Abstract
Orphan drugs are a treatment for rare diseases. From that, comes the importance of orphan drug development and discovery. For an orphan drug to be approved by the FDA, it does not have to be similar to any approved orphan drug. So chemists opinions are important to determine the probability of similarity. It is too hard to check all orphan drugs for any rare disease. It takes a long time and big effort, so we introduce in this study a system that classifies the orphan drugs according to their probability of structural similarity. It also compares between them and the unauthorized orphan drug to determine the closest orphan drug to it. That system helps chemists to study a certain orphan database using the five features. That system provides better results. It provides chemists with the clusters of orphan drugs after adding the drug that needs to be authorized to its cluster.
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References
Health resources and services administration (2012), http://www.hrsa.gov/opa/programrequirements/orphandrugexclusion/index.html
How to apply for orphan product designation (2013), http://www.fda.gov/ForIndustry/DevelopingProductsforRareDiseasesConditions/HowtoapplyforOrphanProductDesignation/ucm135122.htm
European medicines agency. rare disease (orphan) designations (2014), http://www.ema.europa.eu/ema/
Bender, A., Glen, R.C.: Molecular similarity: a key technique in molecular informatics. OBC 2(22), 3204–3218 (2004)
Bender, A., Jenkins, J.L., Scheiber, J., Sukuru, S.C.K., Glick, M., Davies, J.W.: How similar are similarity searching methods? a principal component analysis of molecular descriptor space. J. Chem. Inf. Model., JCIM 49(1), 108–119 (2009)
Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)
Chu, C.W., Holliday, J.D., Willett, P.: Combining multiple classifications of chemical structures using consensus clustering. Bioorg. Med. Chem. 20(18), 5366–5371 (2012)
Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13(1), 21–27 (1967)
Dutt, R., Madan, A.: Predicting biological activity: Computational approach using novel distance based molecular descriptors. Comput. Biol. Med. 42(10), 1026–1041 (2012)
Fernández-Blanco, E., Aguiar-Pulido, V., Munteanu, C.R., Dorado, J.: Random forest classification based on star graph topological indices for antioxidant proteins. J. Theor. Biol. 317, 331–337 (2013)
Franco, P., Porta, N., Holliday, J.D., Willett, P.: The use of 2d fingerprint methods to support the assessment of structural similarity in orphan drug legislation. J. Cheminformatics 6(1), 5 (2014)
Geppert, H., Vogt, M., Bajorath, J.: Current trends in ligand-based virtual screening: molecular representations, data mining methods, new application areas, and performance evaluation. J. Chem. Inf. Model., JCIM 50(2), 205–216 (2010)
Heemstra, H., Vrueh, R., Weely, S., Bller, H., Leufkens, H.: Predictors of orphan drug approval in the european union. Eur. J. Clin. Pharmacol. 64(5), 545–552 (2008), http://dx.doi.org/10.1007/s00228-007-0454-6
Jain, A.N., Nicholls, A.: Recommendations for evaluation of computational methods. J. Comput. Aided Mol. Des. 22(3-4), 133–139 (2008)
Joppi, R., Garattini, S., et al.: Orphan drugs, orphan diseases. the first decade of orphan drug legislation in the eu. Eur. J. Clin. Pharmacol. 69(4), 1009–1024 (2013)
Morgan, S., Grootendorst, P., Lexchin, J., Cunningham, C., Greyson, D.: The cost of drug development: a systematic review. Health Policy 100(1), 4–17 (2011)
Riniker, S., Landrum, G.A.: Open-source platform to benchmark fingerprints for ligand-based virtual screening. J. Cheminformatics 5, 26 (2013)
Ripphausen, P., Nisius, B., Bajorath, J.: State-of-the-art in ligand-based virtual screening. Drug Discovery Today 16(9), 372–376 (2011)
Todeschini, R., Consonni, V., Xiang, H., Holliday, J., Buscema, M., Willett, P.: Similarity coefficients for binary chemoinformatics data: overview and extended comparison using simulated and real data sets. J. Chem. Inf. Model., JCIM 52(11), 2884–2901 (2012)
Truchon, J.F., Bayly, C.I.: Evaluating virtual screening methods: good and bad metrics for the early recognition problem. J. Chem. Inf. Model., JCIM 47(2), 488–508 (2007)
Westermark, K., Holm, B.B., Söderholm, M., Llinares-Garcia, J., Rivière, F., Aarum, S., Butlen-Ducuing, F., Tsigkos, S., Wilk-Kachlicka, A., N’Diamoi, C., et al.: European regulation on orphan medicinal products: 10 years of experience and future perspectives. Nature Reviews. Drug Discovery 10(5), 341–349 (2011)
Willett, P.: Combination of similarity rankings using data fusion. J. Chem. Inf. Model., JCIM 53(1), 1–10 (2013)
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Aziz, A.A., Zein, M., Atef, M., Adl, A., Ghany, K.K.A., Hassanien, A.E. (2015). An Orphan Drug Legislation System. In: Filev, D., et al. Intelligent Systems'2014. Advances in Intelligent Systems and Computing, vol 323. Springer, Cham. https://doi.org/10.1007/978-3-319-11310-4_34
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DOI: https://doi.org/10.1007/978-3-319-11310-4_34
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11309-8
Online ISBN: 978-3-319-11310-4
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