ABSTRACT:
Purpose: This study evaluates the potential of artificial intelligence (AI), specifically ChatGPT, in assisting with the analysis of paracetamol dissolution tests and the construction of calibration curves. The primary research question investigates whether AI-generated data can approximate real laboratory results for pharmaceutical quality control, with an emphasis on exploring the limitations and applications of AI in pharmaceutical analysis. Methods: Paracetamol solutions were prepared, and their absorbance was measured using a UV-Vis spectrophotometer to construct calibration curves. Dissolution tests were performed in phosphate buffer solution, and both real and AI-generated data were analyzed. ChatGPT generated hypothetical data for the calibration curves and dissolution profiles, which were then compared to actual laboratory results. Statistical methods such as mean squared error (MSE), percent error, and correlation coefficients (R²) were employed to evaluate the accuracy of the AI-generated data. A paired t-test was used to determine the statistical significance of differences between the datasets. Results: AI-generated data followed general trends in both the calibration curve and dissolution tests but exhibited significant discrepancies in accuracy. Absorbance values from the AI model were consistently lower than real measurements, while AI overestimated drug release in early dissolution stages. The MSE for dissolution tests was 47.80, while for calibration curves, it was 0.0583. Despite these differences, AI-generated data aligned more closely with real data at later dissolution time points. Conclusions: While AI tools like ChatGPT can approximate trends in pharmaceutical analysis, real laboratory data are essential for accuracy, particularly in the early stages of testing. AI shows potential as a supplementary tool for theoretical understanding but cannot replace hands-on experimentation.
Cite this article:
Ruba Malkawi. Evaluating Artificial Intelligence Tools in the Pharmaceutical Industry: A Case Study on Paracetamol Dissolution and Calibration Curves. Research Journal of Pharmacy and Technology. 2025;18(5):2269-4. doi: 10.52711/0974-360X.2025.00325
Cite(Electronic):
Ruba Malkawi. Evaluating Artificial Intelligence Tools in the Pharmaceutical Industry: A Case Study on Paracetamol Dissolution and Calibration Curves. Research Journal of Pharmacy and Technology. 2025;18(5):2269-4. doi: 10.52711/0974-360X.2025.00325 Available on: https://rjptonline.org/AbstractView.aspx?PID=2025-18-5-48
REFERENCES:
1. Kumar S, Malviya R, Sharma PK. Solid dispersion: Pharmaceutical technology for the improvement of various characteristics of active pharmaceutical ingredients. African Journal of Basic and Applied Sciences. 2011; 3(4): 116–25.
2. Malkawi R. Revolutionizing drug delivery innovation: leveraging AI-driven chatbots for enhanced efficiency. International Journal of Applied Pharmaceutics. 2024; 16(2): 52–6.
3. Malkawi R, Malkawi WI, Al-Mahmoud Y, Tawalbeh J. Current trends on solid dispersions: past, present, and future. Adv Pharmacol Pharm Sci. 2022; 14(4): 1–17.
4. Jarwan B, Tawalbeh J, Malkawi R. Assessment of phenol and antioxidant content of olive varieties and their potential health benefits for colon health. The Scientific World Journal. 2023; 5: 41–52.
5. Malkawi R, Jarwan B, Tawalbeh J. Assessing the awareness, attitude, and knowledge of senior pharmacy students in Jordanian universities regarding antibiotic use and resistance: a study of the medical curriculum. J Pharm Res Int. 2023 Mar 25; 35(5):41–52.
6. Zhao A, Wu Y. Future implications of ChatGPT in pharmaceutical industry: drug discovery and development. Front Pharmacol. 2023Jun 17; 14(5): 1–14.
7. Ji Y, Lemberg M, Prudic A, Paus R, Sadowski G. Modeling and analysis of dissolution of paracetamol/Eudragit® formulations. Chemical Engineering Research and Design. 2017 May 1; 121(8): 22–31.
8. Abebe K, Beressa TB, Yimer BT. In-vitro evaluations of quality control parameters of paracetamol tablets marketed in gondar city, northwest ethiopia. Drug Healthc Patient Saf. 2020 Dec 21; 12(10): 273–9.
9. Menon D, Shilpa K. “Chatting with ChatGPT”: Analyzing the factors influencing users’ intention to use the open AI’s ChatGPT using the UTAUT model. Heliyon. 2023 Nov; 9(11): 209–62.
10. Oh AL, Hassali MA, Al-Haddad MS, Sulaiman SAS, Shafie AA, Awaisu A. Public knowledge and attitudes towards antibiotic usage: a cross-sectional study among the general public in the state of Penang, Malaysia. The Journal of Infection in Developing Countries. 2010 Nov 9; 5(5): 338–47.
11. Juhi A, Pipil N, Santra S, Mondal S, Behera JK, Mondal H, et al. The capability of ChatGPT in predicting and explaining common drug-drug interactions. Cureus. 2023 Mar; 15(3): 1–7.
12. Malone DC, Abarca J, Skrepnek GH, Murphy JE, Armstrong EP, Grizzle AJ, et al. Pharmacist workload and pharmacy characteristics associated with the dispensing of potentially clinically important drug-drug interactions. Med Care. 2007 May; 45(5): 456–62.
13. Heck TG. What artificial intelligence knows about 70 kDa heat shock proteins, and how we will face this ChatGPT era. Cell Stress Chaperones. 2023 May; 28(3): 225–9.
14. Parmar A, Lakshminarayanan R, Iyer A, Mayandi V, Leng Goh ET, Lloyd DG, et al. Synthesis and Biological Evaluation of a Teixobactin Analogue. Org Lett. 2015 Dec 18; 17(24): 6182–5.
15. Sallam M. ChatGPT utility in healthcare education, research, and practice: systematic review on the promising perspectives and valid concerns. Healthcare . 2023 Mar; 11(6): 1–20.
16. Fonseca-Berzal C, Palmeiro-Roldán R, Escario J, Torrado S, Arán V, Torrado-Santiago S, et al. Novel solid dispersions of benznidazole: Preparation, dissolution profile and biological evaluation as alternative antichagasic drug delivery system. Exp Parasitol. 2015 Feb 1; 149: 84–91.
17. Makawi R, Al-olimat S, Tawalbeh J. Unlocking the potential: enhancing solubility and bioavailability of acyclovir through solid dispersion formulations. International Journal of Applied Pharmaceutics. 2024 Sep 7; 15(5): 111–8.