NIH funding for vaccine readiness before the COVID-19 pandemic

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3-8-2021

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

Anthony E. Kiszewski

Ekaterina Galkina Cleary

Matthew J. Jackson

Fred D. Ledley

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NIH funding for vaccine readiness before the COVID-19 pandemic

Anthony E. Kiszewski

, Ekaterina Galkina Cleary

b,c

, Matthew J. Jackson

a,b

Fred D. Ledley

a,b,d,1,

⇑

Department of Natural & Applied Sciences, Bentley University, Waltham, MA 02452, United States

Center for Integration of Science and Industry, Bentley University, Waltham, MA 02452, United States

Department of Mathematical Sciences, Bentley University, Waltham, MA 02452, United States

Department of Management, Bentley University, Waltham, MA 02452, United States

article info

Article history:

Received 17 December 2020

Received in revised form 26 February 2021

Accepted 4 March 2021

Available online 8 March 2021

Keywords:

NIH Funding

Vaccine

COVID-19

SARS-CoV-2

abstract

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.

Ó 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license

(http://creativecommons.org/licenses/by-nc-nd/4.0/).

1. Introduction

The COVID-19 pandemic has triggered a rapid mobilization of

global vaccine development. With infection fatality rates

approaching 1% [1], the prospect of long-term sequelae in those

who recover [2,3], and a high level of population immunity

required to halt transmission [4], initiatives have proceeded at

‘‘warp speed” [5,6]. The urgency of vaccine development necessi-

tated the application of existing vaccine technologies.

Within six months of the ﬁrst description of the SARS-CoV-2

virus [7], candidate vaccines employing many diverse methodolo-

gies entered development [8]. Some candidates incorporated

technologies that had already been validated in successful products.

Others incorporated technologies that had been previously shown

to generate immune responses against human coronaviruses

including Middle East Respiratory Syndrome virus (MERS-CoV) [9]

and SARS-CoV-1 [10]. Some candidates applied technologies from

registered veterinary vaccines against coronaviruses in domesti-

cated species, including (chicken) Infectious Bronchitis Virus (IBV)

[11] and bovine Betacoronavirus-1 [12]. Nascent technologies, such

as genetically modiﬁed viral vectors or mRNA also played an impor-

tant role, even though these technologies were not previously vali-

dated in clinical trials or registered products.

Rapid succe ss of these initiatives was not guaranteed. Despite

successful development of many vaccines, the challenge remained

daunting. As late as 2013, evidence showed that the failure rate for

vaccines entering development was as high as 94%, and that the

average time from preclinical studies to approval was 10.7 years [13].

Research on technological innovation has demonstrated that

the maturation level, or readiness, of many different technologies

has been a critical determinant in their ability to generate products

that satisfy market needs [14–19]. Using a bibliometric model for

the advance of basic biomedical research, we have found that few

targeted therapeutics are successfully developed before research

on both the drug target and therapeutic modality pass an analyti-

cally described established point, and that timelines of clinical

development are signiﬁcantly shorter when clinical trials com-

mence after this point [20,21]. Similarly, the maturation of vaccine

technologies is recognized to be a factor in the success of vaccine

development. For example, a model of vaccine development

https://doi.org/10.1016/j.vaccine.2021.03.022

0264-410X/Ó 2021 The Authors. Published by Elsevier Ltd.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Abbreviations: CEPI, Coalition for Epidemic Preparedness Innovations; MeSH,

Medical Subject Headings; WHO, World Health Organization; PMID, PubMed

Identiﬁers; TLR9, toll-like receptor 9; TIME, Technology Innovation Maturation

Evaluation (model); RePORT, (NIH) Research Portfolio Online Reporting Tools.

⇑

Corresponding author at: Center for Integration of Science and Industry, Bentley

University, 175 Forest Street, Waltham, MA 02452, United States.

E-mail address: ﬂ[email protected] (F.D. Ledley).

Address reprint requests to Dr. Fred D. Ledley at the Center for Integration of

Science and Industry, Bentley University, 175 Forest Street, Waltham, MA 02452,

United States.

Vaccine 39 (2021) 2458–2466

Contents lists available at ScienceDirect

Vaccine

journal homepage: www.elsevier.com/locate/vaccine

prepared for the Coalition for Epidemic Preparedness Innovations

(CEPI) posits that technologies with ‘‘no licensure track-record,”

have a substantially higher risk of failure than ‘‘well-established”

technologies [22]. This is consistent with the suggestion that the

high failure rate of vaccines in development prior to 2013 was

due, in part, to the large number of candidate vaccines using unpro-

ven, nucleotide (DNA or RNA) technologies [13].

Research on the basic science and technologies underlying new

drugs or vaccines, and the maturation of these technologies to the

point that they can support efﬁcient development, is funded pri-

marily by the public sector, principally governments. We have pre-

viously examined the scale of NIH funding for the basic research

and technologies underlying new drug approvals by identifying

NIH funding cited in published research papers [21,23]. These

studies show that the NIH contributed more than $170 billion for

research related to the 356 drugs approved from 2010–2019 or

their drug targets, with more than 85% of this funding involving

research on the drug targets rather than the drugs themselves.

We have also observed that the majority of this research is funded

through investigator-initiated research projects [21].

The present work examines the maturation of research on the

technologies being used in candidate COVID-19 vaccines as well

as the NIH funding supporting this research over the past twenty

years. Speciﬁcally, we examine published research on vaccines

incorporating attenuated or inactivated viruses, synthetic (recom-

binant) proteins, DNA or mRNA, or recombinant viral vectors as

well as research on formulations including conventional adjuvants,

virus-like particles, nanoparticles and toll-like receptor 9 (TLR9)

agonists. We also examine research and NIH funding for vaccines

aimed at coronaviruses and three unrelated viral pathogens that

have been associated with epidemic transmission: Zika, Ebola,

and dengue. We consider the impact of this prior research on accel-

erated efforts to develop a vaccine for COVID-19, and the impor-

tance of sustained public-sector funding in establishing a

foundation for responding to pandemic outbreaks.

2. Materials and methods

Technologies utilized in candidate vaccines against COVID-19

were identiﬁed from the World Health Organization (WHO)

‘‘DRAFT Landscape of Candidate COVID-19 vaccines” [24]. Viruses

with epidemic potential were identiﬁed from Plotkin [25] or the

WHO Blueprint [26].

Searches were performed using PubMed (www.pubmed.gov;

accessed June 3, 2020), with the updated Automatic Term Mapping

(May, 2020) optimized with Medical Subject Headings (MeSH)

terms or Boolean modiﬁers to increase speciﬁcity (Supplemental

Table 1). PubMed Identiﬁers (PMIDs) with their respective publica-

tion year were recorded.

PMIDs acknowledging NIH funding were identiﬁed in NIH

RePORTER data tables (https://exporter.nih.gov/ExPORTER_Cata-

log.aspx; accessed June 3, 2020) as described previously [23]. Each

PMID was associated with a project year corresponding to the pro-

ject number and year of publication using the ‘‘Link Tables for Pro-

ject to Publication Associations.” Costs (since 2000) were derived

from the ‘‘Project Data Table.” Publications occurring before the

ﬁrst year of the grant award or more than four years after the last

year of the grant were excluded. Publications 1–4 years after the

last year of the grant were associated with the project costs of

the last year. All values are described after eliminating duplicate

PMIDs, NIH-funded PMIDs, project years, and project costs arising

from the identiﬁcation of PMIDs in multiple searches, multiple

sources of funding for some PMIDs, and multiple PMIDs related

to many projects. ‘‘Unique” values across technologies are

described after eliminating duplicates across technologies. Activity

codes and the funding institute were determined from the project

codes. Grant categories were derived from ‘‘NIH Types of Grant

Programs 2020” (https://grants.nih.gov/grants/funding/funding_

program.htm; accessed May, 2020). Costs are given in constant

dollars inﬂation-adjusted to 2018 using the U.S. Bureau of Labor

Statistics All Urban Consumer Prices (Current Series) (https://

www.bls.gov/data/; accessed May, 2020). All analyses were per-

formed in PostgreSQL and Excel.

The bibliographic Technology Innovation Maturation Evaluation

(TIME) model assesses the maturation of a body of published

research by modeling the rate of research accumulation, as

described previously [20]

. The model quantiﬁes the ‘‘S-curve” of

technology maturation described in other sectors [14,15,18,19].

The TIME model ﬁts an exponentiated logistic function to the accu-

mulation of PubMed identiﬁed publications over time. The equa-

tion has the form:

N ¼ L

1þe

rðtt



where N is the cumulative number of publications, L is the upper

limit of publications, r is the growth rate, t is time, and t

is the mid-

point of exponential growth. This asymmetric, sigmoidal function

exhibits the common logistic sigmoid function (‘‘S-curve”) over

log scales. The established (Te) point is deﬁned as the point of min-

imum acceleration or logN’’(t)

min

. Results are visualized as annual

publications, cumulative publications, or log cumulative publica-

tions to assess the suitability of the calculated curve ﬁt.

3. Results

3.1. Research on vaccine technologies

As of July 31, 2020, the WHO listed 165 candidate vaccines

against COVID-19 [24]. PubMed searches were performed for ten

of the technologies utilized in this portfolio of products. The ten

technologies and the number of publications through 2019 are

shown in Table 1. The time course of publications on the ten tech-

nologies together is shown in Fig. 1A. The time course of publica-

tion on individual technologies is shown in the interactive

graphic at https://tabsoft.co/31EkYeK.

Technologies involving whole virus preparations, including live

attenuated and inactivated viral vaccines, have been widely used in

vaccines for polio, inﬂuenza, MMR, and other products since the

1950s. Research on these technologies has continued to accumu-

late since 1980 without evident acceleration or deceleration

(Fig. 1A). These technologies were used in four of the ﬁrst ten can-

didate vaccines to enter clinical trials against COVID-19, but only

12 (7.3%) candidate vaccines through July 31, 2020.

Synthetic vaccines incorporating recombinant proteins

emerged with biotechnology in the 1980s. Exponential growth of

research on synthetic vaccines was evident after 1985, but growth

has slowed since the late 1990s (Fig. 1A). While only one of the ﬁrst

ten candidate COVID-19 vaccines to enter clinical trials employed

synthetic vaccine technologies, they are used in 66 (40%) candidate

vaccines through July 31, 2020.

Vaccines employing recombinant viral vectors emerged in the

mid-1990s as an outgrowth of research on gene therapy. A period

of exponential growth was evident in the 1990s, followed by slow-

ing from the early 2000s to the present (Fig. 1A). Two of the ﬁrst

ten candidate COVID-19 vaccines in clinical trials used recombi-

nant adenoviral vectors. Overall, recombinant viral vectors are

employed in 31 (19%) candidate vaccines through July 2020.

DNA-based vaccine technologies also emerged in the mid-

1990s as an outgrowth of research on gene therapy and exhibited

a similar pattern of exponential advance and slowing. A related

A.E. Kiszewski, Ekaterina Galkina Cleary, M.J. Jackson et al.

Vaccine 39 (2021) 2458–2466

2459

technology involving administration of mRNA derives from meth-

ods for translating puriﬁed mRNA in Xenopus oocytes and evidence

that injection of puriﬁed viral mRNA into cultured cells could pro-

duce infectious virus [27]. These technologies have advanced sig-

niﬁcantly in recent years with the application of synthetic

nucleotides, self-replicating mRNA, and improved formulations.

One DNA vaccine and two mRNA vaccines were among the earliest

candidate vaccines to enter human trials. At the end of July 2020,

there were 36 (22%) nucleic acid-based vaccines in development,

including 14 DNA vaccines and 22 mRNA vaccines.

Several other vaccine technologies are shown in Fig. 1A includ-

ing the use of lipid nanoparticles or virus-like particles designed to

enhance delivery and antigenicity of recombinant proteins and the

incorporation of TLR9 agonists to stimulate speciﬁc immune path-

ways. These technologies were employed in 24 (15%) of the 165

candidate vaccines under development through July 2020.

Three technologies that originated in the 1950s, including live

attenuated virus, inactivated virus, and adjuvants, which have

been widely used in approved vaccines, have advanced continually

since 1980 without evident acceleration or deceleration (Fig. 1A).

The maturation of seven technologies emerging since 1980 was

examined using the TIME model (Fig. 1B). This model ﬁts the

cumulative number of publications to an exponentiated logistic

function and estimates the established (Te) point of this research

as the point of maximum deceleration (Fig. 1B) [20,21]. Curve ﬁts

for each technology are shown in Supplemental Figs. 1, 2, and 3.

Four technologies (synthetic, DNA, viral vector, and TLR9 ago-

nists) exhibited a logistic pattern of growth and had estimated estab-

lished points prior to 2010. Three technologies (virus-like particles,

mRNA, and nanoparticles) exhibited an exponential, rather than

logistic, pattern of growth (Fig. 1B, Supplemental Figs. 1, 2, and 3).

This is indicative of technologies that have not yet reached the estab-

lished point. Closer examination of mRNA technologies indicates

that the rate of advance has slowed, though the point of maximum

slowing (the established point) cannot be calculated with conﬁdence.

3.2. NIH funding for published research on vaccine technologies

The total numbers of PMIDs, number and percent of NIH-funded

PMIDs, project years, and project costs for ten vaccine technologies

are shown in Table 1. We identiﬁed 51,530 unique publications

related to these ten vaccine technologies, including 8,420 (16%)

acknowledging NIH funding. This funding comprised 16,358

unique project years and $17.2 billion in project costs. The largest

amount of funding was for synthetic (recombinant) vaccines ($9.65

billion) followed by adjuvants ($5.6 billion), DNA vaccines ($4.6

billion), and live attenuated virus ($4 billion). More than $1 billion

in NIH funding was associated with published research on inacti-

vated viral vector-based vaccines as well as TLR9 agonists as adju-

vants. More than $500 million was invested in mRNA vaccines,

virus-like particles, and nanoparticle-based vaccines.

The time course of research publications, NIH-funded research

publications, project years, and project costs for these ten vaccine

technologies together are shown in Fig. 2A–B. Data for each of the

technologies individually is shown in the interactive graphic at

https://tabsoft.co/31EkYeK.

Publication activity increased steadily from 1980 to 2010, with

the percent of publications acknowledging NIH funding rising from

4% in 1980 to 20% in 2010. After 2010, annual publications and the

percent of NIH-funded publications both decreased.

The type of research funding is shown in Fig. 3

A and Supple-

mental Table 2. While 40% of project years associated with

research on these technologies represented investigator-initiated

research projects, these accounted for only 8.9% of project costs.

A greater fraction of funding involved cooperative agreements

(44%), intramural programs (9.4%), and research program projects

and centers (22.7%). This pattern resembles that observed for the

foundational research underlying remdesivir [28], but differs shar-

ply from that for other drugs approved from 2010–2019, where the

majority of project years and costs involved investigator-initiated

research projects [21].

3.3. Research and NIH funding for diseases with pandemic potential

Table 1 shows the number of PMIDs, NIH-funded PMIDs, project

years, and project costs associated with vaccine development for

each of these pathogens as well as for HIV, which has been the sub-

ject of intensive vaccine research since the 1980s. Fig. 4A shows

the annual NIH project costs for Ebola, Zika, dengue, and coron-

avirus vaccines since 2000 compared to the project costs for

Table 1

Publications in PubMed (PMID) and NIH funding associated with research on technologies used in candidate COVID-19 vaccines as well as vaccine development for selected

epidemic threats.

PMID (1960–2019) NIH-funded PMID (1980–2019)

Project Years (1980–2019) Project Costs (2000–2019)

Selected vaccine technologies (unique)

51530 8420 (16%) 16358 $17,171

Synthetic Vaccines

21742 3935 (18%) 9755 $9653

Adjuvants, general 14347 2132 (15%) 5369 $5642

DNA (nucleic acid) vaccines

7621 1464 (19%) 3742 $4585

Live, Attenuated Virus 8147 1399 (17%) 3382 $4053

Viral vector-based 1191 379 (32%) 1010 $1651

Inactivated virus 5929 515 (8.7%) 1199 $1469

TLR9 agonists (adjuvant) 1227 353 (29%) 1082 $1096

mRNA vaccines 767 174 (23%) 534 $943

Virus-like particles 801 161 (20%) 418 $583

Nanoparticle-based 761 121 (16%) 334 $519

Vaccines against selected epidemic threats

HIV 5806 2024 (35%) 6684 $9184

Coronavirus 2435 388 (16%) 625 $767

Ebola 450 115 (26%) 294 $639

Zika 375 135 (36%) 390 $555

Dengue 725 110 (15%) 231 $331

Number of NIH-funded PMID and % of all PMID.

Costs given in millions of dollars inﬂation adjusted to 2018.

The ‘‘unique” number of PMID, NIH-funded PMID, Project Years, and Project Costs is greater than the sum of values for the individual technologies examined due to PMID

addressing multiple technologies, being identiﬁed in multiple searches or having multiple sources of funding as well as Projects that produce multiple PMID. Duplication

between technologies is eliminated in calculating ‘‘unique” values.

‘‘Synthetic vaccines” is a MeSH term describing vaccines incorporating recombinant protein antigens.

Search performed for DNA vaccines included many reports describing mRNA technologies.

A.E. Kiszewski, Ekaterina Galkina Cleary, M.J. Jackson et al. Vaccine 39 (2021) 2458–2466

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research on HIV vaccines. Fig. 4B shows the annual NIH project

costs for vaccine research on Ebola, Zika, dengue, and coron-

aviruses individually.

NIH funding for research on HIV vaccines increased more than

fourfold from 2000–2014, and has subsequently declined by half.

There was little NIH funding for research on Ebola, Zika, dengue,

or coronaviruses prior to 2014. NIH funding associated with publi-

cations on vaccines for Ebola virus exhibited a sharp peak in 2009,

a larger peak from 2013–2015, and another resurgence in 2019.

These peaks corresponded to a relatively small outbreak of Ebola

in 2007, the major outbreak in 2014, which had limited spread to

Europe and the US, and a recent event that began in 2018

(https://www.cdc.gov/vhf/ebola/history/chronology.html). A sin-

gle peak of NIH funding associated with publications related to

vaccines for Zika corresponds to the 2015–2017 outbreak in South

America and the Caribbean that also affected several US states. A

rise in funding associated with publications related to dengue

was apparent in 2016, but dropped rapidly commensurate with

the decline in reported cases in 2017 and 2018 (https://www.

who.int/news-room/fact-sheets/detail/dengue-and-severe-

dengue).

The pattern of NIH funding for published research on coron-

avirus vaccines shows a similar correspondence with outbreaks

of disease. NIH project costs associated with published research

on coronaviruses peaked in 2005, then dropped steadily to 2013.

A second peak of NIH funding associated with published research

on coronavirus vaccines was evident in association with the MERS

outbreak in 2012, peaking in 2015. Funding related explicitly to

coronavirus vaccines declined rapidly after 2015 as this threat

waned and other, more mediate threats, such as Zika, emerged.

Fig. 3B and Supplemental Table 2 show the activity code cate-

gories for the NIH projects associated with published research on

coronavirus vaccines. While the majority of project years were

associated with investigator-initiated research projects, only 14%

Fig. 1. Publications related to ten vaccine technologies used in candidate COVID-19 vaccines. A. Cumulative PMIDs from a PubMed search over time. Data is shown on a

logarithmic scale. B. Maturation of research using the TIME model. Cumulative publications are shown as symbols. Curves ﬁtting the (exponentiated logistic) TIME model are

shown as solid lines. The calculated established points for these technologies are shown in the box. Curves exhibiting exponential advance for technologies that have not yet

reached an established point (Te) are shown as dotted lines. Supplemental Figs. 1–3 provide additional curve ﬁts.

A.E. Kiszewski, Ekaterina Galkina Cleary, M.J. Jackson et al.

Vaccine 39 (2021) 2458–2466

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of project costs were associated with research projects, while 34%

went to cooperative agreements, 26% to intramural programs, and

22% to research program projects and centers.

4. Discussion

This analysis was undertaken in the context of evidence that

there is a 94% failure rate for vaccines entering development, aver-

age clinical development time of more than 10 years [13], the exi-

gency for rapid development of a COVID-19 vaccine [5,6], and

evidence that maturity of the underlying technologies is a signiﬁ-

cant factor in the efﬁciency of drug development [20,21]. Using

quantitative methods, we examined published research on ten

technologies being employed in candidate COVID-19 vaccines,

the maturity level, or readiness, of these technologies for clinical

development, and the NIH funding for this research through the

end of 2019. We also described NIH funding for vaccine develop-

ment directed towards speciﬁc zoonotic threats including coron-

avirus, Zika, Ebola, and dengue.

Some of the technologies incorporated in candidate COVID-19

vaccines (live attenuated or inactivated virus, non-speciﬁc adju-

vants) date from the mid-20

century and have been used to cre-

ate many successful vaccine products including vaccines for polio,

inﬂuenza, and childhood diseases. While we found that the

research literature on these technologies continues to grow, there

is no evidence of exponential acceleration since 1980, suggesting

that these technologies had become well-established before that

time.

The advance of research on synthetic (recombinant) vaccines,

DNA vaccines, viral vectors, and TLR9 agonist-based adjuvants

exhibited the characteristic ‘‘S-curve” pattern of technology

advances with estimated established points prior to 2010. In con-

trast, research on mRNA vaccines, virus-like particles, and

nanoparticles exhibited an exponential pattern of growth with

only early evidence of slowing, and the established point cannot

be estimated with conﬁdence.

It should be emphasized that the TIME model does not identify

individual publications or milestones in technology development.

Rather, the model posits that the complete body of published

research—including original insights or inventions, replication or

reﬁnement of previous observations, and/or refutation of erro-

neous results—contributes to the foundational knowledge required

for efﬁcient product development.

Studies of drug development demonstrate that few new drugs

were approved before research on the biological target or class of

drugs passed the established point [20,21,29]. Similarly, few

Fig. 2. NIH support for published research on ten vaccine technologies used in candidate COVID-19 vaccines. A. Annual PMIDs, NIH-funded PMIDs, and the fraction of PMIDs

receiving NIH support for vaccine technologies. B. Annual project years and project costs associated with NIH-funded PMIDs 2000–2019.

A.E. Kiszewski, Ekaterina Galkina Cleary, M.J. Jackson et al. Vaccine 39 (2021) 2458–2466

2462

products utilizing monoclonal antibodies [19], nucleotide therapeu-

tics [30], or gene therapies [31] were approved before research on

these modalities passed the established point. Evidence also shows

that the timelines of clinical development from Phase 1 to approval

is signiﬁcantly shorter when clinical trials commence after the

underlying technologies have passed this point [20,21]. While the

association between the point of maximum slowing of publication

activity (the established point) and increased efﬁciency of product

development is entirely empirical, it is likely that the slowing of

publication activity reﬂects a stage in the advance of research when

experiments are less likely to produce novel, publishable ﬁndings or

introduce unanticipated new areas of investigation. So too, clinical

investigations undertaken once the research has advanced to this

stage may be more likely to achieve the predicted outcomes.

It should be cautioned that, while the TIME model has been

extensively validated in studies of drug development, it has not

been previously applied to vaccines. Published literature on vac-

cine development, however, suggests that the maturation of tech-

nology is an important factor in vaccine development as well. The

success of the Salk vaccine was preceded by unsuccessful efforts to

develop products using analogous technologies, and the rollout of

the Salk vaccine itself was complicated by an outbreak of polio

caused by inadequate inactivation of early batches of the product

[32]. Similarly, early versions of vaccines for RSV and measles led

to enhanced disease on re-exposure [33]. More recently, the dra-

matic decline in the success rate for vaccine development from

2003–2013 has been ascribed, in part, to numerous failures of then

nascent DNA technologies [34]. In contrast, the remarkable safety

of vaccines currently approved by the FDA [35], as well as the rou-

tine development of annual vaccines for evolving strains of inﬂu-

enza virus, are consistent with the efﬁciency of established

vaccine technologies.

A relationship between technological maturity and the efﬁ-

ciency of vaccine development is incorporated in an economic

model of vaccine development prepared for CEPI. In this model,

vaccine technologies with no ‘‘licensure track-record” are assumed

to have a higher risk of vaccine failure and higher development

costs than ‘‘well-established” technologies [22]. While there is no

‘‘licensure track-record” for mRNA vaccines, this analysis suggests

that research on mRNA vaccine technologies is just now achieving

a level of technology maturation associated with product success.

The dramatic success of mRNA COVID-19 vaccines is also con-

sistent with research on innovation suggesting that technologies

which are not yet capable of meeting the performance standards

of established markets, often generate important products that

have a lower performance threshold [14,15,19]. In this context,

the fact that the level and duration of gene expression required

to stimulate an immune response may be lower than that required

to achieve sustained, therapeutic levels of a protein, as well as the

fact that clinical outcomes can be achieved without repetitive,

long-term administration of the product, may have contributed

to the early success of vaccines based on these technologies. It is

also noteworthy that mRNA vaccines were able to enter preclinical

and clinical development months earlier than vaccines using con-

ventional technologies because nucleotide products can be synthe-

sized, produced, and formulated using established, standard

methods. In contrast, candidate vaccines using live attenuated

virus, inactivated virus, or puriﬁed proteins required production

and puriﬁcation of virus or antigens with novel properties, requir-

ing custom processes for production and quality control.

Viral vector vaccines employing adenoviral vectors also pro-

gressed rapidly through production and preclinical testing using

standardized procedures. This progress was facilitated by the fact

that two MERS vaccines utilizing these technologies had been pre-

Fig. 3. NIH funding for published research on ten vaccine technologies and coronavirus vaccines by activity category. A. Number of project years and project costs for research

on ten vaccine technologies by activity category 2000–2019. B. Number of project years and project costs for research on coronavirus vaccines 2000–2019.

A.E. Kiszewski, Ekaterina Galkina Cleary, M.J. Jackson et al.

Vaccine 39 (2021) 2458–2466

2463

viously produced and tested in Phase 1 clinical trials, before the

waning of MERS precluded further testing [36,37], and these tech-

nologies have advanced past the analytically-deﬁned established

point estimated by the model. The cost of vaccine discovery and

development typically relies on both public and private sector

investments. A 1997 report by the National Vaccine Advisory Com-

mittee concluded that ‘‘two thirds of all new vaccines provided

worldwide have been produced by a US network of independent

industrial, governmental, and academic partners engaged in vac-

cine research and development” [38]. A 2001 report by the World

Bank, working with Gavi, the Vaccine Alliance, described the role of

public-sector institutions as being primarily focused on basic

science ‘‘measured by the number and value of the research manu-

scripts they publish in the scientiﬁc literature” [39,40]. In contrast,

industry focused on development, manufacture, and marketing of

vaccine products [39], and more than 90% of all vaccine doses

procured in the past ﬁve years have been manufactured by

for-proﬁt organizations [41].

A lack of sustainable investment is recognized as a limiting

factor in developing vaccines for pandemic threats, particularly

those initially impacting low-income countries [42]. Three of the

four speciﬁc diseases examined in this report (coronavirus, Zika,

and Ebola) were identiﬁed by the WHO as pandemic risks in the

2016 ‘‘R&D Blueprint for Action to prevent Epidemics” [26] and

by CEPI as priority epidemic infectious diseases [22]. Our analysis

describes a sporadic pattern of NIH funding related to these patho-

gens. In each case, we show that outbreaks of disease trigger an

increase in grant-funded publication activity, which then wanes

rapidly. The dramatic decrease in grant-funded publications related

to coronavirus vaccines after 2015, despite recognition of coron-

avirus as a pandemic threat in the 2016 WHO report, is particularly

striking. While this decrease may, in part, be due to the inability to

Fig. 4. NIH costs for published search on vaccines for HIV and selected zoonotic pandemic threats (coronavirus, Ebola, Zika, dengue) 2000–2019. A. Annual NIH costs

associated with published research on HIV vaccines compared to the cumulative total of research on coronavirus, Zika, Ebola, and dengue. B. Annual costs associated with

published research on coronavirus, Zika, Ebola, and dengue. Symbols indicate years of major outbreaks as well as the years of vaccine approval.

A.E. Kiszewski, Ekaterina Galkina Cleary, M.J. Jackson et al. Vaccine 39 (2021) 2458–2466

2464

conduct clinical trials of candidate products in the absence of active

human infections, and uncertainty regarding the strains that may

emerge in the future, mechanisms should be explored to sustain

research aimed at preventing recognized pandemic threats, rather

than simply responding to outbreaks of disease.

Our work also demonstrates that while the largest fractions of

PMIDs and project years represented investigator-initiated

research projects, a majority of the funding was associated with

cooperative agreements or intramural research, two mechanisms

for funding government-initiated research programs, or for

research capacity through research program projects and centers.

This pattern has been observed previously in examining the foun-

dational research underlying development of remdesivir [28], but

is distinctly different from NIH funding related to drugs in other

therapeutic areas, where the large majority of research funding

involves investigator-initiated research projects or research pro-

gram projects and centers [21]. The lack of investigator-initiated,

vaccine-focused research projects funded by the NIH is consistent

with the observation that traditional grant mechanisms may not

represent a robust mechanism for funding vaccine technologies

or development [42], and demonstrates the importance of contin-

ued strategic initiatives by governments or non-governmental

organizations.

Finally, this analysis emphasizes that NIH funding does not

guarantee successful vaccine development. Despite decades of

research and billions of dollars in NIH funding, there are no

approved vaccines for HIV. In contrast, the NIH contributed little

funding to the published research describing vaccines for Ebola

or dengue viruses prior to approval of these products.

4.1. Limitations of this research

There are several important limitations to this study. First, this

analysis is restricted to published research included in PubMed,

and may not capture reports, patents, regulatory ﬁlings, unpub-

lished clinical studies, or trade secrets. Also, this method is depen-

dent on the efﬁciency of PubMed searches, and may not identify

research without abstracts in PubMed or research that predates

emergence of standard vocabularies and might be missed by

search algorithms.

Second, it is not possible to associate costs with every NIH-

funded publication due to incomplete RePORTER data and incon-

sistencies between publication dates and project years. Moreover,

given an estimated lag of up to three years between research fund-

ing and publication [43], funding for research performed since

2018 may be underrepresented in this analysis.

Third, this analysis does not account for research funded by

other government agencies including the Department of Defense

or National Science Foundation, the $1.8 billion that the U.S. con-

tributed to Gavi since 2009 [44], or research funded by other gov-

ernments, philanthropies, academic institutions, or industry. It

should also be noted that this analysis does not include research

publications or funding in 2020 in response to the COVID-19

pandemic.

5. Conclusion

The Operation Warp Speed and Accelerating COVID-19 Thera-

peutic Interventions and Vaccines (ACTIV) initiatives leveraged a

variety of existing technologies in the race for a COVID-19 vaccine.

This work illustrates the importance of a broad foundation of basic

research over the past two decades, which advanced research on

vaccine technologies that could be rapidly deployed in the

response to the COVID-19 pandemic. To the extent that technolog-

ical maturity contributes to the efﬁciency of new product develop-

ment, NIH funding for this research in the years prior to the

pandemic played an important role in the speed and success of

COVID-19 vaccine development. This investment in basic research,

along with investments related to developing and procuring the

vaccine in response to the pandemic, should be considered in fully

assessing the public sector’s contribution to these products, their

price, and the distribution of proﬁts arising from their sale.

These ﬁndings also demonstrate the importance of sustained

public sector funding for foundational technologies in the ability

to respond rapidly to emerging public health threats. Signiﬁcantly,

the majority of the NIH funding identiﬁed in this study came in the

form of government-initiated cooperative agreements or intramu-

ral research, rather than investigator-initiated research projects.

Even so, we observed a lack of sustained research or funding

related to recognized, epidemic threats, including coronavirus,

over the past two decades. Further consideration needs to be given

to creating robust mechanisms for sustained funding of research

on vaccines for recognized pandemic threats to ensure a rapid

response to future outbreaks of disease.

6. Authorship

All authors attest they meet the ICMJE criteria for authorship.

7. Data sharing

Data used in this article are available from scholars.bentley.edu

(Digital Commons).

Declaration of Competing Interest

The authors declare the following ﬁnancial interests/personal

relationships which may be considered as potential competing

interests: Dr. Ledley is Principle Investigator of a grant from the

National Biomedical Research Foundation (a 501(c)(3) non-proﬁt)

to Bentley University.

Acknowledgements

This work was supported by a grant from the National Biomedical

Research Foundation, Waltham, MA, to Bentley University. The

funding source had no role in study design; in the collection, anal-

ysis and interpretation of data; in the writing of the report; or in

the decision to submit the article for publication.

The authors thank Prateet Shah for his contributions to this

manuscript as well as Drs. Michael Boss and Nancy Hsiung for their

constructive suggestions.

Appendix A. Supplementary data

Supplementary data to this article can be found online at

https://doi.org/10.1016/j.vaccine.2021.03.022.

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