Amorim AMB, Piochi LF, Gaspar AT, Preto AJ, Rosário-Ferreira N, Moreira IS (2024) Advancing drug safety in drug development: bridging computational predictions for enhanced toxicity prediction. Chem Res Toxicol 37(6):827–849. https://doi.org/10.1021/acs.chemrestox.3c00352
Article CAS PubMed PubMed Central Google Scholar
Araújo AM, Carvalho F, Guedes de Pinho P, Carvalho M (2021) Toxicometabolomics: small molecules to answer big toxicological questions. Metabolites 11(10):692–722. https://doi.org/10.3390/metabo11100692
Article CAS PubMed PubMed Central Google Scholar
Bohus E, Coen M, Keun HC, Ebbels TM, Beckonert O, Lindon JC, Holmes E, Noszál B, Nicholson JK (2008) Temporal metabonomic modeling of l-arginine-induced exocrine pancreatitis. J Proteome Res 7(10):4435–4445. https://doi.org/10.1021/pr800407j
Article CAS PubMed Google Scholar
Bollard ME, Contel NR, Ebbels TM, Smith L, Beckonert O, Cantor GH, Lehman-McKeeman L, Holmes EC, Lindon JC, Nicholson JK, Keun HC (2010) NMR-based metabolic profiling identifies biomarkers of liver regeneration following partial hepatectomy in the rat. J Proteome Res 9(1):59–69. https://doi.org/10.1021/pr900200v
Article CAS PubMed Google Scholar
Cantor GH, Beckonert O, Bollard ME, Keun HC, Ebbels TM, Antti H, Wijsman JA, Bible RH, Breau AP, Cockerell GL, Holmes E, Lindon JC, Nicholson JK (2013) Integrated histopathological and urinary metabonomic investigation of the pathogenesis of microcystin-LR toxicosis. Vet Pathol 50(1):159–171. https://doi.org/10.1177/0300985812443839
Article CAS PubMed Google Scholar
Ebbels TMD, Keun HC, Beckonert OP, Bollard ME, Lindon JC, Holmes E, Nicholson JK (2007) Prediction and classification of drug toxicity using probabilistic modeling of temporal metabolic data: the consortium on metabonomic toxicology screening approach. J Proteome Res 6(11):4407–4422. https://doi.org/10.1021/pr0703021
Article CAS PubMed Google Scholar
Ericsson AC, Franklin CL (2021) The gut microbiome of laboratory mice: considerations and best practices for translational research. Mamm Genome 32(4):239–250. https://doi.org/10.1007/s00335-021-09863-7
Article PubMed PubMed Central Google Scholar
Garrod S, Bollard ME, Nicholls AW, Connor SC, Connelly J, Nicholson JK, Holmes E (2005) Integrated metabonomic analysis of the multiorgan effects of hydrazine toxicity in the rat. Chem Res Toxicol 18(2):115–122
Article CAS PubMed Google Scholar
Lindon JC, Nicholson JK, Holmes E, Antti H, Bollard ME, Keun H, Beckonert O, Ebbels TM, Reily MD, Robertson D, Stevens GJ, Luke P, Breau AP, Cantor GH, Bible RH, Niederhauser U, Senn H, Schlotterbeck G, Sidelmann UG, Laursen SM, Tymiak A, Car BD, Lehman-McKeeman L, Colet JM, Loukaci A, Thomas C (2003) Contemporary issues in toxicology the role of metabonomics in toxicology and its evaluation by the COMET project. Toxicol Appl Pharmacol 187(3):137–146. https://doi.org/10.1016/s0041-008x(02)00079-0
Article CAS PubMed Google Scholar
Lindon JC, Keun HC, Ebbels TM, Pearce JM, Holmes E, Nicholson JK (2005) The Consortium for Metabonomic Toxicology (COMET): aims, activities and achievements. Pharmacogenomics 6(7):691–699. https://doi.org/10.2217/14622416.6.7.691
Article CAS PubMed Google Scholar
Molon-Noblot S, Boussiquet-Leroux C, Owen RA, Irisarri E, Durand-Cavagna G, Peter CP, Duprat P (1992) Rat urinary bladder hyperplasia induced by oral administration of carbonic anhydrase inhibitors. Toxicol Pathol 20(1):93–102. https://doi.org/10.1177/019262339202000111
Article CAS PubMed Google Scholar
Nicholson JK et al (1989) Quantitative high resolution 1H NMR urinalysis studies on the biochemical effects of cadmium in the rat. Mol Pharmacol 36(3):398–404
Article CAS PubMed Google Scholar
Nicholson et al (2002). https://doi.org/10.1038/nrd728
Olesti E, González-Ruiz V, Wilks MF, Boccard J, Rudaz S (2021) Approaches in metabolomics for regulatory toxicology applications. Analyst 146(6):1820–1834. https://doi.org/10.1039/D0AN02212H
Article CAS PubMed Google Scholar
Owen RA, Durand-Cavagna G, Molon-Noblot S, Boussiquet-Leroux C, Berry PH, Tonkonoh N, Peter CP, Gordon LR (1993) Renal papillary cytoplasmic granularity and potassium depletion induced by carbonic anhydrase inhibitors in rats. Toxicol Pathol 21(5):449–455. https://doi.org/10.1177/019262339302100504
Article CAS PubMed Google Scholar
Radi ZA (2019) Kidney pathophysiology, toxicology, and drug-induced injury in drug development. Int J Toxicol 38(3):215–227. https://doi.org/10.1177/1091581819831701
Article CAS PubMed Google Scholar
Ramaiah SK (2007) A toxicologist guide to the diagnostic interpretation of hepatic biochemical parameters. Food Chem Toxicol 45(9):1551–1557. https://doi.org/10.1016/j.fct.2007.06.007
Article CAS PubMed Google Scholar
Robertson DG (2005) Metabonomics in toxicology: a review. Toxicol Sci 85(2):809–822. https://doi.org/10.1093/toxsci/kfi102
Article CAS PubMed Google Scholar
Robertson DG, Watkins PB, Reily MD (2011) Metabolomics in toxicology: preclinical and clinical applications. Toxicol Sci 120(S1):S146–S170. https://doi.org/10.1093/toxsci/kfq358
Article CAS PubMed Google Scholar
Robosky LC, Wells DF, Egnash LA, Manning ML, Reily MD, Robertson DG (2005) Metabonomic identification of two distinct phenotypes in Sprague-Dawley (Crl:CD(SD)) rats. Toxicol Sci 87(1):277–284. https://doi.org/10.1093/toxsci/kfi214
Article CAS PubMed Google Scholar
Sanins et al (1992). https://doi.org/10.1007/BF01970674
Shockcor JP, Holmes E (2002) Metabonomic applications in toxicity screening and disease diagnosis. Curr Top Med Chem 2(1):35–51. https://doi.org/10.2174/1568026023394498
Article CAS PubMed Google Scholar
Silverman W, Locovei S, Dahl G (2008) Probenecid, a gout remedy, inhibits pannexin 1 channels. Am J Physiol Cell Physiol 295(3):C761–C767. https://doi.org/10.1152/ajpcell.00227.2008
Article CAS PubMed PubMed Central Google Scholar
Smith JR, Bolton ER, Dwinell MR (2019) The Rat: A Model Used in Biomedical Research. In: Hayman G, Smith J, Dwinell M, Shimoyama M (eds) Rat Genomics. Methods in molecular biology, vol 2018. Humana, New York, pp 1–41
Stark C, Steger-Hartmann T (2015) Nonclinical Safety and Toxicology. In: Nielsch U, Fuhrmann U, Jaroch S (eds) New Approaches to Drug Discovery, vol 232. Springer, Cham, pp 261–283
Tosca EM, Bartolucci R, Magni P, Poggesi I (2021) Modeling approaches for reducing safety-related attrition in drug discovery and development: a review on myelotoxicity, immunotoxicity, cardiovascular toxicity, and liver toxicity. Expert Opin Drug Discov 16(11):1365–1390. https://doi.org/10.1080/17460441.2021.1931114
Article CAS PubMed Google Scholar
Tran TTV, Wibowo AS, Tayara H, Chong KT (2023) Artificial intelligence in drug toxicity prediction: recent advances, challenges, and future perspectives. J Chem Inf Model 63(9):2628–2643. https://doi.org/10.1021/acs.jcim.3c00200
Article CAS PubMed Google Scholar
Veselkov KA, Pahomov VI, Lindon JC, Volynkin VS, Crockford D, Osipenko GS, Davies DB, Barton RH, Bang JW, Holmes E, Nicholson JK (2010) A metabolic entropy approach for measurements of systemic metabolic disruptions in patho-physiological States. J Proteome Res 9(7):3537–3544. https://doi.org/10.1021/pr1000576
Article CAS PubMed Google Scholar
Walker PA, Ryder S, Lavado A, Dilworth C, Riley RJ (2020) The evolution of strategies to minimise the risk of human drug-induced liver injury (DILI) in drug discovery and development. Arch Toxicol 94:2559–2585. https://doi.org/10.1007/s00204-020-02763-w
Article CAS PubMed PubMed Central Google Scholar
Wilke RA, Lin DW, Roden DM, Watkins PB, Flockhart D, Zineh I, Giacomini KM, Krauss RM (2007) Identifying genetic risk factors for serious adverse drug reactions: current progress and challenges. Nat Rev Drug Discov 6(11):904–916. https://doi.org/10.1038/nrd2423
Comments (0)