Synergistic drug combination prediction in multi-input neural network_Journal of Biomedical Engineering

Authors：

CHEN Xi ^1,2 ,  QIN Yufang ^1,2 , CHEN Ming ^1,2 , ZHANG Chongyang ^1,2

1. College of Information Technology, Shanghai Ocean University, Shanghai 201306, P.R.China;
2. Key Laboratory of Fisheries Information, Ministry of Agriculture, Shanghai 201306, P.R.China;

Corresponding author：

QIN Yufang, Email: yfqin@shou.edu.cn

Keywords：

deep learning; gene expression; anticancer drugs; drug combination

DOI：

10.7507/1001-5515.201907049

Video：

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Synergistic effects of drug combinations are very important in improving drug efficacy or reducing drug toxicity. However, due to the complex mechanism of action between drugs, it is expensive to screen new drug combinations through trials. It is well known that virtual screening of computational models can effectively reduce the test cost. Recently, foreign scholars successfully predicted the synergistic value of new drug combinations on cancer cell lines by using deep learning model DeepSynergy. However, DeepSynergy is a two-stage method and uses only one kind of feature as input. In this study, we proposed a new end-to-end deep learning model, MulinputSynergy which predicted the synergistic value of drug combinations by integrating gene expression, gene mutation, gene copy number characteristics of cancer cells and anticancer drug chemistry characteristics. In order to solve the problem of high dimension of features, we used convolutional neural network to reduce the dimension of gene features. Experimental results showed that the proposed model was superior to DeepSynergy deep learning model, with the mean square error decreasing from 197 to 176, the mean absolute error decreasing from 9.48 to 8.77, and the decision coefficient increasing from 0.53 to 0.58. This model could learn the potential relationship between anticancer drugs and cell lines from a variety of characteristics and locate the effective drug combinations quickly and accurately.

Citation： CHEN Xi, QIN Yufang, CHEN Ming, ZHANG Chongyang. Synergistic drug combination prediction in multi-input neural network. Journal of Biomedical Engineering, 2020, 37(4): 676-682, 691. doi: 10.7507/1001-5515.201907049 Copy

1.	Huang Yuan, Jiang Donghai, Sui Meihua, et al. Fulvestrant reverses doxorubicin resistance in multidrug-resistant breast cell lines Independent of estrogen receptor expression. Oncol Rep, 2017, 37(2): 705-712.
2.	Tooker P, Yen W C, Ng S C, et al. Bexarotene (LGD1069, targretin), a selective retinoid X receptor agonist, prevents and reverses gemcitabine resistance in NSCLC cells by modulating gene amplification. Cancer Res, 2007, 67(9): 4425-4433.
3.	Al-Lazikani B, BANERJI Udai, Workman P. Combinatorial drug therapy for cancer in the post-genomic era. Nat Biotechnol, 2012, 30(7): 679-692.
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6.	Chou T-C. Theoretical basis, experimental design, and computerized simulation of synergism and antagonism in drug combination studies. Pharmacol Rev, 2006, 58(3): 621-681.
7.	He L, Kulesskiy E, Saarela J, et al. Methods for high-throughput drug combination screening and synergy scoring. Methods Mol Biol, 2018, 1711: 351-398.
8.	Morris M, Clarke D, Osimiri L, et al. Systematic analysis of quantitative logic model ensembles predicts drug combination effects on cell signaling networks. CPT Pharmacometrics Syst Pharmacol, 2016, 5(10): 544-553.
9.	Bulusu K C, Guha R, Mason D J, et al. Modelling of compound combination effects and applications to efficacy and toxicity: state-of-the-art, challenges and perspectives. Drug Discov Today, 2016, 21(2): 225-238.
10.	Aliper A, Plis S, Artemov A, et al. Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data. Mol Pharm, 2016, 13(7): 2524-2530.
11.	Chang Y, Park H, Yang H J, et al. Cancer drug response profile scan (CDRscan): A deep learning model that predicts drug effectiveness from cancer genomic signature. Sci Rep, 2018, 8(1): 8857.
12.	Feng Q, Dueva E, Cherkasov A, et al. Padme: A deep learning-based framework for drug-target interaction prediction. arXiv preprint arXiv: 2018: 1807.09741.
13.	Zong N, Wong R S N, Ngo V. Tripartite network-based repurposing method using deep learning to compute similarities for drug-target prediction. Methods Mol Biol, 2019, 1903: 317-328.
14.	Preuer K, Lewis R P I, Hochreiter S, et al. DeepSynergy: predicting anti-cancer drug synergy with Deep Learning. Bioinformatics, 2018, 34(9): 1538-1546.
15.	Hochreiter S, Clevert D A, Obermayer K. A new summarization method for Affymetrix probe level data. Bioinformatics, 2006, 22(8): 943-949.
16.	O'neil J, Benita Y, Feldman I, et al. An unbiased oncology compound screen to identify novel combination strategies. Mol Cancer Ther, 2016, 15(6): 1155-1162.
17.	Di Veroli G Y, Fornari C, Wang D, et al. Combenefit: an interactive platform for the analysis and visualization of drug combinations. Bioinformatics, 2016, 32(18): 2866-2868.
18.	Loewe S. The problem of synergism and antagonism of combined drugs. Arzneimittelforschung, 1953, 3(6): 285-290.
19.	Iorio F, Knijnenburg T A, Vis D J, et al. A landscape of pharmacogenomic interactions in cancer. Cell, 2016, 166(3): 740-754.
20.	Mauri A. alvaDesc: A tool to calculate and analyze molecular descriptors and fingerprints//Roy K. Ecotoxicological QSARs. Methods in pharmacology and toxicology. New York: Humana, 2020: 801-820.

1. Huang Yuan, Jiang Donghai, Sui Meihua, et al. Fulvestrant reverses doxorubicin resistance in multidrug-resistant breast cell lines Independent of estrogen receptor expression. Oncol Rep, 2017, 37(2): 705-712.
2. Tooker P, Yen W C, Ng S C, et al. Bexarotene (LGD1069, targretin), a selective retinoid X receptor agonist, prevents and reverses gemcitabine resistance in NSCLC cells by modulating gene amplification. Cancer Res, 2007, 67(9): 4425-4433.
3. Al-Lazikani B, BANERJI Udai, Workman P. Combinatorial drug therapy for cancer in the post-genomic era. Nat Biotechnol, 2012, 30(7): 679-692.
4. Ferreira D, Adega F, Chaves R. The importance of cancer cell lines as in vitro models in cancer methylome analysis and anticancer drugs testing//López-Camarillo C, Aréchaga-Ocampo E. Oncogenomics and cancer proteomics-novel approaches in biomarkers discovery and therapeutic targets in cancer. Rijeka: InTech, 2013: 139-166.
5. Jürgen B. Integration of virtual and high-throughput screening. Nat Rev Drug Discov, 2002, 1(11): 882-894.
6. Chou T-C. Theoretical basis, experimental design, and computerized simulation of synergism and antagonism in drug combination studies. Pharmacol Rev, 2006, 58(3): 621-681.
7. He L, Kulesskiy E, Saarela J, et al. Methods for high-throughput drug combination screening and synergy scoring. Methods Mol Biol, 2018, 1711: 351-398.
8. Morris M, Clarke D, Osimiri L, et al. Systematic analysis of quantitative logic model ensembles predicts drug combination effects on cell signaling networks. CPT Pharmacometrics Syst Pharmacol, 2016, 5(10): 544-553.
9. Bulusu K C, Guha R, Mason D J, et al. Modelling of compound combination effects and applications to efficacy and toxicity: state-of-the-art, challenges and perspectives. Drug Discov Today, 2016, 21(2): 225-238.
10. Aliper A, Plis S, Artemov A, et al. Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data. Mol Pharm, 2016, 13(7): 2524-2530.
11. Chang Y, Park H, Yang H J, et al. Cancer drug response profile scan (CDRscan): A deep learning model that predicts drug effectiveness from cancer genomic signature. Sci Rep, 2018, 8(1): 8857.
12. Feng Q, Dueva E, Cherkasov A, et al. Padme: A deep learning-based framework for drug-target interaction prediction. arXiv preprint arXiv: 2018: 1807.09741.
13. Zong N, Wong R S N, Ngo V. Tripartite network-based repurposing method using deep learning to compute similarities for drug-target prediction. Methods Mol Biol, 2019, 1903: 317-328.
14. Preuer K, Lewis R P I, Hochreiter S, et al. DeepSynergy: predicting anti-cancer drug synergy with Deep Learning. Bioinformatics, 2018, 34(9): 1538-1546.
15. Hochreiter S, Clevert D A, Obermayer K. A new summarization method for Affymetrix probe level data. Bioinformatics, 2006, 22(8): 943-949.
16. O'neil J, Benita Y, Feldman I, et al. An unbiased oncology compound screen to identify novel combination strategies. Mol Cancer Ther, 2016, 15(6): 1155-1162.
17. Di Veroli G Y, Fornari C, Wang D, et al. Combenefit: an interactive platform for the analysis and visualization of drug combinations. Bioinformatics, 2016, 32(18): 2866-2868.
18. Loewe S. The problem of synergism and antagonism of combined drugs. Arzneimittelforschung, 1953, 3(6): 285-290.
19. Iorio F, Knijnenburg T A, Vis D J, et al. A landscape of pharmacogenomic interactions in cancer. Cell, 2016, 166(3): 740-754.
20. Mauri A. alvaDesc: A tool to calculate and analyze molecular descriptors and fingerprints//Roy K. Ecotoxicological QSARs. Methods in pharmacology and toxicology. New York: Humana, 2020: 801-820.

Journal of Biomedical Engineering

Synergistic drug combination prediction in multi-input neural network

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