The AI-Guided Clinical Trial Architect: A Genetic Algorithm and MCDM Platform for Adaptive, Multi-Objective Patient Cohort Selection and Trial Simulation
Article Sidebar
Main Article Content
Abstract
Introduction: Clinical trial design faces a critical challenge in balancing multiple, often conflicting, objectives such as statistical power, patient safety, cohort diversity, recruitment speed, and cost. While Multi-Objective Genetic Algorithms (MOGA) and Multi-Criteria Decision-Making (MCDM) have been applied independently in pharmaceutical contexts, their synergistic potential for clinical trial architecture remains under-explored. This paper introduces the AI-Guided Clinical Trial Architect (AI-CTA), a novel computational platform that integrates a MOGA with a fuzzy MCDM framework for adaptive, multi-objective patient cohort selection and trial simulation. Methods: The methodology involves a multi-phase workflow: data encoding via entropy-based weighting, evolutionary exploration of cohort configurations using a fuzzy-enhanced NSGA-II, and final selection through a Fuzzy TOPSIS analysis that incorporates expert-derived linguistic weights to handle uncertainty. Results & Discussion: A comprehensive case study for an oncology trial (n=200 from 1,850 candidates) demonstrates the platform's efficacy. The MOGA successfully generated a Pareto-optimal set of cohorts, from which the FMCDM module identified an optimal cohort achieving a superior balance of objectives (Closeness Coefficient: 0.656), validating the platform's ability to derive non-intuitive, robust solutions. Conclusion: By unifying the explorative power of MOGA with the deliberative precision of FMCDM, the AI-CTA provides a transformative, transparent, and computationally robust environment for designing more efficient, equitable, and economically viable clinical trials.
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Journal of Pharmacy at https://journals.iium.edu.my/ktn/index.php/jp is licensed under a Creative Commons Attribution 4.0 International License.
References
Aljohani, A. (2025). AI-driven decision-making for personalized elderly care: A fuzzy MCDM-based framework for enhancing treatment recommendations. BMC Medical Informatics and Decision Making, 25(1), 1–16. https://doi.org/10.1186/s12911-025-02831-2 DOI: https://doi.org/10.1186/s12911-025-02953-5
Alsaedi, A. G. N., Varnamkhasti, M. J., Mohammed, H. J., & Aghajani, M. (2024). Integrating multi-criteria decision analysis with deep reinforcement learning: A novel framework for intelligent decision-making in Iraqi industries. International Journal of Mathematical Modelling & Computations, 14(2). https://doi.org/10.30495/ijm2c.2024.20729
Alsaig, A., Alsaig, A., & Alagar, V. (2023, December). A critical review of multi criteria decision analysis method for decision making and prediction in big data healthcare applications. In International Conference on Applied Intelligence (pp. 89–100). Springer Nature Singapore. https://doi.org/10.1007/978-981-97-0992-2_8 DOI: https://doi.org/10.1007/978-981-97-0827-7_8
Ashaduzzaman, M., Nawar, N. I., Khan, S. A., Biswas, A. A., & Islam, M. M. (2024). Strategic implementation of artificial intelligence and machine learning in the pharmaceutical sector: A dual hesitant fuzzy group decision making approach. Decision Analytics Journal, 10, 100424. https://doi.org/10.1016/j.dajour.2024.100424 DOI: https://doi.org/10.46254/BA07.20240185
Bachorz, R. A., Lawless, M. S., Miller, D. W., & Jones, J. O. (2025). Multi-criteria decision analysis in drug discovery. Applied Biosciences, 4(1), 2. https://doi.org/10.3390/applbiosci4010002 DOI: https://doi.org/10.3390/applbiosci4010002
Chen, S. (2024). Pharmaceutical laboratory course scheduling system optimization based on genetic algorithm. In Intelligent Computing Technology and Automation (pp. 497–506). IOS Press. https://doi.org/10.3233/atia240115 DOI: https://doi.org/10.3233/ATDE231224
Dao, T. K. (2025, February). A hybrid approach for multi-objective optimization in supply chain management with multi-objective genetic algorithm. In *Genetic and Evolutionary Computing: Proceedings of the Sixteenth International Conference on Genetic and Evolutionary Computing, August 28-30, 2024, Miyazaki, Japan (Vol. 2)* (Vol. 1322, p. 277). Springer Nature. https://doi.org/10.1007/978-981-99-9761-9_26
El Midaoui, M., Qbadou, M., & Mansouri, K. (2021). Logistics chain optimization and scheduling of hospital pharmacy drugs using genetic algorithms: Morocco case. *International Journal of Web-Based Learning and Teaching Technologies, 16*(2), 54–64. https://doi.org/10.4018/ijwltt.2021030104 DOI: https://doi.org/10.4018/IJWLTT.2021030104
Hassan, N., & Jalali Varnamkhasti, M. (2015). Classical and urgencies assignment methods in p-median problems with fuzzy genetic algorithm. Pakistan Journal of Statistics, 31(5), 643–651. http://www.pakjs.com/journals//31-5-15.pdf
Jalali Varnamkhasti, M., Lee, L. S., Abu Bakar, M. R., & Leong, W. J. (2012). A genetic algorithm with fuzzy crossover operator and probability. Advances in Operations Research, 2012(1), 956498. https://doi.org/10.1155/2012/956498 DOI: https://doi.org/10.1155/2012/956498
Jokar, F., Jalali Varnamkhasti, M., & Hadi-Vencheh, A. (2025). Hybrid multi-criteria decision-making (MCDM) approaches with random forest regression for interval-based fuzzy uncertainty management. International Journal of Mathematical Modelling & Computations, 15(1), 49–66. https://doi.org/10.30495/ijm2c.2025.20996
Khan, S. A., Gupta, H., Gunasekaran, A., Mubarik, M. S., & Lawal, J. (2023). A hybrid multi?criteria decision?making approach to evaluate interrelationships and impacts of supply chain performance factors on pharmaceutical industry. Journal of Multi?Criteria Decision Analysis, 30(1-2), 62–90. https://doi.org/10.1002/mcda.1813 DOI: https://doi.org/10.1002/mcda.1800
Kolasa, K., Zah, V., & Kowalczyk, M. (2018). How can multi-criteria decision analysis support value assessment of pharmaceuticals? -Findings from a systematic literature review. Expert Review of Pharmacoeconomics & Outcomes Research, 18(4), 379–391. https://doi.org/10.1080/14737167.2018.1485097 DOI: https://doi.org/10.1080/14737167.2018.1467759
Laganà, I. R., & Colapinto, C. (2022). Multiple criteria decision?making in healthcare and pharmaceutical supply chain management: A state?of?the?art review and implications for future research. Journal of Multi?Criteria Decision Analysis, 29(1-2), 122–134. https://doi.org/10.1002/mcda.1781 DOI: https://doi.org/10.1002/mcda.1778
Lebedev, G., Fartushnyi, E., Fartushnyi, I., Shaderkin, I., Klimenko, H., Kozhin, P., & Fomina, I. (2020). Technology of supporting medical decision-making using evidence-based medicine and artificial intelligence. Procedia Computer Science, 176, 1703–1712. https://doi.org/10.1016/j.procs.2020.09.193 DOI: https://doi.org/10.1016/j.procs.2020.09.195
Marsh, K., Lanitis, T., Neasham, D., Orfanos, P., & Caro, J. (2014). Assessing the value of healthcare interventions using multi-criteria decision analysis: A review of the literature. PharmacoEconomics, 32(4), 345–365. https://doi.org/10.1007/s40273-014-0134-1 DOI: https://doi.org/10.1007/s40273-014-0135-0
Mei, K. (2014). Optimization of the fill time window and makespan in a central fill pharmacy application using multi-objective genetic algorithms [Master's thesis, State University of New York at Binghamton]. ProQuest Dissertations & Theses Global.
Modibbo, U. M., Hassan, M., Ahmed, A., & Ali, I. (2022). Multi-criteria decision analysis for pharmaceutical supplier selection problem using fuzzy TOPSIS. Management Decision, 60(3), 806–836. https://doi.org/10.1108/md-07-2021-0862 DOI: https://doi.org/10.1108/MD-10-2020-1335
Mohamed, M. F., Eltoukhy, M. M., Al Ruqeishi, K., & Salah, A. (2023). An adapted multi-objective genetic algorithm for healthcare supplier selection decision. Mathematics, 11(6), 1537. https://doi.org/10.3390/math11061537 DOI: https://doi.org/10.3390/math11061537
Mohammed, A. J., & Alwan, F. M. (2024). A genetic algorithm approach for improving the probabilistic inventory model with the continuous review: A practical application in the department of pharmacy. Journal of Economics and Administrative Sciences, 30(143), 474–494. https://doi.org/10.33095/jeasv30i143.3636 DOI: https://doi.org/10.33095/zba63g94
Moosivand, A., Rangchian, M., Zarei, L., Peiravian, F., Mehralian, G., & Sharifnia, H. (2021). An application of multi-criteria decision-making approach to sustainable drug shortages management: Evidence from a developing country. Journal of Pharmaceutical Health Care and Sciences, 7(1), 14. https://doi.org/10.1186/s40780-021-00196-w DOI: https://doi.org/10.1186/s40780-021-00200-3
Naser, M. M. R., Jalali Varnamkhasti, M., Mohammed, H. J., & Aghajani, M. (2024). Artificial intelligence as a catalyst for operational excellence in Iraqi industries: Implementation of a proposed model. International Journal of Mathematical Modelling & Computations, 14(2). https://doi.org/10.30495/ijm2c.2024.20730
Nowicka, M. M. (2023). Design and multi-criteria optimization of cell classifier circuits in cancer therapy [Doctoral dissertation, ETH Zurich]. ETH Zurich Research Collection. https://doi.org/10.3929/ethz-b-000633238
Oliinyk, A., Fedorchenko, I., Zaiko, T., Goncharenko, D., Stepanenko, A., & Kharchenko, A. (2019, October). Development of genetic methods of network pharmacy financial indicators optimization. In *2019 IEEE International Scientific-Practical Conference Problems of Infocommunications, Science and Technology (PIC S&T)* (pp. 607–612). IEEE. https://doi.org/10.1109/picst47496.2019.9061348 DOI: https://doi.org/10.1109/PICST47496.2019.9061396
Prasad, T. V. S. R. K., & Kolla, S. (2014). Multi criteria ABC analysis using artificial–intelligence-based classification techniques–case study of a pharmaceutical company. International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences, 2, 35–42. http://ijiremps.org/vol2_issue1.html
Roldán, Ú. B., Badia, X., Marcos-Rodríguez, J. A., de la Cruz-Merino, L., Gómez-González, J., Melcón-de Dios, A., & Calleja-Hernández, M. Á. (2018). Multi-criteria decision analysis as a decision-support tool for drug evaluation: A pilot study in a pharmacy and therapeutics committee setting. International Journal of Technology Assessment in Health Care, 34(5), 519–526. https://doi.org/10.1017/s0266462318000542 DOI: https://doi.org/10.1017/S0266462318000569
Sadabadi, S. A., Hadi-Vencheh, A., Jamshidi, A., & Jalali, M. (2021). A linear programming technique to solve fuzzy multiple criteria decision-making problems with an application. *RAIRO-Operations Research, 55*(1), 83–97. https://doi.org/10.1051/ro/2020004 DOI: https://doi.org/10.1051/ro/2020116
Schuwirth, N., Reichert, P., & Lienert, J. (2012). Methodological aspects of multi-criteria decision analysis for policy support: A case study on pharmaceutical removal from hospital wastewater. European Journal of Operational Research, 220(2), 472–483. https://doi.org/10.1016/j.ejor.2012.01.055 DOI: https://doi.org/10.1016/j.ejor.2012.01.055
Varnamkhasti, M. J. (2011). ANFISGA-adaptive neuro-fuzzy inference system genetic algorithm. Global Journal of Computer Science and Technology, 11(1), 13–18. https://computerresearch.org/index.php/computer/article/view/655
Varnamkhasti, M. J., & Vali, M. (2012). Sexual selection and evolution of male and female choice in genetic algorithm. Scientific Research and Essays, 7(31), 2788–2804. https://doi.org/10.5897/sre12.201 DOI: https://doi.org/10.5897/SRE11.1990
Ykhlef, M., & ElGibreen, H. (2010). Mining pharmacy database using evolutionary genetic algorithm. International Journal of Electronics and Telecommunications, 56(4), 427–432. https://doi.org/10.2478/v10177-010-0056-6 DOI: https://doi.org/10.2478/v10177-010-0058-4