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Paper ID:IJSREM63170

Artificial Intelligence in Dose Selection and Optimization: A Comprehensive Review on Transforming Modern Drug Development

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM63170

Volume:

10 |

5 |

May 2026

Pages:

1-17

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM63170

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

KS

Karan Singh

Pharmaceuitcal Sciences

Minerva College of Pharmacy

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DKKV

Dr. Kapil Kumar Verma

Pharmaceuitcal Sciences

Minerva College of Pharmacy

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TD

Tushar Dhiman

Pharmaceuitcal Sciences

Minerva College of Pharmacy

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NK

Nitesh Kumar

Pharmaceuitcal Sciences

Minerva College of Pharmacy

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AD

Anshita Dhiman

Pharmaceuitcal Sciences

Minerva College of Pharmacy

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Mentors

N

Nikita

Pharmaceuitcal Sciences

Minerva College of Pharmacy

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Abstract

Abstract Artificial Intelligence (AI) has been recognized as an important technology in the development of the drug industry. One of the important stages in the development of the drug industry involves the selection and optimization of the appropriate drug dose to ensure the maximum efficacy and the least side effects. The AI system can process vast amounts of biological, pharmacological, and clinical data to determine the best drug dose. AI technology, with the use of pharmacokinetic and pharmacodynamic data, helps in understanding the absorption, distribution, metabolism, and excretion of the drug, which are important factors in the determination of the appropriate drug dose. In addition, AI helps in the development of personalized medicine, where individualized dose prediction can be made based on characteristics such as age, genetics, body weight, and disease conditions. This helps in improving treatment outcomes and minimizing the risk of adverse drug reactions. AI-based systems can help researchers in designing efficient clinical trials, predicting toxicity, and reducing the time and costs of developing drugs.

KEYWORDS

Keywords Artificial Intelligence (AI) Drug Development Dose Selection Dose Machine Learning Clinical Trials Optimization.

Cite This Article

Karan Singh, Dr. Kapil Kumar Verma, Tushar Dhiman, Nitesh Kumar, Anshita Dhiman, Nikita, "Artificial Intelligence in Dose Selection and Optimization: A Comprehensive Review on Transforming Modern Drug Development," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-17, May 2026. https://doi.org/10.55041/IJSREM63170

Full Article/Conclusion

Artificial Intelligence (AI) is bringing a major change in how drug doses are selected and optimized. It helps researchers and doctors understand complex data better and choose the most effective and safe dose for patients. Compared to traditional methods, AI makes the process faster, more accurate, and more reliable. AI also supports personalized medicine by considering individual patient differences such as age, genetics, and health condition. This leads to better treatment outcomes and fewer side effects. In addition, AI improves clinical trials, reduces costs, and increases the chances of successful drug development. Although there are some challenges like data quality, model transparency, and regulatory issues, continuous improvements in technology and proper guidelines can overcome these problems. In the future, AI will play an even bigger role in healthcare by making treatments more precise, adaptive, and patient-focused. Overall, AI has the potential to make drug development safer, smarter, and more effective for everyone.

References

[1] Varela-Rey I, Bandín-Vilar E, Toja-Camba FJ, Cañizo-Outeiriño A, Cajade-Pascual F, Ortega-Hortas M, et al. Artificial Intelligence and Machine Learning Applications to Pharmacokinetic Modeling and Dose Prediction of Antibiotics: A Scoping Review. Antibiotics 2024;13:1203. https://doi.org/10.3390/antibiotics13121203. [2] Ros H, Chan N, Cook MT, Shorthouse D. Artificial intelligence and machine learning guided optimization in drug delivery. Adv Drug Deliv Rev 2026;232:115781. https://doi.org/10.1016/j.addr.2026.115781. [3] Joshi S, Sheth S. Artificial Intelligence (AI) in Pharmaceutical Formulation and Dosage Calculations. Pharmaceutics 2025;17:1440. https://doi.org/10.3390/pharmaceutics17111440. [4] Agirbasli M. Influence of Diabetes on CABG Patency. J Am Coll Cardiol 2017;70:2604. https://doi.org/10.1016/j.jacc.2017.07.801. [5] Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med 2019;25:44–56. https://doi.org/10.1038/s41591-018-0300-7. [6] M M, S M, Kate SS, Taneja I, Jaiswal S, Pratap B, et al. A review on integrated machine learning and deep learning driven artificial intelligence models for pharmacokinetics and toxicokinetics predictions, and their application. Drug Metabolism and Disposition 2026;54:100240. https://doi.org/10.1016/j.dmd.2026.100240. [7] Sheiner LB, Steimer J-L. Pharmacokinetic/Pharmacodynamic Modeling in Drug Development. Annu Rev Pharmacol   2000;40:67–95. https://doi.org/10.1146/annurev.pharmtox.40.1.67. [8] Tao R, Chan S, Wang J, Wang X, Shi L, Lan X, et al. Pharmacokinetic-pharmacodynamic target attainment analyses as support for meropenem dosing regimens in critically ill adult and elderly patients with Pseudomonas aeruginosa infections. Front Pharmacol 2025;16. https://doi.org/10.3389/fphar.2025.1643553. [9] Sheiner LB, Steimer J-L. Pharmacokinetic/Pharmacodynamic Modeling in Drug Development. Annu Rev Pharmacol Toxicol2000;40:67–95. https://doi.org/10.1146/annurev.pharmtox.40.1.67. [10]      Erratum. Clin Pharmacol Ther 1981;29:670–670. https://doi.org/10.1038/clpt.1981.93.

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Publication Date

5/21/2026

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Review Rounds: 2

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