Model-Based Prediction of Irinotecan-Induced Grade 4 Neutropenia in Cancer Patients: Influence of Incorporating Germline Genetic Factors in the Model.

Neutropenia is the major dose-limiting toxicity of irinotecan-based therapy. The objective of this study was to assess whether inclusion of germline genetic variants into a population pharmacokinetic/pharmacodynamic model can improve prediction of irinotecan-induced grade 4 neutropenia and identify novel variants of clinical value. A semimechanistic population pharmacokinetic/pharmacodynamic model was used to predict neutrophil response over time in 197 patients receiving irinotecan. Covariate analysis was performed for demographic/clinical factors and 4,781 genetic variants in 84 drug response- and toxicity-related genes to identify covariates associated with neutrophil response. We evaluated the predictive value of the model for grade 4 neutropenia reflecting different clinical scenarios of available data on identified demographic/clinical covariates, baseline and post-treatment absolute neutrophil counts (ANCs), individual pharmacokinetics, and germline genetic variation. Adding 8 genetic identified covariates (rs10929302 (UGT1A1), rs1042482 (DPYD), rs2859101 (HLA-DQB3), rs61754806 (NR3C1), rs9266271 (HLA-B), rs7294 (VKORC1), rs1051713 (ALOX5), and ABCB1 rare variant burden) to a model using only baseline ANCs improved prediction of irinotecan-induced grade 4 neutropenia from area under the receiver operating characteristic curve (AUC-ROC) of 50-64% (95% confidence interval (CI), 54-74%). Individual pharmacokinetics further improved the prediction to 74% (95% CI, 64-84%). When weekly ANC was available, the identified covariates and individual pharmacokinetics yielded no additional contribution to the prediction. The model including only ANCs at baseline and at week 1 achieved an AUC-ROC of 78% (95% CI, 69-88%). Germline DNA genetic variants may contribute to the prediction of irinotecan-induced grade 4 neutropenia when incorporated into a population pharmacokinetic/pharmacodynamic model. This approach is generalizable to drugs that induce neutropenia and ultimately allows for personalized intervention to enhance patient safety.

Deep Learning Methods Applied to Drug Concentration Prediction of Olanzapine.

Pharmacometrics and the utilization of population pharmacokinetics play an integral role in model-informed drug discovery and development (MIDD). Recently, there has been a growth in the application of deep learning approaches to aid in areas within MIDD. In this study, a deep learning model, LSTM-ANN, was developed to predict olanzapine drug concentrations from the CATIE study. A total of 1527 olanzapine drug concentrations from 523 individuals along with 11 patient-specific covariates were used in model development. The hyperparameters of the LSTM-ANN model were optimized through a Bayesian optimization algorithm. A population pharmacokinetic model using the NONMEM model was constructed as a reference to compare to the performance of the LSTM-ANN model. The RMSE of the LSTM-ANN model was 29.566 in the validation set, while the RMSE of the NONMEM model was 31.129. Permutation importance revealed that age, sex, and smoking were highly influential covariates in the LSTM-ANN model. The LSTM-ANN model showed potential in the application of drug concentration predictions as it was able to capture the relationships within a sparsely sampled pharmacokinetic dataset and perform comparably to the NONMEM model.

Model-Based Prediction of Irinotecan-Induced Grade 4 Neutropenia in Advanced Cancer Patients: Influence of Demographic and Clinical Factors.

Severe neutropenia is the major dose-liming toxicity of irinotecan-based chemotherapy. The objective was to assess to what extent a population pharmacokinetic/pharmacodynamic model including patient-specific demographic/clinical characteristics, individual pharmacokinetics, and absolute neutrophil counts (ANCs) can predict irinotecan-induced grade 4 neutropenia. A semimechanistic population pharmacokinetic/pharmacodynamic model was developed to describe neutrophil response over time in 197 patients with cancer receiving irinotecan. For covariate analysis, sex, race, age, pretreatment total bilirubin, and body surface area were evaluated to identify significant covariates on system-related parameters (mean transit time (MTT) and É£) and sensitivity to neutropenia effects of irinotecan and SN-38 (SLOPE). The model-based simulation was performed to assess the contribution of the identified covariates, individual pharmacokinetics, and baseline ANC alone or with incremental addition of weekly ANC up to 3 weeks on predicting irinotecan-induced grade 4 neutropenia. The time course of neutrophil response was described using the model assuming that irinotecan and SN-38 have toxic effects on bone marrow proliferating cells. Sex and pretreatment total bilirubin explained 10.5% of interindividual variability in MTT. No covariates were identified for SLOPE and γ. Incorporating sex and pretreatment total bilirubin (area under the receiver operating characteristic curve (AUC-ROC): 50%, 95% CI 50-50%) or with the addition of individual pharmacokinetics (AUC-ROC: 62%, 95% CI 53-71%) in the model did not result in accurate prediction of grade 4 neutropenia. However, incorporating ANC only at baseline and week 1 in the model achieved a good prediction (AUC-ROC: 78%, 95% CI 69-88%). These results demonstrate the potential applicability of a model-based approach to predict irinotecan-induced neutropenia, which ultimately allows for personalized intervention to maximize treatment outcomes.

Integration of DNA sequencing with population pharmacokinetics to improve the prediction of irinotecan exposure in cancer patients.

BACKGROUND: Irinotecan (CPT-11) is an anticancer agent widely used to treat adult solid tumours. Large interindividual variability in the clearance of irinotecan and SN-38, its active and toxic metabolite, results in highly unpredictable toxicity. METHODS: In 217 cancer patients treated with intravenous irinotecan single agent or in combination, germline DNA was used to interrogate the variation in 84 genes by next-generation sequencing. A stepwise analytical framework including a population pharmacokinetic model with SNP- and gene-based testing was used to identify demographic/clinical/genetic factors that influence the clearance of irinotecan and SN-38. RESULTS: Irinotecan clearance was influenced by rs4149057 in SLCO1B1, body surface area, and co-administration of 5-fluorouracil/leucovorin/bevacizumab. SN-38 clearance was influenced by rs887829 in UGT1A1, pre-treatment total bilirubin, and EGFR rare variant burden. Within each UGT1A1 genotype group, elevated pre-treatment total bilirubin and/or presence of at least one rare variant in EGFR resulted in significantly lower SN-38 clearance. The model reduced the interindividual variability in irinotecan clearance from 38 to 34% and SN-38 clearance from 49 to 32%. CONCLUSIONS: This new model significantly reduced the interindividual variability in the clearance of irinotecan and SN-38. New genetic factors of variability in clearance have been identified.

Uridine Glucuronosyltransferase 2B7 Polymorphism-Based Pharmacogenetic Dosing of Epirubicin in FEC Chemotherapy for Early-Stage Breast Cancer.

BACKGROUND: Epirubicin is metabolized by uridine glucuronosyltransferase 2B7 (UGT2B7). Patients homozygous for the minor allele (CC) in the UGT2B7 -161 promoter polymorphism have lower clearance and significantly higher rates of leukopenia compared to wild-type homozygote (TT) or heterozygote (CT) patients. This study was designed to determine if TT and CT genotype patients could tolerate a higher epirubicin dose compared to CC genotype patients. PATIENTS AND METHODS: We studied women with histologically confirmed non-metastatic, invasive breast cancer who were scheduled to receive at least three cycles of FE100C in the (neo)adjuvant setting. Patients received standard-dose FE100C during the first 21-day cycle. Based on genotype, the epirubicin dose was escalated in the second and third cycles to 115 and 130 mg/m2 or to 120 and 140 mg/m2 for CT and TT genotype patients, respectively. The main outcome measurements were myelosuppression and dose-limiting toxicity. These were analyzed for relationships with the three genotypes. RESULTS: Forty-five patients were enrolled (10 CC, 21 CT, and 14 TT genotypes) and received 100 mg/m2 of epirubicin in the first cycle. Twelve and 10 TT patients were dose escalated at the second and third cycles, respectively; 16 CT patients were dose escalated at the second and third cycles. Leukopenia, but not febrile neutropenia, was genotype and dose dependent and increased in patients with CT and TT genotypes as their dose was increased. However, the third-cycle leukopenia rates were comparable to patients with the CC genotype receiving standard-dose epirubicin. CONCLUSION: Pharmacogenetically guided epirubicin dosing is well tolerated and allowed dose escalation without increased toxicity.

Optimal Sampling Strategies for Irinotecan (CPT-11) and its Active Metabolite (SN-38) in Cancer Patients.

Irinotecan (CPT-11) is an anticancer agent widely used in the treatment of a variety of adult solid tumors. The objective of this study was to develop an optimal sampling strategy model that accurately estimates pharmacokinetic parameters of CPT-11 and its active metabolite, SN-38. This study included 221 patients with advanced solid tumors or lymphoma receiving CPT-11 single or combination therapy with 5-fluorouracil (5-FU)/leucovorin (LV) (FOLFIRI) plus bevacizumab from 4 separate clinical trials. Population pharmacokinetic analysis of CPT-11 and SN-38 was performed by non-linear mixed effects modeling. The optimal sampling strategy model was developed using D-optimality with expected distribution approach. The pharmacokinetic profiles of CPT-11 and SN-38 were best described by a 3- and 2-compartment model, respectively, with first-order elimination. Body surface area and co-administration with 5-FU/LV plus bevacizumab were significant covariates (p < 0.01) for volumes of the central compartment of CPT-11 and SN-38, and clearance of CPT-11. Pre-treatment total bilirubin and co-administration with 5-FU/LV and bevacizumab were significant covariates (p < 0.01) for clearance of SN-38. Accurate and precise predictive performance (r2 > 0.99, -2 < bias (%ME) < 0, precision (% RMSE) < 12) of both CPT-11 and SN-38 was achieved using: (i) 6 fixed sampling times collected at 1.5, 3.5, 4, 5.75, 22, 23.5 hours post-infusion; or (ii) 1 fixed time and 2 sampling windows collected at 1.5, [3-5.75], [22-23.5] hours post-infusion. The present study demonstrates that an optimal sampling design with three blood samples achieves accurate and precise pharmacokinetic parameter estimates for both CPT-11 and SN-38.

Mathematical modeling of intracrine androgen metabolism in prostate cancer: Methodological aspects.

BACKGROUND: Progression of castration-recurrent/resistant prostate cancer (CRPC) relies in part on dihydrotestosterone derived from intratumoral androgen metabolism. Mathematical modeling provides a valuable tool for studies of androgen metabolism in CRPC. This modeling approach integrates existing knowledge about complex biologic systems and provides a means of interrogating the effects of various interventions. We sought to model a single reaction in the androgen biosynthesis network, namely the oxidation of androsterone (AND) to androstanedione (5α-dione) by four 3α-oxidoreductase enzymes, as an initial effort to establish the feasibility of our modeling approach. METHODS: Models were constructed for two cell culture systems, a non-prostate cancer cell line (CV-1) and a prostate cancer cell line (LAPC-4), using the SimBiology app (version 5.3) in MATLAB (version 8.6). The models included components for substrate (AND), product (5α-dione), each of the four enzymes, and each of the four enzyme-substrate complexes. Each enzymatic reaction consisted of a reversible enzyme-substrate binding step and an irreversible catalysis step. Rates of change for each component were described using ordinary differential equations. RESULTS: Mathematical models were developed with model parameter values derived from literature sources or from existing experimental data, which included gene expression measurements and substrate and product concentrations determined using liquid chromatography-tandem mass spectrometry. The models for both cell lines adequately described substrate and product concentrations observed after 12 h treatment with AND. CONCLUSIONS: This modeling approach represents an adaptable, extensible and mechanistic framework that reflects androgen metabolism. The models can be expanded systematically to describe the complex androgen metabolic pathways important for study of novel therapies for CRPC.

Population Pharmacokinetic Modeling To Estimate the Contributions of Genetic and Nongenetic Factors to Efavirenz Disposition.

Efavirenz pharmacokinetics is characterized by large between-subject variability, which determines both therapeutic response and adverse effects. Some of the variability in efavirenz pharmacokinetics has been attributed to genetic variability in cytochrome P450 genes that alter efavirenz metabolism, such as CYP2B6 and CYP2A6 While the effects of additional patient factors have been studied, such as sex, weight, and body mass index, the extent to which they contribute to variability in efavirenz exposure is inconsistently reported. The aim of this analysis was to develop a pharmacometric model to quantify the contribution of genetic and nongenetic factors to efavirenz pharmacokinetics. A population-based pharmacokinetic model was developed using 1,132 plasma efavirenz concentrations obtained from 73 HIV-seronegative volunteers administered a single oral dose of 600 mg efavirenz. A two-compartment structural model with absorption occurring by zero- and first-order processes described the data. Allometric scaling adequately described the relationship between fat-free mass and apparent oral clearance, as well as fat mass and apparent peripheral volume of distribution. Inclusion of fat-free mass and fat mass in the model mechanistically accounted for correlation between these disposition parameters and sex, weight, and body mass index. Apparent oral clearance of efavirenz was reduced by 25% and 51% in subjects predicted to have intermediate and slow CYP2B6 metabolizer status, respectively. The final pharmacokinetic model accounting for fat-free mass, fat mass, and CYP2B6 metabolizer status was consistent with known mechanisms of efavirenz disposition, efavirenz physiochemical properties, and pharmacokinetic theory. (This study has been registered at ClinicalTrials.gov under identifier NCT00668395.).

A Uridine Glucuronosyltransferase 2B7 Polymorphism Predicts Epirubicin Clearance and Outcomes in Early-Stage Breast Cancer.

BACKGROUND: Epirubicin is metabolized by uridine glucuronosyltransferase 2B7 (UGT2B7), an enzyme rich in single nucleotide polymorphisms (SNPs). We studied whether the -161 C > T germline SNP in UGT2B7 was related to epirubicin metabolism and whether differences exist in the toxicity and efficacy of epirubicin-based chemotherapy among patients who were TT homozygotes, CT heterozygotes, and CC homozygotes. PATIENTS AND METHODS: A total of 132 women with non-metastatic breast cancer receiving FEC (5-fluorouracil 500 mg/m(2), epirubicin 100 mg/m(2), cyclophosphamide 500 mg/m(2)) were prospectively enrolled. Toxicity was assessed in cycle 1 using the National Cancer Institute Common Toxicity Criteria, version 2.0. RESULTS: The sequence at -161 was studied in 132 subjects; 37 were TT homozygotes, 63 were CT heterozygotes, 26 were CC homozygotes, and 6 could not be genotyped. The CC genotype patients had decreased epirubicin clearance (median, 103.3 L/hr) compared with the CT/TT genotype patients (median, 134.0 L/hr; P = .002). The CC homozygous patients had an increased risk of grade 3 to 4 leukopenia compared with the TT homozygotes or heterozygotes (P = .038 and P = .032, respectively). TT homozygotes or heterozygotes had an increased risk of early recurrence (P = .039; χ(2) test). CONCLUSION: The results of the present prospective pharmacogenetic study suggest that the UGT2B7 -161 C > T SNP correlate with drug metabolism, toxicity, and efficacy in patients receiving epirubicin chemotherapy. Further studies of this UGT2B7 SNP as a predictor of epirubicin toxicity and efficacy are warranted.

Biomarkers for Diagnosis and Prognosis of Sinusoidal Obstruction Syndrome after Hematopoietic Cell Transplantation.

Reliable, noninvasive methods for diagnosing and prognosing sinusoidal obstruction syndrome (SOS) early after hematopoietic cell transplantation (HCT) are needed. We used a quantitative mass spectrometry-based proteomics approach to identify candidate biomarkers of SOS by comparing plasma pooled from 20 patients with and 20 patients without SOS. Of 494 proteins quantified, we selected 6 proteins (L-Ficolin, vascular cell adhesion molecule-1 [VCAM1], tissue inhibitor of metalloproteinase-1, von Willebrand factor, intercellular adhesion molecule-1, and CD97) based on a differential heavy/light isotope ratio of at least 2 fold, information from the literature, and immunoassay availability. Next, we evaluated the diagnostic potential of these 6 proteins and 5 selected from the literature (suppression of tumorigenicity-2 [ST2], angiopoietin-2 (ANG2), hyaluronic acid [HA], thrombomodulin, and plasminogen activator inhibitor-1) in samples from 80 patients. The results demonstrate that together ST2, ANG2, L-Ficolin, HA, and VCAM1 compose a biomarker panel for diagnosis of SOS. L-Ficolin, HA, and VCAM1 also stratified patients at risk for SOS as early as the day of HCT. Prognostic Bayesian modeling for SOS onset based on L-Ficolin, HA, and VCAM1 levels on the day of HCT and clinical characteristics showed >80% correct prognosis of SOS onset. These biomarkers may provide opportunities for preemptive intervention to minimize SOS incidence and/or severity.

The Impact of Sertraline Co-Administration on the Pharmacokinetics of Olanzapine: A Population Pharmacokinetic Analysis of the STOP-PD.

BACKGROUND AND OBJECTIVE: Clinical evidence and expert opinion support using a combination of an antipsychotic and an antidepressant when treating major depression with psychotic features. We characterized the impact of sertraline co-administration on olanzapine clearance in psychotic depression using population pharmacokinetic methods. METHODS: The Study of Pharmacotherapy for Psychotic Depression (STOP-PD) randomized 259 participants to olanzapine plus placebo or olanzapine plus sertraline. Olanzapine was started at 2.5-5 mg/day and sertraline at 25-50 mg/day. Doses were increased to a maximum of 20 mg/day for olanzapine and 200 mg/day for sertraline. Up to four olanzapine concentration samples were collected during the 12-week trial and 12-week continuation phase. We used NONMEM (Version VII) for population pharmacokinetic analysis, assessing effects of the covariates sex, African American origin, smoking, age, and sertraline co-administration. RESULTS: Population pharmacokinetic analysis comprised 336 samples from 175 individuals. The structural model published by Bigos et al. was sufficient to describe the olanzapine data adequately: a one-compartment model with first-order absorption and elimination, using an additive residual error structure with the absorption rate constant fixed to 0.5. Sertraline co-administration significantly increased olanzapine apparent clearance (p < 0.005) by 25-35 % depending on the patient characteristics included. Male sex was associated with a significantly increased clearance. Age and race did not have a significant impact on clearance. CONCLUSIONS: Contrary to expectations from the knowledge of cytochrome P450 interactions, sertraline increased olanzapine apparent clearance. Plausible explanations include patients treated with sertraline having poorer adherence to olanzapine, or the impact of sertraline inhibition of transporters resulting in increased intracellular concentrations and thus access to metabolizing enzymes.

Pharmacokinetic modeling of tranexamic acid for patients undergoing cardiac surgery with normal renal function and model simulations for patients with renal impairment.

Tranexamic acid (TXA), an effective anti-fibrinolytic agent that is cleared by glomerular filtration, is used widely for cardiopulmonary bypass (CPB) surgery. However, an effective dosing regimen has not been fully developed in patients with renal impairment. The aims of this study were to characterize the inter-patient variability associated with pharmacokinetic parameters and to recommend a new dosing adjustment based on the BART dosing regimen for CPB patients with chronic renal dysfunction (CRD). Recently published data on CPB patients with normal renal function (n = 15) were re-examined with a two-compartment model using the ADAPT5 and NONMEMVII to identify covariates that explain inter-patient variability and to ascertain whether sampling strategies might affect parameter estimation. A series of simulations was performed to adjust the BART dosing regimen for CPB patients with renal impairment. Based on the two-compartmental model, the number of samples obtained after discontinuation of TXA infusion was found not to be critical in parameter estimation (p > 0.05). Both body weight and creatinine clearance were identified as significant covariates (p < 0.005). Simulations showed significantly higher than normal TXA concentrations in CRD patients who received the standard dosing regimen in the BART trial. Adjustment of the maintenance infusion rate based on the percent reduction in renal clearance resulted in predicted plasma TXA concentrations that were safe and therapeutic (~100 mg·L(-1) ). Our proposed dosing regimen, with consideration of renal function, is predicted to maintain effective target plasma concentrations below those associated with toxicity for patients with renal failure for CPB.

Clinical trial simulation to evaluate population pharmacokinetics and food effect: capturing abiraterone and nilotinib exposures.

The objectives of this study were to determine (1) the accuracy with which individual patient level exposure can be determined and (2) whether a known food effect can be identified in a trial simulation of a typical population pharmacokinetic trial. Clinical trial simulations were undertaken using NONMEM VII to assess a typical oncology pharmacokinetic trial design. Nine virtual trials for each compound were performed for combinations of different levels of between-occasion variability, number of patients in the trial, and magnitude of a food covariate on oral clearance. Less than 5% and 20% bias and precision were obtained in individual clearance estimated for both abiraterone and nilotinib using this design. This design resulted in biased and imprecise population clearance estimates for abiraterone. The between-occasion variability in most trials was captured with less than 30% of percent bias and precision. The food effect was detectable as a statistically significant covariate on oral clearance for abiraterone and nilotinib with percent bias and precision of the food covariate less than 20%. These results demonstrate that clinical trial simulation can be used to explore the ability of specific trial designs to evaluate the power to identify individual and population level exposures, covariate, and variability effects.

Nicotine and cotinine exposure from electronic cigarettes: a population approach.

BACKGROUND AND OBJECTIVES: Electronic cigarettes (e-cigarettes) are a recent technology that has gained rapid acceptance. Still, little is known about them in terms of safety and effectiveness. A basic question is how effectively they deliver nicotine; however, the literature is surprisingly unclear on this point. Here, a population pharmacokinetic model was developed for nicotine and its major metabolite cotinine with the aim to provide a reliable framework for the simulation of nicotine and cotinine concentrations over time, based solely on inhalation airflow recordings and individual covariates [i.e., weight and breath carbon monoxide (CO) levels]. METHODS: This study included ten adults self-identified as heavy smokers (at least one pack of cigarettes per day). Plasma nicotine and cotinine concentrations were measured at regular 10-min intervals for 90 min while human subjects inhaled nicotine vapor from a modified e-cigarette. Airflow measurements were recorded every 200 ms throughout the session. A population pharmacokinetic model for nicotine and cotinine was developed based on previously published pharmacokinetic parameters and the airflow recordings. All of the analyses were performed with the non-linear mixed-effect modeling software NONMEM(®) version 7.2. RESULTS: The results show that e-cigarettes deliver nicotine effectively, although the pharmacokinetic profiles are lower than those achieved with regular cigarettes. Our pharmacokinetic model effectively predicts plasma nicotine and cotinine concentrations from the inhalation volume, and initial breath CO. CONCLUSION: E-cigarettes are effective at delivering nicotine. This new pharmacokinetic model of e-cigarette usage might be used for pharmacodynamic analysis where the pharmacokinetic profiles are not available.

Modelling the sitagliptin effect on dipeptidyl peptidase-4 activity in adults with haematological malignancies after umbilical cord blood haematopoietic cell transplantation.

BACKGROUND AND OBJECTIVES: Dipeptidyl peptidase-4 (DPP4) inhibition is a potential strategy to increase the engraftment rate of haematopoietic stem/progenitor cells. A recent clinical trial using sitagliptin, a DPP4 inhibitor approved for type 2 diabetes mellitus, has been shown to be a promising approach in adults with haematological malignancies after umbilical cord blood (UCB) haematopoietic cell transplantation (HCT). On the basis of data from this clinical trial, a semi-mechanistic model was developed to simultaneously describe DPP4 activity after multiple doses of sitagliptin in subjects with haematological malignancies after a single-unit UCB HCT. METHODS: The clinical study included 24 patients who received myeloablative conditioning followed by oral sitagliptin with single-unit UCB HCT. Using a nonlinear mixed-effects approach, a semi-mechanistic pharmacokinetic-pharmacodynamic model was developed to describe DPP4 activity from these trial data, using NONMEM version 7.2 software. The model was used to drive Monte Carlo simulations to probe the various dosage schedules and the attendant DPP4 response. RESULTS: The disposition of sitagliptin in plasma was best described by a two-compartment model. The relationship between sitagliptin concentrations and DPP4 activity was best described by an indirect response model with a negative feedback loop. Simulations showed that twice daily or three times daily dosage schedules were superior to a once daily schedule for maximal DPP4 inhibition at the lowest sitagliptin exposure. CONCLUSION: This study provides the first pharmacokinetic-pharmacodynamic model of sitagliptin in the context of HCT, and provides a valuable tool for exploration of optimal dosing regimens, which are critical for improving the time to engraftment in patients after UCB HCT.

The relationship of polymorphisms in ABCC2 and SLCO1B3 with docetaxel pharmacokinetics and neutropenia: CALGB 60805 (Alliance).

Docetaxel-related neutropenia was associated with polymorphisms in the drug transporters ABCC2 and SLCO1B3 in Japanese cancer patients. We hypothesized that this association is because of reduced docetaxel clearance, associated with polymorphisms in those genes. We studied 64 US cancer patients who received a single cycle of 75 mg/m of docetaxel monotherapy. We found that the ABCC2 polymorphism at rs-12762549 trended to show a relationship with reduced docetaxel clearance (P=0.048), but not with neutropenia. There was no significant association of the SLCO1B3 polymorphisms with docetaxel clearance or neutropenia. We conclude that the relationship between docetaxel-associated neutropenia and polymorphisms in drug transporters identified in Japanese patients was not confirmed in this cohort of US cancer patients.

Population pharmacokinetic analysis of 17-dimethylaminoethylamino-17-demethoxygeldanamycin (17-DMAG) in adult patients with solid tumors.

PURPOSE: To identify sources of exposure variability for the tumor growth inhibitor 17-dimethylaminoethylamino-17-demethoxygeldanamycin (17-DMAG) using a population pharmacokinetic analysis. METHODS: A total 67 solid tumor patients at 2 centers were given 1 h infusions of 17-DMAG either as a single dose, daily for 3 days, or daily for 5 days. Blood samples were extensively collected and 17-DMAG plasma concentrations were measured by liquid chromatography/mass spectrometry. Population pharmacokinetic analysis of the 17-DMAG plasma concentration with time was performed using nonlinear mixed effect modeling to evaluate the effects of covariates, inter-individual variability, and between-occasion variability on model parameters using a stepwise forward addition then backward elimination modeling approach. The inter-individual exposure variability and the effects of between-occasion variability on exposure were assessed by simulating the 95 % prediction interval of the AUC per dose, AUC(0-24 h), using the final model and a model with no between-occasion variability, respectively, subject to the five day 17-DMAG infusion protocol with administrations of the median observed dose. RESULTS: A 3-compartment model with first order elimination (ADVAN11, TRANS4) and a proportional residual error, exponentiated inter-individual variability and between occasion variability on Q2 and V1 best described the 17-DMAG concentration data. No covariates were statistically significant. The simulated 95% prediction interval of the AUC(0-24 h) for the median dose of 36 mg/m(2) was 1,059-9,007 mg/L h and the simulated 95 % prediction interval of the AUC(0-24 h) considering the impact of between-occasion variability alone was 2,910-4,077 mg/L h. CONCLUSIONS: Population pharmacokinetic analysis of 17-DMAG found no significant covariate effects and considerable inter-individual variability; this implies a wide range of exposures in the population and which may affect treatment outcome. Patients treated with 17-DMAG may require therapeutic drug monitoring which could help achieve more uniform exposure leading to safer and more effective therapy.

Resampling phase III data to assess phase II trial designs and endpoints.

PURPOSE: The best phase II design and endpoint for growth inhibitory agents is controversial. We simulated phase II trials by resampling patients from a positive (sorafenib vs. placebo; TARGET) and a negative (AE941 vs. placebo) phase III trial in metastatic renal cancer to compare the ability of various designs and endpoints to predict the known results. EXPERIMENTAL DESIGN: A total of 770 and 259 patients from TARGET and the AE 941 trial, respectively, were resampled (5,000 replicates) to simulate phase II trials with α = 0.10 (one-sided). Designs/endpoints: single arm, two-stage with response rate (RR) by Response Evaluation Criteria in Solid Tumors (RECIST; 37 patients); and randomized, two arm (20-35 patients per arm) with RR by RECIST, mean log ratio of tumor sizes (log ratio), progression-free survival (PFS) rate at 90 days (PFS-90), and overall PFS. RESULTS: Single-arm trials were positive with RR by RECIST in 55% and 1% of replications for sorafenib and AE 941, respectively. Randomized trials versus placebo with 20 patients per arm were positive with RR by RECIST in 55% and 7%, log ratio in 88% and 25%, PFS-90 in 64% and 15%, and overall PFS in 69% and 9% of replications for sorafenib and AE 941, respectively. CONCLUSIONS: Compared with the single-arm design and the randomized design comparing PFS, the randomized phase II design with the log ratio endpoint has greater power to predict the positive phase III result of sorafenib in renal cancer, but a higher false positive rate for the negative phase III result of AE 941.

An exploratory study of body composition as a determinant of epirubicin pharmacokinetics and toxicity.

PURPOSE: Although body composition has emerged as an important predictor of drug efficacy and toxicity, explanations for this association are unclear. Our goal was to investigate relationships between lean body mass (LBM), liver size/function and epirubicin pharmacokinetics (PK) and toxicity. METHODS: Data from a clinical study (n = 24) of patients with breast cancer receiving adjuvant intravenous FE(100)C chemotherapy were used to examine relationships between LBM, liver size, and epirubicin clearance. Muscle tissue and liver mass were measured by analysis of computerized tomography cross-sectional images, and an extrapolation of muscle mass to total LBM compartment was employed. Population PK analysis of epirubicin was undertaken to test effects of body composition on epirubicin clearance and area under the curve (AUC). RESULTS: Estimated LBM was extremely variable in this cohort ranging from 32.9 to 67.3 kg. LBM was associated with neutrophil nadir (r = 0.5, P = 0.023), and mean LBM was lower for patients presenting with toxicity compared to those where toxicity was absent (41.6 vs. 56.2 kg, P = 0.002); 33% of variance in clearance was explained by LBM and aspartate aminotransferase (AST). Liver mass was not related to epirubicin clearance likely due to larger livers presenting with larger fat content, but liver attenuation (degree of fat infiltration) and AST were associated with AUC. CONCLUSION: To our knowledge, this is the first study to examine relationships between LBM, liver mass/function and epirubicin PK and toxicity. This exploratory work investigates the notion of organs and tissues having distinctive contributions to the distribution and metabolism of antineoplastic drugs.