388821 Nequalsone: A Model-Based Clinical Decision Support Tool for Implementing Personalized Treatment Strategies
Cancer treatment has been a challenge for more than a century to clinicians and researchers alike. Every individual is unique, both genetically and phenotypically, and hence respond to the treatment differently. Dosing decisions based on individual patients’ characteristics offer maximum efficacy without causing severe toxicities. While this has largely been empirical in the past, quantitative predictive methods are gaining traction in the recent times as a tool to predict the oncoming efficacy/toxicity and make dose adjustments accordingly. Quantitative tools, suitably empowered by systems theoretic approach, could serve as a decision-support mechanism to physicians. However, use of such sophisticated models and algorithms have not found its way into clinical practice as they demand extensive knowledge of mathematical, statistical and computational background and skills. Obviously, clinicians, already burdened with their clinical practice, are not in a position to make full use of these tools. In other words, there are no user-friendly software available for clinicians to assist them in their day-to-day decision-making with regard to dosage adjustments.
In this work, we present nEqualsOne, a one-of-its-kind, model-based standalone software, to quantitatively predict the clinical outcome for a specific patient and determine the optimal chemotherapeutic dose to achieve a desired clinical response. It is a point-and-click, interactive software where the practicing clinicians can process the available patient information through mathematical models and predict as well as optimize the dose for each individual patient based on a defined clinical target. Finally, the dosing decision is made by the clinicians taking into account some information that are not considered in the software. We demonstrate an application for an important chemotherapeutic drug known as 6-Mercaptopurine in tailoring treatment for leukemia patients.