Using Real-World Data to Predict Clinical and Economic Benefits of a Future Drug Based on its Target Product Profile

For a new drug to be developed, the desired properties are described in a target product profile.

We propose a framework for using real-world data to measure the disease-specific costs of the current standard of care and then to project the costs of the proposed new product for early data-driven portfolio decisions to select drug candidates for development.

We sampled from a cohort of patients representing the current standard of care to generate a hypothetical cohort of patients that fits a given target product profile for a new (hypothetical) treatment. The healthcare costs were determined and compared between standard of care and the new treatment. The approach differed according to the number of outcomes defined in the target product profile, and the cases for one, two, and three outcome variables are described.

Based on assumed hypothetical treatment effect, absolute risk and cost reductions were estimated in a worked example. The median costs per day for one patient were estimated to be $10.37 and $8.39 in the original and hypothetical cohorts, respectively. This means that the assumed target product profile would result in cost savings of $1.98 per day and patient-not accounting for any additional drug costs.

We present a simple approach to assess the potential absolute clinical and economic benefit of a new drug based on real-world data and its target product profile. The approach allows for early data-driven portfolio decisions to select drug candidates based on their expected cost savings.