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.

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.

Projected US savings from biosimilars, 2021-2025.

OBJECTIVES: Biologics account for an increasing share of US prescription drug spending. Biosimilars could lower biologic prices through competition, but barriers to increasing both supply and uptake remain. We projected US biosimilar savings from 2021 to 2025 under different scenarios. STUDY DESIGN: We projected US spending on biologics over a 5-year period under 3 scenarios: (1) a baseline scenario holding quarter 4 (Q4) of 2020 market conditions constant; (2) under main assumptions allowing for biosimilar market growth and entry; and (3) an upper-bound scenario assuming greater biosimilar uptake, more robust price competition, and quicker biosimilar entry. METHODS: We first analyzed 2014-2020 US volume and price data from IQVIA's MIDAS database for biologics already facing biosimilar competition to inform model parameter values. We used these inputs to project biosimilar entry, biosimilar volume shares, biosimilar prices, and reference biologic prices. We calculated 2021-2025 new savings from biosimilar competition vs the Q4 2020 baseline. RESULTS: Estimated biosimilar savings from 2021 to 2025 under our main approach were $38.4 billion, or 5.9% of projected spending on biologics over the same period. Biologics first facing biosimilar competition from 2021 to 2025 accounted for $26.1 billion of savings, with $12.2 billion from evolving market conditions for already-marketed biosimilars. Furthermore, $24.6 billion of savings under our main approach were from downward pressure on reference biologic prices rather than lower biosimilar prices. Savings were substantially higher ($124.5 billion) under the upper-bound scenario. CONCLUSIONS: Biosimilar savings from 2021 to 2025 were $38.4 billion under our main assumptions. Greater savings may be feasible if managed care and other settings increase biosimilar utilization and promote competition.

Physician Understanding and Willingness to Prescribe Biosimilars: Findings from a US National Survey.

BACKGROUND: Biosimilars have the potential to increase patient access and significantly reduce healthcare costs in the US. However, uptake in the US has been slower than anticipated, limiting the benefits of biosimilar competition. Understanding the factors that affect uptake is critically important to realize the benefits of biosimilars. METHODS: A US national survey study was conducted electronically from December 11, 2019 to January 8, 2020. The survey was administered to 507 US healthcare professionals practicing in dermatology, gastroenterology, hematology, oncology, nephrology, or rheumatology. The survey evaluated prescriber attitudes toward biosimilars in general, as well as prescriber decision making, using a series of hypothetical scenarios with fictional biological products. RESULTS: Fewer than half had a baseline understanding of key elements of biosimilarity, even among respondents who had previously prescribed a biosimilar. Regardless of previous experience, all respondents benefited from receiving additional information about biosimilarity, indicating the potential benefits of educational efforts for prescribers across all specialties and levels of experience. Prescriber choice was driven primarily by formulary status; however, respondents identified a variety of factors that would influence their willingness to prescribe a biosimilar, including financial savings to the patient, pharmacovigilance, patient experience, and education on the FDA approval process. Over one-third of participants indicated a preference for reference products and nearly half indicated a hesitancy to try biosimilars until they have been on the market longer. Naming conventions for biosimilars did not affect prescribers' willingness to prescribe biosimilars. CONCLUSIONS: Gaps in prescriber knowledge and hesitancy toward biosimilars remain significant challenges for biosimilar uptake. While formulary status of a biosimilar product strongly influences prescriber choice, additional prescriber education on biosimilarity is needed.

Biosimilars And Follow-On Products In The United States: Adoption, Prices, And Users.

Biologic drugs account for a disproportionate share of the increase in pharmaceutical spending in the US and worldwide. Against this backdrop, many look to the expanding market for biosimilars-follow-on products to biologic drugs-as a vehicle for controlling pharmaceutical spending. This study explores the early years of entry of biosimilars and related follow-on products in the US. Using monthly sales data from the period 2005-19 for ten drug classes, we examine how quickly biosimilars/follow-on products gained market share and the subsequent trajectory of prevailing (national average invoice) prices. Our analysis suggests that although uptake has been slower than what is typically seen in generic drug markets, the most recent entrants have captured market share more rapidly than comparable earlier biosimilars/follow-on products. We also document that from biosimilar/follow-on products' time of entry, their lower prices help offset the overall trend in average annual reference-product price increases. Our findings can provide insight into future policy reforms aimed at increasing competition and use of biosimilars, leading to expanded patient access and significant cost savings.

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.

Economic Approaches to Estimating Benefits of Regulations Affecting Addictive Goods.

The question of how to evaluate lost consumer surplus in benefit-cost analyses has been contentious. There are clear health benefits of regulations that curb consumption of goods with health risks, such as tobacco products and foods high in fats, calories, sugar, and sodium. Yet, if regulations cause consumers to give up goods they like, the health benefits they experience may be offset by some utility loss, which benefit-cost analyses of regulations need to take into account. This paper lays out the complications of measuring benefits of regulations aiming to curb consumption of addictive and habitual goods, rooted in the fact that consumers' observed demand for such goods may not be in line with their true preferences. Focusing on the important case of tobacco products, the paper describes four possible approaches for estimating benefits when consumers' preferences may not be aligned with their behavior, and identifies one as having the best feasibility for use in applied benefit-cost analyses in the near term.

Evaluating chronic disease for heterogeneous populations: the case of arthritis.

BACKGROUND: Cost-effectiveness evaluation for health care programs often involves the use of quality-adjusted life-year (QALY) estimates to measure morbidity losses from health conditions. Current techniques for measuring morbidity losses are often subjective, inflexible, impractical, and subject to bias. OBJECTIVE: We sought to examine the impact of population heterogeneity on QALY values for arthritis sufferers by estimating an alternative health-adjusted life-year (HALY) measure based on self-assessed health status. RESEARCH DESIGN: We present a feasible approach for the assessment of improved QALY estimates for chronic conditions affecting heterogeneous populations. An ordered probit model, using data from the National Health Interview Survey (NHIS), is used to calculate expected HALY losses from arthritis for distinct population subgroups. These measures are used to scale existing QALY measures that have been calculated for distinct homogeneous populations. RESULTS: : We find that QALY losses from chronic arthritis vary by age, time since onset, and type of arthritis. When we apply these results to prevention programs aimed at reducing the incidence of Salmonella enteritidis infections (and the resulting reactive arthritis sequelae), we find that age-invariant QALYs underestimate the true discounted lifetime QALY losses from arthritis by 15%. CONCLUSIONS: Our results indicate that a failure to account for population heterogeneity can lead to biased health loss estimates. The modified HALY measure presented here can be used to help inform policymakers faced with heterogeneous populations.

Valuing avoided morbidity using meta-regression analysis: what can health status measures and QALYs tell us about WTP?

Many economists argue that willingness-to-pay (WTP) measures are most appropriate for assessing the welfare effects of health changes. Nevertheless, the health evaluation literature is still dominated by studies estimating nonmonetary health status measures (HSMs), which are often used to assess changes in quality-adjusted life years (QALYs). Using meta-regression analysis, this paper combines results from both WTP and HSM studies applied to acute morbidity, and it tests whether a systematic relationship exists between HSM and WTP estimates. We analyze over 230 WTP estimates from 17 different studies and find evidence that QALY-based estimates of illness severity--as measured by the Quality of Well-Being (QWB) Scale--are significant factors in explaining variation in WTP, as are changes in the duration of illness and the average income and age of the study populations. In addition, we test and reject the assumption of a constant WTP per QALY gain. We also demonstrate how the estimated meta-regression equations can serve as benefit transfer functions for policy analysis. By specifying the change in duration and severity of the acute illness and the characteristics of the affected population, we apply the regression functions to predict average WTP per case avoided.