ARTIFICIAL INTELLIGENCE IN FORMULATION DESIGN A NEW ERA IN PHARMACEUTICS
Main Article Content
Keywords
.
Abstract
The pharmaceutical business is undergoing a transformation thanks to formulation design and the incorporation of artificial intelligence (AI) into drug development. Drug research, formulation development, manufacturing, quality control, and post-market surveillance are just a few of the areas in the pharmaceutical business that have seen a paradigm shift as a result of the introduction of artificial intelligence (AI). By evaluating large datasets to improve formulations and forecast patients behavior’s, these technologies make precision medicine accessible. AI-powered models improve the stability, bioavailability, and pinpoint precision of therapeutic agents based on nanoparticles. By streamlining the development and research processes, AI can help lower the price of development. In addition to predicting the pharmacokinetics and toxicity of potential drugs, machine learning techniques aid in the design of experiments. By selecting and optimizing lead compounds, this capability lessens the need for expensive and time-consuming animal testing. The rapid expansion of biomedical data offers benefits for using AI at every level of drug research and development. When AI is successfully used and integrated into multiple steps, the pharmaceutical sector needs to solve a number of inherent limitations and problems.
References
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26. Cheng, F. & Zhao, Z. Machine learning-based prediction of drug-drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties. J Am Med Inform Assoc 21, (2014).
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35. Zarrinpar, A. et al. Individualizing liver transplant immunosuppression using a phenotypic personalized medicine platform. Sci Transl Med 8, (2016).
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46. Budd, J. et al. Digital technologies in the public-health response to COVID-19. Nat Med 26, 1183–1192 (2020).
47. Kinoshita, R. et al. Containment, contact tracing and asymptomatic transmission of novel coronavirus disease (Covid-19): A modelling study. J Clin Med 9, 1–9 (2020).
48. Bragazzi, N. L. et al. How big data and artificial intelligence can help better manage the covid-19 pandemic. Int J Environ Res Public Health 17, (2020).
49. Brinati, D. et al. Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: A Feasibility Study. J Med Syst 44, (2020).
50. Li, W. T. et al. Using machine learning of clinical data to diagnose COVID-19: A systematic review and meta-analysis. BMC Med Inform Decis Mak 20, (2020).
51. Dara, S., Dhamercherla, S., Jadav, S. S., Babu, C. M. & Ahsan, M. J. Machine Learning in Drug Discovery: A Review. Artificial Intelligence Review 2021 55:3 55, 1947–1999 (2021).
52. Bagherian, M. et al. Machine learning approaches and databases for prediction of drug- target interaction: a survey paper. Brief Bioinform 22, 247–269.
53. Chapman, A. B. et al. Detecting Adverse Drug Events with Rapidly Trained Classification Models. Drug Saf 42, 147–156 (2019).
54. Shah, H., Chavda, V. & Soniwala, M. M. Applications of bioinformatics tools in medicinal biology and biotechnology. Wiley Online LibraryH Shah, V Chavda, MM Soniwala Bioinformatics tools for pharmaceutical drug product development, 2023•Wiley Online Library 95–116 (2023) doi:10.1002/9781119865728.CH6.
55. Kant, S., Deepika, · & Roy, · Saheli. Artificial intelligence in drug discovery and development: transforming challenges into opportunities. Discover Pharmaceutical Sciences 2025 1:1 1, 1–14 (2025).
56. Malheiro, V., Santos, B., Figueiras, A. & Mascarenhas-Melo, F. The Potential of Artificial Intelligence in Pharmaceutical Innovation: From Drug Discovery to Clinical Trials. Pharmaceuticals 2025, Vol. 18, Page 788 18, 788 (2025).
57. Mansur, M. A. Artificial Intelligence in Drug Discovery Towards Strategic Applications Challenges and Implementation Frameworks for Accelerated Pharmaceutical Innovation. European Journal of Artificial Intelligence and Machine Learning 4, 16–23 (2025).
58. Chang, W. C., Tanoshima, R., Ross, C. J. D. & Carleton, B. C. Challenges and Opportunities in Implementing Pharmacogenetic Testing in Clinical Settings. Annu Rev Pharmacol Toxicol 61, 65–84 (2021).
59. Kumar, M. et al. Opportunities and challenges in application of artificial intelligence in pharmacology. Pharmacological Reports 2023 75:1 75, 3–18 (2023).
60. Wei, W., Zhao, J., … D. R.-C. pharmacology & 2021, undefined. Machine learning challenges in pharmacogenomic research. pmc.ncbi.nlm.nih.govWQ Wei, J Zhao, D Roden, JF PetersonClinical pharmacology and therapeutics, 2021•pmc.ncbi.nlm.nih.gov.
61. Primorac, D. et al. Pharmacogenomics at the center of precision medicine: challenges and perspective in an era of Big Data. Taylor & FrancisD Primorac, L Bach-Rojecky, D Vađunec, A Juginović, K Žunić, V Matišić, A Skelin, B ArsovPharmacogenomics, 2020•Taylor & Francis 21, 141–156 (2020).
62. Kalinin, A. A. et al. Deep learning in pharmacogenomics: from gene regulation to patient stratification. Taylor & FrancisAA Kalinin, GA Higgins, N Reamaroon, S Soroushmehr, A Allyn-Feuer, ID Dinov, K NajarianPharmacogenomics, 2018•Taylor & Francis 19, 629–650 (2018).
63. Taherdoost, H. & Ghofrani, A. AI’s role in revolutionizing personalized medicine by reshaping pharmacogenomics and drug therapy. Intelligent Pharmacy 2, 643–650 (2024).
64. Sajadieh, S. M. M. & Noh, S. Do. From Simulation to Autonomy: Reviews of the Integration of Artificial Intelligence and Digital Twins. International Journal of Precision Engineering and Manufacturing - Green Technology (2025) doi:10.1007/S40684-025- 00750-Z.
65. Pokhriyal, P., Chavda, V. P. & Pathak, M. Future Prospects and Challenges in the Implementation of AI and ML in Pharma Sector. Wiley Online LibraryP Pokhriyal, VP Chavda, M PathakBioinformatics tools for pharmaceutical drug product development, 2023•Wiley Online Library 401–416 (2023) doi:10.1002/9781119865728.CH17.
66. Levin, S. et al. Machine-Learning-Based Electronic Triage More Accurately Differentiates Patients With Respect to Clinical Outcomes Compared With the Emergency Severity Index. Ann Emerg Med 71, 565-574.e2 (2018).
67. Artificial Intelligence in Software as a Medical Device | FDA. https://www.fda.gov/medical-devices/software-medical-device-samd/artificial- intelligence-software-medical-device.
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Sep 30, 2025
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Aryan Patel, Dr. Shweta Paroha, & Dr. Pragnesh Patani. (2025). ARTIFICIAL INTELLIGENCE IN FORMULATION DESIGN A NEW ERA IN PHARMACEUTICS. Journal of Population Therapeutics and Clinical Pharmacology, 32(7), 1974-1985. https://doi.org/10.53555/4895sf22
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Author Biographies
Aryan Patel
khyati College of Pharmacy, Palodia, Ahmedabad
Dr. Shweta Paroha
khyati College of Pharmacy, Palodia, Ahmedabad
Dr. Pragnesh Patani
khyati College of Pharmacy, Palodia, Ahmedabad
How to Cite
Aryan Patel, Dr. Shweta Paroha, & Dr. Pragnesh Patani. (2025). ARTIFICIAL INTELLIGENCE IN FORMULATION DESIGN A NEW ERA IN PHARMACEUTICS. Journal of Population Therapeutics and Clinical Pharmacology, 32(7), 1974-1985. https://doi.org/10.53555/4895sf22