1Technological Innovation Management, University of the Andes, Bogotá, Colombia; 2Department of Biomedical Engineering, Director of Technological Innovation Management Programs, University of the Andes, Bogotá, Colombia; 3Department of Biological Sciences, Faculty of Sciences, Director of the Biomedical Research Institute Group, University of the Andes, Bogotá, Colombia

Abstract: Pharmacogenomics is the integration of genomics and pharmacology to optimize drug response and reduce side effects. In terms of personalized or individualized medicine, PGx is defined as the identification and analysis of specific genetic variants associated with particular drug treatments for each patient. Under a precision public health (PPH) approach, population-level data are analyzed to generate public health strategies. The objective of this study was to conduct a scoping review of technological tools, examining their evolution, the predominance of high-income countries in their development, and the gaps and needs for genomic data and advances in low- and middle-income countries (LMICs). This review was conducted in accordance with the ScPRISMA guidelines. A search was conducted in PubMed, Web of Science and Embase until January 2024. A total of 40 documents were selected, which revealed the continuous evolution and progressive development of pharmacogenomic tools. The technological tools developed come from high-income countries, particularly the United States, Canada, China, and several European nations, where international collaboration has been essential to maintain and expand these tools, which have evolved to keep pace with the rapid generation of genomic data. This trend shows a scarce development of technological tools for public health precision in LMICs, which evidences the need to increase investment in genomic research infrastructure in this aspect and in the development of capacities to guarantee global accessibility and boost PPH for all populations.

Keywords: personalized medicine, pharmacogenomics, precision public health, technological tools

Introduction

The completion of the Human Genome Project at the turn of the twenty-first century constituted a significant milestone for the healthcare system, generating substantial quantities of data.1,2 This development has created a need for technological tools to store, organise, and analyse valuable information, facilitating decision-making related to diagnosis, prognosis, and treatment for a number of different stakeholders, including health professionals, private sector companies, public sector organizations, and academics.3,4

Pharmacogenomics PGx is the developing discipline on the borderline between genomic medicine and pharmacology. This area of research is focused on optimizing drug prescription with the help of knowledge and data about genomic variations. This discipline will progress based on studies demonstrating that genetic polymorphism is associated with variations affecting enzymes related to drug transport and metabolism. These polymorphisms affect the way in which organisms absorb, distribute, metabolize, and eliminate drugs, leading to significant differences in drug response, efficacy, and safety.5,6

Accordingly, genomic variations have the potential to significantly affect enzyme function.5,6 Such variations can result in complete loss of enzyme function, a reduction in enzyme activity, or even a gain of function. Individuals carrying some types of variations in their genes are more prone to side effects or receiving less effective dosifications when taking specific medications.7 Among those variations, genomic differences can create phenotypes associated with drug metabolism, grouping those people into four metabolism rate groups: poor metabolizers, intermediate metabolizers, extensive metabolizers, and ultrarapid metabolizers.5

Precision medicine is a medical approach utilizing genomic information to diagnose, treat, and prevent diseases.8 In terms of personalized or individualized medicine, PGx is defined as the identification and analysis of specific genetic variants associated with particular drug treatments for each patient.9 Alternatively, under a PPH approach, PGx is considered to be a process that focuses on identifying and analyzing population-level data toward developing strategies that benefit public health for groups of people having similar.10,11 This latter population-based approach leverages data to analyze genetic differences associated with drug responses, with the goal of developing more accurate treatments for public health decision-making.11

Thanks to technological advances and innovations in genotypic and phenotypic characterization, the field of pharmacogenomics has made great progress through the analysis of large amounts of data and the optimization of drug selection and dosing with technologies such as Genome-Wide Association Studies (GWAS) and Next-Generation Sequencing (NGS).12,13 This progress has identified important benefits in several therapeutic areas. For example, in cardiology, 25 studies it has been demonstrated that individuals with loss-of-function alleles in CYP2C19 treated with clopidogrel have a higher incidence of secondary vascular events (HR 1.72; 95% CI: 1.43 to 2.08; 18 studies), stroke (HR 1.46; 95% CI: 1.09 to 1.95; 5 studies) and ischemic stroke (HR 1.99; 95% CI: 1.49 to 2.65; 12 studies), than those without such alleles.14 On the other hand, a higher risk of death from toxicity related to cancer treatment with fluoropyrimidine use has been demonstrated in oncology studies for carriers of DPYD variants (OR = 34.86, 95% CI 13.96–87.05; p < 0.05).15 These technological innovations have resulted in improvements in the quality of prescribing practices, increased effectiveness of drug use, reduced adverse reactions, improved clinical outcomes, and lower costs of drug interventions and treatments for the healthcare sector.7

This population-based public health approach is crucial for implementing technological advances in PGx derived from precision medicine in low- and middle-income countries. The frequencies of pharmacogenomic variants may vary between populations due to ancestry, however the lack of genomic data on diverse ancestral populations could exacerbate existing health inequalities.7,16,17 Accordingly, initiatives such as the Human Heredity and Health in Africa Consortium (H3Africa) have sought to promote research in 51 projects in 30 African countries, with the aim of developing infrastructure and identifying genetic factors related to health.18 Similarly, in the Republic of Congo, studies have focused on polymorphisms associated with adverse drug responses to HIV/AIDS, malaria, and tuberculosis.19 In the latter, pharmacogenetics has enabled the selection of more effective and less toxic drugs, which could reduce resistance and optimize resources in limited health systems.20

In Asia, countries such as India are also making important advances in the development of pharmacogenomic databases that address genetic diversity in drug metabolism. The IndiGen initiative has sequenced more than 1000 genomes, identifying that on average, each individual carries eight variants that influence drug selection and dosing, which underlines the relevance of orienting pharmaceutical public policies to seek more effective therapeutic results according to the specificities of the population.21

Similarly, in Latin American countries, extensive admixture between African, European, and Native American populations creates a particular genetic diversity,22,23 as evidenced by studies in Colombian,24,25 Brazilian,26 and Chilean27,28 populations. These countries often lack data on local and regional variations in key pharmacogenes, which could exacerbate health disparities and hinder the application of precision medicine and PGx.29,30

The implementation of PGx in clinical practice, tailored to the priorities of each subpopulation structure in every country, is essential to address populations’ specific needs for better outcomes and fewer adverse reactions.30 By adopting this population-based approach, low- and middle-income countries (LMICs) can harness the potential of PGx to tailor medical treatments and interventions more effectively to the needs of groups of individuals, thereby generating a more cost-effective solution for health systems, but also offering a practical pathway to address the unique care challenges encountered in these settings.24

The diverse application and potential of PGx in LMICs will, therefore, help in the optimization of strategies for PPH. Moreover, technological tools have high potential to enhance development in medical, personalized treatments, considering health care challenges unique to developing better health outcomes with reduced side effects. Availability and applicability of available technological tools of PGx in LMICs are still vague. For this reason, a scoping review was conducted to trace an overview of the evolution of pharmacogenomic technological tools, discussing dominance by high-income countries in their development, and highlighting gaps and needs in genomic data and technological advancements in LMICs.

Methods Search Strategy

The procedure for this scoping review follows the ScPRISMA guidelines;31 see Supplementary Material 1. An Excel spreadsheet with a defined taxonomy was determined to be the ideal instrument for collecting information, outlining the review step-by-step, and integrating analysis variables based on the following question: ¿What is the evolution and geographical distribution of technological tools in pharmacogenomics? And ¿what are the gaps in low- and middle-income countries?

The search focused on scientific articles emphasizing technological tools for analyzing PGx information that could be useful for decision-making via a population-based approach. The search was conducted via scientific databases such as PubMed, Web of Science and Embase. The combination of keywords and boolean operators used in this study are pharmacogenetics, pharmacogenomics, PGx, drug response, drug metabolism, drug efficacy, variant, polymorphism, biomarker, SNP, database, web, platform, public health, community, population, ancestry, personalized, individual, and precision.

Inclusion and Exclusion Criteria

The search for articles was conducted up to January 2024 and was limited to human-related articles, with no date restrictions. Articles written in English, Spanish, or any other language that could be translated into any of these languages were included. Excluded where articles were studies conducted in animals or in vitro or without full text; articles focused on general or conceptual aspects of PGx and technological tools; technological tools unrelated to PGx; papers on methods or techniques that did not include a technological tool for PGx data storage, organization, and analysis. Subsequently, duplicate papers were excluded.

Three rounds of review were conducted. In the first round, the titles of the documents were examined. The second round involved reviewing the keywords and abstracts of the remaining documents. Finally, a screening of the full texts of the articles was carried out to eliminate those that did not meet the inclusion and exclusion criteria.

Data Extraction

The relevant information from the selected documents was extracted via an Excel form. This form included specific fields for capturing key details such as the document title, year of publication, year of tool development, geographical region, and functionalities of the technological tool. This data extraction ensured a thorough and organized collection, which facilitated the subsequent analysis of the information obtained. This approach ensured that all essential elements were covered in accordance with the stated objectives and that the required data were accurately collected.

Results PRISMA Flowchart

The search in PubMed, Web of Science and Embase returned 902 documents (see Supplementary Material 2). Of these, 285 duplicates (31,59%) were removed. After the title and abstract review, 438 studies whose topic did not correspond to the aim of this study were excluded. Full-text review of 88 documents was done whose content was assessed for suitability. In the end, 48 documents were eliminated, while 40 at 45.45% documents met all the criteria for information analysis. The flow chart is shown in Figure 1.

Figure 1 PRISMA flowchart.

Timeline and Geographical Origin

Table 1 lists the articles related to technological tools in PGx, categorized by their year of publication and the country of origin of the authors of the 40 identified manuscripts with 34 technological tools. These documents span nearly two decades, illustrating the chronological evolution of technological innovations and developments in the field.

Table 1 Technological Tool Included in the Analysis

Two peaks in publication activity were found in 2013 and 2020, with six and five articles, respectively, presenting new tools in the field. However, the year of publication might not reflect the year of development of the tool, as the identified articles could refer to further improvements and advances in developments.

Other results obtained in these 40 documents are the analysis of the geographical distribution of the authors. Authors affiliated with institutions located in the United States represent 52.5% of the articles.32–34,38,41,42,44,48–51,54,55,57,59,61,62,64,66,70,71 Furthermore, an analysis of the authors’ country of origin revealed that 92.5% of the articles came from high-income countries with a GNI per capita of $13,846 or more,72 such as the United States, Canada, Singapore, Greece, Sweden, the United Arab Emirates, Germany, Italy, Austria, Spain, New Zealand, South Korea, and Puerto Rico.

Similarly, multiple technological tools were developed through collaboration among researchers from several countries (32.35% of the tools identified). Collaboration among multidisciplinary teams was also observed, highlighting its importance. For example, PACdb was a joint effort between authors from institutions in the USA and Singapore;41 Virtual Pharmacist48 and GWASdb50 between authors from China and the USA; ePGA between authors from Greece and the United Arab Emirates;53 PharmacoDB between authors from Canada and the USA;57 PGxLOD between authors from France and the USA;59 PharmVar between authors from Austria, the USA, Spain, New Zealand, and Canada;61 MTBP with authors from Sweden, Spain, the United Kingdom, France, the Netherlands, Germany, Italy, USA,66 and CREAMMIST between authors from Thailand and Singapore.68 In addition, two tools with no names were identified. The first tool, formed by authors from organizations in India, Germany, and Italy, was mentioned in the paper Guin et al 2019, as included in Table 2, the “Guin tool”.60 A second tool was identified in the paper by Zarei et al 2020, as included in Table 2, “Zarei tool”.64 The tool was showcased in an article by authors affiliated with institutions in the USA and Puerto Rico.

Table 2 Main Characteristics of Technological Tools for Pharmacogenomics Identified

Characteristics of Technological Tools for PGx

We reviewed the functionality of the various technological tools involved by studying in detail their characteristics. Where possible and available, tools were accessed to know how they function. Details of these are shown in Table 2. It was also established from the review if these tools make use of information harvested or data from other PGx tools or databases.

We noted that 41.1% of tools are no longer accessible: PharmGED,35 KPD37 “Gamazon tool”,38 The Human P450 (CYP) Allele Nomenclature Database,39,40 PACdb,41 SNPshot,42 DIRECT,44 DEER Database,45 SPP,46 DruGeVar,47 Virtual Pharmacist,48 “Funk tool”,49 the “Guin tool”,60 and Pg-path.63 This reflects the dynamic nature of the PGx field, which evolves rapidly. Newer technologies often replace older ones, whereas some tools evolve by incorporating functionalities or datasets from their predecessors.

In reviewing functionalities and information sources, many tools rely on data from previously developed databases through links or data integration. For example, the Human Cytochrome P450 (CYP) Allele Nomenclature Database was used by PharmGKB and was later replaced by PharmVar.61 DrugBank is also frequently utilized by other tools, including PharmGKB,34 SNPshot,42 the DEER Database,45 SPP,46 Virtual Pharmacist,48 GWASdb,50 PharmacoDB,56 PreMedKB,58 PGxLOD,59 PGxMine,62 Pg-path,63 PharmVIP,65 and PharmaKoVariome.67 Finally, PharmGKB serves as a foundation for tools such as the “Gamazon Tool”,38 PACdb,41 SNPshot,42 SPP,46 Drugevar,47 Virtual Pharmacist,48 “Funk tool”,49 SPHINX,51 ePGA,52,53 PreMedKB,58 PGxLOD,59 “Guin tool”,60 PharmVIP,65 PharmCAT.70

PGx Technological Tools for PPH: Addressing Gaps and Needs in the Context of LMICs

Among these results, population and ancestry analysis emerges as a particularly notable functionality that is especially relevant for LMICs. Tools such as KPD,37 PACdb,41 SNPshot,42 FINDbase,43 SPP,46 Virtual Pharmacist,48 SPHINX,51 ePGA,52,53 PharmVIP65 and PharmaKoVariome67 offer the ability to analyse population- and ancestry-specific pharmacogenomic data. Specifically, these tools aim to detail how genetic variants, in the context of ancestral diversity and population-specific genetic background, influence the efficacy, dosing, and potential side effects of drugs. This is particularly relevant in low- and middle-income countries, where such tools could be used to deepen the analysis of genetic variations in diverse populations. In this way, they will contribute to optimizing drug therapies in these countries, closing the gaps in technological advances.

Another valuable functionality we found is the ability of users to upload their own data, notably with the Virtual Pharmacists tool,48 ePGA,52 PharmVIP,65 MTBP,66 and PharmCAT.70 This feature enables the integration of various types of PGx data, including SNP genotyping results from microarrays, as well as FASTQ and variant call format files. This capability not only facilitates the collection and organization of data from new studies so that the tool provides up-to-date information but also makes it possible to include data from diverse sources and populations, including genetic data from LMICs.

Discussion Mapping the Timeline and Geography

Our results show a steady increase in the literature related to technological tools for PGx. This trend coincides with statistics from the Centers for Disease Control and Prevention (CDC), which indicate an increase in the number of PGx study publications, from 12 in 2012 to 333 in 2021.73 This kind of growth probably reflects the demand due to the need for tools to store, organize, and analyze PGx data generated by these studies. This underlines the growing interest and investment in a data-driven approach for integrating PGx information to translate theoretical research into practical clinical applications.74

This evolutionary trend is also consistent with the increasing interest in genomic databases, which have grown from 88 in 2013 to more than 700 in 2022, including different data types such as whole-genome sequences, whole-exome sequences, genome-wide genotypes, RNA-Seq, and epigenomic information, and includes the management of population data and PGx research practices.75 This confirms the importance of implementing technological innovations to utilize PGx data.

Furthermore, through the documentary review of the articles, it was found that several technological tools were developed years before the publications. For example, PharmGKB, with articles from 2005, 2008, and 2013,32–34 was initially established in 2000. Subsequent publications have highlighted additional functionalities integrated into the tool. Similarly, the Human Cytochrome P450 (CYP) Allele Nomenclature Database originally was developed in 1999, with publications in 2010 and 2013 article detailing updates.39,40 The FindBASE article, published in 2013,43 reflects a development that occurred in 2007. GWASdb, developed in 2012, introduced its version 2.0 in a 2016 publication.50 The SPHINX is a 2016 publication51 but reflects a tool developed in 2013. PharmacoDB, developed in 2017, has two associated articles: one dating from 2018 and another from 2022.56,57 The latter describes version 2.0, emphasizing new data and the adoption of advanced technologies to ensure scalability. PharmVar, established in 2017, was the subject of a 2020 article focused specifically on the CYP2D6 gene,76 the identified publication of PharmCAT70 of the year 2023, but whose development took place in 2018 and finally PancanQTL71 whose publication corresponds to a second version PancanQTLv2.0 in 2024 but whose initial version was in 2018. Therefore, these publications suggest the continuous evolution and progressive development of technological tools and databases for PGx over the last two decades.

Another relevant result is the origin of the authors of the publications analyzed. The results show that academic production is concentrated in developed countries, with few exceptions. Only in Asia was the participation of low- and middle-income countries identified, such as Thailand and India, whose authors participated in three articles. In Latin America and Africa, no technological tools developed by authors from low- and middle-income countries were found. This concentration highlights the critical role played by leaders in PGx research and underlines the need for greater representation from other regions in pharmacogenomics research, as well as its applicability to diverse populations.73 This demonstrates that the development of PGx technologies is a critical area that requires resources, skills, experience, and the training of professionals, as well as specialized facilities and laboratories, which are lacking in many low- and middle-income countries in Latin America and Africa, representing a significant gap in access to PGx due to the lack of technology and financing in these countries, which, added to political and fiscal factors, can represent additional barriers to the implementation of PGx databases and technological tools.77–79

The need to implement pharmacogenomics (PGx) technologies is especially important in contexts where information on ancestral genomic variants is lacking. In Africa, a recent genome-wide study of 426 individuals from 13 African countries revealed more than 3 million new genomic variants, highlighting the knowledge gap on the genetic diversity of these populations and the urgency of prioritizing PGx research in Africa.80,81

According to Kuguyo,82 biorepositories could be an alternative in advancing pharmacogenomics and translational research, and a cancer biobank has now been established in Zimbabwe to collect and analyze genomic data, which is essential to overcome barriers in that country, where a lack of representative studies and limitations in storage and analysis infrastructure complicate the progress of PGx. Other initiatives are focused on implementing preventive pharmacogenetic screening programs in South Africa, which they claim could reduce adverse drug reactions by 30%, reflecting the enormous potential of these technologies to improve public health outcomes, but caution that African genetic diversity and socio-economic challenges require specific local approaches.83 This approach could also serve as a model for other under-represented regions, which could leverage Pgx technology tools such as those shown in this article. Meanwhile, initiatives such as CÓDIGO-Colombia in Latin America underline the importance of strengthening local infrastructures that facilitate the large-scale use of existing population data.84

Kanhaiya85 emphasized that building healthcare informatics systems require synergy between healthcare information components and highly qualified multidisciplinary teams comprising physicians, biomedical researchers, computer scientists, and data scientists. Such teams play essential roles in effectively managing, integrating, analyzing, and interpreting data to advance PGx. Further, there is a requirement for computational and collaborative data management infrastructures, with investment in state-of-the-art omics technology and other digital tools in high-performance computing and multistorage facilities based on the cloud.85 This highlights the importance of having sufficient resources to maintain these technological tools. Many of the reviewed tools herein still exist, but ensuring continuity in maintenance and upgrades is important in sustaining initiatives in PGx implementation.

PGx Technological Tools for PPH: Addressing Gaps in the Context of LMICs

These 34 tools underscore the commitments of the scientific community toward the advancement of PGx analysis. From enzymatic variation to text mining, through studies of genetic variations having an impact on the prediction of response to drugs and treatment optimization, these tools enhance clinical practice. It makes it easier and more accessible for researchers and clinicians alike to do their job with the help of online resources and applications that provide ease in medication selection and administration while contributing to the prevention of adverse reactions and improving the efficacy of therapeutic interventions.86

The use of technological tools for PPH in low- and middle-income countries is crucial to the translation of pharmacogenomics advances to help respond to global health challenges. According to Magavern,87 the application of population-level PGx is critical in harnessing the power of genomic knowledge that has not yet been fully tapped in genomic diversity-rich regions like Oceania, Africa, and Latin America. However, this review revealed a lack of options for capturing, organizing, and analyzing pharmacogenomic data in these regions.

In this review, 11 identified tools—KPD, PACdb, SNPshot, FINDbase, SPP, Virtual Pharmacist, SPHINX, ePGA, PharmVIP, and PharmaKoVariome—provide specific functionalities to analyze population- and ancestry-specific pharmacogenomic data, helping to address biases and optimize drug therapies for diverse populations. Ancestry plays a crucial role in PGx by mitigating biases that arise in studies predominantly involving majority groups. Therefore, ancestry should be taken into account in these various populations living in LMICs for both equitable and safe practices.88,89

The advancement of a range of technological instruments was made possible through the collaborative efforts of researchers from a multitude of nations. The differences in technological advancement in population genomics are sharp reminders of why collaboration between nations would be needed, especially within those countries with no kind of pharmacogenomic tools, to help narrow the gap—underscoring capacity-building activities within underrepresented regions. Population genomics offers significant potential to increase initiatives worldwide by enhancing the understanding of specific populations’ genetic profiles for drug policies and dosage recommendations.90

Online tools and resources are invaluable for conducting a variety of analyses. Often, access to multiple sources is necessary to gain comprehensive insights. In countries lacking their own tools, health professionals and researchers can use online resources or adapt existing resources to analyze available data as an initial step in population analysis for resource-limited regions. The ability to upload data to technological tools and conduct population and ancestry analyses helps narrow the technology gap for low- and middle-income countries, enhancing their capacity to utilize pharmacogenomic insights effectively.

Conclusions

In this review, we identified groups of tools and discussed how their functionalities can support population analyses in LMICs where PGx has not yet been widely implemented. The use of these tools created by international experts in PGx gives valuable resources that allow for prioritization of candidate genes and promotes PPH interventions for diverse populations.

Development of technological tools for storing, organizing, and analyzing generated PGx information has been a basic pillar about understanding, harmonizing, dissemination, and definition of key factors related to the impact of genetic variations on drug absorption, distribution, metabolism, and elimination. This review highlights the significant advancements in technological tools and their role as fundamental sources of information for precision health decision-making.

Collaboration is essential for collecting, organizing, and analyzing genomic information from diverse populations. Such collaborative efforts enhance the accuracy of public health decision-making and effectively address specific PGx gaps in LMICs. By leveraging these technological tools and fostering international cooperation, we can advance the implementation of PPH and improve health outcomes globally, which would enhance the accuracy of public health decision-making and effectively address specific PGx gaps.

Limitations and Future Work

This review examined technological tools capable of capturing, storing, and analysing PGx data; however, it may be that the number of existing tools is bigger than that reviewed here. It is recommended developing services facilitating large-scale use of data existing populations and multiple population inclusions, as is currently being developed at CÓDIGO-Colombia85 and Africa.

There is also an urgent requirement for the development and strengthening of such technological tools in LMICs. Future research has to evaluate not only the possibility of implementing PGx technologies in these regions but also strategies elaboration concerning public health initiatives targeting underrepresented populations.

Abbreviations

LMICs, Low and Middle-Income Countries; PGx, Pharmacogenomics; PPH, Precision public health; PRISMA ScR, Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews.

Data Sharing Statement

All data generated or analyzed during this study are included in this published article and its Supplementary Material.

Ethics Approval

Ethics approval was not required for this scoping review.

Funding

This work was supported by a FAPA project (PVI0122029) from Universidad de Los Andes, granted to Prof. Augusto Valderrama-Aguirre.

Disclosure

Professor Augusto Valderrama-Agirre reports grants from Universidad de Los Andes, outside the submitted work. The authors report no other conflicts of interest in this work.

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