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Current Drug Metabolism

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ISSN (Print): 1389-2002
ISSN (Online): 1875-5453

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Review Article

In Silico Tools to Thaw the Complexity of the Data: Revolutionizing Drug Research in Drug Metabolism, Pharmacokinetics and Toxicity Prediction

Author(s): Hema Sree Kommalapati, Pushpa Pilli, Vijaya Madhyanapu Golla, Nehal Bhatt and Gananadhamu Samanthula*

Volume 24, Issue 11, 2023

Published on: 06 December, 2023

Page: [735 - 755] Pages: 21

DOI: 10.2174/0113892002270798231201111422

Price: $65

Open Access Journals Promotions 2
Abstract

In silico tool is the flourishing pathway for Researchers and budding chemists to strain the analytical data in a snapshot. Traditionally, drug research has heavily relied on labor-intensive experiments, often limited by time, cost, and ethical constraints. In silico tools have paved the way for more efficient and cost-effective drug development processes. By employing advanced computational algorithms, these tools can screen large libraries of compounds, identifying potential toxicities and prioritizing safer drug candidates for further investigation. Integrating in silico tools into the drug research pipeline has significantly accelerated the drug discovery process, facilitating early-stage decision-making and reducing the reliance on resource-intensive experimentation. Moreover, these tools can potentially minimize the need for animal testing, promoting the principles of the 3Rs (reduction, refinement, and replacement) in animal research. This paper highlights the immense potential of in silico tools in revolutionizing drug research. By leveraging computational models to predict drug metabolism, pharmacokinetics, and toxicity. Researchers can make informed decisions and prioritize the most promising drug candidates for further investigation. The synchronicity of In silico tools in this article on trending topics is insightful and will play an increasingly integral role in expediting drug development.

Keywords: In silico tools, drug metabolism and pharmacokinetics (DMPK), toxicity studies, computational models, simulations, ADME.

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[1]
Shaker, B.; Ahmad, S.; Lee, J.; Jung, C.; Na, D. In silico methods and tools for drug discovery. Comput. Biol. Med., 2021, 137, 104851.
[http://dx.doi.org/10.1016/j.compbiomed.2021.104851] [PMID: 34520990]
[2]
Barh, D. Chapter 21- In silico models: From simple networks to complex diseases. In: Animal Biotechnology; Elsevier, 2014; pp. 385-404.
[http://dx.doi.org/10.1016/B978-0-12-416002-6.00021-3]
[3]
Ekins, S.; Mestres, J.; Testa, B. In silico pharmacology for drug discovery: Methods for virtual ligand screening and profiling. Br. J. Pharmacol., 2007, 152(1), 9-20.
[http://dx.doi.org/10.1038/sj.bjp.0707305] [PMID: 17549047]
[4]
Sliwoski, G.; Kothiwale, S.; Meiler, J.; Lowe, E.W., Jr Computational methods in drug discovery. Pharmacol. Rev., 2014, 66(1), 334-395.
[http://dx.doi.org/10.1124/pr.112.007336] [PMID: 24381236]
[5]
Pelkonen, O.; Turpeinen, M.; Raunio, H. In vivo-in vitro-in silico pharmacokinetic modelling in drug development: Current status and future directions. Clin. Pharmacokinet., 2011, 50(8), 483-491.
[http://dx.doi.org/10.2165/11592400-000000000-00000] [PMID: 21740072]
[6]
Hemmerich, J.; Ecker, G.F. In silico toxicology: From structure-activity relationships towards deep learning and adverse outcome pathways. Wiley Interdiscip. Rev. Comput. Mol. Sci., 2020, 10(4), e1475.
[http://dx.doi.org/10.1002/wcms.1475] [PMID: 35866138]
[7]
Hinkson, I.V.; Madej, B.; Stahlberg, E.A. Accelerating therapeutics for opportunities in medicine: A paradigm shift in drug discovery. Front. Pharmacol., 2020, 11, 770.
[http://dx.doi.org/10.3389/fphar.2020.00770] [PMID: 32694991]
[8]
Gajula, S.N.R.; Nathani, T.N.; Patil, R.M.; Talari, S.; Sonti, R. Aldehyde oxidase mediated drug metabolism: An underpredicted obstacle in drug discovery and development. Drug Metab. Rev., 2022, 54(4), 427-448.
[http://dx.doi.org/10.1080/03602532.2022.2144879] [PMID: 36369949]
[9]
Gajula, S.N.R.; Vora, S.A.; Dikundwar, A.G.; Sonti, R. In vitro drug metabolism studies using human liver microsomes. In: Dosage Forms; IntechOpen, 2022.
[10]
Rao Gajula, S.N.; Pillai, M.S.; Samanthula, G.; Sonti, R. Cytochrome P450 enzymes: A review on drug metabolizing enzyme inhibition studies in drug discovery and development. Bioanalysis, 2021, 13(17), 1355-1378.
[http://dx.doi.org/10.4155/bio-2021-0132] [PMID: 34517735]
[11]
Rao Gajula, S.N.; Reddy, G.N.; Reddy, D.S.; Sonti, R. Pharmacokinetic drug-drug interactions: An insight into recent US FDA-approved drugs for prostate cancer. Bioanalysis, 2020, 12(22), 1647-1664.
[http://dx.doi.org/10.4155/bio-2020-0242] [PMID: 33156691]
[12]
Dahlgren, D.; Lennernäs, H. Intestinal permeability and drug absorption: Predictive experimental, computational and in vivo approaches. Pharmaceutics, 2019, 11(8), 411.
[http://dx.doi.org/10.3390/pharmaceutics11080411] [PMID: 31412551]
[13]
Wanat, K. Biological barriers, and the influence of protein binding on the passage of drugs across them. Mol. Biol. Rep., 2020, 47(4), 3221-3231.
[http://dx.doi.org/10.1007/s11033-020-05361-2] [PMID: 32140957]
[14]
Gajula, S.N.R.; Bale, D.N.J.; Nanjappan, S.K. Analytical and omics approaches in the identification of oxidative stress-induced cancer biomarkers. In: Handbook of Oxidative Stress in Cancer: Mechanistic Aspects; Springer: Singapore, 2020.
[15]
Gajula, S.N.R.; Khairnar, A.S.; Jock, P.; Kumari, N.; Pratima, K.; Munjal, V.; Kalan, P.; Sonti, R. LC-MS/MS: A sensitive and selective analytical technique to detect COVID-19 protein biomarkers in the early disease stage. Expert Rev. Proteomics, 2023, 20(1-3), 5-18.
[http://dx.doi.org/10.1080/14789450.2023.2191845] [PMID: 36919634]
[16]
Gajula, S.N.R. Chapter 5- Metabolomics: A recent advanced omics technology in herbal medicine research. In: Medicinal and Aromatic Plants; Elsevier, 2021; pp. 97-117.
[17]
Ortwine, D.F.; Aliagas, I. Physicochemical and DMPK in silico models: Facilitating their use by medicinal chemists. Mol. Pharm., 2013, 10(4), 1153-1161.
[http://dx.doi.org/10.1021/mp3006193] [PMID: 23402361]
[18]
Pähler, A.; Brink, A. Software aided approaches to structure-based metabolite identification in drug discovery and development. Drug Discov. Today. Technol., 2013, 10(1), e207-e217.
[http://dx.doi.org/10.1016/j.ddtec.2012.12.001] [PMID: 24050249]
[19]
Kirchmair, J.; Williamson, M.J.; Tyzack, J.D.; Tan, L.; Bond, P.J.; Bender, A.; Glen, R.C. Computational prediction of metabolism: Sites, products, SAR, P450 enzyme dynamics, and mechanisms. J. Chem. Inf. Model., 2012, 52(3), 617-648.
[http://dx.doi.org/10.1021/ci200542m] [PMID: 22339582]
[20]
Dixit, V.A.; Lal, L.A.; Agrawal, S.R. Recent advances in the prediction of non‐ CYP450 ‐mediated drug metabolism. Wiley Interdiscip. Rev. Comput. Mol. Sci., 2017, 7(6), e1323.
[http://dx.doi.org/10.1002/wcms.1323]
[21]
Wu, F.; Zhou, Y.; Li, L.; Shen, X.; Chen, G.; Wang, X.; Liang, X.; Tan, M.; Huang, Z. Computational approaches in preclinical studies on drug discovery and development. Front Chem., 2020, 8, 726.
[http://dx.doi.org/10.3389/fchem.2020.00726] [PMID: 33062633]
[22]
Cheng, F.; Li, W.; Liu, G.; Tang, Y. In silico ADMET prediction: Recent advances, current challenges and future trends. Curr. Top. Med. Chem., 2013, 13(11), 1273-1289.
[http://dx.doi.org/10.2174/15680266113139990033] [PMID: 23675935]
[23]
Krüger, A.; Gonçalves Maltarollo, V.; Wrenger, C.; Kronenberger, T. ADME profiling in drug discovery and a new path paved on silica. In: Drug discovery and development-new advances; IntechOpen, 2019.
[24]
Subramanian, K. TruPK - human pharmacokinetic models for quantitative ADME prediction. Expert Opin. Drug Metab. Toxicol., 2005, 1(3), 555-564.
[http://dx.doi.org/10.1517/17425255.1.3.555] [PMID: 16863461]
[25]
Storelli, F.; Yin, M.; Kumar, A.R.; Ladumor, M.K.; Evers, R.; Chothe, P.P.; Enogieru, O.J.; Liang, X.; Lai, Y.; Unadkat, J.D. The next frontier in ADME science: Predicting transporter-based drug disposition, tissue concentrations and drug-drug interactions in humans. Pharmacol. Ther., 2022, 238, 108271.
[http://dx.doi.org/10.1016/j.pharmthera.2022.108271] [PMID: 36002079]
[26]
Khan, M.; Sylte, I.; Khan, M.; Sylte, I. Predictive QSAR modeling for the successful predictions of the ADMET properties of candidate drug molecules. Curr. Drug Discov. Technol., 2007, 4(3), 141-149.
[http://dx.doi.org/10.2174/157016307782109706] [PMID: 17985997]
[27]
Pantaleão, S.Q.; Fernandes, P.O.; Gonçalves, J.E.; Maltarollo, V.G.; Honorio, K.M. Recent advances in the prediction of pharmacokinetics properties in drug design studies: A review. ChemMedChem, 2022, 17(1), e202100542.
[http://dx.doi.org/10.1002/cmdc.202100542] [PMID: 34655454]
[28]
Rydberg, P.; Gloriam, D.E.; Zaretzki, J.; Breneman, C.; Olsen, L. SMARTCyp: A 2D method for prediction of cytochrome P450-mediated drug metabolism. ACS Med. Chem. Lett., 2010, 1(3), 96-100.
[http://dx.doi.org/10.1021/ml100016x] [PMID: 24936230]
[29]
Panneerselvam, S.; Yesudhas, D.; Durai, P.; Anwar, M.; Gosu, V.; Choi, S. A combined molecular docking/dynamics approach to probe the binding mode of cancer drugs with cytochrome P450 3A4. Molecules, 2015, 20(8), 14915-14935.
[http://dx.doi.org/10.3390/molecules200814915] [PMID: 26287147]
[30]
Olsen, L.; Montefiori, M.; Tran, K.P.; Jørgensen, F.S. SMARTCyp 3.0: Enhanced cytochrome P450 site-of-metabolism prediction server. Bioinformatics, 2019, 35(17), 3174-3175.
[http://dx.doi.org/10.1093/bioinformatics/btz037] [PMID: 30657882]
[31]
Frechen, S.; Rostami-Hodjegan, A. Quality assurance of PBPK modeling platforms and guidance on building, evaluating, verifying and applying PBPK models prudently under the umbrella of qualification: Why, when, what, how and by whom? Pharm. Res., 2022, 39(8), 1733-1748.
[http://dx.doi.org/10.1007/s11095-022-03250-w] [PMID: 35445350]
[32]
Plus, S. Worldwide model-informed drug development., Available from: https://www.simulations-plus.com/
[33]
Arafat, M.; Sarfraz, M.; AbuRuz, S. Development and in vitro evaluation of controlled release viagra® containing poloxamer-188 using gastroplus™ pbpk modeling software for in vivo predictions and pharmacokinetic assessments. Pharmaceuticals, 2021, 14(5), 479.
[http://dx.doi.org/10.3390/ph14050479] [PMID: 34070160]
[34]
Honório, T.S.; Pinto, E.C.; Rocha, H.V.A.; Esteves, V.S.A.D.; dos Santos, T.C.; Castro, H.C.R.; Rodrigues, C.R.; de Sousa, V.P.; Cabral, L.M. In vitro-in vivo correlation of Efavirenz tablets using GastroPlus®. AAPS PharmSciTech, 2013, 14(3), 1244-1254.
[http://dx.doi.org/10.1208/s12249-013-0016-4] [PMID: 23943401]
[35]
George, J.K.; Singh, S.K.; Verma, P.R.P. In vivo in silico pharmacokinetic simulation studies of carvedilol-loaded nanocapsules using GastroPlus™. Ther. Deliv., 2016, 7(5), 305-318.
[http://dx.doi.org/10.4155/tde-2015-0004] [PMID: 27075951]
[36]
Okumu, A.; DiMaso, M.; Löbenberg, R. Computer simulations using GastroPlus™ to justify a biowaiver for etoricoxib solid oral drug products. Eur. J. Pharm. Biopharm., 2009, 72(1), 91-98.
[http://dx.doi.org/10.1016/j.ejpb.2008.10.019] [PMID: 19056493]
[37]
Rao Gajula, S.N.; Talari, S.; Nathani, T.N.; Munjal, V.; Rahman, Z.; Dandekar, M.P.; Sonti, R. Effect of chronopharmacology and food on in vivo pharmacokinetic profile of mavacamten. Bioanalysis, 2023, 15(12)
[http://dx.doi.org/10.4155/bio-2023-0030]
[38]
Gajula, S.N.R.; Talari, S.; Chilvery, S.; Chandraiah, G.; Sonti, R. A unique in vivo pharmacokinetic profile, in vitro metabolic stability, and hepatic first-pass metabolism of garcinol, a promising novel anticancer phytoconstituent, by liquid chromatography-mass spectrometry. RPS Pharma. Pharmacol. Reports, 2023, rqad017,
[39]
Anchi, P.; Chilvery, S.; Tekalkar, S.; bolla, L.; Rao Gajula, S.N.; Sonti, R.; Godugu, C. Nimbolide loaded sustained release microparticles as single-dose formulations for effective management of arthritis. J. Drug Deliv. Sci. Technol., 2022, 75, 103638.
[http://dx.doi.org/10.1016/j.jddst.2022.103638]
[40]
Song, J.C.; Gao, H.; Qiu, H.B.; Chen, Q.B.; Cai, M.H.; Zhang, M.Z.; Lu, Z.J. The pharmacokinetics of dexmedetomidine in patients with obstructive jaundice: A clinical trial. PLoS One, 2018, 13(11), e0207427.
[http://dx.doi.org/10.1371/journal.pone.0207427] [PMID: 30427948]
[41]
Riva, A.; Ronchi, M.; Petrangolini, G.; Bosisio, S.; Allegrini, P. Improved oral absorption of quercetin from quercetin phytosome®, a new delivery system based on food grade lecithin. Eur. J. Drug Metab. Pharmacokinet., 2019, 44(2), 169-177.
[http://dx.doi.org/10.1007/s13318-018-0517-3] [PMID: 30328058]
[42]
Burmańczuk, A.; Wojciechowska, B.; Gbylik-Sikorska, M.; Gajda, A.; Markiewicz, W.; Sosin, E.; Grabowski, T. Baicalin decreases somatic cell count in mastitis of dairy cows. Ann. Anim. Sci., 2021, 21(2), 485-496.
[http://dx.doi.org/10.2478/aoas-2021-0019]
[43]
Farrier, D.S. PK Solutions 2.0. Noncompartmental pharmacokinetics data analysis; Summit Research Services: Ashland, USA, 2003.
[44]
Gomez, D.S.; Sanches-Giraud, C.; Silva, C.V., Jr; Oliveira, A.M.R.R.; da Silva, J.M., Jr; Gemperli, R.; Santos, S.R.C.J. Imipenem in burn patients: Pharmacokinetic profile and PK/PD target attainment. J. Antibiot. , 2015, 68(3), 143-147.
[http://dx.doi.org/10.1038/ja.2014.121] [PMID: 25227503]
[45]
Al-Gahtany, M.; Karunakaran, G.; Munisamy, M. Pharmacogenetics of CYP3A5 on carbamazepine pharmacokinetics in epileptic patients developing toxicity. BMC Genomics, 2014, 15(S2), P2.
[http://dx.doi.org/10.1186/1471-2164-15-S2-P2]
[46]
Johansson, F.; Paterson, R. Physiologically based in silico models for the prediction of oral drug absorption. In: Drug Absorption Studies: In Situ, in vitro and in silico Models; Springer: Boston, MA, 2008; pp. 486-509.
[47]
Madden, J.C.; Pawar, G.; Cronin, M.T.D.; Webb, S.; Tan, Y.M.; Paini, A. In silico resources to assist in the development and evaluation of physiologically-based kinetic models. Comput. Toxicol., 2019, 11, 33-49.
[http://dx.doi.org/10.1016/j.comtox.2019.03.001]
[48]
Johnson, K.C. Mechanistic modeling of gastrointestinal motility with integrated dissolution for simulating drug absorption. ADMET DMPK, 2020, 8(3), 314-324.
[http://dx.doi.org/10.5599/admet.829] [PMID: 35300303]
[49]
Willmann, S.; Lippert, J.; Sevestre, M.; Solodenko, J.; Fois, F.; Schmitt, W. PK-Sim®: A physiologically based pharmacokinetic ‘whole-body’ model. BIOSILICO, 2003, 1(4), 121-124.
[http://dx.doi.org/10.1016/S1478-5382(03)02342-4]
[50]
Zhang, X.; Luo, T.; Yang, H.; Ma, W.Y.; He, Q.; Xu, M.; Yang, Y. Physiologically-based pharmacokinetic modeling of tenofovir disoproxil fumarate in pregnant women. Curr. Drug Metab., 2022, 23(14), 1115-1123.
[http://dx.doi.org/10.2174/1389200224666230130093314] [PMID: 36718061]
[51]
Basu, S.; Lien, Y.T.K.; Vozmediano, V.; Schlender, J.F.; Eissing, T.; Schmidt, S.; Niederalt, C. Physiologically based pharmacokinetic modeling of monoclonal antibodies in pediatric populations using PK-Sim. Front. Pharmacol., 2020, 11, 868.
[http://dx.doi.org/10.3389/fphar.2020.00868] [PMID: 32595502]
[52]
Dallmann, A.; Ince, I.; Solodenko, J.; Meyer, M.; Willmann, S.; Eissing, T.; Hempel, G. Physiologically based pharmacokinetic modeling of renally cleared drugs in pregnant women. Clin. Pharmacokinet., 2017, 56(12), 1525-1541.
[http://dx.doi.org/10.1007/s40262-017-0538-0] [PMID: 28391404]
[53]
Rüdesheim, S.; Selzer, D.; Fuhr, U.; Schwab, M.; Lehr, T. Physiologically‐based pharmacokinetic modeling of dextromethorphan to investigate interindividual variability within CYP2D6 activity score groups. CPT Pharmacomet. Syst. Pharmacol., 2022, 11(4), 494-511.
[http://dx.doi.org/10.1002/psp4.12776] [PMID: 35257505]
[54]
Liu, X.I.; Dallmann, A.; Brooks, K.; Best, B.M.; Clarke, D.F.; Mirochnick, M.; van den Anker, J.N.; Capparelli, E.V.; Momper, J.D. Physiologically‐based pharmacokinetic modeling of remdesivir and its metabolites in pregnant women with COVID‐19. CPT Pharmacometrics Syst. Pharmacol., 2023, 12(2), 148-153.
[http://dx.doi.org/10.1002/psp4.12900] [PMID: 36479969]
[55]
Ioakimidis, L.; Thoukydidis, L.; Mirza, A.; Naeem, S.; Reynisson, J. Benchmarking the reliability of QikProp. Correlation between experimental and predicted values. QSAR Comb. Sci., 2008, 27(4), 445-456.
[http://dx.doi.org/10.1002/qsar.200730051]
[56]
Laoui, A.; Polyakov, V.R. Web services as applications’ integration tool: QikProp case study. J. Comput. Chem., 2011, 32(9), 1944-1951.
[http://dx.doi.org/10.1002/jcc.21778] [PMID: 21455963]
[57]
Gajula, S.N.R.; Nadimpalli, N.; Sonti, R. Drug metabolic stability in early drug discovery to develop potential lead compounds. Drug Metab. Rev., 2021, 53(3), 459-477.
[http://dx.doi.org/10.1080/03602532.2021.1970178] [PMID: 34406889]
[58]
Reddy, K.K.; Singh, S.K.; Tripathi, S.K.; Selvaraj, C.; Suryanarayanan, V. Shape and pharmacophore-based virtual screening to identify potential cytochrome P450 sterol 14α-demethylase inhibitors. J. Recept. Signal Transduct. Res., 2013, 33(4), 234-243.
[http://dx.doi.org/10.3109/10799893.2013.789912] [PMID: 23638723]
[59]
Byvatov, E.; Baringhaus, K.H.; Schneider, G.; Matter, H. A virtual screening filter for identification of cytochrome P450 2C9 (CYP2C9) inhibitors. QSAR Comb. Sci., 2007, 26(5), 618-628.
[http://dx.doi.org/10.1002/qsar.200630143]
[60]
Muthiah, I.; Rajendran, K.; Dhanaraj, P. In silico molecular docking and physicochemical property studies on effective phytochemicals targeting GPR116 for breast cancer treatment. Mol. Cell. Biochem., 2021, 476(2), 883-896.
[http://dx.doi.org/10.1007/s11010-020-03953-x] [PMID: 33106912]
[61]
Yang, H.; Sun, L.; Li, W.; Liu, G.; Tang, Y. In silico prediction of chemical toxicity for drug design using machine learning methods and structural alerts. Front Chem., 2018, 6, 30.
[http://dx.doi.org/10.3389/fchem.2018.00030] [PMID: 29515993]
[62]
Rim, K.T. In silico prediction of toxicity and its applications for chemicals at work. Toxicol. Environ. Health Sci., 2020, 12(3), 191-202.
[http://dx.doi.org/10.1007/s13530-020-00056-4] [PMID: 32421081]
[63]
Raies, A.B.; Bajic, V.B. In silico toxicology: Computational methods for the prediction of chemical toxicity. Wiley Interdiscip. Rev. Comput. Mol. Sci., 2016, 6(2), 147-172.
[http://dx.doi.org/10.1002/wcms.1240] [PMID: 27066112]
[64]
Parthasarathi, R. Chapter 5- In silico approaches for predictive toxicology. In: In vitro Toxicology; Elsevier, 2018; pp. 91-109.
[http://dx.doi.org/10.1016/B978-0-12-804667-8.00005-5]
[65]
Segall, M.D.; Barber, C. Addressing toxicity risk when designing and selecting compounds in early drug discovery. Drug Discov. Today, 2014, 19(5), 688-693.
[http://dx.doi.org/10.1016/j.drudis.2014.01.006] [PMID: 24451294]
[66]
Ahuja, V.; Krishnappa, M.; Kandarova, H. In silico toxicity prediction using Derek Nexus® for skin sensitization, phototoxicity, hepatotoxicity and in vitro hERG inhibition. Toxicol. Lett., 2021, 350, S250-S250.
[67]
Patlewicz, G.; Fitzpatrick, J.M. Current and future perspectives on the development, evaluation, and application of in silico approaches for predicting toxicity. Chem. Res. Toxicol., 2016, 29(4), 438-451.
[http://dx.doi.org/10.1021/acs.chemrestox.5b00388] [PMID: 26686752]
[68]
Judson, P. DEREK-predicting toxicity. Knowledge-based expert systems in chemistry: Artificial intelligence in decision making, 2nd ed; Royal Society of Chemistry: London, 2019, pp. 125-133.
[69]
Golla, V.M.; Kushwah, B.S.; Dhiman, V.; Velip, L.; Samanthula, G. LC-HRMS and NMR studies for characterization of forced degradation impurities of ponatinib, a tyrosine kinase inhibitor, insights into in-silico degradation and toxicity profiles. J. Pharm. Biomed. Anal., 2023, 227, 115280.
[http://dx.doi.org/10.1016/j.jpba.2023.115280] [PMID: 36773542]
[70]
Arvidson, K.B. FDA toxicity databases and real-time data entry. Toxicol. Appl. Pharmacol., 2008, 233(1), 17-19.
[http://dx.doi.org/10.1016/j.taap.2007.12.033] [PMID: 18656494]
[71]
Burton, J.; Worth, A.P.; Tsakovska, I.; Diukendjieva, A. In silico models for acute systemic toxicit. Methods Mol. Biol., 2016, 177-200.
[http://dx.doi.org/10.1007/978-1-4939-3609-0_10]
[72]
Hsieh, J.H.; Sedykh, A.; Mutlu, E.; Germolec, D.R.; Auerbach, S.S.; Rider, C.V. Harnessing in silico, in vitro, and in vivo data to understand the toxicity landscape of polycyclic aromatic compounds (PACs). Chem. Res. Toxicol., 2021, 34(2), 268-285.
[http://dx.doi.org/10.1021/acs.chemrestox.0c00213] [PMID: 33063992]
[73]
Huang, R.; Xia, M.; Sakamuru, S.; Zhao, J.; Shahane, S.A.; Attene-Ramos, M.; Zhao, T.; Austin, C.P.; Simeonov, A. Modelling the Tox21 10 K chemical profiles for in vivo toxicity prediction and mechanism characterization. Nat. Commun., 2016, 7(1), 10425.
[http://dx.doi.org/10.1038/ncomms10425] [PMID: 26811972]
[74]
Madden, J.C.; Enoch, S.J.; Paini, A.; Cronin, M.T.D. A review of in silico tools as alternatives to animal testing: Principles, resources and applications. Altern. Lab. Anim., 2020, 48(4), 146-172.
[http://dx.doi.org/10.1177/0261192920965977] [PMID: 33119417]
[75]
Lo Piparo, E.; Worth, A. Review of QSAR models and software tools for predicting developmental and reproductive toxicity; JRC European Commission, 2010.
[76]
Rusyn, I.; Sedykh, A.; Low, Y.; Guyton, K.Z.; Tropsha, A. Predictive modeling of chemical hazard by integrating numerical descriptors of chemical structures and short-term toxicity assay data. Toxicol. Sci., 2012, 127(1), 1-9.
[http://dx.doi.org/10.1093/toxsci/kfs095] [PMID: 22387746]
[77]
Roper, C. Chapter 40- Tox21 and adverse outcome pathways. In: An Introduction to Interdisciplinary Toxicology; Elsevier, 2020; pp. 559-568.
[http://dx.doi.org/10.1016/B978-0-12-813602-7.00040-5]
[78]
Idakwo, G.; Thangapandian, S.; Luttrell, J.IV.; Zhou, Z.; Zhang, C.; Gong, P. Deep learning-based structure-activity relationship modeling for multi-category toxicity classification: A case study of 10K Tox21 chemicals with high-throughput cell-based androgen receptor bioassay data. Front. Physiol., 2019, 10, 1044.
[http://dx.doi.org/10.3389/fphys.2019.01044] [PMID: 31456700]
[79]
Hardy, B.; Douglas, N.; Helma, C.; Rautenberg, M.; Jeliazkova, N.; Jeliazkov, V.; Nikolova, I.; Benigni, R.; Tcheremenskaia, O.; Kramer, S.; Girschick, T.; Buchwald, F.; Wicker, J.; Karwath, A.; Gütlein, M.; Maunz, A.; Sarimveis, H.; Melagraki, G.; Afantitis, A.; Sopasakis, P.; Gallagher, D.; Poroikov, V.; Filimonov, D.; Zakharov, A.; Lagunin, A.; Gloriozova, T.; Novikov, S.; Skvortsova, N.; Druzhilovsky, D.; Chawla, S.; Ghosh, I.; Ray, S.; Patel, H.; Escher, S. Collaborative development of predictive toxicology applications. J. Cheminform., 2010, 2(1), 7.
[http://dx.doi.org/10.1186/1758-2946-2-7] [PMID: 20807436]
[80]
Jeliazkova, N.; Jeliazkov, V. AMBIT RESTful web services: An implementation of the OpenTox application programming interface. J. Cheminform., 2011, 3(1), 18.
[http://dx.doi.org/10.1186/1758-2946-3-18] [PMID: 21575202]
[81]
Williams, A.J.; Harland, L.; Groth, P.; Pettifer, S.; Chichester, C.; Willighagen, E.L.; Evelo, C.T.; Blomberg, N.; Ecker, G.; Goble, C.; Mons, B. Open PHACTS: Semantic interoperability for drug discovery. Drug Discov. Today, 2012, 17(21-22), 1188-1198.
[http://dx.doi.org/10.1016/j.drudis.2012.05.016] [PMID: 22683805]
[82]
Samwald, M.; Jentzsch, A.; Bouton, C.; Kallesøe, C.S.; Willighagen, E.; Hajagos, J.; Marshall, M.S.; Prud’hommeaux, E.; Hassanzadeh, O.; Pichler, E.; Stephens, S. Linked open drug data for pharmaceutical research and development. J. Cheminform., 2011, 3(1), 19.
[http://dx.doi.org/10.1186/1758-2946-3-19] [PMID: 21575203]
[83]
Banerjee, P.; Eckert, A.O.; Schrey, A.K.; Preissner, R. ProTox-II: A webserver for the prediction of toxicity of chemicals. Nucleic Acids Res., 2018, 46(W1), W257-W263.
[http://dx.doi.org/10.1093/nar/gky318] [PMID: 29718510]
[84]
Pawar, B. Essentials of Pharmatoxicology in Drug Research; Elsevier, 2023.
[85]
Drwal, M.N.; Banerjee, P.; Dunkel, M.; Wettig, M.R.; Preissner, R. ProTox: A web server for the in silico prediction of rodent oral toxicity. Nucleic Acids Res., 2014, 42(W1), W53-W58.
[http://dx.doi.org/10.1093/nar/gku401] [PMID: 24838562]
[86]
Vo, A.H.; Van Vleet, T.R.; Gupta, R.R.; Liguori, M.J.; Rao, M.S. An overview of machine learning and big data for drug toxicity evaluation. Chem. Res. Toxicol., 2020, 33(1), 20-37.
[http://dx.doi.org/10.1021/acs.chemrestox.9b00227] [PMID: 31625725]
[87]
N, S.; M, R.K.; N, A.K.; S, B.; N K, U.P. In silico evaluation of multispecies toxicity of natural compounds. Drug Chem. Toxicol., 2021, 44(5), 480-486.
[http://dx.doi.org/10.1080/01480545.2019.1614023] [PMID: 31111731]
[88]
Toropov, A.A.; Toropova, A.P.; Mukhamedzhanoval, D.V.; Gutman, I. Simplified molecular input line entry system (SMILES) as an alternative for constructing quantitative structure-property relationships; QSPR, 2005.
[89]
Tetko, I.V.; Bruneau, P.; Mewes, H.W.; Rohrer, D.C.; Poda, G.I. Can we estimate the accuracy of ADME-Tox predictions? Drug Discov. Today, 2006, 11(15-16), 700-707.
[http://dx.doi.org/10.1016/j.drudis.2006.06.013] [PMID: 16846797]
[90]
Talapatra, S.N.; Sarkar, A. Acute toxicity prediction of synthetic and natural preservatives in rat by using QSAR modeling software. Int. J. Adv. Res. , 2015, 3(7), 1424-1438.
[91]
Schultz, T.W.; Diderich, R.; Kuseva, C.D.; Mekenyan, O.G. The OECD QSAR toolbox starts its second decade. Computat. Toxicol. Methods Protocol, 2018, 55-77.
[92]
Kuseva, C.; Schultz, T.W.; Yordanova, D.; Ivanova, H.; Tankova, K.; Pavlov, T.; Chapkanov, A.; Chankov, G.; Georgiev, M.; Gissi, A.; Sobanski, T.; Mekenyan, O.G. Category consistency in the OECD QSAR Toolbox: Assessment and reporting tool to justify read-across. Comput. Toxicol., 2019, 11, 65-71.
[http://dx.doi.org/10.1016/j.comtox.2019.03.002]
[93]
Yordanova, D.; Schultz, T.W.; Kuseva, C.; Ivanova, H.; Pavlov, T.; Chankov, G.; Karakolev, Y.; Gissi, A.; Sobanski, T.; Mekenyan, O.G. Alert performance: A new functionality in the OECD QSAR Toolbox. Comput. Toxicol., 2019, 10, 26-37.
[http://dx.doi.org/10.1016/j.comtox.2018.12.003]
[94]
Yordanova, D.; Kuseva, C.; Tankova, K.; Pavlov, T.; Chankov, G.; Chapkanov, A.; Gissi, A.; Sobanski, T.; Schultz, T.W.; Mekenyan, O.G. Using metabolic information for categorization and read-across in the OECD QSAR Toolbox. Comput. Toxicol., 2019, 12, 100102.
[http://dx.doi.org/10.1016/j.comtox.2019.100102]
[95]
Dimitrov, S.D.; Diderich, R.; Sobanski, T.; Pavlov, T.S.; Chankov, G.V.; Chapkanov, A.S.; Karakolev, Y.H.; Temelkov, S.G.; Vasilev, R.A.; Gerova, K.D.; Kuseva, C.D.; Todorova, N.D.; Mehmed, A.M.; Rasenberg, M.; Mekenyan, O.G. QSAR Toolbox - workflow and major functionalities. SAR QSAR Environ. Res., 2016, 27(3), 203-219.
[http://dx.doi.org/10.1080/1062936X.2015.1136680] [PMID: 26892800]
[96]
El Mchichi, L.; El Aissouq, A.; Kasmi, R.; Belhassan, A.; El-Mernissi, R.; Ouammou, A.; Lakhlifi, T.; Bouachrine, M. In silico design of novel Pyrazole derivatives containing thiourea skeleton as anti-cancer agents using: 3D QSAR, Drug-Likeness studies, ADMET prediction and molecular docking. Mater. Today Proc., 2021, 45, 7661-7674.
[http://dx.doi.org/10.1016/j.matpr.2021.03.152]
[97]
Goudzal, A.; El Aissouq, A.; El Hamdani, H.; Ouammou, A. QSAR modeling, molecular docking studies and ADMET prediction on a series of phenylaminopyrimidine-(thio) urea derivatives as CK2 inhibitors. Mater. Today Proc., 2022, 51, 1851-1862.
[http://dx.doi.org/10.1016/j.matpr.2020.08.044]
[98]
Kumar, A.; Kini, S.G.; Rathi, E. A recent appraisal of artificial intelligence and in silico ADMET prediction in the early stages of drug discovery. Mini Rev. Med. Chem., 2021, 21(18), 2788-2800.
[http://dx.doi.org/10.2174/1389557521666210401091147] [PMID: 33797376]
[99]
Roncaglioni, A.; Lombardo, A.; Benfenati, E. The VEGAHUB Platform: The philosophy and the tools. Altern. Lab. Anim., 2022, 50(2), 121-135.
[http://dx.doi.org/10.1177/02611929221090530] [PMID: 35382564]
[100]
Mombelli, E. In silico prediction of chemically induced mutagenicity: A weight of evidence approach integrating information from QSAR models and read-across predictions. In: In silico Methods for Predicting Drug Toxicity; Springer, 2022; pp. 149-183.
[http://dx.doi.org/10.1007/978-1-0716-1960-5_7]
[101]
Benfenati, E.; Roncaglioni, A.; Lombardo, A.; Manganaro, A. Integrating QSAR, read-across, and screening tools: the VEGAHUB platform as an example. In: Advances in Computational Toxicology: Methodologies and Applications in Regulatory Science; Springer, 2019; pp. 365-381.
[102]
Nasrullah, I.; Kartasasmita, R.E.; Damayanti, S. Advances in computer science research. 3rd International Conference on Computation for Science and Technology (ICCST-3) 2015, pp. 49-58.
[103]
Grisoni, F.; Consonni, V.; Villa, S.; Vighi, M.; Todeschini, R. QSAR models for bioconcentration: Is the increase in the complexity justified by more accurate predictions? Chemosphere, 2015, 127, 171-179.
[http://dx.doi.org/10.1016/j.chemosphere.2015.01.047] [PMID: 25703779]
[104]
Galati, S.; Di Stefano, M.; Martinelli, E.; Macchia, M.; Martinelli, A.; Poli, G.; Tuccinardi, T. VenomPred: A machine learning based platform for molecular toxicity predictions. Int. J. Mol. Sci., 2022, 23(4), 2105.
[http://dx.doi.org/10.3390/ijms23042105] [PMID: 35216217]
[105]
Prival, M.J. Evaluation of the TOPKAT system for predicting the carcinogenicity of chemicals. Environ. Mol. Mutagen., 2001, 37(1), 55-69.
[http://dx.doi.org/10.1002/1098-2280(2001)37:1<55:AID-EM1006>3.0.CO;2-5] [PMID: 11170242]
[106]
Mazzatorta, P.; Estevez, M.D.; Coulet, M.; Schilter, B. Modeling oral rat chronic toxicity. J. Chem. Inf. Model., 2008, 48(10), 1949-1954.
[http://dx.doi.org/10.1021/ci8001974] [PMID: 18803370]
[107]
Bakhtyari, N.G.; Raitano, G.; Benfenati, E.; Martin, T.; Young, D. Comparison of in silico models for prediction of mutagenicity. J. Environ. Sci. Health Part C Environ. Carcinog. Ecotoxicol. Rev., 2013, 31(1), 45-66.
[http://dx.doi.org/10.1080/10590501.2013.763576] [PMID: 23534394]
[108]
Patlewicz, G.; Rodford, R.; Walker, J.D. Quantitative structure activity relationships for predicting mutagenicity and carcinogenicity. Environ. Toxicol. Chem., 2003, 22(8), 1885-1893.
[http://dx.doi.org/10.1897/01-461] [PMID: 12924587]
[109]
Plošnik, A.; Vračko, M.; Dolenc, M.S. Mutagenic and carcinogenic structural alerts and their mechanisms of action. Arh. Hig. Rada Toksikol., 2016, 67(3), 169-182.
[http://dx.doi.org/10.1515/aiht-2016-67-2801] [PMID: 27749264]
[110]
Sohlenius-Sternbeck, A.K.; Terelius, Y. Evaluation of ADMET predictor in early discovery drug metabolism and pharmacokinetics project work. Drug Metab. Dispos., 2022, 50(2), 95-104.
[http://dx.doi.org/10.1124/dmd.121.000552] [PMID: 34750195]
[111]
Dulsat, J.; López-Nieto, B.; Estrada-Tejedor, R.; Borrell, J.I. Evaluation of free online ADMET tools for academic or small biotech environments. Molecules, 2023, 28(2), 776.
[http://dx.doi.org/10.3390/molecules28020776] [PMID: 36677832]
[112]
Reynisson, J.; Mirza, A. Benchmarking the reliability of QikProp. Correlation between experimental and predicted values. QSAR Comb. Sci., 2008, 27(4), 445-456.
[http://dx.doi.org/10.1002/qsar.200730051]
[113]
Dave, V.; Yadav, R.B.; Yadav, S.; Sharma, S.; Sahu, R.K.; Ajayi, A.F. A critique of computer simulation software’s used in pharmacokinetics and pharmacodynamics analysis. Curr. Clin. Pharmacol., 2019, 13(4), 216-235.
[http://dx.doi.org/10.2174/1574884713666181025144845] [PMID: 30360723]
[114]
Foster, R.S.; Fowkes, A.; Cayley, A.; Thresher, A.; Werner, A.L.D.; Barber, C.G.; Kocks, G.; Tennant, R.E.; Williams, R.V.; Kane, S.; Stalford, S.A. The importance of expert review to clarify ambiguous situations for (Q)SAR predictions under ICH M7. Genes Environ., 2020, 42(1), 27.
[http://dx.doi.org/10.1186/s41021-020-00166-y] [PMID: 32983286]
[115]
Danieli, A.; Colombo, E.; Raitano, G.; Lombardo, A.; Roncaglioni, A.; Manganaro, A.; Sommovigo, A.; Carnesecchi, E.; Dorne, J.L.C.M.; Benfenati, E. The VEGA tool to check the applicability domain gives greater confidence in the prediction of in silico models. Int. J. Mol. Sci., 2023, 24(12), 9894.
[http://dx.doi.org/10.3390/ijms24129894] [PMID: 37373049]
[116]
Djukić-Ćosić, D.; Baralić,, K.; Jorgovanović, D.; Živančević, K.; Javorac, D.; Stojilković, N.; Radović, B.; Marić, D.; Ćurčić,, M.; Djordjević, A.B. In silico toxicology methods in drug safety assessment. Arch. Pharma., 2021, 71, 257-278.
[117]
Van Norman, G.A. Limitations of animal studies for predicting toxicity in clinical trials: Is it time to rethink our current approach? JACC Basic Transl. Sci., 2019, 4(7), 845-854.
[http://dx.doi.org/10.1016/j.jacbts.2019.10.008] [PMID: 31998852]

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