The estimated annual financial impact of gene therapy in the United States

Abstract

Gene therapy is a new class of medical treatment that alters part of a patient's genome through the replacement, deletion, or insertion of genetic material. While still in its infancy, gene therapy has demonstrated immense potential to treat and even cure previously intractable diseases. Nevertheless, existing gene therapy prices are high, raising concerns about its affordability for U.S. payers and its availability to patients. We assess the potential financial impact of novel gene therapies by developing and implementing an original simulation model which entails the following steps: identifying the 109 late-stage gene therapy clinical trials underway before January 2020, estimating the prevalence and incidence of their corresponding diseases, applying a model of the increase in quality-adjusted life years for each therapy, and simulating the launch prices and expected spending of all available gene therapies annually. The results of our simulation suggest that annual spending on gene therapies will be approximately $20.4 billion, under conservative assumptions. We decompose the estimated spending by treated age group as a proxy for insurance type, finding that approximately one-half of annual spending will on the use of gene therapies to treat non-Medicare-insured adults and children. We conduct multiple sensitivity analyses regarding our assumptions and model parameters. We conclude by considering the tradeoffs of different payment methods and policies that intend to ensure patient access to the expected benefits of gene therapy.

Fig. 1. A flowchart showing the performance of the simulation.

After extracting the information on each disease from the clinical trial databases, we simulate whether the disease will obtain an approval. If it fails to do so, the simulation will end for this disease in this iteration. Otherwise, we estimate the expected number of patients to be treated, compute the corresponding cost of treatment, and store the results. At each step of the computation, we sourced data from the published literature and impute missing information.

Fig. 2

The empirical distribution of duration against our fitted gamma distribution.

Fig. 3. Cumulative number of approvals between January 2020 and December 2034 observed from 1,000,000 simulation runs.

The line represents the mean and the shaded region represents the 5th and 95th percentiles of our simulation.

Fig. 4. Number of patients treated between January 2020 and December 2034, obtained from 1,000,000 simulation runs.

a Monthly number of patients treated with gene therapy across all diseases, among existing and new patients. b Stacked chart depicting the proportion of existing and new patients treated in that month, by disease category. c Cumulative number of patients treated. The line represents the mean and the shaded region represents the 5th and 95th percentiles of our simulation.

Fig. 5. Simulated monthly spending on patients treated with gene therapy.

a Monthly spending on treating existing and new patients with gene therapy. b Stacked chart depicting the proportion of spending on treating existing and new patients in that month, by disease category. The line represents the mean and the shaded region represents the 5th and 95th percentiles from our simulation.

Fig. 6. Cumulative spending on treating patients with gene therapy.

The line represents the mean and the shaded region represents the 5th and 95th percentiles of our simulation.

Fig. 7. QALYs gained by treating existing and new patients with gene therapy.

a QALYs gained by treating existing and new patients with gene therapy. b Cumulative QALY gained by treating patients with gene therapy overall. c Cumulative QALY gained by treating patients with gene therapy broken out by new and existing patients. The line represents the mean and the shaded region represents the 5th and 95th percentiles of our simulation.

Fig. 8. Tornado charts showing the sensitivity of the variables on the different metrics.

a Tornado chart of the impact of the variables on the peak value. b Tornado chart of the impact of the variables on the cumulative spending (both nominal and discounted). c Tornado chart of the impact of the variables on the date of peak value. Since we compute by calendar month, a small machine precision error may change the results by 1 month. The black bars represent the effect of increasing the variable by 20% and the red bars represent the effect of decreasing the variable by 20%.

Fig. 9

A plot of the number of programs initiated over time in our dataset.

Fig. 10. Comparison between the results of the simulations with and without assuming additional gene therapy programs entering the pipeline.

a Cumulative number of approvals. b Cumulative number of patients treated. c Cumulative spending on gene therapy. The line represents the mean and the shaded region represents the 5th and 95th percentiles of our simulation.