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Evaluation of co-administration in the treatment of patients with SARS-CoV-2 and drugs to treat comorbidities using artificial neural networks
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Abstract
The aim of this work is to develop a clinical decision support tool to evaluate the result of the interaction of the drugs used to treat SARS-CoV-2 and the co-administration of drugs to treat comorbidities.
Neural networks (RNs) are used to predict drug interaction outcomes. The model has been developed using an artificial neural network of the PNN (Probabilistic Neural Network) / GRNN (General Regression Neural Network) type. Once the artificial neural network was trained, tested and validated, a determination coefficient of 82.79% was obtained, with which it is possible to have good and fast predictions to know the result of the interaction of specific drugs for SARS-CoV-2 with other drugs to treat different diseases.
For the training of the RN, a database was used, considering nine drugs to treat SARS-CoV-2 and 10 categories of drugs to treat other types of conditions. In the literature there are studies that demonstrate the importance of correct drug co-administration. However, there are currently no tools that integrate the use of neural networks and the new knowledge obtained from responses to drug interactions, which can be used first-hand by treatment specialists, decreasing with the risk of said interaction. (However, few efforts have been made to integrate artificial intelligence into this process.) Therefore, this paper aims to address this gap in knowledge. This system can be used as a decision support tool to evaluate and select between the best treatment to use in a patient with SARS-CoV-2, and avoid complications due to the negative interaction that it can cause with other medications.
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Awortwe, C. y Cascorbi, I. (2020). Meta-analysis on outcome-worsening comorbidities of COVID-19 and related potential drug-drug interactions. Pharmacological Research, 161. https://doi.org/10.1016/j.phrs.2020.105250
Aggarwal, C. (2018). Neural Networks and Deep Learning. Springer.
Beck, B. R.; Shin, B.; Choi, Y.; Park, S. y Kang, K. (2020). Predicting commercially available antiviral drugs that may act on the novel coronavirus (2019-nCoV), Wuhan, China through a drug-target interaction deep learning model. bioRxiv, 18, 784-790. https://doi.org/10.1101/2020.01.31.929547
Capra, R., De Rossi, N., Mattioli, F., Romanelli, G., Scarpazza, C., Pia Sormani, M., y Cossi, S. (2020). Impact of low dose tocilizumab on mortality rate in patients with COVID-19. European Journal of Internal Medicine, 76, 31-35. https://doi.org/10.1016/j.ejim.2020.05.009
Cheng, C.-Y., Lee, Y.-L., Chen, C.-P., Lin, Y.-C., Liu, C.-E., Liao, C.-H., y Cheng, S.-H. (2020). Lopinavir/ritonavir did not shorten the duration of SARS CoV-2 shedding in patients with mild pneumonia in Taiwan. Journal of Microbiology, Immunology and Infection, 53, 488-492. https://doi.org/10.1016/j.jmii.2020.03.032
Cortegiani, A., Ingoglia, G., Ippolito, M., Giarratano, A. y Einav, S. (2020). A systematic review on the efficacy and safety of chloroquine for the treatment of COVID-19. Journal of Critical Care, 57, 279-283. https://doi.org/10.1016/J.JCRC.2020.03.005
Gallardo, M. d., y Ávila, R. Á. (2008). Artificial intelligence applied to medicine: prospects and problems. ACIMED, 17(5). Recuperado el 24 de 07 de 2020, de http://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S1024-94352008000500005&lng=es&tlng=es.
GNS, H. S., GR, S., Murahari, M., y Krishnamurthy, M. (2019). An update on Drug Repurposing: Re-written saga of the drug’s fate. Biomedicine & Pharmacotherapy, 110, 700-716. https://doi.org/10.1016/j.biopha.2018.11.127
Hassan, A., Shahin, I., y Alsabek, M. B. (2020, November). COVID-19 detection system using recurrent neural networks. In 2020 International conference on communications, computing, cybersecurity, and informatics (CCCI) (pp. 1-5). IEEE.
Imran, A., Posokhova, I., Qureshi, H. N., Masood, U., Riaz, S., Ali, K., . . . Nabeel, M. (2020). AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app. Informatics in Medicine Unlocked. https://doi.org/10.1016/j.imu.2020.100378
Javier Trujillano, J. M. (2004). Aproximación metodológica al uso de redes neuronales artificiales para la predicción de resultados en medicina. Medicina Clínica, 122, 59-67. https://www.elsevier.es/es-revista-medicina-clinica-2-articulo-aproximacion-metodologica-al-uso-redes-13057536
Kubat, M. (2017). Artificial neural networks. In An Introduction to Machine Learning. Springer.
Liverpool University. (2020). COVID-19 Drug Interactions. Obtenido de https://www.covid19-druginteractions.org/checker
Liu, C., Zoph, B., Neumann, M., Shlens, J., Hua, W., Li, L. J., ... y Murphy, K. (2018). Progressive neural architecture search. In Proceedings of the European conference on computer vision (ECCV) (pp. 19-34).
Luo, R., Tian, F., Qin, T., Chen, E., y Liu, T. Y. (2018). Neural architecture optimization. Advances in neural information processing systems, 31.
Manic, G., Obrist, F., Kroemer, G., Vitale, I., y Galluzzi, L. (2014). Chloroquine and hydroxychloroquine for cancer therapy. Molecular & cellular oncology. https://doi.org/10.4161/mco.29911
Mohanty, S., Rashid, M. H., Mridul, M., Mohanty, C., y Swayamsiddha, S. (2020). Application of Artificial Intelligence in COVID-19 drug repurposing. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, (14), 1027-1031. https://doi.org/10.1016/j.dsx.2020.06.068
Narin, A., Kaya, C., y Pamuk, Z. (2021). Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks. Pattern Analysis and Applications, 24, 1207-1220.
Pilkington, V., Toby, P., y Andrew, H. (2020). A review of the safety of favipiravir – a potential treatment in the COVID-19 pandemic? Journal of Virus Eradication 6(2), 44-51. https://doi.org/10.1016/S2055-6640(20)30016-9
Quesada-Varela, V., Avila-Tomás, J., y Mayer-Pujadas, M. (2020). Artificial intelligence and its applications in medicine I: introductory background to AI and robotics. Atención primaria 52(10), 778-774. https://doi.org/10.1016/j.aprim.2020.04.013
Reyna, J. (2020). El SARS-CoV-2,una nueva zoonosis pandémica que amenaza al mundo. Vacunas, 21(1), 17-22. https://doi.org/10.1016/j.vacun.2020.03.001
S. Khalili, J., Zhu, H., Amanda Mak, N. S., Yan, Y., y Zhu, Y. (2020). Novel coronavirus treatment with ribavirin: Groundwork for an evaluation concerning COVID‐19. Journal of Medical Virology 92(7), 740–746. https://doi.org/10.1002/jmv.25798
Sallard, E., Lescure, F.-X., Yazdanpanah, Y., Mentre, F., y Peiffer-Smadja, N. (2020). Type 1 interferons as a potential treatment against COVID-19. Antiviral Research, 178. https://doi.org/10.1016/j.antiviral.2020.104791
Schrezenmeier, E., y Dörner, T. (2020). Mechanisms of action of hydroxychloroquine and chloroquine: implications for rheumatology. Nature Reviews Rheumatology, 16, 155-166. https://doi.org/10.1038/s41584-020-0372-x
Singh, A. K., Singh, A., Singh, R., y Misra, A. (2020). Remdesivir in COVID-19: A critical review of pharmacology, pre-clinical and clinical studies. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(4), 641-648. https://doi.org/10.1016/j.dsx.2020.05.018
Specht, D. F. (1990). Probabilistic Neural Networks. Neural Networks, 3(1), 109-118. https://doi.org/10.1016/0893-6080(90)90049-Q
Specht, D. F. (1991). A General Regression Neural Network. IEEE Transactions on Neural Networks, 2(6), 568-576.
Suárez, V., Suárez Quezada, M., Oros Ruiz, S., y Ronquillo De Jesús, E. (2020). Epidemiología de COVID-19 en México: del 27 de febrero al 30 de abril de 2020. Revista Clínica Española 220(8), 463-471. https://doi.org/10.1016/j.rce.2020.05.007
Trilla, A. (2020). Un mundo, una salud: la epidemia por el nuevo coronavirus COVID-19. Medicina Clínica, 154(5), 175-177. https://doi.org/10.1016/j.medcli.2020.02.002
Wang, M., Cao, R., Zhang, L., Yang, X., Liu, J., y Xu, M. (2020). Remdesivir and chloroquine effectively inhibit the recently. Cell Research, 30, 269-271. https://doi.org/10.1038/S41422-020-0282-0
Yu, W.-L., Toh, H., Liao, C.-T., y Chang, W.-T. (2020). A Double-Edged Sword—Cardiovascular Concerns of Potential Anti-COVID-19 Drugs. Cardiovascular Drugs and Therapy, 35, 205-214. https://doi.org/10.1007/S10557-020-07024-7