Leveraging biomarkers and translational medicine for preclinical safety - Lessons for advancing the validation of alternatives to animal testing
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Abstract
This article explores the potential of principles established in translational medicine for the use of biomarkers to advance the validation of alternatives to animal testing in preclinical safety assessment. It examines especially how such principles can enhance the predictive power, mechanistic understanding, and human relevance of new approach methodologies (NAMs). Key concepts from translational medicine, such as fit-for-purpose validation, evidence-based approaches, and integrated testing strategies, are already being applied to the development and validation of NAMs. The article discusses challenges in implementing biomarker-based approaches, including standardization, demonstration of relevance, regulatory acceptance, and addressing biological complexity. It also highlights opportunities for advancement through collaborative efforts, technological innovations, and regulatory evolution. Case studies demonstrate successful applications of biomarkers in preclinical safety, while future perspectives explore emerging trends like multi-omics integration, microphysiological systems, and artificial intelligence. The article emphasizes the potential of biomarkers and translational science approaches in creating more predictive, efficient, and ethical preclinical safety assessment paradigms in the use of NAMs. Use of biomarkers can enable the mechanistic validation of human-relevant models and provide a means to relate changes in NAMs to animal or clinical study results. By leveraging these tools, the field can work towards reducing reliance on animal testing while improving the accuracy and human relevance of safety predictions.
Plain language summary
This article examines how biomarkers and translational science principles can improve safety testing without using animals. Biomarkers are quantifiable indicators of biological processes. Some of these can predict disease progression or drug effects. Translational science aims to apply laboratory findings towards clinical benefits. The article explores how combining these approaches can create better, more human-relevant and validated alternatives to animal testing. It discusses challenges that the field faces, including standardization of methods and getting regulatory acceptance. It also highlights opportunities, like integration with emerging technologies and increased global collaboration. The ultimate goal is to improve human health by streamlining NAM validation processes, i.e., show that new safety tests are more accurate, efficient, and ethical than current animal-based methods.
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Ahmad, A., Imran, M. and Ahsan, H. (2023). Biomarkers as biomedical bioindicators: Approaches and techniques for the detection, analysis, and validation of novel biomarkers of diseases. Pharmaceutics 15, 1630. doi:10.3390/pharmaceutics15061630
Amur, S. G., Sanyal, S., Chakravarty, A. G. et al. (2015a). Building a roadmap to biomarker qualification: Challenges and opportunities. Biomark Med 9, 1095-1105. doi:10.2217/bmm.15.90
Amur, S., LaVange, L., Zineh, I. et al. (2015b). Biomarker qualification: Toward a multiple stakeholder framework for biomarker development, regulatory acceptance, and utilization. Clin Pharmacol Ther 98, 34-46. doi:10.1002/cpt.136
Bakker, E., Hendrikse, N. M., Ehmann, F. et al. (2022). Biomarker qualification at the European medicines agency: A review of biomarker qualification procedures from 2008 to 2020. Clin Pharmacol Ther 112, 69-80. doi:10.1002/cpt.2554
Ball, N. M., Bars, R., Botham, P. A. et al. (2022). A framework for chemical safety assessment incorporating new approach methodologies within REACH. Arch Toxicol 96, 743-766. doi:10.1007/s00204-021-03215-9
Balmer, N. V., Weng, M. K., Zimmer, B. et al. (2012). Epigenetic changes and disturbed neural development in a human embryonic stem cell-based model relating to the fetal valproate syndrome. Hum Mol Genet 21, 4104-4014. doi:10.1093/hmg/dds239
Beilmann, M., Boonen, H., Czich, A. et al. (2019). Optimizing drug discovery by investigative toxicology: Current and future trends. ALTEX 36, 3-17. doi:10.14573/altex.1808181
Berman, S. and Siegel, J. (2022). Biomarker qualification at the European medicines agency: A look under the hood. Clin Pharmacol Ther 112, 28-30. doi:10.1002/cpt.2609
Blaauboer, B., Boekelheide, K., Clewell, H. J. et al. (2012). The use of biomarkers of toxicity for integrating in vitro hazard estimates into risk assessment for humans. ALTEX 29, 411-425. doi:10.14573/altex.2012.4.411
Bleavins, M. R., Carini, C., Jurima-Romet, M. et al. (2010). Biomarkers in Drug Development: A Handbook of Practice, Application and Strategy. Hoboken, NJ, USA: Wiley-Blackwell.
Bodaghi, A., Fattahi, N. and Ramazani, A. (2023). Biomarkers: Promising and valuable tools towards diagnosis, prognosis and treatment of Covid-19 and other diseases. Heliyon 9, e13323. doi:10.1016/j.heliyon.2023.e13323
Bonventre, J. V., Vaidya, V. S., Schmouder, R. et al. (2010). Next-generation biomarkers for detecting kidney toxicity. Nat Biotechnol 28, 436-440. doi:10.1038/nbt0510-436
Bottini, A. A., Amcoff, P. and Hartung, T. (2007). Food for thought … on globalization of alternative methods. ALTEX 24, 255-261. doi:10.14573/altex.2007.4.255
Bouhifd, M., Hartung, T., Hogberg, H. T. et al. (2013). Review: Toxicometabolomics. J Appl Toxicol 33, 1365-1383. doi:10.1002/jat.2874
Bravo-Merodio, L., Williams, J. A., Gkoutos, G. V. et al. (2019). -Omics biomarker identification pipeline for translational medicine. J Transl Med 17, 155. doi:10.1186/s12967-019-1912-5
Brown, K. D., Campbell, C. and Roberts, G. V. (2020). Precision medicine in kidney disease: The patient’s view. Nat Rev Nephrol 16, 625-627. doi:10.1038/s41581-020-0319-0
Burgdorf, T., Piersma, A., Landsiedel, R. et al. (2019). Workshop on the validation and regulatory acceptance of innovative 3R approaches in regulatory toxicology – Evolution versus revolution. Toxicol In Vitro 59, 1-11. doi:10.1016/j.tiv.2019.03.039
Califf, R. M. (2018). Biomarker definitions and their applications. Exp Biol Med (Maywood) 243, 213-221. doi:10.1177/1535370217750088
Caloni, F., De Angelis, I. and Hartung, T. (2022). Replacement of animal testing by integrated approaches to testing and assessment (IATA): A call for in vivitrosi. Arch Toxicol 96, 1935-1950. doi:10.1007/s00204-022-03299-x
Chang, S. Y., Weber, E. J., Sidorenko, V. S. et al. (2017). Human liver-kidney model elucidates the mechanisms of aristolochic acid nephrotoxicity. JCI Insight 2, e95978. doi:10.1172/jci.insight.95978
Chen, R., Sanyal, S., Thompson, A. et al. (2018). Evaluating the use of KIM-1 in drug development and research following FDA qualification. Clin Pharmacol Ther 104, 1175-1181. doi:10.1002/cpt.1093
Clerbaux, L. A., Amigó, N., Amorim, M. J. et al. (2022). COVID-19 through adverse outcome pathways: Building networks to better understand the disease – 3rd CIAO AOP design workshop. ALTEX 39, 322-335. doi:10.14573/altex.2112161
Costa, E., Girotti, S., Pauro, F. et al. (2022). The impact of FDA and EMA regulatory decision-making process on the access to CFTR modulators for the treatment of cystic fibrosis. Orphanet J Rare Dis 17, 188. doi:10.1186/s13023-022-02350-5
Cummings, J., Raynaud, F., Jones, L. et al. (2010). Fit-for-purpose biomarker method validation for application in clinical trials of anticancer drugs. Br J Cancer 103, 1313-1317. doi:10.1038/sj.bjc.6605910
Daneshian, M., Akbarsha, M. A., Blaauboer, B. et al. (2011). A framework program for the teaching of alternative methods (replacement, reduction, refinement) to animal experimentation. ALTEX 28, 341-352. doi:10.14573/altex.2011.4.341
Das, S., Dey, M. K., Devireddy, R. et al. (2024). Biomarkers in cancer detection, diagnosis, and prognosis. Sensors 24, 37. doi:10.3390/s24010037
Davis, K. D., Aghaeepour, N., Ahn, A. H. et al. (2020). Discovery and validation of biomarkers to aid the development of safe and effective pain therapeutics: Challenges and opportunities. Nat Rev Neurol 16, 381-400. doi:10.1038/s41582-020-0362-2
Del Giudice, G., Migliaccio, G., D’Alessandro, N. et al. (2023). Advancing chemical safety assessment through an omics-based characterization of the test system-chemical interaction. Front Toxicol 5, 1294780. doi:10.3389/ftox.2023.1294780
Delp, J., Cediel-Ulloa, A., Suciu, I. et al. (2021). Neurotoxicity and underlying cellular changes of 21 mitochondrial respiratory chain inhibitors. Arch Toxicol 95, 591-615. doi:10.1007/s00204-020-02970-5
Edington, C. D., Chen, W. L. K., Geishecker, E. et al. (2018). Interconnected microphysiological systems for quantitative biology and pharmacology studies. Sci Rep 8, 4530. doi:10.1038/s41598-018-22749-0
El-Achkar, T. M., Eadon, M. T., Kretzler, M. et al. (2024). Precision medicine in nephrology: An integrative framework of multidimensional data in the kidney precision medicine project. Am J Kidney Dis 83, 402-410. doi:10.1053/j.ajkd.2023.08.015
Ewart, L., Fabre, K., Chakilam, A. et al. (2017). Navigating tissue chips from development to dissemination: A pharmaceutical industry perspective. Exp Biol Med 242, 1579-1585. doi:10.1177/1535370217715441
Ewart, L., Apostolou, A., Briggs, S. A. et al. (2022). Performance assessment and economic analysis of a human liver-chip for predictive toxicology. Commun Med (Lond) 2, 154. doi:10.1038/s43856-022-00209-1
FDA-NIH Biomarker Working Group (2016-). Monitoring biomarker. 2016 Dec 22 [Updated 2021 Jan 25]. In BEST (Biomarkers, EndpointS, and other Tools). Silver Spring, MD, USA: Food and Drug Administration (US) Co-published by Bethesda, MD, USA: National Institutes of Health (US). https://www.ncbi.nlm.nih.gov/books/nbk402282/
Grillberger, K., Cöllen, E., Trivisani, C. I. et al. (2023). Structural insights into neonicotinoids and N-unsubstituted metabolites on human nAChRs by molecular docking, dynamics simulations, and calcium imaging. Int J Mol Sci 24, 13170. doi:10.3390/ijms241713170
Gromova, M., Vaggelas, A., Dallmann, G. et al. (2020). Biomarkers: Opportunities and challenges for drug development in the current regulatory landscape. Biomark Insights 15, 1177271920974652. doi:10.1177/1177271920974652
Guo, L., Coyle, L., Abrams, R. M. C. et al. (2013). Refining the human iPSC-cardiomyocyte arrhythmic risk assessment model. Toxicol Sci 136, 581-594. doi:10.1093/toxsci/kft205
Hargrove-Grimes, P., Low, L. A. and Tagle, D. A. (2021). Microphysiological systems: What it takes for community adoption. Exp Biol Med (Maywood) 246, 1435-1446. doi:10.1177/15353702211008872
Hargrove-Grimes, P., Low, L. A. and Tagle, D. A. (2022). Microphysiological systems: Stakeholder challenges to adoption in drug development. Cells Tissues Organs 211, 269-281. doi:10.1159/000517422
Hartung, T., Bremer, S., Casati, S. et al. (2004). A modular approach to the ECVAM principles on test validity. Altern Lab Anim 32, 467-472. doi:10.1177/026119290403200503
Hartung, T. and Leist, M. (2008). Food for thought … on the evolution of toxicology and phasing out of animal testing. ALTEX 25, 91-96. doi:10.14573/altex.2008.2.91
Hartung, T., Blaauboer, B. and Leist, M. (2009). Food for thought … on education in alternative methods in toxicology. ALTEX 26, 255-263. doi:10.14573/altex.2009.4.255
Hartung, T. (2013). Look back in anger – What clinical studies tell us about preclinical work. ALTEX 30, 275-291. doi:10.14573/altex.2013.3.275
Hartung, T., Luechtefeld, T., Maertens, A. et al. (2013a). Integrated testing strategies for safety assessments. ALTEX 30, 3-18. doi:10.14573/altex.2013.1.003
Hartung, T., Stephens, M. and Hoffmann, S. (2013b). Mechanistic validation. ALTEX 30, 119-130. doi:10.14573/altex.2013.2.119
Hartung T. (2015). The human whole blood pyrogen test – Lessons learned in twenty years. ALTEX 32, 79-100. doi:10.14573/altex.1503241
Hartung, T. (2017). Utility of the adverse outcome pathway concept in drug development. Expert Opin Drug Metab Toxicol 13, 1-3. doi:10.1080/17425255.2017.1246535
Hartung, T. (2021). Pyrogen testing revisited on occasion of the 25th anniversary of the whole blood test. ALTEX 38, 3-19. doi:10.14573/altex.2101051
Hartung, T. (2023a). A call for a human exposome project. ALTEX 40, 4-33. doi:10.14573/altex.2301061
Hartung, T. (2023b). ToxAIcology – The evolving role of artificial intelligence in advancing toxicology and modernizing regulatory science. ALTEX 40, 559-570. doi:10.14573/altex.2309191
Hartung, T. (2023c). Artificial intelligence as the new frontier in chemical risk assessment. Front Artif Intell 6, 1269932. doi:10.3389/frai.2023.1269932
Hartung, T. (2024a). The (misleading) role of animal models in drug development. Front Drug Discov 4, 1355044. doi:10.3389/fddsv.2024.1355044
Hartung, T. (2024b). The validation of regulatory test methods – Conceptual, ethical, and philosophical foundations. ALTEX 41, 525-544. doi:10.14573/altex.2409271
Hartung, T., Maertens, A., Luechtefeld, T. (2024). E-validation – Unleashing AI for validation. ALTEX 41, 567-587. doi:10.14573/altex.2409211
Hendrikse, N. M., Llinares Garcia, J., Vetter, T. et al. (2022). Biomarkers in medicines development – From discovery to regulatory qualification and beyond. Front Med 9, 878942. doi:10.3389/fmed.2022.878942
Hewitt, N. J., Bühring, K. U., Dasenbrock, J. et al. (2001). Studies comparing in vivo:in vitro metabolism of three pharmaceutical compounds in rat, dog, monkey, and human using cryopreserved hepatocytes, microsomes, and collagen gel immobilized hepatocyte cultures. Drug Metab Dispos 29, 1042-1050.
Hood, L., Heath, J. R., Phelps, M. E. et al. (2004). Systems biology and new technologies enable predictive and preventative medicine. Science 306, 640-643. doi:10.1126/science.1104635
Horien, C. (2017). Biomarkers, translational medicine, and drug development: An interview with Chirag R. Parikh, MD, PhD. Yale J Biol Med 90, 153-156.
Institute of Medicine (US) Committee on Qualification of Biomarkers and Surrogate Endpoints in Chronic Disease (2010). 4 case studies. In C. M. Micheel and J. R. Ball (eds), Evaluation of Biomarkers and Surrogate Endpoints in Chronic Disease. Washington, DC, USA: National Academies Press (US). https://www.ncbi.nlm.nih.gov/books/nbk220298/
Judson, R., Kavlock, R., Martin, M. et al. (2013). Perspectives on validation of high-throughput pathway-based assays supporting the 21st century toxicity testing vision. ALTEX 30, 51-66. doi:10.14573/altex.2013.1.051
Kimmel, P. L., Jefferson, N., Norton, J. M. (2019). How community engagement is enhancing NIDDK research. Clin J Am Soc Nephrol 14, 768-770. doi:10.2215/cjn.14591218
Kleinstreuer, N. and Hartung, T. (2024). Artificial intelligence (AI) – It’s the end of the tox as we know it (and I feel fine). Arch Toxicol 98, 735-754. doi:10.1007/s00204-023-03666-2
Kopec, A. K., Yokokawa, R., Khan, N. et al. (2021). Microphysiological systems in early stage drug development: Perspectives on current applications and future impact. J Toxicol Sci 46, 99-114. doi:10.2131/jts.46.99
Krewski, D., Westphal, M., Andersen, M. E. et al. (2014). A framework for the next generation of risk science. Environ Health Perspect 122, 796-805. doi:10.1289/ehp.1307260
Kumar, C. and van Gool, A. J. (2013). Introduction: Biomarkers in translational and personalized medicine. In P. Horvatovich and R. Bischoff (eds), Comprehensive Biomarker Discovery and Validation for Clinical Application (Chapter 1, 3-39). The Royal Society of Chemistry.
Lee, J. W., Devanarayan, V., Barrett, Y. C. et al. (2006). Fit-for-purpose method development and validation for successful biomarker measurement. Pharm Res 23, 312-328. doi:10.1007/s11095-005-9045-3
Leist, M., Hartung, T. and Nicotera, P. (2008). The dawning of a new age of toxicology. ALTEX 25, 103-114. doi:10.14573/altex.2008.2.103
Leist, M., Ghallab, A., Graepel, R. et al. (2017). Adverse outcome pathways: Opportunities, limitations and open questions. Arch Toxicol 31, 221-229. doi:10.1007/s00204-017-2045-3
Lin, Z. and Chou, W. C. (2022). Machine learning and artificial intelligence in toxicological sciences. Toxicol Sci 189, 7-19. doi:10.1093/toxsci/kfac075
Linkov, I., Massey, O., Keisler, J. et al. (2015). From “weight of evidence” to quantitative data integration using multicriteria decision analysis and Bayesian methods. ALTEX 32, 3-8. doi:10.14573/altex.1412231
Loser, D., Hinojosa, M. G., Blum, J. et al. (2021). Functional alterations by a subgroup of neonicotinoid pesticides in human dopaminergic neurons. Arch Toxicol 95, 2081-2107. doi:10.1007/s00204-021-03031-1
Luciano, J. S., Andersson, B., Batchelor, C. et al. (2011). The translational medicine ontology and knowledge base: Driving personalized medicine by bridging the gap between bench and bedside. J Biomed Semant 2, Suppl 2, S1. doi:10.1186/2041-1480-2-s2-s1
Maertens, A., Anastas, N., Spencer, P. J. et al. (2014). Green toxicology. ALTEX 31, 243-249. doi:10.14573/altex.1406181
Maertens, A. and Hartung, T. (2018). Green toxicology – Know early about and avoid toxic product liabilities. Toxicol Sci 161, 285-289. doi:10.1093/toxsci/kfx243
Maertens, A., Golden, E. and Hartung, T. (2019). Avoiding regrettable substitutions: Green toxicology for sustainable chemistry. ACS Sustain Chem Eng 9, 7749-7758. doi:10.1021/acssuschemeng.0c09435
Maertens, A., Luechtefeld, T. and Hartung, T. (2024). Alternative methods go green! Green toxicology as a sustainable approach for assessing chemical safety and designing safer chemicals. ALTEX 41, 3-19. doi:10.14573/altex.2312291
Magurany, K. A., Chang, X., Clewell, R. et al. (2023). A pragmatic framework for the application of new approach methodologies in one health toxicological risk assessment. Toxicol Sci 192, 155-177. doi:10.1093/toxsci/kfad012
Mahony, C. (2019). Building confidence in non-animal methods: Practical examples of collaboration between regulators, researchers and industry. Comput Toxicol 10, 78-80. doi:10.1016/j.comtox.2019.01.003
Malik, M., Yang, Y., Fathi, P. et al. (2021). Critical considerations for the design of multi-organ microphysiological systems (MPS). Front Cell Dev Biol 9, 721338. doi:10.3389/fcell.2021.721338
Mankoff, S. P., Brander, C., Ferrone, S. et al. (2004). Lost in translation: Obstacles to translational medicine. J Transl Med 2, 14. doi:10.1186/1479-5876-2-14
Mansouri, M., Lam, J. and Sung, K. E. (2024). Progress in developing microphysiological systems for biological product assessment. Lab Chip 24, 1293-1306. doi:10.1039/d3lc00876b
Marciano, L. P. A., Kleinstreuer, N., Chang, X. et al. (2024). A novel approach to triazole fungicides risk characterization: Bridging human biomonitoring and computational toxicology. Sci Total Environ 953, 176003. doi:10.1016/j.scitotenv.2024.176003
Marx, U., Andersson, T. B., Bahinski, A. et al. (2016). Biology-inspired microphysiological system approaches to solve the prediction dilemma of substance testing using animals. ALTEX 33, 272-321. doi:10.14573/altex.1603161
Marx, U., Akabane, T., Andersson, T. B. et al. (2020). Biology-inspired microphysiological systems to advance medicines for patient benefit and animal welfare. ALTEX 37, 364-394. doi:10.14573/altex.2001241
Mattes, W. and Walker, E. (2009). Translational toxicology and the work of the predictive safety testing consortium. Clin Pharmacol Ther 85, 327-330. doi:10.1038/clpt.2008.270
Mattes, W. B., Gribble Walker, E., Abadie, E. et al. (2010). Research at the interface of industry, academia and regulatory science. Nat Biotechnol 28, 432-433. doi:10.1038/nbt0510-432
Meigs, L., Smirnova, L., Rovida, C. et al. (2018). Animal testing and its alternatives – The most important omics is economics. ALTEX 35, 275-305. doi:10.14573/altex.1807041
Mondou, M., Maguire, S., Pain, G. et al. (2021). Envisioning an international validation process for new approach methodologies in chemical hazard and risk assessment. Environ Adv 4, 100061. doi:10.1016/j.envadv.2021.100061
Monticello, T. M., Jones, T. W., Dambach, D. M. et al. (2017). Current nonclinical testing paradigm enables safe entry to first-In-human clinical trials: The IQ consortium nonclinical to clinical translational database. Toxicol Appl Pharmacol 334, 100-109. doi:10.1016/j.taap.2017.09.006
Murad, N. and Melamud, E. (2022). Global patterns of prognostic biomarkers across disease space. Sci Rep 12, 21893. doi:10.1038/s41598-022-25209-y
Nendza, M. and Ahlers, J. (2022). Aquatic toxicity integrated testing and assessment strategies (ITS) for difficult substances: Case study with thiochemicals. Environ Sci Eur 34, 17. doi:10.1186/s12302-022-00591-6
Nguyen, V. V. T., Gkouzioti, V., Maass, C. et al. (2023). A systematic review of kidney-on-a-chip-based models to study human renal (patho-)physiology. Dis Model Mech 16, dmm050113. doi:10.1242/dmm.050113
OECD (2023). Guideline No. 497: Defined Approaches on Skin Sensitisation. OECD Guidelines for the Testing of Chemicals, Section 4. OECD Publishing, Paris. doi:10.1787/b92879a4-en
Olson, H., Betton, G., Robinson, D. et al. (2000). Concordance of the toxicity of pharmaceuticals in humans and in animals. Regul Toxicol Pharmacol 32, 56-67. doi:10.1006/rtph.2000.1399
Owen, M., Bose, N., Nisenbaum, L. et al. (2023). The critical role of biomarkers for drug development targeting the biology of aging. J Prev Alzheimers Dis 10, 729-742. doi:10.14283/jpad.2023.111
Ozer, J. S., Dieterle, F., Troth, S. et al. (2010). A panel of urinary biomarkers to monitor reversibility of renal injury and a serum marker with improved potential to assess renal function. Nat Biotechnol 28, 486-494. doi:10.1038/nbt.1627
Pamies, D., Leist, M., Coecke, S. et al. (2022). Guidance document on Good Cell and Tissue Culture Practice 2.0 (GCCP 2.0). ALTEX 39, 30-70. doi:10.14573/altex.2111011
Pamies, D., Ekert, J., Zurich, M. G. et al. (2024). Recommendations on fit-for-purpose criteria to establish quality management for microphysiological systems and for monitoring their reproducibility. Stem Cell Reports 19, 604-617. doi:10.1016/j.stemcr.2024.03.009
Pang, L., Cai, C., Aggarwal, P. et al. (2024). Predicting oncology drug-induced cardiotoxicity with donor-specific iPSC-CMs – A proof-of-concept study with doxorubicin. Toxicol Sci 200, 79-94. doi:10.1093/toxsci/kfae041
Parish, S., Aschner, M., Casey, W. et al. (2020). An evaluation framework for new approach methodologies for human health safety assessment. Regul Toxicol Pharmacol 112, 104592. doi:10.1016/j.yrtph.2020.104592
Ramirez, T., Daneshian, M., Kamp, H. et al. (2013). Metabolomics in toxicology and preclinical research. ALTEX 30, 209-225. doi:10.14573/altex.2013.2.209
Ratner, M. (2017). FDA deems in vitro data on mutations sufficient to expand cystic fibrosis drug label. Nat Biotechnol 35, 606-606. doi:10.1038/nbt0717-606
Rempel, E., Hoelting, L., Waldmann, T. et al. (2015). A transcriptome-based classifier to identify developmental toxicants by stem cell testing: Design, validation and optimization for histone deacetylase inhibitors. Arch Toxicol 89, 1599-1618. doi:10.1007/s00204-015-1573-y
Robinson, W. H., Lindstrom, T. M., Cheung, R. K. et al. (2013). Mechanistic biomarkers for clinical decision making in rheumatic diseases. Nat Rev Rheumatol 9, 267-276. doi:10.1038/nrrheum.2013.14
Roth, A. and MPS-WS Berlin 2019 (2021). Human microphysiological systems for drug development. Science 373, 1304-1306. doi:10.1126/science.abc3734
Rovida, C., Alépée, N., Api, A. M. et al. (2015). Integrated testing strategies (ITS) for safety assessment. ALTEX 32, 171-181. doi:10.14573/altex.1506201
Sasseville, V. G., Mansfield, K. G. and Brees, D. J. (2014). Safety biomarkers in preclinical development: Translational potential. Vet Pathol 51, 281-291. doi:10.1177/0300985813505117
Sauer, J. M. and Porter, A. C. (2018). Preclinical biomarker qualification. Exp Biol Med (Maywood) 243, 222-227. doi:10.1177/1535370217743949
Sauer, J. M. and Porter, A. C. (2020). The regulatory acceptance of translational safety biomarkers. Curr Opin Toxicol 23-24, 80-86. doi:10.1016/j.cotox.2020.06.001
Schindler, S., Spreitzer, I., Löschner, B. et al. (2006). International validation of pyrogen tests based on cryopreserved human primary blood cells. J Immunol Methods 316, 42-51. doi:10.1016/j.jim.2006.07.023
Schmeisser, S., Miccoli, A., von Bergen, M. et al. (2023). New approach methodologies in human regulatory toxicology - Not if, but how and when!. Environ Int 178, 108082. doi:10.1016/j.envint.2023.108082
Schomaker, S., Ramaiah, S., Khan, N. et al. (2019). Safety biomarker applications in drug development. J Toxicol Sci 44, 225-235. doi:10.2131/jts.44.225
Schwartz, M. P., Hou, Z., Propson, N. E. et al. (2015). Human pluripotent stem cell-derived neural constructs for predicting neural toxicity. Proc Natl Acad Sci U S A 112, 12516-12521. doi:10.1073/pnas.1516645112
Seidel, F., Cherianidou, A., Kappenberg, F. et al. (2022). High accuracy classification of developmental toxicants by in vitro tests of human neuroepithelial and cardiomyoblast differentiation. Cells 11, 3404. doi:10.3390/cells11213404
Serelli-Lee, V., Ito, K., Koibuchi, A. et al. (2022). A state-of-the-art roadmap for biomarker-driven drug development in the era of personalized therapies. J Pers Med 12, 669. doi:10.3390/jpm12050669
Sillé, F. and Hartung, T. (2024). Metabolomics in preclinical drug safety assessment: Current status and future trends. Metabolites 14, 98. doi:10.3390/metabo14020098
Sillé, F. C. M., Busquet, F., Fitzpatrick, S. et al. (2024). The implementation moonshot project for alternative chemical testing (IMPACT) toward a human exposome project. ALTEX 41, 344-362. doi:10.14573/altex.2407081
Skolariki, K., Exarchos, T. P. and Vlamos, P. (2023). Computational models for biomarker discovery. Adv Exp Med Biol 1424, 289-295. doi:10.1007/978-3-031-31982-2_33
Song, X. and Dobbin, K. K. (2022). Evaluating biomarkers for treatment selection from reproducibility studies. Biostatistics 23, 173-188. doi:10.1093/biostatistics/kxaa018
Stucki, A. O., Barton-Maclaren, T. S., Bhuller, Y. et al. (2022). Use of new approach methodologies (NAMs) to meet regulatory requirements for the assessment of industrial chemicals and pesticides for effects on human health. Front Toxicol 4, 964553. doi:10.3389/ftox.2022.964553
Suciu, I., Delp, J., Gutbier, S. et al. (2023). Definition of the neurotoxicity-associated metabolic signature triggered by berberine and other respiratory chain inhibitors. Antioxidants 13, 49. doi:10.3390/antiox13010049
Sung, N. S., Crowley, W. F. Jr., Genel, M. et al. (2003). Central challenges facing the national clinical research enterprise. JAMA 289, 1278-1287. doi:10.1001/jama.289.10.1278
Tagle, D. A. (2019). The NIH microphysiological systems program: Developing in vitro tools for safety and efficacy in drug development. Curr Opin Pharmacol 48, 146-154, doi:10.1016/j.coph.2019.09.007
Takeda, M., Miyagawa, S., Fukushima, S. et al. (2018). Development of in vitro drug-induced cardiotoxicity assay by using three-dimensional cardiac tissues derived from human induced pluripotent stem cells. Tissue Eng Part C Methods 24, 56-67. doi:10.1089/ten.tec.2017.0247
Tonoyan, L. and Siraki, A. G. (2024). Machine learning in toxicological sciences: Opportunities for assessing drug toxicity. Front Drug Discov 4, 1336025. doi:10.3389/fddsv.2024.1336025
Tran, T. T. V., Wibowo, A. S., Tayara, H. et al. (2023). Artificial intelligence in drug toxicity prediction: Recent advances, challenges, and future perspectives. J Chem Inf Model 63, 2628-2643. doi:10.1021/acs.jcim.3c00200
Troth, S. P., Vlasakova, K., Amur, S. et al. (2019). Translational safety biomarkers of kidney injury. Semin Nephrol 39, 202-214. doi:10.1016/j.semnephrol.2018.12.008
Tsaioun, K., Blaauboer, B. J. and Hartung, T. (2016). Evidence-based absorption, distribution, metabolism, excretion and toxicity (ADMET) and the role of alternative methods. ALTEX 33, 343-358. doi:10.14573/altex.1610101
van der Zalm, A. J., Barroso, J., Browne, P. et al. (2022). A framework for establishing scientific confidence in new approach methodologies. Arch Toxicol 96, 2865-2879. doi:10.1007/s00204-022-03365-4
Vlasakova, K., Bourque, J., Bailey, W. J. et al. (2022). Universal accessible biomarkers of drug-induced tissue injury and systemic inflammation in rat: Performance assessment of TIMP-1, A2M, AGP, NGAL, and albumin. Toxicol Sci 187, 219-233. doi:10.1093/toxsci/kfac030
von Aulock, S., Busquet, F., Locke, P. et al. (2022). Engagement of scientists with the public and policymakers to promote alternative methods. ALTEX 39, 543-559. doi:10.14573/altex.2209261
Wagner, P. D. and Srivastava, S. (2012). New paradigms in translational science research in cancer biomarkers. Transl Res 159, 343-353. doi:10.1016/j.trsl.2012.01.015
Waldmann, T., Grinberg, M., König, A. et al. (2017). Stem cell transcriptome responses and corresponding biomarkers that indicate the transition from adaptive responses to cytotoxicity. Chem Res Toxicol 30, 905-922. doi:10.1021/acs.chemrestox.6b00259
Wang, X. and Ward, P. A. (2012). Opportunities and challenges of disease biomarkers: A new section in the journal of translational medicine. J Transl Med 10, 240. doi:10.1186/1479-5876-10-240
Weaver, J. L., Wu, W., Hyland, P. L. et al. (2022). Expanding approved patient populations for rare disease treatment using in vitro data. Clin Pharmacol Ther 112, 58-61. doi:10.1002/cpt.2414
Wehling, M. (2015). Principles of Translational Science in Medicine: From Bench to Bedside (2nd edition). Elsevier Science.
Wilmer, M. J., Ng, C. P., Lanz, H. L. et al. (2016). Kidney-on-a-chip technology for drug-induced nephrotoxicity screening. Trends Biotechnol 34, 156-170. doi:10.1016/j.tibtech.2015.11.001
Yang, X., Ribeiro, A. J. S., Pang, L. et al. (2022). Use of human iPSC-CMs in nonclinical regulatory studies for cardiac safety assessment. Toxicol Sci 190, 117-126. doi:10.1093/toxsci/kfac095