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An Exploratory Analysis of the BioMedical Literature and Clinical Trials for Discovering Drug Repurposing Links: A COVID-19 Case Study

Our research on COVID-19 Drug Repurposing concluded that it may be necessary to combine FDA-approved drugs to achieve a viable treatment. This analysis was conducted against a large body of biomedical literature and confirmed by analyzing the clinical trials that are active and recruiting. For example, the research highlighted combinations of drugs that included ritonavir. A drug that has proven to be a base ingredient for already commercialized products known as Kaltera (when combined with lopinavir), and known as Paxlovid (when boosted and combined with nirmatrelvir). Clearly, this research has proven promising in the way that it predicts drug combinations that have been confirmed by evidence from COVID-19 related clinical trials. In this talk, we describe the computational methods and the full findings of this research.

Dr. Ahmed Abdeen Hamed, Ph.D., completed his Ph.D. at the University of Vermont in 2014. His dissertation presented novel network models and algorithms that explored social media data, news articles, and biomedical literature. Particularly, his work investigated digital recruitment, adverse drug events, and rankings. While he was in the Pharma industry, he designed a network algorithm that provided ranking to small molecules based on their specificity. In 2019, Dr. Hamed also served as an assistant Professor of data science and artificial intelligence at Norwich University. He has led the development of several academic programs (in data science, business analytics, and information systems) for both undergraduate and graduate levels. He also served as a program director for those academic programs. During the pandemic, Dr. Hamed was one of the very early scientists who investigated the possibility of providing a COVID-19 treatment. He published a paper that recommended 30-drugs which to date is considered the foundation for many of the publications that followed after.

Dr. Hamed’s research continues to strive to solve real-world problems. He is currently focusing on advancing our knowledge to understand disease and treatment. His research on drug repurposing is currently focusing on advancing our understanding of COVID-19 treatment but constructing knowledge from the clinical trials and biomedical literature. Dr. Hamed joined Sano to continue to pursue his clinical research using computational means of data science and artificial intelligence. He will be collaborating with the other Sano teams and beyond while he will be supervising graduate Ph.D. Students and train PostDoctoral fellows.

With many years of experience, in both industry and academia, he has achieved the following:

* Actively published in highly specialized and well-ranked journals
* A first inventor for molecule ranking and drug discovery for the Pharma industry
* Helped a startup company to be awarded a multi-million dollar grant for building a recommendation engine
* Was selected among The FastCompany MostCreative in 2016

Top-5 Publications:

* Hamed, A.A.; Fandy, T.E.; Tkaczuk, K.L.; Verspoor, K.; Lee, B.S. COVID-19 Drug Repurposing: A Network-Based Framework for Exploring Biomedical Literature and Clinical Trials for Possible Treatments. Pharmaceutics 2022, 14, 567.

* Gates LE, Hamed AA; The Anatomy of the SARS-CoV-2 Biomedical Literature: Introducing the CovidX Network Algorithm for Drug Repurposing Recommendation
J Med Internet Res 2020;22(8):e21169 doi: 10.2196/21169

* Abdeen, M.A.R.; Hamed, A.A.; Wu, X. Fighting the COVID-19 Infodemic in News Articles and False Publications: The NeoNet Text Classifier, a Supervised Machine Learning Algorithm. Appl. Sci. 2021, 11, 7265.

* Hamed, A. A., Leszczynska, A., & Schreiber, M. (2019, March). MolecRank: a specificity-based network analysis algorithm. In International Conference on Advanced Machine Learning Technologies and Applications (pp. 159-168). Springer, Cham.

* Hamed, A. A., Wu, X., Erickson, R., & Fandy, T. (2015). Twitter KH networks in action: Advancing biomedical literature for drug search. Journal of biomedical informatics, 56, 157-168.

Pharma Patent:

* Hamed, A.A. and Leszczynska, A., Merck Sharp and Dohme Corp, 2021. Systems and methods for providing a specificity-based network analysis algorithm for searching and ranking therapeutic molecules. U.S. Patent 10,978,178.

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Dr. Ahmed Abdeen Hamed, Ph.D.

Sano – Centre for Computational Personalised Medicine – International Research Foundation

Monday, 9th May 2022, 2:00-3:30 PM (CEST)

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