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

Background aimsDrug 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.MethodsUsing 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.ResultsPrices 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.ConclusionsOur 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.