Malaria is a vector-borne infectious disease caused by Plasmodium falciparum, Plasmodium vivax, Plasmodium malariae, Plasmodium ovale, and Plasmodium knowlesi and transmitted by Anopheles mosquitoes. There are two proven effective strategies to combat malaria (White, 2004). One is to block the Anopheles mosquitoes from approaching humans, by blocking the transmission route of Anopheles mosquitoes and using insecticide-equipped mosquito nets. The other is to use antimalarial drugs for case management. In 2006, the World Health Organization (WHO) recommended the use of artemisinin-based combination therapies (ACTs) as first-line treatment for falciparum malaria (Eastman and Fidock, 2009). If ACTs fail in Southeast Asia and other regions, there will be no fully effective drug capable of completely replacing them in the future. As the new antimalarial drugs are unlikely to become generally available within the next few years, active measures should be given the highest priority to preserve the current antimalarials.

Widespread drug use leads to antimalarial drug resistance, which is an inevitable risk factor in malaria treatment. This risk cannot be disregarded, particularly with unregulated drug use. Early detection and proper medical intervention can effectively lower malaria mortality rates and prevent the emergence of Plasmodium resistance (Landier et al., 2018; Schultz et al., 2023). Over the last few years, antimalarial drug resistance has posed a significant challenge to worldwide malaria control initiatives, especially in the Greater Mekong subregion (Ménard et al., 2018). There are alarming early reports of disturbing mutations in other regions too (Anantabotla et al., 2019; Hussien et al., 2020; Narh et al., 2020; Uwimana et al., 2020; Balikagala et al., 2021; Uwimana et al., 2021; Conrad et al., 2023; Fola et al., 2023; Mihreteab et al., 2023). The global prevalence of Plasmodium infection and deaths declined prior to the outbreak, and healthcare services were compromised due to the public health emergencies that exacerbated the common health crisis (Bhatt et al., 2015; McLean et al., 2018; Kiarie et al., 2022). In accordance with the prognosis of the WHO, the insufficient prevention, diagnosis, and treatment of the illness is expected to lead to the doubling of malaria-related fatalities during the pandemic (Norman et al., 2022). According to the 2022 World Malaria Report, there were 247 million cases of malaria globally in 2021, an increase from 245 million cases in 2020. The number of deaths attributed to the disease decreased from 625,000 in 2020 to 619,000 in 2021 (Fact sheet about malaria, n.d). Among available antimalarials, factors that contribute include parasite mutation rates, total parasite load, drug strength, adherence to treatment guidelines, dosing appropriateness, pharmacokinetic properties, inadequate drug contact due to falsified drugs, and poor-quality antimalarials that may lead to or eliminate resistance (Shibeshi et al., 2020). In 2021, amid antimalarial drug resistance, the African region has contributed to 95% of the total global malaria cases and 96% of all deaths related to malaria, with children under five accounting for 80% of all malaria-related deaths in Africa. While artemisinin-based malaria treatment continues to be highly effective in Africa with no evidence of complete resistance, the mounting genetic mutations linked to artemisinin resistance may ultimately result in enhanced overall artemisinin resistance as well as selective pressure on partner drugs (Owoloye et al., 2021). Therefore, continuous monitoring of treatment efficacy and genetic surveillance across Africa is imperative.

Regular monitoring of drug efficacy is necessary to enhance treatment strategies in malaria-endemic countries and ensure early detection and response to drug resistance. The WHO recommends surveillance of antimalarial drug resistance every 2 years to control the spread of antimalarial drug resistance. The predominant techniques employed to oversee antimalarial drug resistance encompass drug efficacy studies, in vitro/in vivo studies of malaria parasites, and molecular studies that evaluate recognized markers of antimalarial drug resistance (Ariey et al., 2014; Ménard et al., 2016; Chen et al., 2021). To identify antimalarial drug resistance, specific gene mutations and molecular markers associated with antimalarial drug resistance are utilized. Detection of antimalarial drug resistance serves as a useful monitoring mechanism to enhance the control of Plasmodium susceptibility. Bibliometrics is a qualitative and quantitative visualization study of the retrieved literature, involving the application of mathematical and statistical methods to scholarly publications. Researchers evaluate research trends and research priorities within the field by analyzing literature databases, which have extensive applications in the pharmaceutical field. This study aimed to visually analyze the research status and development trends of the antimalarial drug resistance, by means of bibliometrics and knowledge mapping.

The study employed Web of Science Core Collection (WOSCC) as the literature search database. The retrieved literature was analyzed for research trends, references, and keywords with the aid of CiteSpace and Microsoft Excel. Relevant literature from 1 January 2015 to 29 March 2023 was included, with the date of publication as the selection criterion. The search terms used were “(TS= (Antimalarial Drug) AND TS=(Resistance))”.

CiteSpace (6.2.R1) was utilized for bibliometric analysis, a tool created by Professor Chen Chaomei. The most current version of CiteSpace can be downloaded from the official website and no longer requires the JAVA environment. Its primary function is to visually and analytically represent information found in the academic literature within a given field. The primary objective of visualizing knowledge domains is to identify and track their development by illustrating scientific collective networks within scientific domains, such as scientific networks, social networks of co-authors, citation networks, and networks of collaborators. CiteSpace achieves this by representing relations between scholars or publications through network nodes and connectivity, with the size of a node proportional to the publication output. The publication date is indicated by the color of each node. Technical terms are explained upon their first use. Mediator centrality is assigned to every node in a network, determining the probability of each shortest path passing through it. Mediated centrality is defined for each node in the network to determine the probability that any shortest path in the network passes via the node. Mediator nodes with high centrality may be found at the center of two significant clusters or sub-networks, hence the term mediator. CiteSpace illustrates nodes with high mediator centrality as purple rings, with the thickness representing the magnitude of the value of mediator centrality. The co-cited literature and keywords were clustered using the log-likelihood ratio (LLR) method. The silhouette (S) value denotes the mean contour value of the cluster. It is generally accepted that clustering is justifiable when S > 0.5 and persuasive when S > 0.7.

For this research, CiteSpace was used with the following specific parameters: time slice spanned January 2015 to March 2023; year slice interval was 1 year; term source used included title, abstract, author, and keywords; node type encompassed author, institution, country, keyword, literature, cited author, and cited journal; and the top 50 articles were compiled based on the selection criteria.

Microsoft Excel was utilized to construct tables that illustrate annual national publication trends.

A total of 2,561 papers related to antimalarial drug resistance in the WOSCC were searched, 2,559 papers were shortlisted after excluding duplicates using CiteSpace, and all papers were included in the analysis after excluding duplicates. From 1 January 2015 to 29 March 2023, a total of 2,561 articles with the subject terms “antimalarial drug” and “resistance” were retrieved from the WOSCC, consisting of the following article types: Article (1,979), Article; Book Chapter (6), Article; Early Access (33), Article; Proceedings Paper (7), Correction (2), Editorial Material (31), Meeting Abstract (31), News Item (1), Review (462), Review; Book Chapter (5), and Review; Early Access (4).

There were 386 papers in 2021, compared to 363 in 2020, 343 in 2022, and approximately 300 in other years, with the largest amount of literature published ( Figure 1 ). In general, the rise in the number of academic papers on malaria may be linked to growing apprehension about the emergence of resistance genes in Africa over the past few years.

This paper presents the inaugural bibliometric visualization of antimalarial drug resistance. However, noteworthy limitations exist. The study solely comprises pertinent literature from the Web of Science (WOS) core database, excluding relevant content in other search engines such as PubMed and Google Scholar. As CiteSpace predominantly supports the WOS database, the core database acted as the data source to accommodate the software requirements. Other databases were seldom utilized for the analysis of the software. The software’s limitation to automatically eliminate duplicate entries from distinct databases prohibits its ability to conduct simultaneous analyses on two or more databases. In addition, the study’s merging of near-synonyms cannot completely eliminate possible analysis errors due to subjectivity.

The volume of literature has consistently shown an upward trend every year, signifying a persistent interest within the field. The visual analysis of authors originating from diverse nations, institutions, and journals displays their academic contributions without subjectivity while offering potential collaboration for future research. An overview of literature and keywords within the field reveals research priorities and patterns in antimalarial drug resistance. For instance, developing new drugs represents a current priority, while screening new drug targets using network models could emerge as a future trend. Scholarly peers can reference this analysis to gain insights into the direction of research in this field.

JZ: Visualization, Data curation, Writing – original draft. MS: Writing – review & editing. MI: Writing – review & editing. HZ: Writing – review & editing, Conceptualization, Visualization.