Emerging technologies and their impact on regulatory science

1. Allan, J, Belz, S, Hoeveler, A, Hugas, M, Okuda, H, Patri, A, Rauscher, H, Silva, P, Slikker, W, Sokull-Kluettgen, B, Tong, W, Anklam, E. Regulatory landscape of nanotechnology and nanoplastics from a global perspective. Regul Toxicol Pharmacol 2021; 122:104885
Google Scholar | Crossref | Medline2. Honmva, M. An assessment of mutagenicity of chemical substances by (quantitative) structure-activity relationship. Genes Environ 2020; 42:23
Google Scholar | Crossref | Medline3. Yamamoto, E, Taquahashi, Y, Kuwagata, M, Saito, H, Matsushita, K, Toyoda, T, Sato, F, Kitajima, S, Ogawa, K, Izutsu, K-i, Saito, Y, Hirabayashi, Y, Iimura, Y, Honma, M, Okuda, H, Goda, Y. Visualizing the spatial localization of ciclesonide and its metabolites in rat lungs after inhalation of 1-μm aerosol of ciclesonide by desorption electrospray ionization-time of flight mass spectrometry imaging. Int J Pharma 2021; 595:120241
Google Scholar | Crossref4. Lambert, D, Pightling, A, Griffiths, E, Van Domselaar, G, Evans, P, Berthelet, S, Craig, D, Chandry, PS, Stones, R, Brinkman, F, Angers-Loustau, A, Kreysa, J, Tong, W, Blais, B. Baseline practices for the application of genomic data supporting regulatory food safety. J AOAC Int 2017; 100:721–31
Google Scholar | Crossref | Medline5. Blais, BW, Tapp, K, Dixon, M, Carrillo, CD. Genomically informed strain-specific recovery of Shiga toxin-producing Escherichia coli during foodborne illness outbreak investigations. J Food Prot 2019; 82:39–44
Google Scholar | Crossref | Medline6. Rott, ME, Kesanakurti, P, Berwarth, C, Rast, H, Boyes, I, Phelan, J, Jelkmann, W. Discovery of negative-sense RNA viruses in trees infected with apple rubbery wood disease by next-generation sequencing. Plant Dis 2018; 102:1254–63
Google Scholar | Crossref | Medline7. Lung, O, Fisher, M, Erickson, A, Nfon, C, Ambagala, A. Fully automated and integrated multiplex detection of high consequence livestock viral genomes on a microfluidic platform. Transbound Emerg Dis 2019; 66:144–55
Google Scholar | Crossref | Medline8. Authority, EFS. Modern methodologies and tools for human hazard assessment of chemicals. EFSA J 2014; 12:3638
Google Scholar9. Ball, R, Robb, M, Anderson, SA, Dal Pan, G. The FDA's sentinel initiative – a comprehensive approach to medical product surveillance. Clin Pharmacol Ther 2016; 99:265–68
Google Scholar | Crossref | Medline | ISI10. Platt, R, Brown, JS, Robb, M, McClellan, M, Ball, R, Nguyen, MD, Sherman, RE. The FDA sentinel initiative – an evolving national resource. N Engl J Med 2018; 379:2091–93
Google Scholar | Crossref | Medline11. Brown, JS, Maro, JC, Nguyen, M, Ball, R. Using and improving distributed data networks to generate actionable evidence: the case of real-world outcomes in the Food and Drug Administration's sentinel system. J Am Med Inform Assoc 2020; 27:793–97
Google Scholar | Crossref | Medline12. Ball, R, Toh, S, Nolan, J, Haynes, K, Forshee, R, Botsis, T. Evaluating automated approaches to anaphylaxis case classification using unstructured data from the FDA sentinel system. Pharmacoepidemiol Drug Saf 2018; 27:1077–84
Google Scholar | Crossref | Medline13. Jamal-Hanjani, M, Wilson, GA, McGranahan, N, Birkbak, NJ, Watkins, TBK, Veeriah, S, Shafi, S, Johnson, DH, Mitter, R, Rosenthal, R, Salm, M, Horswell, S, Escudero, M, Matthews, N, Rowan, A, Chambers, T, Moore, DA, Turajlic, S, Xu, H, Lee, SM, Forster, MD, Ahmad, T, Hiley, CT, Abbosh, C, Falzon, M, Borg, E, Marafioti, T, Lawrence, D, Hayward, M, Kolvekar, S, Panagiotopoulos, N, Janes, SM, Thakrar, R, Ahmed, A, Blackhall, F, Summers, Y, Shah, R, Joseph, L, Quinn, AM, Crosbie, PA, Naidu, B, Middleton, G, Langman, G, Trotter, S, Nicolson, M, Remmen, H, Kerr, K, Chetty, M, Gomersall, L, Fennell, DA, Nakas, A, Rathinam, S, Anand, G, Khan, S, Russell, P, Ezhil, V, Ismail, B, Irvin-Sellers, M, Prakash, V, Lester, JF, Kornaszewska, M, Attanoos, R, Adams, H, Davies, H, Dentro, S, Taniere, P, O'Sullivan, B, Lowe, HL, Hartley, JA, Iles, N, Bell, H, Ngai, Y, Shaw, JA, Herrero, J, Szallasi, Z, Schwarz, RF, Stewart, A, Quezada, SA, Le Quesne, J, Van Loo, P, Dive, C, Hackshaw, A, Swanton, C. Tracking the evolution of non-small-cell lung cancer. N Engl J Med 2017; 376:2109–21
Google Scholar | Crossref | Medline14. AbdulJabbar, K, Raza, SEA, Rosenthal, R, Jamal-Hanjani, M, Veeriah, S, Akarca, A, Lund, T, Moore, DA, Salgado, R, Al Bakir, M, Zapata, L, Hiley, CT, Officer, L, Sereno, M, Smith, CR, Loi, S, Hackshaw, A, Marafioti, T, Quezada, SA, McGranahan, N, Le Quesne, J, Swanton, C, Yuan, Y. Geospatial immune variability illuminates differential evolution of lung adenocarcinoma. Nat Med 2020; 26:1054–62
Google Scholar | Crossref | Medline15. Pennycuick, A, Teixeira, VH, AbdulJabbar, K, Raza, SEA, Lund, T, Akarca, AU, Rosenthal, R, Kalinke, L, Chandrasekharan, DP, Pipinikas, CP, Lee-Six, H, Hynds, RE, Gowers, KHC, Henry, JY, Millar, FR, Hagos, YB, Denais, C, Falzon, M, Moore, DA, Antoniou, S, Durrenberger, PF, Furness, AJ, Carroll, B, Marceaux, C, Asselin-Labat, ML, Larson, W, Betts, C, Coussens, LM, Thakrar, RM, George, J, Swanton, C, Thirlwell, C, Campbell, PJ, Marafioti, T, Yuan, Y, Quezada, SA, McGranahan, N, Janes, SM. Immune surveillance in clinical regression of preinvasive squamous cell lung cancer. Cancer Discov 2020; 10:1489–99
Google Scholar | Crossref | Medline16. Reddy, S, Allan, S, Coghlan, S, Cooper, P. A governance model for the application of AI in health care. J Am Med Inform Assoc 2020; 27:491–97
Google Scholar | Crossref | Medline17. Viceconti, M, Pappalardo, F, Rodriguez, B, Horner, M, Bischoff, J, Musuamba Tshinanu, F. In silico trials: verification, validation and uncertainty quantification of predictive models used in the regulatory evaluation of biomedical products. Methods 2021; 185:120–27
Google Scholar | Crossref | Medline18. SEQC/MAQC-III Consortium . A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium. Nat Biotechnol 2014; 32:903–14
Google Scholar | Crossref | Medline | ISI19. Tong, L, Wu, PY, Phan, JH, Hassazadeh, HR, Consortium, S, Tong, W, Wang, MD. Impact of RNA-seq data analysis algorithms on gene expression estimation and downstream prediction. Sci Rep 2020; 10:17925
Google Scholar | Crossref | Medline20. Tong, L, Mitchel, J, Chatlin, K, Wang, MD. Deep learning based feature-level integration of multi-omics data for breast cancer patients survival analysis. BMC Med Inform Decis Mak 2020; 20:225
Google Scholar | Crossref21. Tong, L, Wu, H, Wang, MD. Integrating multi-omics data by learning modality invariant representations for improved prediction of overall survival of cancer. Methods 2021; 189:74–85
Google Scholar | Crossref | Medline22. Barredo Arrieta, A, Díaz-Rodríguez, N, Del Ser, J, Bennetot, A, Tabik, S, Barbado, A, Garcia, S, Gil-Lopez, S, Molina, D, Benjamins, R, Chatila, R, Herrera, F. Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf Fusion 2020; 58:82–115
Google Scholar | Crossref23. Boulemtafes, A, Derhab, A, Challal, Y. A review of privacy-preserving techniques for deep learning. Neurocomputing 2020; 384:21–45
Google Scholar | Crossref24. Hardwick, SA, Deveson, IW, Mercer, TR. Reference standards for next-generation sequencing. Nat Rev Genet 2017; 18:473–84
Google Scholar | Crossref | Medline25. Zook, JM, Catoe, D, McDaniel, J, Vang, L, Spies, N, Sidow, A, Weng, Z, Liu, Y, Mason, CE, Alexander, N, Henaff, E, McIntyre, AB, Chandramohan, D, Chen, F, Jaeger, E, Moshrefi, A, Pham, K, Stedman, W, Liang, T, Saghbini, M, Dzakula, Z, Hastie, A, Cao, H, Deikus, G, Schadt, E, Sebra, R, Bashir, A, Truty, RM, Chang, CC, Gulbahce, N, Zhao, K, Ghosh, S, Hyland, F, Fu, Y, Chaisson, M, Xiao, C, Trow, J, Sherry, ST, Zaranek, AW, Ball, M, Bobe, J, Estep, P, Church, GM, Marks, P, Kyriazopoulou-Panagiotopoulou, S, Zheng, GX, Schnall-Levin, M, Ordonez, HS, Mudivarti, PA, Giorda, K, Sheng, Y, Rypdal, KB, Salit, M. Extensive sequencing of seven human genomes to characterize benchmark reference materials. Sci Data 2016; 3:160025
Google Scholar | Crossref26. Deveson, IW, Chen, WY, Wong, T, Hardwick, SA, Andersen, SB, Nielsen, LK, Mattick, JS, Mercer, TR. Representing genetic variation with synthetic DNA standards. Nat Methods 2016; 13:784–91
Google Scholar | Crossref | Medline27. Cescon, D, Bratman, S, Chan, S, Siu, L. Circulating tumor DNA and liquid biopsy in oncology. Nature Cancer 2020; 1:276–90
Google Scholar | Crossref28. Pirmohamed, M, James, S, Meakin, S, Green, C, Scott, AK, Walley, TJ, Farrar, K, Park, BK, Breckenridge, AM. Adverse drug reactions as cause of admission to hospital: prospective analysis of 18 820 patients. BMJ 2004; 329:15–19
Google Scholar | Crossref | Medline29. Davies, EC, Green, CF, Taylor, S, Williamson, PR, Mottram, DR, Pirmohamed, M. Adverse drug reactions in hospital in-patients: a prospective analysis of 3695 patient-episodes. PLoS One 2009; 4:e4439
Google Scholar | Crossref30. Breiteneder, H, Peng, YQ, Agache, I, Diamant, Z, Eiwegger, T, Fokkens, WJ, Traidl-Hoffmann, C, Nadeau, K, O'Hehir, RE, O'Mahony, L, Pfaar, O, Torres, MJ, Wang, DY, Zhang, L, Akdis, CA. Biomarkers for diagnosis and prediction of therapy responses in allergic diseases and asthma. Allergy 2020; 75:3039–68
Google Scholar | Crossref | Medline31. Evans, WE, Relling, MV. Moving towards individualized medicine with pharmacogenomics. Nature 2004; 429:464–68
Google Scholar | Crossref | Medline | ISI32. Turner, RM, Newman, WG, Bramon, E, McNamee, CJ, Wong, WL, Misbah, S, Hill, S, Caulfield, M, Pirmohamed, M. Pharmacogenomics in the UK National Health Service: opportunities and challenges. Pharmacogenomics 2020; 21:1237–46
Google Scholar | Crossref | Medline33. van der Wouden, CH, Cambon-Thomsen, A, Cecchin, E, Cheung, KC, Dávila-Fajardo, CL, Deneer, VH, Dolžan, V, Ingelman-Sundberg, M, Jönsson, S, Karlsson, MO, Kriek, M, Mitropoulou, C, Patrinos, GP, Pirmohamed, M, Samwald, M, Schaeffeler, E, Schwab, M, Steinberger, D, Stingl, J, Sunder-Plassmann, G, Toffoli, G, Turner, RM, van Rhenen, MH, Swen, JJ, Guchelaar, HJ. Implementing pharmacogenomics in Europe: design and implementation strategy of the ubiquitous pharmacogenomics consortium. Clin Pharmacol Ther 2017; 101:341–58
Google Scholar | Crossref | Medline | ISI34. Pirmohamed, M, Burnside, G, Eriksson, N, Jorgensen, AL, Toh, CH, Nicholson, T, Kesteven, P, Christersson, C, Wahlström, B, Stafberg, C, Zhang, JE, Leathart, JB, Kohnke, H, Maitland-van der Zee, AH, Williamson, PR, Daly, AK, Avery, P, Kamali, F, Wadelius, M. A randomized trial of genotype-guided dosing of warfarin. N Engl J Med 2013; 369:2294–303
Google Scholar | Crossref | Medline | ISI35. Jorgensen, AL, Prince, C, Fitzgerald, G, Hanson, A, Downing, J, Reynolds, J, Zhang, JE, Alfirevic, A, Pirmohamed, M. Implementation of genotype-guided dosing of warfarin with point-of-care genetic testing in three UK clinics: a matched cohort study. BMC Med 2019; 17:76
Google Scholar | Crossref | Medline36. Kimmel, SE, French, B, Kasner, SE, Johnson, JA, Anderson, JL, Gage, BF, Rosenberg, YD, Eby, CS, Madigan, RA, McBane, RB, Abdel-Rahman, SZ, Stevens, SM, Yale, S, Mohler, ER, Fang, MC, Shah, V, Horenstein, RB, Limdi, NA, Muldowney, JAS, Gujral, J, Delafontaine, P, Desnick, RJ, Ortel, TL, Billett, HH, Pendleton, RC, Geller, NL, Halperin, JL, Goldhaber, SZ, Caldwell, MD, Califf, RM, Ellenberg, JH. A pharmacogenetic versus a clinical algorithm for warfarin dosing. New Engl J Med 2013; 369:2283–93
Google Scholar | Crossref | Medline | ISI37. Pirmohamed, M, Ostrov, DA, Park, BK. New genetic findings lead the way to a better understanding of fundamental mechanisms of drug hypersensitivity. J Allergy Clin Immunol 2015; 136:236–44
Google Scholar | Crossref | Medline38. Beger, RD, Sun, J, Schnackenberg, LK. Metabolomics approaches for discovering biomarkers of drug-induced hepatotoxicity and nephrotoxicity. Toxicol Appl Pharmacol 2010; 243:154–66
Google Scholar | Crossref | Medline39. Viant, MR, Ebbels, TMD, Beger, RD, Ekman, DR, Epps, DJT, Kamp, H, Leonards, PEG, Loizou, GD, MacRae, JI, van Ravenzwaay, B, Rocca-Serra, P, Salek, RM, Walk, T, Weber, RJM. Use cases, best practice and reporting standards for metabolomics in regulatory toxicology. Nat Commun 2019; 10:3041
Google Scholar | Crossref | Medline40. Evans, AM, O'Donovan, C, Playdon, M, Beecher, C, Beger, RD, Bowden, JA, Broadhurst, D, Clish, CB, Dasari, S, Dunn, WB, Griffin, JL, Hartung, T, Hsu, PC, Huan, T, Jans, J, Jones, CM, Kachman, M, Kleensang, A, Lewis, MR, Monge, ME, Mosley, JD, Taylor, E, Tayyari, F, Theodoridis, G, Torta, F, Ubhi, BK, Vuckovic, D. Dissemination and analysis of the quality assurance (QA) and quality control (QC) practices of LC-MS based untargeted metabolomics practitioners. Metabolomics 2020; 16:113
Google Scholar | Crossref | Medline41. Cao, Z, Kamlage, B, Wagner-Golbs, A, Maisha, M, Sun, J, Schnackenberg, LK, Pence, L, Schmitt, TC, Daniels, JR, Rogstad, S, Beger, RD, Yu, LR. An integrated analysis of metabolites, peptides, and inflammation biomarkers for assessment of preanalytical variability of human plasma. J Proteome Res 2019; 18:2411–21
Google Scholar |

留言 (0)

沒有登入
gif