Technological Prospection on Artificial Intelligence Sector Applied to Repurposing Drugs Against COVID-19
DOI:
https://doi.org/10.9771/cp.v16i2.50158Keywords:
Pandemic, Artificial Intelligence, Drugs.Abstract
In a post-pandemic world where the transmission chains of the SARS-CoV-2 virus will still be present, there is a clear necessity to understand machine learning algorithms in artificial intelligence systems with the aim of testing the repurposing drugs against COVID-19. To prospect the scientific-technological production on the subject, a search for the term repurposing AND drugs AND machine learning AND COVID was carried out in the Web of Science, Orbit and Lens databases (2017 to 2022). We identified 71 bibliographic records with authors structured in two groups, with the 2nd group answering by the most recent documents. Nine classes of IPCs were identified with the main technological domains related to the topic and all distributed in 42 active patent documents. Of these, 4 were granted to “RO5” and “Precisionlife” companies. Of the 50 most promising startups in 2022, only two develop this type of technology, which reinforces the existence of a small number of players in this sector and highlights the promising horizon for this area of technology production.
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BECK, B. R.; SHIN, B.; CHOI, Y.; PARK, S.; KANG, K. Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model. Computational and structural biotechnology journal, 18, 784-790. 2020. Disponível em: https://doi.org/10.1016/j.csbj.2020.03.025. Acesso em: 08/12/202
BULLOCK, J. et al. Mapping the landscape of artificial intelligence applications against COVID-19. 2020. p. 1e14. Disponível em: http://arxiv.org/abs/2003.11336. Acesso em: 8 dez. 2022.
COVINGTON. M. et al. Preclinical characterization of itacitinib (INCB039110), a novel selective inhibitor of JAK1, for the treatment of inflammatory diseases. Eur. J. Pharmacol., [s.l.], v. 885, p. 173505, 2020. Disponível em: https://doi.org/10.1016/j.ejphar.2020.173505. Acesso em: 8 dez. 2022.
DAS, S.; BEAULAH, K.; GARDNER, S. Systematic indication extension for drugs using patient stratification insights generated by combinatorial analytics. Cell Patterns, [s.l.], v. 3, n. 6, June, 2022. Disponível em: https://doi.org/10.1016/j.patter.2022.100496. Acesso em: 8 dez. 2022.
DE FREITAS, R. A. B. et al. Prospecção científica sobre epidemiologia e prevenção da COVID-19 aliada a inteligência artificial. Cadernos de Prospecção, Salvador, v. 13, n. 2, edição especial COVID-19, p. 543-543, abril, 2020. Disponível em: https://doi.org/10.9771/cp.v13i2.36190. Acesso em: 8 dez. 2022.
DIMASI, J. A.; GRABOWSKI, H. G.; HANSEN, R. W. Innovation in the pharmaceutical industry: new estimates of R&D costs. J. Health Econ., [s.l.], v. 47, p. 20-33, 2016. Disponível em: https://doi.org/10.1016/j.jheale co.2016.01.012. Acesso em: 8 dez. 2022.
GONG, W. et al. SARS-CoV-2 variants and COVID-19 vaccines: Current challenges and future strategies. International Reviews of Immunology, [s.l.], p. 1-22, 2022.
INPI – INSTITUTO NACIONAL DA PROPRIEDADE INDUSTRIAL. Observatório de Tecnologias Relacionadas à COVID-19 – Medicamentos. 2020. Disponível em: https://www.gov.br/inpi/pt-br/servicos/patentes/tecnologias-para-covid-19/patentes-de-medicamentos-covid-19. Acesso em: 8 dez. 2022.
KOLLURI, S. et al. Machine learning and artificial intelligence in pharmaceutical research and development: a review. The AAPS Journal, [s.l.], v. 24, n. 1, p. 1-10, 2022. Disponível em: https://doi.org/10.1208/s12248-021-00644-3. Acesso em: 8 dez. 2022.
KOROMINA, M.; PANDI, M. T.; PATRINOS, G. P. Rethinking drug repositioning and development with artificial intelligence, machine learning, and omics. Omics: A Journal of Integrative Biology, [s.l.], v. 23, n. 11, p. 539-548, 2019. Disponível em: https://doi.org/10.1089/omi.2019.0151. Acesso em: 8 dez. 2022.
MCCARTHY, J. What is artificial intelligence. URL: http://www-formal. stanford. edu/jmc/whatisai. Html, 2004. Disponível em: http://cse.unl.edu/~choueiry/S09-476-876/Documents/whatisai.pdf. Acesso em: 8 dez. 2022.
MOHAPATRA, S. et al. Repurposing therapeutics for COVID-19: Rapid prediction of commercially available drugs through machine learning and docking. PLoS ONE, [s.l.], v. 15, n. 11, p. e0241543, 2020. Disponível em: https://doi.org/10.1371/journal.pone.0241543. Acesso em: 8 dez. 2022.
NGUYEN, T. T.; WAURN, G.; CAMPUS, P. Artificial intelligence in the battle against coronavirus (COVID-19): A survey and future research directions. 2020. Disponível em: https://doi.org/10.13140/RG.2.2.36491.23846. Acesso em: 8 dez. 2022.
PAWEŁCZYK, A.; ZAPRUTKO, L. Anti-COVID drugs: repurposing existing drugs or search for new complex entities, strategies and perspectives. Future Medicinal Chemistry, [s.l.], v. 12, n. 19, p. 1.743-1.757, 2020. Disponível em: https://doi.org/10.4155/fmc-2020-0204. Acesso em: 8 dez. 2022.
PIRES, E. A.; RIBEIRO, N. M.; QUINTELLA, C. M. Sistemas de Busca de Patentes: análise comparativa entre Espacenet, Patentscope, Google Patents, Lens, Derwent Innovation Index e Orbit Intelligence. Cadernos de Prospecção, Salvador, v. 13, n. 1, p. 13-13, março, 2020. Disponível em: https://doi.org/10.9771/cp.v13i1.35147. Acesso em: 8 dez. 2022.
RAHMAN, S. et al. Epidemiology, pathogenesis, clinical presentations, diagnosis and treatment of COVID-19: a review of current evidence. Expert Ver. Clin. Pharmacol., [s.l.], v. 14, n. 5, p. 601-621, 2021. Disponível em: https://dx.doi.org/10.1080/17512433.2021.1902303. Acesso em: 8 dez. 2022.
RUIZ ESTRADA, M. A. The uses of drones in case of massive Epidemics contagious diseases relief humanitarian aid: Wuhan-COVID-19 crisis. SSRN Electron. J., [s.l.], 2020. Disponível em: https://doi.org/10.2139/ssrn.3546547. Acesso em: 8 dez. 2022.
SABER-AYAD, M.; SALEH, M. A.; ABU-GHARBIEH, E. The rationale for potential pharmacotherapy of COVID-19. Pharmaceuticals, [s.l.], v. 13, n. 5, p. 96, 2020. Disponível em: doi:10.3390/ph13050096. Acesso em: 8 dez. 2022.
SAVIN, I.; CHUKAVINA, K.; PUSHKAREV, A. Topic-based classification and identification of global trends for startup companies. Small Business Economics, [s.l.], p. 1-31, 2022. Disponível em: https://doi.org/10.1007/s11187-022-00609-6. Acesso em: 08/12/2022
SENIOR, A. et al. Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13). Proteins, [s.l.], v. 87, p. 1.141-1.148, 2019. Disponível em: https:// doi.org/10.1002/prot.25834. Acesso em: 8 dez. 2022.
SENIOR, A.W. et al. Improved protein structure prediction using potentials from deep learning. Nature, [s.l.], v. 577, n. 7.792, p. 706-710, 2020. Disponível em: https://doi.org/10.1038/s41586-019-1923-7. Acesso em: 8 dez. 2022.
TANOLI, Z.; VÄHÄ-KOSKELA, M.; AITTOKALLIO, T. Artificial intelligence, machine learning, and drug repurposing in cancer. Expert Opinion on Drug Discovery, [s.l.], v. 16, n. 9, p. 977-989, 2021. Disponível em: https://doi.org/10.1080/17460441.2021.1883585. Acesso em: 8 dez. 2022.
URBINA, F.; PUHL, A. C.; EKINS, S. Recent advances in drug repurposing using machine learning. Current Opinion in Chemical Biology, [s.l.], v. 65, p. 74-84, 2021. Disponível em: https://doi.org/10.1016/j.cbpa.2021.06.001. Acesso em: 8 dez. 2022.
VANDEN EYNDE, J. J. COVID-19: a brief overview of the discovery clinical trial. Pharmaceuticals, [s.l.], v. 13, n. 4, p. 65, 2020.
WONG, C. H.; SIAH, K. W.; LO, A. W. Estimation of clinical trial success rates and related parameters. Biostatistics, [s.l.], v. 20, n. 2, p. 273-286, 2019. Disponível em: https://doi.org/10.1093/biostatistics/kxx069. Acesso em: 8 dez. 2022.
YANG, Z. et al. Ro5 Bioactivity Lab: Identification of Drug Candidates for COVID-19 (ChemRxiv). Cambridge: Cambridge Open Engage, 2020. Disponível em:
26434/chemrxiv.12275741.v1. Acesso em: 8 dez. 2022.
ZHANG, Daniel et al. The AI Index 2022 Annual Report. In: AI INDEX STEERING COMMITTEE, STANFORD INSTITUTE FOR HUMAN-CENTERED AI, STANFORD UNIVERSITY, March, 2022. Anais […]. [S.l.], 2022. Disponível em: https://arxiv.org/ftp/arxiv/papers/2205/2205.03468.pdf. Acesso em: 8 dez. 2022.
ZHOU, P.; YANG, X. L.; WANG, X. G. A pneumonia out- break associated with a new coronavirus of probable bat origin. Nature, [s.l.], v. 579, n. 7.798, p. 270-273, 2020. Disponível em: https://doi.org/10.1038/s41586-020-2012-7. Acesso em: 8 dez. 2022.
ZHOU, Y. et al. Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2. Cell Discov., [s.l.], v. 6, n. 14, p. 2020. Disponível em: https://doi.org/10.1038/s41421-020-0153-3. Acesso em: 8 dez. 2022.
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