Costs of Drug Development and Research and Development Intensity in the US, 2000-2018.

Importance: Understanding the cost of drug development can help inform the development of policies to reduce costs, encourage innovation, and improve patient access to drugs. Objective: To estimate the cost of drug development by therapeutic class and trends in pharmaceutical research and development (R&D) intensity over time. Design, Setting, and Participants: In this economic evaluation study, an analytical model of drug development constructed using public and proprietary sources that collectively cover data from 2000 to 2018 was used to estimate the cost of bringing a drug to market, overall and for specific therapeutic classes. The analysis for the study was completed in October 2020. Main Outcomes and Measures: Three measures of development cost from nonclinical through postmarketing stages were estimated: mean out-of-pocket cost or cash outlay, mean expected cost, and mean expected capitalized cost. Pharmaceutical R&D intensity, defined as the ratio of R&D spending to total sales, from 2008 to 2019, based on the time frame for available data, was also analyzed. Results: The estimated mean cost of developing a new drug was approximately $172.7 million (2018 dollars) (range, $72.5 million for genitourinary to $297.2 million for pain and anesthesia), inclusive of postmarketing studies. The cost increased to $515.8 million when cost of failures was included. When the costs of failures and capital were included, the mean expected capitalized cost of drug development increased to $879.3 million (range, $378.7 million for anti-infectives to $1756.2 million for pain and anesthesia); results varied widely by therapeutic class. The pharmaceutical industry as a whole experienced a decline of 15.6% in sales but increased R&D intensity from 11.9% to 17.7% from 2008 to 2019. By contrast, R&D intensity of large pharmaceutical companies increased from 16.6% to 19.3%, whereas sales increased by 10.0% (from $380.0 to $418.0 billion) over the same 2008 to 2019 period, even though the cost of drug development remained relatively stable or may have even decreased. Conclusions and Relevance: In this economic evaluation of new drug development costs, even though the cost of drug development appears to have remained stable, R&D intensity of large pharmaceutical companies remained relatively unchanged, despite substantial growth in revenues during this period. These findings can inform the design of drug-related policies and their potential impacts on innovation and competition.

Assessing the state of antibacterial drug discovery through patent analysis.

Patent filings suggest increasing intensity of antibacterial drug discovery in recent years, but the share of patents published by commercial companies has declined.

Estimated Cost of Developing a Therapeutic Complex Medical Device in the US.

Importance: The US medical device market is the world's largest, but estimates of the cost to bring a medical device to market are not available to help inform policy making and regulatory efforts to enhance device safety and innovation. Objective: To estimate the mean expected capitalized cost of developing a novel therapeutic complex medical device. Design, Setting, and Participants: In this economic evaluation, an analytical model of novel therapeutic complex medical device development using data from public and proprietary sources with coverage from 2000 through 2018 was used to estimate the cost, duration, and phase transition success probability associated with each stage of development. Data analysis was completed in September 2021. Exposures: Conduct of nonclinical and clinical studies; payment of FDA user fees for novel therapeutic complex medical devices. Main Outcomes and Measures: Mean development cost (in 2018 US dollars) incurred by developers for an FDA-approved novel therapeutic complex medical device, accounting for failures and cost of capital. Results: In this economic analysis, the mean development cost for a novel therapeutic complex medical device was $54 million (95% CI, $25 million-$200 million) excluding any postapproval studies that might be required. After accounting for the cost of failed studies and cost of capital, the mean capitalized cost of bringing a novel therapeutic complex medical device to the US market was $522 million (95% CI, $205 million-$3382 million). The key factors associated with this cost were the phase transition probabilities: 46.9% for nonclinical to feasibility study, 48.0% for feasibility to pivotal study, 75.7% pivotal study to FDA premarket approval submission, and 80.5% for FDA premarket approval submission to approval. The nonclinical development stage constituted the largest portion of overall cost at 85.0% with the FDA review stage with the highest phase transition probability accounting for only a small fraction at 0.5%. Conclusions and Relevance: In this economic evaluation study, the cost of therapeutic complex medical device development from proof of concept through postapproval stages was assessed accounting for the cost of failures and the cost of capital. Existing estimates did not account for all stages of development, capitalization, or failure costs, which this study suggests were substantial.

The Use of Expert Elicitation among Computational Modeling Studies in Health Research: A Systematic Review.

BACKGROUND: Expert elicitation (EE) has been used across disciplines to estimate input parameters for computational modeling research when information is sparse or conflictual. OBJECTIVES: We conducted a systematic review to compare EE methods used to generate model input parameters in health research. DATA SOURCES: PubMed and Web of Science. STUDY ELIGIBILITY: Modeling studies that reported the use of EE as the source for model input probabilities were included if they were published in English before June 2021 and reported health outcomes. DATA ABSTRACTION AND SYNTHESIS: Studies were classified as "formal" EE methods if they explicitly reported details of their elicitation process. Those that stated use of expert opinion but provided limited information were classified as "indeterminate" methods. In both groups, we abstracted citation details, study design, modeling methodology, a description of elicited parameters, and elicitation methods. Comparisons were made between elicitation methods. STUDY APPRAISAL: Studies that conducted a formal EE were appraised on the reporting quality of the EE. Quality appraisal was not conducted for studies of indeterminate methods. RESULTS: The search identified 1520 articles, of which 152 were included. Of the included studies, 40 were classified as formal EE and 112 as indeterminate methods. Most studies were cost-effectiveness analyses (77.6%). Forty-seven indeterminate method studies provided no information on methods for generating estimates. Among formal EEs, the average reporting quality score was 9 out of 16. LIMITATIONS: Elicitations on nonhealth topics and those reported in the gray literature were not included. CONCLUSIONS: We found poor reporting of EE methods used in modeling studies, making it difficult to discern meaningful differences in approaches. Improved quality standards for EEs would improve the validity and replicability of computational models. HIGHLIGHTS: We find extensive use of expert elicitation for the development of model input parameters, but most studies do not provide adequate details of their elicitation methods.Lack of reporting hinders greater discussion of the merits and challenges of using expert elicitation for model input parameter development.There is a need to establish expert elicitation best practices and reporting guidelines.

An Expert Elicitation on the Effects of a Ban on Menthol Cigarettes and Cigars in the United States.

INTRODUCTION: The US Food and Drug Administration announced its intention to ban menthol in cigarettes. However, information is needed on how a federal ban would affect population health. AIMS AND METHODS: We conducted an expert elicitation to gauge the impact of a menthol cigarette and cigar ban in the United States. We developed and pilot tested a questionnaire that focused on tobacco use transitions of current smokers (age 18-24 menthol, age 35-54 menthol, and age 35-54 nonmenthol) and potential menthol smokers (age 12-24). Using a structured expert elicitation, we estimated mean net transitions under a ban from cigarette use to combustible tobacco product, smokeless tobacco, novel nicotine delivery product (NNDPs, such as e-cigarettes) use, or no tobacco use. RESULTS: Eleven experts provided responses. Of those ages 12-24 who would have initiated menthol cigarette use in the absence of a ban, the experts estimated that 41% would still initiate combustible products under a ban, while 18% would initiate with NNDPs and 39% would not initiate regular tobacco use. Combustible use by menthol smokers ages 35-54 was expected to decline by 20% postban relative to preban rates, half switching to NNDPs and half quitting all tobacco use. Menthol smokers ages 18-24 were expected to reduce combustible use by 30%, with 16% switching to NNDPs. Greater reductions in combustible use were estimated for African Americans across the three age groups. Negligible impacts were expected for current adult nonmenthol smokers. CONCLUSIONS: According to expert opinion, a menthol ban is expected to substantially reduce smoking initiation and combustible tobacco product use among current menthol smokers. IMPLICATIONS: The US Food and Drug Administration recently announced its intention to ban menthol in cigarettes, but information on the potential impact on smoking and other nicotine product use is limited. We conducted an expert elicitation to gauge the impact of a menthol cigarette and cigar ban in the United States. A panel of experts estimated that menthol smokers ages 35-54 would reduce combustible tobacco use by 20%, with half switching to e-cigarettes and half quitting all nicotine use. Larger reductions were expected at younger ages, and menthol smoking initiation was reduced by 59% with 18% instead using e-cigarettes. African Americans were expected to have greater reductions in combustible tobacco use than the rest of the population.

Societal willingness to pay to avoid mortality and morbidity from Clostridioides difficile and carbapenem-resistant Enterobacteriaceae infections in the United States.

BACKGROUND: Clostridioides difficile infection (CDI) is among the most common health care-associated infections in the United States and is increasingly affecting the elderly. Although carbapenem-resistant Enterobacteriaceae (CRE) infections are still relatively uncommon, there are reported increases in the rate of infection for certain strains, such as Klebsiella pneumoniae. This study examines the burden of mortality and morbidity for CDI and CRE infections in the United States and estimates the societal willingness to pay to avoid them. METHODS: We use an analytic model to estimate the number of incident cases and associated health outcomes for CDI and CRE infections. RESULTS: The number of CDI and CRE infection incident cases in the United States in 2016, is estimated at 468,567 and 9,620, respectively. These infections result in a total of 17,630 estimated deaths and 8,624 lost quality-adjusted life years among patients who survive per year. CONCLUSIONS: Given the significant mortality and morbidity from these infections, the estimated societal willingness to pay to avoid them is high at $176.7 billion per year, of which 93.9% ($166.0 billion) is for CDI. Our estimates far exceed the medical care costs for CDIs and CRE infections reported in the literature despite not capturing the additional costs borne by third-party payers. As incident cases increase or resistant strains develop, the societal willingness to pay is also expected to increase.

Promoting Antibacterial Drug Development: Select Policies and Challenges.

BACKGROUND: The development pipeline for antibacterial drugs has not met the demand of hospitals and healthcare providers struggling to cope with increasing problems of antibacterial resistance. Although the challenges associated with antibacterial drug development have been known for some time, previous efforts to address them have not been sufficient. There remains an urgent need for targeted incentives to foster antibacterial drug development while encouraging prudent use. OBJECTIVE: We examine the effects of two types of incentives, a 5-year delay in competition from generics and a lump-sum US$50 million prize payment upon successful US Food and Drug Administration approval, on antibacterial drug company returns. METHODS: We use the decision-tree framework developed in a study for the US Department of Health and Human Services, which models the drug company's decision process as a revenue maximizer under uncertainty. RESULTS: Our results show that, to maximize societal benefit, such incentives need to take into consideration the indication(s) the new antibacterial drug is designed to treat as well as the drug development stage. CONCLUSIONS: Optimal policies should maximize the difference between societal benefit, primarily measured as the reduction in public health burden from the development of a new antibacterial drug that treats an infectious disease while ensuring prudent use, and social cost. Here, we show that the two types of policies examined under-incentivize early-stage developers (i.e., do not achieve the desired outcome) and over-incentivize late-stage developers (i.e., achieve the desired outcome but at a cost that is higher than needed) ceteris paribus.

Key cost drivers of pharmaceutical clinical trials in the United States.

BACKGROUND: The increasing cost of clinical research has significant implications for public health, as it affects drug companies' willingness to undertake clinical trials, which in turn limits patient access to novel treatments. Thus, gaining a better understanding of the key cost drivers of clinical research in the United States is important. PURPOSE: The study which is based on a report prepared by Eastern Research Group, Inc., for the US Department of Health and Human Services, examined different factors, such as therapeutic area, patient recruitment, administrative staff, and clinical procedure expenditures, and their contribution to pharmaceutical clinical trial costs in the United States by clinical trial phase. METHODS: The study used aggregate data from three proprietary databases on clinical trial costs provided by Medidata Solutions. We evaluated per-study costs across therapeutic areas by aggregating detailed (per patient and per site) cost information. We also compared average expenditures on cost drivers with the use of weighted mean and standard deviation statistics. RESULTS: Therapeutic area was an important determinant of clinical trial costs by phase. The average cost of a Phase 1 study conducted at a US site ranged from US$1.4 million (pain and anesthesia) to US$6.6 million (immunomodulation), including estimated site overhead and monitoring costs of the sponsoring organization. A Phase 2 study cost from US$7.0 million (cardiovascular) to US$19.6 million (hematology), whereas a Phase 3 study cost ranged from US$11.5 million (dermatology) to US$52.9 (pain and anesthesia) on average. Across all study phases and excluding estimated site overhead costs and costs for sponsors to monitor the study, the top three cost drivers of clinical trial expenditures were clinical procedure costs (15%-22% of total), administrative staff costs (11%-29% of total), and site monitoring costs (9%-14% of total). LIMITATIONS: The data were from 2004 through 2012 and were not adjusted for inflation. Additionally, the databases used represented a convenience, that is, non-probability, sample and did not allow for statistically valid estimates of cost drivers. Finally, the data were from trials funded by the global pharmaceutical and biotechnology industry only. Hence, our study findings are limited to that segment. CONCLUSION: Therapeutic area being studied as well as number and types of clinical procedures involved were the key drivers of direct costs in Phase 1 through Phase 3 studies. Research shows that strategies exist for reducing the price tag of some of these major direct cost components. Therefore, to increase clinical trial efficiency and reduce costs, gaining a better understanding of the key direct cost drivers is an important step.