Through a computer monitor darkly: artificial intelligence in absorption, distribution, metabolism and excretion science.

References

• Bender A, Cortés-Ciriano I. 2021a. Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 1: Ways to make an impact, and why we are not there yet. Drug Discovery Today. 26(2):511-524.
   Google Scholar
• Bender A, Cortes-Ciriano I.2021b. Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 2: a discussion of chemical and biological data. Drug Discovery Today. 26(4):1040-1052.
   Google Scholar
• Benet LZ, Hoener BA. 2002. Changes in plasma protein binding have little clinical relevance. Clin Pharm Therap 71(3):15-121.
   Google Scholar
• Benet LZ, Sodhi JK. 2020. Investigating the theoretical basis for in vitro–in vivo extrapolation (ivive) in predicting drug metabolic clearance and proposing future experimental pathways. AAPS J. 22(5):120.
   Google Scholar
• Chan P, Van Gerven T, Dubois, JL, Bernaerts K. 2021. Virtual chemical laboratories: A systematic literature review of research, technologies and instructional design. Computers and Education Open, 2:100053.
   Google Scholar
• Chen S, Li T, Yang L, Zhai F, Jiang X, Xiang R, Ling G.2022. Artificial intelligence-driven prediction of multiple drug interactions. Briefings in Bioinformatics, 23(6): bbac427.
   Google Scholar
• Chou WC, Lin Z.2023. Machine learning and artificial intelligence in physiologically based pharmacokinetic modeling. Toxicol Sci.191(1):1-14.
   Google Scholar
• Creanza TM, Delre P, Ancona N, Lentini G, Saviano M, Mangiatordi GF.2021. Structure- based prediction of hERG-related cardiotoxicity: A benchmark study. J Chem Inf. Model. 61(9):4758-4770.
   Google Scholar
• Devagiri JS, Paheding S, Niyaz Q, Yang X, Smith S. 2022. Augmented Reality and Artificial Intelligence in industry: Trends, tools, and future challenges. Expert Systems with Applications: 18002.
   Google Scholar
• Fourches D, Muratov E, Tropsha, A, 2010. Trust, but verify: on the importance of chemical structure curation in cheminformatics and QSAR modeling research. J Chem Inf. Model. 50(7):1189.
   Google Scholar
• Gandhi YA, Morris ME. 2012. Reevaluation of a quantitative structure pharmacokinetic model for biliary excretion in rats. Drug Met Disp. 40(7):1259-1262.
   Google Scholar
• Gao CA, Howard FM, Markov NS, Dyer EC, Ramesh S, Luo Y, Pearson AT.2022. Comparing scientific abstracts generated by ChatGPT to original abstracts using an artificial intelligence output detector, plagiarism detector, and blinded human reviewers. BioRxiv: 2022-12.
   Google Scholar
• Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P, 2021. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Molecular Diversity. 25:1315-1360.
   Google Scholar
• Hassani H, Silva ES, Unger S, TajMazinani M, Mac Feely S. 2020. Artificial intelligence (AI) or intelligence augmentation (IA): what is the future? Ai. 1(2):8.
   Google Scholar
• Iwata H, Matsuo T, Mamada H, Motomura T, Matsushita M, Fujiwara T, Maeda K, Handa K. 2022. Predicting total drug clearance and volumes of distribution using the machine learning-mediated multimodal method through the imputation of various nonclinical data. J Chem Inf Model. 62(17):4057-4065.
   Google Scholar
• King MR, ChatGPT.2023. A conversation on artificial intelligence, chatbots, and plagiarism in higher education. Cell Mol Bioengineering. 6(1):1-2.
   Google Scholar
• Kirchmair J,Williamson MJ, Tyzack JD, Tan L, Bond PJ, Bender A, Glen RC. 2012. Computational prediction of metabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms. J Chem Inf Model. 52(3):617-648.
   Google Scholar
• Hauben M. 2023. Artificial Intelligence and Data Mining for the Pharmacovigilance of Drug– Drug Interactions. ClinTherap. S0149-2918.
   Google Scholar
• Hines PA, Guy RH, Humphreys AJ, Papaluca-Amati M. 2019. The European Medicines Agency’s goals for regulatory science to 2025. Nature Rev Drug Disc.18(6):403-404.
   Google Scholar
• Kimoto E, Bi, YA, Kosa RE, Tremaine LM, Varma MV. 2017. Hepatobiliary clearance prediction: species scaling from monkey, dog, and rat, and in vitro–in vivo extrapolation of sandwich-cultured human hepatocytes using 17 drugs. J Pharm Sci. 106(9): 2795-2804.
   Google Scholar
• Lalik K, Wątorek F. 2021. Predictive maintenance neural control algorithm for defect detection of the power plants rotating machines using augmented reality goggles. Energies, 14(22):7632.
   Google Scholar
• Lee JW, Maria-Solano MA, Vu TNL, Yoon S, Choi, S. 2022. Big data and artificial intelligence (AI) methodologies for computer-aided drug design (CADD). Biochem Soc Trans. 50(1): 241-252.
   Google Scholar
• Liu G, Catacutan DB, Rathod K, Swanson, K., Jin W, Mohammed J C, Chiappino-Pepe, A, Syed SA, Fragis M, Rachwalski K., Magolan, J. 2023. Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii. Nature Chem Biol. May 25th: 1-9.
   Google Scholar
• Lombardo F, Obach RS, Shalaeva MY, Gao F. 2002. Prediction of volume of distribution values in humans for neutral and basic drugs using physicochemical measurements and plasma protein binding data. J Med Chem. 45(13):2867-2876.
   Google Scholar
• Lombardo F, Waters NJ, Argikar UA, Dennehy MK, Zhan J, Gunduz M, Harriman SP, ,Berellini G, Rajlic IL, Obach, RS. 2013a. Comprehensive assessment of human pharmacokinetic prediction based on in vivo animal pharmacokinetic data, part 1: volume of distribution at steady state. J Clin.Pharm. 53(2):167-177.
   Google Scholar
• Lombardo F, Waters NJ, Argikar UA, Dennehy MK, Zhan J, Gunduz M, Harriman SP, Berellini,G, Rajlic, IL, Obach RS. 2013b. Comprehensive assessment of human pharmacokinetic prediction based on in vivo animal pharmacokinetic data, part 2: clearance. J Clin Pharm. 53(2):178–191.
   Google Scholar
• Lombardo F, Jing, Y. 2016. In silico prediction of volume of distribution in humans. Extensive data set and the exploration of linear and nonlinear methods coupled with molecular interaction fields descriptors. J Chem Inf Model. 56(10):2042-2052.
   Google Scholar
• MacKrill K. 2023. Impact of media coverage on side effect reports from the COVID-19 vaccine. J. Psych. Res.164: .111093.
   Google Scholar
• Martin GL, Jouganous J, Savidan R, Bellec A, Goehrs C, Benkebil M, Miremont G Micallef J, Salvo F, Pariente A, Létinier L. 2022. Validation of artificial intelligence to support the automatic coding of patient adverse drug reaction reports, using nationwide pharmacovigilance data. Drug Safety, 45(5):535-548.
   Google Scholar
• Mulhall A, de Louvois J, Hurley R. 1983. Chloramphenicol toxicity in neonates: its incidence and prevention. Br Med. J (Clin Res Ed.), 287(6403):1424-1427.
   Google Scholar
• Obach RS. 1999. Prediction of human clearance of twenty-nine drugs from hepatic microsomal intrinsic clearance data: an examination of in vitro half-life approach and nonspecific binding to microsomes. Drug Met Disp.27(11): 350-1359.
   Google Scholar
• Obrezanova,O. 2023. Artificial intelligence for compound pharmacokinetics prediction. Curr Opin.Structural Biol.79: 102546.
   Google Scholar
• O’Donovan DH, De Fusco C, Kuhnke L, Reichel A. 2023. Trends in Molecular Properties, Bioavailability, and Permeability across the Bayer Compound Collection: Miniperspective. J Med Chem. 66(4):2347-2360.
   Google Scholar
• Öeren M, Hunt PA, Wharrick CE, Tabatabaei-Ghomi H, Segall MD. 2023. Predicting routes of phase I and II metabolism based on quantum mechanics and machine learning. Xenobiotica, (just-accepted): 1-49.
   Google Scholar
• Øie S, Tozer TN. 1979. Effect of altered plasma protein binding on apparent volume of distribution. J Pharm Sci. 68(9):1203-1205.
   Google Scholar
• Paul D, SanapG, Shenoy S, Kalyane D, Kalia K, Tekade R.K. 2021. Artificial intelligence in drug discovery and development. Drug Disc Today. 26(1):.80
   Google Scholar
• Press B, Di Grandi D.2008. Permeability for intestinal absorption: Caco-2 assay and related issues. Current Drug Met. 9(9):893-900.
   Google Scholar
• Ruzickova E, Skoupa N, Dolezel P, Smith, DA, Mlejnek P. 2019. The lysosomal sequestration of tyrosine kinase inhibitors and drug resistance. Biomolecules. 9(11): 675.
   Google Scholar
• Schneckener S, Grimbs S, Hey, J, Menz S, Osmers M., Schaper S, Hillisch A, Göller, AH. 2019. Prediction of oral bioavailability in rats: Transferring insights from in vitro correlations to (deep) machine learning models using in silico model outputs and chemical structure parameters. J Chem Inf.Model.59. (11):4893-4905.
   Google Scholar
• Seery MK. 2020. Establishing the laboratory as the place to learn how to do chemistry. J Chem Ed. 97(6):1511-1514.
   Google Scholar
• Smith DA, Beaumont K, Maurer TS. Di L. 2015. Volume of distribution in drug design: miniperspective. J Med Chem. 58(15):5691-5698.
   Google Scholar
• Smith DA, Beaumont K, Maurer TS, Di L. 2018. Clearance in drug design: miniperspective. J Med Chem. 62(5):2245-2255.
   Google Scholar
• Smith DA. Jones BC. 1992. Speculations on the substrate structure-activity relationship (SSAR) of cytochrome P450 enzymes. Biochem Pharmacol. 44(11):2089-2098.
   Google Scholar
• Smith DA, van de Waterbeemd H. 1999. Pharmacokinetics and metabolism in early drug discovery. Curr Opinion Chem Biol. 3(4):373-378.
   Google Scholar
• Smith, D.A., Di, L. and Kerns, E.H., 2010. The effect of plasma protein binding on in vivo efficacy: misconceptions in drug discovery. Nature Rev. Drug. Disc., 9(12):929-939.
   Google Scholar
• Smith, D.A. and Rowland, M., 2019. Intracellular and intraorgan concentrations of small molecule drugs: theory, uncertainties in infectious diseases and oncology, and promise. Drug Met Disp. 47(6):665-672.
   Google Scholar
• Weininger D.1988. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem Inf. Comp. Sci. 28(1): 1-36.
   Google Scholar
• Weininger D, Weininger A, Weininger JL. 1989. SMILES. 2. Algorithm for generation of unique SMILES notation. J.Chem Inf Comp Sci. 29(2): 97-101.
   Google Scholar
• Wiest DB, Cochran JB, Tecklenburg FW. 2012. Chloramphenicol toxicity revisited: a 12- year-old patient with a brain abscess. J Ped Pharmacol Therap. 17(2):182-188.
   Google Scholar
• Zang Q,Mansouri K, Williams AJ, Judson RS, Allen DG, Casey WM, Kleinstreuer NC. 2017. In silico prediction of physicochemical properties of environmental chemicals using molecular fingerprints and machine learning. . J Chem Inf Model. 57(1):36-49.
   Google Scholar
• Zhang Y, Deng Z, Xu X, Feng Y, Junliang S. 2023. Application of Artificial Intelligence in Drug–Drug Interactions Prediction: A Review. J Chem Inf Model .
   Google Scholar