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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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Abstract

Background: The risk of drug drug interactions (DDI) has become a major issue in cancer patient care. However, data in sarcoma patients are scarce. We aimed to evaluate the frequency of DDI with antitumor treatments, identify the risk factors for DDI and 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 twenty-two soft-tissue and 80 bone sarcoma patients (103 males, median age 50 years,) were included before chemotherapy (86%) or tyrosine kinase inhibitor (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). Marital status (p=0.003) was the single factor associated with complementary medicine use. A pharmacist performed 157 medication reconciliation 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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Prevalence of Drug-Drug Interactions in Sarcoma
Patients: Key Role of the Pharmacist Integration for
Toxicity Risk Management
Audrey Bellesoeur
Department of Medical Oncology, Cochin Hospital, AP-HP, Paris, France; University of Paris Descartes,
ARIANE, CARPEM, Paris, France
Ithar Gataa
Department of Medical Oncology, Cochin Hospital, AP-HP, Paris, France; University of Paris Descartes,
ARIANE, CARPEM, Paris, France
Anne Jouinot
Department of Medical Oncology, Cochin Hospital, AP-HP, Paris, France; University of Paris Descartes,
ARIANE, CARPEM, Paris, France
Sarah El Mershati
Department of Clinical Pharmacy, Cochin Hospital, AP-HP, Paris, France
Anne Catherine Piketty
Department of Medical Oncology, Cochin Hospital, AP-HP, Paris, France; University of Paris Descartes,
ARIANE, CARPEM, Paris, France
Camille Tlemsani
Department of Medical Oncology, Cochin Hospital, AP-HP, Paris, France; University of Paris Descartes,
ARIANE, CARPEM, Paris, France
David Balakirouchenane
Department of Pharmacokinetics and Pharmacochemisty, Cochin Hospital, AP-HP, Paris, France;
CARPEM, Paris, France
Anthia Monribot
Department of Clinical Pharmacy, Cochin Hospital, AP-HP, Paris, France
Michel Vidal
Department of Pharmacokinetics and Pharmacochemisty, Cochin Hospital, AP-HP, Paris, France;
CARPEM, Paris, France
Rui Batista
Department of Clinical Pharmacy, Cochin Hospital, AP-HP, Paris, France
Sixtine De Percin
Department of Medical Oncology, Cochin Hospital, AP-HP, Paris, France; University of Paris Descartes,
ARIANE, CARPEM, Paris, France
Clementine Villeminey
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Department of Medical Oncology, Cochin Hospital, AP-HP, Paris, France; University of Paris Descartes,
ARIANE, CARPEM, Paris, France
Jerome Alexandre
Department of Medical Oncology, Cochin Hospital, AP-HP, Paris, France; University of Paris Descartes,
ARIANE, CARPEM, Paris, France
Francois Goldwasser
Department of Medical Oncology, Cochin Hospital, AP-HP, Paris, France; University of Paris Descartes,
ARIANE, CARPEM, Paris, France
Benoit Blanchet
Department of Pharmacokinetics and Pharmacochemisty, Cochin Hospital, AP-HP, Paris, France;
CARPEM, Paris, France
pascaline Boudou-Rouquette
Department of Medical Oncology, Cochin Hospital, AP-HP, Paris, France; University of Paris Descartes,
ARIANE, CARPEM, Paris, France
Audrey Thomas-Schoemann ( schoemann.audrey@gmail.com )
Department of Clinical Pharmacy, Cochin Hospital, AP-HP, Paris, France
Research Article
Keywords: sarcoma, drug-drug interactions, complementary and alternative medicine, pharmacist
interventions
DOI: https://doi.org/10.21203/rs.3.rs-150677/v1
License: This work is licensed under a Creative Commons Attribution 4.0 International License. 
Read Full License
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Abstract
Background:The risk of drug drug interactions (DDI) has become a major issue in cancer patient care.
However, data in sarcoma patients are scarce. We aimed to evaluate the frequency of DDI with antitumor
treatments, identify the risk factors for DDI and 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 identied
using Micromedex electronic software.
Results: One hundred twenty-two soft-tissue and 80 bone sarcoma patients (103 males, median age 50
years,) were included before chemotherapy (86%) or tyrosine kinase inhibitor (14%). The median number
of medications was 3; 34 patients (22% of patients with medication reconciliation) reported
complementary medicine use. 37 potential DDI classied as major, were identied (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 identied as risk factors of DDI (p<0.02). Marital status (p=0.003) was the
single factor associated with complementary medicine use. A pharmacist performed 157 medication
reconciliation 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.
Introduction
Sarcomas are a heterogeneous group of tumors of mesenchymal 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 rst-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 diseases, i.e
gemcitabine, trabectedin and taxanes (1)(2)(3). The introduction of molecularly targeted agents has
provided new therapeutic options for STS treatment, such as pazopanib and regorafenib in non-
adipocytic STS(4), imatinib in dermatobrosarcoma(5) and crizotinib in inammatory myobroblastic
tumor(6). Multitargeted tyrosine kinase inhibitors, have also demonstrated activity in patients with
advanced bone sarcoma, i.e regorafenib, cabozantinib and lenvatinib (7)(8)(9).
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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), dened 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 benecial, it is possible that
others may induce herb –drug interactions or vitamin- drug interactions that could impact ecacy or
safety prole 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
multidisciplinary 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 decision.
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 (17)(18)
(19)(20) or small studies focusing on one anticancer treatment (21)(22)(23)(24).
The primary aim of this prospective observational study was to evaluate the frequency and severity of
DDI with anticancer treatment (chemotherapy and tyrosine kinase inhibitor). The secondary aims were to
identify clinical factors associated with DDI, to assess both the frequency of sarcoma 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 inhibitors (TKI) from January 2014 to
October 2017. Chemotherapy (CT) consisted mostly of doxorubicin, ifosfamide, gemcitabine, 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 consisted of cancer patients referred
by oncologists for multidisciplinary 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 optimization. The protocol was approved by the appropriate ethics committee (578AB88). All
methods were carried out in accordance with relevant guidelines and regulation.
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Drug-drug interaction (DDI)
A pharmacist proceeded to complete medication reconciliation, 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 specicity (0.78) by comparison of ve common drug-drug
interaction software programs regarding accuracy and comprehensiveness(25). Severity of DDI was
categorized by using a 4-point scale: contraindicated, major, moderate and minor.
Interventions of pharmacist before treatment initiation were classied as treatment discontinuation,
treatment replacement, dosing adjustments, or plasma drug monitoring.
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-Reactive
Protein (CRP), ALanine Transaminase (ALT), ASpartate Transaminase (AST), Alkaline Phosphatase (ALP)
and bilirubin.
Study endpoints
The main objective was to evaluate the frequency and severity of DDI with CT and TKI. The secondary
objectives were to evaluate complementary medicine CM use, the risk factors associated with DDI, and
the qualitative role of a pharmacist 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 Fishers test for qualitative variables and t-test or Mann-Whitney
test for quantitative variables. Variables signicantly associated with endpoint in univariate analyses
were then combined into multivariable logistic regression model.
All p-values were two-sided, and the level of signicance was set at p < 0.05.
Calculations were performed using R statistical software (version 3.6.1, R Stats Package).
Results
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Patients characteristics
Two hundred and two patients were included in this study. One hundred twenty-two soft-tissue and 80
bone sarcoma patients were included before antitumor treatment: CT (86%) or TKI (14%) (Figure 1). The
median number of comorbidities was 1 (range: 0-2). Most of the patients (65%) had advanced-stage or
metastatic disease. Patients baseline characteristics are summarized in Table 1. The median number of
drugs per patient was 3 (IQR2-5), 65 patients (32%) had at least 5 drugs.
Overall, 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
reconciliation (MR) by a pharmacist (Figure 2).
The most common prescription medications were analgesics and anticoagulants (Supplemental Table
S1).
Potential drug-drug interactions
A total of 37 potential DDIs were identied with Micromedex software in 29 pretherapeutic assessments
(12% of the 243 pre-treatment assessments). All these DDI were classied as major. Therapeutic classes
involved in DDI were antidepressant (57%), anti-acid (19%), anti-epileptic (11%), anti-inammatory (5%),
analgesic (5%), and antibiotic (3%). Two types of DDI mechanisms were observed: DDI that cause QT
interval prolongation (54%) and pharmacokinetic DDI (46%). 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 signicantly 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 antidepressants (OR 1.25 [1.11-
1.41]; p<0.001). Interestingly, patients treated with gemcitabine-base protocols were exposed to a lower
risk of DDI (OR 0.86 [0.78-0.96]; p= 0.0059).
Use of complementary medicines in sarcoma patients
34 patients (22% of the patients with medication reconciliation) used biologically-based CM. It included
herbs infusion (29%), herbs capsules (34%), vitamins and minerals (22%), homeopathy (5%), other CM
(10%) (Figure 3). 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 initiation
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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 replacements 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).
Discussion
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. Previous studies have suggested a high risk of DDI in cancer patients. For
example, Nightingale G et al have recently detected in 142 elderly cancer patients (> 65 years), 315 major
potential DDI in 61% patients with Micromedex software(26). Indeed, DDI prevalence is known to vary
with cancer type and polypharmacy (16)(27). Furthermore, we chose to focus our study on DDI that
involved antitumor 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(28). 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
(29)
As far as we know, we identied for the rst time baseline 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 signicant 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 signicantly decreased in case of PPI administration. Overall, 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-dependent unlike TKI. Therefore,
pharmacists could favor to perform medication reconciliation in sarcoma patients treated with TKI rather
than in patients treated with gemcitabine-protocol.
The use of antidepressants and proton pump inhibitors (PPI) by sarcoma patients during pretherapeutic
assessment were also identied as independent risk factors for DDI (p < 0.001 and p = 0.026 respectively).
Page 8/18
Many cytotoxic anticancer drugs can prolong the QTc interval (eg, anthracyclines). This prolongation is
also frequently reported with some TKIs such as pazopanib or sunitinib, and probably caused by
interaction with hERG K + channels (30). Prolongation of the QTc interval and subsequent development of
Torsades de pointes is a rare but severe toxicity of some TKIs (31) (32). 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 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 exposure, resulting in decreased ecacy.
There have been contradicting results on the clinical effects of PPI on sorafenib or sunitinib absorption
(33,34). By contrast, Mir et al recently showed that coadministration of gastric suppressive agents with
pazopanib was associated with signicantly shortened PFS and OS, proving that this DDI could result in
a decreased ecacy of the anti-cancer treatment (35). 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 consumption of herbal medicines is a common practice among cancer patients(36). 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 consistent 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(37). Similarly, a French study observed in patients, two years
after cancer diagnosis that CM use was signicantly associated with younger age, female gender and a
higher education level. Only a study by Judson PL et al observed a similar inuence of the marital status
on CM use (38)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 Kava for
example (39), herb-drug interactions (14) or an increased risk of conventional anticancer treatment
refusal(36). Therefore, CM use lead to 21 pharmacist interventions (29%) to limit or monitor the use of
these non-conventional treatments. Pharmacists and oncologists should really discuss with patients the
nature of CM, fostering 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
dialogue.
Finally, the pharmacist performed 71 interventions among 59 patients (37% of patients with a medication
reconciliation). 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 treatments. However, an early and
systematic pharmacist intervention seems also crucial in patients receiving cytotoxic chemotherapy
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(ifosfamide, doxorubicine) which are at high-risk for DDI. Frequent DDI observed in sarcoma patients are
described in Table4, that could be a useful tool to help oncologists and pharmacists to prevent DDI.
Another factor associated with pharmacist intervention 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)(16).
Conclusion
In conclusion, the present study highlighted that sarcoma patients have a moderate risk for DDIs, with
12% of the pretherapeutic 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 pharmacokinetic drug drug
interactions or herb-drug interactions, as we previously described(15,40–42) .
Therefore, we recommend a systematic multidisciplinary risk assessment including a medication review
by a pharmacist 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.
Abbreviations
ALP
Alkaline Phosphatase
ALT
ALanine Transaminase
AST
ASpartate Transaminase
CRP
C-Reactive Protein
CT
chemotherapy
CM
complementary medecine
DDI
drug drug interactions
IQR
interquartile range
MR
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medication reconciliation
PPI
proton pump inhibitor
STS
soft tissue sarcoma
TKI
tyrosine kinase inhibitor
Declarations
Ethic approval and consent to participate:
The studies involving human participants were reviewed and approved by Local ethical commitee 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 regulations.
Availability of data and materials
All data generated or analysed during this study are included in this published article [and its
supplementary information les]
Disclosure of Potential Conict of interest: The authors declare that this study was conducted in the
absence of any commercial or nancial relationship that could be constructed as a potential conict of
interest
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, AJ, SEM, ACP, CT, DB, AM, MV, RB, SDP, CV, JA, FG, BB, PBR, ATS,
Final approval of the version to be published: AB, IG, AG, SEM, ACP, CT, DB, AM, MV, RB, SDP, CV, JA, FG,
PBR, ATS
Acknowledgments: DRCI grant for explore database
Page 11/18
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Tables
Table 1. Baseline patients’ characteristics of the study cohort
ALT, alanine amino transferase; AST, aspartate amino transferase; ALP, alkaline phosphatase; ECOG PS,
eastern cooperative oncology group performance status; NA, not available. Quantitative results are
expressed as median [interquartile range]
Page 15/18
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]
Page 16/18
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]
Table 2. Baseline factors associated with a high risk of drug-drug interactions
CI95%, condence interval 95%; ECOG PS, Eastern Cooperative Oncology Group Performance Status; OR,
odds ratio
Page 17/18
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
Table 3 – Factors associated with clinical pharmacist interventions
Page 18/18
CI95%, condence interval 95%; CM, complementary medicine, ECOG PS, Eastern Cooperative Oncology
Group Performance Status; OR, odds ratio;
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-
58.4 (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
Due to technical limitations, table4 is only available as a download in the Supplemental Files section.
ResearchGate has not been able to resolve any citations for this publication.
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Purpose: Abiraterone acetate combined with prednisone improves survival in metastatic castration-resistant prostate cancer (mCRPC) patients. This oral anticancer agent may result in drug-drug interactions (DDI). We aimed to evaluate the prevalence of DDI with abiraterone and the possible determinants for the occurrence of these DDI. Methods: We performed a single centre retrospective review from electronic medical records of mCRPC patients treated with abiraterone from 2011 to 2015. Potential DDI with abiraterone were identified using Micromedex and were categorized by a 4-point scale severity. Results: Seventy-two out of ninety-five mCRPC pts (median age: 77 years [68-82]) had comorbidities. The median number of drugs used per patient was 7 [5-9]. 66 potential DDI with abiraterone were detected in 49 patients (52%): 39 and 61% were classified as major and moderate DDI, respectively. In the univariate analysis, pain (p?<?0.0001), hypo-albuminemia (p?=?0.032), and higher ECOG performance status (PS) (p?=?0.013) were significantly associated with a higher risk of DDI with abiraterone. Pain (p?<?0.0001) and PS (p?=?0.018) remained significant in the multivariate analysis. Conclusions: Polypharmacy is an issue among mCRPC patients. In our study, half of the patients have potential DDI with abiraterone. Patients with pain and poor PS are at higher risk of DDI with abiraterone. A medication review by a pharmacist is of crucial importance to prevent DDI with abiraterone.