Towards sustainability and affordability of expensive cell and gene therapies? Applying a cost-based pricing model to estimate prices for Libmeldy and Zolgensma.

BACKGROUND AIMS: Drug prices are regarded as one of the most influential factors in determining accessibility and affordability to novel therapies. Cell and gene therapies such as OTL-200 (brand name: Libmeldy) and AVXS-101 (brand name: Zolgensma) with (expected) list prices of 3.0 million EUR and 1.9 million EUR per treatment, respectively, spark a global debate on the affordability of such therapies. The aim of this study was to use a recently published cost-based pricing model to calculate prices for cell and gene therapies, with OTL-200 and AVXS-101 as case study examples. METHODS: Using the pricing model proposed by Uyl-de Groot and Löwenberg, we estimated a price for both therapies. We searched the literature and online public sources to estimate (i) research and development (R&D) expenses adjusted for risk of failure and cost of capital, (ii) the eligible patient population and (iii) costs of drug manufacturing to calculate a base-case price for OTL-200 and AVXS-101. All model input parameters were varied in a stepwise, deterministic sensitivity analysis and scenario analyses to assess their impact on the calculated prices. RESULTS: Prices for OTL-200 and AVXS-101 were estimated at 1 048 138 EUR and 380 444 EUR per treatment, respectively. In deterministic sensitivity analyses, varying R&D estimates had the greatest impact on the price for OTL-200, whereas for AVXS-101, changes in the profit margin changed the calculated price substantially. Highest prices in scenario analyses were achieved when assuming the lowest number of patients for OTL-200 and highest R&D expenses for AVXS-101. The lowest R&D expenses scenario resulted in lowest prices for either therapy. CONCLUSIONS: Our results show that, using the proposed model, prices for both OTL-200 and AVXS-101 lie substantially below the currently (proposed) list prices for both therapies. Nevertheless, the uncertainty of the used model input parameters is considerable, which translates in a wide range of estimated prices. This is mainly because of a lack of transparency from pharmaceutical companies regarding R&D expenses and the costs of drug manufacturing. Simultaneously, the disease indications for both therapies remain heavily understudied in terms of their epidemiological profile. Despite the considerable variation in the estimated prices, our results may support the public debate on value-based and cost-based pricing models, and on "fair" drug prices in general.

Increasing the information provided by probabilistic sensitivity analysis: The relative density plot.

BACKGROUND: Results of probabilistic sensitivity analyses (PSA) are frequently visualized as a scatterplot, which is limited through overdrawing and a lack of insight in relative density. To overcome these limitations, we have developed the Relative Density plot (PSA-ReD). METHODS: The PSA-ReD combines a density plot and a contour plot to visualize and quantify PSA results. Relative density, depicted using a color gradient, is transformed to a cumulative probability. Contours are then plotted over regions with a specific cumulative probability. We use two real-world case studies to demonstrate the value of the PSA-ReD plot. RESULTS: The PSA-ReD method demonstrates proof-of-concept and feasibility. In the real-world case-studies, PSA-ReD provided additional visual information that could not be understood from the traditional scatterplot. High density areas were identified by color-coding and the contour plot allowed for quantification of PSA iterations within areas of the cost-effectiveness plane, diminishing overdrawing and putting infrequent iterations in perspective. Critically, the PSA-ReD plot informs modellers about non-linearities within their model. CONCLUSIONS: The PSA-ReD plot is easy to implement, presents more of the information enclosed in PSA data, and prevents inappropriate interpretation of PSA results. It gives modelers additional insight in model functioning and the distribution of uncertainty around the cost-effectiveness estimate.