Skip to main content

Frequency, severity, and factors associated with clinically significant drug-drug interactions among patients with cancer attending Mbarara Regional Referral Hospital Cancer Unit, Uganda

Abstract

Background

Cancer is a major public health problem with pharmacotherapy being the cornerstone of its management. Cancer patients receive multiple drugs concurrently risking Drug-Drug Interactions (DDIs). DDIs, though avoidable, can significantly contribute to morbidity, mortality, and increased healthcare costs in this population of patients. Currently, there is no published study from Uganda on clinically significant DDIs (cs-DDIs) among cancer patients. This study identifies frequency, severity, and factors associated with cs-DDIs at Mbarara Regional Referral Hospital Cancer Unit (MRRHCU).

Method

A cross-sectional study was conducted among 300 cancer patients receiving chemotherapy from a tertiary care hospital in western Uganda from January–February 2022. A questionnaire and data collection form were used to collect patient data. Lexicomp® Drug interaction software was used to screen the patient drug information for DDIs and assess their severity. Predictors of DDIs were identified using logistic regression using SPSS (Statistical Package for Social Sciences).

Result

Three hundred participants were enrolled with a mean age of 48 ± 23.3 years. One hundred eighty-one patients experienced 495 cs-DDIs; with a mean of 1.7 ± 2.2. The prevalence of cs-DDI was 60.3% (55.0-66.0% at 95% CI). Digestive organ neoplasms were the most commonly (80, 26.7%) diagnosed category, and ‘plant alkaloids and other natural products were the most frequently (143, 47.7%) used chemotherapeutic drug classes. About three-quarters of cs-DDIs were rated as category C risk (367, 74.1%) whereas over two-thirds (355, 71.7%) were moderate in severity.. Being female (aOR = 2.43 [1.23–4.48 at 95% CI]; P-value = 0.011) and use of ≥ 6 drugs concurrently (aOR = 18.82 [9.58–36.95 at 95% CI]; P-value < 0.001)) were significantly associated with cs-DDIs.

Conclusion

More than half of the participants experienced at-least one cs-DDI which is generally higher than what was reported in high-income settings. About three-quarters were category C and moderate in severity, and require enhanced monitoring for safety and treatment outcome. Being female and using ≥ 6 drugs were significantly associated with cs-DDIs.

Peer Review reports

Introduction

Drug interaction is defined as a change in the effect of a drug as a result of its interaction with another drug(s), food, or disease [1]. The types of drug interactions include drug-drug interactions (DDIs), food-drug interactions, disease-drug interactions, and drug-supplement interactions. DDIs are the commonest, attributing approximately 20% to 30% of all adverse events (AEs) [1]. DDIs may cause a reduction in therapeutic efficacy [2] or unexpected adverse effects [3, 4] negatively affecting treatment outcomes. The drugs involved can be prescription-only medicines or over-the-counter medicines. DDIs can be classified as pharmacokinetic or pharmacodynamic in nature [5, 6], with pharmacokinetic DDIs being the commonest [7]. A systemic review and meta-analysis approximated that 1/10 hospitalized patients are exposed to cs-DDIs [2], 20–40% [5]. Studies have shown that DDIs have become prevalent among cancer patients, which may be due to disease sequelae that require pharmacologic management, hence contributing to polypharmacy which is one of the leading DDI risk factors. The factors associated with DDIs include lack of DDI identification causality tools and DDI lists [2], long hospital stay, polypharmacy particularly among the elderly because of comorbidities and complex therapy regimens [5, 8], increased approval of combination drug formulations some of which exhibit highly complex drug interaction profiles [9] and self-medication [10] have contributed greatly to the burden.

Among ambulatory cancer patients, 27–58% of them are at risk of DDIs involving anticancer agents like doxorubicin, cyclophosphamide, and methotrexate among others, with methotrexate being the most implicated agent [11, 12]. In Uganda, a retrospective analysis showed a 23% prevalence of potential DDIs in only the major wards (medical, surgical, obstetrics/gynecology, and pediatrics) [13] excluding the cancer unit. In Kenya, a study among cervical cancer patients identified a 46.9% prevalence of DDIs [14]. Generally, in East Africa, DDIs have not been studied exclusively in the cancer units. Previous studies emphasized specific types of drug interactions like herbal-drug interactions (NDA, 2018), and other specific diseases like HIV (89.3% prevalence of potential DDIs) [15] and malaria [16] among others. DDI databases with a warning system combined with a pharmacist’s assessment can be used to identify, prevent, and resolve DDIs [2]. In Uganda, according to published data, there was no study done about DDIs, specifically among cancer patients on chemotherapy. This study aimed to determine the frequency, severity, and factors associated with cs-DDIs in cancer patients at MRRHCU.

Methods

Study design and setting

A cross-sectional study was carried out at the MRRHCU, Southwestern Uganda (269.7 km from the capital city Kampala), a public sector tertiary care teaching hospital from January to February 2022. The cancer unit has a bed capacity of 20 and 18 beds for adults and pediatric patients respectively; with two oncology specialists, one pharmacist, and seven nurses. At the study site, there was neither an active screening system for DDIs nor standardized tools for DDI detection and prevention.

Study participants and selection criteria

All adult and pediatric patients with cancer that received chemotherapy from January–February 2022 at MRRHCU were considered in the current study. The study included all patients with cancer of either sex who had a confirmed cancer diagnosis who gave their written consent to participate in the study and who were prescribed and receiving at least one chemotherapy drug. Whereas patients who withdrew from the study before completion of enrollment and those receiving only one drug were excluded from the study.

Data collection and screening/assessing DDIs

A questionnaire and data collection form were developed, validated, and used to document patients’ baseline socio-demographics, past medical history, and drug use from each eligible patient and their medication file during their attendance at the unit. Both English and Runyankole questionnaires were developed and pre-tested with 15 patients (5% of the estimated sample size) at MRRHCU before actual data collection to ensure the feasibility of the data collection tool. Results from the pretest were excluded from the final analysis. The chemotherapeutic agents and any other concurrent medications prescribed were recorded in the data collection form, by the principal investigator assisted by the assistants.

Two research assistants, a nurse, and a medical laboratory scientist, both bachelor's degree holders were trained on the data collection protocols and ethical considerations before the study started to ensure data quality. Participants voluntarily consented before enrolment after their clinical review by the clinical team. All medications to be taken by the patient were recorded by the research assistants and the principal investigator through direct interviews and patients’ medical records review using a data collection tool on recruitment into the study.

Upon getting the list of drugs the patient was taking, the drugs were entered into the Lexicomp® drug interaction software for potential DDI screening and assessment on risk category and severity. The software assigned a risk rating of A (no known interaction), B (no action needed), C (monitor therapy), D (consider therapy modification), and X (avoid combination). Combinations rated C, D and X indicated cs-DDIs that needed the clinician’s attention while A and B were less clinical. It assigned major, moderate, or minor in severity terms. To minimize the possible consequent bias, the identified cs-DDIs were not intervened except for those believed by investigators to be potentially life-threatening, and these recommendations were made during ward rounds without letting the clinical team know it was from the study’s findings.

Data processing and analysis

Statistical Package for Social Sciences (SPSS), version 21, a software program, was used for data management and quantitative analysis (logistic regression, descriptive, and factor analysis). A descriptive analysis of socio-demographic, clinical, and drug-related variables was presented using a mean with an interquartile range and percentages.

The frequency of cs-DDIs among participants was calculated by dividing the number of participants with at least one cs-DDIs by the total number of participants and expressed as a percentage. Univariate and multivariate logistic regression was employed to determine independent factors associated with cs-DDIs. Variables with p < 0.25 at univariate analysis were included in the multivariate logistic regression. In the multivariate model, P-values < 0.05 were considered statistically significant.

Results

Participant characteristics

Three hundred fifteen patients were approached of whom 10 were on follow-up on complete remission after chemotherapy, 2 declined to receive treatment after review, and 3 declined to consent to participate. In total, 300 patients were studied including 239(79.9%) outpatients, the majority (250, 83.3%) from the adult ward. Over half (163, 54.3%) were males whereas the majority (184, 61.33%) were below 60 years of age and the mean age was 48.0 ± 23.3. Over half (167, 55.7%) of the participants had a previous admission (Table 1).

Table 1 The socio-demographic characteristics of cancer patientsUganda

Types of cancers documented

Digestive organs neoplasms (80, 26.7%) followed by male genital organs neoplasms (51, 17.0%), and lymphoid tissue neoplasms (40, 13.3%) were the most neoplasms diagnosed (Fig. 1).

Fig. 1
figure 1

The types of cancers documented among patients at the cancer unit, Uganda. Others: Kaposi sarcoma, peripheral nerve sheath tumor, neck tumor

Chemotherapeutic agents used

The study participants used a mean of 1.87 ± 0.82 chemotherapeutic drugs. Out of the 8 classes of chemotherapeutic drugs, ‘plant alkaloids and other natural products were used by almost half (143, 47.7%) of the patients followed by ‘antimetabolites’ used by 103 (34.3%). vincristine (57, 19%), docetaxel (44, 14.7%), doxorubicin (44, 14.7%), cyclophosphamide (41, 13.7%), and goserelin (35, 11.7%) were the top five most commonly used specific chemotherapeutic agents (Table 2).

Table 2 The classes and specific chemotherapeutic agents used by cancer patients, Uganda

Frequency of cs-DDIs

Out of 300 participants, 181 experienced at least one cs-DDIs giving a 60.3% (55.0%-66.0% at 95% CI) frequency. 495 cs-DDIs with a mean of 1.7 ± 2.2 were identified.

Risk category and severity of the identified cs-DDIs

Of the 495 cs-DDIs identified, 74.1% (367) were category C (Fig. 2). In severity, minor, moderate, and severe DDIs were 12 (2.4%), 71.7% (355), and 25.9% (128) respectively (Fig. 3).

Fig. 2
figure 2

The risk categories of clinically significant drug-drug interaction among cancer patients at the cancer unit, Uganda. c- Risk rating that requires therapy monitoring; d – Risk rating that requires therapy modification; x- Risk rating that requires therapy avoidance

Fig. 3
figure 3

The Severity of clinically significant drug-drug interaction among cancer patients at the cancer unit, Uganda

Frequency of cs-DDIs across classes of cancers

Endocrine gland neoplasms (100%), skin neoplasms (100%), and female genital organs neoplasms (87.5%) were associated with the highest frequency of cs-DDIs (Table 3).

Table 3 Prevalence of clinically significant DDIs among the different cancer classes diagnosed among cancer patients Uganda

Drugs implicated in cs-DDIs

Doxorubicin (33, 6.7%), cyclophosphamide (33, 6.7%), and methotrexate (30, 6.1%) were the specific chemotherapeutic agents mostly involved in the cs-DDIs. On the other hand, morphine (103, 21.0%), metoclopramide (92, 19.0%), and dexamethasone (54, 11.0%) were the pre-medications mostly implicated in the cs-DDIs (Table 4). Morphine-metoclopramide was the commonest (n = 48) cs-DDI. Among the anti-cancers, cyclophosphamide-doxorubicin contributed to the highest cs-DDI (n = 28) followed by taxanes-platinum analogues (n = 18); both of which are major in severity. Among the other medications, morphine-metoclopramide followed by dexamethasone-NSAIDS (n = 14) were the most frequent (Table 5).

Table 4 The specific chemotherapeutic agents and premedications implicated in clinically significant Drug-drug interactions among cancer patients, Uganda
Table 5 The most frequent cs-DDIs among cancer patients, Uganda

Factors associated with cs-DDIs

Seven variables including age (COR = 2.36 [1.22–4.55 at 95% CI]; P-value = 0.011), gender (COR = 1.61 [1.00–2.57 at 95% CI]; P-value = 0.049), ward (COR = 2.02 [1.09–3.72 at 95% CI]; P-value = 0.025), marital status (COR = 0.36 [0.13–0.99 at 95% CI]; P-value = 0.048), presence of comorbidity (COR = 2.37 [1.46–3.83 at 95% CI]; P-value < 0.001), the total number of drugs (COR = 16.49 [9.25–29.38 at 95% CI]; P-value < 0.001),and duration since diagnosis (COR = 1.70 [1.02–2.82 at 95% CI]; P-value = 0.04), were significantly associated with cs-DDIs at univariate logistic regression. Ten variables including seven that showed statistical significance at the univariate level and three other variables with a p-value < 0.25, including admission status, level of education, and previous admission were included in the multivariate analysis (Table 6). In multivariate analysis, only two variables, female gender (aOR = 2.43 [1.23–4.48 at 95% CI]; P-value = 0.011)) and concurrent use of ≥ 6 drugs (aOR = 18.82 [9.58–36.95 at 95% CI]; P-value < 0.001)) were found to be significantly associated with cs-DDIs (Table 6). Female patients were about 2.43 times more likely (aOR = 2.43: 1.23–4.80 at 95% CI; p value = 0.011) to experience a cs-DDI as compared to the males. Patients receiving six or more drugs were about 18.82 times more likely (aOR = 18.82 [9.58, 36.95 at 95% CI]; P-value < 0.001) to experience a cs-DDI as compared to those receiving less than six drugs.

Table 6 Univariate and Multivariate logistic regression of the factors associated with clinically significant DDIs among cancer patients, Uganda

Discussion

The present study showed that 60.3% of the cancer patients attending MRRHCU experienced at-least one cs-DDI during the study period. This frequency is comparable to 69.73% [17] in Iran among all cancer patients, 67% in the Netherlands [11] among oncology patients from a retrospective cohort study, and another retrospective observational study among hospitalized cancer patients showed a 50% prevalence in Cyprus [18]. Additionally, all the above studies used Lexicomp® drug interaction software like the current study, apart from the study in the Netherlands that used Micromedex drug interactions software which is comparable to Lexicomp® drug interaction software in accuracy [19].

Our study’s cs-DDIs prevalence is considerably higher than some prevalences previously reported among cancer patients. This has been noted from studies reporting 26.8% prevalence among cancer in-patients in the Netherlands [20], and 41% and 46% among cancer out-patients in Spain [21] and the Netherlands [22] respectively. Lower prevalences could be due to considering only one kind/group of patients compared to the current study that considered all patient groups. Secondly, in the Netherlands studies, manual screening methods (peer-reviewed reports) and drug interaction Fact Software version 4.0, 2006 were used to screen for cs-DDIs, which are noted to be less accurate compared to Lexicomp which was used in the current study [19]. This could explain the lower prevalence observed. A clinical trial exclusively among cancer patients in the United States of America revealed that 9.4% of them experienced csDDIs [23]. The restriction to only clinical trial patients and a specific antineoplastic agent could explain the lower prevalence of csDDIs. A low DDI vigilance noted in the low-income state health care settings [24], including oncology units [25], and our study’s inclusion of all cancer patients, may explain the higher csDDI prevalence in the current study. Other possible explanations for this discrepancy include differences in the DDI assessment tool used [20] and differences in the study population [26, 27]. Lower prevalences in previous studies may also be explained by consideration of only antineoplastic interactions excluding interactions between supportive and comorbid medications [21, 23, 26] and considering only risk categories D and X as being clinically significant [21]. Likewise, more than half (58.24%) of the DDIs identified in this study occurred with non-anticancer medications. The most frequently involved antineoplastics were doxorubicin, cyclophosphamide, and methotrexate. These drugs were also reported to be the most frequently involved in previous studies among cancer patients [12, 28]. The high prevalence of cs-DDIs warrants due attention to their prevention among cancer patients as they can lead to unexpected adverse effects and hence a poor prognosis including death [3, 29]. In our study, about three quarters (74.14%) of the cs-DDIs were risk category C that required monitoring, 21.4% were category D required therapy modification and 4.45% were risk category X requiring avoiding therapy, with most of them moderate (71.72%) in severity. The proportion of risk category C cs-DDIs in the current study is comparable to 78.6% from a study in Iran [30]. The proportion of the moderate cs-DDIs in severity observed in our study is comparable with 77% proportion of the moderate cs-DDIs reported among cancer patients in Netherlands [31]. At an Indian teaching hospital, a prospective study among only admitted cancer patients using Drug Interaction Fact software Version 4, revealed a lower prevalence of 56.8% [32]. This may have been due to the exclusion of the out patients and use of a different DDI checker. Another cohort study among breast cancer patients also revealed a lower prevalence of 25.3% [12]. This could be due to considering one cancer group. A 24.2% proportion of moderate to major cs-DDIs reported among clinical trial cancer patients [23] was lower owing to the consideration of only DDIs associated with chemotherapy agents. Being an interventional study design also allowed the investigators to avoid medications with potential DDIs whenever possible. The current high proportion of moderate to major drug-drug interactions among cancer patients requires more emphasis to prevent ADEs due to drug interactions in addition to high ADR risk associated with the antineoplastic agents [12, 33, 34], in the bid to assure patient safety.

In the current study, the use of six and more drugs was significantly associated with clinically significant drug interactions. This finding was comparable with those from previous studies in cervical cancer patients in Pakistan [12, 35] and among cancer patients in the Netherlands [20, 22], all of which reported a higher number of concurrently used medications was significantly associated with cs-DDI. Due to disease sequelae related to cancer and comorbidities, cancer patients are at high risk of polypharmacy [33], and the use of six or more drugs [36, 37]. This predisposes them to cs-DDIs [35, 38].

Being female was shown to be another determinant of the occurrence of clinically significant DDI in the present study. In previous studies, polypharmacy and comorbidities have been noted as prevalent among female neoplasms (Breast cancer and cervical cancer) [39,40,41]. More studies have shown that comorbidities and polypharmacy were associated with clinically significant DDIs among breast cancer females [12, 42] and cervical cancer patients [41, 43]. This explains the current study’s 87.5% of patients with female-specific neoplasms and 67.7% of patients with breast neoplasms experiencing at least one csDDI; higher than the overall prevalence of 60.3%. This is comparable with the findings of a study among cancer inpatients in Brazil that showed females experience more DDIs than men [44]. Moreover, the higher health-seeking behavior among females [45] in Uganda can explain the higher number of medications and thus, more risk of incurring a clinically significant DDI than in males.

Due to cancer disease and therapy sequelae, for example, pain and ADRs among others, which require pharmacologic intervention, cancer patients are exposed to polypharmacy a noted risk factor of cs-DDIs [20, 35]. This informs the development of medication discrepancy preventive strategies like the involvement of clinical pharmacists in the healthcare management team at the cancer unit, who will reduce csDDIs through conducting medication reconciliation [46]. The development of a verified comprehensive medication reconciliation form and a DDI list at the cancer unit will be so key in detecting, reporting, preventing, and resolving medication discrepancies [47], to mitigate the burden of cs-DDIs and their consequences. Failure to recognize the magnitude of such interactions and implementation of preventive strategies leads to increased health costs, prolonged hospitalization, permanent disability, and sometimes death [48].

Limitations

The use of one drug-drug interaction checker might limit the comparison with other studies which employed different checkers. We did not include the performance status of the participants as an independent variable in the multivariable logistic regression. Lastly, we did not follow up on the patients with cs-DDIs for occurrences of adverse drug events or treatment failure due to the interaction.

Conclusion

The current study showed that more than half the patients with cancer attending MRRHCU experienced at least one cs-DDIs, and about three-quarters of these were of category C in risk rating and moderate in severity. Morphine-Metoclopramide was the commonest cs-DDI. Cyclophosphamide-doxorubicin was the most frequent cs-DDI among the anti-cancer agents. Female gender and concurrent use of six or more drugs were found to be significantly associated with cs-DDIs. DDI vigilance at the MRRHCU should be enhanced through the use of DDI lists, DDI checker software, involvement of clinical pharmacists, and increased patient health education in a way of preventing cs-DDIs and thus, mitigating the potential adverse effects. Future researchers should address the potential outcomes of the csDDIs and their correlation with the performance status of cancer patients by using perspective and multi-centered study designs.

Availability of data and materials

All the data supporting the conclusions of this article are included in this manuscript and any more data needed can be availed by corresponding on request.

Abbreviations

ADE:

Adverse Drug Event

ADR:

Adverse Drug Reaction

aOR:

Adjusted odds ratio

OR:

Odds ratio

cs-DDI:

Clinically significant drug-drug interaction

MRRH:

Mbarara Regional Referral Hospital

MRRHCU:

Mbarara Regional Referral Hospital Cancer Unit

MUST:

Mbarara University of Science and Technology

MUST-REC:

Mbarara University of Science and Technology Research Ethics Committee

SPSS:

Statistical Package for Social Sciences

References

  1. Köhler G, Bode-Böger S, Busse R, Hoopmann M, Welte T, Böger R. Drug-drug interactions in medical patients: effects of in-hospital treatment and relation to multiple drug use. Int J Clin Pharmacol Ther. 2000;38(11):504–13.

    Article  Google Scholar 

  2. Gonzaga de Andrade Santos TN, Mendonçada Cruz Macieira G, Cardoso Sodré Alves BM, Onozato T, Cunha Cardoso G, Ferreira Nascimento MT, et al. Prevalence of clinically manifested drug interactions in hospitalized patients: a systematic review and meta-analysis. PloS One. 2020;15(7):e0235353.

    Article  CAS  Google Scholar 

  3. Scripture CD, Figg WD. Drug interactions in cancer therapy. Nat Rev Cancer. 2006;6(7):546–58.

    Article  CAS  Google Scholar 

  4. Sönnerstam E, Sjölander M, Lövheim H, Gustafsson M. Clinically relevant drug-drug interactions among elderly people with dementia. Eur J Clin Pharmacol. 2018;74(10):1351–60.

    Article  Google Scholar 

  5. Palleria C, Di Paolo A, Giofrè C, Caglioti C, Leuzzi G, Siniscalchi A, et al. Pharmacokinetic drug-drug interaction and their implication in clinical management. J Res Med Sci. 2013;18(7):601.

    Google Scholar 

  6. Diksis N, Melaku T, Assefa D, Tesfaye A. Potential drug-drug interactions and associated factors among hospitalized cardiac patients at Jimma University Medical Center. Southwest Ethiopia SAGE open medicine. 2019;7:2050312119857353.

    Google Scholar 

  7. Kim SH, Suh Y, Ah Y-M, Jun K, Lee J-Y. Real-world prevalence of potential drug-drug interactions involving oral antineoplastic agents: a population-based study. Support Care Cancer. 2020;28(8):3617–26.

    Article  Google Scholar 

  8. Shore N, Zurich C, Fricke R, Gieschen H, Graudenz K, Koskinen M, et al. Evaluation of clinically relevant drug-drug interactions and population pharmacokinetics of darolutamide in patients with nonmetastatic castration-resistant prostate cancer: results of pre-specified and post hoc analyses of the phase III ARAMIS trial. Target Oncol. 2019;14(5):527–39.

    Article  Google Scholar 

  9. Zhou H, Davis HM. Risk-based strategy for the assessment of pharmacokinetic drug-drug interactions for therapeutic monoclonal antibodies. Drug Discovery Today. 2009;14(17–18):891–8.

    Article  CAS  Google Scholar 

  10. Niwandinda F, Lukyamuzi EJ, Ainebyona C, Ssebunya VN, Murungi G, Atukunda EC. Patterns and practices of self-medication among students enrolled at Mbarara University of Science and Technology in Uganda. Integr Pharm Res Pract. 2020;9:41.

    Article  Google Scholar 

  11. Voll ML, Yap KD, Terpstra WE, Crul M. Potential drug-drug interactions between anti-cancer agents and community pharmacy dispensed drugs. Pharm World Sci. 2010;32(5):575–80.

    Article  CAS  Google Scholar 

  12. Bibi R, Azhar S, Iqbal A, Jabeen H, Kalsoom UE, Iqbal MM, et al. Prevalence of potential drug-drug interactions in breast cancer patients and determination of their risk factors. J Oncol Pharm Pract. 2021;27(7):1616–22.

    Article  CAS  Google Scholar 

  13. Lubinga S, Uwiduhaye E. Potential drug-drug interactions on in-patient medication prescriptions at Mbarara Regional Referral Hospital (MRRH) in western Uganda: prevalence, clinical importance, and associated factors. Afr Health Sci. 2011;11(3):499–507.

  14. Degu A, Njogu P, Weru I, Karimi P. Assessment of drug therapy problems among patients with cervical cancer at Kenyatta National Hospital, Kenya. Gynecol Oncol Res Pract. 2017;4(1):1–15.

    Article  Google Scholar 

  15. Eneh PC, Hullsiek KH, Kiiza D, Rhein J, Meya DB, Boulware DR, et al. Prevalence and nature of potential drug-drug interactions among hospitalized HIV patients presenting with suspected meningitis in Uganda. BMC Infect Dis. 2020;20(1):572.

    Article  Google Scholar 

  16. Walimbwa SI, Lamorde M, Waitt C, Kaboggoza J, Else L, Byakika-Kibwika P, et al. Drug interactions between dolutegravir and artemether-lumefantrine or artesunate-amodiaquine. Antimicrob Agents Chemother. 2018;63(2):e01310-e1318.

    Google Scholar 

  17. Hadjibabaie M, Badri S, Ataei S, Moslehi AH, Karimzadeh I, Ghavamzadeh A. Potential drug-drug interactions at a referral hematology-oncology ward in Iran: a cross-sectional study. Cancer Chemother Pharmacol. 2013;71(6):1619–27.

    Article  CAS  Google Scholar 

  18. Laban A, Birand N, Chukwunyere U, Abdi A, Basgut B. Evaluation of drug-drug interactions in cancer patients treated at a university hospital in North Cyprus using two interaction databases. Nigerian J Clin Pract. 2021;24(7):1067.

    CAS  Google Scholar 

  19. Kheshti R, Aalipour M, Namazi S. A comparison of five common drug-drug interaction software programs regarding accuracy and comprehensiveness. J Res Pharm Pract. 2016;5(4):257–63.

    Article  CAS  Google Scholar 

  20. van Leeuwen RWF, Jansman FGA, van den Bemt P, de Man F, Piran F, Vincent I, et al. Drug-drug interactions in patients treated for cancer: a prospective study on clinical interventions. Ann Oncol. 2015;26(5):992–7.

    Article  Google Scholar 

  21. Lopez-Martin C, Garrido Siles M, Alcaide-Garcia J, Faus FV. Role of clinical pharmacists to prevent drug interactions in cancer outpatients: a single-center experience. Int J Clin Pharm. 2014;36(6):1251–9.

    Article  Google Scholar 

  22. van Leeuwen RWF, Brundel DHS, Neef C, van Gelder T, Mathijssen RHJ, Burger DM, et al. Prevalence of potential drug–drug interactions in cancer patients treated with oral anticancer drugs. Br J Cancer. 2013;108(5):1071–8.

    Article  Google Scholar 

  23. Marcath LA, Coe TD, Hoylman EK, Redman BG, Hertz DL. Prevalence of drug-drug interactions in oncology patients enrolled on National Clinical Trials Network oncology clinical trials. BMC Cancer. 2018;18(1):1–8.

    Article  Google Scholar 

  24. Kiguba R, Ndagije HB, Nambasa V, Bird SM. Adverse Drug Reaction Onsets in Uganda’s VigiBase(®): Delayed International Visibility, Data Quality, and Illustrative Signal Detection Analyses. Pharmaceut Med. 2018;32(6):413–27.

    Google Scholar 

  25. Baldo P, Fornasier G, Ciolfi L, Sartor I, Francescon S. Pharmacovigilance in oncology. Int J Clin Pharm. 2018;40(4):832–41.

    Article  Google Scholar 

  26. Ussai S, Petelin R, Giordano A, Malinconico M, Cirillo D, Pentimalli F. A pilot study on the impact of known drug-drug interactions in cancer patients. J Exp Clin Cancer Res. 2015;34(1):89.

    Article  Google Scholar 

  27. Degu A, Njogu P, Weru I, Karimi P. Assessment of drug therapy problems among patients with cervical cancer at Kenyatta National Hospital. Kenya Gynecol Oncol Res Pract. 2017;4:15.

    Article  Google Scholar 

  28. Voll ML, Yap KD, Terpstra WE, Crul M. Potential drug-drug interactions between anti-cancer agents and community pharmacy dispensed drugs. Pharm World Sci. 2010;32(5):575–80.

    Article  CAS  Google Scholar 

  29. Sönnerstam E, Sjölander M, Lövheim H, Gustafsson M. Clinically relevant drug-drug interactions among elderly people with dementia. Eur J Clin Pharmacol. 2018;74(10):1351–60.

    Article  Google Scholar 

  30. Mousavi S, Ghanbari G. Potential drug-drug interactions among hospitalized patients in a developing country. Caspian J Intern Med. 2017;8(4):282–8.

    Google Scholar 

  31. Riechelmann RP, Tannock IF, Wang L, Saad ED, Taback NA, Krzyzanowska MK. Potential drug interactions and duplicate prescriptions among cancer patients. J Natl Cancer Inst. 2007;99(8):592–600.

    Article  Google Scholar 

  32. Kannan G, Anitha R, Rani V, Thennarasu P, Alosh J, Vasantha J, et al. A study of drug-drug interactions in cancer patients of a south Indian tertiary care teaching hospital. J Postgrad Med. 2011;57(3):206–10.

    Article  CAS  Google Scholar 

  33. Blower P, de Wit R, Goodin S, Aapro M. Drug-drug interactions in oncology: Why are they important and can they be minimized? Crit Rev Oncol Hematol. 2005;55(2):117–42.

    Article  Google Scholar 

  34. Yadesa TM, Kitutu FE, Tamukong R, Alele PE. Prevalence, incidence, and characteristics of adverse drug reactions among older adults hospitalized at Mbarara regional referral hospital, Uganda: a prospective cohort study. Clin Interv Aging. 2021;16:1705.

    Article  Google Scholar 

  35. Ismail M, Khan S, Khan F, Noor S, Sajid H, Yar S, et al. Prevalence and significance of potential drug-drug interactions among cancer patients receiving chemotherapy. BMC Cancer. 2020;20(1):335.

    Article  CAS  Google Scholar 

  36. Komiya H, Umegaki H, Asai A, Kanda S, Maeda K, Shimojima T, et al. Factors associated with polypharmacy in elderly home-care patients. Geriatr Gerontol Int. 2018;18(1):33–41.

    Article  Google Scholar 

  37. Bushardt RL, Massey EB, Simpson TW, Ariail JC, Simpson KN. Polypharmacy: misleading, but manageable. Clin Interv Aging. 2008;3(2):383–9.

    Article  Google Scholar 

  38. Ramasubbu SK, Mahato SK, Agnihotri A, Pasricha RK, Nath UK, Das BD. Prevalence, severity, and nature of risk factors associated with drug-drug interactions in geriatric patients receiving cancer chemotherapy: a prospective study in a tertiary care teaching hospital. Cancer Treat Res Commun. 2021;26:100277.

    Article  Google Scholar 

  39. Topaloğlu US, Özaslan E. Comorbidity and polypharmacy in patients with breast cancer. Breast Cancer. 2020;27(3):477–82.

    Article  Google Scholar 

  40. Keats MR, Cui Y, DeClercq V, Grandy SA, Sweeney E, Dummer TJB. The burden of multimorbidity and polypharmacy among cancer survivors: a population-based nested case-control study. Support Care Cancer. 2021;29(2):713–23.

    Article  Google Scholar 

  41. S Mustapha MM, L Mustapha, I Yunusa, B Basgut. A survey on drug-related problems in cervical cancer patients receiving chemotherapy in Ahmadu Bello University Teaching Hospital Zaria. Bayero Journal of Pure and Applied Sciences. 2018.; Vol. 10 No. 1 (2017)( Vol. 10 No. 1 (2017): Special Conference Edition.).

  42. Domínguez-Alonso JA, Conde-Estévez D, Bosch D, Pi-Figueras M, Tusquets I. Breast cancer, placing drug interactions in the spotlight: is polypharmacy the cause of everything? Clin Transl Oncol. 2021;23(1):65–73.

    Article  Google Scholar 

  43. Degu A, Njogu P, Weru I, Karimi P. Assessment of drug therapy problems among patients with cervical cancer at Kenyatta National Hospital, Kenya. GynecolOncol Res Pract. 2017;4(1):15.

    Article  Google Scholar 

  44. Cruciol-Souza JM, Thomson JC. Prevalence of potential drug-drug interactions and its associated factors in a Brazilian teaching hospital. J Pharm Pharm Sci. 2006;9(3):427–33.

    CAS  Google Scholar 

  45. David Lawson. Development Economics and Public Policy. Manchester: University of Manchester; 2004. p. 1–31.

  46. Tenti E, Schirone A, Passardi A, Casadei GA. Medication reconciliation in oncology and monitoring of preventable drug interactions. Int J Mol Med. 2015;36:S26.

    Google Scholar 

  47. NCC-MERP. About Medication Errors. 2022. [Available from: https://www.nccmerp.org/about-medication-errors.

  48. Zheng WY, Richardson LC, Li L, Day RO, Westbrook JI, Baysari MT. Drug-drug interactions and their harmful effects in hospitalized patients: a systematic review and meta-analysis. Eur J Clin Pharmacol. 2018;74(1):15–27.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The authors extend their sincere gratitude to the study participants and health workers at the MRRHCU. Thanks to the study assistants for cooperating with us through the data collection process.

Funding

No funding was received for conducting this study. The researchers used personal funds.

Author information

Authors and Affiliations

Authors

Contributions

All authors made a significant contribution to the work reported, 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.

Corresponding author

Correspondence to Bonny Luzze.

Ethics declarations

Ethics approval and consent to participate

In this study, all methods were performed in accordance with the Declaration of Helsinki. Ethical approval was obtained from the MUST research ethics committee (REC) as an institutional review board (IRB) for authorization to conduct the research (reference No: MUST – 2021- 203). We also obtained site clearance from the MRRH. Written informed consent was sought before recruitment from adult participants while parental consent and child assent was obtained for children below eighteen years. English and Runyankole versions of consent and assent forms were used. Information was stored on a password-protected computer and only accessed by the principal investigator. The participants received all the services provided at the health facility and the study procedures did not negatively interfere with patients’ care.

Consent for publication

All authors agreed to the submission of this manuscript for publication in addition to the consent to publish which was included in the informed consent form and attained ethical and participant approval.

Competing interests

The authors have no relevant financial or non-financial interests to declare.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Luzze, B., Atwiine, B., Lugobe, H.M. et al. Frequency, severity, and factors associated with clinically significant drug-drug interactions among patients with cancer attending Mbarara Regional Referral Hospital Cancer Unit, Uganda. BMC Cancer 22, 1266 (2022). https://doi.org/10.1186/s12885-022-10396-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12885-022-10396-8

Keywords