Lung Cancer 83 (2014) 265–271
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Correlation between erlotinib pharmacokinetics, cutaneous toxicity and clinical outcomes in patients with advanced non-small cell lung cancer (NSCLC) Marcello Tiseo a,∗ , Roberta Andreoli b,c , Francesco Gelsomino a , Paola Mozzoni b,c , Cinzia Azzoni d , Marco Bartolotti a , Beatrice Bortesi a , Matteo Goldoni b,c , Enrico Maria Silini d , Giuseppe De Palma e , Antonio Mutti b , Andrea Ardizzoni a,∗ a
Oncology Unit, University Hospital, Parma, Italy Department of Clinical and Experimental Medicine, University Hospital, Parma, Italy c INAIL Research Area, CERT, University of Parma, Parma, Italy d Section of Pathological Anatomy, University Hospital, Parma, Italy e Department of Experimental and Applied Medicine, Section of Occupational Health and Industrial Hygiene, University of Brescia, Brescia, Italy b
a r t i c l e
i n f o
Article history: Received 14 September 2013 Received in revised form 30 November 2013 Accepted 3 December 2013 Keywords: Erlotinib NSCLC Pharmacokinetic Skin toxicity
a b s t r a c t Objectives: An association between skin toxicity and outcome has been reported for NSCLC patients treated with erlotinib. Several explanations have been suggested, including pharmacokinetic and pharmacogenomic variability. The purposes of this study were to characterize erlotinib pharmacokinetic and to correlate drug serum and urine levels to toxicity and outcomes in advanced NSCLC patients. Methods: Patients with stage IV NSCLC consecutively treated with erlotinib in second- or third-line were enrolled. Biological samples (blood, urine and tumor specimens) were collected. Erlotinib levels in serum and urine samples of all patients after 7 (T1) and 30 (T2) days of treatment were quantified by LC–MS/MS analysis, along with urinary 6-hydroxycortisol/cortisol ratio, as marker of metabolic phenotype of the CYP3A4/5 enzyme. Results: 56 patients were recruited and for 46 all samples were available. At T1 erlotinib levels were 3.90 [2.13] mol/l and 0.37 [2.90] mol/mol creat in serum and urinary samples, respectively; at T2 drug concentrations were significantly lower (2.02 [4.05] mol/l and 0.23 [4.47] mol/mol creat, respectively). Patients with grade 3 skin toxicity showed serum T1 drug levels significantly higher than those with grade 0–2 (6.84 [2.28] vs. 3.08 [1.97] mol/l, respectively, p = 0.004) and had longer progression-free and overall survival. An inverse correlation between erlotinib serum levels and urinary 6-hydroxycortisol/cortisol ratio was observed in patients with grade 3 skin toxicity. Conclusions: These findings suggest that the pharmacokinetics and metabolism of erlotinib are related to skin toxicity and may support further studies where erlotinib dosing is tailored according to pharmacokinetic parameters. © 2013 Elsevier Ireland Ltd. All rights reserved.
1. Introduction Recently, epidermal growth factor receptor (EGFR) tyrosinekinase inhibitors (TKIs), such as erlotinib and gefitinib, have become a treatment option for advanced non-small cell lung cancer (NSCLC) [1,2]. Activating mutations of the EGFR tyrosine-kinase domain are a strong predictive factor of response to TKIs [2,3]. Therefore, the availability of sufficient tumor tissue to perform this mutational analysis represents a priority in the therapeutic strategy of
∗ Corresponding authors at: Oncology Unit, University Hospital of Parma, Via Gramsci 14, 43100 Parma, Italy. Tel.: +39 0521 702316; fax: +39 0521 995448. E-mail addresses: [email protected] (M. Tiseo), [email protected] (A. Ardizzoni). 0169-5002/$ – see front matter © 2013 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.lungcan.2013.12.001
advanced NSCLC, in particular in first-line and maintenance settings. However, for many NSCLC patients EGFR gene status remains unknown, since only small tumor samples, not adequate for molecular analysis, are available. Moreover, EGFR TKIs, in particular erlotinib, demonstrated to be effective also in a small percentage of patients without EGFR mutations [1,4]. In fact, erlotinib was proven to be effective in secondand third-line treatments and also in maintenance therapy of unselected patients with advanced NSCLC, including EGFR wild type ones [1,5]. At present, there are no known pre-treatment predictive factors to identify the minority of patients with advanced EGFR wild-type NSCLCs who may derive some benefit from erlotinib treatment. Therefore, early predictors of efficacy during erlotinib treatment
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would be very useful in the clinical approach to EGFR wild-type patients or those with unknown EGFR status. Several trials indicated skin toxicity as a potential predictive factor of better outcome in NSCLC patients treated with EGFR TKIs, such as erlotinib [6–11]. In a phase I study, 73% of patients treated with erlotinib experienced skin toxicity and these had drug plasma concentrations at 24 h significantly higher than those with no skin toxicity [11]. In a phase II study, skin rash of any grade was registered in 75% of 57 NSCLC patients treated with erlotinib and its appearance and severity resulted a predictive factor of better tumor response and survival [6]. Also in the phase III trial BR.21, a significant correlation between development of rash and erlotinib efficacy was observed [1,7]. In fact, response rates (RRs) were higher in patients with skin rash of grade ≥2 than those with skin rash of grade 1 or 0 (13% vs. 10% vs. 0%, respectively) as well as progression-free (PFS) (4.0 vs. 3.2 vs. 1.7 months, respectively; p < 0.001) and overall survival (OS) were longer (11.1 vs. 7.1 vs. 3.3 months, respectively; p < 0.001) [7]. Similar results were reported in Italian data of TRUST study [8]. The association between rash and clinical outcome has been observed in other tumors treated with either erlotinib or other EGFR inhibitors [12–14]. Factors predicting the development of rash are unknown and the association between rash and outcome remains so far unexplained. Several explanations have been suggested, including pharmacokinetic and pharmacogenomic variability [15,16]. Given the scarcity of data on the correlation between erlotinib effects and its pharmacokinetic parameters [11,16,17], we studied erlotinib serum and urine levels in patients with advanced NSCLC and sought to identify possible relationship with toxicity and clinical outcomes aiming to identify novel early predictive factors of drug efficacy.
2. Patients and methods 2.1. Patient population Patients with advanced NSCLC candidate to erlotinib treatment were enrolled prospectively from January 2008 to July 2010. Inclusion criteria were in accordance with standard eligibility criteria for erlotinib treatment in pretreated patients [18]: age 18 years or more, stage IV measurable disease, Eastern Cooperative Oncology Group (ECOG) performance status (PS) of 0–2, progression after at least one line of chemotherapy, adequate liver and renal function. Erlotinib was administered orally at the dose of 150 mg once daily until disease progression, or unacceptable toxicity. Clinical and biochemical evaluation were performed monthly until treatment discontinuation. Patients underwent CT scan evaluation every two months or in case of clinical suspicion of disease progression. Radiological response was assessed by CT scan according to RECIST (Response Evaluation Criteria in Solid Tumors) version 1.0 criteria [19]. Toxicities were managed by symptomatic treatment and/or erlotinib dose interruptions or reductions as appropriate on the basis of the erlotinib prescribing information [18]; in case of grade 3 toxicity (according to the National Cancer Institute’s Common Terminology Criteria for Adverse Events version 3.0 [CTCAE]), dosage was reduced to 100 mg/day and 50 mg/day and discontinued in case of CTCAE grade 4 toxicity. No routine prophylactic skin treatment was prescribed. The study was approved by the local ethical review board and conducted according to Good Clinical Practice guidelines and to the Declaration of Helsinki. Written informed consent was obtained from all patients.
2.2. Pharmacokinetic and molecular biology methods Blood and urine samples were collected, between 8.00 and 10.00 a.m. before erlotinib administration, at three different times: T0, baseline; T1, after 7 days of erlotinib therapy, before the 8th dose; and T2, after 30 days of therapy. Erlotinib levels in serum and urine samples of patients were quantified by liquid chromatography tandem mass spectrometry (LC–MS/MS) analysis at T0, T1 and T2 [20] (see supplemental file). To characterize the metabolic phenotype of CYP3A4/5, we determined urinary 6-hydroxycortisol (6-OH-cortisol) and cortisol concentrations by LC–MS/MS at T0, at T1 and at T2 and calculated their ratio [21] (see supplemental file). EGFR and K-ras gene mutation assessment was performed on available cytological and histological specimens [22]. 2.3. Statistical methods Statistical analysis was carried out with the SPSS software (version 18.0 for Windows® , Chicago, IL, USA). Log-normal distribution was assessed by the Kolmogorov–Smirnov test and the logarithm of variables was used for all the inferential statistics. Data were calculated both as geometric mean (GM) and geometric standard deviation (GSD) and as median and interquartile range; in the paper, data were expressed only as GM and GSD. Comparisons between T1 vs. T2 were calculated on 46 patients, all other statistical analyses were carried out on 56 patients. When a between subjects’ factors was used (e.g. smoking status), a mixed effects ANOVA was used, testing the interaction between the repeated measures and the independent factor. Differences between two groups were assessed using the Student’s t-test for independent samples, and one-way ANOVA followed by multiple comparison tests (Bonferroni post hoc test) was applied in the case of comparisons among more than two groups; the Pearson’s r was used to assess the correlation between variables. Receiver Operating Characteristic (ROC) curves were built to evaluate the efficacy of an erlotinib cut-off value predicting a severe skin toxicity and response to treatment. The areas under ROC curves of drug concentrations for the prediction of a severe skin toxicity (Grade 0–2 versus Grade 3) and response (DCR, complete response + partial response + stable disease according RECIST criteria vs. PD, progression of disease) were calculated with 95% confidence interval (CI). The diagnostic accuracy values of sensitivity and specificity were determined at the flex point of the curve (sensitivity + specificity = maximum). To assess the relationship between treatment protocols and disease progression or mortality, the Kaplan–Meier survival curves were compared with the log-rank test. The multivariate Cox proportional hazard model was used to determine the potential variables able to predict disease progression or mortality, considering age, gender, EGFR status, smoking habits, histopathology, skin toxicity, biological parameters such as serum concentration levels of erlotinib and urinary 6-OH-cortisol/cortisol ratio at T1. The hazard ratio (HR) of disease progression or death and 95%CI were obtained. For continuous variables (erlotinib serum levels and urinary 6-OH-cortisol/cortisol ratio) the geometric mean was chosen as cut off. The level of significance was set at p < 0.05 for all performed tests. 3. Results 3.1. Patient characteristics The main characteristics of the 56 patients are reported in Table 1. EGFR and K-ras gene status was assessed in 28 (50%) and 24 (42.8%) cases, respectively, due to insufficient amount of tumor
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Table 1 Demographic, clinico-pathologic and molecular patients’ characteristics. No. of patients
56
Mean age (±SD)
65.6 (±9.14)
Gender – no. (%) Male Female
21 (37.5) 35 (62.5)
ECOG performance status score – no. (%) 0–1 2
46 (82.2) 10 (17.8)
Smoking habit – no. (%) Smokers Former smokers Never-smokers
11 (19.6) 21 (37.5) 24 (42.9)
Histology – no. (%) ADC SQCC NOS
43 (76.8) 6 (10.7) 7 (12.5)
EGFR status – no. (%) Mutated/Wild-type/Unknown
9 (16.1)/19 (33.9)/28(50)
K-RAS status – no. (%) Mutated/Wild-type/Unknown Stage IV– no. (%)
4 (7.1)/22(39.3)/30(53.6) 56 (100)
Erlotinib treatment line – no. (%) II III
26 (46.4) 30 (53.6)
OS Median [25th-75th] PFS Median [25th-75th]
183 [87.5–293] 68 [46.3–182]
Response – no. (%) PR SD PD
10 (17.9) 13 (23.2) 33 (58.9)
Dose reduction – no. (%) Yes No
13 (23.2) 43 (76.8)
Cutaneous toxicity – no. (%) G0 G1 G2 G3
15 (26.8) 12 (21.4) 16 (28.6) 13 (23.2)
Diarrhea – no. (%) G0 G1 G2
37 (66.1) 16 (28.6) 3 (5.3)
SD, standard deviation; ADC, adenocarcinoma; SQCC, squamous cell carcinoma; NOS, not otherwise specified carcinoma; OS, overall survival; PFS, progression-free survival; PR, partial response; SD, stable disease; PD, progression disease.
cells or poor quality of DNA in the remaining specimens. Nine patients (16.1%) were EGFR-mutated (6 cases of exon 19 deletion, 1 case of exon 20 insertion and 2 cases of L858R mutation in exon 21). Four other tumors (7.1%) were K-ras mutated. Ten patients (17.9%) obtained a partial response, with a DCR of 41.1%. Skin toxicity of grade ≥2 was observed in 29 patients (51.8%); 13 patients (23.2%) required erlotinib dose reduction after T1 because of grade 3 toxicity (11 patients between days 7 and 30 of erlotinib and 2 during the 2nd month of therapy). Overall median PFS and OS were 68 days (95%CI 26.5–535 days) and 183 days (95%CI 41.5–535 days), respectively. 3.2. Serum and urine erlotinib levels Erlotinib levels were determined at T1 in all patients both in serum and urinary samples. Drug concentrations were 3.90 [2.13] mol/l and 0.37 [2.90] mmol/mol creat in serum and urine, respectively. At T2, biological samples were available in 46 patients; drug concentrations were significantly lower than those at T1: 2.02
Fig. 1. Scatter plots of erlotinib concentrations in serum (A) and urine (B) samples of patients according to sampling time and skin toxicity. Values are expressed as geometric means. Concentrations are expressed as mol/l in serum and in mmol/mol creat in urine. *p < 0.05, **p < 0.01, Student’s t-test for paired samples, T1 vs. T2. # p = 0.003, Student’s t-test for independent samples, Grade 0–2 vs. Grade 3 at T1.
[4.05] vs. 3.66 [2.17] mol/l (p = 0.005) in serum samples and 0.23 [4.47] vs. 0.38 [2.99] mmol/mol creat (p = 0.008) in urinary samples, respectively (Fig. 1A and B). Serum and urinary drug concentrations were significant correlated both at T1 (r = 0.317, p = 0.017) and at T2 (r = 0.659, p < 0.0001). Looking at smoking status, the urinary erlotinib concentrations at T2 were lower in non-smokers than in current smokers (p = 0.03, Bonferroni’s post hoc test), while in former smokers were not different from both categories. Therefore, smoking status was not considered as a confounding factor at T1 and on serum erlotinib at T2, while was tested for urine at T2. Considering only the 46 patients for which biological samples at all different times were available, differences in serum erlotinib levels were observed according to skin toxicity and sampling time. At T1, patients with grade 3 skin toxicity (Group G3, n = 10) had significantly higher serum drug levels than those with grade 0–2 (Group G0-2, n = 36) [6.84 (2.28) vs. 3.08 (1.97) mol/l, p = 0.003] (Fig. 1A). At T2, erlotinib serum levels of G3 patients (in 9 patients erlotinib dose was reduced from 150 to 100 mg/die between days 7 and 30) were higher than in patients with G0-2, who received all 150 mg/die [2.97 (3.38) vs. 1.82 (4.23) mol/l], although this difference was not statistically significant (Fig. 1A). No significant differences were observed in urine drug levels (Fig. 1B) and no interaction between urinary erlotinib and smoking status was observed. In the two groups of patients with different skin toxicity, a statistically significant decrease in erlotinib serum levels from T1 to T2 was observed (Fig. 1A), whereas, in urine samples (Fig. 1B) the difference was statistically significant only in Group G3 [0.41 (2.967) vs. 0.16 (3.89) mmol/mol creat, p = 0.009]. No interaction between urinary erlotinib and smoking status was observed. Patient characteristics and other toxicity (diarrhea) did not influence erlotinib levels except for gender (see supplemental file).
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Fig. 2. (A) Scatter plots of erlotinib concentrations in serum at T1, expressed as geometric means, and skin toxicity; one way ANOVA; (B) receiver operating curve (ROC) curve analysis; the better erlotinib serum value in predicting skin toxicity (Grade 0–2 vs. Grade 3) was 4.60 mol/l with a sensitivity of 70% and a specificity of 70%.
3.3. Serum erlotinib levels and patient outcomes Erlotinib serum levels, cutaneous toxicity (Fig. 2A) and tumor response were correlated at T1 (see supplemental file). An analysis using ROC curves was performed aiming to detect a cut-off value of erlotinib serum level able to predict skin toxicity (grade 0–2 vs. grade 3). Area under the curve (AUC) was 0.706 (95%CI: 0.542–0.870); 4.60 mol/l was the best serum erlotinib value in predicting grade 3 skin toxicity, with a sensitivity of 70% and a specificity of 70% (Fig. 2B). This cut-off was particularly effective in non-smokers with a sensitivity of 85.7% and a specificity of 82.4%. The AUC of ROC curve, performed aiming to obtain a cut-off value of erlotinib serum level able to predict DCR vs. PD, was 0.605 and the test was not statistically significant.
Group G3 experienced a statistically significant benefit vs. Group G0-2 in terms of PFS [239 (46–562) vs. 61 (15–535) days, p = 0.009, Fig. 3A] and OS [330 (92.0–562) vs. 140 (31.0–535) days, p = 0.006, Fig. 3B]. This benefit was statistically significant also when PFS and OS were correlated to all grades of skin toxicity (p = 0.029, Fig. 3C; p < 0.0001, Fig. 3D, respectively). The results of univariate analysis of PFS and OS were reported in Table 2 using Cox’s Hazard model; only skin toxicity (G3 vs. G0-2) was significant factor both for OS and PFS. 3.4. Analysis of CYP3A4/5 metabolic phenotype No significant differences were observed in all patients’ CYP3A4/5 activity at different times, even if an increased trend
Fig. 3. Kaplan–Meier analysis for progression-free survival (PFS; A and C) and overall survival (OS; B and D) according to skin toxicity (A and B Grade 0–2 vs. Grade 3; C and D all grades).
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Table 2 Results of univariate analysis of prognostic value to progression-free survival and overall survival using Cox’s hazard model. Factors
Age ( vs. ≤5.65)
PFS
OS
HR
95% CI
p
HR
1.297 0.487 0.667 0.478 0.940 0.272 1.765 2.065
0.563–2.987 0.237–1.003 0.220–2.019 0.225–1.015 0.410–2.156 0.098–0.755 0.852–3.657 0.871–4.897
0.541 0.051 0.474 0.055 0.885 0.012 0.127 0.100
1.206 0.778 1.117 0.442 0.493 0.230 1.424 1.427
95% CI 0.536–2.711 0.383–1.581 0.362–3.446 0.195–1.000 0.216–1.124 0.080–0.660 0.677–2.996 0.659–3.089
p 0.650 0.488 0.848 0.050 0.093 0.006 0.351 0.366
PFS, progression-free survival; OS, overall survival; HR, hazard ratio; CI, confidence interval; ADC, adenocarcinoma; others*, EGFR wild-type and unknown.
in the ratio values, related to an enhanced enzyme activity, was observed from T0 to T2. However, in Group G3 CYP3A4/5 activity significantly increased from T0 to T1, but decreased from T1 to T2 (see supplemental file and supplemental Fig. S1). According to skin toxicity, statistically significant differences in the 6-OH-cortisol/cortisol ratio between Group G3 and Group G02 at T0 [3.14 (2.29) vs. 5.96 (2.40), p = 0.044] and at T2 [4.14 (1.80) vs. 7.62 (2.29), p = 0.035] were observed. An inverse correlation between serum erlotinib levels and urinary 6-OH-cortisol/cortisol ratio was observed in Group G3 at each sampling time (Fig. 4); the Pearson correlation coefficients were r = −0.694 (p = 0.026) at T1 and r = −0.757 (p = 0.011) at T2. 3.5. EGFR and K-ras mutation status and patient outcomes EGFR gene status was assessed in 28 (50%) patients but only for 21 of them all biological samples were available (14 patients were wild-type and 7 were mutated, 5 in exon 19 and 2 in exon 21). At T1, mutated patients had higher erlotinib serum levels then wildtype ones [5.71 (2.32) vs. 3.52 (2.32) mol/l], even if the difference
was not statistically significant different (p = 0.231). Conversely, urinary erlotinib concentrations were lower in mutated patients then in wild-type ones [0.20 (1.78) vs. 0.52 (4.68) mmol/mol creat, p = 0.059]. No other significant differences in erlotinib levels were observed according to EGFR status (data not shown). According to skin toxicity, EGFR mutated patients were 3 in Group G3 (3 out of 7, 42%), while EGFR wild-type ones were 2 in Group G3 (2 out of 14, 14%). Mutated patients had a significant longer PFS than EGFR wild-type ones [182 (47–562) vs. 50.5 (16–535) days, p = 0.015] and this difference was confirmed also in the subgroup with G3 skin toxicity (p = 0.010). We observed a longer OS for EGFR mutated vs. wild-type patients, but this difference was not statistically significant (p = 0.136). Considering EGFR mutated patients, a significantly benefit in terms of PFS was reported in G3 vs. G0-2 (p = 0.040). EGFR status did not influenced patients’ CYP3A4/5 activity at different times. Univariate analysis of PFS and OS using Cox’s Hazard model in the patient group with known EGFR status were reported in Supplemental Table S2; skin toxicity (G3 vs. G0-2) confirmed its prognostic role both in PFS and OS.
Fig. 4. Relationships between metabolic phenotype, as 6-OH-cortisol/cortisol ratio in urine samples, and erlotinib serum level at T1 (A, B) and at T2 (C, D) distinguished by skin toxicity (Grade 0–2, A and C; Grade 3, B and D).
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K-ras gene status was assessed in 26 (46%) patients but only for 20 of them all biological samples (T0, T1 and T2) were available. Considering that only 2 out of 20 patients were K-ras mutated, no statistically significant correlations were observed (data not shown).
4. Discussion Regardless of the presence of EGFR sensitizing gene mutations, the appearance of skin rash might be an early factor able to identify a subset of NSCLC patients with a higher probability of obtaining a benefit from erlotinib treatment [6–10,23,24]. Unfortunately, its appearance is variable and unpredictable due to several factors including pharmacokinetics and pharmacogenetic heterogeneity [16,17,25–27], drug interactions [28,29] and smoking habits [17,30]. We assessed erlotinib pharmacokinetic and correlated drug serum and urine levels to toxicity and clinical outcomes in advanced NSCLC patients aiming to identify predictive factors of erlotinib efficacy. Patients who developed skin toxicity, in particular those with grade 3, had a significant advantage in PFS and OS, confirming its role as potential predictive factor in erlotinib treatment [6–8,10,23,24]. To assess whether skin toxicity might reflect differences in erlotinib pharmacokinetics, we studied serum and urine drug level in all patients and in patients with different skin toxicity at three time points. T0 was chosen to avoid confounding factors and to characterize the metabolic phenotype before erlotinib assumption; T1 is the expression of unbiased drug level before the occurrence of skin rash, that usually display its maximum intensity during week 2 [6]; T2 was scheduled at the monthly standard clinical follow-up visit. Patients with grade 3 skin toxicity had significantly higher drug levels than those with grade 0–2 translating into improved survival. In particular, erlotinib serum levels at day 7 were able to predict, with a sensitivity and a specificity of 70%, the appearance of grade 3 skin toxicity, similarly at others studies [16,31,32]; sensitivity and specificity were higher considering only non-smokers. Erlotinib levels were significantly higher after a week of treatment than at 30 days in all patients and within groups of skin toxicity. Accordingly, cutaneous toxicity tended to show up within one week, to achieve its peak after approximately two weeks and then to decrease. These results, as for other target therapies [33], encourage studies of drug monitoring to determine the best effective dose for each patient. The relationships between higher serum erlotinib levels and skin toxicity may be correlated to individual characteristics, such as genetic polymorphisms that may affect drug metabolism [16]. However, the main enzymes involved in erlotinib metabolism (CYP3A4/5, CYP1A2 and CYP1A1) show a high inducibility by erlotinib itself [34] and other several substrates. Moreover, the respective genetic polymorphisms potentially affecting the metabolic phenotype are somewhat rare in Caucasian population. Both these reasons induced us to prefer the evaluation of metabolic phenotype instead of genetic polymorphisms. The 6OH-cortisol/cortisol ratio is a marker of the CYP3A4/5 activity; in particular, low values of the urinary ratio indicate a reduced enzymatic activity, whereas increasing ratio levels are related to an enzyme induction. Erlotinib is a substrate of CYP3A4/5 enzymes and as expected, we observed an inverse correlation between serum erlotinib levels and metabolic phenotype, but only in the group of G3. Moreover, we observed an increase in the 6-OH-cortisol/cortisol ratio related to decreasing plasma concentrations of erlotinib between T1 and T2, suggesting an induction of CYP3A4/5, probably dose dependent
[34]. Surprisingly, we observed an increase of enzymes activity in the group G3 in which we had the higher level of unmetabolized drug and a negative correlation between serum erlotinib levels and metabolic phenotype. In our opinion, these data support the hypothesis that in these patients the metabolism of erlotinib is to a some extent inhibited. Our data do not allow a better characterization of this inhibition, if due to genetic or acquired factors. Similar evidences have been reported [35], with a common limitation that the phenotypic metabolic feature is conditioned by several factors, such as other drugs and foods, possible inducers or inhibitors of enzymatic activity. Response to erlotinib treatment is highly dependent on EGFR gene mutations, that represent the main predictors of TKI efficacy. Therefore, we analyzed our data according to EGFR mutational status. This was available in only 50% patients, a common drawback in clinical practice where small tumor samples, not adequate for molecular analysis, represent often the only tissue available. Considering that the majority of patients in this study were female and never smokers, there is a reasonable probability that EGFR mutation rate in this population is higher than expected (10–15%) [3]: indeed in the 28 patients screened, EGFR mutations were detected in 32% of cases. We demonstrated that patients with EGFR mutated tumors had higher erlotinib serum levels when compared to EGFR wild-type ones. Higher affinity of erlotinib with EGFR mutated versus wild-type form could justify a minor drug biodisponibility to metabolization pathway and an increase in serum sample levels of subjects with EGFR mutated tumors. Further studies are necessary to confirm this explanation. Again, in the mutated group, we confirmed the role of skin toxicity as a potential predictive factor in erlotinib treatment. Probably, also in EGFR mutated patients, the different pharmacokinetic parameters and dosing are relevant, considering that might affect the evolution of TKI resistance [36]. Despite the main limitations of this study, such as the small sample size, even though it represents one of the few studies on this pharmacokinetic topic [16,36,37], and the limited number of sampling time points, although this represents an easier clinical applicability, we consider our results promising. In conclusion, erlotinib serum level and 6-OH-cortisol/cortisol ratio assessment after 7 days of erlotinib treatment could be useful to identify patients showing a higher probability to develop skin toxicity and to predict clinical outcome early in unselected population with pretreated advanced NSCLC. Early identification of erlotinib activity through serum samples might help to develop more effective therapy tailoring especially in NSCLC patients in whom genetic analysis of tumor tissue in unfeasible. Our findings may support further studies where erlotinib dosing is tailored according to pharmacokinetic parameters.
Conflict of interest statement No conflicts of interest for all other authors.
Acknowledgments This work was supported by Associazione Italiana per la Ricerca sul Cancro (AIRC), Milan grant IG 8856.
Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at http://dx.doi.org/10.1016/j.lungcan. 2013.12.001.
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Correlation between erlotinib pharmacokinetics, cutaneous toxicity and clinical outcomes in patients with advanced non-small cell lung cancer (NSCLC) Marcello Tiseo a,∗ , Roberta Andreoli b,c , Francesco Gelsomino a , Paola Mozzoni b,c , Cinzia Azzoni d , Marco Bartolotti a , Beatrice Bortesi a , Matteo Goldoni b,c , Enrico Maria Silini d , Giuseppe De Palma e , Antonio Mutti b , Andrea Ardizzoni a,∗ a
Oncology Unit, University Hospital, Parma, Italy Department of Clinical and Experimental Medicine, University Hospital, Parma, Italy c INAIL Research Area, CERT, University of Parma, Parma, Italy d Section of Pathological Anatomy, University Hospital, Parma, Italy e Department of Experimental and Applied Medicine, Section of Occupational Health and Industrial Hygiene, University of Brescia, Brescia, Italy b
a r t i c l e
i n f o
Article history: Received 14 September 2013 Received in revised form 30 November 2013 Accepted 3 December 2013 Keywords: Erlotinib NSCLC Pharmacokinetic Skin toxicity
a b s t r a c t Objectives: An association between skin toxicity and outcome has been reported for NSCLC patients treated with erlotinib. Several explanations have been suggested, including pharmacokinetic and pharmacogenomic variability. The purposes of this study were to characterize erlotinib pharmacokinetic and to correlate drug serum and urine levels to toxicity and outcomes in advanced NSCLC patients. Methods: Patients with stage IV NSCLC consecutively treated with erlotinib in second- or third-line were enrolled. Biological samples (blood, urine and tumor specimens) were collected. Erlotinib levels in serum and urine samples of all patients after 7 (T1) and 30 (T2) days of treatment were quantified by LC–MS/MS analysis, along with urinary 6-hydroxycortisol/cortisol ratio, as marker of metabolic phenotype of the CYP3A4/5 enzyme. Results: 56 patients were recruited and for 46 all samples were available. At T1 erlotinib levels were 3.90 [2.13] mol/l and 0.37 [2.90] mol/mol creat in serum and urinary samples, respectively; at T2 drug concentrations were significantly lower (2.02 [4.05] mol/l and 0.23 [4.47] mol/mol creat, respectively). Patients with grade 3 skin toxicity showed serum T1 drug levels significantly higher than those with grade 0–2 (6.84 [2.28] vs. 3.08 [1.97] mol/l, respectively, p = 0.004) and had longer progression-free and overall survival. An inverse correlation between erlotinib serum levels and urinary 6-hydroxycortisol/cortisol ratio was observed in patients with grade 3 skin toxicity. Conclusions: These findings suggest that the pharmacokinetics and metabolism of erlotinib are related to skin toxicity and may support further studies where erlotinib dosing is tailored according to pharmacokinetic parameters. © 2013 Elsevier Ireland Ltd. All rights reserved.
1. Introduction Recently, epidermal growth factor receptor (EGFR) tyrosinekinase inhibitors (TKIs), such as erlotinib and gefitinib, have become a treatment option for advanced non-small cell lung cancer (NSCLC) [1,2]. Activating mutations of the EGFR tyrosine-kinase domain are a strong predictive factor of response to TKIs [2,3]. Therefore, the availability of sufficient tumor tissue to perform this mutational analysis represents a priority in the therapeutic strategy of
∗ Corresponding authors at: Oncology Unit, University Hospital of Parma, Via Gramsci 14, 43100 Parma, Italy. Tel.: +39 0521 702316; fax: +39 0521 995448. E-mail addresses: [email protected] (M. Tiseo), [email protected] (A. Ardizzoni). 0169-5002/$ – see front matter © 2013 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.lungcan.2013.12.001
advanced NSCLC, in particular in first-line and maintenance settings. However, for many NSCLC patients EGFR gene status remains unknown, since only small tumor samples, not adequate for molecular analysis, are available. Moreover, EGFR TKIs, in particular erlotinib, demonstrated to be effective also in a small percentage of patients without EGFR mutations [1,4]. In fact, erlotinib was proven to be effective in secondand third-line treatments and also in maintenance therapy of unselected patients with advanced NSCLC, including EGFR wild type ones [1,5]. At present, there are no known pre-treatment predictive factors to identify the minority of patients with advanced EGFR wild-type NSCLCs who may derive some benefit from erlotinib treatment. Therefore, early predictors of efficacy during erlotinib treatment
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would be very useful in the clinical approach to EGFR wild-type patients or those with unknown EGFR status. Several trials indicated skin toxicity as a potential predictive factor of better outcome in NSCLC patients treated with EGFR TKIs, such as erlotinib [6–11]. In a phase I study, 73% of patients treated with erlotinib experienced skin toxicity and these had drug plasma concentrations at 24 h significantly higher than those with no skin toxicity [11]. In a phase II study, skin rash of any grade was registered in 75% of 57 NSCLC patients treated with erlotinib and its appearance and severity resulted a predictive factor of better tumor response and survival [6]. Also in the phase III trial BR.21, a significant correlation between development of rash and erlotinib efficacy was observed [1,7]. In fact, response rates (RRs) were higher in patients with skin rash of grade ≥2 than those with skin rash of grade 1 or 0 (13% vs. 10% vs. 0%, respectively) as well as progression-free (PFS) (4.0 vs. 3.2 vs. 1.7 months, respectively; p < 0.001) and overall survival (OS) were longer (11.1 vs. 7.1 vs. 3.3 months, respectively; p < 0.001) [7]. Similar results were reported in Italian data of TRUST study [8]. The association between rash and clinical outcome has been observed in other tumors treated with either erlotinib or other EGFR inhibitors [12–14]. Factors predicting the development of rash are unknown and the association between rash and outcome remains so far unexplained. Several explanations have been suggested, including pharmacokinetic and pharmacogenomic variability [15,16]. Given the scarcity of data on the correlation between erlotinib effects and its pharmacokinetic parameters [11,16,17], we studied erlotinib serum and urine levels in patients with advanced NSCLC and sought to identify possible relationship with toxicity and clinical outcomes aiming to identify novel early predictive factors of drug efficacy.
2. Patients and methods 2.1. Patient population Patients with advanced NSCLC candidate to erlotinib treatment were enrolled prospectively from January 2008 to July 2010. Inclusion criteria were in accordance with standard eligibility criteria for erlotinib treatment in pretreated patients [18]: age 18 years or more, stage IV measurable disease, Eastern Cooperative Oncology Group (ECOG) performance status (PS) of 0–2, progression after at least one line of chemotherapy, adequate liver and renal function. Erlotinib was administered orally at the dose of 150 mg once daily until disease progression, or unacceptable toxicity. Clinical and biochemical evaluation were performed monthly until treatment discontinuation. Patients underwent CT scan evaluation every two months or in case of clinical suspicion of disease progression. Radiological response was assessed by CT scan according to RECIST (Response Evaluation Criteria in Solid Tumors) version 1.0 criteria [19]. Toxicities were managed by symptomatic treatment and/or erlotinib dose interruptions or reductions as appropriate on the basis of the erlotinib prescribing information [18]; in case of grade 3 toxicity (according to the National Cancer Institute’s Common Terminology Criteria for Adverse Events version 3.0 [CTCAE]), dosage was reduced to 100 mg/day and 50 mg/day and discontinued in case of CTCAE grade 4 toxicity. No routine prophylactic skin treatment was prescribed. The study was approved by the local ethical review board and conducted according to Good Clinical Practice guidelines and to the Declaration of Helsinki. Written informed consent was obtained from all patients.
2.2. Pharmacokinetic and molecular biology methods Blood and urine samples were collected, between 8.00 and 10.00 a.m. before erlotinib administration, at three different times: T0, baseline; T1, after 7 days of erlotinib therapy, before the 8th dose; and T2, after 30 days of therapy. Erlotinib levels in serum and urine samples of patients were quantified by liquid chromatography tandem mass spectrometry (LC–MS/MS) analysis at T0, T1 and T2 [20] (see supplemental file). To characterize the metabolic phenotype of CYP3A4/5, we determined urinary 6-hydroxycortisol (6-OH-cortisol) and cortisol concentrations by LC–MS/MS at T0, at T1 and at T2 and calculated their ratio [21] (see supplemental file). EGFR and K-ras gene mutation assessment was performed on available cytological and histological specimens [22]. 2.3. Statistical methods Statistical analysis was carried out with the SPSS software (version 18.0 for Windows® , Chicago, IL, USA). Log-normal distribution was assessed by the Kolmogorov–Smirnov test and the logarithm of variables was used for all the inferential statistics. Data were calculated both as geometric mean (GM) and geometric standard deviation (GSD) and as median and interquartile range; in the paper, data were expressed only as GM and GSD. Comparisons between T1 vs. T2 were calculated on 46 patients, all other statistical analyses were carried out on 56 patients. When a between subjects’ factors was used (e.g. smoking status), a mixed effects ANOVA was used, testing the interaction between the repeated measures and the independent factor. Differences between two groups were assessed using the Student’s t-test for independent samples, and one-way ANOVA followed by multiple comparison tests (Bonferroni post hoc test) was applied in the case of comparisons among more than two groups; the Pearson’s r was used to assess the correlation between variables. Receiver Operating Characteristic (ROC) curves were built to evaluate the efficacy of an erlotinib cut-off value predicting a severe skin toxicity and response to treatment. The areas under ROC curves of drug concentrations for the prediction of a severe skin toxicity (Grade 0–2 versus Grade 3) and response (DCR, complete response + partial response + stable disease according RECIST criteria vs. PD, progression of disease) were calculated with 95% confidence interval (CI). The diagnostic accuracy values of sensitivity and specificity were determined at the flex point of the curve (sensitivity + specificity = maximum). To assess the relationship between treatment protocols and disease progression or mortality, the Kaplan–Meier survival curves were compared with the log-rank test. The multivariate Cox proportional hazard model was used to determine the potential variables able to predict disease progression or mortality, considering age, gender, EGFR status, smoking habits, histopathology, skin toxicity, biological parameters such as serum concentration levels of erlotinib and urinary 6-OH-cortisol/cortisol ratio at T1. The hazard ratio (HR) of disease progression or death and 95%CI were obtained. For continuous variables (erlotinib serum levels and urinary 6-OH-cortisol/cortisol ratio) the geometric mean was chosen as cut off. The level of significance was set at p < 0.05 for all performed tests. 3. Results 3.1. Patient characteristics The main characteristics of the 56 patients are reported in Table 1. EGFR and K-ras gene status was assessed in 28 (50%) and 24 (42.8%) cases, respectively, due to insufficient amount of tumor
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Table 1 Demographic, clinico-pathologic and molecular patients’ characteristics. No. of patients
56
Mean age (±SD)
65.6 (±9.14)
Gender – no. (%) Male Female
21 (37.5) 35 (62.5)
ECOG performance status score – no. (%) 0–1 2
46 (82.2) 10 (17.8)
Smoking habit – no. (%) Smokers Former smokers Never-smokers
11 (19.6) 21 (37.5) 24 (42.9)
Histology – no. (%) ADC SQCC NOS
43 (76.8) 6 (10.7) 7 (12.5)
EGFR status – no. (%) Mutated/Wild-type/Unknown
9 (16.1)/19 (33.9)/28(50)
K-RAS status – no. (%) Mutated/Wild-type/Unknown Stage IV– no. (%)
4 (7.1)/22(39.3)/30(53.6) 56 (100)
Erlotinib treatment line – no. (%) II III
26 (46.4) 30 (53.6)
OS Median [25th-75th] PFS Median [25th-75th]
183 [87.5–293] 68 [46.3–182]
Response – no. (%) PR SD PD
10 (17.9) 13 (23.2) 33 (58.9)
Dose reduction – no. (%) Yes No
13 (23.2) 43 (76.8)
Cutaneous toxicity – no. (%) G0 G1 G2 G3
15 (26.8) 12 (21.4) 16 (28.6) 13 (23.2)
Diarrhea – no. (%) G0 G1 G2
37 (66.1) 16 (28.6) 3 (5.3)
SD, standard deviation; ADC, adenocarcinoma; SQCC, squamous cell carcinoma; NOS, not otherwise specified carcinoma; OS, overall survival; PFS, progression-free survival; PR, partial response; SD, stable disease; PD, progression disease.
cells or poor quality of DNA in the remaining specimens. Nine patients (16.1%) were EGFR-mutated (6 cases of exon 19 deletion, 1 case of exon 20 insertion and 2 cases of L858R mutation in exon 21). Four other tumors (7.1%) were K-ras mutated. Ten patients (17.9%) obtained a partial response, with a DCR of 41.1%. Skin toxicity of grade ≥2 was observed in 29 patients (51.8%); 13 patients (23.2%) required erlotinib dose reduction after T1 because of grade 3 toxicity (11 patients between days 7 and 30 of erlotinib and 2 during the 2nd month of therapy). Overall median PFS and OS were 68 days (95%CI 26.5–535 days) and 183 days (95%CI 41.5–535 days), respectively. 3.2. Serum and urine erlotinib levels Erlotinib levels were determined at T1 in all patients both in serum and urinary samples. Drug concentrations were 3.90 [2.13] mol/l and 0.37 [2.90] mmol/mol creat in serum and urine, respectively. At T2, biological samples were available in 46 patients; drug concentrations were significantly lower than those at T1: 2.02
Fig. 1. Scatter plots of erlotinib concentrations in serum (A) and urine (B) samples of patients according to sampling time and skin toxicity. Values are expressed as geometric means. Concentrations are expressed as mol/l in serum and in mmol/mol creat in urine. *p < 0.05, **p < 0.01, Student’s t-test for paired samples, T1 vs. T2. # p = 0.003, Student’s t-test for independent samples, Grade 0–2 vs. Grade 3 at T1.
[4.05] vs. 3.66 [2.17] mol/l (p = 0.005) in serum samples and 0.23 [4.47] vs. 0.38 [2.99] mmol/mol creat (p = 0.008) in urinary samples, respectively (Fig. 1A and B). Serum and urinary drug concentrations were significant correlated both at T1 (r = 0.317, p = 0.017) and at T2 (r = 0.659, p < 0.0001). Looking at smoking status, the urinary erlotinib concentrations at T2 were lower in non-smokers than in current smokers (p = 0.03, Bonferroni’s post hoc test), while in former smokers were not different from both categories. Therefore, smoking status was not considered as a confounding factor at T1 and on serum erlotinib at T2, while was tested for urine at T2. Considering only the 46 patients for which biological samples at all different times were available, differences in serum erlotinib levels were observed according to skin toxicity and sampling time. At T1, patients with grade 3 skin toxicity (Group G3, n = 10) had significantly higher serum drug levels than those with grade 0–2 (Group G0-2, n = 36) [6.84 (2.28) vs. 3.08 (1.97) mol/l, p = 0.003] (Fig. 1A). At T2, erlotinib serum levels of G3 patients (in 9 patients erlotinib dose was reduced from 150 to 100 mg/die between days 7 and 30) were higher than in patients with G0-2, who received all 150 mg/die [2.97 (3.38) vs. 1.82 (4.23) mol/l], although this difference was not statistically significant (Fig. 1A). No significant differences were observed in urine drug levels (Fig. 1B) and no interaction between urinary erlotinib and smoking status was observed. In the two groups of patients with different skin toxicity, a statistically significant decrease in erlotinib serum levels from T1 to T2 was observed (Fig. 1A), whereas, in urine samples (Fig. 1B) the difference was statistically significant only in Group G3 [0.41 (2.967) vs. 0.16 (3.89) mmol/mol creat, p = 0.009]. No interaction between urinary erlotinib and smoking status was observed. Patient characteristics and other toxicity (diarrhea) did not influence erlotinib levels except for gender (see supplemental file).
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Fig. 2. (A) Scatter plots of erlotinib concentrations in serum at T1, expressed as geometric means, and skin toxicity; one way ANOVA; (B) receiver operating curve (ROC) curve analysis; the better erlotinib serum value in predicting skin toxicity (Grade 0–2 vs. Grade 3) was 4.60 mol/l with a sensitivity of 70% and a specificity of 70%.
3.3. Serum erlotinib levels and patient outcomes Erlotinib serum levels, cutaneous toxicity (Fig. 2A) and tumor response were correlated at T1 (see supplemental file). An analysis using ROC curves was performed aiming to detect a cut-off value of erlotinib serum level able to predict skin toxicity (grade 0–2 vs. grade 3). Area under the curve (AUC) was 0.706 (95%CI: 0.542–0.870); 4.60 mol/l was the best serum erlotinib value in predicting grade 3 skin toxicity, with a sensitivity of 70% and a specificity of 70% (Fig. 2B). This cut-off was particularly effective in non-smokers with a sensitivity of 85.7% and a specificity of 82.4%. The AUC of ROC curve, performed aiming to obtain a cut-off value of erlotinib serum level able to predict DCR vs. PD, was 0.605 and the test was not statistically significant.
Group G3 experienced a statistically significant benefit vs. Group G0-2 in terms of PFS [239 (46–562) vs. 61 (15–535) days, p = 0.009, Fig. 3A] and OS [330 (92.0–562) vs. 140 (31.0–535) days, p = 0.006, Fig. 3B]. This benefit was statistically significant also when PFS and OS were correlated to all grades of skin toxicity (p = 0.029, Fig. 3C; p < 0.0001, Fig. 3D, respectively). The results of univariate analysis of PFS and OS were reported in Table 2 using Cox’s Hazard model; only skin toxicity (G3 vs. G0-2) was significant factor both for OS and PFS. 3.4. Analysis of CYP3A4/5 metabolic phenotype No significant differences were observed in all patients’ CYP3A4/5 activity at different times, even if an increased trend
Fig. 3. Kaplan–Meier analysis for progression-free survival (PFS; A and C) and overall survival (OS; B and D) according to skin toxicity (A and B Grade 0–2 vs. Grade 3; C and D all grades).
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Table 2 Results of univariate analysis of prognostic value to progression-free survival and overall survival using Cox’s hazard model. Factors
Age ( vs. ≤5.65)
PFS
OS
HR
95% CI
p
HR
1.297 0.487 0.667 0.478 0.940 0.272 1.765 2.065
0.563–2.987 0.237–1.003 0.220–2.019 0.225–1.015 0.410–2.156 0.098–0.755 0.852–3.657 0.871–4.897
0.541 0.051 0.474 0.055 0.885 0.012 0.127 0.100
1.206 0.778 1.117 0.442 0.493 0.230 1.424 1.427
95% CI 0.536–2.711 0.383–1.581 0.362–3.446 0.195–1.000 0.216–1.124 0.080–0.660 0.677–2.996 0.659–3.089
p 0.650 0.488 0.848 0.050 0.093 0.006 0.351 0.366
PFS, progression-free survival; OS, overall survival; HR, hazard ratio; CI, confidence interval; ADC, adenocarcinoma; others*, EGFR wild-type and unknown.
in the ratio values, related to an enhanced enzyme activity, was observed from T0 to T2. However, in Group G3 CYP3A4/5 activity significantly increased from T0 to T1, but decreased from T1 to T2 (see supplemental file and supplemental Fig. S1). According to skin toxicity, statistically significant differences in the 6-OH-cortisol/cortisol ratio between Group G3 and Group G02 at T0 [3.14 (2.29) vs. 5.96 (2.40), p = 0.044] and at T2 [4.14 (1.80) vs. 7.62 (2.29), p = 0.035] were observed. An inverse correlation between serum erlotinib levels and urinary 6-OH-cortisol/cortisol ratio was observed in Group G3 at each sampling time (Fig. 4); the Pearson correlation coefficients were r = −0.694 (p = 0.026) at T1 and r = −0.757 (p = 0.011) at T2. 3.5. EGFR and K-ras mutation status and patient outcomes EGFR gene status was assessed in 28 (50%) patients but only for 21 of them all biological samples were available (14 patients were wild-type and 7 were mutated, 5 in exon 19 and 2 in exon 21). At T1, mutated patients had higher erlotinib serum levels then wildtype ones [5.71 (2.32) vs. 3.52 (2.32) mol/l], even if the difference
was not statistically significant different (p = 0.231). Conversely, urinary erlotinib concentrations were lower in mutated patients then in wild-type ones [0.20 (1.78) vs. 0.52 (4.68) mmol/mol creat, p = 0.059]. No other significant differences in erlotinib levels were observed according to EGFR status (data not shown). According to skin toxicity, EGFR mutated patients were 3 in Group G3 (3 out of 7, 42%), while EGFR wild-type ones were 2 in Group G3 (2 out of 14, 14%). Mutated patients had a significant longer PFS than EGFR wild-type ones [182 (47–562) vs. 50.5 (16–535) days, p = 0.015] and this difference was confirmed also in the subgroup with G3 skin toxicity (p = 0.010). We observed a longer OS for EGFR mutated vs. wild-type patients, but this difference was not statistically significant (p = 0.136). Considering EGFR mutated patients, a significantly benefit in terms of PFS was reported in G3 vs. G0-2 (p = 0.040). EGFR status did not influenced patients’ CYP3A4/5 activity at different times. Univariate analysis of PFS and OS using Cox’s Hazard model in the patient group with known EGFR status were reported in Supplemental Table S2; skin toxicity (G3 vs. G0-2) confirmed its prognostic role both in PFS and OS.
Fig. 4. Relationships between metabolic phenotype, as 6-OH-cortisol/cortisol ratio in urine samples, and erlotinib serum level at T1 (A, B) and at T2 (C, D) distinguished by skin toxicity (Grade 0–2, A and C; Grade 3, B and D).
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K-ras gene status was assessed in 26 (46%) patients but only for 20 of them all biological samples (T0, T1 and T2) were available. Considering that only 2 out of 20 patients were K-ras mutated, no statistically significant correlations were observed (data not shown).
4. Discussion Regardless of the presence of EGFR sensitizing gene mutations, the appearance of skin rash might be an early factor able to identify a subset of NSCLC patients with a higher probability of obtaining a benefit from erlotinib treatment [6–10,23,24]. Unfortunately, its appearance is variable and unpredictable due to several factors including pharmacokinetics and pharmacogenetic heterogeneity [16,17,25–27], drug interactions [28,29] and smoking habits [17,30]. We assessed erlotinib pharmacokinetic and correlated drug serum and urine levels to toxicity and clinical outcomes in advanced NSCLC patients aiming to identify predictive factors of erlotinib efficacy. Patients who developed skin toxicity, in particular those with grade 3, had a significant advantage in PFS and OS, confirming its role as potential predictive factor in erlotinib treatment [6–8,10,23,24]. To assess whether skin toxicity might reflect differences in erlotinib pharmacokinetics, we studied serum and urine drug level in all patients and in patients with different skin toxicity at three time points. T0 was chosen to avoid confounding factors and to characterize the metabolic phenotype before erlotinib assumption; T1 is the expression of unbiased drug level before the occurrence of skin rash, that usually display its maximum intensity during week 2 [6]; T2 was scheduled at the monthly standard clinical follow-up visit. Patients with grade 3 skin toxicity had significantly higher drug levels than those with grade 0–2 translating into improved survival. In particular, erlotinib serum levels at day 7 were able to predict, with a sensitivity and a specificity of 70%, the appearance of grade 3 skin toxicity, similarly at others studies [16,31,32]; sensitivity and specificity were higher considering only non-smokers. Erlotinib levels were significantly higher after a week of treatment than at 30 days in all patients and within groups of skin toxicity. Accordingly, cutaneous toxicity tended to show up within one week, to achieve its peak after approximately two weeks and then to decrease. These results, as for other target therapies [33], encourage studies of drug monitoring to determine the best effective dose for each patient. The relationships between higher serum erlotinib levels and skin toxicity may be correlated to individual characteristics, such as genetic polymorphisms that may affect drug metabolism [16]. However, the main enzymes involved in erlotinib metabolism (CYP3A4/5, CYP1A2 and CYP1A1) show a high inducibility by erlotinib itself [34] and other several substrates. Moreover, the respective genetic polymorphisms potentially affecting the metabolic phenotype are somewhat rare in Caucasian population. Both these reasons induced us to prefer the evaluation of metabolic phenotype instead of genetic polymorphisms. The 6OH-cortisol/cortisol ratio is a marker of the CYP3A4/5 activity; in particular, low values of the urinary ratio indicate a reduced enzymatic activity, whereas increasing ratio levels are related to an enzyme induction. Erlotinib is a substrate of CYP3A4/5 enzymes and as expected, we observed an inverse correlation between serum erlotinib levels and metabolic phenotype, but only in the group of G3. Moreover, we observed an increase in the 6-OH-cortisol/cortisol ratio related to decreasing plasma concentrations of erlotinib between T1 and T2, suggesting an induction of CYP3A4/5, probably dose dependent
[34]. Surprisingly, we observed an increase of enzymes activity in the group G3 in which we had the higher level of unmetabolized drug and a negative correlation between serum erlotinib levels and metabolic phenotype. In our opinion, these data support the hypothesis that in these patients the metabolism of erlotinib is to a some extent inhibited. Our data do not allow a better characterization of this inhibition, if due to genetic or acquired factors. Similar evidences have been reported [35], with a common limitation that the phenotypic metabolic feature is conditioned by several factors, such as other drugs and foods, possible inducers or inhibitors of enzymatic activity. Response to erlotinib treatment is highly dependent on EGFR gene mutations, that represent the main predictors of TKI efficacy. Therefore, we analyzed our data according to EGFR mutational status. This was available in only 50% patients, a common drawback in clinical practice where small tumor samples, not adequate for molecular analysis, represent often the only tissue available. Considering that the majority of patients in this study were female and never smokers, there is a reasonable probability that EGFR mutation rate in this population is higher than expected (10–15%) [3]: indeed in the 28 patients screened, EGFR mutations were detected in 32% of cases. We demonstrated that patients with EGFR mutated tumors had higher erlotinib serum levels when compared to EGFR wild-type ones. Higher affinity of erlotinib with EGFR mutated versus wild-type form could justify a minor drug biodisponibility to metabolization pathway and an increase in serum sample levels of subjects with EGFR mutated tumors. Further studies are necessary to confirm this explanation. Again, in the mutated group, we confirmed the role of skin toxicity as a potential predictive factor in erlotinib treatment. Probably, also in EGFR mutated patients, the different pharmacokinetic parameters and dosing are relevant, considering that might affect the evolution of TKI resistance [36]. Despite the main limitations of this study, such as the small sample size, even though it represents one of the few studies on this pharmacokinetic topic [16,36,37], and the limited number of sampling time points, although this represents an easier clinical applicability, we consider our results promising. In conclusion, erlotinib serum level and 6-OH-cortisol/cortisol ratio assessment after 7 days of erlotinib treatment could be useful to identify patients showing a higher probability to develop skin toxicity and to predict clinical outcome early in unselected population with pretreated advanced NSCLC. Early identification of erlotinib activity through serum samples might help to develop more effective therapy tailoring especially in NSCLC patients in whom genetic analysis of tumor tissue in unfeasible. Our findings may support further studies where erlotinib dosing is tailored according to pharmacokinetic parameters.
Conflict of interest statement No conflicts of interest for all other authors.
Acknowledgments This work was supported by Associazione Italiana per la Ricerca sul Cancro (AIRC), Milan grant IG 8856.
Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at http://dx.doi.org/10.1016/j.lungcan. 2013.12.001.
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