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Pharmacogenomic and Pharmacokinetic Determinants of Erlotinib Toxicity

Charles M. Rudin, Wanqing Liu, Apurva Desai, Theodore Karrison, Xuemin Jiang, Linda Janisch, Soma Das, Jacqueline Ramirez, Balasubramanian Poonkuzhali, Erin Schuetz, Donna Lee Fackenthal, Peixian Chen, Deborah K. Armstrong, Julie R. Brahmer, Gini F. Fleming, Everett E. Vokes, Michael A. Carducci, Mark J. Ratain

From the Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD; Departments of Medicine, Health Studies, and Human Genetics, University of Chicago, Chicago, IL; and Department of Pharmaceutical Sciences, St Jude Children's Research Hospital, Memphis, TN

Address reprint requests to Charles M. Rudin, MD, PhD, Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, David H. Koch Cancer Research Building, Room 544, 1550 Orleans St, Baltimore MD 21231; e-mail: rudin{at}jhmi.edu

	ABSTRACT

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Purpose To assess the pharmacogenomic and pharmacokinetic determinants of skin rash and diarrhea, the two primary dose-limiting toxicities of the epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor erlotinib.

Patients and Methods A prospective clinical study of 80 patients with non–small-cell lung cancer, head and neck cancer, and ovarian cancer was performed. Detailed pharmacokinetics and toxicity of erlotinib were assessed. Polymorphic loci in EGFR, ABCG2, CYP3A4, and CYP3A5 were genotyped, and their effects on pharmacokinetics and toxicities were evaluated.

Results A novel diplotype of two polymorphic loci in the ABCG2 promoter involving –15622C/T and 1143C/T was identified, with alleles conferring lower ABCG2 levels associated with higher erlotinib pharmacokinetic parameters, including area under the curve (P = .019) and maximum concentration (P = .006). Variability in skin rash was best explained by a multivariate logistic regression model incorporating the trough erlotinib plasma concentration (P = .034) and the EGFR intron 1 polymorphism (P = .044). Variability in diarrhea was associated with the two linked polymorphisms in the EGFR promoter (P < .01), but not with erlotinib concentration.

Conclusion Although exploratory in nature, this combined pharmacogenomic and pharmacokinetic model helps to define and differentiate the primary determinants of skin and gastrointestinal toxicity of erlotinib. The findings may be of use both in designing trials targeting a particular severity of rash and in considering dose and schedule modifications in patients experiencing dose-limiting toxicities of erlotinib or similarly targeted agents. Further studies of the relationship between germline polymorphisms in EGFR and the toxicity and efficacy of EGFR inhibitors are warranted.

	INTRODUCTION

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Erlotinib is the only epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor currently approved for marketing in the United States. The most common adverse effects of erlotinib are skin rash and diarrhea.^1-3 Both of these toxicities can be severe and can lead to discontinuation of therapy. A strong but unexplained association between skin rash and survival has been noted for patients given erlotinib for several epithelial malignancies, including lung cancer, head and neck cancer, and ovarian cancer.³ Intriguingly, both this toxicity spectrum and the association between rash and clinical benefit have been observed across classes of EGFR inhibitors.

Rash and diarrhea associated with EGFR inhibitor use both demonstrate high interindividual variability. Several potential explanations for this observation have been suggested, including pharmacodynamic and pharmacokinetic (PK) variability.^4,5 Defining determinants of interindividual variability may provide critical insight, guiding the design of future clinical research by defining rational strategies for maximizing clinical benefit and minimizing adverse effects in patients treated with these agents.

Germline polymorphisms can have a major effect on drug pharmacokinetics and pharmacodynamics.⁶ EGFR, encoding the direct target of erlotinib, is highly polymorphic.^7,8 An intronic microsatellite polymorphism has been associated with EGFR expression, with the repeat length of cytosine-adenosine (CA) nucleotides inversely correlating with EGFR mRNA and protein level, as well as erlotinib sensitivity in vitro.^9-12 There are marked interethnic differences at this intronic locus.⁷

More recent studies have identified single nucleotide polymorphisms (SNPs) in the 5'-regulatory region of EGFR. Two, –216G/T (rs712829) and –191C/A (rs712830), are in the essential promoter region of EGFR. The variant –216G/T has been associated with increased EGFR promoter activity and gene expression mediated by an altered interaction with Sp1, whereas –191C/A is close to one of major transcription start sites.⁸ Recently, –216G/T was reported to be associated with gefitinib response and toxicity in lung cancer patients.¹³ A nonsynonymous SNP at codon 497 of EGFR (rs11543848), a G to A alteration, results in substitution of the amino acid Arg (R) by Lys (K).¹⁴ This is the only common missense polymorphism of EGFR reported to date, and the K allele seems to decrease the activity of EGFR.¹⁵ Whether these polymorphisms are involved in the mechanism underlying side effects and responsiveness to EGFR tyrosine kinase inhibitors (TKIs) in cancer patients remains incompletely understood.

Previous studies of EGFR inhibitors found an association between drug steady-state plasma concentrations and the severity of skin rash and diarrhea.^16,17 Variation in genes involved in the pharmacokinetics of TKIs may contribute to these adverse reactions. Erlotinib is a substrate for both CYP3A4 and CYP3A5.¹⁸ These two genes are highly and polymorphically expressed.^19,20 Polymorphisms in the CYP3A5 gene can lead to significant interindividual and interracial differences in CYP3A-dependent drug metabolism.^21,22 CYP3A5*3 is a common A>G transition within intron 3 of CYP3A5 (rs776746), which creates a cryptic splicing site and leads to a truncated CYP3A5 protein production.²¹ G/G homozygotes lack CYP3A5 expression, whereas individuals with at least one wild-type allele (A/A or A/G) express CYP3A5.²¹ A common A>G transition in the 5' regulatory region of CYP3A4 (CYP3A4*1B, rs2740574) has been associated with prostate cancer risk^23-25 and may also moderately increase CYP3A4 activity,²⁶ though a substantial effect of this SNP on the hepatic expression of CYP3A4 has not been demonstrated.^27-30 These two polymorphisms are linked.³¹ It is unknown whether haplotypes of these two SNPs affect the metabolism of erlotinib and influence the interindividual variability in erlotinib toxicity.

In addition to drug metabolizing enzymes, drug transporters may also be involved in the pharmacokinetics of erlotinib. Recent studies suggest that gefitinib and erlotinib are substrates of ABCG2.^32-35 Two nonsynonymous ABCG2 SNPs, 421 C>A (Q141K, rs2231142) and 34G>A (V12M, rs2231137), are common.^36-39 The 141K polymorphism has been associated with lower expression and activity of ABCG2 and with higher accumulation of both gefitinib and erlotinib.^35,36,40 A recent clinical study showed an association between 141K and diarrhea in patients treated with gefitinib.⁴¹ We have recently identified four functional polymorphisms in the 5'-regulatory region of ABCG2 (Poonkuzhali et al, manuscript submitted for publication). The –15994G>A (rs7699188) promoter rSNP (predicted to result in the gain of an HNF4 site) was significantly associated with higher ABCG2 expression in multiple tissues. Carriers of the –15622C>T (novel) rSNP showed lower ABCG2 expression in multiple tissues. An intron 1 SNP 16702G>A (rs2046134) was associated with high expression in liver and was predicted to result in the gain of a GATA4 site. Finally, 1143C>T (rs2622604) was associated with low expression in intestine. Whether these polymorphisms affect the pharmacokinetics of erlotinib and other TKIs has not been reported.

We hypothesized that germline polymorphisms in EGFR and other candidate genes influence erlotinib toxicity. We conducted a prospective study of 80 patients with lung, head and neck, and ovarian cancer receiving standard dose (150 mg daily) erlotinib to evaluate the impact of the genetic polymorphisms mentioned above on skin rash and diarrhea, the two major adverse reactions.

	PATIENTS AND METHODS

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Patients
This was a two-institution study conducted at the University of Chicago and the Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins in Baltimore, MD. The study was reviewed and approved by the institutional review boards of both institutions, and signed informed consent was obtained from all patients. Patients with lung (n = 43), head and neck (n = 9), and ovarian cancer (n = 28) were treated with 150 mg of oral erlotinib once daily.

Genetic Polymorphisms
Four polymorphisms (–216G/T, –191C/A, intron 1 (CA)_n, and 497G/A) in the EGFR gene, CYP3A4*1B, CYP3A5*3, and six polymorphisms (421C/A, 34G/A, –15994G/A, –15622C>T, 16702G/A, and 1143C/T) in the ABCG2 gene were genotyped in the blood DNA (n = 80). Methods for genotyping and haplotype estimation are included in the Appendix (online only).

Erlotinib Pharmacokinetic Analysis
Plasma samples were collected and erlotinib concentration was measured using high-performance liquid chromatography. Details of the assay are included in the Appendix.

Statistics and Data Analysis
Methods for PK data analyses are provided in the Appendix. Logistic regression was used to examine the association between PK parameters and toxicity. t tests and analysis of variance were performed to evaluate the association between the various polymorphisms and PK parameters. Fisher's exact tests were used to analyze the association between genetic polymorphisms and toxicity. Multiple analyses were performed to test for associations under dominant, recessive, and additive genetic models. Multivariable logistic regression models⁴² were fit to examine the effects of genetic polymorphisms on toxicity while controlling for PK. Only statistically significant (P < .05, boldfaced and italicized) or marginally significant (.05 P .10, boldfaced) P values are shown in the tables. Further details regarding the statistical methods are provided in the Appendix.

	RESULTS

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Population PK Modeling: Correlation Between PK Data and Toxicities
Patient characteristics are listed in Appendix Table A1 (online only). Table 1 presents the population parameter estimates. No patient characteristics were significantly associated with any pharmacokinetic parameters.

It should be noted that the number of patients with certain polymorphisms was small (for example, there were only four patients with EGFR497 A/A, seven patients with CYP3A4*1B G/G, five patients with ABCG216,702 A/A, and nine patients with ABCG2-15994 A/A). In the case of EGFR497 A/A, the mean AUC, C_max, and C_trough were noticeably higher than for the other genotypes. Consequently, lack of statistical significance could be due to low statistical power for comparisons involving small sample sizes.

Correlation Between Genetic Polymorphisms and Toxicity
Polymorphisms in the EGFR promoter were associated with both skin rash and diarrhea (Table 4). The two promoter polymorphisms, –216 G/T and –191 C/A, were associated with grade 2 diarrhea (P = .009 and P = .008, respectively, under a dominant model). Similar associations were found when comparing with the –216/-191 diplotypes or haplotypes. Only one of 43 patients with either the 1/1 diplotype (T-C/T-C) or the 1/2,3 diplotype (T-C and either G-C or G-A) had grade 2 diarrhea, compared with eight (23%) of 35 patients in the 2,3/2,3 category (ie, no T-C combination; P = .027 for 2 degrees of freedom test; P = .007 under a recessive model). The relative frequency of haplotype 1 (T-C) was lower in patients with grade 2 diarrhea as compared with patients with grade 0 to 1 diarrhea (P = .003; data not shown).

CYP3A4 polymorphisms were marginally associated with skin rash. Individuals with lower CYP3A4 expression (A/A) were more likely to develop rash (46 [78%] of 59 patients) than those with higher CYP3A4 levels (eight [62%] of 13 of A/Gs and three [43%] of seven of G/G homozygotes; P = .077). Similarly, the CYP3A5*3 G polymorphism was also marginally associated with grade 2 rash (P = .094, dominant model) and any grade diarrhea (P = .062, recessive model). Finally, patients in the 2,3,4/2,3,4 diplotype category had a lower rate of grade 2 skin rash than those in the 1/1 or 1/2,3,4 groups (P = .095, recessive model). The relative frequency of haplotype 1 (A-G; lower CYP3A expression) was marginally higher in the patients with rash than in those without rash (P = .089, data not shown) and significantly higher in those with any grade diarrhea as compared with those without diarrhea (P = .029; data not shown).

A marginally significant association was found between ABCG2 16,702 G/A polymorphism and any grade skin toxicity (P = .089). G/G and A/A patients were more likely to develop toxicity (77% and 80%, respectively) compared with G/A (50%). A more consistent relationship is seen for grade 2 skin rash, with the G/G genotype exhibiting a higher rate of toxicity as compared with the G/A or A/A polymorphisms under a dominance model (P = .027). A marginally significant association was also detected between –15,622 C/T and any grade diarrhea (P = .066): 20 (41%) of 49 C/C patients developed diarrhea, compared with 20 of 31 (65%) C/T or T/T patients.

Multivariate Analysis
Logistic regression analyses were performed to evaluate the effects of genetic polymorphisms on toxicity controlling for PK (and vice versa). Because C_trough was significantly associated with grade 2 rash (Table 2) and C_trough levels captured the majority of associations between polymorphisms and PK (Table 3), this variable was chosen as the PK parameter for multivariate analysis. Results are presented in Table 5 for skin rash and in Table 6 for diarrhea. Because of small numbers, multivariable analysis of grade 2 diarrhea was not performed.

	DISCUSSION

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We undertook this prospective study to evaluate the clinical impact on skin rash and diarrhea of the large number of genetic polymorphisms we and others have identified in genes encoding the target for erlotinib, as well as genes associated with its membrane transport and metabolism.^{7,8,13,17,18,33,41} Our results indicate that determinants of skin toxicity may include trough erlotinib plasma concentration and variability in the EGFR intron 1 polymorphism (P = .034 and P = .044, respectively, under a multivariable model). In contrast, diarrhea was correlated with the two linked polymorphisms in the EGFR promoter (P < .01), but not with erlotinib concentration. We emphasize that a large number of candidate polymorphic loci were evaluated and multiple analyses of each genetic polymorphism were performed. The multiple testing could lead to the detection of spurious associations, and therefore our findings are in need of further confirmation in independent data sets.

Erlotinib is a metabolic substrate for the phase II enzymes CYP3A4 and CYP3A5.¹⁸ However, CYP3A4 and CYP3A5 polymorphisms determining enzyme expression and activity levels demonstrated only marginal associations with either erlotinib pharmacokinetic parameters or observed toxicity.

We report associations between newly discovered polymorphisms in the multidrug transporter gene ABCG2 and erlotinib PK, with lower expressing ABCG2 alleles correlating with increased erlotinib concentrations, and diplotypes of two linked polymorphisms resulting in strong correlations with erlotinib AUC and C_max. Marginal and significant associations were also seen with ABCG2 polymorphic loci and toxicity. We were unable to confirm correlations between previously reported ABCG2 polymorphisms and drug accumulation or diarrhea in patients treated with the related EGFR inhibitor gefitinib.^35,41 It is unclear whether this represents a pharmacologic difference between gefitinib and erlotinib. Both agents are known substrates for ABCG2 transport, and both inhibit ABCG2 activity at high concentration.³⁵

Previous series have reported separately on gene polymorphisms and on pharmacokinetic variability as correlates of toxicity.^13,35,41 An important aspect of the current report is the integrated analysis of genotypic and pharmacokinetic variability. Multivariate analysis to evaluate the potential interactions between putative determinants of toxicity suggests that interindividual pharmacokinetic variability may be a dominant determinant of erlotinib skin toxicity. Pharmacokinetic variability remains a statistically significant determinant of erlotinib toxicity across all polymorphic loci analyzed (P values for C_trough ranging from .026 to .052).

Multivariate analysis further suggests that erlotinib associated-diarrhea may be mechanistically distinct from rash. Rash, but not diarrhea, seemed to be correlated with erlotinib pharmacokinetics. This may reflect that gastrointestinal toxicity from erlotinib is primarily luminal and thus may be relatively independent of erlotinib blood levels. EGFR is highly expressed in intestinal lumen. The observed correlations with EGFR promoter polymorphisms suggest that EGFR expression may be a more important determinant of erlotinib-associated diarrhea than previously recognized.

Taken together, these data indicate that the toxicities experienced by patients taking erlotinib are multifactorial and determined by distinct parameters in different tissues. The interindividual variability in erlotinib pharmacokinetics, a primary correlate of skin toxicity, has not been adequately explained. That skin toxicity in this study was primarily correlated with erlotinib exposure levels, together with the observed association between skin toxicity and survival, supports exploration of therapeutic strategies based on dose escalation of erlotinib to development of clinically significant (grade 2) rash. Clinical outcome of patients enrolled in such studies has not been reported. Alternative determinants of interindividual susceptibility to rash and diarrhea, not evaluated in this study, are likely and remain to be identified. A clear understanding of the basis of variability in toxicity to EGFR-directed therapy may ultimately guide use of the currently available agents at optimal doses and in patients most likely to benefit.

	AUTHORS' DISCLOSURES OF POTENTIAL CONFLICTSOF INTEREST

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Although all authors completed the disclosure declaration, the following author(s) indicated a financial or other interest that is relevant to the subject matter under consideration in this article. Certain relationships marked with a "U" are those for which no compensation was received; those relationships marked with a "C" were compensated. For a detailed description of the disclosure categories, or for more information about ASCO's conflict of interest policy, please refer to the Author Disclosure Declaration and the Disclosures of Potential Conflicts of Interest section in Information for Contributors.

Employment or Leadership Position: None Consultant or Advisory Role: Charles M. Rudin, Genentech (C); Deborah K. Armstrong, Genentech (C); Julie R. Brahmer, Eli Lilly (C), Cephalon (C), Genentech (C); Michael A. Carducci, GlaxoSmithKline (C); Mark J. Ratain, Genentech (C) Stock Ownership: Mark J. Ratain, Appleva, Illumian Honoraria: None Research Funding: Julie R. Brahmer, Wyeth, AstraZeneca, Pfizer, Mederex Expert Testimony: None Other Remuneration: None

	AUTHOR CONTRIBUTIONS

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Conception and design: Charles M. Rudin, Apurva Desai, Theodore Karrison, Mark J. Ratain

Financial support: Michael A. Carducci, Mark J. Ratain

Administrative support: Wanqing Liu, Michael A. Carducci, Mark J. Ratain

Provision of study materials or patients: Charles M. Rudin, Apurva Desai, Deborah K. Armstrong, Julie R. Brahmer, Gini F. Fleming, Everett E. Vokes, Mark J. Ratain

Collection and assembly of data: Charles M. Rudin, Wanqing Liu, Apurva Desai, Linda Janisch, Soma Das, Jacqueline Ramirez, Balasubramanian Poonkuzhali, Erin Schuetz, Donna Lee Fackenthal, Peixian Chen, Deborah K. Armstrong, Julie R. Brahmer, Everett E. Vokes

Data analysis and interpretation: Charles M. Rudin, Wanqing Liu, Apurva Desai, Theodore Karrison, Xuemin Jiang, Soma Das, Balasubramanian Poonkuzhali, Erin Schuetz, Donna Lee Fackenthal, Peixian Chen, Mark J. Ratain

Manuscript writing: Charles M. Rudin, Wanqing Liu, Theodore Karrison, Jacqueline Ramirez, Donna Lee Fackenthal, Peixian Chen, Michael A. Carducci, Mark J. Ratain

Final approval of manuscript: Charles M. Rudin, Wanqing Liu, Apurva Desai, Theodore Karrison, Jacqueline Ramirez, Balasubramanian Poonkuzhali, Erin Schuetz, Donna Lee Fackenthal, Peixian Chen, Deborah K. Armstrong, Julie R. Brahmer, Gini F. Fleming, Everett E. Vokes, Michael A. Carducci, Mark J. Ratain

	Appendix

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Toxicity Assessment and Grading
Toxicity assessment was performed at each study visit by study investigators. All symptomatic and objective toxicities and adverse events were recorded, assigned a relation to study drug, and graded using the National Cancer Institute Common Toxicity Criteria version 2.0

Genotyping of Genetic Polymorphisms
Blood DNA was extracted by using PUREGENE DNA Purification Kit (Gentra Systems, Inc, Minneapolis, MN). Four polymorphisms (-216G/T, -191C/A, intron 1(CA)_n, and 497G/A) in the EGFR gene, CYP3A4*1B, CYP3A5*3 and six polymorphisms (-15994G/A, -15622C/T, intron 1 16702G/A, intron 1 1143C/T, 421C/A and 34G/A) in the ABCG2 gene were genotyped. Polymerase chain reaction (PCR) was performed to amplify the DNA sequences containing the polymorphisms of interest. Genotyping related DNA sequences and annealing temperatures are listed in Appendix Table A2. For -191C/A and -216G/T, PCRs were set up in a 40-µL volume containing 3 mM MgCl₂, 1xQ-solution (Qiagen, Santa Clarita, CA), 100 µM each of dNTP, 125 nM of forward and reverse primers, 1 unit Hotstart Taq DNA polymerase (Qiagen), and 25 ng of DNA. Reactions were denatured initially at 98°C for 10 minutes and then cycled 35 times at 98°C for 15 seconds, annealing at 62°C for 15 seconds and 72°C for 20 seconds. PCR products were then purified and directly sequenced as previously described.¹ For other polymorphisms, PCRs were performed in a 15-µL volume containing 125 nM of each primer for the two PCR amplicons, 30 ng of genomic DNA, 2.5 mM of MgCl2, 100 µM of each dNTP, and 0.375 U of AmpliTa Qgold polymerase (Applied Biosystems, Foster City, CA) in the buffer provided by the manufacturer. Duplex PCR was used to amplify the ABCG2 polymorphisms. Genotyping of the EGFR intron 1 (CA)_n polymorphism was performed as previously described.² Other polymorphisms, EGFR 497G/A, CYP3A4*1B, CYP3A5*3, and ABCG2 polymorphisms were genotyped using single-base extension and denaturing high-performance liquid chromatography (DHPLC). Briefly, amplified PCR products were purified by treatment with shrimp alkaline phosphatase (Roche, Neuilly sur Seine, France) and exonuclease I (USB) at 37°C for 45 minutes before the SBE reaction. SBE reactions were carried out in 12.6 µL containing 1 µM of each SBE primer, 250 µM each of four ddNTPs, 7.2 µL of purified PCR product and 1.5 U of ThermoSequenase (Amersham Pharmacia Biotech) in 1x Reaction buffer provided by the manufacturer. Reactions were run in a 9600 thermal cycler (Applied Biosystems) under the following conditions: 96°C for 2 min, followed by 60 cycles of 96°C for 30 s, 55°C for 30 s and 60°C for 30 s. Wave 3500HT DHPLC system (Transgenomic Inc.) was used for separating SBE products. Before run on DHPLC, the samples were denatured at 96°C for 4 minutes and held at 4°C. For analyzing SBE on Wave DHPLC, 8 µL of SBE products of each sample was injected. We used "Mutation Detection" application type as a template, selected "Normal" clean as clean type (ie, 100% buffer B clean off step after each injection), and manually set the following variables for this application: The flow rate was set at 1.5 mL/min by using an high-throughput column, oven temperature was set at 70°C, and the gradient used for elution of the SBE products was from 24% to 36.5% buffer B over 2.5 minutes (buffer B contains 25% acetonitrile). The extended products were eluted in the order of C<G<T<A dependent on the hydrophobicity differences of the four bases. The known genotype controls were included in each run.

Erlotinib Pharmacokinetic Analysis
Blood samples for pharmacokinetic analysis were collected into sodium heparinized vacutainer tubes on days 1, 15, and 29 of cycle 1. On day 1, samples were drawn before the first treatment dose and 0.5, 1, 2, 4, and 6 hours after treatment initiation. Blood samples were also collected before treatment on days 15 and 29 of the first cycle. Briefly, plasma aliquots (500 µL) were vortexed and centrifuged (650 rcf, 10 minutes, 4°C) after addition of 80 µL of internal standard (3.4 µg/mL of chloroquine) and 5 mL of methyl t-butyl ether. After centrifugation, the analytes were extracted by acidification with 1.2 mL of 5% hydrochloric acid and back-extracted using 1.2 mL of 1 N sodium hydroxide and 5 mL of methyl t-butyl ether. The supernatants were then evaporated to dryness using nitrogen gas (37°C) and reconstituted in 250 µL of mobile phase, and 200 µL aliquots were injected into the high-performance liquid chromatography (Hitachi High Technologies, San Jose, CA). The high-performance liquid chromatography conditions were identical to those published previously,³ with the only difference being that our mobile phase composition was 32/68 (volume/volume) acetonitrile/50 mM of potassium phosphate buffer containing 0.2% triethylamine (pH 4.8). Under our conditions, the retention times of chloroquine and OSI-774 were 2 and 18 minutes, respectively. Erlotinib concentrations were calculated using a standard curve (range, 15.3 to 4103.3 ng/mL). Samples with concentrations that fell outside of the range of the standard curve were reanalyzed by diluting with blank plasma before extraction, and the final concentration values were determined after applying the appropriate dilution factor. Intra-assay reproducibility (% CV = 1.1 to 10.0) and accuracy (range, 94.6 to 107.3) were determined by performing three measurements of the same seven standards on the same day. Inter-assay reproducibility (% CV = 4.5 to 11.2) and accuracy (range, 96.3 to 104.5) were determined by assays of the same seven standards in triplicate on 3 days.

Statistics and Data Analysis
NONMEM software (version V⁴; GloboMax LLC, Hanover, MD) was used for the pharmacokinetic data analyses. Dose, erlotinib concentration, and patient characteristics were fit simultaneously. Covariate models were selected based on the following selection criteria: a reduction of the objective function value (OFV) 3.84 units (P .05, df = 1) for forward inclusion of a covariate in the model and a reduction of OFV 6.64 units (P .01, df = 1) for backward elimination of a covariate from the model; physiologic relevance; decrease in the interpatient variability; and improvement in the diagnostic plots. The first-order conditional estimation method with interaction was used for the model development.

Individual exposures (area under the curve [AUC], maximum concentration [C_max], and trough level [C_trough] at steady-state) were estimated from the final population pharmacokinetic model by use of equations (1), (2), and (3), yielding a one-compartment model with first-order absorption and elimination. The patient characteristics considered for the model included age, height, weight, hematocrit, creatinine, albumin, and total bilirubin.

(1)

(2)

(3)

where AUC is the area under the drug concentration-time curve at steady-state, F is the bioavailability of drug, CL is the total drug clearance, C_SS (t) is the drug concentration at steady state at time (t), k_a is the absorption rate constant, V is the volume of distribution, k is the elimination rate constant, is the dosing interval, and t_{SS, max} is the time at the maximum of concentration.

Logistic regression was performed to examine the association between PK parameters (AUC, C_max, and C_trough) and toxicity (cycle 1 rash and diarrhea). In these and all subsequent analyses, two sets of comparisons were performed, one dichotomizing toxicity as grade 0 versus grade 1 or worse (ie, none v any toxicity) and a second categorizing toxicity as grade 0 or 1 versus grade 2 or worse. t tests and analysis of variance were performed to evaluate the association between the various polymorphisms and PK parameters. As the modes of inheritance were either unknown or not clearly identifiable from the plotted data, four types of analyses were generally conducted, an analysis of variance on two degrees of freedom (df) to test the null hypothesis of equal mean levels of the PK parameters across the three genotypes, followed by t tests and linear regression analysis for the evaluation of dominant, recessive, and additive models. For example, for the hypothetical variant A>B, we have genotypes A/A, A/B, and B/B. In the additive model, the independent variable would be coded as follows: A/A=0, A/B=1, B/B=2. The dominant model would be coded as follows: A/A=0, A/B=1, B/B=1. The recessive model would be coded as follows: A/A=0, A/B=0, B/B=1

Fisher exact tests were used to analyze the association between genetic polymorphisms and toxicity. For the EGFR intron 1 (CA)_n polymorphism, we examined the association between the number of CA repeats and toxicity as follows. The number of CA repeats on a given allele ranged from 14 to 22, with one additional allele having 25 repeats. We prospectively hypothesized that patients with alleles 16 repeats would have a different phenotype from those with longer repeats. We therefore classified patients as "s/s" if the number of repeats was 16 on both alleles, "l/l" if the number of repeats was > 16 on both alleles, and "s/l" if the number of repeats on one allele was 16 and the number for the other allele was > 16. However, we also considered additional cut points, r, of 17, 18, 19, and 20. The data were analyzed using Fisher's exact test as well as Armitage's test for a linear trend in proportions.⁵

P values between .05 and .10 were regarded as marginally suggestive of an association (boldfaced), whereas P < .05 was considered statistically significant (boldfaced and italicized). Only significant or marginally significant P values are shown in the tables, with other results denoted as not significant ("NS"). Note, however, that for the CA repeat analysis, because five different cut points were considered, a Bonferroni correction would require P < .05/5= .01 for statistical significance.

	References

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION AUTHORS' DISCLOSURES OF... AUTHOR CONTRIBUTIONS Appendix References REFERENCES

 
1 Liu W, Innocenti F, Wu MH, et al: A functional common polymorphism in a Sp1 recognition site of the epidermal growth factor receptor gene promoter. Cancer Res 65:46-53, 2005[Abstract/Free Full Text]

2 Liu W, Innocenti F, Chen P, et al: Interethnic difference in the allelic distribution of human epidermal growth factor receptor intron 1 polymorphism. Clin Cancer Res 9:1009-1012, 2003[Abstract/Free Full Text]

3 Zhang W, Siu LL, Moore MJ, et al: Simultaneous determination of OSI-774 and its major metabolite OSI-420 in human plasma by using HPLC and UV detection. J Chromatogr B Analyt Technol Biomed Life Sci 814:143-147, 2005[Medline]

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5 Armitage P: Statistical Methods in Medical Research. Oxford, United Kingdom, Blackwell Scientific Publications, 1971

	NOTES

 
Clinical conduct of this study was supported by National Institutes of Health Grants No. R21 CA10132, U01 CA69852, U01 CA-70095, and GM61393 and by the O'Connor Foundation. Pharmacologic studies were supported by the UCCRC Pharmacology Core Facility (http://pharmacology.bsd.uchicago.edu/) through the University of Chicago Cancer Research Center Cancer Center Support Grant, P30 CA14599.

Authors' disclosures of potential conflicts of interest and author contributions are found at the end of this article.

	REFERENCES

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1. Fukuoka M, Yano S, Giaccone G, et al: Multi-institutional randomized phase II trial of gefitinib for previously treated patients with advanced non-small-cell lung cancer (The IDEAL 1 Trial). J Clin Oncol 21:2237-2246, 2003[Abstract/Free Full Text]

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Submitted June 25, 2007; accepted November 14, 2007.

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