83.Issue 11 november 25 - INDO AMERICAN JOURNAL OF PHARMACEUTICAL SCIENCES

Volume : 12, Issue : 11, November – 2025

Title:

ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL INDUSTRY

Authors :

Vishnu B. Mundhe, Venkatesh S. Nagare, Dr. Swati P. Deshmukh

Abstract :

It has the potential to enhance its use in pharmaceutical research and biotechnology. 3D printing covers a broad range of technical applications in the pharmaceutical field, including new drug delivery systems, developing new excipients, improving drug compatibility, and creating customized dosage forms. In the future, 3D printing could be regulated and used across pharmaceutical and other industries, with proper attention to safety and security.
The main potential of AI in the pharmaceutical industry is to cut costs and boost efficiency.A lot of research shows that dynamic learning can create highly accurate AI models that use half or even less information than traditional AI and information subsampling methods. Although the exact reason for this boost in productivity isn’t fully clear, it seems that reducing repetition and bias, along with getting more meaningful data to overcome decision limits, play a big role in this improved performance. 3D printing technology can create complex structures in a cost-effective and time-saving way.
Keywords: Artificial intelligence, History, Technology, Classification, Challenges, Recent Development, Future Scope.

Cite This Article:

Please cite this article in press Vishnu B. Mundhe et al., Artificial Intelligence In Pharmaceutical Industry, Indo Am. J. P. Sci, 2025; 12(11).

REFERENCES:

1. Mak KK, Pichika MR. Artificial intelligence in drug development: present status and future prospects. Drug Discov Today. 2019;24(3):773-780.
2. Russell S, Dewey D, Tegmark M. Research priorities for robust and beneficial artificial intelligence. AI Mag. 2015;36(4):105-114.
3. Duch W, Setiono R, Zurada JM. Computational intelligence methods for rule-based data understanding.Proc IEEE. 2004;92(5):771-805
4. Dasta JF. Application of artificial intelligence to pharmacy and medicine.Hosp Pharm. 1992;27(4):312-315.
5. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial in healthcare: past, present and future. Stroke Vasc Neurol. 2017;2(4).
6. Gobburu JV, Chen EP. Artificial neural networks as a novel approach to integrated pharmacokinetic–pharmacodynamic analysis. J Pharm Sci. 1996;85(5):505-510.
7. Sakiyama Y. The use of machine learning and nonlinear statistical tools for ADME prediction.Expert Opin Drug MetabToxicol. 2009;5(2):149-169.
8. Agatonovic-Kustrin S, Beresford R. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J Pharm Biomed Anal. 2000;22(5):717-727.
9. Zhang ZH, Wang Y, Wu WF, Zhao X, Sun XC, Wang HQ. Development of glipizide push-pull osmotic pump controlled release tablets by using expert system and artificial neural network. Yao XueXueBao. 2012;47(12).
10.Bishop CM. Model-based machine learning. Trans R Soc A Math PhysEng Sci. 2013;371(1984):20120222.
11. Merk D, Friedrich L, Grisoni E, Schneider G. De novo design of bioactive small molecules by artificial intelligence. Mol Inform. 2018;37(1-2):1700153.
12. Hopgood AA. Intelligent systems for engineers and scientists: a practical guide to artificial intelligence. CRC Press; 2021.
13. Asha P, Srivani P, Ahmed AAA, Kolhe A, Nomani MZM. Artificial intelligence in medical imaging: an analysis of innovative technique and its future promise. Mater Today Proc. 2022;S6:2236-2239.
14. Flasinski M. Introduction to artificial intelligence. Switzerland: Springer International Publishing; 2016.
15. Kostis EJ, Pavlovic DA, Zivkovic MD. Applications of artificial intelligence in medicine and pharmacy: ethical aspects. Acta Med Medianae. 2019;58(3):128-137.
16. Markoff J. On ‘Jeopardy!’ Watson win is all but trivial. The New York Times. 2011; 16:2011
17. Manikiran SS, Prasanthi NL. Artificial intelligence: milestones and role in pharma and healthcare sector. Pharma Times. 2019;51:9-56.
18. Cherkasor A, Hilpert K, Jenssen H, Fjell CD, Waldbrook M, Mullaly SC, et al. Use of artificial intelligence in the design of small peptide antibiotics effective against a broad spectrum of highly antibiotic-resistant superbugs. ACS Chem Biol. 2009;4(1):65-74.
19. Agatonovic-Kustrin S, Beresford R. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J Pharm Biomed Anal. 2000;22(5):717-727.
20. Ranschaert ER, Morozov S, Algra PR. Artificial intelligence in medical imaging: opportunities, applications and risks. Springer; 2019.
21. Nelson SD, Walsh CG, Olsen CA, McLaughlin AJ, LeGrand JR, Schutz N, et al. Demystifying artificial intelligence in pharmacy. Am J Health Syst Pharm. 2020;77(19):1556-1570.
22. Dasta JF. Application of artificial intelligence to pharmacy and medicine.Hosp Pharm. 1992;27(4):312-315.
23. Mishra V. Artificial intelligence: the beginning of a new era in pharmacy profession. Asian J Pharm. 2018;12(02).
24. Flynn A. Using artificial intelligence in health-system pharmacy practice: finding new patterns that matter. Am J Health Syst Pharm. 2019;76(9):622-627.
25. Donepudi PK. AI and machine learning in retail pharmacy: systematic review of related literature. ABC J Adv Res. 2018;7(2):109-112.
26. Mishra V. Artificial intelligence: the beginning of a new era in pharmacy profession. Asian J Pharm. 2018;12(02).
27. Duch W, Swaminathan K, Meller J. Artificial intelligence approaches for rational drug design and discovery. Curr Pharm Des. 2007;13(14):1497-1508.
28. Krishnaveni C, Arvapalli S, Sharma JVC. [Title missing].Int J Innov Pharm Sci Res. [Year missing];[Volume(Issue)]:[Pages].
29. Kalis B, Colier M, Fu R. 10 promising AI applications in health care. Harv Bus Rev. 2018.
30. Mak KK, Pichika MR. Artificial intelligence in drug development: present status and future prospects. Drug Discov Today. 2019;24(3):773-780.
31. Chen W, Desai D, Good C, Crison J, Timmins P, Paruchuri S, Wang I, Ha K. Mathematical model-based accelerated development of extended-release metformin hydrochloride tablet formulation. AAPS PharmSciTech. 2016;17(4):1007-1013.
32. Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade RK. Artificial intelligence in drug discovery and development. Drug Discov Today. 2021;26(1):80.
33. Reklaitis R. Towards intelligent decision support for pharmaceutical product development. [Journal/Year missing].
34. Wang X. Intelligent quality management using knowledge discovery in databases. In: 2009 International Conference on Computational Intelligence and Software Engineering. IEEE; 2009.p.1-4.
35. Hay M, Thomas DW, Craighead JL, Economides C, Rosenthal J. Clinical development success rates for investigational drugs. [Journal/Year missing]. 2014.
36. Park Y, Goto D, Yang KF, Downton K, Lecomte P, Olson M, Mullins CD. A literature review of factors affecting price and competition in the global pharmaceutical market.Value Health. 2016;19(3):A265.
37. Wilson B, Km G. Artificial intelligence and related technologies enabled nanomedicine for advanced cancer treatment. Nanomedicine. 2020;15(5):433-435.
38. Prasad LK, Smyth H. 3D printing technologies for drug delivery: a review. Drug DevInd Pharm. 2016;42(7):1019-1031.
39. Srinivas L, Jaswitha M, Manikanta V, Bhavya B, Himavant BD. 3D printing in pharmaceutical technology: a review. Int Res J Pharm. 2019;10(2):8-17.
40. Katakam P, Dey B, Assaleh FH, Hwisa NT, Adiki SK, Chandu BR, et al. Top-down and bottom-up approaches in 3D printing technologies for drug delivery challenges. Crit Rev Ther Drug Carrier Syst. 2015;32(1):61-87.
41. Chakraborty R. Fundamentals of genetic algorithms: AI course lecture 39–40. Dostopnonanaslovu: http://www.myreaders.infoilassels/applets100_Genetic_Algorithms.pdf. 2010. Accessed 13 Apr 2014.
42. Goldberg D, Sastry K. Genetic algorithms: the design of innovation. Berlin: Springer; 2007.
43. Man KF, Tang KS, Kwong S. Genetic algorithms: concepts and applications [in engineering design]. IEEE Trans Ind Electron. 1996;43(5):519-534.
44. Krishnaveni C, Arvapalli S, Sharma J, Divya K. Artificial intelligence in pharma industry – a review. Int J Innov Pharm Sci Res. 2019;7(10):37-50.
46. Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol Divers. 2021;25(3):1315-1360.