Drug Repositioning / Repurposing and Deep Learning

Although discovering completely new drugs has taken the wind of technology and Deep Learning behind it, climate change, resource problems and economic recessions that have been felt well in the 2020s cause problems in the development and supply of new drugs (Murage et al., 2021; Neergheen-Bhujun et al., 2017; Shukar et al., 2021). Similarly, it is claimed that there is a slowdown in new drug discoveries despite exponential data processing speeds and hardware capacities (Bridle, 2018; Bridle, 2020). In addition, when we look at the average rates in new drug discoveries, it is understood that only 13.8% of drug applications are successful (Wong et al., 2019). Under all these conditions, drug repositioning (targeting), which looks at drug discovery from a different window, has become an alternative in creating faster, lower-cost and safer drug solutions as a solution based on directing drugs with known success and effectiveness to different diseases (Singh et al., 2020). Thanks to drug repositioning, faster drug-disease solutions are obtained by overcoming some phases in new drug discovery, drug components known to be safe and effective can be effectively directed to alternative diseases, and economical and optimum use of available resources is ensured (Park, 2019; Pushpakom et al., 2019).

In this context, many success stories have emerged thanks to drug repositioning. The most well-known of these are the identification of broad-spectrum therapeutics against multiple infections by repositioning antimicrobials (Firth and Prathapan, 2021), the identification of Thalidomide use for Leprosy and Multiple Myeloma (Amare et al., 2021), and the identification of Sildenafil used for Erectile Dysfunction as a treatment for Pulmonary Hypertension and Alzheimer’s (Cheng et al., 2021; Ghofrani et al., 2006). In addition to these developments, Deep Learning techniques have also become an indispensable element for drug repositioning applications – just like in new drug discoveries – (Aliper et al., 2016; Li et al., 2020; Issa et al., 2021; Pan et al., 2022; Pham et al., 2021). Currently, the effective role of Deep Learning in drug repositioning applications against COVID-19 is remarkable (Abdel-Basset et al., 2020; Choi et al., 2020; Deepthi et al., 2021; Hooshmand et al., 2021; Li et al., 2020; Pham et al., 2021; Wang et al., 2021; Zeng et al., 2020). However, it is possible to state that Deep Learning-based drug repositioning research has great potential in many important diseases, especially cancer (Issa et al., 2021; Quazi and Fatima, 2023; Yu et al., 2022).

References

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• Aliper, A., Plis, S., Artemov, A., Ulloa, A., Mamoshina, P., Zhavoronkov, A. 2016. “Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data”. Molecular Pharmaceutics, 13(7), 2524-2530.
• Amare, G. G., Meharie, B. G., Belayneh, Y. M. 2021. “A drug repositioning success: The repositioned therapeutic applications and mechanisms of action of thalidomide”. Journal of Oncology Pharmacy Practice, 27(3), 673-678.
• Bridle, J. 2018. New Dark Age: Technology and the End of the Future. Verso Books.
• Bridle, J. 2020. New Dark Age: Technology and the End of the Future (Turkish Version). Metis Publications.
• Cheng, F., Fang, J., Zhang, P., Zhou, Y., Chiang, C. W., Pieper, A. A., Cummings, J. L. 2021. “Sildenafil reduces the incidence of Alzheimer’s disease”. Alzheimer’s & Dementia, 17, e051847.
• Choi, Y., Shin, B., Kang, K., Park, S., Beck, B. R. 2020. “Target-centered drug repurposing predictions of human angiotensin-converting enzyme 2 (ACE2) and transmembrane protease serine subtype 2 (TMPRSS2) interacting approved drugs for coronavirus disease 2019 (COVID-19) treatment through a drug-target interaction deep learning model”. Viruses, 12(11), 1325.
• Deepthi, K., Jereesh, A. S., Liu, Y. 2021. “A deep learning ensemble approach to prioritize antiviral drugs against novel coronavirus SARS-CoV-2 for COVID-19 drug repurposing”. Applied Soft Computing, 113, 107945.
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• Pham, T. H., Qiu, Y., Zeng, J., Xie, L., Zhang, P. 2021. “A deep learning framework for high-throughput mechanism-driven phenotype compound screening and its application to COVID-19 drug repurposing”. Nature Machine Intelligence, 3(3), 247-257.
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• Quazi, S., Fatima, Z. 2023. “Role of Artificial Intelligence and Machine Learning in Drug Discovery and Drug Repurposing”. In Structural and Functional Aspects of Biocomputing Systems for Data Processing (pp. 182-197). IGI Global.
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• Singh, T. U., Parida, S., Lingaraju, M. C., Kesavan, M., Kumar, D., Singh, R. K. 2020. “Drug repurposing approach to fight COVID-19”. Pharmacological Reports, 72(6), 1479-1508.
• Wang, Y., Yang, Y., Chen, S., Wang, J. 2021. “DeepDRK: a deep learning framework for drug repurposing through kernel-based multi-omics integration”. Briefings in Bioinformatics, 22(5), bbab048.
• Wong, C. H., Siah, K. W., Lo, A. W. 2019. “Estimation of clinical trial success rates and related parameters”. Biostatistics, 20(2), 273-286.
• Yu, J. L., Dai, Q. Q., Li, G. B. 2022. “Deep learning in target prediction and drug repositioning: Recent advances and challenges”. Drug Discovery Today, 27(7), 1796-1814.
• Zeng, X., Zhu, S., Lu, W., Liu, Z., Huang, J., Zhou, Y., …, Cheng, F. 2020. “Target identification among known drugs by deep learning from heterogeneous networks”. Chemical Science, 11(7), 1775-1797.