Evidence map›Paper›PMID 38941099›Full record

ArticleJAMA network open2024

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

Aylin Sertkaya, Trinidad Beleche, Amber Jessup, Benjamin D Sommers

Abstract read
In one paragraph

Article in JAMA network open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 119 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
119citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

119 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. In Silico Clinical Trials in Drug Development: A Systematic Review.Therapeutic innovation & regulatory science · 2026
    Pooled it
  2. Pooled it
  3. Ticagrelor Compared to Clopidogrel in Acute Coronary Syndromes trial (TC4): a Bayesian pragmatic cluster randomized controlled trial.CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne · 2025
    Trial
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  11. Repurposing biosimilars, rethinking costs: a framework for sustainable drug pricing for repurposed bevacizumab for intravitreal injections.The European journal of health economics : HEPAC : health economics in prevention and care · 2026
    Article
  12. Identifying and Deploying Benchmarks for Clinical Research at Sites.Therapeutic innovation & regulatory science · 2026
    Article
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  14. Article
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  18. In Silico Isomerization Produces Apt Negative Data for VHTS Validation.Journal of chemical information and modeling · 2026
    Article
  19. Review
  20. Review

59 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Aylin SertkayaEastern Research Group, Inc, Lexington, Massachusetts.
Trinidad BelecheOffice of the Assistant Secretary for Planning and Evaluation, Office of Science and Data Policy, US Department of Health and Human Services, Washington, DC.
Amber JessupOffice of the Assistant Secretary for Planning and Evaluation, Office of Science and Data Policy, US Department of Health and Human Services, Washington, DC.
Benjamin D SommersUS Department of Health and Human Services, Washington, DC.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Drug DevelopmentDrug CostsDrug IndustryHumansPharmaceutical ResearchUnited States

Identifiers

PMID38941099
PMCPMC11214120

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.