control and Care, Jena University Hospital, Jena, Germany
| | - Peter Loskill
- NMI Natural and Medical Sciences Institute at the University of Tübingen, Reutlingen, Germany,Department for Microphysiological Systems, Institute for Biomedical Engineering, Faculty of Medicine, Eberhard Karls University Tübingen, Österbergstraße 3, 72074 Tübingen, Germany,3R-Center for In vitro Models and Alternatives to Animal Testing, Eberhard Karls University Tübingen, Tübingen, Germany
| |
Collapse
|
6
|
Spînu N, Cronin MT, Madden JC, Worth AP. A matter of trust: Learning lessons about causality will make qAOPs credible. COMPUTATIONAL TOXICOLOGY 2022; 21:100205. [PMID: 35224319 PMCID: PMC8855346 DOI: 10.1016/j.comtox.2021.100205] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 11/01/2021] [Accepted: 11/25/2021] [Indexed: 12/03/2022]
Abstract
Quantitative AOPs are toxicodynamic models based on Adverse Outcome Pathways. Mathematical models, e.g. Bayesian networks, can inform the quantification of AOPs. Model credibility is enhanced by applying the principles of causality theory. A causal diagram is illustrated for an AOP for Parkinsonian motor deficits. Computational resources to help assess causality are listed.
Toxicology in the 21st Century has seen a shift from chemical risk assessment based on traditional animal tests, identifying apical endpoints and doses that are “safe”, to the prospect of Next Generation Risk Assessment based on non-animal methods. Increasingly, large and high throughput in vitro datasets are being generated and exploited to develop computational models. This is accompanied by an increased use of machine learning approaches in the model building process. A potential problem, however, is that such models, while robust and predictive, may still lack credibility from the perspective of the end-user. In this commentary, we argue that the science of causal inference and reasoning, as proposed by Judea Pearl, will facilitate the development, use and acceptance of quantitative AOP models. Our hope is that by importing established concepts of causality from outside the field of toxicology, we can be “constructively disruptive” to the current toxicological paradigm, using the “Causal Revolution” to bring about a “Toxicological Revolution” more rapidly.
Collapse
|
7
|
Landesmann B, Paini A. JRC Summer School on Non-animal Approaches in Science, May 2021. Altern Lab Anim 2022; 49:235-300. [DOI: 10.1177/02611929211065919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
| | - Alicia Paini
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| |
Collapse
|
8
|
Belfield SJ, Enoch SJ, Firman JW, Madden JC, Schultz TW, Cronin MTD. Determination of "fitness-for-purpose" of quantitative structure-activity relationship (QSAR) models to predict (eco-)toxicological endpoints for regulatory use. Regul Toxicol Pharmacol 2021; 123:104956. [PMID: 33979632 DOI: 10.1016/j.yrtph.2021.104956] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Revised: 03/30/2021] [Accepted: 05/06/2021] [Indexed: 10/21/2022]
Abstract
In silico models are used to predict toxicity and molecular properties in chemical safety assessment, gaining widespread regulatory use under a number of legislations globally. This study has rationalised previously published criteria to evaluate quantitative structure-activity relationships (QSARs) in terms of their uncertainty, variability and potential areas of bias, into ten assessment components, or higher level groupings. The components have been mapped onto specific regulatory uses (i.e. data gap filling for risk assessment, classification and labelling, and screening and prioritisation) identifying different levels of uncertainty that may be acceptable for each. Twelve published QSARs were evaluated using the components, such that their potential use could be identified. High uncertainty was commonly observed with the presentation of data, mechanistic interpretability, incorporation of toxicokinetics and the relevance of the data for regulatory purposes. The assessment components help to guide strategies that can be implemented to improve acceptability of QSARs through the reduction of uncertainties. It is anticipated that model developers could apply the assessment components from the model design phase (e.g. through problem formulation) through to their documentation and use. The application of the components provides the possibility to assess QSARs in a meaningful manner and demonstrate their fitness-for-purpose against pre-defined criteria.
Collapse
Affiliation(s)
- Samuel J Belfield
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK
| | - Steven J Enoch
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK
| | - James W Firman
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK
| | - Judith C Madden
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK
| | - Terry W Schultz
- University of Tennessee, College of Veterinary Medicine, Knoxville, TN, 37996-4500, USA
| | - Mark T D Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK.
| |
Collapse
|
9
|
Sapounidou M, Ebbrell DJ, Bonnell MA, Campos B, Firman JW, Gutsell S, Hodges G, Roberts J, Cronin MTD. Development of an Enhanced Mechanistically Driven Mode of Action Classification Scheme for Adverse Effects on Environmental Species. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:1897-1907. [PMID: 33478211 DOI: 10.1021/acs.est.0c06551] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
This study developed a novel classification scheme to assign chemicals to a verifiable mechanism of (eco-)toxicological action to allow for grouping, read-across, and in silico model generation. The new classification scheme unifies and extends existing schemes and has, at its heart, direct reference to molecular initiating events (MIEs) promoting adverse outcomes. The scheme is based on three broad domains of toxic action representing nonspecific toxicity (e.g., narcosis), reactive mechanisms (e.g., electrophilicity and free radical action), and specific mechanisms (e.g., associated with enzyme inhibition). The scheme is organized at three further levels of detail beyond broad domains to separate out the mechanistic group, specific mechanism, and the MIEs responsible. The novelty of this approach comes from the reference to taxonomic diversity within the classification, transparency, quality of supporting evidence relating to MIEs, and that it can be updated readily.
Collapse
Affiliation(s)
- Maria Sapounidou
- School of Pharmacy and Bimolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, U.K
| | - David J Ebbrell
- School of Pharmacy and Bimolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, U.K
| | - Mark A Bonnell
- Science and Risk Assessment Directorate, Environment & Climate Change Canada, 351 St. Joseph Blvd, Gatineau, Quebec K1A 0H3, Canada
| | - Bruno Campos
- Safety and Environmental Assurance Centre, Unilever, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, U.K
| | - James W Firman
- School of Pharmacy and Bimolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, U.K
| | - Steve Gutsell
- Safety and Environmental Assurance Centre, Unilever, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, U.K
| | - Geoff Hodges
- Safety and Environmental Assurance Centre, Unilever, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, U.K
| | - Jayne Roberts
- Safety and Environmental Assurance Centre, Unilever, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, U.K
| | - Mark T D Cronin
- School of Pharmacy and Bimolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, U.K
| |
Collapse
|
10
|
Madden JC, Enoch SJ, Paini A, Cronin MTD. A Review of In Silico Tools as Alternatives to Animal Testing: Principles, Resources and Applications. Altern Lab Anim 2020; 48:146-172. [PMID: 33119417 DOI: 10.1177/0261192920965977] [Citation(s) in RCA: 64] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Across the spectrum of industrial sectors, including pharmaceuticals, chemicals, personal care products, food additives and their associated regulatory agencies, there is a need to develop robust and reliable methods to reduce or replace animal testing. It is generally recognised that no single alternative method will be able to provide a one-to-one replacement for assays based on more complex toxicological endpoints. Hence, information from a combination of techniques is required. A greater understanding of the time and concentration-dependent mechanisms, underlying the interactions between chemicals and biological systems, and the sequence of events that can lead to apical effects, will help to move forward the science of reducing and replacing animal experiments. In silico modelling, in vitro assays, high-throughput screening, organ-on-a-chip technology, omics and mathematical biology, can provide complementary information to develop a complete picture of the potential response of an organism to a chemical stressor. Adverse outcome pathways (AOPs) and systems biology frameworks enable relevant information from diverse sources to be logically integrated. While individual researchers do not need to be experts across all disciplines, it is useful to have a fundamental understanding of what other areas of science have to offer, and how knowledge can be integrated with other disciplines. The purpose of this review is to provide those who are unfamiliar with predictive in silico tools, with a fundamental understanding of the underlying theory. Current applications, software, barriers to acceptance, new developments and the use of integrated approaches are all discussed, with additional resources being signposted for each of the topics.
Collapse
Affiliation(s)
- Judith C Madden
- School of Pharmacy and Biomolecular Sciences, 4589Liverpool John Moores University, Liverpool, UK
| | - Steven J Enoch
- School of Pharmacy and Biomolecular Sciences, 4589Liverpool John Moores University, Liverpool, UK
| | - Alicia Paini
- 99013European Commission, Joint Research Centre (JRC), Ispra, Italy
| | - Mark T D Cronin
- School of Pharmacy and Biomolecular Sciences, 4589Liverpool John Moores University, Liverpool, UK
| |
Collapse
|