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How your RTSM can give you what you need to save on supply cost
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ponsors today are faced with ever increasing costs in running a clinical trial, and the opportunity to anticipate to save costs is one that cannot be let by.
Figuring in this dilemma is the end cost to a sponsor of producing the Investigational Product (IP), while meeting specificities of manufacture, storage, temperature control, distribution logistics to sites. This is compounded by the uncertainties that are encountered in any clinical trial, and the wastage due to the impact of any of the above. McKinsey in its research article* reports an alarming average of 50% clinical trial medication waste in pharma. It goes without saying therefore that the industry
today requires advanced software systems to mitigate and deal with this. The capability of those systems is not only about informing, but also about foreseeing potential barriers as well as mitigating them. Dr. Irena Seredina and Jasvinder Osan, Executive Director and VP-Business Strategy at S-Clinica respectively, discuss how S-Clinica’s experience and understanding of this space – which borders on the unique combination of a deep understanding of how clinical trials run, the science behind this, coupled with advanced mathematical algorithms using predictive technologies is helping clients around the world anticipate and mitigate
risks, while continuing to keep bring their supply management costs down. RTSM (Randomisation and Trial Supply
Management) systems – also in some nuances referred to as IRT (Interactive Response Technologies) have traditionally been recognised as the point within the eClinical ecosystem to obtain the information on the IP kit availability and needs at sites during a clinical trial. As studies become increasingly complex and expensive, there has been an increased expectation from study teams to not only manage, but also forecast their IP kit supply needs, and optimising their supply strategy. To address this, specialised forecasting systems are usually procured by sponsors within the clinical landscape, integrated loosely or working in tandem with the RTSM solution in use. While a step in the right direction, it does still fall
short. The two systems have independent algorithms – one in the RTSM managing the supply, and the second in the forecasting solution, mimicking (or trying to mimic) the former. Irena Seredina, a medical doctor by training and an SME has an interesting analogy : “It’s like planning your trip to go by air, and then actually ending up travelling by boat.” “We developed ClinVision with a biotech company,” explains Seredina. “They needed a
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