Available via license: CC BY 4.0
Content may be subject to copyright.
Page 1/21
Factors affecting the innovativeness of new drugs:
Analysis of rst-in-class drugs
Soon Kyu Jung
Sungkyunkwan University
Dong-Wook Yang
Sungkyunkwan University
Sang-Won Lee
Sungkyunkwan University
Research Article
Keywords: rst-in-class, innovativeness, new molecular entities, biopharmaceutical industry, new drug,
strategy
Posted Date: May 14th, 2024
DOI: https://doi.org/10.21203/rs.3.rs-4382919/v1
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Read Full License
Additional Declarations: No competing interests reported.
Page 2/21
Abstract
Purpose:
Analyzing how the US Food and Drug Administration (FDA) designates rst-in-class drugs,
which is an indicator of the innovativeness of new drugs, is fundamental to promoting innovative
research in the pharmaceutical industry. In this study, we identify the factors that inuence the approval
of rst-in-class drugs by the US FDA
.
Methods
: Datasets of new drugs approved by the US FDA between 2010 and 2022 were constructed
using the EvaluatePharma database and Cortellis Competitive Intelligence. Using the set of drugs (n =
484) approved by the US FDA, we performed logistics regression analyses to identify the factors
associated with the innovativeness of the new molecular entities. Molecular type, orphan drug status,
disease, pharmacological action, drug target, rm size, country of developer, and drug development
strategy were used as independent variables in the analyses. In addition, we examined differences over
time.
Results
: We identied statistically signicant differences relating to antibody biologics, orphan drugs,
oncology drugs, protein targets, self-originated or in-licensed strategy of US companies, and the
acquisition strategy of non-US companies in the approval of rst-in-class drugs. These effects differed in
the two periods studied: after 2017 and before 2016.
Conclusion
: By identifying the factors that inuence the innovativeness of new drugs, pharmaceutical
companies can develop strategies for developing innovative new drugs, and policymakers can gain
insights into how to promote innovation in the pharmaceutical industry.
Introduction
Innovativeness in the pharmaceutical industry is pivotal for both public health policy and business
strategy. The development of more innovative drugs presents the chance to provide treatment for
patients with unmet medical needs; therefore, incentives for promoting innovative drugs are the primary
issue for policymakers [1].
Pharmaceutical companies are aware that the development of innovative drugs is crucial for increasing
sales and improving company recognition; therefore, understanding the factors that inuence drug
innovation is important because obtaining approval for a rst-in-class drug from the US FDA is critical to
a pharmaceutical company’s drug development strategy [2]. For example, a rst-in-class new drug that
meets an unmet medical need may be approved by the US FDA through an expedited program, such as a
fast-track review.
Despite the accepted importance of innovative drugs, their evaluation and classication is a
controversial topic. There are no universally accepted denitions because their value is focused on the
health outcomes for patients rather than the value of the product. In a systematic review of published
articles, more than ten denitions of innovation relating to new drugs were identied [3, 4]. Classication
Page 3/21
of the innovativeness of drugs varies for different studies. The classication of innovation depends on
the element of the drug on which it is based [5, 6].
Among the criteria for assessing a drug’s innovativeness, novelty is a primary indicator [7–9]. The US
Food and Drug Administration’s (FDA) classication of new molecular entities (NMEs) as rst-in-class is
a widely recognized measure of a drug’s novelty [10, 11]. The distinction between rst-in-class and
follow-on drugs is an important indicator of a drug’s novelty because rst-in-class drugs are closest to
innovative drugs in that they offer treatment for unmet medical needs.
Innovation or inuencing factors in the pharmaceutical industry from various perspectives has been
investigated through the analysis of drugs designated as rst-in-class by the US FDA. An analysis of rst-
in-class drugs designated by the US FDA from 1999 to 2008 revealed that 67% of these drugs were
small-molecule drugs and 33% were biologics [12]. The origins of rst-in-class drugs were studied by
investigating drugs approved by the US FDA from 1999 to 2013 [13]. Of the 113 new drugs approved
during that period, 78 (69.0%) were developed using target-based approaches. In an analysis of trends
regarding the introduction of new chemical entities worldwide, US rms surpassed their European
counterparts with respect to innovative performance or the introduction of rst-in-class drugs in
national-level analyses from 1993 to 2003 [14].
Miller et al. [15] analyzed US FDA NMEs from 1983 to 2014 and found that more than 50% of orphan
drugs were designated as rst-in-class drugs and were more innovative than nonorphan drugs. First-in-
class orphan drugs from 2011 to 2013 were also analyzed to determine their innovativeness. Greater
innovation is required to develop orphan drugs because most of them are rst-in-class drugs [16].
The aim of this study was to identify the multifaceted factors contributing to the innovativeness of new
pharmaceuticals, using multiple regression analysis to explore the factors that inuence the FDA’s rst-
in-class drug designations and the features of companies that successfully develop such drugs.
Analyzing how the US FDA designates rst-in-class new drugs, the factors inuencing this process, and
the characteristics of companies developing rst-in-class new drugs are fundamental to research on
innovative new drugs. Therefore, among the various factors affecting the development process, we
analyzed those that could be divided into multinomial classications. Factors affecting the
innovativeness of new drugs were analyzed at the product and company levels. In addition, we
investigated whether these characteristics changed over time. By examining these factors at both
product and company levels and assessing their evolution over time, we aim to more comprehensively
elucidate the drivers of pharmaceutical innovation.
Methods
Data Acquisition
In this study, the term “drugs” refers to new drugs approved by the US FDA between January 1, 2010 and
December 31, 2022. All products containing new compounds were included; however, products in the
Page 4/21
category of in vivo diagnostics were excluded from this analysis. In total, 484 NMEs were approved by
the US FDA during a 13-year period. The researchers obtained regulatory information for these drugs
from the US FDA website [17] and determined if they were rst-in-class and orphan drugs from the Novel
New Drugs or New Drug Therapy Approvals Report published by the US FDA [18]. The researchers also
identied the names of the companies that obtained approval from the US FDA website [19].
For information on each approved drug, we used the EvaluatePharma database [20] and Cortellis
Competitive Intelligence [21]. Using EvaluatePharma, we determined the technology and therapeutic
category data of the drug prole and classied the NMEs as oncology or non-oncology drugs. We
obtained data on the molecular type of the drugs and identied their pharmacological action, country the
developing company was located, and the development strategy of NMEs from the EvaluatePharma
database. Owing to the lack of data in EvaluatePharma, this study also collected data from Cortellis
Competitive Intelligence to assign the category of target-based actions of drugs.
Dependent Variable
The dependent variable in this study was whether drugs approved by the US FDA were included in the
rst-in-class group. This is because “rst-in-class” is used as a universal indicator that reects the
innovativeness of drugs. The number and rate of US FDA’s rst-in-class NMEs between 2010 and 2022
are presented in Fig.1.
Independent Variables
To determine the factors affecting the innovativeness of new drugs, we collected the following
information: the status of orphan drug designation by the US FDA; the molecule type; the approved
indication for oncology; pharmacological action; and the target of the drug in the body. In addition, to
identify the factors that inuenced rst-in-class development in terms of companies, the following
information was obtained: rm size; country of drug developer; and drug development strategy.
Molecule Type: Each new drug was classied as one of three types: chemical, biologics (non-antibody),
and antibody biologics. Biologics (non-antibody) included DNA and RNA therapeutics, protein and
peptide therapeutics, and plant extracts. Antibody biologics included monoclonal and recombinant
antibodies.
Orphan Drug: The drugs were classied as one of two types according to the US FDA designation:
orphan and non-orphan drugs.
Disease: The drugs were classied into two types according to their therapeutic indications: oncology
and non-oncology.
Pharmacological Action: The drugs were categorized according to their pharmacological actions:
activators and suppressors among others. Activators of a drug’s pharmacological action are substances
that increase the effect of a drug by binding to a specic receptor or enzyme [22]. In this study,
Page 5/21
stimulators and agonists were classied as activators. Suppressors (substances that prevent drugs from
binding to targets, thereby reducing the effectiveness of drugs) included inhibitors and antagonists.
Drug Target: Drug targets are biological molecules involved in the pathogenesis of a disease and can be
affected by a drug. The target of a drug, which refers to the site where the drug acts, can be classied
into different categories based on their structure and function, such as ligands, membrane transporters,
and ion channels [23]. In this study, drugs were classied into ve categories: proteins; ion channels;
ligands; receptors; and others. Enzymes were classied as proteins. The others category includes
transporters and unknown targets.
Firm Size
Companies were classied as either “Top 20” or “Others” based on their annual sales from 2010 to 2022
as described by Kim et al. [24] and Lanthier et al. [5]. The top 20 companies are those commonly referred
to as “big pharma.” According to this classication, the top 20 companies were Pzer (including Wyeth),
Novartis, Roche (including Genentech), Merck & Co, Sano, Johnson & Johnson (including Janssen),
Gilead Sciences, GlaxoSmithKline, AbbVie (including Abbott), Teva, Amgen, AstraZeneca, Bayer, Eli Lilly,
BMS, Novo Nordisk, Takeda, Allergan (including Actavis), Boehringer Ingelheim, and Astellas.
Country and Strategy
In this study, two independent variables (country and drug development strategy) were combined to
investigate differences arising from countries and strategies for developing new drugs. Therefore, this
variable was divided into six categories.
Country
The country of each drug developer was classied as US or non-US. For multinational companies,
classication was based on whether their headquarters were in the US. The criterion applied for the
company’s headquarters was the data from Cortellis Competitive Intelligence [25]. The developer of each
product was based on the US FDA applicant and the information on the approval letter on the US FDA
homepage.
Drug Development Strategy: The drug development strategy for new drugs was divided into three types:
self-originated; in-licensed; and acquired. A self-originated strategy is one where the originator develops
their own candidate internally.
Statistical Analysis
From the set of drugs approved by the US FDA (n = 484), we performed multiple logistic regression
analyses to identify the factors associated with the innovativeness of NMEs. Dummy variables were
created for all categorical variables. This logistic regression model was constructed by excluding
variables with variance ination factors (VIF) of 10 or more through multicollinearity analysis. All
Page 6/21
statistical analyses were performed using Stata 16 (StataCorp, College Station, TX, USA), with statistical
signicance forest at
p
values of
<
0.05.
(Insert Fig.1 Here)
Results
The number of observations and descriptive statistics for each dataset are presented in Tables1 and
Fig.2, respectively.
Page 7/21
Table 1
Descriptive statistics of variables in each dataset
Variable Category First-in-class
drugs Follow-on
drugs Total
N(%) N(%) N
Molecule Type Chemical 113 (28.3) 216 (12.6) 329
Biologics (Non-
antibody) 29 (14.6) 34 (11.9) 63
Antibody Biologics 56 (57.1) 36 (75.5) 92
Orphan Drug Yes 119 (60.1) 105 (36.7) 224
No 79 (39.9) 181 (63.3) 260
Disease Oncology 54 (27.3) 89 (31.1) 143
Non-oncology 144 (72.7) 197 (68.9) 341
Pharmacological
Action Activator 39 (19.7) 59 (20.6) 98
Suppressor 133 (67.2) 189 (66.1) 322
Others 26 (13.1) 38 (13.3) 64
Drug Target Protein 98 (49.5) 113 (39.5) 211
Ion channel 5 (0.5) 10 (3.5) 15
Ligand 11 (5.6) 20 (7.0) 31
Receptor 67 (33.8) 110 (38.5) 177
Others 17 (8.6) 33 (11.5) 50
Firm Size Top 20 82 (41.4) 114 (39.9) 196
Others 116 (58.6) 172 (60.1) 288
Country US 132 (66.7) 157 (54.9) 289
Non-US 66 (33.3) 129 (45.1) 195
Development
Strategy
In-licensed 64 (32.3) 98 (34.3) 162
Acquisition 48 (24.3) 55 (19.2) 103
Self-originated 86 (43.4) 133 (46.5) 219
(Insert Table1 Here)
(Insert Fig.2 Here)
Page 8/21
Among the 484 NMEs from 2010 to 2022, 329 were chemicals and 113 were rst-in-class drugs. Of the
63 biologics (excluding antibody biologics), 29 were rst-in-class. Of the 92 antibody biologics, 56
(60.9%) were rst-in-class. Among the rst-in-class drugs, 56 (28.3%) were antibody biologics, exceeding
the 36 (12.6%) antibody biologics among follow-on drugs.
Of the 484 NMEs, 224 were orphan drugs. Among rst-in-class drugs, 119 (60.1%) were orphan drugs,
exceeding the 105 (36.7%) orphan drugs among follow-on drugs. There were 143 oncology drugs.
Among the rst-in-class drugs, 54 (27.3%) were oncology drugs and 114 (72.7%) were non-oncology
drugs. Regarding drug targets, 211 classied as were proteins, 177 as receptors, 31 as ligands, 15 as ion
channels, and 50 as others. Among the drug targets for rst-in-class drugs, 98 (49.5%) were categorized
as proteins, 67 (33.8%) as receptors, 11 (5.6%) as ligands, 5 (0.5%) as ion channels, and 17 (8.6%) as
others.
From 2010 to 2022, of the 484 NME license granted by the US FDA, 289 were for US companies and 195
were for non-US companies, based on their headquarters. US companies accounted for 66.7% (132) of
the rst-in-class drugs and 54.9% (157) of the approved follow-on drugs. In the analysis of the
combination of country and development strategy, the 123 US and self-originated NMEs comprised 60
(48.8%) rst-in-class drugs and the 96 non-US and self-originated NMEs comprised 26 (27.1%) rst-in-
class drugs.
A model that affects the approval of rst-in-class drugs from the US FDA was constructed and analyzed
using Stata 16 and logistic regression. Each analysis yielded a standard error (SE) that was robust to
heteroscedasticity. In addition, the VIF was calculated to conrm multicollinearity; this was not a concern
for any model.
(Insert Table2 Here)
Page 9/21
Table 2
Results of logistic regression analysis
Category OR Robust SE 95% CI
P
-value
Molecule Type
Chemical Reference
Biologics (Non-antibody) 1.173 0.390 0.612–2.250 0.630
Antibody Biologics 3.084 0.924 1.715–5.548 0.000**
Orphan Drug
No Reference
Yes 3.581 0.850 2.249–5.701 0.000**
Disease
Non-oncology Reference
Oncology 0.359 0.100 0.208–0.619 0.000**
Pharmacological Action
Others Reference
Activatorsa0.869 0.342 0.402–1.879 0.722
Suppressorsb0.904 0.303 0.469–1.745 0.764
Drug Target
Others Reference
Protein 2.262 0.920 1.020–5.018 0.045*
Ion Channel 1.151 0.840 0.275–4.816 0.847
Ligand 1.126 0.639 0.370–3.427 0.834
Receptor 1.961 0.823 0.861–4.465 0.109
Firm Size
Others Reference
aAgonist and stimulator.
bAntagonist and inhibitor.
*
p
< 0.05, **
p
< 0.01.
Page 10/21
Category OR Robust SE 95% CI
P
-value
Top 20 1.232 0.274 0.796–1.905 0.349
Strategy and Country
Non-US & Self-originated Reference
Non-US & In-licensed 1.118 0.447 0.510–2.449 0.780
Non-US & Acquisition 2.260 0.901 1.035–4.938 0.041*
US & Self-originated 2.680 0.822 1.469–4.889 0.001*
US & In-Licensed 1.996 0.637 1.068–3.731 0.030*
US & Acquisition 2.118 0.823 0.988–4.538 0.054
Constant 0.118 0.049 0.052–0.266 0.000
Pseudo R20.1155
Mean VIF 1.87
Hosmer–Lemeshow x2 = 5.18 (
p
= 0.738)
aAgonist and stimulator.
bAntagonist and inhibitor.
*
p
< 0.05, **
p
< 0.01.
The results of logistic regression showed that antibody biologics (odds ratio (OR) = 3.084,
p
< 0.01),
orphan drugs (OR = 3.581,
p
< 0.01), oncology drugs (OR = 0.359, p < 0.01), protein targets (OR = 2.262,
p
< 0.05), self-originated strategy by US companies (OR = 2.680,
p
< 0.01), in-licensed by US companies
(OR = 1.996,
p
< 0.05), and acquisition strategy by non-US companies (OR = 2.260,
p
< 0.05) were
statistically signicant factors in the FDA’s approval of rst-in-class drugs (Table2). The results indicate
that the probability of the approval of rst-in-class drugs was 3.084 times higher for antibody biologics
than that for chemical drugs, that for orphan drugs was 3.581 times higher than that for nonorphan
drugs, and that for oncology drugs was 0.359 times lower than that for non-oncology drugs. The
probability of the approval of rst-in-class new drugs that targeted proteins was 2.262 times higher than
that of other targets.
The probability of the approval of rst-in-class of new drugs developed through self-originated strategy
by US pharmaceutical companies, in-licensed strategy by US companies, and acquisition strategy by non-
US companies is 2.680, 1.996, and 2.260 higher, respectively, than those developed through the self-
originated strategy by non-US companies. The probability of the rst-in-class approval of new drugs
developing other variables was not observed to have a signicant effect.
Page 11/21
Factors that inuenced the US FDA approval of rst-in-class drugs were analyzed using logistic
regression in two different periods: 2011–2016 and 2017–2022 (Table3). To perform data analysis over
a 6-year period, data from 2010 were excluded. We divided the time frames into the two periods around
2016 because of certain changes in the US FDA in 2016. In 2016, the US FDA’s approval of new drugs
was the lowest since 2007; on 13 December 2016, the 21st Century Cure Act was signed into law, and
included measures intended to streamline drug approvals.
Page 12/21
Table 3
Results of logistic regression analysis for 2011–2016 vs. 2017–2022
2011–2016 2017–2022
Category OR (95% CI) P-value OR (95% CI) P-value
Molecule Type
Chemical Reference
Biologics (Non-antibody) 2.370 (0.691–8.126) 0.170 1.002 (0.404–2.486) 0.996
Antibody Biologics 3.138 (1.005–9.799) 0.049*3.123 (1.447–6.742) 0.004**
Orphan Drug
No Reference
Yes 3.252 (1.377–7.677) 0.007** 3.997 (2.103–7.597) 0.000**
Disease
Non-oncology Reference
Oncology 0.424 (0.156–1.155) 0.093 0.279 (0.135–0.576) 0.001**
Action
Others Reference
Activatorsa1.398 (0.397–4.923) 0.602 0.799 (0.266–2.402) 0.689
Suppressorsb1.957 (0.662–5.788) 0.225 0.597 (0.235–1.515) 0.277
Drug Target
Others Reference
Protein 0.901 (0.271–2.992) 0.864 4.444 (1.382–14.292) 0.012*
Ion Channel 2.667 (0.526–13.512) 0.236 1.000 (omitted)
Ligand 0.190 (0.029–1.242) 0.083 3.530 (0.765–16.287) 0.106
Receptor 0.946 (0.267–3.358) 0.932 2.952 (0.901–9.670) 0.074
Firm Size
aAgonist and stimulator.
bAntagonist and inhibitor.
*
p
< 0.05, **
p
< 0.01.
Page 13/21
2011–2016 2017–2022
Others Reference
Top 20 1.197 (0.568–2.523) 0.636 1.133 (0.609–2.109) 0.693
Strategy and Country
Non-US & Self-originated Reference
Non-US & In-licensed 1.430 (0.384–5.325) 0.594 1.146 (0.397–3.311) 0.801
Non-US & Acquisition 2.641 (0.602–11.586) 0.198 3.669 (1.165–11.562) 0.026*
US & Self-originated 2.051 (0.757–5.561) 0.158 4.090 (1.667–10.032) 0.002**
US & In-Licensed 1.899 (0.700–5.147) 0.208 2.341 (0.916–5.980) 0.075
US & Acquisition 2.646 (0.754–9.287) 0.129 1.731 (0.629–4.762) 0.288
Constant 0.141 0.003 0.082 0.000
Pseudo R20.1216 0.1540
Mean VIF 2.10 1.87
Hosmer–Lemeshow x2 = 7.34 (
p
= 0.501) x2 = 6.37 (
p
= 0.606)
aAgonist and stimulator.
bAntagonist and inhibitor.
*
p
< 0.05, **
p
< 0.01.
(Insert Table3 Here)
Following the test of the signicance of the regression coecient, antibody biologics were observed to
have a high probability of approval with an OR = 3.138 (
p
< 0.05) and 3.123 (
p
< 0.01). In addition, in both
periods, the probability of the rst-in-class approval of orphan drugs was high at OR = 3.252, 3.997, and
signicant at
p
< 0.01. Whether the new drugs were oncology drugs was not signicant from 2011 to
2016, but was signicant from 2017 to 2022 (OR = 0.279,
p
< 0.01). This means that over time, new
anticancer drugs might have a negative impact on rst-in-class designation. The selection of the drug
target as protein was not signicant between 2011 and 2016, but was signicant (OR = 4.444,
p
< 0.05)
between 2016 and 2022. Recently, protein targets have been considered as factors that inuence
innovative drugs.
For the corporation-based variable, the country of the applicant and development strategy of new drugs
changed over time. The development of new drugs by US pharmaceutical companies through the self-
originated strategy and by non-US companies through acquisition strategy were signicant between
Page 14/21
2017 and 2022, with OR = 3.669 (
p
< 0.05) and 4.090 (
p
< 0.01), respectively, but not between 2011 and
2016. From the above results, the factors inuencing the rst-in-class drugs approved by the US FDA
have changed over time.
Discussion
In this study, we found that antibody biologics and protein target drugs had a statistically signicant
effect on the approval prospects of rst-in-class drugs by the US FDA. These results have important
implications for pharmaceutical companies looking to develop innovative new drugs because they
facilitate the determination of whether their new drug candidates fall into these categories and therefore
gauge the innovativeness of the drug. Therefore, understanding the global trends around where new
drugs are being recognized for innovativeness is important.
Since 2010, innovation in the pharmaceutical industry has been driven by antibody biologics, and the
study of new drugs has been an active research area. Following the approval of BMS’s immune
checkpoint inhibitor Yervoy [26] in 2011, numerous innovative antibody biologics have since been
approved and marketed in the US. Representative antibody biologics include MSD’s Keytruda [27],
Amgen’s Blincyto [28], and Janssen’s Rybrevant [29]. Despite these advances, antibody biologics are one
of the fastest growing therapies in the world, driven by the maturation of emerging markets such as
China. In particular, approvals for antibody–drug conjugates (ADCs) and bispecic antibodies have
recently increased, with 11 of the 17 approved ADCs and 5 of the 9 approved bispecic antibodies having
been approved by the US FDA since 2017 [30].
In addition, the development of new drugs targeting a protein had a positive impact on rst-in-class
approval. Potential protein drug targets are linked to disease processes; however, the number of proteins
that modern pharmaceuticals act on and that of potentially druggable proteins are unknown [31].
Therefore, protein-targeted new drugs can be regarded as novel.
Our ndings suggest that orphan drugs were also a crucial factor in rst-in-class drugs because they
reect innovation in the sense of addressing unmet medical needs. Our result is consistent with that of
Miller et al. [15], who stated that orphan drugs are more innovative than nonorphan drugs. Although
orphan drugs are inaccessible to small and medium-sized pharmaceutical companies (i.e., a company
outside of the top 20) owing to the limited market and high risk associated with orphan drug
development, the Orphan Drug Act has been credited with stimulating the development of innovative
drugs for rare diseases. In our analysis, small and medium-sized companies developed 66.5% of the
approved orphan NMEs, and 53.5% of nonorphan drug NMEs. This indicates increased engagement of
small- and medium-sized companies in orphan drug development. Therefore, small- and medium-sized
companies looking to develop innovative new drugs may have the opportunity to take advantage of the
various supportive policies and armative programs offered by the US government under the Orphan
Drug Act [32].
Page 15/21
The high level of innovation associated with orphan drugs has important implications for policymakers.
Although orphan drug approval by the US FDA is not directly linked to new drug applications, orphan
drugs are more likely to be included in expedited programs. In our study, 60.7% and 49.1% of the 224
orphan drugs received priority review and fast-track review, respectively, compared with 55.0% and
36.8%, respectively, for all NMEs. In addition, 68.6% of orphan drugs approved between 2017 and 2022
were designated for priority review, exceeding those approved between 2011 and 2016 (44.7%). This
suggests that the US FDA recognizes that orphan drugs result in signicant therapeutic improvement
and is willing to expedite the regulatory review period to accelerate the approval pathway.
Conversely, the development of oncology drugs is not a good strategy for obtaining rst-in-class drug
approval. Because novelty is considered crucial in innovative new drugs, it is dicult to consider a drug
innovative when numerous cancer medicines have already been introduced to the market. When
developing oncology drugs, clear advantages, in terms of drug innovation, compared with products
already available on the market or drugs in development by other companies, is important. For example,
over the past decade, targeted drug agents for the treatment of leukemia have been actively developed,
and innovation was recognized in the early stages. However, none of the three treatments for leukemia
developed after 2020 were included in the rst-in-class category [33]. The approval of rst-in-class drugs
is important for pharmaceutical companies to increase the value of new drugs in development because
reimbursement pressures have increased for drugs that are no more innovative than existing medicines
[34]. Lanthier et al. [35] found that nearly two-thirds of rst-in-class drugs do not face a subsequent
follow-on drug because of increasing reimbursement pressure on subsequent products without clear
advantages. This means that the development of follow-on drugs, which lack advantages relative to
other available treatments, is not attractive for established drug classes such as oncology. Finding a less
competitive market, where receiving reimbursement for new treatments may be easier, is necessary for
innovative new drug development.
In addition, we found that strategies for developing rst-in-class new drugs should differ between
companies outside the US and those in the US. New drugs developed by US pharmaceutical companies
using a self-originated or in-licensed strategy were statistically signicantly positively associated with
the approval of rst-in-class drugs because US pharmaceutical companies have a high capacity for the
in-house R&D of innovative new drugs and are active in introducing new drug candidates through
technology deals. Conversely, new drugs developed by non-US companies using an acquisition strategy
were relatively innovative; therefore, non-US companies appear to be advantageous for the development
of innovative new drugs by acquiring already-developed drugs or companies. This is an important insight
for policymakers in non-US governments looking to stimulate their pharmaceutical industries. They
should consider policies that enable their companies to use acquisition strategies to develop innovative
new drugs.
Finally, our analysis highlights how the factors that inuenced the US FDA’s approval of rst-in-class new
drugs have changed over time. We observed that oncology drugs or protein-targeted drugs were
statistically signicant between 2017 and 2022 but not between 2011 and 2016. Oncology drugs were a
Page 16/21
negative factor from 2017 to 2022, but not from 2011 to 2016. This implies that the factors affecting the
innovativeness of new drugs approved by the US FDA since 2017 are different than before, and that
these factors have been relatively inuential in recent years. In this sense, pharmaceutical companies
and regulatory agencies should be proactive in identifying future emerging technologies that may impact
drug development and prepare to incorporate their innovativeness into the regulatory process.
The following limitations of this study should be considered. First, while the US FDA’s rst-in-class
designation is a representative indicator of the innovation of new drugs, it is not the only acceptable
indicator in all studies. The determination of new drug innovation may vary depending on the denition
and criteria used to evaluate it. The aim of logistic regression analysis is to identify the most appropriate
model for the relationship between one dependent variable and several independent variables. Therefore,
the interpretation of statistical analysis can only explain the high or low probability of variable occurring.
In this study, the setting of independent variables was limited to the object of interest for the
researchers; therefore, the factors inuencing the development of innovative new drugs could not be
reected in depth.
Second, this is essentially a retrospective analysis and cannot predict or recommend the future
innovativeness of drugs. Moreover, because the innovativeness of new drugs changes depending on
various factors, including technological advancements, regulatory environment, and society. Therefore,
an analysis focusing solely on drug characteristics and corporate factors faces inherent limitations in
predicting future trends. Nonetheless, this study is noteworthy as it explores the factors that inuence
drug innovation by identifying the changes in the US FDA’s rst-in-class designations.
Conclusions
In this study, the factors affecting the development of rst-in-class new drugs were identied by
analyzing US FDA NME data. The factors inuencing the granting of rst-in-class status by the US FDA
were identied through the logistic regression of seven independent variables. Three results were
identied and discussed.
First, antibody biologics, orphan drugs, oncology drugs, and protein targets were found to be the drugs
types with statistically signicant prospects of receiving rst-in-class drugs approval at the product level.
Second, non-US pharmaceutical companies using acquisition strategies statistically signicantly
affected rst-in-class designation at the company level. This implies that companies outside the US and
those in the US should have different strategies for developing rst-in-class new drugs. Finally, the
factors inuencing the FDA rst-in-class drugs changed over time. Pharmaceutical companies engaged
in developing innovative new drugs will need to monitor the changing trends related to the
innovativeness of drugs.
Declarations
Page 17/21
Conict of interest
The author(s) declared no potential conicts of interest with respect to the research, authorship, and/or
publication of this article.
Funding
The author(s) received no nancial support of the research, authorship, and/or publication of this article.
Author Contribution
SW Lee devised the research project, the main conceptual ideas, and the proof outline. SK Jung worked
out the technical details, the data acquisition for the statistical analysis, drafted the manuscript, and
designed the gures for the suggested project, and DW Yang worked out the statistical analysis and
interpreted the results. All authors reviewed and approved the nal version of the manuscript.
Acknowledgement
The authors are grateful to comments and suggestions received by the researchers of the Center of
Health Industry Policy, the Korea Health Industry Development Institute
References
1. Arnold D, Troyer J. Does increased spending on pharmaceutical marketing inhibit pioneering
innovation? J Health Polit Policy Law. 2016;41(2):157–79. https://doi.org/10.1215/03616878-
3476093.
2. Dunlap-Hinkler D, Kotabe M, Mudambi R. A story of breakthrough versus incremental innovation:
corporate entrepreneurship in the global pharmaceutical industry. Strateg Entrepr J. 2010;4(2):106–
27. https://doi.org/10.1002/sej.86.
3. de Sola-Morales O, Cunningham D, Flume M. et. al. Dening Innovation with Respect to New
Medicines: A Systematic Review from a Payer Perspective. Int J Technol Assess Health Care.
2018;34(3):224–40. https://doi.org/10.1017/S0266462318000259.
4. Kesselheim AS, Wang B, Avorn J. Dening innovativeness in drug development: a systematic review.
Clin Pharmacol Ther. 2013;94(3):336–48. https://doi.org/10.1038/clpt.2013.115.
5. Lanthier M, Miller KL, Nardinelli C. An improved approach to measuring drug innovation nds steady
rates of rst-in-class pharmaceuticals, 1987–2011. Health Aff (Millwood). 2013;32(8):1433–9.
https://doi.org/10.1377/hlthaff.2012.0541.
Page 18/21
. Morgan S, Lopert R, Greyson D. Toward a denition of pharmaceutical innovation. Open Med.
2008;2(1):e4–7.
7. Stiller I, van Witteloostuijn A, Cambré B. Do current radical innovation measures actually measure
radical drug innovation? Scientometrics. 2020;126(2):1049–78. https://doi.org/10.1007/s11192-
020-03778-x.
. Krieger J, Li D, Papanikolaou D. Missing novelty in drug development. Rev Financ Stud.
2022;35(2):636–79. https://doi.org/10.1093/rfs/hhab024.
9. Wills TJ, Lipkus AH. Structural approach to assessing the innovativeness of new drugs nds
accelerating rate of innovation. ACS Med Chem Lett. 2020;11(11):2114–9.
10. Cohen SK, Caner T. Converting inventions into breakthrough innovations: The role of exploitation and
alliance network knowledge heterogeneity. J Eng Technol Manage. 2016;40:29–44.
https://doi.org/10.1016/j.jengtecman.2016.03.002.
11. Fernald KDS, Pennings HPG, van den Bosch JF. The moderating role of absorptive capacity and the
differential effects of acquisitions and alliances on Big Pharma rms' innovation performance. PLoS
ONE. 2017;12(2):e0172488. https://doi.org/10.1371/journal.pone.0172488.
12. Swinney DC. Phenotypic vs. target-based drug discovery for rst-in-class medicines. Clin Pharmacol
Ther. 2013;93(4):299–301. https://doi.org/10.1038/clpt.2012.236.
13. Eder J, Sedrani R, Wiesmann C. The discovery of rst-in-class drugs: origins and evolution. Nat Rev
Drug Discov. 2014;13(8):577–87. https://doi.org/10.1038/nrd4336.
14. Grabowski HG, Wang YR. The quantity and quality of worldwide new drug introductions, 1982–2003.
Health Aff (Millwood). 2006;25(2):452–60. https://doi.org/10.1377/hlthaff.25.2.452.
15. Miller KL, Lanthier M. Trends in orphan new molecular entities, 1983–2014: Half were rst in class,
and rare cancers were the most frequent target. Health Aff (Millwood). 2016;35(3):464–70.
https://doi.org/10.1377/hlthaff.2015.0921.
1. Phillips MI, Lee JR. Recent orphan drugs that are rst-in-class. Expert Opin Orphan Drugs.
2014;2(8):759–63. https://doi.org/10.1517/21678707.2014.931806.
17. US FDA. Development & approval process. https://www.fda.gov/drugs/developmnet-approval-
process-drugs. Accessed 06 Nov 2023.
1. New drugs at FDA. CDER’s new molecular entities and new therapeutic biological products.
https://www.fda.gov/drugs/development-approval-process-drugs/new-drugs-fda-cders-new-
molecular-entities-and-new-therapeutic-biological-products. Accessed 11 Nov 2023.
19. Drugs@FDA. FDA-approved drugs. https://www.accessdata.fda.gov/scripts/cder/daf/index.cfm.
20. Evaluate Pharma homepage. https://www.evaluate.com/products-services/pharma. Accessed 12
Nov 2023.
21. Cortellis Competitive Intelligence homepage. https://clarivate.com/products/biopharma/portfolio-
strategy-business-development/competitive-intelligence-and-analytics/. Accessed 19 Nov 2023.
Page 19/21
22. Olga DL. Enzyme inhibitors and activators. In: Murat S, editor. Enzyme inhibitors and activators.
Rijeka: IntechOpen; 2017. Ch. 11.
23. Landry Y, Gies JP. Drugs and their molecular targets: an updated overview. Fundam Clin Pharmacol.
2008;22(1):1–18. https://doi.org/10.1111/j.1472-8206.2007.00548.x.
24. Kim H-K, Lee H, Lee S-W. Does rm’s alliances increase new drug development time? A multiple
regression analysis of clinical development time. J Pharm Innov. 2023.
https://doi.org/10.1007/s12247-023-09773-y.
25. Cortellis Competitive Intelligence homepage. https://clarivate.com/products/biopharma/portfolio-
strategy-business-development/competitive-intelligence-and-analytics/. Accessed 19 Nov 2023.
2. Yervoy Homepage. https://www.opdivo.com/. Accessed 16 Dec 2023.
27. Keytruda Homepage. https://www.opdivo.com/. Accessed 16 Dec 2023.
2. Blincyto Homepage. https://www.opdivo.com/. Accessed 16 Dec 2023.
29. Rybrevant Homepage. https://www.rybrevant.com/. Accessed 16 Dec 2023.
30. Lyu X, Zhao Q, Hui J. The global landscape of approved antibody therapies. Antib Ther.
2022;5(4):233–57. https://doi.org/10.1093/abt/tbac021.
31. Bull SC, Doig AJ. Properties of protein drug target classes. PLoS ONE. 2015;10(3):e0117955.
https://doi.org/10.1371/journal.pone.0117955.
32. Ross JS. BMJ. 2023;381:928. https://doi.org/10.1136/bmj.p928. US Orphan Drug Act.
33. Brown DG, Wobst HJ. A decade of FDA-approved drugs (2010–2019): Trends and future directions.
J Med Chem. 2021;64(5):2312–38. https://doi.org/10.1021/acs.jmedchem.0c01516.
34. Ryo O. Chronological analysis of rst-in-class drugs approved from 2011 to 2022: Their
Technological Trend and Origin. Pharmaceutics. 2023;15(1794):1794.
https://doi.org/10.1136/bmj.p928.
35. Lanthier ML, Kerr KW, Miller KL. An analysis of follow-on development in new drug classes, January
1986–June 2018. Clin Pharmacol Ther. 2019;106(5):1125–32. ttps://doi.org/10.1002/cpt.1554.
Figures
Page 20/21
Figure 1
Number and rate of US FDA’s rst-in-class NMEs, 2010–2022
Figure 2
Percentage of rst-in-class drug by country and development strategy
Page 21/21
