A neural network-based method for polypharmacy side effects prediction

BMC Bioinformatics - 2021
Raziyeh Masumshah1, Rosa Aghdam2, Changiz Eslahchi1
1Department of Computer and Data Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran
2School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran

Tóm tắt

Abstract Background Polypharmacy is a type of treatment that involves the concurrent use of multiple medications. Drugs may interact when they are used simultaneously. So, understanding and mitigating polypharmacy side effects are critical for patient safety and health. Since the known polypharmacy side effects are rare and they are not detected in clinical trials, computational methods are developed to model polypharmacy side effects. Results We propose a neural network-based method for polypharmacy side effects prediction (NNPS) by using novel feature vectors based on mono side effects, and drug–protein interaction information. The proposed method is fast and efficient which allows the investigation of large numbers of polypharmacy side effects. Our novelty is defining new feature vectors for drugs and combining them with a neural network architecture to apply for the context of polypharmacy side effects prediction. We compare NNPS on a benchmark dataset to predict 964 polypharmacy side effects against 5 well-established methods and show that NNPS achieves better results than the results of all 5 methods in terms of accuracy, complexity, and running time speed. NNPS outperforms about 9.2% in Area Under the Receiver-Operating Characteristic, 12.8% in Area Under the Precision–Recall Curve, 8.6% in F-score, 10.3% in Accuracy, and 18.7% in Matthews Correlation Coefficient with 5-fold cross-validation against the best algorithm among other well-established methods (Decagon method). Also, the running time of the Decagon method which is 15 days for one fold of cross-validation is reduced to 8 h by the NNPS method. Conclusions The performance of NNPS is benchmarked against 5 well-known methods, Decagon, Concatenated drug features, Deep Walk, DEDICOM, and RESCAL, for 964 polypharmacy side effects. We adopt the 5-fold cross-validation for 50 iterations and use the average of the results to assess the performance of the NNPS method. The evaluation of the NNPS against five well-known methods, in terms of accuracy, complexity, and running time speed shows the performance of the presented method for an essential and challenging problem in pharmacology. Datasets and code for NNPS algorithm are freely accessible at https://github.com/raziyehmasumshah/NNPS.

Từ khóa


Tài liệu tham khảo

Masnoon N, Shakib S, Kalisch-Ellett L, Caughey GE. What is polypharmacy? A systematic review of definitions. BMC Geriatr. 2017;17(1):1–10. https://doi.org/10.1186/s12877-017-0621-2.

World Health Organization. Medication safety in polypharmacy. Med Without Harm. 2019;1(1):1–63.

Wilson M, Mcintosh J, Codina C, Flemming G, Geitona M, Gillespie U, Harrison C, Illario M, Kinnear M, Fernandez-llimos F, Kempen T, Menditto E, Michael N, Scullin C, Wiese B. Alpana Mair Plus the SIMPATHY consortium. Robert Gordon University Aberdeen (2017)

Avery T, Barber N, Ghaleb B, Franklin BD, Armstrong S, Crowe S, Dhillon S, Freyer A, Howard R, Pezzolesi C, Serumaga B, Swanwick G, Olanrenwaju T. Investigating the prevalence and causes of prescribing errors in general practice?: The PRACtICe Study (PRevalence And Causes of prescrIbing errors in general practiCe) A report for the GMC. General Med Counc. 2012;1(May):1–187.

Rodrigues MCS, De Oliveira C. Interações medicamentosas e reações adversas a medicamentos em polifarmácia em idosos: Uma revisão integrativa. Rev Lat Am Enfermagem. 2016;24:1–17. https://doi.org/10.1590/1518-8345.1316.2800.

Shah BM, Hajjar ER. Polypharmacy, adverse drug reactions, and geriatric syndromes. Clin Geriatr Med. 2012;28(2):173–86. https://doi.org/10.1016/j.cger.2012.01.002.

Oluwaseun E. William\_P. Acad Div Child Health. 2015;101(4):1–13.

Verrotti A, Tambucci R, Di Francesco L, Pavone P, Iapadre G, Altobelli E, Matricardi S, Farello G, Belcastro V. The role of polytherapy in the management of epilepsy: suggestions for rational antiepileptic drug selection. Expert Rev Neurother. 2020;20(2):167–73. https://doi.org/10.1080/14737175.2020.1707668.

Hosseini L, Hajibabaee F, Navab E. Reviewing polypharmacy in elderly. Syst Rev Med Sci. 2020;1(1):17–24.

Chen C-m, Kuo L-n, Cheng K-j, Shen W-c, Bai K-j, Wang C-c, Chiang Y-c, Chen H-y. The effect of medication therapy management service combined with a national PharmaCloud system for polypharmacy patients. Comput Methods Programs Biomed. 2016;134(1):109–11.

Zhang P, Wang F, Hu J, Sorrentino R. Label propagation prediction of drug–drug interactions based on clinical side effects. Sci Rep. 2015;5(1):1–10. https://doi.org/10.1038/srep12339.

Valenza PL, McGinley TC, Feldman J, Patel P, Cornejo K, Liang N, Anmolsingh R, McNaughton N. Dangers of polypharmacy. Vignettes Patient Saf. 2017;1(1):47–69. https://doi.org/10.5772/intechopen.69169.

Stephen LJ, Brodie MJ. Antiepileptic drug monotherapy versus polytherapy: pursuing seizure freedom and tolerability in adults. Curr Opin Neurol. 2012;25(2):164–72. https://doi.org/10.1097/WCO.0b013e328350ba68.

Andrew T, Milinis K, Baker G, Wieshmann U. Self reported adverse effects of mono and polytherapy for epilepsy. Seizure. 2012;21(8):610–3. https://doi.org/10.1016/j.seizure.2012.06.013.

Aggarwal A, Mehta S, Gupta D, Sheikh S, Pallagatti S, Singh R, Singla I. Clinical & immunological erythematosus patients characteristics in systemic lupus Maryam. J Dent Educ. 2012;76(11):1532–9. https://doi.org/10.4103/ijmr.IJMR.

Zitnik M, Agrawal M, Leskovec J. Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics. 2018;34(13):457–66. https://doi.org/10.1093/bioinformatics/bty294.

St. Louis E. Truly, “rational” polytherapy: maximizing efficacy and minimizing drug interactions, drug load, and adverse effects. Curr Neuropharmacolo. 2009;7(2):96–105. https://doi.org/10.2174/157015909788848929.

Holmes LB, Mittendorf R, Shen A, Smith CR, Hernandez-Diaz S. Fetal effects of anticonvulsant polytherapies: different risks from different drug combinations. Arch Neurol. 2011;68(10):1273–9. https://doi.org/10.1001/archneurol.2011.133.

Shtar G, Rokach L, Shapira B. Detecting drug–drug interactions using artificial neural networks and classic graph similarity measures. PLoS ONE 14(8), 1–25 (2019). https://doi.org/10.1371/journal.pone.0219796. arXiv:1903.04571

Mekonnen AB, Alhawassi TM, McLachlan AJ, Brien JE. Adverse drug events and medication errors in African hospitals: a systematic review. Drugs Real World Outcomes. 2018;5(1):1–24. https://doi.org/10.1007/s40801-017-0125-6.

Alsulami Z, Conroy S, Choonara I. Medication errors in the Middle East countries: a systematic review of the literature. Eur J Clin Pharmacol. 2013;69(4):995–1008. https://doi.org/10.1007/s00228-012-1435-y.

Sears K, Scobie A, Mackinnon NJ. Patient-related risk factors for self-reported medication errors in hospital and community settings in 8 countries. Can Pharm J. 2012;145(2):88–93. https://doi.org/10.3821/145.2.cpj88.

Lin X, Quan Z, Wang Z-J, Ma T, Zeng X. KGNN: knowledge graph neural network for drug-drug interaction prediction. IJCAI. 2020. https://doi.org/10.24963/ijcai.2020/380.

Davies EA, O’Mahony MS. Adverse drug reactions in special populations—the elderly. Br J Clin Pharmacol. 2015;80(4):796–807. https://doi.org/10.1111/bcp.12596.

Molokhia M, Majeed A. Current and future perspectives on the management of polypharmacy. BMC Fam Pract. 2017;18(1):1–9. https://doi.org/10.1186/s12875-017-0642-0.

Hubbard RE, O’Mahony MS, Woodhouse KW. Medication prescribing in frail older people. Eur J Clin Pharmacol. 2013;69(3):319–26. https://doi.org/10.1007/s00228-012-1387-2.

Liu R, AbdulHameed MDM, Kumar K, Yu X, Wallqvist A, Reifman J. Data-driven prediction of adverse drug reactions induced by drug–drug interactions. BMC Pharmacol Toxicol. 2017;18(1):1–18. https://doi.org/10.1186/s40360-017-0153-6.

Zhang W, Chen Y, Liu F, Luo F, Tian G, Li X. Predicting potential drug–drug interactions by integrating chemical, biological, phenotypic and network data. BMC Bioinform. 2017;18(1):1–12. https://doi.org/10.1186/s12859-016-1415-9.

Lewis R, Guha R, Korcsmaros T, Bender A. Synergy maps: exploring compound combinations using network-based visualization. J Cheminform. 2015;7(1):1–11. https://doi.org/10.1186/s13321-015-0090-6.

Percha B, Garten Y, Altman RB. Discovery and explanation of drug–drug interactions via text mining. Pac Symp Biocomput. 2012;1:410–21.

Vilar S, Friedman C, Hripcsak G. Detection of drug–drug interactions through data mining studies using clinical sources, scientific literature and social media. Brief Bioinform. 2018;19(5):863–77. https://doi.org/10.1093/bib/bbx010.

Chen D, Zhang H, Lu P, Liu X, Cao H. Synergy evaluation by a pathway–pathway interaction network: a new way to predict drug combination. Mol BioSyst. 2016;12(2):614–23. https://doi.org/10.1039/c5mb00599j.

Huang L, Li F, Sheng J, Xia X, Ma J, Zhan M, Wong STC. DrugComboRanker: drug combination discovery based on target network analysis. Bioinformatics. 2014;30(12):228–36. https://doi.org/10.1093/bioinformatics/btu278.

Sun Y, Sheng Z, Ma C, Tang K, Zhu R, Wu Z, Shen R, Feng J, Wu D, Huang D, Huang D, Fei J, Liu Q, Cao Z. Combining genomic and network characteristics for extended capability in predicting synergistic drugs for cancer. Nat Commun. 2015;6:1–10. https://doi.org/10.1038/ncomms9481.

Takeda T, Hao M, Cheng T, Bryant SH, Wang Y. Predicting drug–drug interactions through drug structural similarities and interaction networks incorporating pharmacokinetics and pharmacodynamics knowledge. J Cheminform. 2017;9(1):1–9. https://doi.org/10.1186/s13321-017-0200-8.

Gottlieb A, Stein GY, Oron Y, Ruppin E, Sharan R. INDI: a computational framework for inferring drug interactions and their associated recommendations. Mol Syst Biol. 2012;8(592):1–12. https://doi.org/10.1038/msb.2012.26.

Li X, Xu Y, Cui H, Huang T, Wang D, Lian B, Li W, Qin G, Chen L, Xie LCO. Artif Intell Med. 2017;17(83):35–43.

Li J, Zheng S, Chen B, Butte AJ, Swamidass SJ, Lu Z. A survey of current trends in computational drug repositioning. Brief Bioinform. 2016;17(1):2–12. https://doi.org/10.1093/bib/bbv020.

Zitnik M, Zupan B. Data fusion by matrix factorization. IEEE Trans Pattern Anal Mach Intell. 2015;37(1):41–53. https://doi.org/10.1109/TPAMI.2014.2343973. arXiv:1307.0803.

Ferdousi R, Safdari R, Omidi Y. Computational prediction of drug–drug interactions based on drugs functional similarities. J Biomed Inform. 2017;70:54–64. https://doi.org/10.1016/j.jbi.2017.04.021.

Vilar S, Harpaz R, Uriarte E, Santana L, Rabadan R, Friedman C. Drug–drug interaction through molecular structure similarity analysis. J Am Med Inform Assoc. 2012;19(6):1066–74. https://doi.org/10.1136/amiajnl-2012-000935.

Nickel M, Tresp V, Kriegel HP. A three-way model for collective learning on multi-relational data. In: Proceedings of the 28th international conference on machine learning, ICML 2011, vol. 1, p. 809–16 (2011)

Papalexakis EE, Faloutsos C, Sidiropoulos ND. Tensors for data mining and data fusion: Models, applications, and scalable algorithms. ACM Trans Intell Syst Technol. 2016;8(2):1–44. https://doi.org/10.1145/2915921.

Perozzi B, Al-Rfou R, Skiena S. DeepWalk: Online learning of social representations. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining, vol. 1, no. 1, p. 701–10, 2014. https://doi.org/10.1145/2623330.2623732. arXiv:1403.6652.

Zong N, Kim H, Ngo V, Harismendy O. Deep mining heterogeneous networks of biomedical linked data to predict novel drug-target associations. Bioinformatics. 2017;33(15):2337–44. https://doi.org/10.1093/bioinformatics/btx160.

Martinez CJ, Torrie JH, Allen ON. Correlation analysis of criteria of symbiotic nitrogen. Fixation by soybeans (Glycine max Merr.). Zentralblatt fur Bakteriologie, Parasitenkunde, Infektionskrankheiten und Hygiene. Zweite naturwissenschaftliche Abt.: Allgemeine, landwirtschaftliche und technische Mikrobiologie 124(3), 212–6 (1970). https://doi.org/10.1126/scitranslmed.3003377.Data-Driven

Kuhn M, Letunic I, Jensen LJ, Bork P. The SIDER database of drugs and side effects. Nucleic Acids Res. 2016;44(D1):1075–9. https://doi.org/10.1093/nar/gkv1075.

Menche J, Sharma A, Kitsak M, Ghiassian SD, Vidal M, Loscalzo J, Barabási AL. Uncovering disease–disease relationships through the incomplete interactome. Science. 2015;347(6224):841. https://doi.org/10.1126/science.1257601.

Chatr-Aryamontri A, Breitkreutz BJ, Oughtred R, Boucher L, Heinicke S, Chen D, Stark C, Breitkreutz A, Kolas N, O’Donnell L, Reguly T, Nixon J, Ramage L, Winter A, Sellam A, Chang C, Hirschman J, Theesfeld C, Rust J, Livstone MS, Dolinski K, Tyers M. The BioGRID interaction database: 2015 update. Nucleic Acids Res. 2015;43(D1):470–8. https://doi.org/10.1093/nar/gku1204.

Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, Santos A, Doncheva NT, Roth A, Bork P, Jensen LJ, Von Mering C. The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible. Nucleic Acids Res. 2017;45(D1):362–8. https://doi.org/10.1093/nar/gkw937.

Rolland T, Taşan M, Charloteaux B, Pevzner SJ, Zhong Q, Sahni N, Yi S, Lemmens I, Fontanillo C, Mosca R, Kamburov A, Ghiassian SD, Yang X, Ghamsari L, Balcha D, Begg BE, Braun P, Brehme M, Broly MP, Carvunis AR, Convery-Zupan D, Corominas R, Coulombe-Huntington J, Dann E, Dreze M, Dricot A, Fan C, Franzosa E, Gebreab F, Gutierrez BJ, Hardy MF, Jin M, Kang S, Kiros R, Lin GN, Luck K, Macwilliams A, Menche J, Murray RR, Palagi A, Poulin MM, Rambout X, Rasla J, Reichert P, Romero V, Ruyssinck E, Sahalie JM, Scholz A, Shah AA, Sharma A, Shen Y, Spirohn K, Tam S, Tejeda AO, Trigg SA, Twizere JC, Vega K, Walsh J, Cusick ME, Xia Y, Barabási AL, Iakoucheva LM, Aloy P, De Las Rivas J, Tavernier J, Calderwood MA, Hill DE, Hao T, Roth FP, Vidal M. A proteome-scale map of the human interactome network. Cell. 2014;159(5):1212–26. https://doi.org/10.1016/j.cell.2014.10.050.

Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks. J Mach Learn Res. 2010;9(1):249–56.

Bottou L. Stochastic gradient descent tricks. Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 7700 LECTURE NO(1), 421–436 (2012). https://doi.org/10.1007/978-3-642-35289-8-25

Serban B, Panti Z, Nica M, Pleniceanu M, Popa M, Ene R, Cîrstoiu C. Statistically based survival rate estimation in patients with soft tissue tumors. Rom J Orthop Surg Traumatol. 2019;1(2):84–9. https://doi.org/10.2478/rojost-2018-0085.

Arbyn M, Weiderpass E, Bruni L, de Sanjosé S, Saraiya M, Ferlay J, Bray F. Estimates of incidence and mortality of cervical cancer in 2018: a worldwide analysis. Lancet Glob Health. 2020;8(2):191–203. https://doi.org/10.1016/S2214-109X(19)30482-6.

Januszewicz A, Guzik T, Prejbisz A, Mikołajczyk T, Osmenda, G, Januszewicz W. 158\_Prejbisz\_ONLINE. PALSKIE 126Janusze(1), 86–93 (2016)

Atci IB, Yilmaz H, Yaman M, Baran O, Türk O, Solmaz B, Kocaman Ü, Ozdemir NG, Demirel N, Kocak A. Incidence, hospital costs and in-hospital mortality rates of surgically treated patients with traumatic cranial epidural hematoma. Rom Neurosurg. 2018;32(1):110–5. https://doi.org/10.2478/romneu-2018-0013.

Evans EC, Matteson KA, Orejuela FJ, Alperin M, Balk EM, El-Nashar S, Gleason JL, Grimes C, Jeppson P, Mathews C, Wheeler TL, Murphy M. Salpingo-oophorectomy at the time of benign hysterectomy: a systematic review. Obstet Gynecol. 2016;128(3):476–85. https://doi.org/10.1097/AOG.0000000000001592.

Thông tin
Thông tin xuất bản

BMC Bioinformatics

Thông tin tác giả