Artificial Intelligence in Optimizing Formulations and Excipients: Revolutionizing Pharmaceutical Product Development
Main Article Content
Abstract
The pharmaceutical industry is undergoing a paradigm shift with the integration of artificial intelligence (AI) technologies into research and development workflows. One of the most promising applications lies in the optimization of pharmaceutical formulations and excipient selection—a traditionally empirical and time-consuming process. AI, particularly machine learning and neural networks, has demonstrated immense potential in modeling complex formulation behaviors, predicting excipient compatibility, and streamlining the path to robust product development. By analyzing large datasets and identifying hidden patterns, AI tools facilitate rapid screening of excipients, forecasting of stability profiles, and real-time decision-making in formulation design. Furthermore, AI-driven models can complement or even surpass traditional statistical methods such as design of experiments by enabling multi-objective optimization across various formulation parameters. Case studies involving tablets, lipid-based carriers, and nanotechnology-based systems illustrate the practical success of AI in enhancing formulation performance and reproducibility. Despite challenges such as data quality, algorithm transparency, and regulatory acceptance, the trajectory of AI adoption is accelerating, with its convergence with Quality by Design and process analytical technologies forming the foundation of next-generation pharmaceutical development. As AI technologies mature and become more interpretable and accessible, they are poised to redefine the role of formulation scientists and enable more personalized, efficient, and predictive formulation strategies. This manuscript explores the core principles, current applications, limitations, and future outlook of AI in pharmaceutical formulation science.
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Yadav, A., Singh, G., Singh, M., & Chaudhary, A. (2025). Artificial Intelligence in Optimizing Formulations and Excipients: Revolutionizing Pharmaceutical Product Development. Journal of Advanced Scientific Research, 16(07), 9-18. https://doi.org/10.55218/JASR.2025160702
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Review Articles
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References
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2. Mak KK, Pichika MR. Artificial intelligence in drug development: present status and future prospects. Drug Discov Today. 2019 Mar;24(3):773–80.
3. Ali KA, Mohin S, Mondal P, Goswami S, Ghosh S, Choudhuri S. Influence of artificial intelligence in modern pharmaceutical formulation and drug development. Futur J Pharm Sci. 2024 Mar 29;10(1):53.
4. Galata DL, Könyves Z, Nagy B, Novák M, Mészáros LA, Szabó E, et al. Real-time release testing of dissolution based on surrogate models developed by machine learning algorithms using NIR spectra, compression force and particle size distribution as input data. Int J Pharm. 2021 Mar;597:120338.
5. Benkő E, Ilič IG, Kristó K, Regdon G, Csóka I, Pintye-Hódi K, et al. Predicting Drug Release Rate of Implantable Matrices and Better Understanding of the Underlying Mechanisms through Experimental Design and Artificial Neural Network-Based Modelling. Pharmaceutics. 2022 Jan 19;14(2):228.
6. Momeni M, Afkanpour M, Rakhshani S, Mehrabian A, Tabesh H. A prediction model based on artificial intelligence techniques for disintegration time and hardness of fast disintegrating tablets in pre-formulation tests. BMC Med Inform Decis Mak. 2024 Mar 27;24(1):88.
7. Serrano DR, Luciano FC, Anaya BJ, Ongoren B, Kara A, Molina G, et al. Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine. Pharmaceutics. 2024 Oct 14;16(10):1328.
8. Bekhit AA, Nasralla SN, El-Agroudy EJ, Hamouda N, El-Fattah AA, Bekhit SA, et al. Investigation of the anti-inflammatory and analgesic activities of promising pyrazole derivative. European Journal of Pharmaceutical Sciences. 2022 Jan;168:106080.
9. Hornick T, Mao C, Koynov A, Yawman P, Thool P, Salish K, et al. In silico formulation optimization and particle engineering of pharmaceutical products using a generative artificial intelligence structure synthesis method. Nat Commun. 2024 Nov 7;15(1):9622.
10. Yang Y, Ye Z, Su Y, Zhao Q, Li X, Ouyang D. Deep learning for in vitro prediction of pharmaceutical formulations. Acta Pharm Sin B. 2019 Jan;9(1):177–85.
11. Abdalla Y, Taub M, Hilton E, Akkaraju P, Milanovic A, Orlu M, et al. VECT-GAN: A variationally encoded generative model for overcoming data scarcity in pharmaceutical science. 2025 Jan 17;
12. Dong J, Wu Z, Xu H, Ouyang D. FormulationAI: a novel web-based platform for drug formulation design driven by artificial intelligence. Brief Bioinform. 2023 Nov 22;25(1).
13. Jiang J, Ma X, Ouyang D, Williams RO. Emerging Artificial Intelligence (AI) Technologies Used in the Development of Solid Dosage Forms. Pharmaceutics. 2022 Oct 22;14(11):2257.
14. Wang N, Dong J, Ouyang D. AI-directed formulation strategy design initiates rational drug development. Journal of Controlled Release. 2025 Feb;378:619–36.
15. Malik A, Rajaguru P, Azzawi R. Smart Manufacturing with Artificial Intelligence and Digital Twin: A Brief Review. In: 2022 8th International Conference on Information Technology Trends (ITT). IEEE; 2022. p. 177–82.
16. Moreno-Benito M, Lee KT, Kaydanov D, Verrier HM, Blackwood DO, Doshi P. Digital twin of a continuous direct compression line for drug product and process design using a hybrid flowsheet modelling approach. Int J Pharm. 2022 Nov;628:122336.
17. Phalak P, Tomba E, Jehoulet P, Kapitan-Gnimdu A, Soladana PM, Vagaggini L, et al. Digital Twin Implementation for Manufacturing of Adjuvants. Processes. 2023 Jun 3;11(6):1717.
18. Kumar A, Gupta G Das, Raikwar S. Artificial Intelligence Technologies used for the Assessment of Pharmaceutical Excipients. Curr Pharm Des. 2024 Feb;30(6):407–9.
19. Shah P, Kendall F, Khozin S, Goosen R, Hu J, Laramie J, et al. Artificial intelligence and machine learning in clinical development: a translational perspective. NPJ Digit Med. 2019 Jul 26;2(1):69.
20. Mak KK, Pichika MR. Artificial intelligence in drug development: present status and future prospects. Drug Discov Today. 2019 Mar;24(3):773–80.
21. Vamathevan J, Clark D, Czodrowski P, Dunham I, Ferran E, Lee G, et al. Applications of machine learning in drug discovery and development. Nat Rev Drug Discov. 2019 Jun 11;18(6):463–77.
22. Singh S, Numan A, Maddiboyina B, Arora S, Riadi Y, Md S, et al. The emerging role of immune checkpoint inhibitors in the treatment of triple-negative breast cancer. Drug Discov Today. 2021 Jul;26(7):1721–7.
23. Ezike TC, Okpala US, Onoja UL, Nwike CP, Ezeako EC, Okpara OJ, et al. Advances in drug delivery systems, challenges and future directions. Heliyon. 2023 Jun;9(6):e17488.
24. Kesisoglou F, Panmai S, Wu Y. Nanosizing — Oral formulation development and biopharmaceutical evaluation. Adv Drug Deliv Rev. 2007 Jul;59(7):631–44.
25. Raw A. Regulatory considerations of pharmaceutical solid polymorphism in Abbreviated New Drug Applications (ANDAs). Adv Drug Deliv Rev. 2004 Feb 23;56(3):397–414.
26. Serajuddin ATM. Salt formation to improve drug solubility. Adv Drug Deliv Rev. 2007 Jul;59(7):603–16.
27. Bai JPF, Burckart GJ, Mulberg AE. Literature Review of Gastrointestinal Physiology in the Elderly, in Pediatric Patients, and in Patients with Gastrointestinal Diseases. J Pharm Sci. 2016 Feb;105(2):476–83.
28. Kang MS, Lee SY, Kim KS, Han DW. State of the Art Biocompatible Gold Nanoparticles for Cancer Theragnosis. Pharmaceutics. 2020 Jul 25;12(8):701.
29. Yang ZY, He JH, Lu AP, Hou TJ, Cao DS. Frequent hitters: nuisance artifacts in high-throughput screening. Drug Discov Today. 2020 Apr;25(4):657–67.
30. Damiati SA. Digital Pharmaceutical Sciences. AAPS PharmSciTech. 2020 Jul 26;21(6):206.
31. Vora LK, Gholap AD, Jetha K, Thakur RRS, Solanki HK, Chavda VP. Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design. Pharmaceutics. 2023 Jul 10;15(7):1916.
32. Singh L, Tiwari RK, Verma S, Sharma V. The Future of Artificial Intelligence in Pharmaceutical Product Formulation. Drug Deliv Lett. 2019 Oct 31;9(4):277–85.
33. Xing L, Glen RC. Novel Methods for the Prediction of logP, pKa, and logD. J Chem Inf Comput Sci. 2002 Jul 1;42(4):796–805.
34. Fink T, Reymond JL. Virtual Exploration of the Chemical Universe up to 11 Atoms of C, N, O, F: Assembly of 26.4 Million Structures (110.9 Million Stereoisomers) and Analysis for New Ring Systems, Stereochemistry, Physicochemical Properties, Compound Classes, and Drug Discovery. J Chem Inf Model. 2007 Mar 1;47(2):342–53.
35. Wen H, Närvänen A, Jokivarsi K, Poutiainen P, Xu W, Lehto VP. A robust approach to make inorganic nanovectors biotraceable. Int J Pharm. 2022 Aug;624:122040.
36. Lusci A, Pollastri G, Baldi P. Deep Architectures and Deep Learning in Chemoinformatics: The Prediction of Aqueous Solubility for Drug-Like Molecules. J Chem Inf Model. 2013 Jul 22;53(7):1563–75.
37. Cheng F, Zhao Z. Machine learning-based prediction of drug–drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties. Journal of the American Medical Informatics Association. 2014 Oct;21(e2):e278–86.
38. Ceschan NE, Rosas MD, Olivera ME, Dugour AV, Figueroa JM, Bucalá V, et al. Development of a Carrier-Free Dry Powder Ofloxacin Formulation With Enhanced Aerosolization Properties. J Pharm Sci. 2020 Sep;109(9):2787–97.
39. Bugay DE. Characterization of the solid-state: spectroscopic techniques. Adv Drug Deliv Rev. 2001 May;48(1):43–65.
40. Rajesh MV, Elumalai K. The transformative power of artificial intelligence in pharmaceutical manufacturing: Enhancing efficiency, product quality, and safety. Journal of Holistic Integrative Pharmacy. 2025 Jun;6(2):125–35.
41. Korshunova M, Huang N, Capuzzi S, Radchenko DS, Savych O, Moroz YS, et al. Generative and reinforcement learning approaches for the automated de novo design of bioactive compounds. Commun Chem. 2022 Oct 18;5(1):129.
42. Suriyaamporn P, Pamornpathomkul B, Patrojanasophon P, Ngawhirunpat T, Rojanarata T, Opanasopit P. The Artificial Intelligence-Powered New Era in Pharmaceutical Research and Development: A Review. AAPS PharmSciTech. 2024 Aug 15;25(6):188.
43. Hang NT, Long NT, Duy ND, Chien NN, Van Phuong N. Towards safer and efficient formulations: Machine learning approaches to predict drug-excipient compatibility. Int J Pharm. 2024 Mar;653:123884.
44. Hamid JU, Gupta S. Development and validation of a system for the prediction of excipient-excipient incompatibility using machine learning tools. Pharmaspire. 2022;14(01):18–27.
45. Patel S, Patel M, Kulkarni M, Patel MS. DE-INTERACT: A machine-learning-based predictive tool for the drug-excipient interaction study during product development—Validation through paracetamol and vanillin as a case study. Int J Pharm. 2023 Apr;637:122839.
46. Ono T, Yonemochi E. Evaluation of the physical properties of dry surface-modified ibuprofen using a powder rheometer (FT4) and analysis of the influence of pharmaceutical additives on improvement of the powder flowability. Int J Pharm. 2020 Apr;579:119165.
47. Jain N, Kaul S, Triveni, Kaith A, Sinha A, Mathur T, et al. 8 QbD and artificial intelligence in nanoparticulate drug delivery systems: recent advances. In: Computational Drug Delivery. De Gruyter; 2024. p. 163–82.
48. Prykhodko O, Johansson SV, Kotsias PC, Arús-Pous J, Bjerrum EJ, Engkvist O, et al. A de novo molecular generation method using latent vector based generative adversarial network. J Cheminform. 2019 Dec 3;11(1):74.
49. Muneer R, Hashmet MR, Pourafshary P, Shakeel M. Unlocking the Power of Artificial Intelligence: Accurate Zeta Potential Prediction Using Machine Learning. Nanomaterials. 2023 Mar 29;13(7):1209.
50. Öztürk K, Kaplan M, Çalış S. Effects of nanoparticle size, shape, and zeta potential on drug delivery. Int J Pharm. 2024 Dec;666:124799.
51. Noorain, Srivastava V, Parveen B, Parveen R. Artificial Intelligence in Drug Formulation and Development: Applications and Future Prospects. Curr Drug Metab. 2023 Sep;24(9):622–34.
52. Rao RV, Savsani VJ. Mechanical Design Optimization Using Advanced Optimization Techniques. London: Springer London; 2012.
53. Mura P, Orlandini S, Cirri M, Maestrelli F, Mennini N, Casella G, et al. A preliminary study for the development and optimization by experimental design of an in vitro method for prediction of drug buccal absorption. Int J Pharm. 2018 Aug;547(1–2):530–6.
54. Das PJ, Preuss C, Mazumder B. Artificial Neural Network as Helping Tool for Drug Formulation and Drug Administration Strategies. In: Artificial Neural Network for Drug Design, Delivery and Disposition. Elsevier; 2016. p. 263–76.
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