Implementation of artificial intelligence in biotechnology for rapid drug discovery and enabling personalized treatment through vaccines and therapeutic products
Keywords:
Biotechnology, healthcare, personalized medicine, drug discovery, high-throughput screening, machine learning, ethics, interpretability, multi-omics data, systems biology, medical devices, diagnostics, nanotechnology, tissue engineering, genomics, proteomics, regenerative medicine, high-throughput screening.Abstract
A new era of healthcare and biomedical research has been brought about by the merging of biotechnology with artificial intelligence (AI). This integration presents previously unheard-of prospects to expand scientific understanding, expedite medication discovery, and tailor therapies. In this study, we examine the ways in which biotechnology and artificial intelligence might work together to improve healthcare in a number of areas, such as individualized treatment plans, quick drug discovery, and the creation of therapeutic items. We go over how artificial intelligence (AI) algorithms use massive amounts of biological and chemical data to forecast medication success, optimize treatment plans, and speed up the search for new treatments. We also look at how biotechnology can be used to translate AI-driven predictions into interventions that are applicable to clinical settings, such as the creation of customized vaccinations, gene therapies, and products for regenerative medicine. We draw attention to the difficulties and constraints that come with integrating biotechnology and AI in healthcare, such as the complexity and interpretability of the data, ethical issues, legal barriers, and societal ramifications. Lastly, we talk about the potential applications of biotechnology and AI in healthcare going forward, such as the creation of more understandable AI algorithms, the fusion of systems biology and multi-omics data, and the development of customized medical devices and regenerative medicine treatments. We can fully utilize biotechnology and AI to change healthcare delivery, enhance patient outcomes, and influence the course of medicine by tackling these issues and embracing these new approaches.
References
Hughes JP, Rees S, Kalindjian SB, Philpott KL. Principles. Br J Pharmacol. 2011; 162(6):1239-49. doi: 10.1111/j.1476-5381.2010.01127.x, PMID 21091654.
Drug development: the journey of a medicine from lab to shelf [Home page on internet] [cited Jun 25 2021]. Available from: https://pharmaceutical-journal.com/article/feature/drug-development-the-journey-of-a-medicine-from-lab-to-shelf.
Van Gool, A. J.; Bietrix, F.; Caldenhoven, E.; et al. Bridging the Translational Innovation Gap through Good Biomarker Practice. Nat. Rev. Drug Discov. 2017, 16, 587.
Kraus, V. B. Biomarkers as Drug Development Tools: Discovery, Validation, Qualification and Use. Nat. Rev. Rheumatol. 2018, 14, 354–362.
West, H. J. Novel Precision Medicine Trial Designs: Umbrellas and Baskets. JAMA Oncol. 2017, 3, 423–423.
The Latest on Drug Failure and Approval Rates [Home page on internet] [cited Jun 15 2021]. Available from: https://blogs.sciencemag.org/pipeline/ archives/2019/05/09/the-latest-on-drug-failure-and-approval-rates.
Mak KK, Pichika MR. Artificial intelligence in drug development: present status and future prospects. Drug Discov Today. 2019; 24(3):773-80. doi:10.1016/j.drudis.2018.11.014, PMID 30472429
Sellwood MA, Ahmed M, Segler MHs, Brown N. Artificial intelligence in drug discovery. Future Med Chem. 2018; 10(17):2025-28. doi: 10.4155/fmc-2018- 0212, PMID 30101607.
Smith JS, Roitberg AE, Isayev O. Transforming computational drug discovery with machine learning and AI. ACS Med Chem Lett. 2018; 9(11):1065-69.
Jing Y, Bian Y, Hu Z, Wang L, Xie XQ. Deep learning for drug design: an artificial intelligence paradigm for drug discovery in the big data era. AAPS J. 2018; 20(3):58. doi: 10.1208/s12248-018-0210-0. PMID 29603063.
Powles J, Hodson H. Google DeepMind and healthcare in an age of algorithms. Health Technol (Berl). 2017;7(4):351-67. doi: 10.1007/s12553- 017-0179-1. PMID 29308344.
Kievit FM, Zhang M. Cancer nanotheranostics: improving imaging and therapy by targeted delivery across biological barriers. Adv Mater. 2011; 23(36):H217-47. doi: 10.1002/adma.201102313, PMID 21842473.
Hassanzadeh P, Atyabi F, Dinarvand R. The significance of artificial intelligence in drug delivery system design. Adv Drug Deliv Rev. 2019; 151- 152:169-90. doi: 10.1016/j.addr.2019.05.001, PMID 31071378.
Staples M, Daniel K, Cima MJ, and Langer R. Application of micro- and nano- electromechanical devices to drug delivery. Pharm Res. 2006; 23(5):847-63. doi: 10.1007/s11095-006-9906-4, PMID 16715375.
Ly DQ. The Matter Compiler—towards atomically precise engineering and manufacture. NtP. 2011; 7(3):199-217. doi: 10.4024/N13LY11A.ntp.07.03.
Meziane F, Vadera S, Kobbacy K, Proudlove N. Intelligent systems in manufacturing: current developments and future prospects. Integr Manuf Syst. 2000; 11(4):218-38. doi: 10.1108/09576060010326221.
Keshavarzi Arshadi A, Webb J, Salem M, Cruz E, Calad-Thomson S, Ghadirian N, et al. Artificial Intelligence for COVID-19 Drug Discovery and Vaccine Development. Front Artif Intell. 2020; 3:65. doi: 10.3389/ frai.2020.00065, PMID 33733182.
Bender A, Cortes-Ciriano I. Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 2: A discussion of chemical and biological data. Drug Discov Today. 2021; 26(4):1040-52. doi: 10.1016/j. drudis.2020.11.037, PMID 33508423.
ENGINEERING S. [Home page on internet] [cited Jun 05 2021]. Available from: https://engineering.stanford.edu/magazine/promise-and-challenges-relying-ai-drug-development
Duvenaud DK, Maclaurin D, Iparraguirre J, Bombarell R, Hirzel T, Alan Aspuru-Guzik, et al. Convolutional networks on graphs for learning molecular fingerprints. Adv Neural Inf Process Syst. 2015; 28:2224-32.
Duvenaud D, et al. Convolutional networks on graphs for learning molecular fingerprints. The 28th International Conference on Neural Information Processing Systems; 2018.
Mayr A, Klambauer G, Unterthiner T, Hochreiter S. DeepTox: toxicity prediction using Deep Learning. Front Environ Sci. 2016; 3:80. doi: 10.3389/ fenvs.2015.00080.
DeepTox MA. Toxicity prediction using deep learning. Front Environ Sci. 2016; 3:80.
Xu Y, Ma Junshui, Liaw A, Sheridan RP, Svetnik V. Demystifying Multitask Deep Neural Networks for Quantitative Structure-Activity Relationships. J Chem Inf Model. 2017; 57(10):2490-504. doi: 10.1021/acs.jcim.7b00087, PMID 28872869.
Sanchez-Lengeling B, Outeiral C, Guimaraes GL, Aspuru-Guzik A. Optimizing distributions over molecular space. An Objective-Reinf Gener Adversarial Network Inverse-Des Chem (Org) ChemRxiv. 2017. doi: 10.26434/chemrxiv.5309668.v3.
Ramsundar B, Eastman P, Walters P, Pande V. Deep learning for the life sciences: applying deep learning to genomics, microscopy, drug discovery, and more. 1st ed. O’Reilly Media, Inc; 2019.
Riant, F.; Bergametti, F.; Ayrignac, X.; et al. Recent Insights into Cerebral Cavernous Malformations: The Molecular Genetics of CCM. FEBS J. 2010, 277, 1070–1075.
Chon, J. Automation and Machine Learning: A Look into Recursion Pharmaceuticals. September 2017. https://www.rarediseasereview.org/publications/2017/9/17/automation-and-machine-learning-a-look-into-recursion-pharmaceuticals
Dahl, G. E.; Jaitly, N.; Salakhutdinov, R. Multi-task neural net- works for QSAR predictions. arXiv preprint arXiv:1406.12312014.
Mayr, A.; Klambauer, G.; Unterthiner, T.; et al. DeepTox: Toxicity Prediction Using Deep Learning. Front. Environ. Sci. 2016, 3, 80.
Ma, J.; Sheridan, R. P.; Liaw, A.; et al. Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships. J. Chem Inform. Model. 2015, 55, 263–274.
Xu, K.; Hu, W.; Leskovec, J.; et al. How Powerful Are Graph Neural Networks? arXiv preprint arXiv:1810.00826 2018.
Duvenaud, D. K.; Maclaurin, D.; Iparraguirre, J.; et al. Advances in Neural Information Processing Systems. In Convolutional Networks on Graphs for Learning Molecular Fingerprints; Cortes, C., Lawrence, N. D., Lee, D. D., Sugiyama, M., Garnett, R., Eds.; Curran Associates, Inc.: Red Hook, NY, 2015; pp 2224–2232.
Glen, R. C.; Bender, A.; Arnby, C. H.; et al. Circular Finger- prints: Flexible Molecular Descriptors with Applications from Physical Chemistry to ADME. IDrugs 2006, 9, 199.
Kearnes, S.; McCloskey, K.; Berndl, M.; et al. Molecular Graph Convolutions: Moving beyond Fingerprints. J. Comput. Aid. Mol. Design 2016, 30, 595–608.
Schütt, K. T.; Arbabzadah, F.; Chmiela, S.; et al. Quantum- Chemical Insights from Deep Tensor Neural Networks. Nat. Commun. 2017, 8, 1–8.
Gilmer, J.; Schoenholz, S. S.; Riley, P. F.; et al. Neural Message Passing for Quantum Chemistry. arXiv preprint arXiv:1704.01212 2017.
Liu, K.; Sun, X.; Jia, L.; et al. Chemi-Net: A Molecular Graph Convolutional Network for Accurate Drug Property Prediction. Int. J. Mol. Sci. 2019, 20, 3389.
Chan, H. S.; Shan, H.; Dahoun, T.; et al. Advancing Drug Discovery via Artificial Intelligence. Trends Pharmacol. Sci. 2019, 40, 592–604.
AlQuraishi, M. AlphaFold at CASP13. Bioinformatics 2019, 35, 4862–4865.
Team, A. Computational Predictions of Protein Structures Associated with COVID-19. DeepMind Website K 2020, 417, Y453.
Jin, W.; Barzilay, R.; Jaakkola, T. Junction Tree Variational Autoencoder for Molecular Graph Generation. arXiv preprint arXiv:1802.04364 2018.
You, J.; Liu, B.; Ying, Z.; et al. Advances in Neural Information Processing Systems. In Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation; Bengio, S., Wallach, H. M., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Eds.; Curran Associates, Inc: Red Hook, NY, 2018; pp 6410–6421.
Shi, C.; Xu, M.; Zhu, Z.; et al. GraphAF: A Flow-Based Autoregressive Model for Molecular Graph Generation. arXiv preprint arXiv:2001.09382 2020.
Barratt MJ, Frail DE. Drug repositioning: Bringing new life to shelved assets and existing drugs. John Wiley & Sons 2012. http://dx.doi.org/10.1002/9781118274408
Cha Y, Erez T, Reynolds IJ, et al. Drug repurposing from the perspective of pharmaceutical companies. Br J Pharmacol 2018; 175(2): 168-80.http://dx.doi.org/10.1111/bph.13798 PMID: 28369768
Ekins S, Puhl AC, Zorn KM, et al. exploiting machine learning for end-to-end drug discovery and development. Nat Mater 2019; 18(5): 435-41.
Zhang L, Tan J, Han D, Zhu H. From machine learning to deep learning: progress in machine intelligence for rational drug dis- covery. Drug Discov Today 2017; 22(11): 1680-5.
Zhao K, So HC. Using drug expression profiles and machine learning approach for drug repurposing.Computational Methods for Drug Repurposing. New York, NY: Humana Press 2019; pp. 219-37.
Park K. A review of computational drug repurposing. Transl Clin Pharmacol 2019; 27(2): 59-63.
Koromina M, Pandi MT, Patrinos GP. Rethinking Drug Repositioning and Development with Artificial Intelligence, Machine Learning, and Omics. OMICS 2019; 23(11): 539-48
Zhao K, So HC. A machine learning approach to drug reposition-in based on drug expression profiles: Applications to schizophre- nia and depression/anxiety disorders. arXiv preprint arXiv:170603014 2017.
Kim E, Choi AS, Nam H. Drug repositioning of herbal compounds via a machine-learning approach. BMC Bioinformatics 2019;20(10)
Yella JK, Yaddanapudi S, Wang Y, Jegga AG. Changing trends in computational drug repositioning. Pharmaceuticals (Basel) 2018; 11(2): 57.
