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Current Drug Metabolism

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

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Research Article

Comparative Analysis of Machine Learning Algorithms Evaluating the Single Nucleotide Polymorphisms of Metabolizing Enzymes with Clinical Outcomes Following Intravenous Paracetamol in Preterm Neonates with Patent Ductus Arteriosus

Author(s): Kannan Sridharan*, George Priya Doss C, Hephzibah Cathryn R, Thirumal Kumar D and Muna Al Jufairi

Volume 25, Issue 2, 2024

Published on: 05 March, 2024

Page: [128 - 139] Pages: 12

DOI: 10.2174/0113892002289238240222072027

Price: $65

Abstract

Aims: Pharmacogenomics has been identified to play a crucial role in determining drug response. The present study aimed to identify significant genetic predictor variables influencing the therapeutic effect of paracetamol for new indications in preterm neonates.

Background: Paracetamol has recently been preferred as a first-line drug for managing Patent Ductus Arteriosus (PDA) in preterm neonates. Single Nucleotide Polymorphisms (SNPs) in CYP1A2, CYP2A6, CYP2D6, CYP2E1, and CYP3A4 have been observed to influence the therapeutic concentrations of paracetamol.

Objectives: The purpose of this study was to evaluate various Machine Learning Algorithms (MLAs) and bioinformatics tools for identifying the key genotype predictor of therapeutic outcomes following paracetamol administration in neonates with PDA.

Methods: Preterm neonates with hemodynamically significant PDA were recruited in this prospective, observational study. The following SNPs were evaluated: CYP2E1*5B, CYP2E1*2, CYP3A4*1B, CYP3A4*2, CYP3A4*3, CYP3A5*3, CYP3A5*7, CYP3A5*11, CYP1A2*1C, CYP1A2*1K, CYP1A2*3, CYP1A2*4, CYP1A2*6, and CYP2D6*10. Amongst the MLAs, Artificial Neural Network (ANN), C5.0 algorithm, Classification and Regression Tree analysis (CART), discriminant analysis, and logistic regression were evaluated for successful closure of PDA. Generalized linear regression, ANN, CART, and linear regression were used to evaluate maximum serum acetaminophen concentrations. A two-step cluster analysis was carried out for both outcomes. Area Under the Curve (AUC) and Relative Error (RE) were used as the accuracy estimates. Stability analysis was carried out using in silico tools, and Molecular Docking and Dynamics Studies were carried out for the above-mentioned enzymes.

Results: Two-step cluster analyses have revealed CYP2D6*10 and CYP1A2*1C to be the key predictors of the successful closure of PDA and the maximum serum paracetamol concentrations in neonates. The ANN was observed with the maximum accuracy (AUC = 0.53) for predicting the successful closure of PDA with CYP2D6*10 as the most important predictor. Similarly, ANN was observed with the least RE (1.08) in predicting maximum serum paracetamol concentrations, with CYP2D6*10 as the most important predictor. Further MDS confirmed the conformational changes for P34A and P34S compared to the wildtype structure of CYP2D6 protein for stability, flexibility, compactness, hydrogen bond analysis, and the binding affinity when interacting with paracetamol, respectively. The alterations in enzyme activity of the mutant CYP2D6 were computed from the molecular simulation results.

Conclusion: We have identified CYP2D6*10 and CYP1A2*1C polymorphisms to significantly predict the therapeutic outcomes following the administration of paracetamol in preterm neonates with PDA. Prospective studies are required for confirmation of the findings in the vulnerable population.

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[1]
Sridharan, K.; Ansari, E.A.; Mulubwa, M.; Raju, A.P.; Madhoob, A.A.; Jufairi, M.A.; Hubail, Z.; Marzooq, R.A.; Hasan, S.J.R.; Mallaysamy, S. Population pharmacokinetic-pharmacodynamic modeling of acetaminophen in preterm neonates with hemodynamically significant patent ductus arteriosus. Eur. J. Pharm. Sci., 2021, 167, 106023.
[http://dx.doi.org/10.1016/j.ejps.2021.106023] [PMID: 34592463]
[2]
Zhao, L.; Pickering, G. Paracetamol metabolism and related genetic differences. Drug Metab. Rev., 2011, 43(1), 41-52.
[http://dx.doi.org/10.3109/03602532.2010.527984] [PMID: 21108564]
[3]
Mazaleuskaya, L.L.; Sangkuhl, K.; Thorn, C.F.; FitzGerald, G.A.; Altman, R.B.; Klein, T.E. PharmGKB summary. Pharmacogenet. Genom., 2015, 25(8), 416-426.
[http://dx.doi.org/10.1097/FPC.0000000000000150] [PMID: 26049587]
[4]
Bardanzellu, F.; Neroni, P.; Dessì, A.; Fanos, V. Paracetamol in patent ductus arteriosus treatment: Efficacious and safe? BioMed Res. Int., 2017, 2017, 1-25.
[http://dx.doi.org/10.1155/2017/1438038] [PMID: 28828381]
[5]
Sridharan, K.; Qader, A.M.; Hammad, M.; Jassim, A.; Diab, D.E.; Abraham, B.; Hasan, H.M.S.N.; Pasha, S.A.A.; Shah, S. Evaluation of the association between single nucleotide polymorphisms of metabolizing enzymes with the serum concentration of paracetamol and its metabolites. Metabolites, 2022, 12(12), 1235.
[http://dx.doi.org/10.3390/metabo12121235] [PMID: 36557273]
[6]
Gaedigk, A.; Ingelman-Sundberg, M.; Miller, N.A.; Leeder, J.S.; Whirl-Carrillo, M.; Klein, T.E. The pharmacogene variation (PharmVar) consortium: Incorporation of the human cytochrome P450 ( CYP ) allele nomenclature database. Clin. Pharmacol. Ther., 2018, 103(3), 399-401.
[http://dx.doi.org/10.1002/cpt.910] [PMID: 29134625]
[7]
Meloche, M.; Khazaka, M.; Kassem, I.; Barhdadi, A.; Dubé, M.P.; de Denus, S. CYP2D6 polymorphism and its impact on the clinical response to metoprolol: A systematic review and meta-analysis. Br. J. Clin. Pharmacol., 2020, 86(6), 1015-1033.
[http://dx.doi.org/10.1111/bcp.14247] [PMID: 32090368]
[8]
Kane, M. CYP2D6 overview: Allele and phenotype frequencies.Medical Genetics Summaries; National Center for Biotechnology Information: US, 2021.
[9]
Yang, Y.; Wong, S.E.; Lightstone, F.C. Understanding a substrate’s product regioselectivity in a family of enzymes: A case study of acetaminophen binding in cytochrome P450s. PLoS One, 2014, 9(2), e87058.
[http://dx.doi.org/10.1371/journal.pone.0087058] [PMID: 24498291]
[10]
Schork, N.J. Artificial intelligence and personalized medicine. Cancer Treat. Res., 2019, 178, 265-283.
[http://dx.doi.org/10.1007/978-3-030-16391-4_11] [PMID: 31209850]
[11]
Sridharan, K.; Al Jufairi, M.; Al Ansari, E.; Al Marzooq, R.; Hubail, Z.; Hasan, S.J.R.; Al Madhoob, A. Intravenous acetaminophen (at 15 mg/kg/dose every 6 hours) in critically ill preterm neonates with patent ductus arteriosus: A prospective study. J. Clin. Pharm. Ther., 2021, 46(4), 1010-1019.
[http://dx.doi.org/10.1111/jcpt.13384] [PMID: 33638909]
[12]
Quinn, J.A.; Munoz, F.M.; Gonik, B.; Frau, L.; Cutland, C.; Mallett-Moore, T.; Kissou, A.; Wittke, F.; Das, M.; Nunes, T.; Pye, S.; Watson, W.; Ramos, A.M.A.; Cordero, J.F.; Huang, W.T.; Kochhar, S.; Buttery, J. Preterm birth: Case definition & guidelines for data collection, analysis, and presentation of immunisation safety data. Vaccine, 2016, 34(49), 6047-6056.
[http://dx.doi.org/10.1016/j.vaccine.2016.03.045] [PMID: 27743648]
[13]
Sridharan, K.; Al Jufairi, M.; Al Ansari, E.; Jasim, A.; Eltayeb Diab, D.; Al Marzooq, R.; Al Madhoob, A. Evaluation of urinary acetaminophen metabolites and its association with the genetic polymorphisms of the metabolising enzymes, and serum acetaminophen concentrations in preterm neonates with patent ductus arteriosus. Xenobiotica, 2021, 51(11), 1335-1342.
[http://dx.doi.org/10.1080/00498254.2021.1982070] [PMID: 34529545]
[14]
Supandi, A.; Saefuddin, A.; Sulvianti, I.D. Two step cluster application to classify villages in kabupaten madiun based on village potential data. Xplore: J. Stat., 2020, 10(1), 12-26.
[http://dx.doi.org/10.29244/xplore.v10i1.272]
[15]
Chen, C.W.; Lin, J.; Chu, Y.W. iStable: Off-the-shelf predictor integration for predicting protein stability changes. BMC Bioinformat., 2013, 14(S2)(Suppl. 2), S5.
[http://dx.doi.org/10.1186/1471-2105-14-S2-S5] [PMID: 23369171]
[16]
Cheng, J.; Randall, A.; Baldi, P. Prediction of protein stability changes for single-site mutations using support vector machines. Proteins, 2006, 62(4), 1125-1132.
[http://dx.doi.org/10.1002/prot.20810] [PMID: 16372356]
[17]
Capriotti, E; Fariselli, P; Casadio, R. I-Mutant2.0: Predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Res., 2005, 33, W306-W310.
[18]
Chen, Y.; Lu, H.; Zhang, N.; Zhu, Z.; Wang, S.; Li, M.; Prem, P.S. PremPS: Predicting the impact of missense mutations on protein stability. PLOS Comput. Biol., 2020, 16(12), e1008543.
[http://dx.doi.org/10.1371/journal.pcbi.1008543] [PMID: 33378330]
[19]
Rodrigues, C.H.M.; Pires, D.E.V.; Ascher, D.B. DYNAMUT2 : Assessing changes in stability and flexibility upon single and multiple point missense mutations. Protein Sci., 2021, 30(1), 60-69.
[http://dx.doi.org/10.1002/pro.3942] [PMID: 32881105]
[20]
Pandurangan, A.P.; Ochoa-Montaño, B.; Ascher, D.B.; Blundell, T.L. SDM: A server for predicting effects of mutations on protein stability. Nucleic Acids Res., 2017, 45(W1), W229-W235.
[http://dx.doi.org/10.1093/nar/gkx439] [PMID: 28525590]
[21]
Venselaar, H.; te Beek, T.A.H.; Kuipers, R.K.P.; Hekkelman, M.L.; Vriend, G. Protein structure analysis of mutations causing inheritable diseases. An e-Science approach with life scientist friendly interfaces. BMC Bioinformat., 2010, 11(1), 548.
[http://dx.doi.org/10.1186/1471-2105-11-548] [PMID: 21059217]
[22]
Goodsell, D.S.; Morris, G.M.; Olson, A.J. Automated docking of flexible ligands: Applications of autodock. J. Mol. Recognit., 1996, 9(1), 1-5.
[http://dx.doi.org/10.1002/(SICI)1099-1352(199601)9:1<1::AID-JMR241>3.0.CO;2-6] [PMID: 8723313]
[23]
Morris, G.M.; Huey, R.; Lindstrom, W.; Sanner, M.F.; Belew, R.K.; Goodsell, D.S.; Olson, A.J. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J. Comput. Chem., 2009, 30(16), 2785-2791.
[http://dx.doi.org/10.1002/jcc.21256] [PMID: 19399780]
[24]
Salentin, S; Schreiber, S; Haupt, VJ; Adasme, MF; Schroeder, M PLIP: Fully automated protein–ligand interaction profiler. Nucleic Acids Res., 2015, 43, W443-W447.
[25]
Kaplan, W.; Littlejohn, T.G. Swiss-PDB viewer (Deep View). Brief. Bioinform., 2001, 2(2), 195-197.
[http://dx.doi.org/10.1093/bib/2.2.195] [PMID: 11465736]
[26]
Abraham, M.J.; Murtola, T.; Schulz, R.; Páll, S.; Smith, J.C.; Hess, B.; Lindahl, E. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX, 2015, 1-2, 19-25.
[http://dx.doi.org/10.1016/j.softx.2015.06.001]
[27]
Schüttelkopf, A.W.; van Aalten, D.M.F. PRODRG : A tool for high-throughput crystallography of protein–ligand complexes. Acta Crystallogr. D Biol. Crystallogr., 2004, 60(8), 1355-1363.
[http://dx.doi.org/10.1107/S0907444904011679] [PMID: 15272157]
[28]
Schmid, N.; Eichenberger, A.P.; Choutko, A.; Riniker, S.; Winger, M.; Mark, A.E.; van Gunsteren, W.F. Definition and testing of the GROMOS force-field versions 54A7 and 54B7. Eur. Biophys. J., 2011, 40(7), 843-856.
[http://dx.doi.org/10.1007/s00249-011-0700-9] [PMID: 21533652]
[29]
Jorgensen, W.L.; Chandrasekhar, J.; Madura, J.D.; Impey, R.W.; Klein, M.L. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys., 1983, 79(2), 926-935.
[http://dx.doi.org/10.1063/1.445869]
[30]
Durham, E.; Dorr, B.; Woetzel, N.; Staritzbichler, R.; Meiler, J. Solvent accessible surface area approximations for rapid and accurate protein structure prediction. J. Mol. Model., 2009, 15(9), 1093-1108.
[http://dx.doi.org/10.1007/s00894-009-0454-9] [PMID: 19234730]
[31]
Kumari, R.; Kumar, R.; Lynn, A. g_mmpbsa--a GROMACS tool for high-throughput MM-PBSA calculations. J. Chem. Inf. Model., 2014, 54(7), 1951-1962.
[http://dx.doi.org/10.1021/ci500020m] [PMID: 24850022]
[32]
Miller, B.R., III; McGee, T.D., Jr; Swails, J.M.; Homeyer, N.; Gohlke, H.; Roitberg, A.E. MMPBSA.py : An efficient program for end-state free energy calculations. J. Chem. Theory Comput., 2012, 8(9), 3314-3321.
[http://dx.doi.org/10.1021/ct300418h] [PMID: 26605738]
[33]
Muroi, Y.; Saito, T.; Takahashi, M.; Sakuyama, K.; Niinuma, Y.; Ito, M.; Tsukada, C.; Ohta, K.; Endo, Y.; Oda, A.; Hirasawa, N.; Hiratsuka, M. Functional characterization of wild-type and 49 CYP2D6 allelic variants for N-desmethyltamoxifen 4-hydroxylation activity. Drug Metab. Pharmacokinet., 2014, 29(5), 360-366.
[http://dx.doi.org/10.2133/dmpk.DMPK-14-RG-014] [PMID: 24647041]
[34]
Fukuyoshi, S.; Kometani, M.; Watanabe, Y.; Hiratsuka, M.; Yamaotsu, N.; Hirono, S.; Manabe, N.; Takahashi, O.; Oda, A. Molecular dynamics simulations to investigate the influences of amino acid mutations on protein three-dimensional structures of cytochrome P450 2D6.1, 2, 10, 14A, 51, and 62. PLoS One, 2016, 11(4), e0152946.
[http://dx.doi.org/10.1371/journal.pone.0152946] [PMID: 27046024]

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