Dubai Medical University College of Pharmacy Students Shine at Emirates Pharmacy Society Conference
On November 16–17, 2025, at the 1st Emirates Pharmacy Society Conference: “Advancing Pharmacy, Leading Tomorrow,” held in Dubai, UAE, fifth-year students from the College of Pharmacy — Dania Abaji, Tasneem Zarzour, Rim Jabr, and Enana Al-Fakih — under the supervision of Dr. Rana Sammour, achieved Second Place in the student research competition.
Their project, titled “MediCycle: AI-Driven Solutions for Sustainable Pharmaceutical Waste Management,” was recognized for its innovative use of AI technology and smart systems to promote environmental sustainability and responsible medication disposal.
The initiative was commended for its potential real-world application, with possible collaborations under discussion with Beeah and other sustainability partners.
In addition, two other students from the College of Pharmacy at Dubai Medical University, Ms. Marwa Hadi and Ms. Jumana Al-Ansari, showcased their innovative research project titled “PharmaTwinX: The Novel Dual Digital Twin AI Model for Precision Medicine Pharmacotherapy” at the First Emirates Pharmacy Society Conference. The project was presented under the supervision of Dr. Ahmad El Ouweini, Assistant Professor of Pharmacy Practice at the College of Pharmacy at Dubai Medical University.
PharmaTwinX introduces a pioneering dual digital-twin AI model that virtually pairs a “Patient Twin” and a “Drug Twin” to simulate and predict individualized drug responses prior to therapy initiation.
The research objective is to develop a proof-of-concept AI platform capable of predicting individualized drug efficacy, safety, and formulation stability by virtually pairing drug and patient models prior to therapy initiation. This innovative approach aims to reduce adverse drug reactions and optimize personalized therapy outcomes through AI-driven simulation and predictive modeling.
Looking ahead, the research team plans to advance the model’s capabilities by integrating wearable sensors, paediatric and geriatric modules, and reinforcement-learning-based dose optimization. These future directions aim to enhance real-world applicability and pave the way for AI-assisted, pharmacist-led precision pharmacotherapy.
