IS ARTIFICIAL INTELLIGENCE VIZ M AND D LEARNING COULD BE THE SOLUTION PROMISING ALTERNATIVE TO ANIMALS: A LEARNING-BASED TOXICITY RECITATION?

Machine and Deep Learning

  • Rahul Kumar Mishra Department of Pharmaceutical Science and Technology, Madan Mohan Malviya University of technology, Gorakhpur Uttar Pradesh-India
  • Rahul Kaushik Associate Professor, Ram-Eesh Institute of Vocational and Technical Education, Greater Noida https://orcid.org/0000-0003-2223-5399
  • Ajay Shukla Institute of Pharmacy, Dr Rammanohar Lohia Avadh University Ayodhya, Uttar Pradesh, India
  • Smriti Ojha Department of pharmaceutical science and technology, Madan Mohan Malviya University of technology, Gorakhpur Uttar Pradesh-India
  • Sudhanshu Mishra Department of pharmaceutical science and technology, Madan Mohan Malviya University of technology, Gorakhpur Uttar Pradesh-India
  • Rama Shankar Dubey Department of pharmaceutical science and technology, Madan Mohan Malviya University of technology, Gorakhpur Uttar Pradesh-India
DOI https://doi.org/10.31069/japsr.v5i2.01

Abstract

Similar to animal and human studies, the data show that when many animal models are extrapolated to humans, reliability is limited when it comes to predicting drug effects. This leads to an unbalanced waste of time and money and nightmares during drug development because the drug works well in animals or pre-clinical models and therefore fails in clinical studies or clinical trials, or vice versa. In this technique, machine and deep learning (M and D) is a subset of artificial intelligence. We hope this will eliminate the need for lengthy searches, reduce the number of animals sacrificed in the strategy, and reduce the cost and time required for testing. We recognize that full replacement of animals in toxicological or pre-clinical studies and tests remains a challenge - we acknowledge M and D learning-based animal toxicity prediction can be the key.

Keywords: Machine and Deep learning, Artificial Intelligence, Toxicity recitation, Animal toxicity, Alternative to animals, Pre-clinical studies

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How to Cite
Mishra, R., R. Kaushik, A. Shukla, S. Ojha, S. Mishra, and R. Dubey. “IS ARTIFICIAL INTELLIGENCE VIZ M AND D LEARNING COULD BE THE SOLUTION PROMISING ALTERNATIVE TO ANIMALS: A LEARNING-BASED TOXICITY RECITATION?”. Journal of Applied Pharmaceutical Sciences and Research, Vol. 5, no. 2, Sept. 2022, pp. 1-2, doi:10.31069/japsr.v5i2.01.
Issue
Vol 5 No 2 (2022): Volume 5, Issue 2, 2022
Section
Review Articles

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