Background

Childhood cancer is a serious public health concern globally. World Health Organization (WHO) estimates that there are over 400,000 children who are diagnosed with cancer every year.1 This number highlights a significant burden, however, it may not be the correct estimate, because over 43% of children with cancer go undiagnosed or untreated.2 It is also estimated that approximately 80% of childhood cancer cases occur in low- and middle-income countries, particularly in Africa, where the cure rate remains low.3 This low cure rate can be attributed to various factors, including disparities in individual responses to treatment due to differences in genetic makeup. This concept, where individuals within a population differ in drug response due to genetic variations, is known as pharmacogenomics.4

Although pharmacogenomics has been an established field of research for over five decades, its integration into routine clinical practice has gained significant traction in recent years, particularly in developed countries, where it has demonstrated benefits in treatment outcomes, especially for childhood cancers.5 A useful example of pharmacogenetic application is the prescription of 6-mercaptopurine (6-MP) for treating Acute Lymphocytic Leukemia (ALL), guided by genetic testing for the thiopurine S-methyltransferase (TPMT) gene.6 Individuals with low or absent TPMT activity are poor metabolizers and are at a higher risk of accumulating excessive levels of the active metabolite, 6-thioguanine nucleotides (6-TGNs). This can lead to severe bone marrow suppression and life-threatening myelosuppression, resulting in severe infections or even death. Conversely, individuals with high or normal TPMT activity metabolize 6-MP more efficiently, producing less of the active metabolite, and are generally less likely to experience toxicity at standard doses.7

Despite its promise, the research and application of pharmacogenomics in Africa remain in their infancy, hindered by economic constraints, resource scarcity, and limited training opportunities for healthcare professionals. Ethical issues and policy gaps also present significant barriers to implementation.8 Most of the major consortiums, organizations, and associations advocating for pharmacogenomics, such as the Clinical pharmacogenetic Implementation Consortium (CPIC), the pharmacogenomics Knowledge Base (PharmGKB), the Canadian pharmacogenomics Network for Drug Safety (CPNDS), and the Dutch Pharmacogenetic Working Group, are based outside of Africa and are primarily used in non-African regions.9 Furthermore, Africa faces unique healthcare challenges, including fragmented health systems, inadequate funding, and insufficient infrastructure to support pharmacogenomics research and clinical applications. Consequently, the extent of publications on pharmacogenomics in childhood cancers from the African continent is scant compared to other regions.10

It is projected that by the year 2050, the number of children diagnosed with cancer in Africa will constitute 50% of all global childhood cancer cases.11 Implementing pharmacogenomics practices could play a transformative role in optimizing treatments, especially in resource-constrained settings. These interventions must be accompanied by strategic investments in research, healthcare professional training, and policy development. Therefore, it is crucial to take action now by implementing pharmacogenomics practices to improve cure rates for childhood cancer patients, particularly through better use of limited resources such as medications. Our review aimed to assess the extent of research, report existing findings, and identify the factors influencing the applicability of pharmacogenomics in childhood cancer chemotherapy in Africa.

We examined all aspects of pharmacogenomics in childhood cancer chemotherapy in Africa, guided by the research question: “What is the extent, existing findings, and factors influencing the applicability of pharmacogenomics in childhood cancer in Africa?” From this, we developed the following objectives: [1] to map the existing literature on pharmacogenomics in childhood cancer chemotherapy in Africa, [2] to describe the main findings in the available literature on pharmacogenomics of childhood cancer therapy in Africa, and [3] to explore the factors influencing the applicability of pharmacogenomics in childhood cancer therapy in Africa.

Methods

We utilized the scoping review framework proposed by Arksey and O’Malley to define our research approach. By this framework we identified childhood cancer patients as the population of interest, pharmacogenomics as the key concept, and the available literature from Africa as the context.12 The outcomes of interest included reports on specific genes and variants, chemotherapy-gene association outcomes in children harboring variant genes, and the knowledge and attitudes of healthcare workers (HCWs) regarding pharmacogenomics in pediatric cancer care.

The protocol was prepared and registered on the Open Science Framework (OSF) with the DOI: https://doi.org/10.17605/OSF.IO/DSBWE. We identified relevant literature, selected studies, mapped ideas, and synthesized evidence based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for scoping reviews (PRISMA-ScR),13 as shown in Figure 1 (Additional file 1, supplementary materials). This guide was reviewed by two independent reviewers (DMK and SM), and a consensus was reached after several discussion meetings.

Figure 1 Flow diagram of literature search results and study selection.

Search Strategy

An experienced librarian, Mr. Oliver Bondo, at the Catholic University of Health and Allied Sciences Library, drafted the search strategies, which were shared with all reviewers to ensure a standardized approach for the online searches. A pilot search conducted before the actual search revealed a paucity of literature on the topic, prompting the creation of various search strings, which were incorporated into a checklist for comprehensive literature searches.

The primary search terms included “pharmacogenomics”, “pharmacogenetics”, “genomic medicine”, “childhood cancer”, “children with cancer”, “pediatric oncology”, and regional terms such as “East Africa”, “South Africa”, “Central Africa”, “Northern Africa”, “Western Africa”, and “Africa”, with “Africa” subsequently replaced by individual country names (Additional File 2, supplementary materials). Additionally, “precision medicine”, ‘personalized medicine’, and “individualized medicine” were included as secondary terms, as they have similar meanings to pharmacogenomics and pharmacogenetics. Using Boolean operators, the search string was set as: (pharmacogenomics OR pharmacogenetics) AND (childhood cancers OR pediatric cancers AND chemotherapies) AND Africa. Truncation (*) was applied to some words to capture variations in English, such as “pediatric” and “paediatric”.

The following biomedical research databases were searched: Google Scholar, African Index Medicus (AIM) via the WHO Global Index Medicus, Medline via PubMed, the Cochrane Library, Embase, and African Journals Online (AJOL). The search was limited to articles published in English from the inception of the databases until September 2024. The final search results were exported to Zotero, a reference management software, where they were organized, and duplicates were removed. The complete search strategies for all databases can be found in Additional File 3 (supplementary materials).

Study Eligibility Criteria

The eligible studies were review studies, observational studies (both quantitative and qualitative), and laboratory-based studies. The emphasis was on studies involving the principles of pharmacogenomics in childhood cancer. Therefore, any study that included information on genomic medicine and children with cancer was considered. Furthermore, any study that explored genes known to influence the pharmacokinetics and/or pharmacodynamics of chemotherapies used in the treatment of childhood cancer was included. In addition, studies that examined factors influencing the applicability of pharmacogenomics testing or use (ie, knowledge and attitudes of caregivers) were included, as the groups benefiting from such practices involve children. Moreover, only studies published in English and conducted in, or about African populations were eligible for inclusion.

We excluded studies that were published on genomics but focused on cancer disease genetics, as well as those that were unclear regarding the study population or did not include humans. Likewise, we did not include preprint articles, conference abstracts, thesis, commentaries, comment letters, protocols, and grey literature.

Selection of Sources of Evidence

To identify potentially relevant documents, the four bibliographic databases described earlier were chosen based on the predefined criteria of being reputable, reproducible, and accessible sources of biomedical research published in English. The selection criteria and search techniques were established earlier to standardize the process under the guidance of an experienced librarian. A test was conducted to assess the usability of the selection and search checklist to ensure that all potential sources of evidence were fully utilized.

Screening Process

Because the process was standardized, two reviewers, DMK and SM, used the same checklist to review the titles, abstracts, and full texts of publications from the database sources and created their own lists of included literature. The two lists were then compared, and any inconsistencies and disagreements were resolved through discussion among the reviewers. One final list was chosen and shared with other reviewers for confirmation.

Data Items, Extraction, and Analysis

Through an iterative approach, the identification and extraction of variables were continually refined to obtain the final items, which included the study type, authors, country of publication, study design, time of publication, predefined primary terms, main findings (common gene variants, gene-chemotherapy relationship and healthcare workers’ knowledge and attitudes regarding pharmacogenomics), and overall recommendations from the study.

Critical Appraisal for Individual Sources of Evidence

A structured checklist, regarded as a critical appraisal tool (Additional File 4, supplementary materials), for the standard appraisal of evidence, was created and tested before being used by two reviewers, DMK and SM, to systematically evaluate the extracted items against the objectives of the current review. Because we included studies with different methodologies, it was agreed that the included studies must produce items that fulfill at least one objective of the current review. In addition, the appraisal involved assessing the methodological quality of the included sources of evidence and determining whether the results were valid and could be used to inform practice and policy regarding children with cancer.

Results Search Results

We initially identified 43 publications. During the initial screening, 11 articles were removed because they lacked evidence of including children from Africa. In the next step, we removed 3 duplicate articles, leaving 29 publications for further review. We then screened the titles and abstracts, excluding 8 publications based on our predefined eligibility criteria. The remaining 21 articles were assessed for eligibility through a full-text review. Of these, 9 papers were found to be ineligible: 6 were descriptive reviews lacking cancer information (thus unable to ascertain childhood cancer types), 1 systematic review included a study that had been previously included, and 1 systematic review and 1 lab-based study addressed pharmacogenomics outside the context of pediatric cancer (details in Additional File 5, supplementary materials). As a result, 12 studies were included in this review (Figure 1).

Characteristics of Included Sources of Evidence

The included studies were published between 2010 and 2024. Six out of the 12 studies were authored by researchers from Northern Africa (4 from Egypt, 1 from Tunisia, and 1 from Libya). Additionally, 8 of the 12 studies were laboratory-based, investigating genetic variations in a total of 429 children with cancer (279 with Acute Lymphoblastic Leukemia (ALL) and 150 with Rhabdomyosarcoma (RMS)) and 491 normal adult volunteers. The remaining studies focused on the knowledge and applicability of pharmacogenomics among healthcare workers (HCWs), involving 338 participants (Table 1).

Table 1 Key Features and Results of Reviewed Studies

Summary of the Key Findings

Table 1 summarizes the results from the genetic analysis of samples from children with cancer and adult volunteers, as well as observations on the level of knowledge regarding the concept of pharmacogenomics among relevant healthcare workers and academicians, as reported in the studies included in this scoping review.

Genes, Variants, and Chemotherapies

In eight studies,14,15,19–21,23–25 12 genes associated with the pharmacokinetics of chemotherapies used to treat childhood cancers were analyzed. These genes included CYP3A5, MDR1, MAPT, TPMT, NUDT15, ITPA, IMPDH1, SLC29A1, SLC28A2, SLC28A3, ABCC4, and MTHFR, with various genetic variants identified. Among these, the most common were TPMT variants (*3A and *3C), which are linked to the metabolism of 6-mercaptopurine (6-MP) and were explored in two and three studies, respectively.15,23–25 Additionally, CYP3A5 variants (*3, *6, *7), which encode enzymes involved in the metabolism of vincristine, were identified in two studies.14,20 Variants of the ITPA, IMPDH1, SLC28A2, SLC29A1, and ABCC4 genes, all involved in the transport of 6-MP, were reported in one study.19 Finally, the MTHFR variants C677>T and A1298>C, associated with methotrexate transport across cells, were identified in one study.21

There is considerable variation in the frequencies of these variants across different populations in Africa. One study by Skiles et al reported that 91% of the Kenyan children tested for the CYP3A5 gene were carriers of high expresser variants (homozygous wild-type and heterozygous) for the enzyme involved in vincristine metabolism. The low expressers had higher dose- and body surface area-normalized areas under the curve compared to high expressers (0.28 ± 0.15 hr·m²/l vs 0.15 ± 0.011 hr·m²/l, P = 0.027). The 9% low expressers were those who carried homozygous *3, *6, or *7 alleles, although individual allele frequencies were not provided.14 In contrast, ElShereef et al reported that 61.3% of pediatric Egyptian cancer patients were poor metabolizers (homozygous *3/*3 or *3/*6) of CYP3A5.20

Regarding TPMT gene variants, Chikondowa et al reported a frequency of 9.8% in Zimbabwe, followed by 9.4% in Nigeria as reported by Adehin et al. In both studies, TPMT*3C was the predominant variant.15,25 Salah et al reported a frequency of 1.68% in Tunisia, predominantly TPMT*3A, while Zeglam et al found a frequency of 1.63% in Libya, largely TPMT*3A.23,24 Despite the varying frequencies, all studies linked TPMT variants to the metabolism of 6-MP, used during the maintenance phase of acute lymphoblastic leukemia (ALL) treatment. Hareedy et al in Egypt reported frequencies of 4–14%, 32–38%, 14%, 44%, 9%, and 1–10% for ITPA, IMPDH1, SLC29A1, SLC28A2, SLC28A3, and ABCC4, respectively, all associated with 6-MP transport.19

For MTHFR gene variants, the common variations C677>T had frequencies of 40%, 27.5%, and 32.5% for the TT, CT, and CC genotypes, respectively. The TT homozygous variant was associated with grade 3 and 4 toxicities induced by methotrexate. In contrast, the A1298>C variant had frequencies of 40%, 35%, and 25% for the AA, AC, and CC genotypes, respectively, but was not associated with increased toxicity risk from methotrexate.21

The included studies explored the pharmacogenomics of four cancer chemotherapies, with 6-MP being the most studied, reported in five out of eight studies with genetic analysis results. Four studies focused on enzymes involved in the activation of 6-MP, while one examined its transport across cells. Vincristine, cyclophosphamide, and methotrexate were each reported in one study (Table 2).

Table 2 Gene-Drug Combinations, Frequency of Variants, and Associated Outcomes for Chemotherapeutic Agents

Knowledge, Attitude, and Perception of Pharmacogenomics Among Health Care Workers in Africa

Four studies examined the knowledge, attitudes, and perceptions of healthcare workers (HCWs) regarding pharmacogenomics across different African countries, using various methodologies.16–18,22 In Ghana, Kudzi et al explored these aspects among HCWs, including medical doctors, pharmacists, nurses, and university faculty members. They found that the majority of healthcare professionals (60%) rated their knowledge as “good”, while 21.4% rated it as “poor”. Only a small portion (5.6% and 11.2%) considered their knowledge to be “excellent” or “very good”, respectively. This trend was also observed among university faculty members, even though 40.6% stated that pharmacogenetics was part of their curriculum. However, 37% said it was not included, and 22% were unsure.16

Similarly, Nagy et al reported low overall pharmacogenetics knowledge (mean score = 41) among physicians and pharmacists in Egypt. Despite this, respondents displayed a positive attitude towards the potential benefits of pharmacogenetics, with pharmacists being more receptive than physicians. The study also identified key challenges to implementing clinical pharmacogenetic testing, such as a lack of knowledge, skills, testing devices, and funding.18 These barriers were echoed by Madadi et al in a study from Benin City, Nigeria, where all participants (pharmacists and pharmacologists) were familiar with pharmacogenetics and recognized its benefits.17

In a broader study by Jongeneel et al, involving authors from South Africa, Sudan, Mauritius, Tunisia, Egypt, and Morocco, respondents were from a diverse range of countries, including Morocco, Tunisia, Egypt, Mali, Ghana, Nigeria, Uganda, Rwanda, Malawi, Zimbabwe, Botswana, and South Africa. Nearly all regions of Africa, except Central Africa, were represented. Most respondents, including physicians, postgraduate students, and laboratory heads, were familiar with precision medicine, with over 80% either already using or planning to adopt advanced genomic technologies in their research. However, fewer than half had access to local infrastructure, which led many to rely on external collaborations. About a third of respondents reported that their institutions hosted genomic projects, another third said they did not, and the remaining third were unsure.22 Overall, clinician’s and researcher’s awareness of genomic medicine across these regions was low. Common barriers to implementation included insufficient information on local disease susceptibility, the perception that it was a low-priority investment, and concerns that genomic medicine would primarily benefit wealthier patients. These barriers were consistent with those identified in other studies.

Discussion

The current review presents the extent, existing findings, and factors influencing pharmacogenomics research in childhood cancer therapy in Africa. This concept is still relatively new and rarely applied, as shown by the small number of studies identified in our search. This is despite its foreseeable importance, as reported in other studies examining pharmacogenomics in Africa.26 The African population exhibits the highest level of genetic variation globally, which has significant health implications for many genes relevant to pharmacogenomics. However, it is important to note that this variability is gene-dependent, as some genes, such as UGT2B17 and GSTM1, demonstrate more specific patterns, such as high deletion frequencies in certain populations. These distinctions further shows the importance of region-specific pharmacogenomics research in Africa.27,28

The 12 studies included in this review utilized various study methodologies, with eight reporting on 12 genes related to the pharmacokinetics of four commonly used chemotherapies in 429 children with acute lymphoblastic leukemia (ALL) and rhabdomyosarcoma (RMS). This reflects the low level of laboratory genetic exploration in Africa which may be due to a lack of economic investment in necessary infrastructures.29 The other four studies focused on knowledge, attitudes, perceptions, and challenges related to the clinical application of pharmacogenomics among healthcare workers (HCWs) in Africa. The limited scope of pharmacogenomic studies presented in this review reflects earlier findings by Radouani et al, who reported that only 10% of clinical pharmacogenetic studies in Africa involved cancer.30

In Africa, great emphasis is placed on infectious diseases particularly HIV/AIDS, and Malaria. That is why during our review process we encountered many studies reporting on the CYP2B6 gene and its influence on the pharmacokinetics of Efavirenz, nevirapine, and artemisinin drugs. The same gene also influences the metabolism of cyclophosphamide, a commonly used chemotherapy in the treatment of childhood cancers. This has been widely documented in reviews on pharmacogenomics in Africa.31–33

Half of the studies included in this review were conducted by researchers from North African countries, highlighting the region’s more developed economic and healthcare infrastructure, which supports the funding and execution of clinical pharmacogenomics research. Countries like Egypt, Tunisia, and Libya, which contributed most of the studies, have relatively higher GDPs compared to other African nations, with Egypt’s GDP estimated at approximately 476.7 billion USD, while Tunisia and Libya’s GDPs are around 46.3 billion and 45.75 billion USD, respectively, these are higher than most sub-Saharan African countries except South Africa and Nigeria.34

In this review, most (279) childhood cancer patients tested were children with ALL in the maintenance phase of treatment with 6-mercaptopurine (6-MP). This is because the TPMT gene, which codes for the enzyme involved in the biotransformation of thiopurines, is the most researched globally. Its effects on drug metabolism are well known, and it is incorporated in the clinical guidelines of the Clinical Pharmacogenetics Implementation Consortium (CPIC).35 Additionally, TPMT and 6-MP have an established gene-drug relationship in the pharmacogenomics knowledge base (PharmGKB, https://www.pharmgkb.org/), a large web-based database. The inclusion of this gene in guidelines and its clinical utility has been shown to optimize ALL treatments in developed countries,36 but this practice is not widely adopted in most African countries.37

Interestingly, studies conducted in Sub-Saharan Africa (SSA) showed that approximately 10% of children carried the *3C variant of TPMT, in contrast to children from North Africa, where the majority carried the *3A variant. This can be explained by genetic differences between the two regions, with northern Africans being predominantly Arab and those in SSA being primarily of African descent. Other studies have shown that *3C is more common in African and Asian populations, while 3A is more prevalent in European and Caucasian populations.38 It is also reported that there are notable differences between children with cancer who carry the TPMT*3C variant and those with the 3A variant. For example, TPMT*3C causes a moderate reduction in TPMT enzyme activity, leading to intermediate metabolism of thiopurines. Patients with this variant may have an increased risk of toxicity but not as severe as those with the *3A variant.39,40

Two studies reported on the association of CYP3A5 variants and adverse drug reactions (ADRs). In Kenyan children, carrying variants of this gene was not associated with vincristine-induced neuropathy (VINP), but in Egyptian children with RMS, carrying variants was associated with cyclophosphamide-induced toxicities. These two studies had different objectives but explored the same gene on its influence on two different chemotherapies. However, this observation also explains that carrying a defective variant of a gene will not always translate into phenotypic presentation because there are other explanations that may include different genes, for instance vincristine-induced neuropathy which is also influenced by other genes reported in other studies. The gene, such as CEP72, has been shown to influence the occurrence of VINP.41,42 Additionally, factors like hydration and the use of supportive drugs to prevent hemorrhagic cystitis in patients on cyclophosphamide play a role.43

Moreover, two studies presented results on genes associated with drug transport across cells, all of which yielded significant findings. Variants in the MTHFR gene (related to methotrexate transport) and ITPA, IMPDH1, SLC29A1, SLC28A2, SLC28A3, and ABCC4 (related to 6-MP transport) showed that pharmacogenomic testing may need to involve multiple genes for it to be clinically actionable. These findings align with other reviews that did not exclusively focus on African children.44,45

Pharmacogenomics knowledge among HCWs in Africa is generally low, as indicated by the included studies. Only one study reported that all HCWs were familiar with the concept of pharmacogenetics, but it did not specify the level of familiarity or whether “being familiar” equated to having sufficient knowledge. Familiarity with the concept can be influenced by several factors, including the type of personnel surveyed, their economic status, and the level of healthcare advancement in their region.10 Other included studies categorized HCWs’ knowledge levels, with some self-reporting good knowledge when asked to choose between “excellent”, “very good”, “good”, and “poor”. In general, the level of knowledge on pharmacogenomics is shown to be low among HCWs in Africa.

This lack of knowledge can be attributed to several factors. One is the limited inclusion of pharmacogenomics topics in medical and pharmacy curricula. Traditional pharmacology and clinical training often take precedence, as illustrated by a study from Ghana, where one-third of faculty members at health training institutions reported that pharmacogenomics was not part of their curricula.16,46 Additionally, there are few continuous professional development (CPD) opportunities focused on pharmacogenomics, with limited workshops or conferences available to update HCWs on advances in the field.47,48 This educational gap is compounded by inadequate infrastructure and resources, as the absence of genetic testing facilities and high costs limit practical exposure to pharmacogenomics tools.49

Nevertheless, nearly all of the HCWs included in the studies demonstrated a positive attitude and perception of the application and benefits of clinical pharmacogenomics. This is likely due to their motivation to provide more precise, individualized care, a core principle of pharmacogenomics. By understanding how genetic variations influence drug response, HCWs are increasingly convinced of the clinical value of pharmacogenomics in improving treatment outcomes, especially for chronic diseases like cancer and cardiovascular conditions.50

This review is limited to the African healthcare systems and policies related to pharmacogenomics, particularly in countries where English is one of the official languages. Since it includes studies from all African regions except Central Africa, it provides a broad picture of the genetic variants influencing the pharmacokinetics of chemotherapies commonly used to treat childhood cancers and the knowledge, attitudes, and perceptions of HCWs on pharmacogenomics in Africa.

Conclusion

Based on the studies included in this review, twelve genes known to be associated with adverse drug reactions (ADRs) and/or chemotherapy efficacy in childhood cancer have been explored in Africa. TPMT is the most studied, followed by CYP3A5. The chemotherapies most commonly examined for drug-gene associations include 6-mercaptopurine (6-MP), vincristine, cyclophosphamide, and methotrexate. The pediatric cancers investigated include acute lymphoblastic leukemia (ALL) and rhabdomyosarcoma, highlighting regional disparities in pharmacogenomics studies across Africa. While healthcare workers’ pharmacogenomics knowledge remains low, they show a positive attitude toward its applications. To enhance understanding, targeted educational programs and expanded research on additional genes and cancer types are necessary to improve pharmacogenomics’ application in childhood cancer treatment.

Abbreviations

ADR, Adverse Drug Reactions; ALL, Acute Lymphoblastic Leukemia; CYP, Cytochrome P450; HCWs, Health Care Workers; RMS, Rhabdomyosarcoma; 6-MP, Six-Mercaptopurine.

Data Sharing Statement

All data generated or analyzed during this study are available and included in this published article (and its supplementary information files).

Acknowledgments

We would like to acknowledge Mr. Oliver Bondo, Librarian at the Catholic University of Health and Allied Sciences Library, for his assistance during the literature search.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising, or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

The study was supported by funding provided to DMK by the Higher Education for Economic Transformation (HEET) project.

Disclosure

The authors declare that they have no competing interests.

References

1. who-childhood-cancer-overview-booklet.pdf [Internet]. [cited August 23, 2024]. Available from: https://www.who.int/docs/default-source/documents/health-topics/cancer/who-childhood-cancer-overview-booklet.pdf. Accessed February04, 2025.

2. Oakes K. Nearly half of global childhood cancer cases go undiagnosed. Nature. 2019. doi:10.1038/d41586-019-00828-0

3. Majaliwa E, Smith ER, Cotache-Condor C, et al. Childhood and adolescent cancer care at a tertiary hospital in Northern Tanzania: a retrospective study. JCO Glob Oncol. 2023;9:e2200263. doi:10.1200/GO.22.00263

4. Bachtiar M, Lee CG. Genetics of population differences in drug response. Curr Genet Med Rep. 2013;1:162–170. doi:10.1007/s40142-013-0017-3

5. Sadee W, Wang D, Hartmann K, Toland AE. Pharmacogenomics: driving personalized medicine. Pharmacol Rev. 2023;75(4):789–814. doi:10.1124/pharmrev.122.000810

6. Dean L, Kane M. Mercaptopurine therapy and TPMT and NUDT15 genotype. 2020.

7. Dean L. Thioguanine therapy and TPMT and NUDT15 genotype. Med Genet Summ Internet. 2020;2020:1.

8. Moyo E, Moyo P, Mashe T, Dzobo M, Chitungo I, Dzinamarira T. Implementation of public health genomics in Africa: lessons from the COVID‐19 pandemic, challenges, and recommendations. J Med Virol. 2023;95(1):e28295. doi:10.1002/jmv.28295

9. Gong L, Whirl‐Carrillo M, Klein TE. PharmGKB, an integrated resource of pharmacogenomic knowledge. Curr Protoc. 2021;1(8):e226. doi:10.1002/cpz1.226

10. Dandara C, Masimirembwa C, Haffani YZ, et al. African Pharmacogenomics Consortium: consolidating pharmacogenomics knowledge, capacity development and translation in Africa: consolidating pharmacogenomics knowledge, capacity development and translation in Africa. AAS Open Res. 2019;2:19. doi:10.12688/aasopenres.12965.1

11. Elhassan MMA, Mahmoud I, Qaddoumi I, Vanderpuye V, Parkes J, Ho YS. Pediatric cancer research trends and performance in Africa: a bibliometric analysis from 1991 to 2022. Pediatr Hematol Oncol J. 2024;9:211–218. doi:10.1016/j.phoj.2024.06.006

12. Westphaln KK, Regoeczi W, Masotya M, et al. From Arksey and O’Malley and Beyond: customizations to enhance a team-based, mixed approach to scoping review methodology. MethodsX. 2021;8:101375. doi:10.1016/j.mex.2021.101375

13. Tricco AC, Lillie E, Zarin W, et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. 2018;169(7):467–473. doi:10.7326/M18-0850

14. Skiles JL, Chiang C, Li CH, et al. CYP3A5 genotype and its impact on vincristine pharmacokinetics and development of neuropathy in Kenyan children with cancer. Pediatr Blood Cancer. 2018;65(3):e26854. doi:10.1002/pbc.26854

15. Chikondowa P, Munkombwe D, Chikwambi Z, Kuona P, Masimirembwa C. Pharmacogenetics of 6-mercaptopurine in a black Zimbabwean cohort treated for acute lymphoblastic leukaemia. Pharmacogenomics. 2023;24(8):449–457. doi:10.2217/pgs-2023-0026

16. Kudzi W, Addy B, Dzudzor B. Knowledge of pharmacogenetics among healthcare professionals and faculty members of health training institutions in Ghana. Ghana Med J. 2015;49(1):50–56. doi:10.4314/gmj.v49i1.9

17. Enato EF. Perceptions of health care professionals towards pharmacogenomics in Nigeria: preliminary results. West Afr J Pharm. 2011;22(1):97–101.

18. Nagy M, Lynch M, Kamal S, et al. Assessment of healthcare professionals’ knowledge, attitudes, and perceived challenges of clinical pharmacogenetic testing in Egypt. Pers Med. 2020;17(4):251–260. doi:10.2217/pme-2019-0163

19. Hareedy MS, El Desoky ES, Woillard JB, et al. Genetic variants in 6-mercaptopurine pathway as potential factors of hematological toxicity in acute lymphoblastic leukemia patients. Pharmacogenomics. 2015;16(10):1119–1134. doi:10.2217/PGS.15.62

20. ElShereef CE, Zaki HF, Badary OA, et al. Correlation of genetic polymorphism of CYP3A5 to cyclophosphamide efficacy and toxicity in Rhabdomyosarcoma pediatric Egyptian cancer patients. Asian Pac J Cancer Prev. 2024;25(7):2445–2455. doi:10.31557/APJCP.2024.25.7.2445

21. Tantawy AA, El-Bostany EA, Adly AA, Abou El Asrar M, El-Ghouroury EA, Abdulghaffar EE. Methylene tetrahydrofolate reductase gene polymorphism in Egyptian children with acute lymphoblastic leukemia. Blood Coagul Fibrinolysis. 2010;21(1):28–34. doi:10.1097/MBC.0b013e32833135e9

22. Jongeneel CV, Kotze MJ, Bhaw-Luximon A, et al. A view on genomic medicine activities in Africa: implications for policy. Front Genet. 2022;13:769919. doi:10.3389/fgene.2022.769919

23. Salah LB, Salem CB, B’Chir F, Bouraoui K, Broly F, Saguem S. Thiopurine S-methyltransferase genetic polymorphism in the Tunisian population. Egypt J Med Hum Genet. 2011;12(2):183–186. doi:10.1016/j.ejmhg.2011.08.004

24. Zeglam HB, Benhamer A, Aboud A, et al. Polymorphisms of the thiopurine S -methyltransferase gene among the Libyan population. Libyan J Med. 2015;10(1):27053. doi:10.3402/ljm.v10.27053

25. Adehin A, Bolaji OO, Kennedy MA, Adeagbo BA. Allele frequencies of thiopurine S-methyltransferase (TPMT) variants in the Nigerian population. Pol Ann Med. 2017;24(2):144–147. doi:10.1016/j.poamed.2016.06.007

26. Mpye K, Matimba A, Dzobo K, Chirikure S, Wonkam A, Dandara C. Disease burden and the role of pharmacogenomics in African populations. Glob Health Epidemiol Genomics. 2017;2:e1. doi:10.1017/gheg.2016.21

27. Gomez F, Hirbo J, Tishkoff SA. Genetic variation and adaptation in Africa: implications for human evolution and disease. Cold Spring Harb Perspect Biol. 2014;6(7):a008524. doi:10.1101/cshperspect.a008524

28. Warnich L, I. Drogemoller B, S. Pepper M, Dandara C, E.B. Wright G. Pharmacogenomic research in South Africa: lessons learned and future opportunities in the rainbow nation. Curr Pharmacogenomics Pers Med Former Curr Pharmacogenomics. 2011;9(3):191–207. doi:10.2174/187569211796957575

29. Kabbani D, Akika R, Wahid A, Daly AK, Cascorbi I, Zgheib NK. Pharmacogenomics in practice: a review and implementation guide. Front Pharmacol. 2023;14:1189976. doi:10.3389/fphar.2023.1189976

30. Radouani F, Zass L, Hamdi Y, et al. A review of clinical pharmacogenetics Studies in African populations. Pers Med. 2020;17(2):155–170. doi:10.2217/pme-2019-0110

31. Maseng MJ, Tawe L, Thami PK, et al. Association of CYP2B6 genetic variation with efavirenz and nevirapine drug resistance in HIV-1 patients from Botswana. Pharmacogenomics Pers Med. 2021;16:335–347.

32. Mehlotra RK, Bockarie MJ, Zimmerman PA. CYP2B6 983T> C polymorphism is prevalent in West Africa but absent in Papua New Guinea: implications for HIV/AIDS treatment. Br J Clin Pharmacol. 2007;64(3):391–395. doi:10.1111/j.1365-2125.2007.02884.x

33. Langmia IM, Just KS, Yamoune S, Brockmöller J, Masimirembwa C, Stingl JC. CYP2B6 functional variability in drug metabolism and exposure across populations—implication for drug safety, dosing, and individualized therapy. Front Genet. 2021;12:692234. doi:10.3389/fgene.2021.692234

34. World Economic Outlook - Real GDP growth [Internet]. [cited October 16, 2024]. Available from: https://www.imf.org/external/datamapper/NGDP_RPCH@WEO. Accessed February04, 2025.

35. Khaeso K, Nakkam N, Komwilaisak P, et al. Genetic polymorphisms of drug-metabolizing enzymes involved in 6-mercaptopurine-induced myelosuppression in Thai pediatric acute lymphoblastic leukemia patients. J Pediatr Genet. 2021;10(01):029–34. doi:10.1055/s-0040-1715818

36. Mlakar V, Huezo-Diaz Curtis P, Satyanarayana Uppugunduri CR, Krajinovic M, Ansari M. Pharmacogenomics in pediatric oncology: review of gene—Drug associations for clinical use. Int J mol Sci. 2016;17(9):1502. doi:10.3390/ijms17091502

37. Barbarino JM, Whirl‐Carrillo M, Altman RB, Klein TE. PharmGKB: a worldwide resource for pharmacogenomic information. Wiley Interdiscip Rev Syst Biol Med. 2018;10(4):e1417. doi:10.1002/wsbm.1417

38. Gallardo-Cóndor J, Naranjo P, Atarihuana S, et al. Population-specific distribution of TPMT deficiency variants and ancestry proportions in ecuadorian ethnic groups: towards personalized medicine. Ther Clin Risk Manag. 2023;Volume 19:1005–1018. doi:10.2147/TCRM.S432856

39. Maillard M, Nishii R, Yang W, et al. Additive effects of TPMT and NUDT15 on thiopurine toxicity in children with acute lymphoblastic leukemia across multiethnic populations. JNCI J Natl Cancer Inst. 2024;116(5):702–710. doi:10.1093/jnci/djae004

40. Clunie G, Lennard L. Relevance of thiopurine methyltransferase status in rheumatology patients receiving azathioprine. Rheumatology. 2004;43(1):13–18. doi:10.1093/rheumatology/keg442

41. Stock W, Diouf B, Crews KR, et al. An inherited genetic variant in CEP72 promoter predisposes to vincristine‐induced peripheral neuropathy in adults with acute lymphoblastic leukemia. Clin Pharmacol Ther. 2017;101(3):391–395. doi:10.1002/cpt.506

42. Klumpers MJ, Brand AC, Hakobjan M, et al. Contribution of common and rare genetic variants in CEP72 on vincristine‐induced peripheral neuropathy in brain tumour patients. Br J Clin Pharmacol. 2022;88(7):3463–3473. doi:10.1111/bcp.15267

43. Saito Y, Kumamoto T, Shiraiwa M, et al. Cyclophosphamide‐induced hemorrhagic cystitis in young patients with solid tumors: a single institution study. Asia-Pacific J Clin Oncol. 2018;14(5):e460–4. doi:10.1111/ajco.13048

44. Umerez M, Gutierrez-Camino A, Munoz-Maldonado C, Martin-Guerrero I, Garcia-Orad A. MTHFR polymorphisms in childhood acute lymphoblastic leukemia: influence on methotrexate therapy. Pharmacogenomics Pers Med. 2017;2017:69–78.

45. Adam de Beaumais T, Jacqz-Aigrain E. Pharmacogenetic determinants of mercaptopurine disposition in children with acute lymphoblastic leukemia. Eur J Clin Pharmacol. 2012;68:1233–1242. doi:10.1007/s00228-012-1251-4

46. Wondrasek A, Fryza A, Aziz MA, Leong C, Kowalec K, Maruf AA. Knowledge, perceptions, and attitudes toward pharmacogenomics among pharmacists and pharmacy students: a systematic review. Health Sci Rep. 2024;7(1):e1844. doi:10.1002/hsr2.1844

47. Yadeta TA, Mohamed A, Behir K, et al. Knowledge, perceived needs of continuous professional’s development, and associated factors among healthcare workers in East Ethiopia: a multi-health facility-based cross-sectional study. BMC Med Educ. 2024;24(1):497. doi:10.1186/s12909-024-05486-z

48. Crown N, Sproule BA, Luke MJ, Piquette-Miller M, McCarthy LM. A continuing professional development program for pharmacists implementing pharmacogenomics into practice. Pharmacy. 2020;8(2):55. doi:10.3390/pharmacy8020055

49. Tata EB, Ambele MA, Pepper MS. Barriers to implementing clinical pharmacogenetics testing in Sub-Saharan Africa. A critical review. Precis Med. 2020;29:1.

50. Kapoor R, Tan-Koi WC, Teo YY. Role of pharmacogenetics in public health and clinical health care: a SWOT analysis. Eur J Hum Genet. 2016;24(12):1651–1657. doi:10.1038/ejhg.2016.114