Bhavesh Gohil
Independent Researcher
Gujarat, India
Abstract
Multi-center clinical trials in the field of pharmacy involve complex logistical, financial, and operational planning. The effective allocation of limited resources—personnel, budget, investigational drugs, equipment, and time—becomes increasingly critical as trial scale and complexity grow. Strategic resource allocation models offer structured, data-driven methods to optimize these assets while ensuring regulatory compliance, data quality, and patient safety. This study investigates and evaluates several resource allocation models tailored for multi-center pharmacy clinical trials, including deterministic linear programming, stochastic modeling, and decision-support frameworks. Drawing on empirical data and simulations, the models are compared based on criteria such as efficiency, flexibility, feasibility, and alignment with Good Clinical Practice (GCP) standards. The findings underscore the potential for hybrid approaches, combining predictive analytics and scenario planning, to improve site performance, cost control, and patient recruitment timelines. Strategic allocation frameworks, if embedded early in protocol planning and dynamically adjusted during execution, can significantly improve trial outcomes and streamline pharmaceutical R&D cycles.
Keywords
Clinical Trial Management; Multi-Center Studies; Strategic Resource Allocation; Pharmacy Trials; Operations Optimization; Linear Programming; Stochastic Models; Trial Cost Efficiency; Trial Site Logistics; Predictive Modeling
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