A paradigm of modern drug delivery and artificial intelligence

Authors

  • Banerjee S Burdwan Medical College, Burdwan, West Bengal, India
  • Kumar H Dow University of Health Science, Karachi, Pakistan
  • Mukherjee D Raiganj Government Medical College and Hospital, West Bengal, India
  • Sanyal T Department of Zoology, Krishnagar Government College, Krishnagar, West Bengal, India

Keywords:

Modern drug delivery, AI algorithm, Theraputic instruments, Diagnosis, Pharmaceuticals

Abstract

Computational pharmaceutics is the result of a paradigm change brought about by the convergence of artificial intelligence (AI) and massive amounts of data in the field of pharmaceutics. This cutting-edge field uses the unmatched power of AI algorithms or machine learning techniques to analyse, examine, and forecast the complex nuances of pharmaceutical data. In doing so, computational pharmaceutics gives researchers the ability to model medication distribution and formulation procedures, eliminating the need for lengthy repetitions of trial and error. AI algorithms create therapeutic sequences with enhanced stability, affinity for binding, and perfect safety profile by analysing the enormous amounts of data. Target identity, protein folding anticipation, toxicity examination, and clinical experiment optimisation are all part of the AI symphony.

References

References

Navya K., Kamaraj R. and Bharathi M. 2022 The Trending Role of Artificial Intelligence and Its Applications in Formulation of Solid Dosage Forms: A Review. ECS Trans.107: 20049–20055. DOI10.1149/10701.20049ecst.

Han R., Xiong H., Ye Z., Yang Y., Huang T., Jing Q., Lu J., Pan H., Ren F. and Ouyang D. 2019 Predicting Physical Stability of Solid Dispersions by Machine Learning Techniques. J. Control. Release.311:16–25. DOI: 10.1016/j.jconrel.2019.08.030.

Shi G., Lin L., Liu Y., Chen G., Luo Y., Wu Y. and Li H. 2021 Pharmaceutical Application of Multivariate Modelling Techniques: A Review on the Manufacturing of Tablets. RSC Adv. 11: 8323–8345. DOI: 10.1039/d0ra08030f.

Ma X., Kittikunakorn N., Sorman B., Xi H., Chen A., Marsh M., Mongeau A., Piché N., Williams RO. and Skomski D. 2020 Application of Deep Learning Convolutional Neural Networks for Internal Tablet Defect Detection: High Accuracy, Throughput, and Adaptability. J. Pharm. Sci. 109:1547–1557. DOI: 10.1016/j.xphs.2020.01.014.

Ho D., Wang P. and Kee T. 2019 Artificial Intelligence in Nanomedicine. Nanoscale Horiz. 4: 365-377. DOI:10.1039/C8NH00233A.

Wang N., Zhang Y., Wang W., Ye Z., Chen H., Hu G. and Ouyang, D. 2023 How Can Machine Learning and Multiscale Modeling Benefit Ocular Drug Development? Adv. Drug Deliv. Rev. 196:114772. https://doi.org/10.1016/j.addr.2023.114772.

Akbar R., Bashour H., Rawat P., Robert PA., Smorodina E., Cotet TS., Flem-Karlsen K., Frank R., Mehta BB., Vu MH. Zengin T, Gutierrez-Marcos J, Lund-Johansen F, Andersen JT, Greiff V 2022 Progress and Challenges for the Machine Learning-Based Design of Fit-for-Purpose Monoclonal Antibodies. mAbs. 14: 2008790. DOI: 10.1080/19420862.2021.2008790.

Downloads

Published

2024-05-07

How to Cite

Banerjee, S., Kumar, H., Mukherjee, D., & Sanyal, T. (2024). A paradigm of modern drug delivery and artificial intelligence. International Journal of Advanced Research Trends in Science, 3(1), 1–3. Retrieved from https://ijarts.aura-international.org/index.php/j/article/view/21

Issue

Section

Editorial