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Prevalence of drug–drug interactions in sarcoma patients: key role of the pharmacist integration for toxicity risk management


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Background The risk of drug–drug interactions (DDI) has become a major issue in cancer patients. However, data in sarcoma patients are scarce. We aimed to evaluate the frequency and the factors associated with DDI with antitumor treatments, and to evaluate the impact of a pharmacist evaluation before anticancer treatment.Patients and methodsWe performed a retrospective review of consecutive sarcoma patients starting chemotherapy (CT) or Tyrosine kinase inhibitor (TKI). A pharmacist performed medication reconciliation and established an early toxicity risk assessment. Potential DDI with antitumor drugs were identified using Micromedex electronic software.ResultsOne hundred and twenty-two soft-tissue and 80 bone sarcoma patients (103 males, median age 50 years,) were included before CT (86%) or TKI (14%). The median number of medications was 3; 34 patients (22% of patients with medication reconciliation) reported complementary medicine use. 37 potential DDI classified as major, were identified (12% of the 243 pre-therapeutic assessments). In multivariate analysis, TKI (p < 0.0001), proton pump inhibitor (p = 0.026) and antidepressant (p < 0.001) were identified as risk factors of DDI (p < 0.02). Only marital status (p = 0.003) was associated with complementary medicine use. A pharmacist performed 157 medication reconciliations and made 71 interventions among 59 patients (37%). In multivariate analysis, factors associated with pharmacist intervention were: complementary medicines (p = 0.004), drugs number (p = 0.005) and treatment with TKI (p = 0.0002)Conclusions Clinical interventions on DDI are more frequently required among sarcoma patients treated with TKI than CT. Multidisciplinary risk assessment including a medication reconciliation by a pharmacist could be crucial to prevent DDI with TKI.
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Cancer Chemotherapy and Pharmacology (2021) 88:741–751
Prevalence ofdrug–drug interactions insarcoma patients: key role
ofthepharmacist integration fortoxicity risk management
AudreyBellesoeur1,2· ItharGataa1,2· AnneJouinot1,2· SarahElMershati5· Anne‑CatherinePiketty1,2·
CamilleTlemsani1,2· DavidBalakirouchenane3,4,6· AnthiaMonribot5· MichelVidal3,4,6· RuiBatista5·
SixtinedePercin1,2· ClémentineVilleminey1,2· JérômeAlexandre1,2,7· FrançoisGoldwasser1,2,7·
BenoitBlanchet3,4,6· PascalineBoudou‑Rouquette1,2· AudreyThomas‑Schoemann1,2,5,6
Received: 18 March 2021 / Accepted: 4 June 2021 / Published online: 24 July 2021
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021
Background The risk of drug–drug interactions (DDI) has become a major issue in cancer patients. However, data in sarcoma
patients are scarce. We aimed to evaluate the frequency and the factors associated with DDI with antitumor treatments, and
to evaluate the impact of a pharmacist evaluation before anticancer treatment.
Patients and methods We performed a retrospective review of consecutive sarcoma patients starting chemotherapy (CT)
or Tyrosine kinase inhibitor (TKI). A pharmacist performed medication reconciliation and established an early toxicity risk
assessment. Potential DDI with antitumor drugs were identified using Micromedex electronic software.
Results One hundred and twenty-two soft-tissue and 80 bone sarcoma patients (103 males, median age 50years,) were
included before CT (86%) or TKI (14%). The median number of medications was 3; 34 patients (22% of patients with medi-
cation reconciliation) reported complementary medicine use. 37 potential DDI classified as major, were identified (12%
of the 243 pre-therapeutic assessments). In multivariate analysis, TKI (p < 0.0001), proton pump inhibitor (p = 0.026) and
antidepressant (p < 0.001) were identified as risk factors of DDI (p < 0.02). Only marital status (p = 0.003) was associated
with complementary medicine use. A pharmacist performed 157 medication reconciliations and made 71 interventions among
59 patients (37%). In multivariate analysis, factors associated with pharmacist intervention were: complementary medicines
(p = 0.004), drugs number (p = 0.005) and treatment with TKI (p = 0.0002)
Conclusions Clinical interventions on DDI are more frequently required among sarcoma patients treated with TKI than CT.
Multidisciplinary risk assessment including a medication reconciliation by a pharmacist could be crucial to prevent DDI
with TKI.
Keywords Sarcoma· Drug–drug interactions· Complementary medicines· Pharmacist intervention
ALP Alkaline phosphatase
ALT ALanine transaminase
AST ASpartate transaminase
CRP C-reactive protein
CT Chemotherapy
Pascaline Boudou-Rouquette and Audrey Thomas-Schoemann
contributed equally to the work.
Audrey Bellesoeur and Ithar Gataa contributed equally to the
* Audrey Thomas-Schoemann
1 Department ofMedical Oncology, Cochin Hospital, AP-HP,
Paris, France
2 University ofParis Descartes, ARIANE, CARPEM, Paris,
3 Department ofPharmacokinetics andPharmacochemisty,
Cochin Hospital, AP-HP, Paris, France
4 CARPEM, Paris, France
5 Department ofClinical Pharmacy, Cochin Hospital, AP-HP,
27 rue du Faubourg Saint Jacques, 75014Paris, France
6 UMR8038 CNRS, U1268 INSERM, Faculty ofPharmacy,
University Paris Descartes, PRES Sorbonne Paris Cité, Paris,
7 Cochin Institute, INSERM U1016, Paris, France
742 Cancer Chemotherapy and Pharmacology (2021) 88:741–751
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CM Complementarymedicine
DDI Drug–drug interactions
IQR Interquartile range
MR Medication reconciliation
PPI Proton pump inhibitor
STS Soft tissue sarcoma
TKI Tyrosine kinase inhibitor
Sarcomas are a heterogeneous group of tumors of mesenchy-
mal origin that account for 1% of adult tumors. About 80%
of sarcomas originate from soft tissue and 20% from bone.
Over the past 4 decades, doxorubicin and ifosfamide
have remained the cornerstone of the first-line treatment
for unresectable, advanced soft tissue sarcoma (STS) and
bone sarcomas. Beside anthracyclines and ifosfamide,
there are other drugs with moderate activity in these dis-
eases, i.e., gemcitabine, trabectedin and taxanes [13]. The
introduction of molecularly targeted agents has provided
new therapeutic options for STS treatment, such as pazo-
panib and regorafenib in non-adipocytic STS [4], imatinib
in dermatofibrosarcoma [5] and crizotinib in inflammatory
myofibroblastic tumor [6]. Multitargeted tyrosine kinase
inhibitors, have also demonstrated activity in patients with
advanced bone sarcoma, i.e., regorafenib, cabozantinib and
lenvatinib [79].
Drug–drug interactions (DDI) occurred when the effects
of one drug are changed by the presence of another drug,
herbal medicine, food, or drink [10]. DDI in oncology are
of particular importance owing to the narrow therapeutic
index and the toxicity of some anticancer agents. Cancer
patients are at high risk of DDI, because they frequently
take drugs besides antineoplastic agents, such as drugs to
relieve symptoms from cancer, to treat cancer treatment-
induced toxicity or to treat comorbidities. Therefore, one-
third of ambulatory cancer patients are exposed to potential
DDI [11]. Furthermore, about 40% of cancer patients treated
with conventional therapies may also use complementary
medicine (CM), defined as health care approaches that are
not typically part of conventional medical care, such as
herbs and vitamins, naturopathy, homeopathy… [12, 13].
Although some CM may be beneficial, it is possible that
others may induce herb–drug interactions or vitamin–drug
interactions that could impact efficacy or safety profile of
cancer treatments [14, 15].
The risk of DDI has become a major issue in cancer
patient care. In our center, we, therefore, set up a multidis-
ciplinary risk assessment prior to initiation of anticancer
treatments. Early detection of drug–drug interactions by
the pharmacist is a crucial step of this analysis, followed
by a risk assessment meeting leading to a multidisciplinary
Although DDI or CM–drug interactions seem frequent
in cancer patients, DDI prevalence may differ according to
cancer type [16]. Data are sparse in sarcoma patient with
only some case reports [1720] or small studies focusing on
one anticancer treatment [2124].
The primary aim of this prospective observational study
was to evaluate the frequency and severity of DDI with anti-
cancer treatment (chemotherapy and tyrosine kinase inhibi-
tor). The secondary aims were to identify clinical factors
associated with DDI, to assess both the frequency of sar-
coma patients that use CM and to the impact of a pharmacist
evaluation before treatment initiation.
Patients andmethods
Study design andpopulation
The present study included patients with a diagnosis of
advanced or metastatic soft-tissue or bone sarcomas, who
were candidate for chemotherapy or tyrosine kinase inhibi-
tors (TKI) from January 2014 to October 2017. Chemother-
apy (CT) consisted mostly of doxorubicin, ifosfamide, gem-
citabine, trabectedin or paclitaxel. TKIs included pazopanib,
sunitinib, sorafenib or regorafenib.
Informed consent was obtained from all patients prior to
inclusion. Data were collected and recorded in the ExPLoRE
database. This prospective open monocentric cohort con-
sisted of cancer patients referred by oncologists for multi-
disciplinary risk assessment, before the antitumor treatment
initiation. This risk assessment combined interventions from
coordination nurse, pharmacist, dietician, psychologist and
geriatrician if indicated. This evaluation was followed by a
multidisciplinary staff, leading to potential treatment opti-
mization. The protocol was approved by the appropriate eth-
ics committee (578AB88). All methods were carried out in
accordance with relevant guidelines and regulation.
Drug–drug interaction (DDI)
A pharmacist proceeded to complete medication reconcili-
ation, including prescribed drugs, over-the-counter drugs
and CM.
Once a complete patient’s medication list was established,
drug interactions were screened between patient’s treatments
and proposed anticancer and supportive medications, using
Micromedex software. Indeed Micromedex showed the
highest specificity (0.78) by comparison of five common
drug–drug interaction software programs regarding accuracy
and comprehensiveness [25]. We also used two French data-
bases (Theriaque and DDI predictor) to help us in detecting
743Cancer Chemotherapy and Pharmacology (2021) 88:741–751
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clinically relevant drug interactions but we decided to focus
on Micromedex to be able to compare our data with other
international studies. Severity of DDI was categorized using
a 4-point scale: contraindicated (the drugs are contraindi-
cated for concurrent use), major (the interaction may be life-
threatening and/or require medical interventions to minimize
or prevent serious adverse effects), moderate (the interaction
may result in exacerbation of the patient’s condition and/or
require a major alteration in therapy) and minor (this interac-
tion would have limited clinical effects).
Interventions of pharmacist before treatment initia-
tion were classified as treatment discontinuation, treat-
ment replacement, dosing adjustments, or plasma drug
Data collection
Demographic and clinical patients’ data included age and
gender, ethnic origin, marital status, cancer stage, Eastern
Cooperative Oncology Group performance status (ECOG-
PS), body mass index (BMI), comorbidities (number and
type), sarcoma subtype (bone or soft-tissue), presence of
liver metastasis and planned treatment (CT protocol or TKI).
Biological data included albuminemia, creatinin, C-Reac-
tive Protein (CRP), ALanine Transaminase (ALT), ASpar-
tate Transaminase (AST), Alkaline Phosphatase (ALP) and
Study endpoints
The main objective was to evaluate the frequency and sever-
ity of DDI with CT and TKI. The secondary objectives were
to evaluate complementary medicine CM use, the risk fac-
tors associated with DDI, and the qualitative role of a phar-
macist evaluation before anticancer treatment initiation.
Statistical analysis
Descriptive statistics used median and interquartile range
IQR for quantitative variables, and number and percentages
for qualitative one.
Univariate analysis used Chi-square or Fisher’s test for
qualitative variables and t test or Mann–Whitney test for
quantitative variables. Variables significantly associated
with endpoint in univariate analyses were then combined
into multivariable logistic regression model.
All p values were two-sided, and the level of significance
was set at p < 0.05.
Calculations were performed using R statistical software
(version 3.6.1, R Stats Package).
Two hundred and two patients were included in this study.
One hundred and twenty-two soft-tissue and 80 bone sar-
coma patients were included before antitumor treatment:
CT (86%) or TKI (14%) (Fig.1). The median number of
comorbidities was 1 (range 0–2). Most of the patients (65%)
had advanced-stage or metastatic disease. Patient’s baseline
characteristics are summarized in Table1. The median num-
ber of drugs per patient was 3 (IQR 2–5), 65 patients (32%)
had at least 5 drugs.
Overall, 243 pre-treatment multidisciplinary assess-
ments were realized, since some patients received several
lines of treatment during the study period. Among them,
157 patients underwent medication reconciliation (MR) by
a pharmacist (Fig.2).
The most common prescription medications were analge-
sics and anticoagulants (Supplemental TableS1).
Potential drug–drug interactions
A total of 37 potential DDIs were identified with Microme-
dex software in 29 pretherapeutic assessments (12% of the
243 pre-treatment assessments). All these DDI were classi-
fied as major. Therapeutic classes involved in DDI were anti-
depressant (57%), anti-acid (19%), anti-epileptic (11%), anti-
inflammatory (5%), analgesic (5%), and antibiotic (3%). Two
types of DDI mechanisms were observed: DDI that cause
QT interval prolongation (54%) and pharmacokinetic DDI
(46%). These pharmacokinetics DDI may induce a decrease
in chemotherapy concentrations (10 DDI), an increase in
chemotherapy concentrations (3 DDI) or a change in the
level of other medications (4 DDI). Among the patients with
DDI, the proportion of patients who had 1, 2, or 3 DDI were
76%, 21% and 3%, respectively.
Baseline factors associated withDDI
In univariate analysis, number of drugs (p < 0.001), ECOG-
PS (p = 0.04), pain (p = 0.002), antidepressant (p < 0.001),
proton pump inhibitors (PPI) (p < 0.001), and TKI
(p < 0.001) were significantly associated with a higher risk
of DDI (Table2). In the multivariate analysis, three factors
remained statistically associated: TKI (OR 1.28 [1.15–1.42];
p < 0.001), PPI (OR 1.14 [1.02–1.27]; p = 0.026) and anti-
depressants (OR 1.25 [1.11–1.41]; p < 0.001). Interest-
ingly, patients treated with gemcitabine-base protocols
were exposed to a lower risk of DDI (OR 0.86 [0.78–0.96];
p = 0.0059).
744 Cancer Chemotherapy and Pharmacology (2021) 88:741–751
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Use ofcomplementary medicines insarcoma
34 patients (22% of the patients with medication reconcili-
ation) used biologically based CM. It included herbs infu-
sion (29%), herbs capsules (34%), vitamins and minerals
(22%), homeopathy (5%), other CM (10%) (Fig.3). Among
the 10 patients using herb infusions, 6 patients drank green
tea (Camellia sinensis) and the others were using infu-
sion of dandelion root (Taraxacum officinale), inula vis-
cosa (Dittrichia viscosa), verbena (Aloysia citrodora), and
turmeric (Curcuma longa). Some of these herb infusions
were stopped after pharmacist interventions (Green tea and
turmeric). For example, patients were advised not to drink
green tea, because of its potential interactions with antitumor
treatment, as an antioxidant [26] and because some pub-
lished data suggest it may impact the metabolism of oral
antitumor treatment [27]. We didn’t observe any complica-
tion or hepatic failure in these patients that could have been
induced by these herbs. Age, gender, performance status,
disease stage and socio-professional categories were not
associated with CM use. The only factor associated with
CM use was marital status (married or living together used
CM more frequently than single patients, p = 0.008).
Qualitative role ofpharmacist beforetreatment
A pharmacist performed 157 medication reconciliation
(MR) and made 71 interventions among 59 patients (37% of
the patients with a MR) (34 drugs discontinued, 16 replace-
ments of a drug, 2 dose adjustments, and 19 plasma drug
monitoring). The two main therapeutic classes involved in
pharmacist interventions were analgesics (18%) and PPI
(10%). Interestingly, pharmacist interventions were more
frequent among TKI treated patients than in CT treated
patients (63% versus 17%; p < 0.001) and 23 interventions
(32% of the 71 interventions) implied CM.
In multivariate analysis, three factors were statistically
associated with pharmacist intervention:CM (OR 4.87
[2.01–11.8] p = 0.004), number of medications (OR 1.22
[1.06–1.41] p = 0.005) and Tyrosine kinase inhibitors (OR
8.37 [2.71–25.84] p = 0.0002) (Table3).
The results of our study showed that sarcoma patients are
at moderate risk of DDI, with 12% of the 243 pre-treatment
assessments detecting a major potential DDI with their
antitumor treatment according to Micromedex software.
Fig. 1 Antitumor treatment regimen. “Other” antitumoral regimen included: vinorelbine, temozolomide–irinotecan regimen, sorafenib,
regorafenib, sunitinib
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Previous studies have suggested a high risk of DDI in can-
cer patients. For example, Nightingale etal. have recently
detected in 142 elderly cancer patients (> 65years), 315
major potential DDI in 61% patients with Micromedex
software [28]. Indeed, DDI prevalence is known to vary
with cancer type and polypharmacy [16, 29]. Furthermore,
we chose to focus our study on DDI that involved antitu-
mor treatment. In a previous work, we observed a potential
DDI with abiraterone in 52% of 95 prostate cancer patients,
but their median age was 77years [68–82] with a median
number of 7 drugs [30]. In the present study, sarcoma
patients had a median age of 50years [34–62] with a median
of 3 drugs per patient, which together could contribute to
a lower risk of DDI than in elderly cancer patients [31]. A
limitation of this study is the use of a single DDI database,
Micromedex. Indeed, Bossaer etal. have shown significant
variability among 5 DDI databases (Lexicomp, Microme-
dex, Medscape, Eporactes, with regards to oral
antineoplastics and potential drug interactions [32]. They
also have observed that in terms of accuracy, Lexicomp,
Epocrates and scored higher than Micromedex.
Nevertheless this study only investigated newly approved
oral antineoplastics and should not be generalized to all
antitumor treatments, including intravenous chemotherapy.
As far as we know, we identified for the first time base-
line factors associated with DDI in sarcoma patients. The
type of antitumor protocol seems primordial, since patients
treated with TKI were at higher risk to present a major DDI
(p < 0.001). Different factors may explain this result. First,
oral targeted therapies such as TKI are prescribed for long
periods of time, in patients with potential co-morbidities.
Second, all TKIs (pazopanib, sorafenib, sunitinib and
regorafenib) prescribed in our study are metabolized by
cytochrome P450 enzymes (CYP), which could result in
significant variations in their apparent metabolic clearance
in presence of inhibitor or inducer of CYP3A4. Finally, the
bioavailability of TKI such as pazopanib is known to be
significantly decreased in case of PPI administration. Over-
all, an increased risk of 28% of DDI is expected in sarcoma
patients treated with TKI. By contrast, the risk of DDI is
decreased of 14% in patients treated with gemcitabine-base
protocols (p = 0.019). One explanation could be related to
the metabolism of gemcitabine which is not CYP-depend-
ent unlike TKI. Therefore, pharmacists could favor to per-
form medication reconciliation in sarcoma patients treated
with TKI rather than in patients treated with gemcitabine
The use of antidepressants and proton pump inhibitors
(PPI) by sarcoma patients during pretherapeutic assessment
were also identified as independent risk factors for DDI
(p < 0.001 and p = 0.026, respectively). Many cytotoxic
anticancer drugs can prolong the QTc interval (eg, anthra-
cyclines). This prolongation is also frequently reported with
some TKIs such as pazopanib or sunitinib, and probably
caused by interaction with hERG K + channels [33]. Pro-
longation of the QTc interval and subsequent development
of Torsades de pointes is a rare but severe toxicity of some
TKIs [34, 35]. Given most of sarcoma patients previously
received anthracyclines, pharmacists should, therefore,
check for the concomitant use of QTc prolonging drugs such
as TKI and antidepressant.
DDI between TKI and PPI are also well-known drug
interactions. Indeed, some TKIs such as pazopanib show
Table 1 Baseline patients’ characteristics of the study cohort
Quantitative results are expressed as median [interquartile range]
ALT alanine amino transferase, AST aspartate amino transferase, ALP
alkaline phosphatase, ECOG PS eastern cooperative oncology group
performance status, NA not available
Sex n (%) No. of patients (%)
Male/female 103 (51) / 99 (49)
Demographic data
Age (years) 50 [34–62]
Total body weight (kg) 70 [60–81]
Body mass index (kg/m2) 23.8 [21–28]
Number of comorbidities 1 [0–2]
Number of drugs 3 [2–5]
ECOG-PS, n (%)
0/1 143 (71)
2/3 56 (28)
NA 3 (1)
Prevalence of comorbidities, n (%)
Hypertension 31 (15)
Obesity 29 (14)
Dyslipidemia 24 (12)
Diabetes 17 (8)
Renal failure 15 (7)
Depression disorder 12 (6)
Cardiopathy 6 (3)
Sarcome type
Bone sarcoma 80 (40%)
Soft tissue sarcoma 122 (60%)
Stage, n (%)
Localized 74 (37)
Locally advanced 10 (5)
Metastatic 118 (58)
Baseline biological data
ALT (UI/L) 24 [17–35]
AST (UI/L) 24 [19–31]
Bilirubin (µmol/L) 6.0 [4.3–8.6]
ALP (UI/L) 84 [64–111]
Albumin (g/L) 42 [39–45]
Serum creatinine (µmol/L) 66 [56–80]
C-reactive protein (mg/L) 6 [2–18]
746 Cancer Chemotherapy and Pharmacology (2021) 88:741–751
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Fig. 2 Inclusion diagram. 202 sarcoma patients were included. Over-
all, 243 pre-treatment multidisciplinary assessments were realized,
since some patients received several lines of treatment during the
study period. Among them, 157 patients underwent medication rec-
onciliation (MR) by a pharmacist. The pharmacist couldn’t see every
patient in pre-therapeutic assessment for practical reasons (unavail-
Table 2 Baseline factors associated with a high risk of drug–drug interactions
Bold results are expressed as median [interquartile range]
CI95% confidence interval 95%, ECOG PS Eastern Cooperative Oncology Group Performance Status, OR odds ratio
Variables Univariate analysis Multivariate analysis
No DDI (n = 214) DDI (n = 29) p value OR (95%CI) p value
Age 49 [32.2–61] 58 [41–63] 0.11
Total body weight 71 [60–81] 69 [56–80] 0.37
ECOG PS 1 [1–1] 1 [1–2] 0.04
Number of comorbidities 1 [0–2] 1 [0–2] 0.11
Number of drugs 3 [1–5] 7 [4–9] < 0.0001
Hypertension, n (%) 36 (16) 5 (17) 1
Heart disease, n (%) 6 (3) 1 (3) 0.6
Diabetes, n (%) 23 (11) 1 (3) 0.326
Depression, n (%) 8 (4) 4 (14) 0.041
Dyslipidemia, n (%) 29 (14) 1 (3) 0.142
Pain, n (%) 88 (41) 21 (72) 0.0024
Proton pump inhibitors, n (%) 27 (13) 12 (41) 0.00046 1.14 [1.02–1.27] 0.026
Antidepressants, n (%) 17 (8) 12 (41) < 0.0001 1.25 [1.11–1.41] 0.00022
Anticoagulants, n (%) 6 (3) 1 (3) 0.59
Tyrosine kinase inhibitors, n (%) 22 (10) 13 (45) < 0.0001 1.28 [1.15–1.42] < 0.0001
Gemcitabine-based protocol, n (%) 34 (16) 0 (0) 0.019 0.86 [0.78–0.96] 0.0059
Ifosfamide-based protocol, n (%) 89 (42%) 9 (31%) 0.32
Doxorubicin-based protocol, n (%) 32 (15%) 6 (21%) 0.42
Trabectedin monotherapy, n (%) 11 (5%) 0 (0%) 0.37
747Cancer Chemotherapy and Pharmacology (2021) 88:741–751
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pH-dependent solubility and their solubility may be
decreased by the coadministration of PPI which increase
gastric pH [3]. These DDI could reduce TKI plasma expo-
sure, resulting in decreased efficacy. There have been contra-
dicting results on the clinical effects of PPI on sorafenib or
sunitinib absorption [36, 37]. By contrast, Mir etal. recently
showed that coadministration of gastric suppressive agents
with pazopanib was associated with significantly short-
ened PFS and OS, proving that this DDI could result in a
decreased efficacy of the anti-cancer treatment [38]. Overall,
the best way remains to stop PPI in cancer patients treated
with TKI whenever it is possible.
In the present study, 22% of sarcoma patients used CM
such as phytotherapy, vitamins, or homeopathy. The con-
sumption of herbal medicines is a common practice among
cancer patients [39]. The only factor associated with CM
use in our sarcoma patients cohort was marital status. Our
evaluation of factors associated with CM use is not consist-
ent with prior literature. For example, Johnson etal. have
shown in non-metastatic cancer patients that CM use was
associated with younger age, female sex, privative insurance
and higher socioeconomic status [40]. Similarly, a French
study observed in patients, 2years after cancer diagnosis
that CM use was significantly associated with younger age,
female genderanda higher education level. Only a study
by Judson etal. observed a similar influence of the marital
status on CM use [41] and our data consequently warrants
further study in a larger cohort of sarcoma patients.
CM use may expose cancer patients to several risks,
such as CM toxicity observed with Kava for example
[42], herb–drug interactions [14] or an increased risk of
Fig. 3 Biologically based complementary medicines CM description
in sarcoma patients. Complementary medicines include natural prod-
ucts and mind and bodypractices. Only natural products were listed
by the pharmacist. This group includes a variety of products such as
herbs (infusion or capsules), vitamins and minerals, homeopathy or
other CM
Table 3 Factors associated with clinical pharmacist interventions
Bold results are expressed as median [interquartile range]
CI95% confidence interval 95%, CM complementary medicine, ECOG PS Eastern Cooperative Oncology Group Performance Status, OR odds
No intervention
(n = 98)
Intervention (n = 59) p value OR (95%CI) p value
Age 50 [38–62] 50[29–63] 0.563
Total body weight 71 [60–80] 69 [62–79] 0.329
ECOG PS 0.298
Number of comorbidities 1 [0–2] 1 [0–2] 0.358
Number of drugs 3 [2–5] 4 [2–6] 0.006 1.2 (1.1–1.4) 0.006
Hypertension, n (%) 17 (17) 12 (20) 0.674
Cardiopathy, n (%) 5 (5) 2 (3) 0.712
Diabete, n (%) 8 (8) 6 (10) 0.774
Depression, n (%) 2 (2) 5 (8) 0.104
Dyslipidemia, n (%) 11 (11) 9 (15) 0.467
Pain, n (%) 42 (42) 28 (47) 0.621
Renal impairment, n (%) 1 (1) 7 (12) 0.005
Proton pump inhibitors, n (%) 14 (14) 14 (24) 0.196
Antidepressants, n (%) 10 (10) 10 (17) 0.228
Tyrosine kinase inhibitors, n (%) 8 (8) 22 (37) 1.37.10–5 8.4 (2.7–25.8) 0.0002
Gemcitabine-based protocol, n (%) 17 (17) 6 (10) 0.252
Ifosfamide-based protocol, n (%) 39 (39) 12 (20) 0.014
Doxorubicin-based protocol, n (%) 20 (20) 6 (10) 0.122
CM, n (%) 18 (18) 24 (41) 0.00343 4.9 (2.0–11.8) 0.00045
748 Cancer Chemotherapy and Pharmacology (2021) 88:741–751
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conventional anticancer treatment refusal [39]. Therefore,
CM use leads to 21 pharmacist interventions (29%) to limit
or monitor the use of these non-conventional treatments.
In our study, pharmacist interventions relative to CM were
driven by “MSKCC about herbs” data and literature avail-
able on the CM. We didn’t use Micromedex, since it seems
Table 4 Major pharmacokinetic
drug–drug interactions in
sarcoma patients
Moderate Risk of toxicity of the antitumoral treatment
High Risk of decreased efficacy in the antitumoral treatment
High Risk of toxicity of the antitumoral treatment
No pharmacokinetic interaction
Hydrochlorothiaz ide
Ketoconaz ole
Lansoprazol e
Levofl oxac in
Pantoprazol e
St John's wort
EIAEDS enzyme-inducing anti-epileptic drugs (phenobarbital, phenytoine, primidone),
NSAIDS non-steroidal anti-inflammatory drugs
749Cancer Chemotherapy and Pharmacology (2021) 88:741–751
1 3
not to be performant to detect DDI with CM. Indeed, Shakeel
etal. have compared the performance of eight drug–drug
interaction screening tool to detect herb–drug interaction
with antitumor agent [43]. They concluded that Lexicomp
was the best available tool for screening herb–drug interac-
tions but that all tools had poor sensitivity (< 0.20). Further
research is needed to improve herb–drug interactions screen-
ing performance. Nevertheless, pharmacists and oncologists
should really discuss with patients the nature of CM, foster-
ing an open dialogue, where hopes and concerns are shared.
This communication should be initiated early in the cancer
care continuum to facilitate understanding through ongoing
Finally, the pharmacist performed 71 interventions among
59 patients (37% of patients with a medication reconcilia-
tion). Of note, among TKI treated patients, there were more
frequent pharmacist interventions than in chemotherapy
treated patients (63% versus 17%; p < 0.001). In multivariate
analysis, pharmacist intervention was associated with TKI
treatment (OR 8.37 [2.71–25.84] p = 0.0002). This result is
consistent with the analysis of factors associated with DDI
and pharmacist could favor medication reconciliation in
patients treated with TKI, rather than chemotherapy treat-
ments. However, an early and systematic pharmacist inter-
vention seems also crucial in patients receiving cytotoxic
chemotherapy (ifosfamide, doxorubicine) which are at high
risk for DDI. Frequent DDI observed in sarcoma patients are
described in Table4, which could be a useful tool to help
oncologists and pharmacists to prevent DDI.
Another factor associated with pharmacist interven-
tion was the number of concomitant medications (OR 1.22
[1.06–1.41] p = 0.005). Our result is in agreement with a
recent study conducted in 898 cancer patients in whom
a higher risk of drug interactions was associated with an
increased number of drugs (OR:1.66 [1.55–1.78], p < 0.001)
In conclusion, the present study highlighted that sarcoma
patients have a moderate risk for DDIs, with 12% of the pre-
therapeutic assessment having a potential major DDI with
antitumor treatment. Clinical pharmacist interventions on
DDIs were more frequently required among sarcoma patients
treated with TKI than intravenous cytotoxic chemotherapy.
Since the risk of toxicity is higher in TKI-treated patients,
we developed in our center a follow up of these patients by
plasma drug monitoring and systematic phone calls. Plasma
drug monitoring, could be also helpful to detect new phar-
macokinetic drug–drug interactions or herb–drug interac-
tions, as we previously described [15, 4446].
Therefore, we recommend a systematic multidisciplinary
risk assessment including a medication review by a pharma-
cist to prevent DDI in sarcoma patients. This review must
consider not only traditional medication but also CM, which
are implied in a third of the pharmacist interventions.
Supplementary Information The online version contains supplemen-
tary material available at https:// doi. org/ 10. 1007/ s00280- 021- 04311-4.
Acknowledgements DRCI grant for explore database
Authors’ contributions Conception or design of the work: AB, IG, AJ,
PBR, ATS. Data collection: AB, IG, SEM, ACP, CT, DB, AM, SDP,
CV, JA, FG, PBR, ATS. Data analysis and interpretation; AB, IG, AJ,
ATS, FG, BB, PBR. Drafting the article: AB, IG, AJ, SEM, ACP, CT,
DB, AM, FG, BB, ATS, PBR. Critical revision of the article: AB, IG,
ATS. Final approval of the version to be published: AB, IG, AG, SEM,
Availability of data and materials All data generated or analysed during
this study are included in this published article [and its supplementary
information files].
Conflict of interest On behalf of all authors, the corresponding author
states that there is no conflict of interest.
Ethic approval and consent to participate The studies involving human
participants were reviewed and approved by Local ethical committee
for oncology of Cochin Hospital (AP-HP), CARPEM, university of
Paris, France (number:578AB88). The patients/participants provided
their written informed consent to participate in this study. All methods
were carried out in accordance with relevant guidelines and regula-
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Full-text available
Purpose: Pazopanib is active in soft-tissue sarcoma (STS). Because pazopanib absorption is pH-dependent, coadministration with gastric acid-suppressive (GAS) agents such as proton pump inhibitors could affect exposure of pazopanib, and thereby its therapeutic effects. Patients and methods: The EORTC 62043 and 62072 were single-arm phase II and placebo-controlled phase III studies, respectively, of pazopanib in advanced STS. We first compared the outcome of patients treated with pazopanib with or without GAS agents for ≥80% of treatment duration, and subsequently using various thresholds. The impact of concomitant GAS therapy was assessed on progression-free survival (PFS) and overall survival (OS) using multivariate Cox models, exploring and comparing also the potential effect on placebo-treated patients. Results: Of 333 eligible patients, 59 (17.7%) received concomitant GAS therapy for >80% of pazopanib treatment duration. Median PFS was shorter in GAS therapy users versus nonusers: 2.8 vs. 4.6 months, respectively [HR, 1.49; 95% confidence interval (CI), 1.11-1.99; P = 0.01]. Concomitant administration of GAS therapy was also associated with a shorter median OS: 8.0 vs. 12.6 months (HR, 1.81; 95% CI, 1.31-2.49; P < 0.01). The longer the overlapping use of GAS agents and pazopanib, the worse the outcome with pazopanib. These effects were not observed in placebo-treated patients (HR, 0.82; 95% CI, 0.51-1.34; P = 0.43 for PFS and HR, 0.84; 95% CI, 0.48-1.48; P = 0.54 for OS). Conclusions: Coadministration of long-term GAS therapy with pazopanib was associated with significantly shortened PFS and OS. Withdrawal of GAS agents must be considered whenever possible. Therapeutic drug monitoring of pazopanib plasma concentrations may be helpful for patients on pazopanib and GAS therapy.
Full-text available
With an increasing number of cancer patients seeking an improved quality of life, complementary and alternative therapies are becoming more common ways to achieve such improvements. The potential risks of concurrent administration are serious and must be addressed. However, comprehensive evidence for the risks and benefits of combining anticancer drugs with traditional herbs is rare. Pharmacokinetic investigations are an efficient way to understand the influence of concomitant remedies. Therefore, this study aimed to collect the results of pharmacokinetic studies relating to the concurrent use of cancer chemotherapy and complementary and alternative therapies. According to the National Health Insurance (NHI) database in Taiwan and several publications, the three most commonly prescribed formulations for cancer patients are Xiang-Sha-Liu-Jun-Zi-Tang, Jia-Wei-Xiao-Yao-San and Bu-Zhong-Yi-Qi-Tang. The three most commonly prescribed single herbs for cancer patients are Hedyotis diffusa, Scutellaria barbata, and Astragalus membranaceus. Few studies have discussed herb–drug interactions involving these herbs from a pharmacokinetics perspective. Here, we reviewed Jia-Wei-Xiao-Yao-San, Long-Dan-Xie-Gan-Tang, Curcuma longa and milk thistle to provide information based on pharmacokinetic evidence for healthcare professionals to use in educating patients about the risks of the concomitant use of various remedies.
Rationale Drug-drug interactions (DDIs) with oral antineoplastics (OAs) are of increasing concern given the rapid increase in OA approvals and use in cancer patients. A small pilot study of 20 DDIs with OAs showed significant variability in commonly used DDI screening databases in sensitivity of detecting potentially clinically relevant DDIs. This study builds upon that work by expanding the number of potential DDIs analyzed and including a specificity analysis. Methods Newly approved OAs from 2016 to May 2019 (n = 22) were included in this analysis. Prescribing information for each drug was reviewed. A list of explicit and theoretical drug interactions was created for each OA by the two investigators. A board-certified oncology pharmacist adjudicated all DDI pairs for potential clinical significance. In total, 229 DDI pairs were used to analyze sensitivity of 5 DDI databases (Lexicomp®, Micromedex®, Medscape, Eporactes®, & Additionally, 64 “dummy” or false DDI pairs were created to analyze specificity. Sensitivity and specific were analyzed using Cochran’s Qtest, while accuracy was analyzed using chi-square test. Results There was significant variability among the databases with regards to sensitivity (p < 0.0001), specificity (p < 0.0001), and accuracy (p < 0.0001). In terms of accuracy (max score = 400), Lexicomp®(355), Epocrates® (344), and (352) scored higher than MicroMedex® (270) and Medscape (280). Conclusions Considerable variability exists among DDI screening databases with regards to OAs and potential drug interactions. Clinicians should be vigilant in both screening for DDIs with OAs and describing DDIs encountered in clinical practice.
Introduction Patients with cancer are increasingly using herbal supplements, unaware that supplements can interact with oncology treatment. Herb–drug interaction management is critical to ensure optimal treatment outcomes. Several screening tools exist to detect drug–drug interactions, but their performance to detect herb–drug interactions is not known. This study compared the performance of eight drug–drug interaction screening tools to detect herb–drug interaction with anti-cancer agents. Methods The herb–drug interaction detection performance of four subscription (Micromedex, Lexicomp, PEPID, Facts & Comparisons) and free (, Medscape, WebMD, RxList) drug–drug interaction tools was assessed. Clinical relevance of each herb–drug interaction was determined using Natural Medicine and each drug–drug interaction tool. Descriptive statistics were used to calculate sensitivity, specificity, positive predictive value, and negative predictive value. Linear regression was used to compare performance between subscription and free tools. Results All tools had poor sensitivity (<0.20) for detecting herb–drug interaction. Lexicomp had the highest positive predictive value (0.98) and best overall performance score (0.54), while Medscape was the best performing free tool (0.52). The worst subscription tools were as good as or better than the best free tools, and as a group subscription tools outperformed free tools on all metrics. Using an average subscription tool would detect one additional herb–drug interaction for every 10 herb–drug interactions screened by a free tool. Conclusion Lexicomp is the best available tool for screening herb–drug interaction, and Medscape is the best free alternative; however, the sensitivity and performance for detecting herb–drug interaction was far lower than for drug–drug interactions, and overall quite poor. Further research is needed to improve herb–drug interaction screening performance.
Background Because of comorbidities and polypharmacy, older patients with cancer have a greater risk of iatrogenic events. We aimed to characterize potential drug-drug interactions (PDIs) and the risk of unplanned hospitalization in older patients with cancer treated with antineoplastic agents (ANAs). Methods We analyzed all older patients (≥70 years) from the prospective ELCAPA cohort referred for geriatric assessment (2007–2014) prior to treatment with ANA at Henri Mondor Hospital (Créteil, France). PDIs were identified using Lexicomp®, and Theriaque® for French medications. Factors associated with PDIs, and association between PDIs and unplanned hospitalization in the 6 months following geriatric assessment were analyzed using ordered multivariate logistic regression (MLR). Results We included 442 patients (median [interquartile range] age: 77 years [74–80]); number of medications/patient/day: 6 [3–8]); ECOG-PS ≤ 2: 79%; metastasis: 70%). Most patients had a digestive tract cancer (colorectal: 22%; upper digestive tract: 23%). We identified 1742 PDIs; 76.5% of patients had ≥1 PDI; 13% of the PDIs involved an ANA. In a multivariate analysis, cardiovascular disorders (ischemic heart disease, heart failure, atrial fibrillation and/or arterial hypertension) were independently associated with PDIs (p < .001, after adjustment for polypharmacy and tumor site/stage). A high number of PDIs between two daily medications was independently associated with the risk of unplanned hospitalization (adjusted-odds ratio [95% confidence interval] per PDI: 1.05 [1.00;1.11], p = .05), while polypharmacy was not. Conclusion Patients with cardiovascular comorbidities were more likely to have a PDI. A higher number of PDIs may be an independent risk factor for early unplanned hospitalization.
Background: Our goal was to determine (a) the prevalence of multimorbidity and polypharmacy in patients with cancer and (b) the prevalence, predictability, and preventability of adverse drug reactions (ADRs) causing/contributing to hospitalization. Materials and methods: We conducted a 12-month prospective observational study of patients aged ≥16 years admitted to an oncology center. Older adults were aged ≥70 years. Results: We enrolled 350 patients: 52.3% (n = 183) female, mean age 63.6 years (SD 12.1), 36.6% (n = 121) aged ≥70 years. Multimorbidity (≥2 conditions) was identified in 96.9%; 68% had ≥5 conditions. The median number of medications was 6 (interquartile range [IQR] 4-8); 47% were prescribed ≥6 medications and 11.4% ≥11 medications. Older adults had higher numbers of comorbid conditions (7 [IQR 5-10] vs. 5 [IQR 3-7]) and were prescribed more medications (median 7 [IQR 4-9] vs. 4 [IQR 2-7]). ADRs caused/contributed to hospitalization in 21.5% (n = 75): 35.8% (n = 72) of emergency admissions and 4.7% (n = 3) of elective admissions. The most common ADRs were neutropenia with infection (25.3%), dyspepsia/nausea/vomiting (20%), and constipation (20%). Causative medications included systemic anticancer therapies (SACTs; 53.3%), opioids (17.3%), corticosteroids (6.7%), and nonsteroidal anti-inflammatory drugs (5.3%). ADR prevalence was similar in older and younger adults secondary to SACTs (8.3% vs. 13.1%), non-cancer medications (10.7% vs. 8.3%), and both (0% vs. 1.3%). ADRs were predictable in 89.3% (n = 67), definitely avoidable in 29.3% (n = 22), and possibly avoidable in 33.3% (n = 25). No association was identified between ADRs and age, gender, daily medication number, length of stay, or death. No ADR predictor variables were identified by logistic regression. Conclusion: More than 21% of admissions to an oncology service are ADR-related. ADRs are caused by both SACTs and non-cancer-specific medications. The majority are predictable; ≥60% may be preventable. Patients with cancer have high levels of multimorbidity and polypharmacy, which require vigilance for related adverse outcomes. Implications for practice: A diagnosis of cancer often occurs in patients with multimorbidity and polypharmacy. Cancer can cause an altered physiological environment, placing patients at risk of drug-drug interactions, drug-disease interactions, and adverse drug reactions (ADRs). This study identified that ADRs caused or contributed to one in five hospital admissions of patients with cancer. ADRs were caused by systemic anticancer therapies (SACTs) in 53.3% of cases and non-cancer medications in 45.4% of cases, and a combination of both in 1.3%. ADRs occurred in similar frequencies in older and younger patients secondary to SACTs (8.3% vs. 13.1%, p = .295), non-SACTs (10.7% vs. 8.3%, p = .107), and a combination of both (0% vs. 1.3%, p = .240). The majority of ADRs were predictable (89.3%) and potentially preventable (62.6%). These findings support the need for increased awareness of medication-related adversity in patients with cancer and interventions to minimize their occurrence, thus supporting the American Society of Clinical Oncology guidelines that recommend adults ≥65 years of age receiving chemotherapy have geriatric assessment to identify medical and medication issues.
Background: Regorafenib has proven activity in patients with pretreated gastrointestinal stromal tumours and colorectal and hepatocellular carcinoma. We designed REGOBONE to assess the efficacy and safety of regorafenib for patients with progressive metastatic osteosarcoma and other bone sarcomas. This trial comprised four parallel independent cohorts: osteosarcoma, Ewing sarcoma, chondrosarcoma, and chordoma. In this Article, we report the results of the osteosarcoma cohort. Methods: In this non-comparative, double-blind, placebo-controlled, phase 2 trial, patients aged 10 years or older with histologically confirmed osteosarcoma whose disease had progressed after treatment with one to two previous lines of chemotherapy for metastatic disease and an Eastern Cooperative Oncology Group performance status of 0 or 1 were enrolled. Patients were randomly assigned (2:1) to receive either oral regorafenib (160 mg/day, for 21 of 28 days) or matching placebo. Patients in both groups also received best supportive care. Randomisation was done using a web-based system and was stratified (permuted block) by age at inclusion (<18 vs ≥18 years old). Investigators and patients were masked to treatment allocation. Patients in the placebo group, after centrally confirmed progressive disease, could cross over to receive regorafenib. The primary endpoint was the proportion of patients without disease progression at 8 weeks. Analyses were done by modified intention to treat (ie, patients without any major entry criteria violation who initiated masked study drug treatment were included). All participants who received at least one dose of study drug were included in the safety analyses. This study is registered with, number NCT02389244, and the results presented here are the final analysis of the osteosarcoma cohort (others cohorts are ongoing). Findings: Between Oct 10, 2014, and April 4, 2017, 43 adult patients were enrolled from 13 French comprehensive cancer centres. All patients received at least one dose of assigned treatment and were evaluable for safety; five patients were excluded for major protocol violations (two in the placebo group and three in the regorafenib group), leaving 38 patients who were evaluable for efficacy (12 in the placebo group and 26 in the regorafenib group). 17 of 26 patients (65%; one-sided 95% CI 47%) in the regorafenib group were non-progressive at 8 weeks compared with no patients in the placebo group. Ten patients in the placebo group crossed over to receive open-label regorafenib after centrally confirmed disease progression. 13 treatment-related serious adverse events occurred in seven (24%) of 29 patients in the regorafenib group versus none of 14 patients in the placebo group. The most common grade 3 or worse treatment-related adverse events during the double-blind period of treatment included hypertension (in seven [24%] of 29 patients in the regorafenib group vs none in the placebo group), hand-foot skin reaction (three [10%] vs none), fatigue (three [10%] vs one [3%]), hypophosphataemia (three [10%] vs none), and chest pain (three [10%] vs none). No treatment-related deaths occurred. Interpretation: Regorafenib demonstrated clinically meaningful antitumour activity in adult patients with recurrent, progressive, metastatic osteosarcoma after failure of conventional chemotherapy, with a positive effect on delaying disease progression. Regorafenib should be further evaluated in the setting of advanced disease as well as potentially earlier in the disease course for patients at high risk of relapse. Regorafenib might have an important therapeutic role as an agent complementary to standard cytotoxic chemotherapy in the therapeutic armamentarium against osteosarcoma. Funding: Bayer HealthCare.
Importance There is limited information on the association among complementary medicine (CM), adherence to conventional cancer treatment (CCT), and overall survival of patients with cancer who receive CM compared with those who do not receive CM. Objectives To compare overall survival between patients with cancer receiving CCT with or without CM and to compare adherence to treatment and characteristics of patients receiving CCT with or without CM. Design, Setting, and Participants This retrospective observational study used data from the National Cancer Database on 1 901 815 patients from 1500 Commission on Cancer–accredited centers across the United States who were diagnosed with nonmetastatic breast, prostate, lung, or colorectal cancer between January 1, 2004, and December 31, 2013. Patients were matched on age, clinical group stage, Charlson-Deyo comorbidity score, insurance type, race/ethnicity, year of diagnosis, and cancer type. Statistical analysis was conducted from November 8, 2017, to April 9, 2018. Exposures Use of CM was defined as “Other-Unproven: Cancer treatments administered by nonmedical personnel” in addition to at least 1 CCT modality, defined as surgery, radiotherapy, chemotherapy, and/or hormone therapy. Main Outcomes and Measures Overall survival, adherence to treatment, and patient characteristics. Results The entire cohort comprised 1 901 815 patients with cancer (258 patients in the CM group and 1 901 557 patients in the control group). In the main analyses following matching, 258 patients (199 women and 59 men; mean age, 56 years [interquartile range, 48-64 years]) were in the CM group, and 1032 patients (798 women and 234 men; mean age, 56 years [interquartile range, 48-64 years]) were in the control group. Patients who chose CM did not have a longer delay to initiation of CCT but had higher refusal rates of surgery (7.0% [18 of 258] vs 0.1% [1 of 1031]; P < .001), chemotherapy (34.1% [88 of 258] vs 3.2% [33 of 1032]; P < .001), radiotherapy (53.0% [106 of 200] vs 2.3% [16 of 711]; P < .001), and hormone therapy (33.7% [87 of 258] vs 2.8% [29 of 1032]; P < .001). Use of CM was associated with poorer 5-year overall survival compared with no CM (82.2% [95% CI, 76.0%-87.0%] vs 86.6% [95% CI, 84.0%-88.9%]; P = .001) and was independently associated with greater risk of death (hazard ratio, 2.08; 95% CI, 1.50-2.90) in a multivariate model that did not include treatment delay or refusal. However, there was no significant association between CM and survival once treatment delay or refusal was included in the model (hazard ratio, 1.39; 95% CI, 0.83-2.33). Conclusions and Relevance In this study, patients who received CM were more likely to refuse additional CCT, and had a higher risk of death. The results suggest that mortality risk associated with CM was mediated by the refusal of CCT.
Objectives: Drug-drug interactions (DDIs) represent an escalating concern for older adults attributed to polypharmacy, multi-morbidity and organ dysfunction. Few studies have evaluated the prevalence of major DDIs and the variability between DDI detection software which confuses management. Materials and methods: Prevalence of major DDIs was examined as a secondary analysis of outpatients aged ≥65 years. Demographic and clinical information was collected from electronic health records including age, sex, race, cancer type, comorbidities, and medications. All DDIs were screened by a clinical pharmacist using Lexi-Interact® and Micromedex®. Major DDIs were defined as Lexi-Interact® category D or X and/or Micromedex® category major or contraindication. Summary statistics of patient characteristics and DDIs were computed. Results: Our cohort included 142 patients (mean age, 77.7 years; 56% women, 73% Caucasian). The mean medications was 9.8 including 6.7 prescriptions, 2.6 non-prescriptions, and 0.5 herbals. Lexi-Interact® identified 310 major DDIs in 69% of patients (n = 98) with an average of 2.2 DDIs per patient. Micromedex® identified 315 major DDIs in 61% of patients (n = 87) with an average of 2.2 DDIs per patient. DDIs mostly involved opioids, antiplatelets, electrolyte supplements, antiemetics, and antidepressants. Variability existed with the severity rating reporting of the clinical decision support software. Conclusions: There was a high prevalence of major DDIs in older adults with cancer. Utilizing clinical decision support software was beneficial for detecting DDIs however, variability existed with severity reporting. Future studies need to identify the relevant DDIs with clinical implications in order to optimize medication safety in this population.