Association between expedited review designations and the US or global burden of disease for drugs approved by the US Food and Drug Administration, 2010-2019: a cross-sectional analysis.

OBJECTIVES: Pharmaceutical innovation can contribute to reducing the burden of disease in human populations. This research asks whether products approved by the US Food and Drug Administration (FDA) from 2010 to 2019 and expedited review programmes incentivising development of products for serious disease were aligned with the US or global burden of disease. DESIGN: Cross-sectional study. OUTCOME MEASURES: Association of FDA product approvals (2010-2019), first approved indications, designations for expedited review with the burden of disease (disability-adjusted life years (DALYs)), years of life lost (YLL) and years of life lived with disability (YLD) for 122 WHO Global Health Estimates (GHE) conditions in US and global (ex-US) populations. RESULTS: The FDA approved 387 drugs in 2010-2019 with lead indications associated with 59/122 GHE conditions. Conditions with at least one new drug had greater US DALYs (p=0.001), US YLL (p<0.001), global DALYs (p=0.030) and global YLL (p=0.004) but not US YLD (p=0.158) or global YLD (p=0.676). Most approvals were for conditions in the top quartile of US DALYs or YLL, but <27% were for conditions in the top quartile of global DALYs or YLL. The likelihood of a drug having one or more designations for expedited review programmes was negatively associated (OR<1) with US DALYs, US YLD and global YLD. There was a weak negative association with global DALYs and a weak positive association (OR>1) with US and global YLL. CONCLUSIONS: FDA drug approvals from 2010 to 2019 were more strongly aligned with US than global disease burden. Designations for expedited review were not aligned with either the US or global burdens of disease and may inadvertently disincentivise development of products addressing global disease burdens. These results may inform policies to better align pharmaceutical innovation with the burdens of disease.

NIH funding for patents that contribute to market exclusivity of drugs approved 2010-2019 and the public interest protections of Bayh-Dole.

Previous studies have shown that National Institutes of Health (NIH) funding contributed >$187 billion for basic or applied research related to the 356 drugs approved 2010-2019. This analysis asks how much of this funding led to patents cited as providing market exclusivity, patents that would be subject to the provisions of the Bayh-Dole Act that promote and protect the public interest. The method involves identifying published research in PubMed related to the approved drugs (applied research) or their targets (basic research). NIH-funded projects (grants) funding these publications and patents arising from these projects were both identified in RePORT. Patents cited as providing market exclusivity were identified in DrugPatentWatch (which incorporates FDA Orange Book). NIH funded basic or applied research related to all 313 FDA-approved drugs 2010-2019 with at least one patent in DrugPatentWatch. This research comprised 350 thousand publications (9% applied research; 91% basic research) supported by 341 thousand fiscal years (project years) of NIH funding and $164 billion in NIH project year costs (17% applied research; 83% basic research). These NIH projects also produced 22,360 patents, 119 of which were cited in DrugPatentWatch as protecting 34/313 drugs. These patents were associated with 769 project years of NIH funding (0.23% total) and project year costs of $0.95 billion (0.59% total). Overall, only 1.5% of total NIH funding for applied research and 0.38% of total NIH funding for basic research was associated with patents in DrugPatentWatch. This analysis shows that very little of the NIH funding for research that contributes to new drug approvals leads to patents that provide market exclusivity and are subject to the provisions of the Bayh-Dole Act that promote the public interest in practical applications of the research, reasonable use and pricing, and a return on this public sector investment. This suggests that the Bayh-Dole Act is limited in its ability to protect the public interest in the pharmaceutical innovations driven by NIH-funded research.

Spending on Phased Clinical Development of Approved Drugs by the US National Institutes of Health Compared With Industry.

Importance: The launch of the Advanced Research Projects Agency for Health to advance new cures and address public concern regarding drug prices has raised questions about the roles of government and industry in drug development. Objectives: To compare National Institutes of Health (NIH) spending on phased clinical development of approved drugs with that by industry. Design: This cross-sectional study examined NIH funding for published research reporting the results of phased clinical trials of drugs approved between 2010 and 2019 and compared the findings with reported industry spending estimates. Data analysis was performed between May 2021 and August 2022 using PubMed data from January 1999 through October 2021 and NIH Research Portfolio Online Reporting Tools Expenditures and Results data from January 1999 through December 2020. Exposures: Drugs approved between 2010 and 2019. Main Outcome and Measures: National Institutes of Health funding for published research describing applied research on approved drugs, basic research on their biological targets, and phased clinical trials related to drugs approved between 2010 and 2019 were evaluated using Mann-Whitney U tests. All costs were inflation adjusted to 2018. Results: National Institutes of Health funding for basic or applied research related to 386 of 387 drugs approved between 2010 and 2019 totaled $247.3 billion. Of this amount, $8.1 billion (3.3%) was related to phased clinical development. This funding contributed to 12 340 publications on phased clinical trial results involving 240 of 387 (62.0%) drugs. Average NIH spending was $33.8 million per drug, including $13.9 million per drug for phase 1, $22.2 million per drug for phase 2, and $12.9 million per drug for phase 3 trials. Spending by NIH on phased development represented 9.8% to 10.7% of estimated industry spending, including 24.6% to 25.3% of estimated phase 1, 21.4% to 23.2% of phase 2, and 3.7% to 4.3% of phase 3 costs. Considering 60 products for which estimated industry costs were publicly available, NIH spending on clinical trials was significantly lower than estimated industry spending (sum of averages, $54.9 million per drug; mean difference, $326.0 million; 95% CI, $235.6-$416.4 million; 2-tailed paired t test P < .001). More than 90% of NIH funding came through cooperative agreements or program projects and centers, while 3.3% of NIH funding came through investigator-initiated research projects. Conclusions and Relevance: In this cross-sectional study, NIH funding for phased clinical development of drugs approved between 2010 and 2019 represented a small fraction of NIH spending on pharmaceutical innovation. This spending focused primarily on early-phase clinical trials and research capacity and was significantly less than estimated industry spending on clinical development. These results may inform the efficient allocation of government funding to advance pharmaceutical innovation.

Comparison of Research Spending on New Drug Approvals by the National Institutes of Health vs the Pharmaceutical Industry, 2010-2019.

Importance: Government and the pharmaceutical industry make substantive contributions to pharmaceutical innovation. This study compared the investments by the National Institutes of Health (NIH) and industry and estimated the cost basis for assessing the balance of social and private returns. Objectives: To compare NIH and industry investments in recent drug approvals. Design, Setting, and Participants: This cross-sectional study of NIH funding associated with drugs approved by the FDA from 2010 to 2019 was conducted from May 2020 to July 2022 and accounted for basic and applied research, failed clinical candidates, and discount rates for government spending compared with analogous estimates of industry investment. Main Outcomes and Measures: Costs from the NIH for research associated with drug approvals. Results: Funding from the NIH was contributed to 354 of 356 drugs (99.4%) approved from 2010 to 2019 totaling $187 billion, with a mean (SD) $1344.6 ($1433.1) million per target for basic research on drug targets and $51.8 ($96.8) million per drug for applied research on products. Including costs for failed clinical candidates, mean (SD) NIH costs were $1441.5 ($1372.0) million per approval or $1730.3 ($1657.6) million per approval, estimated with a 3% discount rate. The mean (SD) NIH spending was $2956.0 ($3106.3) million per approval with a 10.5% cost of capital, which estimates the cost savings to industry from NIH spending. Spending and approval by NIH for 81 first-to-target drugs was greater than reported industry spending on 63 drugs approved from 2010 to 2019 (difference, -$1998.4 million; 95% CI, -$3302.1 million to -$694.6 million; P = .003). Spending from the NIH was not less than industry spending considering clinical failures, a 3% discount rate for NIH spending, and a 10.5% cost of capital for the industry (difference, -$1435.3 million; 95% CI, -$3114.6 million to $244.0 million; P = .09) or when industry spending included prehuman research (difference, -$1394.8 million; 95% CI, -$3774.8 million to $985.2 million; P = .25). Accounting for spillovers of NIH-funded basic research on drug targets to multiple products, NIH costs were $711.3 million with a 3% discount rate, which was less than the range of reported industry costs with 10.5% cost of capital. Conclusions and Relevance: The results of this cross-sectional study found that NIH investment in drugs approved from 2010 to 2019 was not less than investment by the pharmaceutical industry, with comparable accounting for basic and applied research, failed clinical trials, and cost of capital or discount rates. The relative scale of NIH and industry investment may provide a cost basis for calibrating the balance of social and private returns from investments in pharmaceutical innovation.

Comparing the economic terms of biotechnology licenses from academic institutions with those between commercial firms.

Licenses of drug-related biotechnologies from academic institutions to commercial firms are intended to promote practical applications of public sector research and a return on government investments in biomedical science. This empirical study compares the economic terms of 239 biotechnology licenses from academic institutions to biotechnology companies with 916 comparable licenses between commercial firms. Academic licenses had lower effective royalty rates (median 3% versus 8%, p<0.001), deal size (median $0.9M versus $31.0M, p<0.001), and precommercial payments (median $1.1M versus $25.4M, p<0.001) than corporate licenses. Controlling for the clinical phase of the most advanced product included in the license reduced the median difference in effective royalty rate between academic and corporate licenses from 5% (95% CI 4.3-5.7) to 3% (95% C.I. 2.4-3.6) but did not change the difference in deal size or precommercial payments. Excluding licenses for co-commercialization did not change the effective royalty rate but reduced the median difference in deal size from $15.8M (95% CI 14.9-16.6) to $11.4M (95% CI 10.4-12.3) and precommercial payments from $9.0M (95% CI 8.0-10.0) to $7.6M (95% CI 6.8-8.4). Controlling for deal terms including exclusivity, equity, or R&D in multivariable regression had no substantive effect on the difference in economic terms. This analysis suggests the economic returns associated with biotechnology licenses from academic institutions are systematically lower than licenses between commercial firms and that this difference is only partially accounted for by differences in the intrinsic terms of the license agreements. These results are discussed in the context of a reasonable royalty rate, recognizing that factors extrinsic to the license agreement may reasonably impact the negotiated value of the license, as well as economic theories that view government as an early investor in innovation and technology licenses as a mechanism for achieving a return on investment.

NIH funding for vaccine readiness before the COVID-19 pandemic.

Rapid development of vaccines for COVID-19 has relied on the application of existing vaccine technologies. This work examines the maturity of ten technologies employed in candidate vaccines (as of July 2020) and NIH funding for published research on these technologies from 2000-2019. These technologies vary from established platforms, which have been used successfully in approved products, to emerging technologies with no prior clinical validation. A robust body of published research on vaccine technologies was supported by 16,358 fiscal years of NIH funding totaling $17.2 billion from 2000-2019. During this period, NIH funding for published vaccine research against specific pandemic threats such as coronavirus, Zika, Ebola, and dengue was not sustained. NIH funding contributed substantially to the advance of technologies available for rapid development of COVID-19 vaccines, suggesting the importance of sustained public sector funding for foundational technologies in the rapid response to emerging public health threats.

Comparing long-term value creation after biotech and non-biotech IPOs, 1997-2016.

We compared the financial performance of 319 BIOTECH companies focused on developing therapeutics with IPOs from 1997-2016, to that of paired, non-biotech CONTROL companies with concurrent IPO dates. BIOTECH companies had a distinctly different financial structure with high R&D expense, little revenue, and negative profits (losses), but a similar duration of listing on public markets and frequency of acquisitions. Through 2016, BIOTECH and CONTROL companies had equivalent growth in market cap and shareholder value (>$100 billion), but BIOTECH companies had lower net value creation ($93 billion vs $411 billion). Both cohorts exhibited a high-risk/high reward pattern of return, with the majority losing value, but many achieving growth multiples. While investments in biotechnology are often considered to be distinctively risky, we conclude that value creation by biotech companies after IPO resembles that of non-biotech companies at a similar stage and does not present a disproportionate investment risk.

Late-stage Product Development and Approvals by Biotechnology Companies After Initial Public Offering, 1997-2016.

PURPOSE: This work describes the late-stage product portfolios of the biotechnology companies that completed initial public offerings (IPOs) from 1997 to 2016. We asked whether these emerging companies continue to develop innovative, biologic products and produce the innovation promised by the early biotechnology industry. METHODS: We identified therapeutic products that reached Phase III development from 1997 to 2016, the characteristics of the products, the dates of the initiation of Phase III and product approval, proxy indicators of the innovativeness of each product, and the contribution of each biotechnology company. Companies were characterized by IPO window and clinical status of the most advanced product at IPO. Time from IPO to Phase III or approval, and the estimated probability of a company having a product advance to these milestones, were examined using Kaplan-Meier analysis. FINDINGS: A total of 319 biotechnology companies completed IPOs from 1997 to 2016. These companies contributed to the development of 367 products that progressed to Phase III, and of 144 new drug approvals, through 2016. The estimated probability of a company having a product reach Phase III was 78%, and the estimated probability of a company receiving at least 1 product approval was 52%, with most approvals occurring >5 years after IPO. Small-molecule drugs represented 74% of products reaching Phase III and 78% of approvals. Reformulations represented 36% of Phase III products and 46% of approvals. The estimated probability of product approval was significantly higher for reformulations than new molecular entities (NMEs) and slightly higher for small molecules than biologics. The estimated probability of a company receiving product approval varied significantly by IPO window and was greater for companies with Phase III products at IPO (74%). These companies contributed to the development of 78 NMEs, 44% of which were classified as first in class, initiating development of 69% and contributing to the clinical development of 96%. These products represented 16% of all NMEs and 28% of biologics approved between 1997 and 2016. Seven products achieved per-annum sales of >$1 billion during the study period. IMPLICATIONS: The majority of emerging publicly owned biotechnology companies contribute to products that advance to Phase III development and approval, although these companies are no longer distinctively focused on biologic products.

We Don't Have to Lose STEM Students to Business.

Most undergraduate students who leave STEM majors before graduation choose careers in business. This article argues that better integrating business opportunities and context into the STEM curriculum could advance STEM learning, motivate students to remain in STEM as majors, and cultivate a constructive relationship between business, science, and society.

Profitability of Large Pharmaceutical Companies Compared With Other Large Public Companies.

Importance: Understanding the profitability of pharmaceutical companies is essential to formulating evidence-based policies to reduce drug costs while maintaining the industry's ability to innovate and provide essential medicines. Objective: To compare the profitability of large pharmaceutical companies with other large companies. Design, Setting, and Participants: This cross-sectional study compared the annual profits of 35 large pharmaceutical companies with 357 companies in the S&P 500 Index from 2000 to 2018 using information from annual financial reports. A statistically significant differential profit margin favoring pharmaceutical companies was evidence of greater profitability. Exposures: Large pharmaceutical vs nonpharmaceutical companies. Main Outcomes and Measures: The main outcomes were revenue and 3 measures of annual profit: gross profit (revenue minus the cost of goods sold); earnings before interest, taxes, depreciation, and amortization (EBITDA; pretax profit from core business activities); and net income, also referred to as earnings (difference between all revenues and expenses). Profit measures are described as cumulative for all companies from 2000 to 2018 or annual profit as a fraction of revenue (margin). Results: From 2000 to 2018, 35 large pharmaceutical companies reported cumulative revenue of $11.5 trillion, gross profit of $8.6 trillion, EBITDA of $3.7 trillion, and net income of $1.9 trillion, while 357 S&P 500 companies reported cumulative revenue of $130.5 trillion, gross profit of $42.1 trillion, EBITDA of $22.8 trillion, and net income of $9.4 trillion. In bivariable regression models, the median annual profit margins of pharmaceutical companies were significantly greater than those of S&P 500 companies (gross profit margin: 76.5% vs 37.4%; difference, 39.1% [95% CI, 32.5%-45.7%]; P < .001; EBITDA margin: 29.4% vs 19%; difference, 10.4% [95% CI, 7.1%-13.7%]; P < .001; net income margin: 13.8% vs 7.7%; difference, 6.1% [95% CI, 2.5%-9.7%]; P < .001). The differences were smaller in regression models controlling for company size and year and when considering only companies reporting research and development expense (gross profit margin: difference, 30.5% [95% CI, 20.9%-40.1%]; P < .001; EBITDA margin: difference, 9.2% [95% CI, 5.2%-13.2%]; P < .001; net income margin: difference, 3.6% [95% CI, 0.011%-7.2%]; P = .05). Conclusions and Relevance: From 2000 to 2018, the profitability of large pharmaceutical companies was significantly greater than other large, public companies, but the difference was less pronounced when considering company size, year, or research and development expense. Data on the profitability of large pharmaceutical companies may be relevant to formulating evidence-based policies to make medicines more affordable.

Representation of Industry in Introductory Biology Textbooks: A Missed Opportunity to Advance STEM Learning.

The majority of students who enroll in undergraduate biology courses will eventually be employed in non-STEM (science, technology, engineering, and mathematics) business occupations. This work explores how representations of industry in undergraduate biology textbooks could impact STEM learning for these students and their ability to apply this learning in their chosen work. We used text analysis to identify passages with references to industry in 29 textbooks. Each passage was categorized for relevance to health or environment, for implied positive or negative connotations, and for descriptions of synergy or conflict between science and industry. We found few passages describing applications of STEM learning in non-STEM business occupations and a paucity of content to support context-based learning for students aiming at business careers. A significant number of passages embodied negative connotations regarding industry. Notable passages highlighted irregular or fraudulent business practices or included simplistic caricatures of business practice. We discuss how the representation of industry in these textbooks may impact student engagement, context-based learning, the ability of students to critically apply STEM learning in industry or business occupations, and heuristics that guide intuitive perceptions about the intersection between science and industry.

Contribution of NIH funding to new drug approvals 2010-2016.

This work examines the contribution of NIH funding to published research associated with 210 new molecular entities (NMEs) approved by the Food and Drug Administration from 2010-2016. We identified >2 million publications in PubMed related to the 210 NMEs (n = 131,092) or their 151 known biological targets (n = 1,966,281). Of these, >600,000 (29%) were associated with NIH-funded projects in RePORTER. This funding included >200,000 fiscal years of NIH project support (1985-2016) and project costs >$100 billion (2000-2016), representing ∼20% of the NIH budget over this period. NIH funding contributed to every one of the NMEs approved from 2010-2016 and was focused primarily on the drug targets rather than on the NMEs themselves. There were 84 first-in-class products approved in this interval, associated with >$64 billion of NIH-funded projects. The percentage of fiscal years of project funding identified through target searches, but not drug searches, was greater for NMEs discovered through targeted screening than through phenotypic methods (95% versus 82%). For targeted NMEs, funding related to targets preceded funding related to the NMEs, consistent with the expectation that basic research provides validated targets for targeted screening. This analysis, which captures basic research on biological targets as well as applied research on NMEs, suggests that the NIH contribution to research associated with new drug approvals is greater than previously appreciated and highlights the risk of reducing federal funding for basic biomedical research.

As Technologies for Nucleotide Therapeutics Mature, Products Emerge.

The long path from initial research on oligonucleotide therapies to approval of antisense products is not unfamiliar. This lag resembles those encountered with monoclonal antibodies, gene therapies, and many biological targets and is consistent with studies of innovation showing that technology maturation is a critical determinant of product success. We previously described an analytical model for the maturation of biomedical research, demonstrating that the efficiency of targeted and biological development is connected to metrics of technology growth. The present work applies this model to characterize the advance of oligonucleotide therapeutics. We show that recent oligonucleotide product approvals incorporate technologies and targets that are past the established point of technology growth, as do most of the oligonucleotide products currently in phase 3. Less mature oligonucleotide technologies, such as miRNAs and some novel gene targets, have not passed the established point and have not yielded products. This analysis shows that oligonucleotide product development has followed largely predictable patterns of innovation. While technology maturation alone does not ensure success, these data show that many oligonucleotide technologies are sufficiently mature to be considered part of the arsenal for therapeutic development. These results demonstrate the importance of technology assessment in strategic management of biomedical technologies.

Landscape of Innovation for Cardiovascular Pharmaceuticals: From Basic Science to New Molecular Entities.

PURPOSE: This study examines the complete timelines of translational science for new cardiovascular therapeutics from the initiation of basic research leading to identification of new drug targets through clinical development and US Food and Drug Administration (FDA) approval of new molecular entities (NMEs) based on this research. METHODS: This work extends previous studies by examining the association between the growth of research on drug targets and approval of NMEs associated with these targets. Drawing on research on innovation in other technology sectors, where technological maturity is an important determinant in the success or failure of new product development, an analytical model was used to characterize the growth of research related to the known targets for all 168 approved cardiovascular therapeutics. FINDINGS: Categorizing and mapping the technological maturity of cardiovascular therapeutics reveal that (1) there has been a distinct transition from phenotypic to targeted methods for drug discovery, (2) the durations of clinical and regulatory processes were significantly influenced by changes in FDA practice, and (3) the longest phase of the translational process was the time required for technology to advance from initiation of research to a statistically defined established point of technology maturation (mean, 30.8 years). IMPLICATIONS: This work reveals a normative association between metrics of research maturation and approval of new cardiovascular therapeutics and suggests strategies for advancing translational science by accelerating basic and applied research and improving the synchrony between the maturation of this research and drug development initiatives.

Timelines of translational science: From technology initiation to FDA approval.

While timelines for clinical development have been extensively studied, there is little data on the broader path from initiation of research on novel drug targets, to approval of drugs based on this research. We examined timelines of translational science for 138 drugs and biologicals approved by the FDA from 2010-2014 using an analytical model of technology maturation. Research on targets for 102 products exhibited a characteristic (S-curve) maturation pattern with exponential growth between statistically defined technology initiation and established points. The median initiation was 1974, with a median of 25 years to the established point, 28 years to first clinical trials, and 36 years to FDA approval. No products were approved before the established point, and development timelines were significantly longer when the clinical trials began before this point (11.5 vs 8.5 years, p<0.0005). Technological maturation represents the longest stage of translation, and significantly impacts the efficiency of drug development.

Modeling timelines for translational science in cancer; the impact of technological maturation.

This work examines translational science in cancer based on theories of innovation that posit a relationship between the maturation of technologies and their capacity to generate successful products. We examined the growth of technologies associated with 138 anticancer drugs using an analytical model that identifies the point of initiation of exponential growth and the point at which growth slows as the technology becomes established. Approval of targeted and biological products corresponded with technological maturation, with first approval averaging 14 years after the established point and 44 years after initiation of associated technologies. The lag in cancer drug approvals after the increases in cancer funding and dramatic scientific advances of the 1970s thus reflects predictable timelines of technology maturation. Analytical models of technological maturation may be used for technological forecasting to guide more efficient translation of scientific discoveries into cures.

Patterns of Innovation in Alzheimer's Disease Drug Development: A Strategic Assessment Based on Technological Maturity.

PURPOSE: This article examines the current status of translational science for Alzheimer's disease (AD) drug discovery by using an analytical model of technology maturation. Previous studies using this model have demonstrated that nascent scientific insights and inventions generate few successful leads or new products until achieving a requisite level of maturity. This article assessed whether recent failures and successes in AD research follow patterns of innovation observed in other sectors. METHODS: The bibliometric-based Technology Innovation Maturation Evaluation model was used to quantify the characteristic S-curve of growth for AD-related technologies, including acetylcholinesterase, N-methyl-d-aspartate (NMDA) receptors, B-amyloid, amyloid precursor protein, presenilin, amyloid precursor protein secretases, apolipoprotein E4, and transactive response DNA binding protein 43 kDa (TDP-43). This model quantifies the accumulation of knowledge as a metric for technological maturity, and it identifies the point of initiation of an exponential growth stage and the point at which growth slows as the technology is established. FINDINGS: In contrast to the long-established acetylcholinesterase and NMDA receptor technologies, we found that amyloid-related technologies reached the established point only after 2000, and that the more recent technologies (eg, TDP-43) have not yet approached this point. The first approvals for new molecular entities targeting acetylcholinesterase and the NMDA receptor occurred an average of 22 years after the respective technologies were established, with only memantine (which was phenotypically discovered) entering clinical trials before this point. In contrast, the 6 lead compounds targeting the formation of amyloid plaques that failed in Phase III trials between 2009 and 2014 all entered clinical trials before the respective target technologies were established. IMPLICATIONS: This analysis suggests that AD drug discovery has followed a predictable pattern of innovation in which technological maturity is an important determinant of success in development. Quantitative analysis indicates that the lag in emergence of new products, and the much-heralded clinical failures of recent years, should be viewed in the context of the ongoing maturation of AD-related technologies. Although these technologies were not sufficiently mature to generate successful products a decade ago, they may be now. Analytical models of translational science can inform basic and clinical research results as well as strategic development of new therapeutic products.

Positioning genomics in biology education: content mapping of undergraduate biology textbooks.

Biological thought increasingly recognizes the centrality of the genome in constituting and regulating processes ranging from cellular systems to ecology and evolution. In this paper, we ask whether genomics is similarly positioned as a core concept in the instructional sequence for undergraduate biology. Using quantitative methods, we analyzed the order in which core biological concepts were introduced in textbooks for first-year general and human biology. Statistical analysis was performed using self-organizing map algorithms and conventional methods to identify clusters of terms and their relative position in the books. General biology textbooks for both majors and nonmajors introduced genome-related content after text related to cell biology and biological chemistry, but before content describing higher-order biological processes. However, human biology textbooks most often introduced genomic content near the end of the books. These results suggest that genomics is not yet positioned as a core concept in commonly used textbooks for first-year biology and raises questions about whether such textbooks, or courses based on the outline of these textbooks, provide an appropriate foundation for understanding contemporary biological science.