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The Impact of Price Control Legislation on the Development of New Drugs——Medical Economist


It stands to reason that policies aimed at lowering drug prices or squeezing the profits of life sciences companies may reduce the incentives to fund the research and development needed to bring drugs to market.Previous estimates claim that it is possible to bring a new drug to the market Cost more than 2 billion U.S. dollars, Life science companies will need to bear these costs. A key question then is to what extent potential US policies that reduce returns to life sciences companies will affect new innovations.A sort of Congressional Budget Office Report Designed to answer this question and found:

The expected return of the drug with the highest expected return decreased by 15% to 25%, which is related to an average annual decrease of 0.5% in the number of new drugs entering the market in the first ten years under the policy, increasing to 8% in the third ten years The average annual reduction.

The impact time of this policy on drug development is shown in the figure below.

The reason for the slow impact is that it takes many years for the drug to market. Therefore, the price reduction of medicines has little effect on short-term innovation, but has a significant impact on long-term innovation. To a certain extent, CBO believes that these policies will have the greatest impact on the investment in the first and second phases, but will have less impact on the third phase. The figure below shows this effect graphically, taking into account the uncertainty of some basic parameters. It can be seen that CBO believes that price control may affect the investment decisions of the first and second phases, but once a drug achieves positive results after the second phase, CBO believes that pharmaceutical companies are likely to advance the third phase.

What evidence does the CBO cite? Some key articles cited include:

  • Du Bois et al. 2015. This study estimates the elasticity as 0.23 In terms of market size and new drug development, market size refers to the total sales of competing drugs multiplied by the prices of these drugs.
  • Acemoglu and Linn 2004The author discovers the elasticity of new drugs relative to market size 4.0.
  • 2019 CBOThe CBO estimated the impact of drug price negotiations on the federal government’s savings and future innovations, and found that drug price negotiations will result in a reduction of 8 drug approvals in the United States between 2020 and 2029, but a reduction of 30 drugs between 2030 and 2039 . Assume that 30 new drugs are approved each year.The model uses elastic estimates that increase over time 0.5 18 years later.
  • Estimated market entry research. CBO uses from Blume-Kahout and Sood (2013), Dimas (2013) and a Khmelnitskaya (2020) These estimated working papers.
  • Delanov et al. (2020). This research-together with Blume-Kahout and Sood (2013) ——It shows that it takes several years for drug pricing policy to affect innovation. Both papers used the emergence of Part D of Medicare to quantify this impact.
  • Heckman and Honoré 1990. This Roy model is used to estimate returns. The observed returns from the Part D data used by the CBO provide a biased result because they only include returns for successful drugs. This paper shows that if it is assumed that the benefits and costs are bivariate normally distributed, and the benefits and costs are equal in the long run, then the estimated benefits and costs can be determined.
  • Over time, an estimate of the return on life sciencesDue to the low market share, the initial return of the pharmaceutical company is estimated to be low; it rises over time; and then it falls as new drugs—and ultimately generic drugs—enter the market.The main papers here include Dimasi et al. year 2004 with Batacharia and Vogt 2003.

The new CBO model is an update of the 2019 version.Their model uses the flexibility introduced by new drugs in life science returns 0.45The model uses drug cost estimates from Dimasi, Grabowski and Hansen (2016) And a Roy model to combine revenue and cost information. They found:

The policy example analyzed here is estimated to reduce expected returns by an average of 18%, and the model results show that it leads to an 8% reduction in new drugs after 25 years

The modeling approach basically uses a barrier model, in which the pharmaceutical company only enters the next phase of development (that is, phase 1, 2, or 3) when the expected benefits exceed the cost. The key probabilities that provide information for the model are: (i) the probability of success of the drug depends on the observations in the current stage, (ii) the potential financial benefits of entering the next stage, (iii) the cost of the drug in the next stage (iv) the financing cost (I.e. cost of capital).The probability of success and the cost of the drug phase come from Dimasi et al. 2016The return is estimated to the manufacturer based on Part D of the medical insurance, including confidential data on the rebate paid by the manufacturer. CBO uses the Generalized Method of Moments (GMM) method to solve their simulation models. Use bootstrap to estimate standard deviation.

The CBO did notice that the probability of entering each next stage for stages I, II, and III was 10%, 20%, and 90%, respectively, but these numbers were not chosen because they seem to match the data. In addition, CBO pointed out that price negotiations may increase the cost of capital for pharmaceutical companies, which will further reduce future innovation due to the increase in costs for life science companies.

Although the mass media generally believe that drug price control will not affect future innovation, CBO clearly believes that the top-down price control policy has a long-term impact on innovation.



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