Role of Artificial Intelligence, Big Tech and Machine Learning in Accelerating Biologics Development and Clinical Trials
Authors/Creators
- 1. PhD, Dept. of Anaesthesiology, Gouri Devi Institute of Medical Sciences and College Rajbandh Durgapur
- 2. MBBS MD, Dept. of Anaesthesiology, Gouri Devi Institute of Medical Sciences and College Rajbandh Durgapur
Description
Modern healthcare is being revolutionized and strengthened by artificial intelligence-based technologies that can grasp, learn, and act, whether they are used to identify novel correlations between genetic codes or to guide surgical robots. Vaccines, blood and blood components, allergic somatic cells, gene therapy, tissues, and recombinant therapeutic proteins are only a few examples of biological products, according to the US FDA. Most biologics are complicated mixes that are difficult to identify or classify, in contrast to the majority of medications, which are chemically manufactured and have a known structure. Biotechnological products, including those produced by them, have a propensity to be heat sensitive and microbial contaminated. Because of this, and in contrast to the majority of conventional pharmaceuticals, it is vital to adopt aseptic principles from the first manufacturing processes. A biologic drug (biologic) is a product that is produced from, or contains components of living organisms. Biotechnology has given rise to these organic derivatives which are sourced from humans, animals or microorganisms. As internal modifiers, biologic drugs either enhanced or inhibit biological processes that are part of the key mechanisms of action for critical pathways in healing. The study specifically focuses on the three newest applications of AI-ML in healthcare: AI-ML driven discovery and process development, clinical trials, and patient care. According to the research, companies have highly benefited from the use of AI-ML in health industries i.e. in Pharmaceuticals, Biotechnology, Clinical Trials and Digital Health by automating target identification and thus accelerating the drug discovery lifecycle. We finally anticipate potential recent interventions of AI-ML in several steps of the biological landscape, focusing on clinical trials and clinical research aspects. In this context, artificial intelligence (AI), and especially machine learning (ML), have great potential to accelerate and improve the optimization of protein properties, increasing their activity and safety as well as reducing their development time and manufacturing along with clinical trial costs.
Abstract (English)
Modern healthcare is being revolutionized and strengthened by artificial intelligence-based technologies that can grasp, learn, and act, whether they are used to identify novel correlations between genetic codes or to guide surgical robots. Vaccines, blood and blood components, allergic somatic cells, gene therapy, tissues, and recombinant therapeutic proteins are only a few examples of biological products, according to the US FDA. Most biologics are complicated mixes that are difficult to identify or classify, in contrast to the majority of medications, which are chemically manufactured and have a known structure. Biotechnological products, including those produced by them, have a propensity to be heat sensitive and microbial contaminated. Because of this, and in contrast to the majority of conventional pharmaceuticals, it is vital to adopt aseptic principles from the first manufacturing processes. A biologic drug (biologic) is a product that is produced from, or contains components of living organisms. Biotechnology has given rise to these organic derivatives which are sourced from humans, animals or microorganisms. As internal modifiers, biologic drugs either enhanced or inhibit biological processes that are part of the key mechanisms of action for critical pathways in healing. The study specifically focuses on the three newest applications of AI-ML in healthcare: AI-ML driven discovery and process development, clinical trials, and patient care. According to the research, companies have highly benefited from the use of AI-ML in health industries i.e. in Pharmaceuticals, Biotechnology, Clinical Trials and Digital Health by automating target identification and thus accelerating the drug discovery lifecycle. We finally anticipate potential recent interventions of AI-ML in several steps of the biological landscape, focusing on clinical trials and clinical research aspects. In this context, artificial intelligence (AI), and especially machine learning (ML), have great potential to accelerate and improve the optimization of protein properties, increasing their activity and safety as well as reducing their development time and manufacturing along with clinical trial costs.
Files
IJPCR,Vol14,Issue9,Article74.pdf
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Additional details
Dates
- Accepted
-
2022-09-12
Software
- Repository URL
- https://impactfactor.org/PDF/IJPCR/14/IJPCR,Vol14,Issue9,Article74.pdf
- Development Status
- Active
References
- 1. Ramesh, A. et al. Artificial intelligence in medicine. Ann. R. Coll. Surg. Engl. 2004;86: 334–338 2. Miles, J. and Walker, A. The potential application of artificial intelligence in transport. IEE Proc.-Intell. Transport Syst. 2006;153: 183–198. 3. Yang, Y. and Siau, K. A Qualitative Research on Marketing and Sales in theArtificial Intelligence Age. MWAIS. 2018. 4. Smith, R.G. and Farquhar, A. The road ahead for knowledge management: an AI perspective. AI Mag. 2000; 21:17– 17 5. Lamberti, M.J. et al. A studies on the application and use of artificial intelligence to support drug development. Clin. Ther. 2019;41: 1414–1426 6. Beneke, F. and Mackenrodt, M.-O. Artificial intelligence and collusion. IIC Int. Rev. Intellectual Property Competition Law. 2019;50: 109–134 7. Steels, L. and Brooks, R. The Artificial Life Route to Artificial Intelligence: Building Embodied, Situated Agents. Routledge. 2018. 8. Granda JM, Donina L, Dragone V, Long DL, Cronin L. Controlling an organic synthesis robot with machine learning to search for new reactivity. Nature. 2018;559(7714):377–81. 9. Koh D. Sumitomo Dainippon Pharma and Exscientia achieve breakthrough in AI drug discovery: Healthcare IT News - Portland, ME: Healthcare IT News; 2020. 10. Romero K, Ito K, Rogers JA, Polhamus D, Qiu R, Stephenson D, et al. The future is now: model-based clinical trial design for Alzheimer's disease. Clin Pharmacol Ther. 2015;97(3):210–4. 11. Zhao Y, Zeng D, Socinski MA, Kosorok MR. Reinforcement learning strategies for clinical trials in non small cell lung cancer. Biometrics. 2011; 67(4):1422–33. 12. http://pharma.bayer.com/en/innovation -partnering/technologies-andtrends/small-and-largemolecules/https://perma.cc/T6R7- 3Q7B [Last accessed on 3September 2022). 13. Opderbeck DW. Artificial Intelligence in Pharmaceuticals, Biologics, and Medical Devices: Present and Future Regulatory Models. Fordham L. Rev. 2019;88:553. 14. Gartland KM, Gartland JS. Opportunities in biotechnology. Journal of biotechnology. 2018 Sep 20;282:38- 45. 15. Jones N, Dean AB. Current patenting trends for biologics versus small molecules. Pharmaceutical Patent Analyst. 2012 Jul;1(3):225-7. 16. https://www.clinicaltrialsarena.com/an alysis/ai-artificial-intelligence-clinicaltrials-start-ups/ [Last accessed on 3 September 2022). 17. Vamathevan, J. et al. Applications of machine learning in drug discovery and development. Nat. Rev. Drug Discov. 2019; 18:463–477 18. Ching, T. et al. Opportunities and obstacles for deep learning in biology and medicine. J. R. Soc. Interface 2018;15: 20170387 19. Hay, M. et al. Clinical development success rates for investigational drugs. Nat. Biotechnol. 2014;32: 40–51 20. Harrer, S. et al. Artificial intelligence for clinical trial design. Trends Pharmacol. Sci. 2019;40: 577–591 21. Fogel, D.B. (2018) Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: a review. Contemp. Clin. Trials Commun. 2018;11: 156–164. 22. Mak, K.-K and Pichika, M.R. Artificial intelligence in drug development: present status and future prospects. Drug Discovery Today 2019;24: 773– 780. 23. Barnette DA, Davis MA, Dang NL, Pidugu AS, Hughes T, Swamidass SJ, Boysen G, Miller GP. Lamisil (terbinafine) toxicity: Determining pathways to bioactivation through computational and experimental approaches. Biochemical pharmacology. 2018 Oct 1;156:10-21. 24. Hughes TB, Flynn N, Dang NL, Swamidass SJ. Modeling the Bioactivation and Subsequent Reactivity of Drugs. Chemical researchin toxicology. 2021 Jan 26;34(2):584- 600. 25. Shah P, Kendall F, Khozin S, Goosen R, Hu J, Laramie J, Ringel M, Schork N. Artificial intelligence and machine learning in clinical development: a translational perspective. NPJ digital medicine. 2019 Jul 26;2(1):1-5. 26. Yang, K.K. et al. Machine-learningguided directed evolution for protein engineering. Nat. Methods 2019;16: 687–694. 27. Evans S. R. (2010). Fundamentals of clinical trial design. Journal of experimental stroke & translational medicine, 2010;3(1): 19–27. 28. Stefan Harrer et al., Artificial Intelligence for Clinical Trial Design, ScienceDirect, August 2019, accessed December 18, 2019. 29. Angie Sullivan, Clinical Trial Site Selection: Best Practices, RCRI Inc, accessed December 18, 2019. 30. Jack Kaufman, The innovative startups improving clinical trial recruitment, enrollment, retention, and design, Mobi Health News, November 2018: accessed December 18, 2019. 31. Aditya Kudumala, Leverage operational data with clinical trial analytics: Take three minutes to learn how analytics can help, Deloitte Development LLC, accessed December 18, 2019. 32. Jacob Bell, Pharma is shuffling around jobs, but a skills gap threatens the process, BioPharma Dive, February 2019, accessed December 19, 2019. 33. Greg Reh et al., 2019 Global life sciences outlook: Focus and transform. Accelerating change in life sciences, Deloitte TTL, January 2019, accessed December 18, 2019. 34. Deep Knowledge Analytics, AI for drug discovery, biomarker development and advanced R&D landscape overview 2019/Q3, accessed December 18, 2019. 35. Narayanan H, Dingfelder F, Butté A, Lorenzen N, Sokolov M, Arosio P. Machine learning for biologics: opportunities for protein engineering, developability, and formulation. Trends in pharmacological sciences. 2021 Mar 1;42(3):151-65. 36. DIANE S., Baldé A. K., Camara F., & Diane M. H. Problématique du traitement de limboconjonctiviteetendémique des tropiques. Journal of Medical Research and Health Sciences, 2022;5(9): 2244– 2249.