Öz
Yeni baştan ilaç geliştirme, karmaşık ve oldukça pahalı bir süreçtir. Bu nedenle, son yıllarda yeni hesaplamalı yaklaşımlar geliştirilmiştir. Hesaplamalı yaklaşımlardan biri, onaylanmış ilaçlara yeni tedavi alanını keşfeden ilaç yeniden konumlandırmadır. Çünkü ilaç yeniden konumlandırma, geleneksel ilaç geliştirme süreçlerine kıyasla daha düşük maliyet, daha kısa süre ve risksiz yatırım sağlamaktadır. Son zamanlarda, biyolojik ağ tabanlı ilaç yeniden konumlandırma, proteinler arasındaki fiziksel ilişkileri veya işlevsel benzerlikleri kullandığı ve sonunda canlı sistemlerin daha iyi bir modellenebildiği için daha popüler hale geldi. Bu çalışma, hastalık genleri ve ilaçtan etkilenen genler arasındaki ağ benzerliklerini hesaplayan ve ardından en yüksek benzerlik puanlarına göre en iyi ilaç adaylarına öncelik veren ağ tabanlı yeni bir ilaç yeniden konumlandırma yöntemi sunmaktadır. Genel ağ yapısının oluşturulmasında işlevsel bir protein-protein etkileşim ağı kullanılır. Kansere neden olan genler bu ağ yapısı üzerinde haritalandırılır. İlaçların transkriptom profilleri LINCS L1000 projesinden elde edilir ve yine ağ yapısında ayrı ayrı haritalanır. Bir hastalık ağı ile ilaçtan etkilenen bir ağın benzerliği, Adamic-Adar ve Tercihli Bağlanma ağ merkezilik metrikleriyle hesaplanır. Önerilen ilaç yeniden konumlandırma yaklaşımı, anlamlı benzerlik puanlarına sahip ilaçları seçerek en iyi tedavi adaylarını belirler. Geliştirilen yöntem, insan küçük hücreli olmayan akciğer kanseri (A549) ve prostat kanseri hücre hatları (LNCAP ve PC3) üzerinde denenmiştir. Ağ merkeziliği metriklerinin AUC değerleri her iki kanser türünde de 0.8'i aşmıştır. Aday ilaçlar en yüksek AUC değerlerine göre sıralandığında, FDA onaylı on iki tedaviden beş tanesi prostat kanseri için ilk %20 içinde yer almıştır, bu oldukça umut verici bir sonuçtur. Genel olarak, bu çalışma gelecekte gelişmeye müsait olan özgün bir ağ tabanlı ilaç yeniden konumlandırma yönteminin ilk deneysel sonuçlarını sunmaktadır.
Anahtar Kelimeler
İlaç Yeniden Konumlandırma Akciğer Kanseri Prostat Kanseri Adamic-Adar Tercihli Bağlanma
Destekleyen Kurum
Tübitak
Proje Numarası
318S276
Teşekkür
Bu çalışma Tübitak 318S276 nolu proje tarafından desteklenmektedir.
Kaynakça
- Adamic, L., & Adar, E. (2003). Friends and neighbors on the Web. Social Networks, 25(3), 211-230.
- Barabási, A., & Albert, R. (1999). Emergence of Scaling in Random Networks. Science, 286(5439), 509-512.
- Brynner, R. & Stephens, T. (2001) Dark Remedy: The Impact of Thalidomide and Its Revival as a Vital Medicine (Perseus Publishing, Cambridge).
- Chen, H., Sherr, D., Hu, Z. and DeLisi, C. (2016). A network based approach to drug repositioning identifies plausible candidates for breast cancer and prostate cancer. BMC Medical Genomics, 9(1).
- Gottlieb, A., Stein, G., Ruppin, E., & Sharan, R. (2011). PREDICT: a method for inferring novel drug indications with application to personalized medicine. Molecular Systems Biology, 7(1), 496.
- Hamosh A, Scott AF, Amberger J, Bocchini C, Valle D, McKusick VA (2002) Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res 30: 52–55.
- Li, J., Zhu, X., & Chen, J. (2009). Building Disease-Specific Drug-Protein Connectivity Maps from Molecular Interaction Networks and PubMed Abstracts. Plos Computational Biology, 5(7), e1000450.
- Linghu, B., Snitkin, E., Hu, Z., Xia, Y. and DeLisi, C. (2009). Genome-wide prioritization of disease genes and identification of disease-disease associations from an integrated human functional linkage network. Genome Biology, 10(9), p.R91.
- Ratner, M. L. (2001) Celgene backs into biotech. In Vivo 15, 15.
- Renaud, R. and Xuereb, H. (2002). Erectile-dysfunction therapies. Nature Reviews Drug Discovery, 1(9), pp.663-664. Subramanian, A., Narayan, R., Corsello, S., Peck, D., Natoli, T., & Lu, X. et al. (2017). A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles. Cell, 171(6), 1437-1452.e17.
- The Human Protein Atlas. (2020). Retrieved 17 July 2020, from https://www.proteinatlas.org/.
- Thor, K. B. & Katofiasc, M. A. (1995) Effects of duloxetine, a combined serotonin and norepinephrine reuptake inhibitor, on central neural control of lower urinary tract function in the chloralose-anesthetized female cat. J. Pharmacol. Exp. Thera. 274, 1014–1024.
- Wishart DS, Knox C, Guo AC, Cheng D, Shrivastava S, Tzur D, Gautam B, Hassanali M (2008) DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res 36: D901–D906.
- Xue, H., Li, J., Xie, H., & Wang, Y. (2018). Review of Drug Repositioning Approaches and Resources. International Journal of Biological Sciences, 14(10), 1232-1244.
- Zeng, X., Zhu, S., Liu, X., Zhou, Y., Nussinov, R. and Cheng, F. (2019). deepDR: a network-based deep learning approach to in silico drug repositioning. Bioinformatics.
Öz
De-novo drug development is complicated and quite expensive process. Therefore, new computational approaches have been developed over the last years. One of the computational approaches is drug repositioning that discovers new treatment area of approved drugs. Since drug repositioning provides lower costs, shorter time, and risk-free investment compared to the traditional drug development processes. Recently, biological network-based drug repositioning became more popular, since they use the physical relationships or functional similarities between proteins and eventually provide a better modelling of living systems. The current study presents a new network-based drug repositioning method that computes network similarities between disease genes and drug-affected genes, then prioritizes the best drug candidates based on highest similarity scores. A functional protein-protein interaction network is used in creation of the general network structure. The cancer-causing genes are mapped on this network structure. Transcriptome profiles of drugs are obtained from the LINCS L1000 project and they also mapped individually on the network structure. The similarity of a disease network and a drug-affected network is calculated by Adamic-Adar and Preferential Attachment network centrality metrics. The proposed drug repositioning approach identifies the best treatment candidates by choosing the drugs with significant similarity scores. The developed method was experimented on human non-small cell lung cancer (A549) and prostate cancer cell lines (LNCAP and PC3). The AUC values of network centrality metrics exceeded 0.8 in both cancer types. When candidate drugs are ranked based on the highest AUC values, five out of twelve FDA-approved treatments were ranked in the top 20% for prostate cancer, which is quite promising result. Overall, this study provides initial experimental results of a novel network-based drug repositioning method that is open for future developments.
Anahtar Kelimeler
Drug Repositioning Lung Cancer Prostate Cancer Adamic-Adar Preferential Attachment
Proje Numarası
318S276
Kaynakça
- Adamic, L., & Adar, E. (2003). Friends and neighbors on the Web. Social Networks, 25(3), 211-230.
- Barabási, A., & Albert, R. (1999). Emergence of Scaling in Random Networks. Science, 286(5439), 509-512.
- Brynner, R. & Stephens, T. (2001) Dark Remedy: The Impact of Thalidomide and Its Revival as a Vital Medicine (Perseus Publishing, Cambridge).
- Chen, H., Sherr, D., Hu, Z. and DeLisi, C. (2016). A network based approach to drug repositioning identifies plausible candidates for breast cancer and prostate cancer. BMC Medical Genomics, 9(1).
- Gottlieb, A., Stein, G., Ruppin, E., & Sharan, R. (2011). PREDICT: a method for inferring novel drug indications with application to personalized medicine. Molecular Systems Biology, 7(1), 496.
- Hamosh A, Scott AF, Amberger J, Bocchini C, Valle D, McKusick VA (2002) Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res 30: 52–55.
- Li, J., Zhu, X., & Chen, J. (2009). Building Disease-Specific Drug-Protein Connectivity Maps from Molecular Interaction Networks and PubMed Abstracts. Plos Computational Biology, 5(7), e1000450.
- Linghu, B., Snitkin, E., Hu, Z., Xia, Y. and DeLisi, C. (2009). Genome-wide prioritization of disease genes and identification of disease-disease associations from an integrated human functional linkage network. Genome Biology, 10(9), p.R91.
- Ratner, M. L. (2001) Celgene backs into biotech. In Vivo 15, 15.
- Renaud, R. and Xuereb, H. (2002). Erectile-dysfunction therapies. Nature Reviews Drug Discovery, 1(9), pp.663-664. Subramanian, A., Narayan, R., Corsello, S., Peck, D., Natoli, T., & Lu, X. et al. (2017). A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles. Cell, 171(6), 1437-1452.e17.
- The Human Protein Atlas. (2020). Retrieved 17 July 2020, from https://www.proteinatlas.org/.
- Thor, K. B. & Katofiasc, M. A. (1995) Effects of duloxetine, a combined serotonin and norepinephrine reuptake inhibitor, on central neural control of lower urinary tract function in the chloralose-anesthetized female cat. J. Pharmacol. Exp. Thera. 274, 1014–1024.
- Wishart DS, Knox C, Guo AC, Cheng D, Shrivastava S, Tzur D, Gautam B, Hassanali M (2008) DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res 36: D901–D906.
- Xue, H., Li, J., Xie, H., & Wang, Y. (2018). Review of Drug Repositioning Approaches and Resources. International Journal of Biological Sciences, 14(10), 1232-1244.
- Zeng, X., Zhu, S., Liu, X., Zhou, Y., Nussinov, R. and Cheng, F. (2019). deepDR: a network-based deep learning approach to in silico drug repositioning. Bioinformatics.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Mühendislik |
| Bölüm | Makaleler |
| Yazarlar | |
| Proje Numarası | 318S276 |
| Yayımlanma Tarihi | 30 Kasım 2020 |
| Yayımlandığı Sayı | Yıl 2020 Ejosat Özel Sayı 2020 (ISMSIT) |
