Clinical Trial Supply Chain Management with RTSM/IRT: Best Practices Guide

What is clinical trial supply chain management?

Clinical trials are designed to test the safety and efficacy of new investigational products (IP) to determine their suitability for use in the routine treatment and care of patients. Typically the medication (investigational product) being studied is produced alongside the clinical development program, which can mean that medication is only available in limited quantities, has a short expiry date, and may be costly to produce. RTSM/IRT systems operate like the electronic point of sales (EPOS) systems used by high street and online retailers to manage the inventory of product available and automate re-ordering when available stock falls. RTSM/IRT systems optimize clinical trial medication distribution, reducing waste, and ensuring continuous supply availability at research sites. These systems coordinate complex supply chains spanning manufacturers, depots, and clinical sites worldwide.

RTSM/IRT supply chain management key features and capabilities

Modern RTSM systems provide comprehensive real-time visibility into global inventory levels across the entire supply chain. Site managers and study coordinators can instantly access current stock levels at both depots and sites, while the system actively tracks critical parameters such as expiration dates and temperature conditions. The platform maintains detailed records of lot and batch information, manages quarantined products, and tracks returns and destruction processes. This level of oversight ensures compliance with regulatory requirements while maintaining efficient inventory control.

Reducing overage and wastage using RTSM systems

RTSM systems have revolutionized the approach to medication management in clinical trials. One way RTSM systems enable studies to operate using less medication is because medication units are individually numbered instead of using patient-numbered packs. This means that if a patient withdraws early during a clinical trial, the medication that could have been allocated to them at future dispensing visits can be allocated to any patient within the same treatment group. This significantly reduces medication wastage, while maintaining the study blind. A second way RTSM systems reduce medication requirements is through the automated re-stocking of sites throughout the trial – this focuses the supply chain only to sites recruiting more patients and avoids leaving large quantities of unused medication at inactive sites. Further, real-time expiry tracking enables proactive redistribution of medication to high-enrolling sites before expiration.

Traditional trials often required overage amounts of 200-300% to ensure adequate supply across all sites, leading to significant waste and unnecessary costs. RTSM systems, on the other hand, typically reduce required overage to 30-50%, representing substantial cost savings for sponsors, and enabling studies to start on time when available medication quantities are low.

Distribution and resupply management using RTSM

RTSM platforms have transformed the distribution process through intelligent automation. The system initiates resupply orders based on predetermined triggers, ensuring sites maintain optimal stock levels without manual intervention. Through smart allocation algorithms, available stock is distributed efficiently across sites based on actual needs and upcoming patient visits (predictive resupply). The platform considers various factors in its distribution strategy, including site storage capacity and drug expiry dates, while optimizing shipping routes to minimize costs and delivery times. Integration with courier systems provides end-to-end visibility of the supply chain.

Temperature and cold-chain distribution using RTSM systems

For medications requiring refrigeration or frozen storage, RTSM systems provide crucial cold chain management capabilities. Integration with temperature logging devices enables the continuous monitoring of storage conditions in transit and throughout the supply chain, from manufacturer to patient dispensation. If temperature excursions occur, the system immediately alerts relevant personnel and documents the event, and the system is able to impose and manage quarantine status to prevent medication subject to temperature excursions from being dispensed until fully inspected and approved for use. Stability data management and temperature log documentation ensure that only viable medication reaches patients, while maintaining the necessary documentation for regulatory compliance.

Cost savings using RTSM systems

The implementation of RTSM supply management typically results in substantial cost savings across multiple areas of trial operations. By accurately predicting supply needs, trials can significantly reduce drug overage requirements and minimize wastage of expensive investigational products. The system's intelligent distribution algorithms optimize shipping costs by consolidating deliveries and choosing efficient routes. Through improved expiry management and streamlined returns processing, sponsors can maximize the utilization of available drug supplies while maintaining compliance with regulatory requirements.

 Supply forecasting and planning with RTSM/IRT systems

Because investigational product (IP) manufacture is conducted alongside clinical development, it is often a requirement to plan mid-study production of medication to meet the ongoing needs of a clinical trial, especially those with long durations. Further, real-time medication usage data is invaluable for checking assumptions and ensuring medication quantities allocated to each clinical trial are appropriate for its ongoing operation.

RTSM/IRT systems excel in strategic supply forecasting and planning through their sophisticated predictive modeling capabilities. These systems aggregate multiple data points, including historical enrollment patterns, screen failure rates, patient dropout trends, and site activation timelines to generate accurate supply forecasts. The forecasting engines account for protocol-specific factors such as dosing schedules, titration requirements, and built-in visit windows, while also considering practical constraints like manufacturing lead times and shelf-life limitations. The data can be used to model various enrollment scenarios and their impact on supply needs, enabling study teams to develop robust contingency plans. Real-time monitoring of actual versus predicted supply consumption allows for dynamic adjustment of forecasts, while automated alerts notify supply managers when actual patterns deviate significantly from projections.