Journal of Entrepreneurship, Management and Innovation (2026)

Volume 22 Issue 2: 95-118

DOI: https://doi.org/10.7341/20262224

JEL Codes: O32, O31, O33, O38, I23, H52, R58

Angelo Elias Meri Junior, Ph.D. Candidate in Health Sciences, Federal University of São João del-Rei, 400 Sebastião Gonçalves Coelho Street, Chanadour, Divinópolis, Minas Gerais, Brazil, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Elias de Sousa Tonelini, Master of Pharmaceutical Sciences, Federal University of São João del-Rei, 400 Sebastião Gonçalves Coelho Street, Chanadour, Divinópolis, Minas Gerais, Brazil, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Paulo Afonso Granjeiro, Ph.D., Biotechnological Processes and Macromolecules Purification Laboratory, Federal University of São João del-Rei, 400 Sebastião Gonçalves Coelho Street, Chanadour, Divinópolis, Minas Gerais, Brazil, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Luciane Teixeira Passos Giarola, Ph.D., Department of Mathematics and Statistics, Federal University of São João del-Rei, 170 Frei Orlando Square, Centro, São João del-Rei, Minas Gerais, Brazil, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Rafael Sachetto Oliveira, PhD, Department of Computer Science, Federal University of São João del-Rei, Visconde do Rio Preto Avenue, unnumbered, São João del-Rei, Minas Gerais, Brazil, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Renê Oliveira Couto, PhD, Pharmaceutics Development Laboratory, Federal University of São João del-Rei, 400 Sebastião Gonçalves Coelho Street, Chanadour, Divinópolis, Minas Gerais, Brazil, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
André Oliveira Baldoni, PhD, Center for Education and Research in Clinical Pharmacy (NEPeFaC), Federal University of São João del-Rei, 400 Sebastião Gonçalves Coelho Street, Chanadour, Divinópolis, Minas Gerais, Brazil, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

PURPOSE: To analyze the frequency of innovation generation, measured by the total number of patents and associated factors, including patent licensing in graduate programs in pharmaceutical sciences (GPPS) in Brazil. This research is justified by the need to understand the gap between the country’s high scientific output and the limited conversion of this knowledge into technological innovation. METHODOLOGY: A cross-sectional documentary study was conducted based on data from 69 GPPS registered on the Sucupira Platform of the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) in 2024, representing 100% of the programs in this field in Brazil. Information on the scientific and technical production of researchers was collected from the Lattes Platform for the period 2014 to 2024, while data on the GPPS were obtained from the Sucupira Platform. FINDINGS: Most GPPS (40.6%) are located in the Southeast region of the country, and 68.1% did not license patents. Multiple linear regression was associated with 53.78% of the variability in the number of patents, identifying completed postdoctoral research, undergraduate research supervision, and the Municipal Human Development Index (MHDI) as significant predictors. The multiple correspondence analysis showed an association between patent licensing and higher scientific production, a greater number of faculty members, postdoctoral supervisions, GPPS offering both master’s and doctoral degrees, higher program quality, longer existence, and location in municipalities with higher MHDI. IMPLICATIONS: The results indicate that technological innovation in graduate programs depends on institutional maturity and territorial context. Additionally, they support strengthening National Technology Transfer Offices (NITs), incorporating innovation-related indicators into the evaluation of graduate programs, promoting university-industry collaboration, and adopting region-sensitive innovation policies to reduce territorial inequalities. ORIGINALITY & VALUE: This is the first Brazilian study to map the institutional and regional factors associated with technological innovation in GPPS within the country. Its results also contribute to the international literature on innovation system perspectives in middle-income countries, offering contributions applicable to regions facing similar challenges in translating academic research into technological and social transformation.

Keywords: technology transfer, development indicators, pharmacy, science, technology and innovation indicators, graduate education, academic patenting, patent licensing, pharmaceutical sciences, graduate programs, research commercialization, innovation systems, Brazil, municipal human development index, university–industry collaboration

INTRODUCTION

In the current context of economic globalization, characterized by rapid transformations in markets and organizational structures, technological knowledge and the capacity to innovate emerge as fundamental strategic resources for economic and social development (Kamia & Vargas, 2023). The dissemination of innovations, especially in the health sector, is driven by basic research and scientific and technological infrastructure, leading to the development of new drugs, medical devices, and therapeutic solutions (Silva et al., 2024).

The capacity to innovate has become a crucial competitive differentiator for nations, organizations, and academic institutions in a world increasingly shaped by the knowledge economy. Although Brazil ranks among the largest producers of scientific knowledge worldwide (14th), its position in the 2025 Global Innovation Index falls short of expectations, ranking 52nd (WIPO, 2025). This disparity highlights a gap between academic production and its practical application, including in the field of pharmaceutical sciences. In this sense, despite a solid foundation in pharmacology and biomedical sciences, the conversion of knowledge into technological innovation, such as new drugs, patents, and start-ups, remains limited compared to leading innovation countries (Fernandes et al., 2023).

Pharmaceutical innovation in Brazil is predominantly driven by universities and research centers; however, transforming knowledge into products requires effective coordination among universities, industry, and government, as proposed by the Triple Helix model (Viana et al., 2018). However, regulatory challenges, limited funding, inadequate infrastructure, dependence on imported inputs, and an academic culture still insufficiently oriented toward innovation hinder this process (Couto et al., 2016; Paranhos et al., 2019; Ruas et al., 2025).

With the purpose of overcoming these barriers, Brazil has implemented legal frameworks, such as the Legal Framework for Science, Technology, and Innovation, comprising Parliamentary Amendment No. 85 (2015), Law No. 13,243/2016, and Decree No. 9,283, which aim to promote applied research, enhance intellectual property, strengthen public-private partnerships, and establish strategic alliances among the actors of the Triple Helix (Brazil, 2015, 2018). Despite these regulatory advances, their implementation faces practical challenges, limiting the effectiveness of the Technological Innovation Centers (NITs) and the Scientific and Technological Institutions (ICTs) (Brazil, 2022a, Brazil 2024a).

The generation of scientific knowledge in Brazil remains concentrated in stricto sensu graduate programs, evaluated by the Coordination for the Improvement of Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), established in the 1950s with the aim of strengthening the training of highly qualified human resources (Nunes et al., 2010). Since the formalization of master’s and doctoral courses in the 1960s, the country has made significant progress in consolidating its scientific production and strengthening its presence on the international stage (Brazil, 2020; De Sá Barreto et al., 2014; Dotta & Neves, 2015; McManus et al., 2023). Despite this, such growth has not translated proportionally into the generation of technological assets and innovative processes, particularly in the field of pharmaceutical sciences (Scavuzzi et al., 2023).

The gap between academic production and innovation is attributed, among other factors, to limited interaction between universities and the productive sector, leading to research that is often misaligned with market demands (Vidal & Fukushima, 2021). A study conducted by Casagrande et al. (2023) reinforces the need for incentive policies that promote an entrepreneurial culture in universities and strengthen public-private partnerships (Casagrande et al., 2022). Although the country has expanded its scientific production in recent decades, the conversion of this knowledge into technological assets and market innovations remains incipient, particularly in strategic sectors such as the pharmaceutical industry, which has historically been characterized by external dependence and limited productive autonomy (Castro et al., 2021).

According to Fernandes and collaborators (2023), the consolidation of a National Innovation System requires closer integration between scientific production and the demands of the business sector, especially in areas such as health and biotechnology. This alignment is essential for academic research results to be converted into technological solutions with social and economic impact (Fernandes et al., 2023). It is worth noting that studies systematically analyzing the profile of graduate programs in pharmaceutical sciences (GPPS) and their capacity to contribute to technological development are scarce.

Although there are already published studies that have addressed scientific productivity and innovation indicators separately, there is still an analytical gap related to the integrated assessment of institutional characteristics, academic production, and territorial development in the nexus between patent generation and licensing within graduate programs in pharmaceutical sciences (GPPS), especially in middle-income countries like Brazil (Jesus et al., 2023; Reis et al., 2025; Soares et al., 2020).

In this context, this study seeks to answer the following guiding question: “What institutional, academic, and territorial factors are associated with the generation of innovation, measured by total number of patents and patent licensing, in Brazilian GPPS?”. In view of this scenario, this study aimed to analyze the frequency of innovation generation and the factors associated with innovation within GPPS in Brazil.

This article is structured as follows: the next section presents the literature review, outlining the theoretical framework on innovation systems, graduate education, and pharmaceutical innovation. The subsequent section describes the methodological procedures and data sources. The results section presents the descriptive and multivariate analyses. Finally, the discussion contextualizes the findings in light of the literature; the final section highlights the main outcomes, implications, and limitations of the study.

LITERATURE REVIEW

In 1965, in response to a request from the Minister of Education and Culture, Raymundo Moniz de Aragão, the Federal Council of Education (CFE) initiated the process of regulating graduate education in Brazil, as provided for in the 1961 Law of Guidelines and Bases of National Education (Brazil, 1961; Brazil, 1965). Opinion 977, led by Newton Sucupira with the participation of intellectuals such as Alceu Amoroso Lima and Anísio Teixeira, established the structural foundation for stricto sensu graduate education in the country. Inspired by the U.S. model, this opinion aimed to address deficiencies in teacher training and qualification for scientific production, reflecting Brazil’s educational dependence on more developed countries, as well as promoting an elitist and hierarchical vision of higher education (Brazil, 1965; Shigunov Neto et al., 2021).

The implementation of graduate education in Brazil was marked by strong centralization of resources and development in centers of excellence located primarily in the Southeast, South, and Federal District, as determined by the National Policy of Regional Graduate Centers. This resulted in regional inequalities in access and knowledge production, with support concentrated in certain institutions. These inequalities persisted until the late 1980s, when affirmative action policies began to transform the landscape, allowing for greater inclusion and epistemic diversity (Nazareno & Herbetta, 2020).

The National Graduate System (SNPG) in Brazil represents one of the fundamental pillars for the country’s scientific and technological development. Established and coordinated by CAPES, the SNPG is responsible for supervising, evaluating, and supporting master’s and doctoral programs in higher education institutions (Brazil, 2024c).

Although the evaluation of graduate programs in Brazil is considered robust and internationally recognized, it faces criticism due to its rigidity, excessive focus on research, and limited emphasis on diversity and social impact. This evaluation is carried out mainly by CAPES and occurs in four-year cycles (quadrennial evaluation), encompassing Brazilian master’s and doctoral programs (stricto sensu) (Brazil, 2022b; McManus et al., 2022). As evaluation criteria, scientific production (publications, Qualis journal – a system classifying scientific journals into categories A1–A4, B1–B4, and C), training of master’s and doctoral students, social engagement, internationalization, infrastructure, and institutional self-assessment are considered. Scores from 1 to 7 are assigned to graduate programs, and those receiving a score below 3 may face program deaccreditation. However, scores between 6 and 7 indicate national and international excellence (Brazil, 2022b; Castro & Oliveira Filho, 2022; McManus et al., 2022).

The document “National Indicators of Science, Technology, and Innovation 2022,” prepared by the Ministry of Science, Technology, and Innovation (MCTI), highlights the need to improve Brazil’s research and development ecosystem as a strategy to elevate the country’s position in global innovation rankings. The most recent data, covering the period up to 2021, indicate a contraction of 8.2% in total investment in research and development between 2019 and 2020, decreasing from R$ 95.3 billion to R$ 87.1 billion. In relation to the Gross Domestic Product (GDP), the proportion invested in research and development fell from 1.21% to 1.14%. Although public expenditure grew slightly, the situation reflects a mismatch between national efforts and the need to consolidate a robust scientific and technological base. Even though other economies, such as the United States, have also experienced recent fluctuations in investment, these nations maintain historically higher levels of infrastructure and funding, giving them a comparative advantage in innovation (Brazil, 2022a).

The pillar of innovation is expressed through processes that include idea management, project development, dissemination of scientific knowledge, and administration of different types of intellectual property (Souza et al., 2020). The concept of innovation emphasizes the crucial role of universities in generating cutting-edge scientific and technological knowledge and in contributing to socioeconomic advancement (Bazán et al., 2012). Therefore, improving scientific and technological research, increasing investments in infrastructure, exploring new forms of incentives and funding, training qualified human resources, and adopting research models oriented toward innovation are fundamental strategies to address existing challenges (Sousa & Braga, 2023).

From an analytical perspective, technology transfer and innovation processes can be interpreted through the lens of the Absorptive Capacity Theory, proposed by Cohen and Levinthal (1990). Absorptive capacity relates to an organization’s ability to recognize the value of external knowledge, assimilate it, and apply it for economic or social purposes (Cohen & Levinthal, 1990). Applying this concept to graduate programs, this capacity is shaped by institutional maturity, the accumulation of scientific experience, and qualified human capital. For this reason, patent generation and licensing should not be understood as isolated results, but rather as part of broader institutional and academic capabilities (Aldieri et al., 2018; Kato, 2020; Martinkenaite & Breunig, 2016; Todorova & Durisin, 2007).

Graduate education in pharmaceutical sciences in Brazil has played a fundamental role in advancing health research, contributing significantly to areas such as drug development, biotechnology, and clinical pharmacy. These areas attract students and professionals committed to scientific and technological innovation, with direct impacts on public health and the pharmaceutical sector (Gerenutti et al., 2009; Leite & Galdino, 2013).

The field of pharmaceutical sciences has been the setting for significant advances, particularly in pharmacogenetics, new drug discovery, and biological therapies. The growing collaboration among academic institutions, industry, and international research centers has driven technology transfer and knowledge exchange, strengthening the global presence of Brazilian scientific production (Tigre et al., 2016).

The initial milestone of stricto sensu graduate education in pharmaceutical sciences in Brazil occurred in 1965, with the formalization of an institutional structure for academic qualification and program accreditation by CAPES. However, it was during the 1970s and 1980s that master’s and doctoral programs began to consolidate in public universities, particularly in federal and state institutions, reflecting a gradual maturation of the field (Velloso, 2014).

Multi- and interdisciplinarity are intrinsic to the field of pharmacy, given the complexity of drug and medication development, which requires diverse approaches and strategies, as well as consideration of public policies for the acquisition and use of medicines. The 2017 quadrennial evaluation (reference year 2016) revealed that approximately 75% of faculty had training in various major knowledge areas, with emphasis on pharmacy (24.2%), chemistry (15.5%), pharmacology (8.7%), and biochemistry (7.1%). This diversity is reflected in faculty members’ participation as permanent staff in other graduate programs, which is significantly higher than the SNPG average: 37.9% are involved in an additional program and 17.2% in two programs (Brazil, 2019).

The intellectual output of the field, published in journals across various subject categories, reflects its multidisciplinarity. Analysis of the ten most frequently used scientific journals by faculty in the last quadrennial demonstrates the breadth of scientific production, spanning multiple areas of knowledge as defined by both the Web of Science and Scopus classifications. This diversity in training and activities highlights the interdisciplinary and multifaceted nature of research and development in pharmacy (Carvalho & Real, 2021).

International literature on academic patenting indicates that technological innovation outcomes are influenced by a combination of individual, institutional, and territorial factors (Chávez-Bustamante et al., 2024; Messeni Petruzzelli & Murgia, 2023; Moletta & Pilatti, 2025; Rücker Schaeffer et al., 2018; Wolszczak-Derlacz, 2025). This includes research productivity, faculty size, graduate student supervision, availability of technology transfer offices, and proximity to innovation ecosystems. In this sense, these findings reinforce the relevance of analyzing graduate programs as institutional environments that foster harmony among knowledge production, scientific training, and innovation (Ryazanova & Jaskiene, 2022; Shakasimov, 2025; Zarea et al., 2025).

The National Policy for Science, Technology, and Innovation in Health (PNCTIS), established in 2004 in Brazil, emerged in response to increasing pressure from technology industries for the integration of innovations into the health system at both national and international levels. It is estimated that the incorporation of new technologies accounts for approximately one-third of the growth in health expenditures relative to the Gross Domestic Product (GDP), posing significant challenges to the sustainability of health systems, particularly in low- and middle-income countries with universal coverage, such as Brazil (Oliveira, 2016).

In the global context, health innovation development is predominantly concentrated in oligopolistic markets, where corporations invest heavily in research and development to maintain competitiveness through technological innovation. These companies, by protecting their discoveries through patents, often impose monopolistic prices that are frequently disconnected from actual production costs (Garthwaite, 2025).

In developing countries, demand plays a central role in guiding innovation. Thus, the legal framework and organizational structure of health systems become key elements for directing technological innovation toward social interests; however, market-driven trends can alternatively exacerbate inequalities in access. In this context, the sustainable management of health systems emerges as a global challenge, with particular emphasis on countries like Brazil, where it is imperative to ensure universal and comprehensive access by coordinating sectoral policies capable of meeting the needs of a large and heterogeneous population (Htay et al., 2021; Ogugua et al., 2024).

Innovation should be understood as a multidimensional phenomenon encompassing radical, original innovations as well as incremental, adaptive, and process-oriented ones. In this sense, it indicates that they are highly relevant in developing economic contexts (Edwards-Schachter, 2018; Tiberius et al., 2021). In our study, patent generation and licensing are adopted as analytical indicators of technological innovation, since they represent formalized and comparable results of scientific activity with a perspective of economic and social improvement, thus avoiding a restrictive conceptualization of innovation (Capponi et al., 2022; Wittfoth et al., 2022).

Contemporary academic research, in turn, has emphasized the importance of aligning scientific production with social demands, arguing that research should be designed with practical application in mind. The effective use of scientific results is fundamental to converting knowledge into actions that promote tangible improvements in public health. In this regard, the World Health Organization (WHO) defines health innovation as the development of policies and practices that expand the quality and reach of health care, including preventive, promotional, therapeutic, and care-related actions. Abelson et al. (2016) and Scavuzzi et al. (2023) reinforce this perspective, stating that scientific evidence is essential for generating new knowledge and should guide decisions and practices in the health field, favoring the effective application of knowledge (Abelson et al., 2016; Scavuzzi et al., 2023).

The Triple Helix concept proposes a model of collaboration among the three sectors of government, universities/research institutions, and industry, to promote innovation and economic and social development in a given region. In this context, each sector plays an essential and distinct role in driving innovation and the growth of a knowledge-based economy (Rizzi et al., 2018).

According to Etzkowitz & Zhou (2017), the Triple Helix is internationally recognized as a guide for policies and practices at the local, regional, national, and multinational levels. It provides an effective methodology for analyzing local strengths and weaknesses and identifying gaps in interactions among universities, industries, and governments to formulate successful innovation strategies (Etzkowitz & Zhou, 2017).

The Triple Helix concept is oriented toward continuous development, aiming to create an ecosystem of innovation and entrepreneurship. The interaction among the three main actors - government, universities, and industries - fosters the formation of new secondary institutions, known as hybrid organizations. Although an innovation and entrepreneurship ecosystem generated by the Triple Helix model cannot be exactly replicated in its original form, the model, with its three main actors and other supporting participants, can be adapted and implemented anywhere in the world as a universal framework to promote innovation (Etzkowitz & Zhou, 2017).

In the context of the Triple Helix model, companies should not be seen as passive recipients of academic knowledge. Instead, they should be viewed as fundamental partners whose capacity for absorption, investment in R&D&I, and engagement in innovation activities are essential for transforming academic patents into licensed technologies (Etzkowitz & Zhou, 2017; López Jiménez & Dittmar, 2019). In the Brazilian pharmaceutical sector, structural constraints such as limited investment in R&D, technological dependence on imported inputs, and the predominance of generic drug production impact the incorporation of academic licenses and the generation of proprietary innovations (Fernandes et al., 2021; Leal et al., 2022; Martins et al., 2025).

In Brazil, the application of the Triple Helix model gained momentum with the enactment of the Innovation Law (Law No. 10973/2004), which established a legal framework aimed at promoting science, technology, and innovation. This legislation sought to reduce the technological gap between knowledge generated in scientific institutions and its practical application in the productive sector. Since then, public policies, although still incipient in many states, have been formulated to foster an environment conducive to innovation. Key mechanisms established include subsidized interest financing, the provision of non-reimbursable research funds, tax incentives, support for venture capital formation, and encouragement of researcher hiring (Cóser et al., 2018).

The creation of the Legal Framework for Science, Technology, and Innovation (MLCTI) began in 2015 with Constitutional Amendment 85/2015, which incorporated into the Brazilian Federal Constitution the authority of various levels of government to facilitate access to technology, research, and innovation, as well as culture and education. By including the word “Innovation” in the Brazilian Federal Constitution, the amendment filled a legal gap, enabling the Brazilian state to promote innovation in a structured manner. To consolidate these changes, Law No. 13,243 was enacted in 2016 and regulated by Decree No. 9,283 in 2018. This law aims to remove bureaucratic barriers affecting researchers and innovative entrepreneurs and to create mechanisms to integrate science, technology, and innovation institutions with the business sector (Brazil, 2018).

Although 9 years have passed since the enactment of the Innovation Law, data from the Ministry of Science, Technology, and Innovation (MCTI), presented in the 2024 base-year Formict report, indicate considerable variation in the level of consolidation of Technology Innovation Centers (NITs) in Brazil, across both public and private institutions. Concurrently, differences are observed in the organizational structure of NITs relative to their respective Scientific and Technological Institutions (ICTs), even though the majority of these institutions maintain a dedicated NIT (Brazil, 2025).

It is observed that most ICTs have sought to comply with the requirements of the Innovation Law through the creation of their NITs. However, the difficulties in their effective implementation are evident. According to Katz et al. (2018), the establishment of NITs in Brazilian ICTs still represents a challenge, even considering that these centers are fundamental for providing greater autonomy to ICT managers on innovation-related matters (Katz et al., 2018). Additionally, Paranhos et al. (2018) highlight that, despite the increase in the number of NITs across the country, challenges to their operation remain significant, and support for their strengthening is still necessary (Paranhos et al., 2018).

According to the national science, technology, and innovation indicators presented by the MCTI in the 2024 base-year Formict report, a total of 2,397 applications for protection were filed; of these, 2,146 (89.5%) were submitted by public institutions and 251 (10.5%) by private institutions. Regarding granted applications, 2,374 were identified, of which 2,077 (87.5%) originated from public institutions and 297 (12.5%) from private institutions. Furthermore, regarding technology contracts, among the 306 institutions that completed the report, only 95 (31.0%) reported their existence, comprising 69 (72.6%) from public institutions and 26 (27.4%) from private institutions. These data indicate that public institutions dominate research within the country compared to their private counterparts. Moreover, they highlight the low volume of applications for protection and, even more concerning, the scarcity of knowledge and technology transfer contracts among these institutions. Such indicators underscore the urgent need to advance the process of knowledge and technology transfer (Brazil, 2025).

When analyzing the landscape of the largest resident filers of invention patent applications in Brazil in 2024, as reported by the National Institute of Industrial Property (INPI), the list is predominantly composed of public universities, followed by a few private universities, companies, ICTs, and independent researchers. These data highlight the prominent role of Brazilian public universities and ICTs, demonstrating their scientific excellence, while also indicating the private sector’s low participation in the country’s innovation process (INPI, 2025).

Considering the literature reviewed, this study starts from the premise that the generation and licensing of patents in graduate programs are embedded in a broader ecosystem of institutional, academic, and territorial maturity. Therefore, our study is guided by the hypothesis that higher levels of academic productivity, institutional consolidation, and favorable territorial conditions are positively associated with technological innovation outcomes in GPPS.

METHODOLOGY

Study design

This is a cross-sectional documentary study conducted in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (von Elm et al., 2008).

Setting

The research was conducted in February 2025. Official platforms of the Brazilian graduate system were used, particularly the CAPES Sucupira Platform for collecting information on GPPS and the Lattes Platform of the National Council for Scientific and Technological Development (CNPq) for gathering data on researchers’ output, covering the period from 2014 to 2024. Scientific and technical production, as well as the supervision activities of GPPS faculty in Brazil, were evaluated within the CAPES evaluation area “Pharmacy (19).” In parallel, the Lattes curricula of the faculty were assessed for the period from 2014 to 2024.

Participants and sample

All graduate programs in the major field of Health Sciences and the evaluation area of Pharmacy, filtered on the Sucupira Platform, were included. Programs lacking information on their approval by the National Education Council (NEC) were excluded. All researchers accredited in Brazilian GPPS were included. The sample consisted of 69 (100%) active Brazilian GPPS registered in 2024, encompassing GPPS from all regions of Brazil.

Variables

• outcome variable: total number of patents by each Brazilian GPPS;
• predictor/exposure variables: faculty scientific production, including published articles stratified by CAPES Qualis (A1 to B4), texts in newspapers or magazines, abstracts, full papers in conference proceedings, published books, articles accepted for publication, and presentations at scientific events. Academic activities and supervision were also considered, including postdoctoral supervision, undergraduate research supervision, master’s theses, doctoral dissertations, and ongoing student advisement. Additionally, academic recognition, including awards and titles received, was included;
• institutional and faculty variables: total number of faculty in the GPPS, stratified by affiliation type (permanent/collaborator). Institutional and geographic characteristics of the GPPS were also considered, including the Municipal Human Development Index (MHDI), Brazilian region, academic degree (PhD, master’s, professional master’s, professional master’s/doctoral, master’s/doctoral), program modality (academic, professional), latest CAPES evaluation score, program organization format (networked or not), and years of operation of the GPPS;
• derived variable: created and termed the Qualis index, calculated from the article production of each GPPS using the weights from the CAPES evaluation sheet for the Pharmacy area: “Qualis = A1 × 100 + A2 × 85 + A3 × 70 + A4 × 60 + B1 × 50 + B2 × 35 + B3 × 20 + B4 × 10”, where A1, A2, A3, B1, B2, B3, B4 represent the number of articles published by the GPPS in each stratum.
• variable relating to licensing: patents licensed;
• potential confounders and effect modifiers: MHDI, Brazilian region, faculty members and affiliation type, academic degree and program modality, program organization format, and years of operation, given that these may be associated with scientific production, with the number of patents and their licensing;
• the duration of each GPPS was calculated by subtracting its starting year from the current year (2024), resulting in the number of years the GPPS has been in operation;
• the MHDI was obtained from the CAPES scholarship distribution spreadsheet, in accordance with Ordinance No. 92, dated March 27, 2024 (Brazil, 2024b);
• data on faculty affiliation type with the GPPS (permanent or collaborator) were collected from the CAPES panel, which provided general information about the GPPS and its faculty, as well as from institutions’ websites.

Data sources and measurement

The mapping to obtain data on the graduate programs was conducted using public data from the Sucupira Platform in February 2025.

When accessing the website, in the upper left corner where there are options to explore all the information related to the programs, the following path was followed: graduate observatory <https://sucupira.capes.gov.br/ - busca_observatório> Graduate Program, and the selection filters described in Table 1 were inserted.

Table 1. Filters and selections used in the Sucupira Platform to identify graduate programs of interest

Filters	Selections
Years of collection	2014 to 2024
Regions	Midwest, Northeast, North, Southeast and South
Broad area of knowledge	Health Sciences
Assessment area	Pharmacy
Area of knowledge	Pharmacy
Graduate programs in association	Yes and no
Grade	3 to 7
Program modality	Academic and professional
Academic degree	PhD, Master’s, Professional Master’s, Professional Master’s/PhD, and Master’s/PhD
Situation	In deactivation and in operation

Source: Brazil (2024c).

After applying the filters, the platform generated a Microsoft Excel® spreadsheet containing 69 GPPS. All GPPS were included in the study.

Additionally, the names of all faculty members were collected. Each faculty member’s curriculum was individually accessed on the Lattes Platform (https://lattes.cnpq.br/). This allowed the use of a customized version of the ScriptLattes software (V8.11). Individual registrations for each curriculum and the creation of a faculty group for each GPPS were completed. Subsequently, management of all groups and generation of unique reports for each GPPS were conducted. These reports enabled the analysis of bibliographic and technical production (licensed patents), ongoing and completed supervision, as well as awards and titles.

Bias

A potential information/measurement bias is recognized due to the reliance on self-reported data by faculty on the Lattes Platform, which may be subject to errors in completion or updating, as well as the lack of standardization in some records.

Statistical methods

Categorical variables were described using frequency and percentage measures, while quantitative variables were expressed as median and interquartile range.

The response variable (number of total patents) was initially explored using a histogram. Due to the positively skewed distribution of registered patents, multiple linear regression models were fitted with a square root transformation of the response. A multicollinearity diagnosis was performed using a Spearman correlation matrix to explain variables and the variance inflation factor (VIF), excluding variables with high values and redundant information (VIF > 10). Variable selection was conducted using the Akaike Information Criterion (AIC) via the stepwise method, followed by the Likelihood Ratio Test (LRT) to compare nested models. Estimates of the parameters of the final model were obtained using the ordinary least squares method, and their significance was assessed using the Wald test. The adequacy of the model was verified by the residual-versus-fitted plot, including the Shapiro-Wilk (normality test) and Breusch-Pagan (homoscedasticity test), as well as analysis of influential points (Cook’s distance and leverage) and a simulated envelope graph with a 95% confidence interval.

Additionally, the association between licensed patents and institutional and academic variables was explored using Multiple Correspondence Analysis (MCA), with variables categorized by their medians. The first two axes (dimensions), which accounted for the majority of the data variability, were considered for the construction of perceptual maps.

All statistical analyses were conducted at a significance level of 5% (p < 0.05) using R software (version 4.3.3), with the MASS package (Venables & Ripley, 2002) for regression analysis and the ca package (Nenadic & Greenacre, 2007) for MCA.

RESULTS AND DISCUSSION

Analysis of the 69 GPPS revealed a regional concentration, with predominance in the Southeast region (n = 28; 40.58%), followed by the South (n = 13; 18.84%) and Northeast (n = 13; 18.84%) regions (Table 2). This distribution, as noted by Sousa and Braga (2021), reflects the historical centralization of resources and infrastructure in the more developed regions of the country, which favors the consolidation of more robust and continuous research lines (Guimarães et al., 2020; McManus et al., 2023; Paranhos et al., 2020). This historical concentration helps account for regional asymmetries in innovation performance, but does not fully justify the differences in technology licensing outcomes observed across regions.

Regarding program offerings (Table 2), 60.87% (n = 42) of GPPS offer both master’s and doctoral courses, contributing to the consolidation of more robust and continuous research lines. According to Fernandes et al. (2023), the presence of doctoral programs is strongly associated with the strengthening of institutional technical and scientific capacity, representing a key element for innovation generation. This is confirmed by patent production data, where GPPS with greater academic complexity tend to show higher numbers of patent filings (Fernandes et al., 2023). From the point of view of absorption capacity, doctoral training enhances the ability of institutions to recognize, assimilate, and transform scientific knowledge into potentially applicable technologies.

The predominance of academic programs (n = 61; 88.41%) over professional programs (n = 8; 11.59%) also stands out. Although professional programs aim to bring knowledge closer to practical application, their number remains limited. The expansion of academic master’s and doctoral programs is an effective strategy to foster technological innovation directly related to the demands of the productive sector, particularly in the health field (Mann et al., 2019). Considering the absence of complementary institutional mechanisms for technology transfer, academic expansion alone may not translate into higher licensing rates.

Regarding the Qualis score, the median of 50080.00 (32220.00 – 71090.00) indicates robust scientific production, mostly concentrated in the higher strata (A1 and A2). However, even with this high bibliographic output, a discrepancy is observed when compared to the median number of granted patents, which is only 8.00 (2.00 – 13.00). This gap highlights what Scavuzzi et al. (2023) refer to as the “academic productivity paradox”: a substantial quantitative output that does not necessarily translate into protected and applicable innovation (Scavuzzi et al., 2023). This paradox reflects a possible structural misalignment between academic incentives and innovation-oriented outcomes.

Technical production also showed notable indicators, with emphasis on conference presentations (median: 277.00) and published books (median: 82.00), demonstrating the effort to disseminate scientific knowledge by the GPPS. However, these forms of output, although relevant for scientific training, have low potential for direct conversion into technological assets if not accompanied by technology transfer strategies (Chen et al., 2022; Craiut et al., 2022; Fontes, 2005; Siegel et al., 2023).

In parallel, the faculty members of the programs demonstrated a high number of completed supervisions, particularly in undergraduate research (median: 272.0), master’s theses (median: 179.0), and final course projects (median: 187.0). The high frequency of undergraduate research supervision is a positive finding, as early research engagement contributes to the development of an innovative culture. However, it is necessary that this training be accompanied by an institutional structure that encourages the protection of the knowledge generated. This reinforces the idea that human capital formation must be coupled with institutional absorptive capacity to effectively generate innovation (Kent et al., 2022).

Table 2. Characteristics of the Graduate Programs in Pharmaceutical Sciences in Brazil evaluated from January 2014 to 2024 (n = 69).

Variable			Statistics
Region - n (%):			69 (100.00)
Midwest			7 (10.14)
Northeast			13 (18.84)
North			8 (11.59)
Southeast			28 (40.58)
South			13 (18.84)
Academic degree - n (%):			69.00 (100.00)
PhD			3.00 (4.35)
Master’s degree			16.00 (23.19)
Professional master’s degree			7.00 (10.14)
Professional Master’s/PhD			1.00 (1.45)
Master’s/PhD			42.00 (60.87)
Modality - n (%):			69.00 (100.00)
Academic			61.00 (88.41)
Professional			8.00 (11.59)
In network - n (%):			69.00 (100.00)
No			64.00 (92.75)
Yes			5.00 (7.25)
Grade			4.00 (4.00 – 5.00)
Duration of operation (in years)			14.00 (11.00 – 19.00)
Municipal Human Development Index (MHDI)			0.80 (0.76 – 0.81)
Articles with CAPES Qualis evaluation:			664.00 (452.00 – 997.00)
A1			196.00 (102.00 – 283.00)
A2			159.00 (81.00 – 221.00)
A3			124.00 (70.00 – 187.00)
A4			73.00 (51.00 – 107.00)
B1			58.00 (39.00 – 80.00)
B2			35.00 (24.00 – 43.00)
B3			20.00 (15.00 – 30.00)
B4			14.00 (8.00 – 26.00)
Qualis index			50080.00 (32220.00 – 71090.00)
Texts in journals			11.00 (5.00 – 18.00)
Abstracts published in conference proceedings			553.00 (356.00 – 711.00)
Other types of bibliographic production			6.00 (3.00 – 13.00)
Published/edited books			82.00 (58.00 – 115.00)
Full papers published in conference proceedings			24.00 (11.00 – 47.00)
Articles accepted for publication			5.00 (2.00 – 8.00)
Extended abstracts published in conference proceedings			40.00 (24.00 – 81.00)
Presentations at scientific events			277.00 (208.00 – 385.00)
Completed postdoctoral supervision			18.00 (7.00 – 42.00)
Completed specialization supervision			21.00 (9.00 – 37.00)
Completed supervisions of other types			100.00 (45.00 – 169.00)
Supervisions of all types in progress			129.00 (102.00 – 193.00)
Awards and honors			98.00 (73.00 – 137.00)
Completed doctoral supervision			84.00 (51.00 – 157.00)
Completed master’s supervision			179 (135 – 242)
Completed undergraduate thesis/final course project supervision			187.00 (139.00 – 283.00)
Completed undergraduate research supervision			272.00 (178.00 – 342.00)
Faculty members:			19.00 (15.00 – 24.00)
Collaborating faculty			3.00 (1.00 – 5.00)
Permanent			16.00 (12.00 – 19.00)
Patents			30.00 (13.00 – 60.00)
Filed			24.00 (10.00 – 45.00)
Granted			8.00 (2.00 – 13.00)
Licensed: n (%):			69.00 (100.00)
0			47.00 (68.12)
1			16.00 (23.19)
2			4.00 (5.79)
3			1.00 (1.45)
5			1.00 (1.45)

Note: Statistics are expressed as frequencies and percentages for categorical variables, and as medians and interquartile ranges for quantitative variables.

The patent analysis shows that, although the median of total filings is 30, the majority of programs still do not have licensed patents (n= 47; 68.12%). This finding corroborates Paranhos et al. (2018), who indicate that the lack of effective institutional policies is one of the main barriers to technology transfer in the Brazilian academic environment (Paranhos et al., 2018). This low licensing rate also reflects structural constraints of the Brazilian pharmaceutical industry, including limited R&D investment and difficulties in incorporating external technologies, particularly in segments dominated by generic medicines (Leal et al., 2022).

The increase in patent filings also represents a continuous source of revenue for the government, contributing to public funding. A robust intellectual property system strengthens the country’s international competitiveness and encourages foreign direct investment, creating a more attractive environment for investors and companies (Paranhos et al., 2018). Moreover, patent validation in other countries allows national companies to expand their operations into new markets, contributing to economic growth and supporting talent retention, which is essential for innovation and economic development (Tanane, 2020; Yang & Wang, 2024). A recent study at a Brazilian public university showed that the number of licensed patents still represents a low percentage, approximately 10% of total filings (Granjeiro et al., 2025).

When analyzing the duration of operation, it was found that Graduate Programs in Pharmaceutical Sciences (GPPS) with a longer existence show stronger performance in scientific and technological production indicators. This result is consistent with McManus et al. (2023), who demonstrated that institutional consolidation over time promotes the development of more efficient organizational structures and the continuous improvement of innovation management practices. More mature programs tend to have greater capacity to plan, execute, and sustain initiatives in applied research, collaboration with the productive sector, and the valorization of innovative outcomes, which directly reflect the quality and impact of their academic and technological production (McManus et al., 2023). Thus, institutional maturity represents a cumulative process that strengthens internal routines, technology transfer offices, and strategic engagement with external actors.

Figures 1, 2, and 3 present complementary perspectives on the production and distribution of patents among the GPPS in Brazil. Together, these figures illustrate temporal trends, regional distribution, and the overall distribution pattern of patent counts.

Figure 1. Annual evolution of the total number of patents related to Graduate Programs in Pharmaceutical Sciences in Brazil (n= 69)

According to Figure 1, the analyzed data reveal a slight increase in the total number of patents from 2014 to 2024, with some fluctuations over the years. Between 2014 (n = 210) and 2017 (n = 272), there was a consistent upward trend, followed by a decline in 2018 (n = 215). From that point on, a recovery was observed, culminating in a peak in 2020 (n = 410). Conversely, in the subsequent years, the number of patents decreased again, reaching 317 in 2021 and 207 in 2022. In 2023 (n = 237) and 2024 (n = 271), a gradual recovery can be noted.

These results are consistent with trends previously discussed in the literature, which associate the effects of the COVID-19 pandemic with changes in inventive activity across various fields of knowledge. According to Bloom et al. (2021), the pandemic led to a technical reorientation of innovations, with a significant increase in patent applications related to technologies that support remote work, such as videoconferencing and online collaboration tools, whose share more than doubled compared to the historical average (Bloom et al., 2021).

The peak observed in 2020 should be interpreted with caution. It is likely associated with research activities initiated prior to the COVID-19 pandemic, reflecting a period of higher productivity before its global impact. The subsequent decline observed in 2021 and 2022 may represent the delayed effects of the pandemic on research and patenting activities, as disruptions in scientific work and institutional operations took time to be reflected in patent outputs. The recovery observed in 2023 suggests a gradual resumption of research activities in the post-pandemic period. Overall, these temporal patterns indicate that the impact of the pandemic on GPPS-related patenting may exhibit a lag effect, rather than an immediate response.

Corroborating this analysis, the Center for Economic Policy Research (2022) indicates that, although international patent filings experienced a decline, the impact was more moderate compared to previous crises. This resilience was driven primarily by advances in biotechnology and health-related technologies. On the other hand, the decline observed in subsequent years may be attributed both to a reduction in emergency demands and to the operational instability faced by academic and scientific institutions during the most critical periods of the pandemic (CEPR, 2022).

Furthermore, it is observed that the pandemic significantly altered scientific production (Riccaboni & Verginer, 2022). In 2020, the first year of the health crisis, there was a marked quantitative increase in publications, with an emphasis on topics related to the economics of health. Figure 2 shows the number of patents and the distribution of GPPS stratified by regions of Brazil.

Figure 2. Geographical distribution, being (A) number of patents and (B) number of Graduate Programs in Pharmaceutical Sciences in Brazil (n= 69)

The analysis of total patents by region (Figure 2A) shows a significant contribution from GPPS in the Northeast (n = 973) and Southeast (n = 945), indicating a relevant innovative dynamic in both regions. Although the Southeast has traditionally concentrated the main science, technology, and innovation indicators in the country (IPEA, 2020), the data suggest that the Northeast has achieved a comparable performance. This result should be interpreted with caution, taking into account the recent expansion of graduate programs in the Northeast region, investments in human resource development, and regionally focused public policies aimed at strengthening institutional innovation structures (Gazzoni et al., 2025; McManus et al., 2021; Neves & Barbosa, 2020).

This distribution of patent licensing supports the idea that innovative capacity is not limited to regions with higher industrial density, but can emerge in contexts where there is continued investment in human resource qualification and in promoting the protection of technical-scientific production. In this sense, the findings are consistent with studies advocating the decentralization of innovation as a result of effective regional strategies for institutional strengthening (Fernandes et al., 2023). Thus, it is suggested that the national graduate system has expanded its territorial reach in terms of generating applicable knowledge. Furthermore, rather than indicating superiority over the Southeast, the North-eastern performance highlights how institutional incentives and regional policies can partially offset historical structural disadvantages.

The distribution of GPPS frequency by region (Figure 2B) shows a concentration in the Southeast (n = 28), followed by the Northeast (n = 13), South (n = 13), North (n = 8), and Central-West (n = 7). Our findings corroborate the historical concentration of higher education institutions and research centers in the Southeast axis (IPEA, 2020). However, when compared to technological production data (Figure 2A), it is evident that the number of programs does not necessarily determine the volume of patents. Although the Northeast region exhibited a number of patents comparable to, or even higher than its number of programs, this finding should be interpreted with caution. The present study did not normalize patent counts by variables such as faculty size, program longevity, or other structural characteristics. Therefore, it is not possible to draw conclusions regarding relative efficiency. Several studies have demonstrated that well-structured regional policies and the consolidation of local innovation networks can offset structural limitations and generate significant associations (De Noni et al., 2018; Leckel et al., 2020; Min et al., 2020; Zhao & Li, 2023). Thus, the data reinforce the idea that innovative performance depends not only on the number of programs but also on the quality of institutional coordination and local strategies to promote innovation.

After the descriptive characterization of the GPPS, an analysis was conducted to identify the factors associated with the total number of patents.

Figure 3. Histogram of the patent variable for the 69 Brazilian Graduate Programs in Pharmaceutical Sciences

The response variable (number of total patents) was initially explored using a histogram. The distribution showed right-skewness, indicating that most programs have a low number of patents, with a few programs exhibiting higher values (Figure 3).

A multiple linear regression model was fitted with a square root transformation of the response variable due to its positively skewed distribution. High variance inflation factor values were obtained for the variables completed doctoral supervision (VIF = 11.82), published articles (VIF = 10.46), and Qualis index (VIF = 10.86), therefore they were excluded. After selecting variables using the Akaike Information Criterion (AIC), considering the stepwise method, followed by the use of the Likelihood Ratio Test (LRT) and assessing significance of the parameters by the Wald test, the adjusted model included the following variables: completed postdoctoral supervisions, MHDI, and completed undergraduate research supervisions. Its equation is given by:

where, xki corresponds to k-th variable values in the model to i-th program, the parameters βk are the regression coefficients associated with the respective xk variables (x1: completed postdoctoral supervisions; x2: MHDI; e x3: completed undergraduate research supervisions), and e yi is the number of patents to i-th program.

The parameter estimates are presented in Table 5. All predictor variables were statistically significant (p < 0.05), with MHDI having a negative effect on the total number of patents, while completed postdoctoral supervisions and completed undergraduate research supervisions had a positive effect on the outcome.

Table 5. Parameter estimates for the model of multiple linear regression, along with their respective standard errors and p-values from the Wald test

Parameter	Estimate	Standard error	p-value
β0	26.6976	5.432509	< 0.0001*
β1	0.0415	0.009044	< 0.0001*
β2	-31.3149	6.837956	< 0.0001*
β3	0.0084	0.002068	0.0001*

Note: *Significant at 5%.

The model’s adequacy was confirmed by residual analysis (Figure 4), which met the assumptions of normality (Shapiro-Wilk test; W = 0.9883, p-value = 0.7701), and homoscedasticity (Breusch-Pagan test; BP = 2.3942, df = 3, p-value = 0.4947). Sensitivity was assessed using Cook’s distance and leverage (Figure 4). Analyzing standardized residuals versus leverage, although some observations show higher residuals or moderate leverage, they do not exceed the usual limits of Cook’s distance, indicating that the model fit is not excessively dependent on individual points. The simulated envelope graph has 4.35% of points out of envelope and also indicates that the model is adequate (Figure 5).

Figure 4. Residual plots of the fitted model necessary for evaluating the assumptions of normality, and homoscedasticity

Figure 5. Simulated envelope graph with a 95% confidence interval

The Analysis of Variance (ANOVA) of the model was significant (p < 0.0001), accounting for 53.78% of the total variability in the patents (Table 6).

Table 6. Analysis of variance of the fitted multiple linear regression model

Source of variation	GL	SQ	QM	p-value
Regression	3	319.1966	106.3989	< 0.0001*
Residuals	65	274.302	4.220
Total	68	593.499

Note: *Significant at 5%.

Additionally, a multiple correspondence analysis (MCA) was conducted to explore associations between patent licensing (present/absent) and categorical variables related to academic production, institutional profile, and regional context.

The resulting perceptual map (Figure 6) highlights a clear association between the presence of patent licensing and programs with above-median indicators in terms of scientific production, number of faculty members, postdoctoral supervision, completed master’s and doctoral supervisions. Conversely, the absence of licensing was associated with programs exhibiting less consolidated profiles and below-median indicators. The first two axes of the MCA accounted for 74.2% of the data variability.

Figure 6. Correspondence map for the variables patent licensing (L) and the variables region (Reg), total articles (ART), completed postdoctoral supervisions (PD), total completed master’s and doctoral supervisions (MD), faculty members (DOC), Qualis index (Q), and academic degree (GA). Legend: GA:M – master’s; GA:D – doctorate; GA:MD – master’s/doctorate; GA:MP – professional master’s; GA:MDP – professional master’s/doctorate; Reg:SE – Southeast; Reg:S – South; Reg:NE – Northeast; Reg:CO – Midwest; Reg:N – North; DOC – faculty members; PD – postdoctoral supervision; MD – master’s/doctorate supervisions; ART – articles; Q – Qualis; NOT – CAPES score; TEMP – program duration; IDH – Human Development Index; L – licensed patents

The evaluation of technology transfer has been considered a complex and multifaceted process, involving the analysis of several dimensions such as personnel, processes, budget, valuation, intellectual property, marketing, integration, management, R&D in technologies, relationships, environment, and society. Thus, the more structured a university is, as reflected in the performance of its graduate programs and the activity of its TTO, the better the conditions for successful technology transfer (Silva et al., 2025). Figure 7 presents the perceptual map, which accounts for 90.4% of the variability in the category cloud.

Figure 7. Correspondence map for the variables licensed patents (L) and the variables program rating (NOT), program duration (TEMP), Qualis score (Q), and the Human Development Index (IDH) of the municipality where the programs are located

These findings indicate that patent licensing (L:P) is associated with institutional and contextual variables performing above the median, such as longer program duration (TEMP:1), higher CAPES ratings (NOT:1), superior Qualis index (Q:1), and location in municipalities with higher HDI. Similarly, the absence of licensed patents (L:A) is associated with low output of these same variables (Q:0,TEMP:0, NOT:0, IDH:0, respectively) (Figure 7). This correspondence map accounted for 90.4% of the data variability.

These results support the hypothesis that technology licensing is embedded within a broader ecosystem of academic and institutional maturity, reflecting not only the technical capacity of the GPPS but also their placement in more developed urban contexts. However, regarding the total number of patents, the regression results show that traditional indicators of academic productivity, such as total bibliographic output, did not remain independently associated after adjustment. Instead, variables directly related to human capital formation, such as completed postdoctoral and undergraduate research supervisions, demonstrated a positive association with patent generation. This reinforces the concept that the internal dynamics of qualification and training within programs may be more decisive for inventive output than bibliometric volume alone (Chen & Chen, 2025; McChesney et al., 2025).

As noted by Fernandes et al. (2023), programs with longer duration and positive CAPES evaluations tend to exhibit more robust innovation management structures, which enhance the generation and transfer of technologies. Conversely, the association between the absence of licensing (L:A) and indicators below the median may reveal structural or institutional limitations that restrict the conversion of scientific knowledge into technological assets, particularly in regions with lower HDI or less consolidated programs (Fernandes et al., 2023).

The association between patent licensing and the HDI highlights the relationship of the territorial context to the innovative performance of graduate programs. Municipalities with MHDI above the median show a higher occurrence of programs that license patents, suggesting that more developed contexts in terms of income, education, and longevity create more favorable conditions for technology transfer. We emphasize that the apparent discrepancy between the negative association of the HDI with total patents in the regression model, and the positive association of a higher HDI with patent licensing in the multiple correspondence analysis, should be interpreted with caution. Our findings stem from different analytical approaches and distinct outcomes (patent counts versus the presence of licensing). These results should be considered exploratory and indicative of potential complexity in the relationship between territorial development and innovation outcomes, given that no formal robustness checks or additional analyses were conducted to reconcile them (Garcia & Araújo, 2022; Ye et al., 2024).

This finding is consistent with the literature, which emphasizes the importance of territorial capacities in strengthening innovation ecosystems, especially in countries with pronounced regional inequalities, such as Brazil (Oliveira & Pimentel Neto, 2025). At the same time, the findings also indicate the occurrence of the so-called “Southern paradox”: regions with high HDI may still exhibit low licensing levels due to institutional priorities focused on traditional academic outcomes rather than applied innovation (Bastos & Frenkel, 2017; Soares et al., 2020).

On the other hand, the absence of patent licensing (L:A) is more associated with regions such as the South (Reg:S) and GPPS offering exclusively professional or academic master’s programs (GA:MP and GA:M). Although the Southern region generally exhibits high HDI and a solid educational and productive base, our findings indicate its association with the lack of patent licensing. While this may initially seem counterintuitive, it reflects institutional heterogeneity within the region, marked by differences between more established programs and those with emerging structures, which may be associated with part of this result. Many programs in the South, despite being located in municipalities with high HDI, are characterized by a focus on academic training and traditional scientific production, often at the expense of fostering a culture of innovation and technology transfer (Etzkowitz & Zhou, 2017; Fernandes et al., 2023).

Furthermore, factors such as the absence of internal policies focused on intellectual property, weak engagement with the regional productive sector, and the lack of well-structured NITs can act as barriers to effective licensing, even in socioeconomically favorable environments. Therefore, a high HDI, although an important facilitator, does not guarantee innovative performance on its own; it is also necessary to consider institutional strategies and the level of technological maturity of the programs involved (Silva et al., 2025).

The results indicate that patent licensing in graduate programs is not determined solely by academic indicators or the quality of scientific output, but rather by the interaction of multiple institutional, structural, and territorial factors. Program maturity, reflected in variables such as years of operation, CAPES evaluation scores, and faculty qualifications, combined with the presence of consistent internal innovation policies and the strengthening of NITs, is essential for effective knowledge transfer (Santos et al., 2024; Veiga & Menezes, 2023). This discovery aligns with the Triple Helix model, in which universities, industry, and government share responsibility for the outcomes of innovation. Furthermore, industry is not a passive recipient, but rather an active co-creator of technological development (López Jiménez & Dittmar, 2019).

Concurrently, the socioeconomic context in which the programs are embedded, represented by the MHDI, is associated with the opportunities and constraints regarding university-industry collaboration (Lima et al., 2021; Lima et al., 2022; Nsanzumuhire & Groot, 2020; Yu & Yuizono, 2021). Therefore, promoting patent licensing requires not only investment in academic training and research infrastructure but also the development of more equitable, coordinated innovation ecosystems that are sensitive to regional specificities. Our study underscores the need for integrated public policies that consider both the internal strengthening of institutions and the territorial inequalities shaping the country’s innovative capacity (Lee, 2019).

Limitations include those related to the methodological design followed in this study. The reliance on secondary public domain data, such as that available on the Sucupira and Lattes platforms, stands out, characterizing possible information bias due to inconsistencies in completion, late updating, or underreporting by faculty and programs. Furthermore, because this is a cross-sectional design, the analysis does not allow for establishing causal relationships between the factors investigated and patent production; therefore, only associations are possible. Furthermore, if a patent has co-authors in more than one GPPS, it may have been counted twice. Other contextual variables that were not considered include the existence and effectiveness of institutional policies to encourage innovation, the specific budget allocated to intellectual property protection, or the expertise of the NITs, all of which may be associated with the observed results. Finally, our findings limit generalizability to other areas of knowledge due to the exclusive focus on Pharmaceutical Sciences.

Furthermore, this is the first Brazilian study on the topic and provides innovative results that can enhance public policies. Regression analysis identified significant predictors linked to academic dynamics and MHDI. At the same time, multiple correspondence analyses revealed that patent licensing is strongly associated with a consolidated institutional profile, characterized by longer existence, better CAPES ratings, relevant scientific production, and location in municipalities with a high MHDI. In this sense, our findings reinforce that, while fundamental, bibliographic production alone is not enough to ensure the translation of knowledge into technological innovation. Therefore, it is crucial to advance policies that strengthen intellectual property management, encourage university-business collaboration, and reduce regional differences, converting scientific excellence into technological and social transformation. These findings suggest that science and technology policies should go beyond generic incentives and focus on strengthening institutional maturity, technology transfer capabilities, and regional innovation ecosystems in order to effectively translate scientific excellence into technological and social benefits.

CONCLUSION

Patent licensing, although still in its early stages within the Brazilian GPPS system, showed consistent links with programs located primarily in the Southeast and Northeast regions and characterized by greater institutional contributions. This includes higher CAPES index rankings, a longer history of activity, a broader academic structure, and location in municipalities with higher HDI levels. Our results reinforce the idea that technology commercialization tends to emerge within a broader ecosystem of institutional maturity and a favorable territorial context.

The factors associated with the total number of patents were considered different from those associated with licensing. Multiple regression analysis demonstrated that patent generation was positively associated with completed postdoctoral supervisions and completed undergraduate research supervisions, highlighting the central role of human capital development, both in advanced and initial stages, in fostering inventive activity. In this sense, we suggest that an internal research training structure within GPPS is fundamental for the production of patent applications.

On the other hand, presentations at scientific events and the municipal HDI (MHDI) showed negative associations with the total number of patents. This finding shows that a greater emphasis on academic expansion does not necessarily translate into greater patent production and may demonstrate different institutional priorities between scientific visibility and intellectual property protection. Furthermore, the distinct behavior of the MHDI across the two analytical approaches suggests that territorial development may be more closely associated with the commercialization of technologies than with their initial generation.

In parallel, these findings demonstrate that patent generation and licensing outcomes are related, yet specific, dimensions of innovation performance. While licensing appears to depend more heavily on institutional maturity and territorial context, patent generation is more closely linked to internal academic dynamics and the qualification of human capital. Thus, strategies aimed at strengthening technological innovation in the GPPS should not focus exclusively on increasing bibliographic productivity, but also on reinforcing research training, intellectual property management structures, and aligning scientific activity with innovation-oriented aims, to contribute to the advancement of the Brazilian pharmaceutical sector.

Despite its contributions, this study is limited to the area of pharmaceutical sciences and quantitative indicators of patent activity, without addressing the qualitative dimensions of technology transfer or economic outcomes. As perspectives, future research should advance in three main directions: comparative analyses across different areas of knowledge, qualitative investigations of institutional innovation practices and the performance of technology transfer agencies, and longitudinal studies capable of capturing the causal mechanisms linking academic maturation to technological outcomes.

Finally, this study contributes to the understanding of innovation in universities, demonstrating that scientific excellence alone may be insufficient for generating technology, and highlighting the central role of institutional and territorial maturity in converting knowledge into innovation within the Brazilian pharmaceutical sector.

Acknowledgment

This study was financed in part by National Council for Scientific and Technological Development – CNPq – Finance Code 304131/2022–9, Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG), and by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001.

References

Abelson, J., Wagner, F., DeJean, D., Boesveld, S., Gauvin, F.-P., Bean, S., Axler, R., Petersen, S., Baidoobonso, S., Pron, G., Giacomini, M., & Lavis, J. (2016). Public and patient involvement in health technology assessment: A framework for action. International Journal of Technology Assessment in Health Care, 32(4), 256–264. https://doi.org/10.1017/S0266462316000362

Aldieri, L., Sena, V., & Vinci, C. P. (2018). Domestic R&D spillovers and absorptive capacity: Some evidence for US, Europe and Japan. International Journal of Production Economics, 198, 38–49. https://doi.org/10.1016/j.ijpe.2018.01.015

Bastos, V. D., & Frenkel, J. (2017). Paradoxical results of the innovation policy in Brazil. Revista do BNDES, 47, 359–431. https://web.bndes.gov.br/bib/jspui/bitstream/1408/14020/2/Resultados paradoxais da política de inovação no Brasil_P.pdf

Bazán, B. B., Barandika, A. G., Urtiaga, G. M. K., & Arriortua, M. M. I. (2012). Design of a formation program for Ph students based on the dublin-descriptors: Implementation and evaluation under the EFQM-inspired spirit of excellence and innovation. 6th Edition. INTED2012 Proceedings.

Bloom, N., Davis, S. J., & Zhestkova, Y. (2021). COVID-19 shifted patent applications toward technologies that support working from home. AEA Papers and Proceedings, 111, 263–266. https://doi.org/10.1257/pandp.20211057

Brazil. (2016). Law No. 13,243, of January 11, 2016: Provides for incentives for scientific development, research, scientific and technological training, and innovation. Diário Oficial Da União: Seção 1, Brasília, DF. https://www.planalto.gov.br/ccivil_03/_ato2015-2018/2016/lei/l13243.htm

Brazil. (1961). Law No. 4,024 of December 20, 1961: Establishes the guidelines and bases of national education. Diário Oficial Da República Federativa Do Brasil, Brasília. https://www2.camara.leg.br/legin/fed/lei/1960-1969/lei-4024-20-dezembro-1961-353722-publicacaooriginal-1-pl.html

Brazil. (2015). Constitutional Amendment No. 85, 2015: Amends and adds provisions to the Federal Constitution to update the treatment of science, technology, and innovation activities. https://www.planalto.gov.br/ccivil_03/constituicao/emendas/emc/emc85.htm

Brazil. (2018). Decree No. 9,283 of February 7, 2018: Regulates Law No. 10,973 of December 2, 2004, Law No. 13,243 of January 11, 2016, Art. 24, §3, and Art. 32, §7, of Law No. 8,666 of June 21, 1993, Art. 1 of Law No. 8,010 of March 29, 1990, and Art. 2, Caput, Item I. https://www.planalto.gov.br/ccivil_03/_ato2015-2018/2018/decreto/d9283.htm

Brazil. (2019). Coordination for the Improvement of Higher Education Personnel (CAPES). Evaluation Report of the Pharmacy Area – 2013–2016 Quadriennium. Brasília, DF. https://www.gov.br/capes/pt-br/centrais-de-conteudo/relatorio-quadrienal-farmacia-2019

Brazil. (2022a). Ministry of Science, Technology, and Innovations – MCTI. National indicators of science, technology, and innovation: 2022. Brasília: MCTI. https://www.gov.br/mcti/pt-br/acompanhe-o-mcti/indicadores/paginas/publicacoes/arquivos/indicadores_cti_2022.pdf

Brazil. (2024a). Ministry of Education. Ordinance No. 92, dated March 27, 2024. Establishes the criteria for the distribution of the quota of scholarships or educational grants allocated to the Pro-Rectorate, or equivalent body, responsible for graduate programs, within the scope of institutional graduate support programs administered by the Directorate of Domestic Programs and Scholarships. Brasília: Diário Oficial Da União. https://cad.capes.gov.br/ato-administrativo-detalhar?idAtoAdmElastic=14562

Brazil. (2024b). Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – CAPES. Plataforma Sucupira. https://sucupira.capes.gov.br/

Brasil, A. (2020). Building a national system of research and graduate education: how did the university become the house of science in Brazil? Revista NUPEM, 12(27), 222–253. https://doi.org/10.33871/nupem.2020.12.27.222-253

Brasil, A. (2022b). Multidimensionality through self-evaluation: From theory to practice in the Brazilian graduate system, 53, 97-106. https://doi.org/10.22163/fteval.2022.546

Brazil. (1965). Federal Council of Education. Opinion No. 977, of December 3, 1965. Define e regulamenta os cursos de pós-graduação no Brasil, Brasília - DF. https://www.gov.br/capes/pt-br/centrais-de-conteudo/parecer-cesu-977-1965-pdf

Brazil. (2025). Ministry of Science, Technology, Innovations, and Communications (MCTIC). Secretariat for Technological Development and Innovation. FORMICT Report – Base Year 2024. In Política de Propriedade Intelectual das Instituições Científicas e Tecnológicas e de Inovação do Brasil. https://www.gov.br/mcti/pt-br/acompanhe-o-mcti/propriedade-intelectual-e-transferencia-de-tecnologia/propriedade-intelectual-e-transferencia-de-tecnologia-relatorios

Capponi, G., Martinelli, A., & Nuvolari, A. (2022). Breakthrough innovations and where to find them. Research Policy, 51(1), 104376. https://doi.org/10.1016/j.respol.2021.104376

Carvalho, E. S., & Real, G. C. M. (2021). Intellectual production on Qualis periodicals in the field of education: A dialogue with academic research (2008–2018). Ensaio: Avaliação e Políticas Públicas em Educação, 29(112), 595–617. https://doi.org/10.1590/s0104-40362021002902397

Casagrande, E. E., Bueno, G. W., Santos, D. F. L., & Machado, L. P. (2022). Entrepreneurship in practice: Connections among science, the market, and society. Editora Ibero-Americana de Educação. https://doi.org/10.47519/EIAE.978-65-86839-10-4

Castro, A. B. C., Miranda, A. L. B., Nodari, C. H., & Ferreira Andrade, T. B. (2021). Knowledge transfer: Contributions of knowledge management and absorptive capacity. Innovar, 32(84). https://doi.org/10.15446/innovar.v32n84.99864

Castro, P. H. G. R. P., & Oliveira Filho, D. (2022). Metric indicators for the evaluation of graduate programs in Brazil: from Qualis to multi-criteria. Anais Da Academia Brasileira de Ciências, 94(suppl 3). https://doi.org/10.1590/0001-3765202220210962

CEPR. (2022). Centre for Economic Policy Research – CEPR. The innovation response to the covid crisis: A new ebook. https://cepr.org/voxeu/columns/innovation-response-covid-crisis-new-ebook

Chávez-Bustamante, F., Rojas-Mora, J., & Wong, S. (2024). Balancing act: Navigating the tensions between patenting practices and open science in Chilean academic research. Technology in Society, 77, 102584. https://doi.org/10.1016/j.techsoc.2024.102584

Chen, G., & Chen, J. (2025). A study of postgraduate students’ research training environment and innovation capabilities: The mediating role of academic interest and aspiration. Innovations in Education and Teaching International, 62(2), 674–687. https://doi.org/10.1080/14703297.2024.2325005

Chen, K., Zhang, C., Feng, Z., Zhang, Y., & Ning, L. (2022). Technology transfer systems and modes of national research institutes: evidence from the Chinese Academy of Sciences. Research Policy, 51(3), 104471. https://doi.org/10.1016/j.respol.2021.104471

Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128. https://doi.org/10.2307/2393553

Cóser, I., Brandão, M. A. F., Raposo, N. R. B., & Gonçalves, E. (2018). Determinants of patents in life and health sciences at federal universities in Minas Gerais, Brazil: A panel data analysis for the period 1995–2016. Cadernos de Saúde Pública, 34(6), e00097517. https://doi.org/10.1590/0102-311x00097517

Couto, M. V. L., Neves, A. R. V., Figueiredo, E. R., Silveira, L. D. S., Costa, L. V., & Barroso, W. B. G. (2016). Main obstacles hindering innovation in public pharmaceutical institutions in Brazil. Vigilância Sanitária Em Debate, 4(2), 2-7. https://doi.org/10.3395/2317-269x.00592

Craiut, L., Bungau, C., Bungau, T., Grava, C., Otrisal, P., & Radu, A.-F. (2022). Technology transfer, sustainability, and development, worldwide and in Romania. Sustainability, 14(23), 15728. https://doi.org/10.3390/su142315728

De Noni, I., Orsi, L., & Belussi, F. (2018). The role of collaborative networks in supporting the innovation performances of lagging-behind European regions. Research Policy, 47(1), 1–13. https://doi.org/10.1016/j.respol.2017.09.006

De Sá Barreto, F. C., Domingues, I., & Borges, M. N. (2014). The Brazilian National Graduate Program, past, present and future: A short review. Policy Futures in Education, 12(5), 695–706. https://doi.org/10.2304/pfie.2014.12.5.695

Dotta, A. G., & Neves, O. P. (2015). History of Master’S and Doctoral Degree Training From 1965-2013 in Brazil: a Map of the Public Education Service in the Postgraduate System. European Scientific Journal, 1(October), 11(10) 1857–7881. https://eujournal.org/index.php/esj/article/view/6456

Edwards-Schachter, M. (2018). The nature and variety of innovation. International Journal of Innovation Studies, 2(2), 65–79. https://doi.org/10.1016/j.ijis.2018.08.004

Etzkowitz, H., & Zhou, C. (2017). Triple Helix: University-Industry-Government innovation and entrepreneurship. Estudos Avançados, 31(90), 23–48. https://doi.org/10.1590/s0103-40142017.3190003

Fernandes, D. R. A., Gadelha, C. A. G., & Maldonado, J. M. S. de V. (2021). Vulnerabilities of domestic pharmaceutical and biotechnological industries in the context of the COVID-19 pandemic. Cadernos de Saúde Pública, 37(4), e00254720. https://doi.org/10.1590/0102-311x00254720

Fernandes, D. R. A., Gadelha, C. A. G., & Maldonado, J. M. S. de V. (2023). Innovation strategies for medicines and vaccines within the scope of CEIS – Models, mechanisms, and expectations. Saúde Em Debate, 47(138), 377–392. https://doi.org/10.1590/0103-1104202313801

Fontes, M. (2005). The process of transformation of scientific and technological knowledge into economic value conducted by biotechnology spin-offs. Technovation, 25(4), 339–347. https://doi.org/10.1016/j.technovation.2003.08.004

Garcia, R., & Araújo, V. (2022). Determinants of university and industrial patents in Brazilian regions: a spatial panel approach. Blucher Engineering Proceedings, 555–569. https://doi.org/10.5151/vi-enei-892

Garthwaite, C. (2025). Economic markets and pharmaceutical innovation. Journal of Economic Perspectives, 39(2), 3–26. https://doi.org/10.1257/jep.20251438

Gazzoni, F., Scherer, F. L., Soares, S. de S., Piveta, M. N., & Pivetta, N. P. (2025). Internationalization of higher education institutions in Rio Grande do Sul from the perspective of institutional. Avaliação: Revista da Avaliação da Educação Superior (Campinas), 30. https://doi.org/10.1590/1982-57652025v30id2836311

Gerenutti, M., Vila, M. M. D. C., & Sandano, W. (2009). The research policy and implementation of the master’s program in pharmaceutical sciences at the University of Sorocaba. Avaliação: Revista Da Avaliação Da Educação Superior (Campinas), 14(3), 699–712. https://doi.org/10.1590/S1414-40772009000300010

Granjeiro, P. A., Lima, L. M. E. S., Lacerda, J. A. D. S., Cordeiro, V. L. P., Rodrigues, A. H. P., Campos, P. M., & Baldoni, A. O. (2025). Intellectual property management at a Brazilian public university: The case of UFSJ. Revista de Gestão e Secretariado, 16(7), e5121. https://doi.org/10.7769/gesec.v16i7.5121

Guimarães, A. R., Brito, C. S., & Santos, J. A. B. (2020). Expansion and financing of graduate education and regional inequality in Brazil (2002–2018). Práxis Educacional, 16(41), 47–71. https://doi.org/10.22481/praxisedu.v16i41.7244

Htay, T. T., Saw, Y. M., Levinson, J., Kadri, S. M., Brady, A. H., Acquin, C. S., & Htet, A. S. (2021). Health policy in developing countries. In R. Detels, Q. A. Karim, F. Baum, L. Li & A. H. Leyland (Eds.), Oxford Textbook of Global Public Health (pp. 301–322). Oxford University PressOxford. https://doi.org/10.1093/med/9780198816805.003.0021

INPI. (2025). Brazilian National Institute of Industrial Property. INPI 2024: Depositor Ranking. In Presidência. Diretoria Executiva. Assessoria de Assuntos Econômicos (AECON). https://www.gov.br/inpi/pt-br/central-de-conteudo/noticias/fotos/INPIRankingsdeDepositantes2024final.pdf

IPEA. (2020). Brazilian Institute for Applied Economic Research. Public policies for science and technology in Brazil: Scenario and recent evolution. Technical Note No. 92. Brasília: Ipea.

Jesus, C. S., Cardoso, D. O., & Souza, C. G. (2023). Motivational factors for patenting: A study of the Brazilian researchers profile. World Patent Information, 75, 102241. https://doi.org/10.1016/j.wpi.2023.102241

Kamia, F. D., & Vargas, M. A. (2023). Foreign capital and structural change in the Brazilian private healthcare services market. Cadernos de Saúde Pública, 39(9), e00171222. https://doi.org/10.1590/0102-311xpt171222

Kato, M. (2020). Founders’ human capital and external knowledge sourcing: Exploring the absorptive capacity of start-up firms. Economics of Innovation and New Technology, 29(2), 184–205. https://doi.org/10.1080/10438599.2019.1598670

Katz, I. S. S., Prado, F. O., & Souza, M. A. (2018). Implementation and structuring process of the technological innovation center. Revista Gestão & Tecnologia, 18(1), 225–251. https://doi.org/10.20397/2177-6652/2018.v18i1.1112

Kent, B. A., Holman, C., Amoako, E., Antonietti, A., Azam, J. M., Ballhausen, H., Bediako, Y., Belasen, A. M., Carneiro, C. F. D., Chen, Y.-C., Compeer, E. B., Connor, C. A. C., Crüwell, S., Debat, H., Dorris, E., Ebrahimi, H., Erlich, J. C., Fernández-Chiappe, F., Fischer, F., … Weissgerber, T. L. (2022). Recommendations for empowering early career researchers to improve research culture and practice. PLOS Biology, 20(7), e3001680. https://doi.org/10.1371/journal.pbio.3001680

Leal, G. A., Bresciani, L. P., & Machado, C. (2022). Determining factors for pharmaceutical innovation and the health industrial complex in Brazil and the BRICS. International Journal of Business, Economics and Management, 9(3), 93–108. https://doi.org/10.18488/62.v9i3.3032

Leckel, A., Veilleux, S., & Dana, L. P. (2020). Local open innovation: A means for public policy to increase collaboration for innovation in SMEs. Technological Forecasting and Social Change, 153, 119891. https://doi.org/10.1016/j.techfore.2019.119891

Lee, S. J. (2019). Academic entrepreneurship: exploring the effects of academic patenting activity on publication and collaboration among heterogeneous researchers in South Korea. The Journal of Technology Transfer, 44(6), 1993–2013. https://doi.org/10.1007/s10961-018-9711-3

Leite, S. N., & Galdino, S. L. (2013). Undergraduate and graduate education in pharmacy in Brazil: Convergences and capacity strengthening. Interface - Comunicação, Saúde, Educação, 17(47), 991–994. https://doi.org/10.1590/1807-57622013.0688

Lima, J. C. F., Torkomian, A. L. V., Pereira, S. C. F., Oprime, P. C., & Hashiba, L. H. (2021). Socioeconomic impacts of university–industry collaborations – A systematic review and conceptual model. Journal of Open Innovation: Technology, Market, and Complexity, 7(2), 137. https://doi.org/10.3390/joitmc7020137

Lima, P. A. B., Paião Júnior, G. D., Santos, T. L., Furlan, M., Battistelle, R. A. G., Silva, G. H. R., Ferraz, D., & Mariano, E. B. (2022). Sustainable human development at the municipal level: A data envelopment analysis index. Infrastructures, 7(2), 12. https://doi.org/10.3390/infrastructures7020012

López Jiménez, D., & Dittmar, E. C. (2019). Triple helix model of innovation: university, industry and government interactions. In: EDULEARN19 Proceedings. IATED, 2570–2574. https://doi.org/10.21125/edulearn.2019.0705

Mann, D. M., Chokshi, S. K., Lebwohl, R., Mainiero, M., Dinh-Le, C., Driscoll, K., Robinson, S., & Egger, H. (2019). Building digital innovation capacity at a large academic medical center. Npj Digital Medicine, 2(1), 13. https://doi.org/10.1038/s41746-019-0088-y

Martinkenaite, I., & Breunig, K. J. (2016). The emergence of absorptive capacity through micro–macro level interactions. Journal of Business Research, 69(2), 700–708. https://doi.org/10.1016/j.jbusres.2015.08.020

Martins, F. S., Kollipara, S., Sivadasu, P., Yu, M., Severino, P., & Souto, E. (2025). The innovation paradox in emerging pharmaceutical markets: Barriers and opportunities for sustainable development. Pharmaceutical Research, 42(6), 1047–1058. https://doi.org/10.1007/s11095-025-03856-w

McChesney, E. T., Weng, Y.-H., Winkler, C. E., Selznick, B., & Mayhew, M. J. (2025). The effect of interdisciplinary training on cultivating graduate student innovation capacities. Innovative Higher Education, 51, 925-950. https://doi.org/10.1007/s10755-025-09848-3

McManus, C., Baeta Neves, A. A., Carvalho, C. H., Souza Filho, A. G., Freire, D. M. G., Fioravanti, M. C. S., Báo, S. N., de Medeiros, I. A., Diniz Filho, J. A., Diaz, B. L., de Carvalho, E. R., Audy, J. L. N., Silva Filho, M. de C., Gimenez, J. R., Carvalho, M., da Silva, R. R., & Napimoga, M. H. (2023). Considerations for continued expansion of the Brazilian post-graduate system. Frontiers in Education, 8, 987200. https://doi.org/10.3389/feduc.2023.987200

McManus, C., Baeta Neves, A. A., Oliveira, C., & De Oliveira Castro, H. (2021). Postgraduate internationalisation in Brazil. International Journal of Scientific Research and Management, 9(07), 1791–1805. https://doi.org/10.18535/ijsrm/v9i07.el02

McManus, C., Baeta Neves, A. A., Souza Filho, A. G., Rosa, A. A. S., Carvalho, C. H., Freire, D. M. G., Fioravanti, M. C. S., Báo, S. N., de Medeiros, I. A., Diniz-Filho, J. A., Diaz, B. L., Carvalho, E. R., Audy, J. L. N., Silva-Filho, M. C., Gimenez, J., Carvalho, M., Silva, R. R., & Napimoga, M. H. (2022). Assessment of the Brazilian postgraduate evaluation system. Frontiers in Education, 7, 1036108. https://doi.org/10.3389/feduc.2022.1036108

Messeni Petruzzelli, A., & Murgia, G. (2023). A multilevel analysis of the technological impact of university-SME joint innovations. Journal of Small Business Management, 61(4), 1896–1928. https://doi.org/10.1080/00472778.2021.1874003

Min, S., Kim, J., & Sawng, Y.-W. (2020). The effect of innovation network size and public R&amp;D investment on regional innovation efficiency. Technological Forecasting and Social Change, 155, 119998. https://doi.org/10.1016/j.techfore.2020.119998

Moletta, J., & Pilatti, L. A. (2025). Motivations and challenges for patent filing by universities: A systematic literature review. SAGE Open, 15(3). https://doi.org/10.1177/21582440251358985

Nazareno, E., & Herbetta, A. F. (2020). Brazilian graduate education: Its asymmetrical construction and some attempts at overcoming it. Estudos de Psicologia, 24(2), 103-112 https://doi.org/10.22491/1678-4669.20190013

Nenadic, O., & Greenacre, M. (2007). Correspondence analysis in R, with two- and three-dimensional graphics: The CA package. Journal of Statistical Software, 20(3). https://doi.org/10.18637/jss.v020.i03

Neves, C. E. B., & Barbosa, M. L. O. (2020). Internationalization of higher education in Brazil: advances, obstacles, and challenges. Sociologias, 22(54), 144–175. https://doi.org/10.1590/15174522-99656

Nsanzumuhire, S. U., & Groot, W. (2020). Context perspective on University-Industry Collaboration processes: A systematic review of literature. Journal of Cleaner Production, 258, 120861. https://doi.org/10.1016/j.jclepro.2020.120861

Nunes, E. D., Ferreto, L. E., & Barros, N. F. (2010). Graduate studies in collective health in Brazil: Trajectory. Ciência & Saúde Coletiva, 15(4), 1923–1934. https://doi.org/10.1590/S1413-81232010000400008

Ogugua, J. O., Olorunsogo, T. O., Muonde, M., Maduka, C. P., & Omotayo, O. (2024). Developing countries’ health policy: A critical review and pathway to effective healthcare systems. International Journal of Science and Research Archive, 11(1), 371–382. https://doi.org/10.30574/ijsra.2024.11.1.0069

Oliveira, A. B., & Pimentel Neto, J. G. (2025). Innovation networks in a peripheral-immature context: An analysis of granted patent data in Maranhão, Brazil. ARACÊ, 7(4), 16746–16764. https://doi.org/10.56238/arev7n4-067

Oliveira, J. J. (2016). Science, technology, and innovation in Brazil: Power, politics, and bureaucracy in the decision-making arena. Revista de Sociologia e Política, 24(59), 129–147. https://doi.org/10.1590/1678-987316245907

Paranhos, J., Cataldo, B., & Pinto, A. C. de A. (2018). Creation, institutionalization, and functioning of technological innovation centers in Brazil: Characteristics and challenges. REAd. Revista Eletrônica de Administração (Porto Alegre), 24(2), 253–280. https://doi.org/10.1590/1413-2311.211.84988

Paranhos, J., França, C., & Mercadante, E. (2020). Regional innovation systems in the Brazilian pharmaceutical sector: A comparative study. Cadernos Do Desenvolvimento Fluminense, 14(15), 100–118. https://doi.org/10.12957/cdf.2018.47678

Paranhos, J., Perin, F. S., Mercadante, E., & Soares, C. (2019). Industry-university interaction strategies of large Brazilian pharmaceutical companies. Management Research: Journal of the Iberoamerican Academy of Management, 17(4), 494–509. https://doi.org/10.1108/MRJIAM-11-2018-0884

Reis, A. A., Leite, R. Â. S., Walter, C. E., Reis, I. B., Gonçalves, R., Martins, J., Branco, F., & Au‐Yong‐Oliveira, M. (2025). The hierarchical importance of patent’s characteristics to licensing: An analysis through Random Forest. Expert Systems, 42(2). https://doi.org/10.1111/exsy.13661

Riccaboni, M., & Verginer, L. (2022). The impact of the COVID-19 pandemic on scientific research in the life sciences. PLOS ONE, 17(2), e0263001. https://doi.org/10.1371/journal.pone.0263001

Rizzi, D. I., Mazzioni, S., Poli, O. L., & Moura, G. D. (2018). The triple helix model: Intellectual production in national and international journals. Revista Gestão Universitária Na América Latina - GUAL, 11(2), 110–132. https://doi.org/10.5007/1983-4535.2018v11n2p110

Ruas, C. M., Portela, R., Assis Acurcio, F., Alvares-Teodoro, J., Guerra Júnior, A. A., & Kesselheim, A. S. (2025). Pharmaceutical access in Brazil: challenges and opportunities. Globalization and Health, 21(1), 57. https://doi.org/10.1186/s12992-025-01141-4

Rücker Schaeffer, P., Fischer, B., & Queiroz, S. (2018). Beyond education: The role of research universities in innovation ecosystems. Foresight and STI Governance, 12(2), 50–61. https://doi.org/10.17323/2500-2597.2018.2.50.61

Ryazanova, O., & Jaskiene, J. (2022). Managing individual research productivity in academic organizations: A review of the evidence and a path forward. Research Policy, 51(2), 104448. https://doi.org/10.1016/j.respol.2021.104448

Santos, D. S., Lima, I. D., Tonelli, T. R. F., & Gustavo, C. (2024). Performance of technology transfer offices (TTOs) in the technology transfer process of the technology innovation centers (ISTs) in Brazil between 2007 and 2017. International Research Journal of Economics and Management Studies, 3(7). https://doi.org/10.56472/25835238/IRJEMS-V3I7P147

Scavuzzi, Â. M. A., Reis, V. G., Ramos, M. S., Souza, M. J. A., Winkler, I., & Pereira-Guizzo, C. de S. (2023). Application of scientific knowledge: Knowledge translation at a science and technology institute in public health. Cadernos de Saúde Pública, 39(11). https://doi.org/10.1590/0102-311xpt006523

Shakasimov, M. M. (2025). Characteristics of the early state formation: State and temple economy. Journal of Management and Economics, 5(5), 9–11. https://doi.org/10.55640/jme-05-05-03

Shigunov Neto, A., Giusti Trevisol, M., & Lourdes Pinto de Almeida, M. (2021). From the institutionalization of the graduate studies system to the national graduate studies plan (2011–2020): Challenges and perspectives. Revista Diálogo Educacional, 21(71). https://doi.org/10.7213/1981-416X.21.071.AO07

Siegel, D., Bogers, M. L. A. M., Jennings, P. D., & Xue, L. (2023). Technology transfer from national/federal labs and public research institutes: Managerial and policy implications. Research Policy, 52(1), 104646. https://doi.org/10.1016/j.respol.2022.104646

Silva, L. C. S., Sánchez-García, J. C., Gaia, S., & Resende, D. N. (2025). Assessment of technology transfer processes at Brazilian public universities. SAGE Open, 15(1). https://doi.org/10.1177/21582440251313608

Silva, M. R. S., Luft, M. C. M. S., & Olave, M. E. L. (2024). Reflections on the Implementation of the Self-Assessment Policy in Brazilian Graduate Education: The Perspective of Specialists. Avaliação: Revista da Avaliação da Educação Superior (Campinas), 29. https://doi.org/10.1590/1982-57652024v29id266729

Soares, T. J., Torkomian, A. L. V., & Nagano, M. S. (2020). University regulations, regional development and technology transfer: The case of Brazil. Technological Forecasting and Social Change, 158, 1–14.

Sousa, M. B., & Braga, I. A. (2023). Public policies and social inequality: The reality of the rural space in the Brazilian Northeast. MOITARÁ - Revista Do Serviço Social Da UNIGRANRIO, 1(8), 146–165.

Souza, D. L., Zambalde, A. L., Mesquita, D. L., Souza, T. A. de & Silva, N. L. C. (2020). Researchers’ perspectives on the challenges of research in Brazil. Educação e Pesquisa, 46. https://doi.org/10.1590/s1678-4634202046221628

Tanane, O. (2020). Roles of patents in economic development and integration. ECONOMICS, 8(1), 23–30. https://doi.org/10.2478/eoik-2020-0008

Tiberius, V., Schwarzer, H., & Roig-Dobón, S. (2021). Radical innovations: Between established knowledge and future research opportunities. Journal of Innovation & Knowledge, 6(3), 145–153. https://doi.org/10.1016/j.jik.2020.09.001

Tigre, P. B., Nascimento, C. V. M. F., & Costa, L. S. (2016). Windows of opportunities and technological innovation in the Brazilian pharmaceutical industry. Cadernos de Saúde Pública, 32(suppl 2). https://doi.org/10.1590/0102-311X00103315

Todorova, G., & Durisin, B. (2007). Absorptive capacity: Valuing a reconceptualization. Academy of Management Review, 32(3), 774–786. https://doi.org/10.5465/amr.2007.25275513

Veiga, C. C., & Menezes, A. B. (2023). Barriers to turning inventions into innovations in Brazilian public universities. Revista de Gestão Dos Países de Língua Portuguesa, 22(2), 102–127. https://doi.org/10.12660/rgplp.v22n2.2023.88648

Velloso, A. (2014). Graduate education in Brazil: Legacies and challenges. Almanaque Multidisciplinar De Pesquisa, 1(1). https://publicacoes.unigranrio.edu.br/amp/article/view/2648

Venables, W. N., & Ripley, B. D. (2002). Modern Applied Statistics with S (4th Edition). Springer New York. https://doi.org/10.1007/978-0-387-21706-2

Viana, L., Jabour, D., Ramirez, P., & Cruz, G. (2018). Patents go to the market? University-industry technology transfer from a Brazilian perspective. Journal of Technology Management & Innovation, 13(3), 24–35. https://doi.org/10.4067/S0718-27242018000300024

Vidal, E. I. O., & Fukushima, F. B. (2021). The art and science of writing a scientific review article. Cadernos de Saúde Pública, 37(4). https://doi.org/10.1590/0102-311x00063121

von Elm, E., Altman, D. G., Egger, M., Pocock, S. J., Gøtzsche, P. C., & Vandenbroucke, J. P. (2008). The strengthening the reporting of observational studies in epidemiology (STROBE) statement: Guidelines for reporting observational studies. Journal of Clinical Epidemiology, 61(4), 344–349. https://doi.org/10.1016/j.jclinepi.2007.11.008

WIPO. (2025). World intellectual property organization. Global innovation index. Brazil ranking in the Global Innovation Index 2025. https://www.wipo.int/edocs/gii-ranking/2025/br.pdf

Wittfoth, S., Berger, T., & Moehrle, M. G. (2022). Revisiting the innovation dynamics theory: How effectiveness- and efficiency-oriented process innovations accompany product innovations. Technovation, 112, 102410. https://doi.org/10.1016/j.technovation.2021.102410

Wolszczak-Derlacz, J. (2025). The determinants of European universities patenting and co-patenting with companies. The Journal of Technology Transfer, 50(2), 620–636. https://doi.org/10.1007/s10961-024-10112-7

Yang, W., & Wang, X. (2024). The impact of patent protection on technological innovation: A global value chain division of labor perspective. Technological Forecasting and Social Change, 203, 123370. https://doi.org/10.1016/j.techfore.2024.123370

Ye, L., Zhang, T., Cao, X., Hu, S., & Zeng, G. (2024). Mapping the landscape of university technology flows in China using patent assignment data. Humanities and Social Sciences Communications, 11(1), 473. https://doi.org/10.1057/s41599-024-02982-x

Yu, S., & Yuizono, T. (2021). A proximity approach to understanding university-industry collaborations for innovation in non-local context: Exploring the catch-up role of regional absorptive capacity. Sustainability, 13(6), 3539. https://doi.org/10.3390/su13063539

Zarea, F., Douglas, E. J., Obschonka, M., Davidsson, P., Audretsch, D. B., & Hutmacher, D. W. (2025). When the marketplace comes to the research laboratory: technology transfer efficiency of innovation-driven publicly funded research centers. The Journal of Technology Transfer, 50(6), 2556–2586. https://doi.org/10.1007/s10961-025-10188-9

Zhao, S., & Li, J. (2023). Impact of innovation network on regional innovation performance: do network density, network openness and network strength have any influence? Journal of Science and Technology Policy Management, 14(5), 982–999. https://doi.org/10.1108/JSTPM-05-2022-0084

Biographical notes

Angelo Elias Meri Junior is a PhD candidate in Health Sciences at the Federal University of São João del-Rei (UFSJ), Brazil, and a visiting doctoral researcher at the University at Buffalo, State University of New York (USA). He holds a Master’s degree in Pharmaceutical Sciences from UFSJ. He is a researcher at the Pharmaceutical Development Laboratory (LADEF-UFSJ) and a member of the Center for Teaching and Research in Clinical Pharmacy (NEPeFaC). His research focuses on pharmacoepidemiology, pharmaceutical technology, drug development and quality control, pharmacokinetics, and mathematical modeling and simulation, including Physiologically Based Pharmacokinetic (PBPK) modeling. He also has experience in clinical research, specifically in conducting epidemiological, systematic reviews, and bibliometric studies.

Elias Sousa Tonelini is a pharmacist with a Master’s degree in Pharmaceutical Sciences from the Federal University of São João del-Rei (UFSJ), Brazil. He has professional experience in community and hospital pharmacy, including patient care, medicine dispensing, pharmaceutical counseling, and team management.

Paulo Afonso Granjeiro is a Full Professor at the Federal University of São João del-Rei (UFSJ), Brazil. He holds a PhD in Biochemistry from the University of Campinas (UNICAMP) and completed a senior research internship at the Massachusetts Institute of Technology (MIT) in the USA. His research focuses on protein purification and characterization, biosurfactants, biomaterials, probiotics, and technological innovation. He has extensive experience in academic leadership, entrepreneurship, and innovation ecosystems, including startup acceleration programs and university-based incubators. He is also actively involved in technology transfer, innovation policy, and scientific entrepreneurship initiatives.

Luciane Teixeira Passos Giarola is a Full Professor at the Federal University of São João del-Rei (UFSJ), Brazil, where she teaches undergraduate and graduate courses in statistics. She holds a PhD in Statistics and Agricultural Experimentation from the Federal University of Lavras (UFLA). Her research focuses on applied statistics, with emphasis on survival analysis, time series, multivariate analysis, and categorical data analysis. She has extensive experience in supervising undergraduate and graduate students and collaborates with multidisciplinary research teams in health-related studies.

Rafael Sachetto Oliveira is a professor at the Federal University of São João del-Rei (UFSJ), Brazil. He holds a PhD in Computer Science from the Federal University of Minas Gerais (UFMG). His research interests include computational modeling, parallel computing, and high-performance computing.

Renê Oliveira Couto is an Associate Professor at the Federal University of São João del-Rei (UFSJ), Brazil. He holds a PhD in Pharmaceutical Sciences from the University of São Paulo (USP), with a doctoral exchange at Rutgers University in the USA. He is a permanent faculty member in graduate programs in Pharmaceutical Sciences and Health Sciences. His research focuses on the development and innovation of pharmaceutical, cosmeceutical, and nutraceutical products, with emphasis on biodiversity-based solutions, modified drug delivery systems, and drug repositioning. He also has expertise in experimental design, multivariate analysis, analytical method validation, and systematic reviews. He leads a research group on phytopharmaceutical innovation and is actively involved in science outreach and extension activities.

André Oliveira Baldoni is a Full Professor and Pro-Rector for Research and Graduate Studies at the Federal University of São João del-Rei (UFSJ), Brazil, and a CNPq Productivity Fellow (PQ-2). He holds a PhD in Pharmaceutical Sciences from the University of São Paulo (USP). His research focuses on clinical pharmacy, pharmacotherapy, pharmacoepidemiology, and evidence-based health care, particularly in the context of the Brazilian public health system. He has extensive experience in graduate supervision, academic leadership, and clinical pharmacy services, and coordinates the Center for Education and Research in Clinical Pharmacy (NEPeFaC).

Author contributions statement

Angelo Elias Meri Junior: Data analysis and organization. Review and writing of the final version of the manuscript. Elias Sousa Tonelini: Performed data collection and organization, literature review and contributed to the final version of the manuscript. Paulo Afonso Granjeiro: Reviewed the manuscript and contributed to co-supervision and scientific guidance. Provided guidance and writing on innovation and patents. Luciane Teixeira Passos Giarola: Reviewed the manuscript, performed statistical analyses, and interpreted the data. Oversaw, wrote, and interpreted the statistical analysis. Rafael Sachetto Oliveira: Reviewed the manuscript and provided methodological support for data organization and analysis. Developed the ScriptLattes software for analyzing researchers’ technical and scientific output. Renê Oliveira Couto: Reviewed the manuscript and contributed to the critical review of intellectual content. André Oliveira Baldoni: Reviewed the manuscript, provided supervision, and final approval of the version to be published.

Conflicts of interest

The authors declare no conflicts of interest.

Citation (APA style)

Meri Junior, A. E., Tonelini, E. S., Granjeiro, P. A., Giarola, L. T. P., Oliveira, R. S., Couto, R. O., & Baldoni, A. O. (2026). Innovation in pharmaceutical sciences in Brazil: Factors associated with the total number of patents and patent licensing. Journal of Entrepreneurship, Management and Innovation, 22(2), 95-118. https://doi.org/10.7341/20262224

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