a pharmacokinetic/pharmacodynamic model of tumor lysis … · 2013. 1. 8. · 24 conflict of...

1

A Pharmacokinetic/Pharmacodynamic Model of Tumor Lysis Syndrome in Chronic 1

Lymphocytic Leukemia Patients Treated with Flavopiridol 2

3 Jia Ji1, Diane R. Mould2,3, Kristie A. Blum1,4, Amy S. Ruppert4, Ming Poi1, Yuan Zhao3, Amy J. 4

Johnson1,4, John C. Byrd1,4,5, Michael R. Grever1,4, Mitch A. Phelps1,3 5

6 1The Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210 USA 7

2Projections Research, Inc., Phoenixville, PA 19460 USA 8

3Division of Pharmaceutics, College of Pharmacy, The Ohio State University, Columbus, OH 9

4Division of Hematology, Department of Internal Medicine, College of Medicine, The Ohio 10

State University, Columbus, OH 11

5Division of Medicinal Chemistry, College of Pharmacy, The Ohio State University, Columbus, 12

OH 13

14

Address correspondence to: Mitch A. Phelps, Ph.D. 15 Assistant Professor 16 The Ohio State University 17 College of Pharmacy, 18 500 W. 12th Ave. 19 Columbus, OH 43210 USA 20 614-688-4370 21 [email protected] 22

23

Conflict of Interest Statement: Dr. Michael Grever and Dr. John Byrd have a use patent on 24

flavopiridol that has not been awarded and currently lacks financial value. All other authors have 25

no potential conflict of interest to report. 26

27

Keywords: chronic lymphocytic leukemia, flavopiridol, tumor lysis syndrome, population 28

pharmacokinetics, glucuronide metabolite, logistic regression model29

Research. on August 13, 2021. © 2013 American Association for Cancerclincancerres.aacrjournals.org Downloaded from

Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on January 8, 2013; DOI: 10.1158/1078-0432.CCR-12-1092

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2

Statement of Translational Relevance 30

Tumor lysis syndrome (TLS) is an oncologic emergency requiring immediate intervention to 31

prevent severe kidney damage, cardiac arrhythmias and death. Although rare in patients with 32

refractory chronic lymphocytic leukemia (CLL) and despite aggressive prophylactic measures, 33

hyper-acute TLS occurs frequently in refractory CLL patients treated with single-agent cyclin-34

dependent kinase inhibitors (CDKIs), flavopiridol and dinaciclib. While these agents are 35

impressively active in refractory and cytogenetically high-risk CLL, the prevalent and 36

unpredictable occurrence of TLS limits their broad clinical use. This manuscript presents the first 37

PK/PD model of TLS and explores the unique associations of female gender and metabolite 38

pharmacokinetics with TLS induced by flavopiridol therapy in CLL patients. This model offers a 39

tool enabling estimation of TLS probability in CLL patients prior to therapy with flavopiridol, 40

and it represents a general framework through which the mechanisms of TLS induced by CDKI 41

therapy in CLL patients can be further studied. 42

43




3

Abstract 44

Purpose: Flavopiridol, the first clinically evaluated cyclin dependent kinase inhibitor, 45

demonstrates activity in patients with refractory chronic lymphocytic leukemia, but prevalent and 46

unpredictable tumor lysis syndrome (TLS) presents a major barrier to its broad clinical use. The 47

purpose of this study was to investigate the relationships between pretreatment risk factors, drug 48

pharmacokinetics, and TLS. 49

Experimental Design: A population pharmacokinetic/pharmacodynamic model linking drug 50

exposure and TLS was developed. Plasma data of flavopiridol and its glucuronide metabolite 51

(flavo-G) were obtained from 111 patients treated in early phase trials with frequent sampling 52

following initial and/or escalated doses. TLS grading was modeled with logistic regression as a 53

pharmacodynamic endpoint. Demographics, baseline disease status, and blood chemistry 54

variables were evaluated as covariates. 55

Results: Gender was the most significant pharmacokinetic covariate, with females displaying 56

higher flavo-G exposure than males. Glucuronide metabolite exposure was predictive of TLS 57

occurrence, and bulky lymphadenopathy was identified as a significant covariate on TLS 58

probability. The estimated probability of TLS occurrence in patients with baseline bulky 59

lymphadenopathy < 10 cm or > 10 cm during the first two treatments was 0.111 (SE% 13.0%) 60

and 0.265, (SE% 17.9%) respectively, when flavo-G area under the plasma concentration vs. 61

time curve was at its median value in whole patient group. 62

Conclusions: This is the first population pharmacokinetic/pharmacodynamic model of TLS. 63

Further work is needed to explore potential mechanisms and to determine if the associations 64

between TLS, gender and glucuronide metabolites are relevant in CLL patients treated with other 65

cyclin dependent kinase inhibitors. 66

67




4

Introduction 68

As the first cyclin-dependent kinase inhibitor (CDKI) in clinical trials, flavopiridol has been 69

investigated as a single agent and in combination regimens to treat numerous malignancies. The 70

initial dosing regimen using continuous infusion showed limited clinical activity and few 71

responses (1-13). A novel, pharmacokinetically guided dosing schedule was subsequently 72

developed to achieve target cytotoxic concentrations in patients with chronic lymphocytic 73

leukemia (CLL) (9, 14, 15). This significantly improved efficacy in refractory CLL patients with 74

40% and 53% of patients achieving objective responses in phase I and II trials, respectively (9, 75

15), including one patient who achieved a complete response (CR). Notably, most CLL patients 76

in both trials were heavily pretreated and not responsive to traditional therapies and/or harbored 77

high-risk cytogenetics and bulky lymphadenopathy (16). This improvement in clinical activity 78

with the new flavopiridol dosing regimen highlighted the importance of achieving active 79

concentrations and exposure durations required for drug activity. 80

81

The dose-limiting toxicity for this dosing regimen in CLL patients was tumor lysis syndrome 82

(TLS) and was observed in 53 out of 116 patients studied on these two trials (17). TLS is 83

characterized by a series of metabolic disorders induced by rapid tumor cell death and release of 84

toxic cellular contents into circulation (18, 19). It is defined by abnormal elevation in serum uric 85

acid, potassium, phosphate and lactate dehydrogenase (LDH), leading to serious complications 86

such as neurological abnormalities, kidney damage, cardiovascular events, and potentially death 87

(18, 19). TLS is typically rare in CLL, and its prevalence with the pharmacokinetically guided 88

dosing regimen of flavopiridol highlights the exquisite and acute sensitivity of CLL to cyclin 89

dependent kinase inhibition. Blum and colleagues identified several pre-treatment risk factors for 90




5

TLS in CLL patients treated with flavopiridol (17). TLS occurrence was associated with 91

advanced disease stage, characterized by bulky tumor burden, elevated white blood cell (WBC) 92

count and β2-microglobulin, and reduced albumin level (17). These were consistent with 93

multiple diseases where tumor bulk and disease stage are general risk factors (19). Overall, these 94

produced odds ratios (ORs) for TLS ranging from 1.1 to 2.9. Interestingly, female gender was 95

the most influential of all risk factors identified for TLS occurrence after flavopiridol treatment 96

with an OR of 8.6 (95% CI: 2.6-27.7). Gender has not been previously reported as a risk factor 97

for TLS. While these factors serve as useful indicators for increased risk of TLS, many CLL 98

patients who experienced TLS would not have been identified by the combined risk factors. 99

100

Glucuronide conjugation accounts for the majority of metabolic transformation of flavopiridol 101

(20, 21). Glucuronidation can be influenced by factors such as age, gender, cigarette smoking 102

and obesity (22). In vitro studies showed that isoform UGT1A9 is predominantly responsible for 103

conversion of flavopiridol to 7-O-β-glucopyranuronosyl-flavopiridol (Flavo-7-G), which 104

accounts for 98.5% of glucuronidation product(20, 21). UGT1A1 or UGT1A4 are reported to 105

catalyze formation of 5-O-β-glucopyranuronosyl-flavopiridol (Flavo-5-G), the minor 106

glucuronidation product (20, 21). These metabolites (flavo-G) have been presumed inactive, 107

although studies evaluating their activity have not been reported. Flavo-G conjugates are 108

presumed to be formed predominantly in liver, although UGT activity in other tissues, including 109

lymphocytes, may also contribute. These conjugates, along with parent drug, are eliminated 110

through biliary and renal excretion with the majority of flavopiridol and flavo-G found in 111

bile/feces. Evidence for enterohepatic recirculation of flavopiridol has been reported (23, 24). 112

Glucuronidase activity in the gut may convert flavo-G to flavopiridol, allowing further 113




6

flavopiridol reabsorption. We previously reported that flavo-G, the primary flavopiridol 114

metabolites, were associated with TLS in the phase I study (14). In the study by Blum and 115

colleagues, this trend was apparent in a subset of 86 of the patients with flavo-G PK data. 116

Interestingly, this analysis also identified the association between flavo-G and gender, where 117

females had higher plasma flavo-G concentrations and areas under the concentration-time curves 118

(AUC). 119

120

With the increased development of targeted therapies, TLS is becoming more prevalent and is 121

now observed more commonly in diseases that were previously characterized as low risk for TLS, 122

such as CLL (25-27. Although flavopiridol has demonstrated impressive activity in refractory 123

CLL, the prevalence of TLS has dampened enthusiasm for its broader use in the clinical setting. 124

Recent clinical experience with other cyclin dependent kinase inhibitors such as dinaciclib, 125

suggests TLS may be a class effect in CLL (28). In addition to having similar pharmacodynamic 126

targets, dinaciclib and flavopiridol also have in common a UGT-mediated elimination pathway. 127

Therefore, understanding the relationships between drug and metabolite exposure and occurrence 128

of TLS is imperative to further the development of CDKIs in CLL. 129

130

The purpose of this study was to model TLS occurrence in CLL patients treated with flavopiridol 131

and to explore the relationships and relative contributions of parent drug, glucuronide metabolite, 132

and pre-treatment risk factors to TLS occurrence. TLS has not previously been modeled using a 133

pharmacokinetic/pharmacodynamic (PK/PD) nonlinear mixed effects approach. Herein we 134

describe the first PK-PD model linking drug and metabolite exposure to TLS. 135

136




7

Methods 137

138

Patients 139

Subjects included in this study were patients with relapsed, symptomatic CLL or small 140

lymphocytic lymphoma (SLL) or prolymphocytic leukemia arising from CLL treated with 141

flavopiridol monotherapy in phase I and II studies. Both studies were conducted at The James 142

Cancer Hospital at The Ohio State University in Columbus, Ohio. The studies were reviewed and 143

approved by the institutional review boards of The Ohio State University and signed informed 144

consent was obtained from all patients. Each patient received a maximum of six cycles with each 145

cycle containing 3 or 4 weekly treatments. Patients were treated at 30 mg/m2 half-hour s infusion 146

followed by 30 mg/m2 as a 4-hour infusion at first dose in Cycle 1. Depending on toxicity 147

occurrence, the 4-hour infusion dose was escalated to 50 mg/m2 either at the second dose in 148

Cycle 1 or at the first dose in Cycle 2. Two patients in the phase I study received 40 mg/m2 each 149

for the half-hour and 4-hour infusions. The details of enrollment criteria, study design, treatment 150

and dosing schedule of both trials were reported elsewhere (9, 14, 15). 151

152

Flavopiridol and Flavopiridol Glucuronide PK Analysis 153

Plasma samples were collected between 0.5 and 24, 36 or 48 hours after the first dose and/or 154

escalated dose. Flavopiridol and flavo-G concentrations were measured using liquid 155

chromatography-tandem mass spectrometry methods as previously described (14, 29, 30). 156

157

Definition, prophylaxis, and management of TLS 158




8

TLS was defined as an acute elevation in uric acid, potassium, phosphate, and/or lactate 159

dehydrogenase (LDH) within 24 hours of flavopiridol administration. Prophylaxis for TLS was 160

given to all patients with allopurinol, rasburicase, sodium bicarbonate-containing intravenous 161

hydration, and oral phosphate binder. All patients were monitored for 24 hours after dosing for 162

serum potassium level, and those who experienced hyperkalemia or hyperphosphatemia were 163

treated with sodium polystyrene sulfonate (Kayexalate™), furosemide, albuterol, insulin and 164

glucose, calcium, oral phosphate binders, or emergent dialysis (17). Patients who developed TLS 165

or other severe adverse events during the first dose were not dose escalated for subsequent 166

treatments. Hyper-acute TLS was designated where dialysis intervention was required within 6 167

hours of initiating therapy. 168

169

Covariates 170

Baseline variables were collected from all patients before the first dose. They included 6 171

demographic variables (body weight, height, body surface area, age, sex, and race), 4 disease 172

state indices (Rai stage, β2-microglobulin, bulky lymphadenopathy > 10 cm, and ECOG status), 173

and 10 blood chemistry variables (albumin, WBC, creatinine, potassium, uric acid, phosphate, 174

total bilirubin, aspartate aminotransferase (AST), alanine aminotransferase (ALT), and LDH). 175

Baseline creatinine clearance was estimated using the Cockcroft-Gault equation (31). 176

177

Population PK Modeling 178

The population PK of flavopiridol and flavo-G were evaluated using nonlinear mixed effect 179

modeling as implemented in NONMEM (version 7 Level 1.2, ICON Development Solutions, 180

Ellicott City MD, USA) with Intel Visual Fortran compiler (version 11.1.060, Intel Corporation, 181




9

Santa Clara, CA, USA). Data were fit using a log transform both sides (LTBS) approach. The 182

first-order conditional estimation (FOCE) method was used throughout the model building 183

process. The minimum objective function value (OFV) was used to compare nested models. A 184

decrease of 6.64 or greater in OFV was considered statistically significant at p<0.01 for nested 185

models. A PK model for flavopiridol was developed first. The PK model for flavo-G was built 186

with the parameters for flavopiridol fixed to their final values. Once the flavo-G model was 187

established, the parameters for both parent drug and metabolite were simultaneously estimated. 188

189

Standard model building approaches were used for both analytes. To establish a base model, 1-, 190

2, and 3 - compartment models were tested. A parameter estimating the fraction of parent drug 191

converted to metabolite (Fm) was described using logit transformation to constrain the value 192

between (0, 1) and was used to link the parent drug and metabolite compartments. Inter-193

individual (IIV) and inter-occasion (IOV) variability were estimated for the PK parameters using 194

an exponential error model. According to the study design, patients started on a low total dose 195

(30mg/m2 bolus + 30mg/m2 infusion dose) and were escalated to a higher total dose (30mg/m2 196

bolus + 50mg/m2 infusion dose) if the low dose was tolerated. Since pharmacokinetic sampling 197

occurred during the first administration of each dose level, inter-occasion variability (IOV) was 198

tested on the two total dose levels in the model of 60 or 80 mg/m2. Terms describing correlations 199

between the inter-individual random effects were included where feasible (e.g. where the 200

correlation was estimable, the model converged successfully, and the OFV was reduced). 201

Residual variability was described using an additive error model (which is proportional after 202

back transformation of the data). 203

204




10

Covariate analysis was performed by comparing hierarchical models based on the likelihood 205

ratio test. Continuous covariates were normalized to the median value, which was assumed for 206

missing covariate data. Initially, each covariate was plotted against individual estimates of inter-207

individual variability (e.g. eta values) for screening. Covariate-parameter relationships that 208

displayed a visual trend in the graphical assessment were then introduced singly into the base 209

model separately using equations (1) and (2) for continuous and dichotomous covariates, 210

respectively. 211

TVCLi = θCL × (NCovi)θ1 (1) 212

TVCLi = θCL × θ2Covi (2) 213

In equation (1), TVCLi is the typical value of clearance adjusted with the normalized continuous 214

covariate, NCovi. NCovi represents the continuous covariate for individual i divided by the 215

median value of that covariate. TVCL and θCL are equivalent when the continuous covariate 216

takes the median value (i.e. when NCov = 1). θ1 denotes the estimate of influence of the 217

continuous covariate. In equation (2), TVCLi is the typical value of clearance when the 218

dichotomous covariate takes the value of 0, e.g. SEX = 0 refers to male patients. Covi is the 219

dichotomous covariate for individual i. θ2 is the proportional change in clearance when the 220

dichotomous covariate takes the value of 1. 221

222

Covariates were added to the base model if the decrease in OFV was at least 3.8 units (p<0.05). 223

Clinical relevance was also considered when determining inclusion of a statistically significant 224

covariate in that the effect of a covariate had to change the parameter value by at least 20% over 225

the range of covariate values in the database (selection criteria of 20% was based on standard 226

considerations for bioequivalency(32)). All significant covariates were then added to one model 227




11

(the full model) and a stepwise backward deletion procedure was conducted, using the criteria of 228

p<0.01 based on the likelihood ratio test. 229

230

Population PK-PD Modeling 231

As with population PK modeling, NONMEM (with the same compiler and installation) was used 232

for population PK/PD modeling with FOCE and LAPLACE LIKE options. Clinical grading of 233

TLS according to the Common Terminology Criteria for Adverse Events is either 0 (no TLS), 3 234

(present), 4 (Life-threatening consequences; urgent intervention indicated), or 5 (death). All 235

patients whose PK/PD data were used in our study were graded either 0, 3 or 4. Therefore, 236

logistic regression was used to characterize the probability of the PD effect, TLS occurrence 237

(grades 3 or 4), and to evaluate its relationship with drug or metabolite exposure. TLS 238

occurrence during Cycle 1 was therefore modeled as a dichotomous outcome variable. TLS 239

presents as metabolic abnormalities secondary to spontaneous or most commonly cytotoxic 240

treatment-induced rapid tumor cell death. When tumor burden is high prior to the treatment and 241

tumor cells are responsive to the first treatment, rapid cell death causes mass cellular debris 242

released into blood circulation and may lead to elevation of electrolyte levels in blood. Following 243

repeated doses, the decreased tumor burden, and potentially decreased sensitivity to drug, results 244

in a decreased likelihood of TLS. In these two studies, we observed incidence of TLS was 245

highest following the first dose at each dose level, and then decreased to lower or no incidence at 246

later doses. TLS was rarely observed in subsequent cycles, particularly if patients had 247

experienced TLS in the first cycle. Given the low rate of occurrence at later cycles, only TLS 248

during cycle 1 was considered, thus the data included one to four observations per subject. 249

Similarly within Cycle 1, the probability of TLS decreased over time. Thus, the effect of time on 250




12

TLS within Cycle 1 was tested by several functions, including exponential, cubic, step, and 251

Bateman functions. Both the maximum concentrations (Cmax) and AUC of flavopiridol and 252

flavo-G were investigated as predictors of TLS. 253

254

First a base model linking drug or metabolite exposure to the probability of TLS was established, 255

then 6 risk factors (sex, β2-microglobulin, bulky lymphadenopathy > 10 cm, WBC, albumin, 256

creatinine clearance) from a multivariate analysis for TLS (17) were evaluated by including one 257

risk factor into the model each time. The model considering effects for time and drug exposure 258

on the logit scale is shown in equation (3). 259

logit [P(TLSik=1)] = αi + βi × (DrugExpik-MedDrugExpi) + η (3) 260

In this equation, The term P(TLSik=1) is the probability of TLS in patient k on day 1 or 8 (i=0) or 261

day 15 or 22 (i=1), where TLS=1 denotes TLS occurrence and TLS=0 denotes no TLS. The 262

effect of drug exposure was evaluated first then added to the base model. Alpha (αi) is the 263

probability of TLS on day 1 or 8 (i=0) or day 15 or 22 (i=1) at median drug exposure. During 264

model development α1 was estimated, but with poor precision, using the function, exp(-265

α1)/(1+exp(-α1)). We therefore evaluated different initial estimates of α1 ranging from -50 to -10. 266

The initial estimate that allowed successful convergence and the lowest OFV (α=-30) was 267

selected. Beta (βi) is the drug exposure factor, where β0 is estimated and β1 is fixed to be 0 (since 268

TLS occurred only on the first or escalated dose). Estimated Cmax and AUC of parent drug and 269

metabolite were tested as predictors of TLS. Cmax or AUC of both compounds at each dose 270

level for each patient was estimated by known dosing amount and post hoc parameters from the 271

final population PK model. AUC values were obtained up to 168 hours after each dose. After 272

drug effect was established, the remaining covariates were examined. Continuous variables were 273




13

added to the model in linear function with addition of term γi × (Covik-MedCovi) into equation 274

(3). Gamma (γi) is the covariate factor where γ0 is estimated in Day 1 or 8 and γ1 is fixed as 0 in 275

Day 15 or 22. Covik is the covariate value for the kth patient and MedCovi is the median 276

covariate value. Dichotomous variables were evaluated with separate estimation of the drug 277

exposure factor between two statuses. In equation (3), term βij × (DrugExpijk-MedDrugExpij) is 278

used, e.g. where drug exposure effect was separately estimated in male (j=0) and female (j=1) 279

patients. Eta(η) is the inter-individual random effect which was fixed to zero in this model as the 280

data were too limited to estimate variability. 281

282

Model Evaluation 283

Model evaluation was conducted on the final models. The parameters of the fixed and random 284

effects were examined for reasonable estimation, standard error, shrinkage and correlation. 285

Goodness-of-fit plots were graphed to evaluate model appropriateness. Normality assumption of 286

the random effects was visually checked by histogram and quantile-quantile plots. Visual 287

predictive check (VPC) of the PK models were generated from 1500 simulations. The PD model 288

was evaluated by comparing observed TLS probability and 95% confidence interval (CI) of 289

predicted TLS probability. Predicted probability was calculated for each patient in each day 290

using bootstrap parameters from 1000 bootstrap runs. These 1000 calculated probabilities were 291

used to construct 95% CI of the probability of TLS for a range of drug exposures in Day 1 or 8. 292

The observed probabilities of TLS were compared to the predicted values. 293

294

Results 295

296




14

There were 111 unique patients for PK analysis from the phase I and II studies, including 8 297

patients who were retreated after relapse. Eighty-one patients received escalated infusion doses 298

of 50 mg/m2 (70-134.5 mg). There were 1374 and 781 plasma concentration observations of 299

flavopiridol and flavo-G available for modeling, respectively. Flavo-G concentrations were 300

measured in 85 out of 111 patients. The patient population was primarily Caucasian American 301

with a median age of 60 years, and was 70% male. There was a wide range of body weight in 302

this patient group (45.1 kg to 153.7 kg). Most patients were at late stage of disease and had 303

variable WBC count. There was 1 patient (0.9%) with missing data for race, ECOG status, total 304

bilirubin, AST, 2 patients (1.8%) with missing data for potassium and ALT, 3 patients (2.7%) 305

with missing uric acid data and 13 patients (11.7%) with missing phosphate data. Forty-three 306

percent (43%) of patients had TLS in the first cycle. A summary of the demographics of the 307

patients in the database is presented in Supplementary Table 1. 308

309

The overall scheme for the PK model of flavopiridol and flavopiridol glucuronide is presented in 310

Figure 1. A two-compartment PK model with first-order elimination described the disposition of 311

flavopiridol well, as previously reported(14, 29). Individual ETAs for clearance and volume 312

were highly correlated (r > 0.9), so a shared ETA was used with an estimated scale factor applied 313

to the shared ETA for volume of distribution (i.e. CL = TVCL x EXP (ETA1) and V = TVV x 314

EXP (THETA(n) x ETA1), where THETA(n) is the estimated variance scale factor). The 315

population parameter estimates for flavopiridol are presented in Table 1. The clearance, central 316

volume of distribution, distribution clearance, and peripheral volume of distribution were 34.1 317

L/h, 75.8 L, 6.77 L/h, and 91.8 L, respectively. The parameters were estimated with good 318

precision (less than 20-30%) and shrinkage (less than 20%). In the final model, an allometric 319




15

function using normalized body weight was applied to clearance parameters with a fixed factor 320

of 0.75 and to volume parameters with a fixed factor of 1. Flavopiridol PK parameters including 321

this body size factor were consistent with our previous model that utilized body surface area as a 322

covariate (14). We also observed a significant effect in univariate analysis from sex with females 323

having 12.3% lower CL compared to males (△OFV = -7.9). However, since sex and body weight 324

were correlated, we used normalized body weight which had better precision and lower 325

shrinkage compared to sex. No other covariates met the cutoff criteria for inclusion in the final 326

flavopiridol model. Plots of the observed concentrations versus the population and individual 327

predictions are presented in Figure 2, and residual plots are presented as supplementary data. The 328

VPC plot in Figure 2 demonstrates the 95% CI and prediction intervals (PI) adequately describe 329

the observed concentrations at each time point with no notable bias. VPC plots provided as 330

supplementary data, with patients stratified by weight (< 80 kg and ≥ 80 kg), demonstrate similar 331

flavopiridol CI and PI between the two categories. 332

333

A two compartment-model was also selected to describe flavo-G plasma concentration-time 334

profiles. To address identifiability limitations, since clearance of parent drug in metabolic or 335

non-metabolic pathways could not be distinguished by plasma sampling of metabolite alone, a 336

parameter for the fraction of parent drug converted to metabolite, Fm, was estimated using 0.5 as 337

an initial estimate based on preliminary DMPK data. During model development. PK parameters 338

of both parent drug and metabolite were simultaneously estimated, and the model successfully 339

converged. However, Fm was fixed during covariate analysis to avoid terminated runs. This 340

approach for estimating Fm has been presented in recent literature(33). This parameter did not 341

include a term for inter-individual variability. Random effects on flavo-G volumes of distribution 342




16

were also unidentifiable and were therefore not included. Using the base model with both 343

flavopiridol and glucuronide metabolite, sex was the only significant covariate on metabolite 344

clearance, suggesting clearance of flavo-G in female patients was approximately half the 345

clearance of male patients. Population parameter estimates of flavo-G are shown in Table 1. 346

Figure 3 shows goodness-of-fit plots of the PK model of flavo-G and its VPC stratified by sex. 347

Residual plots for flavo-G are provided as supplementary data. 348

349

The estimated parameters for the model describing the probability of TLS are shown in Table 2. 350

Because most TLS events occurred in Day 1 or 8, a step function with time effect provided the 351

best estimate of TLS probability within this dataset. A linear function was used to evaluate the 352

relationship between drug exposure and TLS probability on day 1 or 8. Inclusion of flavopiridol 353

Cmax or AUC did not improve the model, while inclusion of flavo-G Cmax or AUC led to a 354

significant decrease in OFV (p<0.01), suggesting that exposure to flavo-G was predictive of TLS. 355

Successful convergence was obtained when flavo-G AUC was included, and it was therefore 356

selected for use in the final model. 357

358

We evaluated the potential influence of patient drop-out during Cycle 1 of the study. Among 7 359

patients who dropped out after Day 1, 4 patients dropped out due to TLS and 3 due to other 360

toxicities. Patients who dropped out at Day 15 or 22 did so for reasons other than TLS. 361

Compared with number of patients having TLS at Day 1 (n=25) or Day 8 (n=26), the number of 362

patients who dropped out due to TLS was low. Overall, drop-outs in this study were deemed to 363

post no significant influence and minimal impact. 364

365




17

For PD model covariate analysis with 6 risk factors, inclusion of β2-microglobulin (∆OFV=-366

16.05), WBC (∆OFV=-10.41), bulky lymphadenopathy > 10 cm (∆OFV=-9.54), or creatinine 367

clearance (∆OFV=-8.34) into the model resulted in significant decreases in OFV (p<0.01) with 368

successful convergence. The other factors evaluated (sex (∆OFV=-3.25) or albumin (∆OFV=-369

1.55)) did not result in significant reductions of OFV and were not considered further. When 370

both β2-microglobulin and bulky lymphadenopathy status were included in the model, OFV 371

change was -19.823 (∆OFV=-19.823) with successful convergence. However, there was a strong 372

correlation between β2-microglobulin level and bulky lymphadenopathy status (p<0.01). 373

Inclusion of creatinine clearance and bulky lymphadenopathy status did not yield a model for 374

which standard errors were estimated. Therefore, only bulky lymphadenopathy status was 375

retained in the final PD model. 376

377

Using the final model, the estimated probability of TLS occurrence in patients with baseline 378

bulky lymphadenopathy < 10 cm during the first two treatments was 0.111 when flavo-G AUC 379

was at its median value of 13.6 μg/mL×h (range 3.21-141μg/mL×h) in all patients. This 380

probability increased to 0.265 when patients had bulky lymphadenopathy > 10 cm at baseline 381

and when flavo-G AUC was at the same median value. The increasing trend of TLS probability 382

with flavo-G AUC, however, was similar in both groups of patients, based on the similarity of 383

estimated slopes. All pharmacodynamic parameters were well estimated with good precision in 384

the final model. Figure 4 shows observed TLS probability versus time in Cycle 1 was well 385

covered by the 95% CI of predicted TLS probability, regardless of gender effect. At Day 1 or 8 386

in Cycle 1, this model gave 95% CI of predicted TLS probability against increasing flavo-G 387

AUC that matched the observed linear trend, with the narrowest overall confidence interval 388




18

among all covariates considered (Figure 4). Plots showing predicted TLS probability vs. parent 389

drug AUC and predicted vs. observed TLS probability are provided in the supplement. 390

391

Discussion 392

393

In patients with refractory CLL, flavopiridol has demonstrated significant efficacy with TLS as 394

the dose-limiting toxicity. Although TLS reflects rapid and high activity for flavopiridol to 395

destroy CLL tumor cells, a recent analysis of clinical outcomes data indicated TLS was not 396

associated with objective response (17). Furthermore, the probability of TLS was not associated 397

with flavopiridol PK. While the probability of TLS was associated with expected pre-treatment 398

variables, including bulky disease and WBC, it was also associated with unexpected variables, 399

including gender and flavo-G PK. Outside of flavopiridol in CLL, TLS had not been previously 400

associated with gender or glucuronide metabolite levels. We therefore sought to further explore 401

the apparent links between TLS, gender and flavo-G. 402

403

The final PK model estimated unexplained proportional residual error of 39.6% and 55.8% in 404

parent compound and metabolite concentration estimates, respectively. Additional covariates that 405

can explain such high variability are yet to be identified. With such high residual error, 406

sequential modeling of parent drug and metabolite were conducted first to estimate PK 407

parameters for each compound, followed by simultaneous modeling with these parameter 408

estimates as initial estimates to ensure convergence. We recently reported that pharmacogenetic 409

factors significantly contribute to flavopiridol and flavo-G (29), and such factors will likely 410

improve the model. However, pharmacogenetic data were not available for a large proportion of 411




19

patients in this dataset. While imputation methods are available for genotype data, these methods 412

require either larger datasets and/or prior validated information relating genotype and phenotype 413

or use of linkage disequilibrium to infer genotypes from determined genotypes. (34-38). We 414

therefore concluded evaluation of pharmacogenetics as a covariate in this model to be 415

impractical. Other missing covariates were imputed with the median value, which is a common 416

imputation method to deal with limited missing data. The disadvantage to this method of 417

imputation is that covariate distribution may be clustered around the median value thus under-418

estimating the covariate effect. We determined this impact should be minimal since the 419

percentage of missing data is is less than 12% in one covariate and less than 3% for all other 420

covariates used. 421

422

For development of the PK model, body weight and gender were determined to improve the 423

models for flavopiridol and flavo-G clearance, respectively. Other groups have shown flavo-G 424

elimination is primarily due to biliary and fecal excretion (21, 23). In support of this, our results 425

suggest flavo-G deposited into urine represents less than 5% of the total dose of flavopiridol 426

(data not shown). Due to inability to robustly estimate Fm, we were not able to effectively 427

determine the impact of gender or other covariates on the fraction of flavopiridol converted to 428

flavo-G. Therefore, the gender effect may be more appropriately attributed to flavo-G formation 429

as opposed to flavo-G clearance. Gender differences in UGT enzyme expression and/or activity 430

have been observed in animal models and in humans (39-41)(42)(43, 44). However, data are not 431

clear with respect to gender specific expression and activity for UGT1A9. Future studies are 432

needed to better define how gender may influence flavo-G formation and/or elimination. 433

434




20

Pharmacodynamic modeling of TLS probability utilized a logistic regression model. The sharp 435

decrease in TLS probability after the first and second doses was handled with a step function 436

based on time. As the most significant risk factor in a multivariate analysis (17), gender was 437

investigated in the covariate analysis for the PD model. However, it was not shown to be a 438

significant PD covariate in this analysis. This may be explained by our use of gender as a 439

covariate on flavo-G PK with females having higher exposure. Since flavo-G AUC was the drug 440

exposure factor used to scale TLS probability, the gender effect was already present and thus was 441

diminished in covariate analysis of the PD model. It is important to note, however, that the 442

exclusion of gender from the current PD model does not necessarily indicate gender is only 443

related to TLS through metabolite PK. It is still possible that females have an increased 444

probability of developing TLS by some unknown mechanism not related to PK. 445

446

Inclusion of β2-microglobulin level gave the highest OFV changes, although it gave the widest 447

confidence intervals for TLS probability. Inclusion of bulky lymphadenopathy status produced a 448

significant decrease in OFV and demonstrated the narrowest confidence intervals in modeling 449

TLS probability with respect to flavopiridol glucuronide AUC. The different behaviors of these 450

two covariates may be due to the different modeling structures for continuous versus 451

dichotomous covariates. Given the strong correlation between these two covariates, we selected 452

only bulky lymphadenopathy status for inclusion in our final model. However, further 453

consideration for both covariates should be given in future models and in larger datasets. 454

455

Our observed set of associated factors, including gender and glucuronide metabolite PK, may 456

indicate unique mechanisms are involved with flavopiridol induced TLS in CLL. Our final and 457




21

current model suggests gender may play a role in TLS development through drug metabolite 458

exposure, although the mechanism for this is not clear. We have previously determined flavo-G 459

metabolites were not cytotoxic to CLL cells ex vivo nor were these metabolites formed at 460

detectable levels within human whole blood or in CLL cells ex vivo (data not shown). Therefore, 461

flavo-G is unlikely involved in direct CLL cell killing. Importantly, TLS is a function of both the 462

rate of tumor cell death and the rate of elimination of toxic cellular debris from systemic 463

circulation. Therefore if flavo-G does contribute to TLS, it may do so by interfering with renal or 464

hepatic clearance of cellular debris through unknown mechanisms. It should be noted, however, 465

that while we have observed the association between flavo-G, gender, and TLS in the two 466

independent trials, the PK concentration-time profiles of flavo-G and flavopiridol overlap 467

considerably, thus preventing us from discerning their individual contributions to the occurrence 468

of TLS. 469

470

Diversity in enzyme activities and expression of genes involved in flavopiridol disposition may 471

influence its efficacy and toxicity. A previous study by Ramirez and colleagues demonstrated 472

significant inter-patient variability of flavopiridol glucuronidation in human hepatic microsomes 473

(45). Many factors, including polymorphism effects of UGT genes on flavopiridol disposition are 474

limited. Zhai and colleagues reported a lack of association of UGT1A1*28 and flavopiridol 475

pharmacokinetics(45), and separate reports demonstrated a lack of 1A7 and 1A9 polymorphic 476

effects on gene expression, flavopiridol transformation in vitro, and no changes in flavopiridol 477

disposition in patients (46, 47). Our group previously observed a lack of significant associations 478

of UGT1A1*28 and UGT1A9*22 in multi variable analyses, although these polymorphisms 479

were associated with flavopiridol and/or flavo-G pharmacokinetics in univariate analyses(29). In 480




22

light of our findings in this current study, a thorough evaluation of polymorphisms in UGT 481

enzymes, particularly UGT1A9, would be warranted in future clinical studies with flavopiridol. 482

If UGT pharmacogenetics proved to be a significant factor contributing to flavo-G disposition 483

and TLS, prospective genotyping may reduce risk for CLL patients receiving flavopiridol 484

therapy. 485

486

This study represents the first population PK-PD approach for modeling TLS. As with other TLS 487

models previously published (48, 49), our model may not be generalizable to other diseases and 488

therapies. Rather than modeling a drug exposure-TLS relationship, which may change with each 489

drug therapy, modeling of a biomarker-TLS relationship may be preferred, if such a biomarker 490

could be identified to be rapidly modulated post flavopiridol therapy and correlate with TLS 491

occurrence and either drug or metabolite levels. However, aside from the current markers used to 492

declare TLS occurrence (potassium, uric acid, phosphate, and LDH), no such marker has been 493

identified.Pre-treatment bulky lymphadenopathy status and β2-microglobulin were correlated 494

with TLS, but these markers were not altered by therapy on a time scale that would be useful for 495

establishing drug exposure-biomarker and biomarker-TLS relationships in a predictive model. 496

Thus we were ultimately left with the glucuronide metabolite as the best observed “biomarker” 497

associated with TLS. The observance of hyper-acute TLS in CLL is rare outside of cyclin 498

dependent kinase inhibitor trials (28, 50). Interestingly, TLS is also observed in CLL patients 499

treated with dinaciclib, a second generation cyclin dependent kinase inhibitor(28). Like 500

flavopiridol, dinaciclib is also eliminated through excretion and metabolism. Although the 501

metabolic and transport pathways have not been elucidated, glucuronidation is clearly important 502




23

in dinaciclib clearance (unpublished data). As new trials with dinaciclib are conducted in CLL, it 503

will be important to monitor the association of gender and dinaciclib on TLS probability. 504

505

Acknowledgement 506

507

This project was supported by grants from the Leukemia and Lymphoma Society (LLS 7080 508

SCOR) and the National Institutes of Health (U01CA76576, U01GM092655 and 509

5KL2RR025754). We thank all trial participants including patients and their families. We 510

express our gratitude to Di Wu, Katie A. Albanese and Katherine L. Farley for analyzing 511

samples. We thank Melissa Vargo and Sarah Mitchell for their assistance in providing clinical 512

data. 513

514 515

516




24

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660 661




28

Figure Legends 662

663 Figure 1. The pharmacokinetic model of flavopiridol and its glucuronide metabolite. Fm = 664

K13/(K10+K13); Flavo, flavopiridol; Flavo-G, flavopiridol glucuronide 665

666

Figure 2. Observed concentrations versus the population (A) or individual (B) predictions, and 667

(C) visual predictive check (VPC) plot using the population pharmacokinetic model for 668

flavopiridol. For panel C, open circles, observed data; grey solid line, median of observed data at 669

nominal time; grey dashed line, 95% confidence interval (CI) of observed data at nominal time; 670

black solid line, median of simulated data at nominal time; black dashed line, 95% prediction 671

interval (PI) of simulated data at nominal time; grey area, 95% CI around median or 95% PI of 672

simulated time. 673

674

Figure 3. Observed concentrations versus the population (A) or individual (B) predictions, and 675

visual predictive check (VPC) plots stratified by male (C) and female (D) patients using the 676

population pharmacokinetic model for flavopiridol glucuronide. For panels C and D, open circles, 677

observed data; grey solid line, median of observed data at nominal time; grey dashed line, 95% 678

confidence interval (CI) of observed data at nominal time; black solid line, median of simulated 679

data at nominal time; black dashed line, 95% prediction interval (PI) of simulated data at 680

nominal time; grey area, 95% CI around median or 95% PI of simulated time. 681

682

Figure 4. Visual predictive check (VPC) plot of TLS probability along with Days in Cycle 1, 683

stratified by male (A), female (B) and all (C) patients. VPC of TLS probability with increasing 684

metabolite AUC (D). For panel D, data was first sorted by observed metabolite AUC and divided 685




29

into 13 bins with approximately 17 observations (AUC/TLS pairs) in each bin. The median 686

metabolite AUC was calculated for each bin, and observed incidence of TLS events was 687

determined for each bin. One thousand (1,000) bootstrap replicates were generated from the 688

final TLS model, and probability of TLS was calculated within each bin from the vectors of the 689

bootstrap parameters. Median and 95% boundaries were then calculated from the 1,000 predicted 690

probabilities at each AUC bin. The median probability is reflective of the typical probability of 691

TLS occurring at a given AUC, while the upper and lower boundaries reflect the 95% CI of the 692

probability curve. 693

694 695




30

Table 1. Population PK parameters of flavopiridol and flavopiridol glucuronide 696 697

Model Term Parameter Estimate (CV%) IIV (CV%) IOV (CV%)

Parent Drug -- Flavopiridol

CL = θCL × exp(IIVCL + IOV) θCL (L/h) 34.1 (3.8) 33.6 (19.1)* 21.7 (20.0)

V1 = θV1 × exp(IIVV1) θV1 (L) 75.8 (3.9) 33.6 (19.1)*

Q = θQ × exp(IIVQ) θQ (L/h) 6.77 (9.2) 74.7 (24.7)

V2 = θV2 × exp(IIVV2) θV2 (L) 91.8 (12.2) 101.0 (17.9) Shared ETA Scale Factor 0.86 (12.3) Corr (ηQ,ηV2) 0.766 Proportional Residual Error ε (%) 39.6 (8.3) Metabolite -- Flavopiridol Glucuronide CLm = θCLm × exp(IIVCLm) θCLm (L/h) 5.17 (9.3) 79.4 (18.5)** V3 = θV3 × exp(IIVV3) θV3 (L) 0.969 (3.1) n.e. Qm = θQm × exp(IIVQm) θQm (L/h) 1.29 (17.1) 79.4 (18.5)** V4 = θV4 × exp(IIVV4) θV4 (L) 6.16 (16.1) n.e. Shared ETA Scale Factor -0.301 (37.2) Fm = θFm θFm (%) 51.3 (71.7) n.e. Factor for Sex 0.462 (19.4) Proportional Residual Error ε (%) 55.8 (9.2) 698 Note: body weight in allometric function applied to all four flavopiridol PK parameters. 699 IIV, inter-individual variability; IOV, inter-occasion variability with each occasion 700 corresponding to each dose level; CV%, coefficient of variation (%); n.e., not estimated. * and 701 ** indicate shared ETAs for parent and metabolite, respectively. The Shared Eta Scale Factors 702 for each were estimated as described in the text. 703 704

705




31

706 Table 2. Population pharmacodynamic parameter estimates for a logistic regression model for 707 probability of TLS occurrence 708 709

Model Term Parameter Estimate (CV%)

PD model

Intercept* (Day 1 or 8, bulky lymphadenopathy < 10 cm) θ1 -2.02 (13.0)

Intercept* (Day 1 or 8, bulky lymphadenopathy > 10 cm) θ3 -1.03 (17.9)

Slope of drug effect (Day 1 or 8, metabolite AUC, regardless of bulky disease status)

θ2 0.0281 (20.4)

Intercept (Day 15 or 22, regardless of bulky disease status) θ4 -30 (43.7)

Slope of drug effect (Day 15 or 22, regardless of bulky disease status)

θ5 0, fixed

IIV η 0, fixed

710 IIV, inter-individual variability; CV%, coefficient of variation (%). * When drug exposure is 711 equal to median drug exposure 712 713 714




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Published OnlineFirst January 8, 2013.Clin Cancer Res Jia Ji, Diane R Mould, Kristie A Blum, et al. with FlavopiridolSyndrome in Chronic Lymphocytic Leukemia Patients Treated A Pharmacokinetic/Pharmacodynamic Model of Tumor Lysis

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