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Steger-Hartmann T, Kreuchwig A, Wang K, Birzele F, Draganov D, Gaudio S, Rothfuss A. Perspectives of data science in preclinical safety assessment. Drug Discov Today 2023:103642. [PMID: 37244565 DOI: 10.1016/j.drudis.2023.103642] [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: 03/15/2023] [Revised: 05/12/2023] [Accepted: 05/22/2023] [Indexed: 05/29/2023]
Abstract
The data landscape in preclinical safety assessment is fundamentally changing because of not only emerging new data types, such as human systems biology, or real-world data (RWD) from clinical trials, but also technological advancements in data-processing software and analytical tools based on deep learning approaches. The recent developments of data science are illustrated with use cases for the three factors: predictive safety (new in silico tools), insight generation (new data for outstanding questions); and reverse translation (extrapolating from clinical experience to resolve preclinical questions). Further advances in this field can be expected if companies focus on overcoming identified challenges related to a lack of platforms and data silos and assuring appropriate training of data scientists within the preclinical safety teams.
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Affiliation(s)
| | - Annika Kreuchwig
- Investigational Toxicology, Bayer AG, Pharmaceuticals, 13353 Berlin, Germany
| | - Ken Wang
- Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences F. Hoffmann-La-Roche AG, Basel, Switzerland
| | - Fabian Birzele
- Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences F. Hoffmann-La-Roche AG, Basel, Switzerland
| | - Dragomir Draganov
- Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences F. Hoffmann-La-Roche AG, Basel, Switzerland
| | - Stefano Gaudio
- Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences F. Hoffmann-La-Roche AG, Basel, Switzerland
| | - Andreas Rothfuss
- Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences F. Hoffmann-La-Roche AG, Basel, Switzerland
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Wright PSR, Briggs KA, Thomas R, Smith GF, Maglennon G, Mikulskis P, Chapman M, Greene N, Phillips BU, Bender A. Statistical analysis of preclinical inter-species concordance of histopathological findings in the eTOX database. Regul Toxicol Pharmacol 2023; 138:105308. [PMID: 36481279 DOI: 10.1016/j.yrtph.2022.105308] [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: 04/01/2022] [Revised: 11/17/2022] [Accepted: 11/25/2022] [Indexed: 12/12/2022]
Abstract
Preclinical inter-species concordance can increase the predictivity of observations to the clinic, potentially reducing drug attrition caused by unforeseen adverse events. We quantified inter-species concordance of histopathological findings and target organ toxicities across four preclinical species in the eTOX database using likelihood ratios (LRs). This was done whilst only comparing findings between studies with similar compound exposure (Δ|Cmax| ≤ 1 log-unit), repeat-dosing duration, and animals of the same sex. We discovered 24 previously unreported significant inter-species associations between histopathological findings encoded by the HPATH ontology. More associations with strong positive concordance (33% LR+ > 10) relative to strong negative concordance (12.5% LR- < 0.1) were identified. Of the top 10 most positively concordant associations, 60% were computed between different histopathological findings indicating potential differences in inter-species pathogenesis. We also observed low inter-species target organ toxicity concordance. For example, liver toxicity concordance in short-term studies between female rats and dogs observed an average LR+ of 1.84, and an average LR- of 0.73. This was corroborated by similarly low concordance between rodents and non-rodents for 75 candidate drugs in AstraZeneca. This work provides new statistically significant associations between preclinical species, but finds that concordance is rare, particularly between the absence of findings.
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Affiliation(s)
- Peter S R Wright
- University of Cambridge, Centre for Molecular Science Informatics, Department of Chemistry, Cambridge, United Kingdom.
| | | | | | - Graham F Smith
- AstraZeneca, Data Science and AI, Clinical Pharmacology and Safety Sciences, R&D, Cambridge, United Kingdom
| | - Gareth Maglennon
- AstraZeneca, Oncology Pathology, Clinical Pharmacology and Safety Sciences, R&D, Melbourn, United Kingdom
| | - Paulius Mikulskis
- AstraZeneca, Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, Gothenburg, Sweden
| | - Melissa Chapman
- AstraZeneca, Toxicology, Clinical Pharmacology and Safety Sciences, R&D, Melbourn, United Kingdom
| | - Nigel Greene
- AstraZeneca, Data Science and Artificial Intelligence, Clinical Pharmacology and Safety Sciences, R&D, Boston, MA, USA
| | - Benjamin U Phillips
- AstraZeneca, Data Sciences and Quantitative Biology, Discovery Sciences, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Andreas Bender
- University of Cambridge, Centre for Molecular Science Informatics, Department of Chemistry, Cambridge, United Kingdom.
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Bender A, Cortes-Ciriano I. Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 2: a discussion of chemical and biological data. Drug Discov Today 2021; 26:1040-1052. [PMID: 33508423 PMCID: PMC8132984 DOI: 10.1016/j.drudis.2020.11.037] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 11/07/2020] [Accepted: 11/30/2020] [Indexed: 12/11/2022]
Abstract
'Artificial Intelligence' (AI) has recently had a profound impact on areas such as image and speech recognition, and this progress has already translated into practical applications. However, in the drug discovery field, such advances remains scarce, and one of the reasons is intrinsic to the data used. In this review, we discuss aspects of, and differences in, data from different domains, namely the image, speech, chemical, and biological domains, the amounts of data available, and how relevant they are to drug discovery. Improvements in the future are needed with respect to our understanding of biological systems, and the subsequent generation of practically relevant data in sufficient quantities, to truly advance the field of AI in drug discovery, to enable the discovery of novel chemistry, with novel modes of action, which shows desirable efficacy and safety in the clinic.
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Affiliation(s)
- Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK; Imaging and Data Analytics, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK.
| | - Isidro Cortes-Ciriano
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, CB10 1SD, UK.
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Watford S, Ly Pham L, Wignall J, Shin R, Martin MT, Friedman KP. ToxRefDB version 2.0: Improved utility for predictive and retrospective toxicology analyses. Reprod Toxicol 2019; 89:145-158. [PMID: 31340180 PMCID: PMC6944327 DOI: 10.1016/j.reprotox.2019.07.012] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Revised: 05/31/2019] [Accepted: 07/12/2019] [Indexed: 02/08/2023]
Abstract
The Toxicity Reference Database (ToxRefDB) structures information from over 5000 in vivo toxicity studies, conducted largely to guidelines or specifications from the US Environmental Protection Agency and the National Toxicology Program, into a public resource for training and validation of predictive models. Herein, ToxRefDB version 2.0 (ToxRefDBv2) development is described. Endpoints were annotated (e.g. required, not required) according to guidelines for subacute, subchronic, chronic, developmental, and multigenerational reproductive designs, distinguishing negative responses from untested. Quantitative data were extracted, and dose-response modeling for nearly 28,000 datasets from nearly 400 endpoints using Benchmark Dose (BMD) Modeling Software were generated and stored. Implementation of controlled vocabulary improved data quality; standardization to guideline requirements and cross-referencing with United Medical Language System (UMLS) connects ToxRefDBv2 observations to vocabularies linked to UMLS, including PubMed medical subject headings. ToxRefDBv2 allows for increased connections to other resources and has greatly enhanced quantitative and qualitative utility for predictive toxicology.
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Affiliation(s)
- Sean Watford
- ORAU, Contractor to U.S. Environmental Protection Agency through the National Student Services Contract, United States; National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency, United States
| | - Ly Ly Pham
- ORAU, Contractor to U.S. Environmental Protection Agency through the National Student Services Contract, United States; ORISE Postdoctoral Research Participant, United States
| | | | | | - Matthew T Martin
- ORAU, Contractor to U.S. Environmental Protection Agency through the National Student Services Contract, United States; Currently at Drug Safety Research and Development, Global Investigative Toxicology, Pfizer, Groton, CT, United States
| | - Katie Paul Friedman
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency, United States.
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Making sense of SEND; the Standard for Exchange of Nonclinical Data. Regul Toxicol Pharmacol 2017; 91:77-85. [DOI: 10.1016/j.yrtph.2017.10.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2017] [Revised: 10/16/2017] [Accepted: 10/17/2017] [Indexed: 11/19/2022]
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McCarthy MW, Walsh TJ. Drug development challenges and strategies to address emerging and resistant fungal pathogens. Expert Rev Anti Infect Ther 2017; 15:577-584. [PMID: 28480775 DOI: 10.1080/14787210.2017.1328279] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
INTRODUCTION Invasive fungal infections represent an expanding threat to public health. The recent emergence of Candida auris, which is often resistant to existing antifungal agents and is associated with a high mortality rate, underscores the urgent need for novel drug development strategies. Areas covered: In this paper, we examine both challenges and opportunities associated with antifungal drug development and explore potential avenues to accelerate the development pipeline, including data sharing, surrogate endpoints, and the role of historical controls in clinical trials. Expert commentary: We review important lessons learned from the study of other rare diseases, including mitochondrial storage diseases and certain forms of cancer that may inform strategies to develop new antifungal agents while highlighting promising new compounds such as SCY-078 for the treatment of invasive fungal infections.
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Affiliation(s)
- Matthew W McCarthy
- a Department of Medicine, Joan and Sanford I Weill Medical College of Cornell University , New York , NY , USA
| | - Thomas J Walsh
- b Weill Cornell Medical Center , Transplantation-Oncology Infectious Diseases Program , New York , NY , USA
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