A population model for the leukopenic effect of etoposide

Preparation of etoposide liposomes for enhancing antitumor efficacy on small cell lung cancer and reducing hematotoxicity of drugs

Etoposide (VP16) is commonly used in the treatment of small cell lung cancer (SCLC) in clinical practice. However, severe adverse reactions such as bone marrow suppression toxicity limit its clinical application. Although several studies on VP16 liposomes were reported, no significant improvement in bone marrow suppression toxicity has been found, and there was a lack of validation of animal models for in vivo antitumor effects. Therefore, we attempted to develop a PEGylated liposomal formulation that effectively encapsulated VP16 (VP16-LPs) and evaluated its therapeutic effect and toxicity at the cellular level and in animal models. First, we optimized the preparation process of VP16-LPs using an orthogonal experimental design and further prepared them into freeze-dried powder to improve storage stability of the product. Results showed that VP16-LPs freeze-dried powder exhibited good dispersibility and stability after redispersion. In addition, compared to marketed VP16 injection, VP16-LPs exhibited sustained drug release characteristics. At the cellular level, VP16-LPs enhanced the cellular uptake of drugs and exhibited strong cytotoxic activity. In animal models, VP16-LPs could target and aggregate in tumors and exhibit a higher anti-tumor effect than VP16-injection after intravenous injection. Most importantly, hematological analysis results showed that VP16-LPs significantly alleviated the bone marrow suppression toxicity of drug. In summary, our study confirmed that PEGylated liposomes could enhance therapeutic efficacy and reduce toxicity of VP16, which demonstrated that VP16-LPs had enormous clinical application potential.

A comprehensive regulatory and industry review of modeling and simulation practices in oncology clinical drug development

Exposure-response (E-R) analyses are an integral component in the development of oncology products. Characterizing the relationship between drug exposure metrics and response allows the sponsor to use modeling and simulation to address both internal and external drug development questions (e.g., optimal dose, frequency of administration, dose adjustments for special populations). This white paper is the output of an industry-government collaboration among scientists with broad experience in E-R modeling as part of regulatory submissions. The goal of this white paper is to provide guidance on what the preferred methods for E-R analysis in oncology clinical drug development are and what metrics of exposure should be considered.

Population Pharmacokinetic-Pharmacodynamic Modeling and Covariate Analyses of Neutropenia and Thrombocytopenia in Patients With Solid Tumors Treated With Lurbinectedin

Lurbinectedin is a selective inhibitor of oncogenic transcription. Reversible myelosuppression is its most relevant toxicity. Pharmacokinetic-pharmacodynamic analyses were conducted to characterize the time course of absolute neutrophil count and platelet count recovery and to detect and quantify the effect of relevant covariates in patients with advanced solid tumors treated with lurbinectedin. Absolute neutrophil count, platelet count, and lurbinectedin total plasma concentration were assessed in 244 patients treated with lurbinectedin with varied dosing schedules and doses. A reference extended semimechanistic pharmacokinetic-pharmacodynamic model of myelosuppression was used. Granulocyte colony-stimulating factor (G-CSF) administration was modeled as a dichotomous covariate, and platelet transfusions were included as a bolus dose into the last compartment of the model, representing the central circulation. Final models were suitable to describe the time course of absolute neutrophil count and platelet count recovery. A lurbinectedin dose of 3.2 mg/m² every 3 weeks can be administered without primary prophylaxis with G-CSF. G-CSF followed by ≤2 dose reductions of 20%, if needed, gradually reduced grade 4 neutropenia from cycle 3 onward. BSA-based dosing reduced the incidence of grade ≥ 3 thrombocytopenia. One-week dose delays because of low absolute neutrophil count occurred in 3.5% of patients, thus supporting every-3-week administration. CYP3A inhibitors produced absolute 11.0% and 6.2% increases in grade ≥ 3 neutropenia and thrombocytopenia, respectively. Neutropenia and thrombocytopenia after lurbinectedin administration to cancer patients are noncumulative, reversible, short lasting, and clinically manageable with secondary prophylaxis of G-CSF or platelet transfusion and, if needed, dose reductions.

Dietary restriction increases protective gut bacteria to rescue lethal methotrexate-induced intestinal toxicity

Methotrexate (MTX) is a typical chemotherapeutic drug that is widely used in the treatment of various malignant diseases as well as autoimmune diseases, with gastrointestinal toxicity being its most prominent complication which could have a significant effect on the prognosis of patients. Yet effective ways to alleviate such complications remains to be explored. Here we show that 30% dietary restriction (DR) for 2 weeks dramatically increased the survival rate of 2-month-old female mice after lethal-dose MTX exposure. DR significantly reduced intestinal inflammation, preserved the number of basal crypt PCNA-positive cells, and protected the function of intestinal stem cells (ISCs) after MTX treatment. Furthermore, ablating intestinal microbiota by broad-spectrum antibiotics completely eliminated the protective effect achieved by DR. 16S rRNA gene deep-sequencing analysis revealed that short-term DR significantly increased the Lactobacillus genus, with Lactobacillus rhamnosus GG gavage partially mimicking the rescue effect of DR on the intestines of ad libitum fed mice exposed to lethal-dose MTX. Together, the current study reveals that DR could be a highly effective way to alleviate the lethal injury in the intestine after high-dose MTX treatment, which is functionally mediated by increasing the protective intestinal microbiota taxa in mice. Keywords: Dietary restriction, Methotrexate, Gut microbiota, Intestinal stem cells, intestinal toxicity.

Pharmacokinetic-Pharmacodynamic Model of Neutropenia in Patients With Myeloma Receiving High-Dose Melphalan for Autologous Stem Cell Transplant

High-dose melphalan (HDM) is part of the conditioning regimen in patients with multiple myeloma (MM) receiving autologous stem cell transplantation (ASCT). However, individual sensitivity to melphalan varies, and many patients experience severe toxicities. Prolonged severe neutropenia is one of the most severe toxicities and contributes to potentially life-threatening infections and failure of ASCT. Granulocyte-colony stimulating factor (G-CSF) is given to stimulate neutrophil proliferation after melphalan administration. The aim of this study was to develop a population pharmacokinetic/pharmacodynamic (PK/PD) model capable of predicting neutrophil kinetics in individual patients with MM undergoing ASCT with high-dose melphalan and G-CSF administration. The extended PK/PD model incorporated several covariates, including G-CSF regimen, stem cell dose, hematocrit, sex, creatinine clearance, p53 fold change, and race. The resulting model explained portions of interindividual variability in melphalan exposure, therapeutic effect, and feedback regulation of G-CSF on neutrophils, thus enabling simulation of various doses and prediction of neutropenia duration.

In Silico Evaluation of Pharmacokinetic Optimization for Antimitogram-Based Clinical Trials

Antimitograms are prototype in vitro tests for evaluating chemotherapeutic efficacy using patient-derived primary cancer cells. These tests might help optimize treatment from a pharmacodynamic standpoint by guiding treatment selection. However, they are technically challenging and require refinements and trials to demonstrate benefit to be widely used. In this study, we performed simulations aimed at exploring how to validate antimitograms and how to complement them by pharmacokinetic optimization. A generic model of advanced cancer, including pharmacokinetic-pharmacodynamic monitoring, was used to link dosing schedules with progression-free survival (PFS), as built from previously validated modules. This model was used to explore different possible situations in terms of pharmacokinetic variability, pharmacodynamic variability, and antimitogram performance. The model recapitulated tumor dynamics and standalone therapeutic drug monitoring efficacy consistent with published clinical results. Simulations showed that combining pharmacokinetic and pharmacodynamic optimization should increase PFS in a synergistic fashion. Simulated data were then used to compute required clinical trial sizes, which were 30% to 90% smaller when pharmacokinetic optimization was added to pharmacodynamic optimization. This improvement was observed even when pharmacokinetic optimization alone exhibited only modest benefit. Overall, our work illustrates the synergy derived from combining antimitograms with therapeutic drug monitoring, permitting a disproportionate reduction of the trial size required to prove a benefit on PFS. Accordingly, we suggest that strategies with benefits too small for standalone clinical trials could be validated in combination in a similar manner.Significance: This work offers a method to reduce the number of patients needed for a clinical trial to prove the hypothesized benefit of a drug to progression-free survival, possibly easing opportunities to evaluate combinations. Cancer Res; 78(7); 1873-82. ©2018 AACR.

Pharmacodynamic modeling of adverse effects of anti-cancer drug treatment

Purpose

Adverse effects related to anti-cancer drug treatment influence patient’s quality of life, have an impact on the realized dosing regimen, and can hamper response to treatment. Quantitative models that relate drug exposure to the dynamics of adverse effects have been developed and proven to be very instrumental to optimize dosing schedules. The aims of this review were (i) to provide a perspective of how adverse effects of anti-cancer drugs are modeled and (ii) to report several model structures of adverse effect models that describe relationships between drug concentrations and toxicities.

Methods

Various quantitative pharmacodynamic models that model adverse effects of anti-cancer drug treatment were reviewed.

Results

Quantitative models describing relationships between drug exposure and myelosuppression, cardiotoxicity, and graded adverse effects like fatigue, hand-foot syndrome (HFS), rash, and diarrhea have been presented for different anti-cancer agents, including their clinical applicability.

Conclusions

Mathematical modeling of adverse effects proved to be a helpful tool to improve clinical management and support decision-making (especially in establishment of the optimal dosing regimen) in drug development. The reported models can be used as templates for modeling a variety of anti-cancer-induced adverse effects to further optimize therapy.

A strategy for residual error modeling incorporating scedasticity of variance and distribution shape

Nonlinear mixed effects models parameters are commonly estimated using maximum likelihood. The properties of these estimators depend on the assumption that residual errors are independent and normally distributed with mean zero and correctly defined variance. Violations of this assumption can cause bias in parameter estimates, invalidate the likelihood ratio test and preclude simulation of real-life like data. The choice of error model is mostly done on a case-by-case basis from a limited set of commonly used models. In this work, two strategies are proposed to extend and unify residual error modeling: a dynamic transform-both-sides approach combined with a power error model (dTBS) capable of handling skewed and/or heteroscedastic residuals, and a t-distributed residual error model allowing for symmetric heavy tails. Ten published pharmacokinetic and pharmacodynamic models as well as stochastic simulation and estimation were used to evaluate the two approaches. dTBS always led to significant improvements in objective function value, with most examples displaying some degree of right-skewness and variances proportional to predictions raised to powers between 0 and 1. The t-distribution led to significant improvement for 5 out of 10 models with degrees of freedom between 3 and 9. Six models were most improved by the t-distribution while four models benefited more from dTBS. Changes in other model parameter estimates were observed. In conclusion, the use of dTBS and/or t-distribution models provides a flexible and easy-to-use framework capable of characterizing all commonly encountered residual error distributions.

Development of etoposide-loaded bovine serum albumin nanosuspensions for parenteral delivery

Nanosuspensions emerge as a promising strategy for delivery of poorly water-soluble drugs. Albumin is a versatile protein carrier for drug delivery and targeting. The purpose of this study was to develop a formulation of etoposide-loaded bovine serum albumin (BSA) nanosuspensions, to study in vitro characterization, and to estimate the in vivo safety and tissue distribution of etoposide-loaded BSA nanosuspensions for parenteral delivery. Etoposide-loaded BSA nanosuspensions were prepared by high-pressure homogenization-solvent precipitation method. The particle size, zeta potential, drug entrapment efficiency, and drug loading of the lyophilized formulation were 182.3 nm, -22.18 mV, 86.44%, and 8.49% respectively. In vitro release files of the formulation presented sustained release properties. Preliminary safety study was conducted to evaluate the delivery system, and results indicated that myelosuppression effect of the etoposide-loaded BSA nanosuspensions group was significantly lower than the Injection® group. Furthermore, results of tissue distribution studies showed that the concentration and AUC of etoposide were increased significantly in lung, liver, spleen while reduced in heart, kidney compared with the etoposide injection® group after i.v. administration of etoposide-loaded BSA nanosuspensions. The formulation played a role in targeting delivery to lung, reduce toxicity, and side effects of etoposide. In conclusion, etoposide-loaded BSA nanosuspensions were promising for parenteral delivery of etoposide.

PKPD Modeling of Predictors for Adverse Effects and Overall Survival in Sunitinib-Treated Patients With GIST

A modeling framework relating exposure, biomarkers (vascular endothelial growth factor (VEGF), soluble vascular endothelial growth factor receptor (sVEGFR)-2, -3, soluble stem cell factor receptor (sKIT)), and tumor growth to overall survival (OS) was extended to include adverse effects (myelosuppression, hypertension, fatigue, and hand–foot syndrome (HFS)). Longitudinal pharmacokinetic–pharmacodynamic models of sunitinib were developed based on data from 303 patients with gastrointestinal stromal tumor. Myelosuppression was characterized by a semiphysiological model and hypertension with an indirect response model. Proportional odds models with a first-order Markov model described the incidence and severity of fatigue and HFS. Relative change in sVEGFR-3 was the most effective predictor of the occurrence and severity of myelosuppression, fatigue, and HFS. Hypertension was correlated best with sunitinib exposure. Baseline tumor size, time courses of neutropenia, and relative increase of diastolic blood pressure were identified as predictors of OS. The framework has potential to be used for early monitoring of adverse effects and clinical response, thereby facilitating dose individualization to maximize OS.

The shape of the myelosuppression time profile is related to the probability of developing neutropenic fever in patients with docetaxel-induced grade IV neutropenia

PURPOSE

Chemotherapy-induced neutropenia is associated with the risk of developing febrile neutropenia (FN). The aim was to describe the time course of myelosuppression in breast cancer patients treated with docetaxel and to investigate how the shape of the predicted myelosuppression time course and earlier proposed risk factors influence the probability of developing FN.

METHODS

Neutrophil counts from 140 breast cancer patients with observed grade IV neutropenia during the first course of docetaxel treatment were included. Twenty-six of the patients (19%) experienced FN. The myelosuppression time course was described using a semi-mechanistic myelosuppression model in NONMEM. The individual myelosuppression model parameters [baseline neutrophil count, mean transit time (MTT) and drug effect parameter (EC(50))], myelosuppression descriptors (nadir, duration of grade IV neutropenia) and earlier suggested risk factors (age, performance status, haemoglobin and liver function) were explored to be related to FN by logistic regression.

RESULTS

The neutrophil time course following docetaxel treatment was well described by the model. EC(50) and MTT were both significantly related to the probability of developing FN where low parameter values result in a rapid decline, low nadir and an increased risk of FN. None of the evaluated risk factors or myelosuppression descriptors were significant.

CONCLUSION

The probability to develop FN in patients who experience grade IV neutropenia was dependent on the myelosuppression time profile. Patients with a rapid neutrophil decline and high drug sensitivity had a higher probability to develop FN. Model-based parameter estimates were superior predictors over descriptive values such as the nadir or duration of neutropenia.

Pharmacokinetic-pharmacodynamic modeling of the effect of fluvoxamine on p-chloroamphetamine-induced behavior

The pharmacokinetic-pharmacodynamic (PK-PD) correlation of the effect of fluvoxamine on para-chloroamphetamine (PCA)-induced behavior was determined in the rat. Rats (n=66) with permanent arterial and venous cannulas received a 30-min intravenous infusion of 1.0, 3.7 or 7.3 mg kg(-1) fluvoxamine. At various time points after the start of fluvoxamine administration, a single dose of PCA (2.5 mg kg(-1)) was injected in the tail vein and resulting behavioral effects, excitation (EXC), flat body posture (FBP) and forepaw trampling (FT), were immediately scored (scores: 0, 1, 2 or 3) over a period of 5 min. In each individual animal the time course of the fluvoxamine plasma concentration was determined up to the time of PCA administration. Observed behavioral effects were related to fluvoxamine plasma concentrations. Fluvoxamine pharmacokinetics was described by a population three-compartment pharmacokinetic model. The effects of fluvoxamine on PCA-induced behavior (probability of EXC, FBP and FT) were directly related to fluvoxamine plasma concentration on the basis of the proportional odds model. For EXC, EC(50) values for the cumulative probabilities P(Y<1), P(Y<2), P(Y<3) were 237+/-39, 174+/-28 and 100+/-20 ng ml(-1), respectively. Slightly higher EC(50) values were obtained for the corresponding effects on FBP and FT. This investigation demonstrates the feasibility of PK-PD modeling of categorical drug effects in animal behavioral pharmacology. This constitutes a basis for the future development of a mechanism-based PK-PD model for fluvoxamine in this paradigm.

Contribution of modelling chemotherapy-induced hematological toxicity for clinical practice

Anticancer chemotherapies are responsible for numerous adverse events. Among these, hematological toxicity is one of the main causes for ending treatment. These toxicities decrease production of red blood cells (anemia), production of white blood cells (neutropenia or granulocytopenia), and production of platelets (thrombocytopenia), which may be life-threatening to the patient. Preventing such discontinuation would be valuable for treating patients more effectively. In order to achieve this goal, numerous mathematical and physiological or semiphysiological models have been developed. The complexity of models has increased over the years, from empiric E(max) models to mechanistic models including physiological mechanisms such as feedback control. This review discusses several approaches of modelling hematological toxicities illustrated with some examples: pharmacodynamic models for the hematological toxicity of 5-fluorouracil, epirubicin, melphalan, paclitaxel, topotecan, and indisulam.

Population Pharmacokinetic-Pharmacodynamic Model for Neutropenia with Patient Subgroup Identification: Comparison across Anticancer Drugs

PURPOSE

Cancer chemotherapy, although based on body surface area, often causes unpredictable myelosuppression, especially severe neutropenia. The aim of this study was to evaluate qualitatively and quantitatively the influence of patient-specific characteristics on the neutrophil concentration-time course, to identify patient subgroups, and to compare covariates on system-related pharmacodynamic variable between drugs.

EXPERIMENTAL DESIGN

Drug and neutrophil concentration, demographic, and clinical chemistry data of several trials with docetaxel (637 patients), paclitaxel (45 patients), etoposide (71 patients), or topotecan (191 patients) were included in the covariate analysis of a physiology-based pharmacokinetic-pharmacodynamic neutropenia model. Comparisons of covariate relations across drugs were made.

RESULTS

A population model incorporating four to five relevant patient factors for each drug to explain variability in the degree and duration of neutropenia has been developed. Sex, previous anticancer therapy, performance status, height, binding partners, or liver enzymes influenced system-related variables and alpha1-acid glycoprotein, albumin, bilirubin, concomitant cytotoxic agents, or administration route changed drug-specific variables. Overall, female and pretreated patients had a lower baseline neutrophil concentration. Across-drug comparison revealed that several covariates (e.g., age) had minor (clinically irrelevant) influences but consistently shifted the pharmacodynamic variable in the same direction.

CONCLUSIONS

These mechanistic models, including patient characteristics that influence drug-specific parameters, form the rationale basis for more tailored dosing of individual patients or subgroups to minimize the risk of infection and thus might contribute to a more successful therapy. In addition, nonsignificant or clinically irrelevant relations on system-related parameters suggest that these covariates could be negligible in clinical trails and daily use.

Pharmacokinetic/pharmacodynamic modelling in oncological drug development

For many oncological agents, myelosuppression is the dose-limiting toxicity and the quantitative characterisation of the relationship between drug dose, plasma concentration and haematological toxicity is of importance in the drug development. Mechanism-based population pharmacokinetic-pharmacodynamic models have been developed for this purpose and the applications of these in candidate selection, first-in-man studies, prodrug and formulation development, dose finding, schedule optimisation, assessing influence of modifying agents, drug combination studies, subgroup identification and feedback individualisation are reviewed.

Semi-physiological model describing the hematological toxicity of the anti-cancer agent indisulam

Indisulam (N-(3-chloro-7-indolyl)-1,4-benzenedisulfonamide, GOAL, E7070) is a novel anti-cancer drug currently in phase II clinical development for the treatment of solid tumors. Phase I dose-escalation studies were conducted comparing four treatment schedules. Neutropenia and thrombocytopenia were dose limiting in all schedules. The aim of this study was to describe the extent and the time course of the hematological toxicity and its possible schedule dependency using a semi-physiological model. Data from 142 patients were analyzed using NONMEM. The semi-physiological model comprised a progenitor blood cell compartment, linked to the central circulation compartment, through 3 transition compartments representing the maturation chain in the bone marrow. Plasma concentrations of the drug were assumed to reduce the proliferation rate in the progenitor compartment according to a linear function. A feedback mechanism was included in the model representing the rebound effect of endogenous growth factors. The model was validated using a posterior predictive check. The model adequately described the extent and time course of neutropenia and thrombocytopenia. The mean transition time (MTT, i.e. maturation time in bone marrow) of neutrophils was increased by 47% in patients who received indisulam as a weekly dose administered for four out of every six weeks. For platelets, MTT was increased by 33% in patients who received this schedule and also in patients who received a continuous 120-h infusion. The validation procedure indicated that the model adequately predicts the nadir value of neutrophils and platelets and the time to reach this nadir. A semi-physiological model was successfully applied to describe the time course and extent of the neutropenia and thrombocytopenia after indisulam administration for four treatment schedules.

Nonlinear mixed-effects model for the dissolution assays of drugs

Sustained or controlled release delivery systems can achieve predictable and reproducible drug release rates. With the aim of optimizing the bioavailability of conventional drugs with minimum side effects, new drug delivery systems continue to attract much attention. This research has been addressed to investigate in vitro metoclopramide release behavior from inert polymeric matrix tablets. Model-dependent and model-independent approaches are commonly used to investigate the release process and a fitted model results for each experimental condition. The aim of this work was to study the release characterization of the whole batch, therefore, we have developed a population model, taking into account the differences between tablets, experimental conditions, heterogeneity, and autocorrelation of residuals. The study of dissolution profiles was carried out using a mixed-effect model, which has provided a satisfactory tool for this kind of data.

Pharmacokinetic-pharmacodynamic modeling of methotrexate-induced toxicity in mice

The prediction of chemotherapeutic efficacy is complicated by "protocol dependencies" in dose-effect and dose-toxicity relationships. It has been proposed that pharmacokinetic-pharmacodynamic mathematical models may allow characterization of chemotherapeutic protocol dependencies, and may facilitate the prediction of chemotherapeutic efficacy; however, few demonstrations exist in the literature. The present study examines the pharmacokinetics and toxicodynamics of methotrexate (MTX), a commonly used anticancer agent, after intraperitoneal (i.p.) administration to mice. MTX was administered via bolus or infusion (24, 72, and 168 h), at doses of 2.5-1000 mg/kg. MTX plasma and peritoneal pharmacokinetics were characterized through standard noncompartmental and compartmental techniques. Body weight loss was used as a measure of MTX-induced toxicity. We found that MTX pharmacokinetics were independent of dose (over a range of 3-600 mg/kg) and independent of dosing mode (i.e., i.p. bolus vs. i.p. infusion). However, MTX-induced toxicity was shown to be highly dependent on the dosing protocol used. For example, the maximally tolerated dose (i.e., the dose related to a mean body weight loss of 10%) was 200-fold greater after bolus administration relative to that observed for 72-h infusion (760 mg/kg vs. 3.8 mg/kg). This profound protocol dependence in the relationship between MTX-induced toxicity and MTX exposure was characterized through the use of a time-dissociated pharmacokinetic-pharmacodynamic model (median prediction error: 3.9%).

Pharmacokinetic-pharmacodynamic guided trial design in oncology

The application of pharmacokinetic (PK) and pharmacodynamic (PD) modeling in drug development has emerged during the past decades and it is has been suggested that the investigation of PK-PD relationships during drug development may facilitate and optimize the design of subsequent clinical development. Especially in oncology, well designed PK-PD modeling could be extremely useful as anticancer agents usually have a very narrow therapeutic index. This paper describes the application of the current insights in the use of PK-PD modeling to the design of clinical trials in oncology. The application of PK-PD modeling in each separate stage of (pre)clinical drug development of anticancer agents is discussed. The implementation of this approach is illustrated with the clinical development of docetaxel.

Mechanistic models for myelosuppression

As myelosuppression is the dose-limiting toxicity for most chemotherapeutic drugs, modelers attempt to find relationships between drug and toxicity to optimize treatment. Mechanistic models, i.e. models based on physiology and pharmacology, are preferable over empirical models, as prior information can be utilized and as they generally are more reliable for extrapolations. To account for different dosing-regimens and possible schedule-dependent effects, the whole concentration-time profile should be used as input into the pharmacokinetic-pharmacodynamic model. It is also of importance to model the whole time course of myelosuppression to be able to predict both the degree and duration of toxicity as well as consecutive courses of therapy. A handful of (semi)-mechanistic pharmacokinetic-pharmacodynamic models with the above properties have been developed and are reviewed. Ideally, a model of myelosuppression should separate drug-specific parameters from system related parameters to be applicable across drugs and useful under different clinical settings. Introduction of mechanistic models of myelosuppression in the design and evaluation of clinical trials can guide in the decision of optimal sampling times, contribute to knowledge of optimal doses and treatment regimens at an earlier time point and identify sub-groups of patients at a high risk of myelosuppression.

Model of chemotherapy-induced myelosuppression with parameter consistency across drugs

PURPOSE

To develop a semimechanistic pharmacokinetic-pharmacodynamic model describing chemotherapy-induced myelosuppression through drug-specific parameters and system-related parameters, which are common to all drugs.

PATIENTS AND METHODS

Patient leukocyte and neutrophil data after administration of docetaxel, paclitaxel, and etoposide were used to develop the model, which was also applied to myelosuppression data from 2'-deoxy-2'-methylidenecytidine (DMDC), irinotecan (CPT-11), and vinflunine administrations. The model consisted of a proliferating compartment that was sensitive to drugs, three transit compartments that represented maturation, and a compartment of circulating blood cells. Three system-related parameters were estimated: baseline, mean transit time, and a feedback parameter. Drug concentration-time profiles affected the proliferation of sensitive cells by either an inhibitory linear model or an inhibitory E(max) model. To evaluate the model, system-related parameters were fixed to the same values for all drugs, which were based on the results from the estimations, and only drug-specific parameters were estimated. All modeling was performed using NONMEM software.

RESULTS

For all investigated drugs, the model successfully described myelosuppression. Consecutive courses and different schedules of administration were also well characterized. Similar system-related parameter estimates were obtained for the different drugs and also for leukocytes compared with neutrophils. In addition, when system-related parameters were fixed, the model well characterized chemotherapy-induced myelosuppression for the different drugs.

CONCLUSION

This model predicted myelosuppression after administration of one of several different chemotherapeutic drugs. In addition, with fixed system-related parameters to proposed values, and only drug-related parameters estimated, myelosuppression can be predicted. We propose that this model can be a useful tool in the development of anticancer drugs and therapies.

Multiple-pool cell lifespan model of hematologic effects of anticancer agents

The leukopenic effects of anticancer agents are described using a semi-physiologic multiple-pool cell lifespan model. The time course of myelosuppression in relation to the drug concentration vs. time profile was characterized using a three pool indirect model. The proliferation and maturation stages of myeloid cells in the bone marrow and cell removal from the circulation were quantitated with a cell life-span concept. Drug effects were assumed to take place in the bone marrow based on irreversible linear or capacity-limited cytotoxicity. Mathematical derivations and computer simulations (Adapt II) were used to examine the properties of the model. Data from the literature were also analyzed. Cell response profiles after therapy typically exhibit a lag period, reduction to a nadir, and return to baseline. The predicted values of the time periods of granulopoiesis were 10-14 days for proliferation, and 1-6 days for maturation of progenitor cells in the bone marrow. The proposed irreversible mechanism of cell killing by anticancer drugs explains previously observed relationships between leukocyte nadir counts and exposure to the drug and/or duration of drug concentrations above some threshold level. The model was applied to literature data for paclitaxel and etoposide effects on leukocyte counts. The predicted value of KC50 for paclitaxel ranged from 0.004 to 0.2 microgram/mL and for etoposide 2 micrograms/mL. The present model accounts for drug-induced leukopenia using a physiologic cell production and loss model and irreversible cytotoxicity in a precursor pool.

Pharmacodynamic modeling of the entire time course of leukopenia after a 3-hour infusion of paclitaxel

The entire time course of leukopenia after anticancer treatment is clinically more relevant than a singly measured nadir count. In order to identify factors associated with neutropenic fever, a mechanistic pharmacodynamic model with two compartments corresponding to leukocytes in bone marrow and peripheral blood was applied to describe the time course of leukopenia. Seventeen patients with breast cancer were treated with 210 mg / m(2) of paclitaxel infused over 3 h as a single agent in a phase II study. Adequate fitting of the time course of leukopenia was achieved in all patients, and time-dependent parameters, including the time period during which leukocyte counts remained below 2000 / microl and the area between the curve for time versus leukocyte counts and the line of a leukocyte count of 2000 / microl (A < 2000), were calculated in each patient. Leukopenia was not significantly correlated with pharmacokinetic parameters, including time above a threshold concentration or the area under the time-concentration curve. A negative correlation between age and the sensitivity parameter of the pharmacodynamic model was observed (r (2) = 0.21, P = 0.07). Patients who experienced neutropenic fever had a larger A < 2000 than patients who did not experience fever (4512 vs. 6 days / microl, P = 0.05), but fever was not significantly related to any pharmacokinetic parameter or the leukocyte nadir count. Febrile episodes were better associated with the time course of leukopenia than the singly measured nadir count, and the pharmacodynamic model presents a novel platform to analyze the entire time course of leukopenia.

Simulation tool for schedule-dependent etoposide exposure based on pharmacokinetic findings published in the literature

It is the aim of this study to establish a simulation tool for etoposide (Eto) which can be used to interpret drug monitoring data in clinical practice and to design new schedules for future protocols. As schedule dependency was observed for Eto, knowledge of concentration-time profiles is important. Pharmacokinetic data from children after low-dose i.v. administration of Eto together with data reported in the literature were used to construct the simulation tool. Validation was performed by independently reproducing various published data. Dose linearity of AUC was shown over the whole dose range of 20-2000 mg/m2 reported in the literature and fits the predictions by the simulation tool. There was no difference in clearance between children and adults. Close agreement was found between predicted and reported concentration-time profiles after various administration schedules. However, subgroups with significantly altered pharmacokinetics of Eto, such as patients with renal impairment or concurrent cisplatin chemotherapy, were excluded from the comparisons. In these patients values predicted for a 'regular' patient might be used as a base for possible dose modifications. In summary, a pharmacokinetic model of high predictive value is presented which allows simulations of Eto concentration-time profiles for low- as well as high-dose conditions and various infusion times.

A cell-kinetic model of CD34+ cell mobilization and harvest: development of a predictive algorithm for CD34+ cell yield in PBPC collections

BACKGROUND

Mobilization and homing of PBPCs are still poorly understood. Thus, a sufficient algorithm for the prediction of PBPC yield in apheresis procedures does not yet exist.

STUDY DESIGN AND METHODS

The decline of CD34+ cells in the peripheral blood during apheresis and their simultaneous increase in the collection bag were determined in a prospective study of 18 consecutive apheresis procedures. A cell-kinetic, four-compartment model describing these changes was developed. Retrospective data from 136 apheresis procedures served to further improve this model. A predictive algorithm for the yield was developed that considered the sex, weight, and height of the patient, the number of CD34+ cells in peripheral blood before apheresis, the inlet flow, and the duration of the apheresis. The accuracy of this algorithm was evaluated by comparison of the predicted and the observed yields of CD34+ cells in 105 prospective autologous and 148 retrospective allogeneic apheresis procedures.

RESULTS

The correlation between predicted and observed yields was good for the autologous and allogeneic groups with a correlation coefficient (r) of 0.8979 and 0.8311 (p<0.0001), respectively. The regression is described by the equations log (measured value [m]) = 1.0118 + 0.8595 x log (predicted value [p]) for the autologous and log (m) = 2.226 + 0.7559 x log (p) for the allogeneic group. The respective equations for the zero-point regression are log (m) = 1.014 x log (p) and log (m) = 1.026 x log (p). The probability that the measured value was 90 percent or more of the predicted value was 83.8 percent for the autologous and 90.5 percent for the allogeneic apheresis procedures.

CONCLUSION

The predictive accuracy of the algorithm and the slope of the zero-point regression curve were higher for allogeneic than autologous PBPC collections. The predictive algorithm may be a useful tool in PBPC harvest, enabling the adaptation of the size of the apheresis to the needs of each patient.

Population pharmacokinetic model for topotecan derived from phase I clinical trials

PURPOSE

To characterize the pharmacokinetics of topotecan in a population model that would identify patient variables or covariates that appreciably impacted on its disposition.

PATIENTS AND METHODS

All data were collected from 82 patients entered in four different phase I trials that were previously reported as separate studies from 1992 to 1996. All patients received topotecan as a 30-minute constant-rate infusion on a daily-times-five schedule and were selected for this study because their daily dose did not exceed 2.0 mg/m(2). Among the 82 patients were 30 patients classified as having renal insufficiency and 13 patients with hepatic dysfunction. The population pharmacokinetic model was built in sequential manner, starting with a covariate-free model and progressing to a covariate model with the aid of generalized additive modeling.

RESULTS

A linear two-compartment model characterized total topotecan plasma concentrations (n = 899). Four primary pharmacokinetic parameters (total clearance, volume of the central compartment, distributional clearance, and volume of the peripheral compartment) were related to various combinations of covariates. The relationship for total clearance (TVCL [L/h] = 32.0 + [0.356(WT - 71) + 0.308(HT - 168.5) - 8.42(SCR - 1.1)] x [1 + 0.671 sex]) was dependent on the patients' weight (WT), height (HT), serum creatinine (SCR), and sex and had a moderate ability to predict (r(2) = 0.64) each patient's individual clearance value. The addition of covariates to the population model improved the prediction errors, particularly for clearance. Removal of 10 outlying patients from the analysis improved the ability of the model to predict individual clearance values (r(2) = 0.77).

CONCLUSION

A population pharmacokinetic model for total topotecan has been developed that incorporates measures of body size and renal function to predict total clearance. The model can be used prospectively to obtain a revised and validated model that can then be used to design individualized dosing regimens.

Optimizing drug regimens in cancer chemotherapy by an efficacy-toxicity mathematical model

In cancer chemotherapy, it is important to design treatment strategies that ensure a desired rate of tumor cell kill without unacceptable toxicity. To optimize treatment, we used a mathematical model describing the pharmacokinetics of anticancer drugs, antitumor efficacy, and drug toxicity. This model was associated with constraints on the allowed plasma concentrations, drug exposure, and leukopenia. Given a schedule of drug administrations, the mathematical model optimized the drug doses that can minimize the tumor burden while limiting toxicity at the level of the white blood cells. The main result is that the optimal drug administration is an initial high-dose chemotherapy up to saturation of constraints associated with normal cell toxicity and a maintenance continuous infusion at a moderate rate. Data related to etoposide investigations were used in a feasibility study. Simulations with the optimized protocol showed better performances than usual clinical protocols. Model-based optimal drug doses provide for greater cytoreduction, while limiting the risk of unacceptable toxicity.

A randomised, concentration-controlled, comparison of standard (5-day) vs. prolonged (15-day) infusions of etoposide phosphate in small-cell lung cancer

PURPOSE

This randomised trial was designed to investigate the activity and toxicity of continuous infusion etoposide phosphate (EP), targeting a plasma etoposide concentration of either 3 micrograms/ml for five days (5d) or 1 microgram/ml for 15 days (15d), in previously untreated SCLC patients with extensive disease.

PATIENTS AND METHODS

EP was used as a single agent. Plasma etoposide concentration was monitored on days 2 and 4 in patients receiving 5d EP and on days 2, 5, 8 and 11 in patients receiving 15d EP, with infusion modification to ensure target concentrations were achieved. Treatment was repeated every 21 days for up to six cycles, with a 25% reduction in target concentration in patients with toxicity.

RESULTS

The study has closed early after entry of 29 patients (14 with 5d EP, 15 with 15d EP). Objective responses were seen in seven of 12 (58%, confidence interval (CI): 27%-85%) evaluable patients after 5d EP, and two of 14 (14%, CI: 4%-42%) evaluable patients after 15d EP (P = 0.038). Grade 3 or 4 neutropenia or leucopenia during the first cycle of treatment was observed in six of 12 patients after 5d EP and 0/14 patients after 15d EP (P = 0.004), with median nadir WBC count of 2.6 x 10(9)/1 after 5d and 5.0 x 10(9)/1 after 15d EP (P = 0.017). Only one of 49 cycles of 15d EP was associated with grade 3 or worse haematological toxicity, compared to 14 of 61 cycles of 5d EP.

CONCLUSIONS

Although the number of patients entered into this trial was small, the low activity seen at 1 microgram/ml in the 15d arm suggests that this concentration is below the therapeutic window in this setting. Further concentration-controlled studies with prolonged EP infusions are required.

Population pharmacokinetics and pharmacodynamics of cisplatin in patients with cancer: analysis with the NONMEM program

The population pharmacokinetics and pharmacodynamics of cisplatin (CDDP) were evaluated based on a mixed-effect model using the NONMEM program. Unchanged CDDP in plasma was measured as a biologically active platinum species during CDDP chemotherapy, using high-performance liquid chromatography. Plasma concentration measurements (157) of unchanged CDDP from 26 patients with cancer receiving 80 mg/m2 CDDP by infusion over 2 hours, 3.5 hours, or 4 hours were analyzed according to a one-compartment model. The influences of individual characteristics such as body weight, dose schedule, course, and clinical laboratory values (renal function markers, albumin) on total body clearance (Cl) and volume of distribution (Vd) were examined. In the final pharmacokinetic model, body surface area and dose schedule affected Cl of unchanged CDDP. The Cl of CDDP was increased by 27.3% after the 2-hour infusion schedule compared with Cl after the longer infusions. The Vd was estimated as 13.4 L/m2. The interindividual variability for Cl and Vd and residual variability were 22.9%, 30.9%, and 35.5%, respectively. The relationships between maximum concentration (Cmax) of unchanged CDDP and maximum blood urea nitrogen (BUNmax), or minimum creatinine clearance (ClCr,min) over a 1-month period after CDDP administration were evaluated according to linear, exponential, or maximum response (Emax) models. The linear or Emax model described pharmacodynamics most successfully, with relatively large interindividual variability for both slope and EC50 (more than 25%). Residual variability was 15.3% and 17.1% in BUNmax and Clcrmin, respectively. The population means and interindividual and residual variability of pharmacokinetics and pharmacodynamics of CDDP were evaluated using the NONMEM program. The results of this study show that the population pharmacokinetic and pharmacodynamic approach could be useful to manage CDDP nephrotoxicity using sparse data in a clinical setting.

Indirect-response model for the time course of leukopenia with anticancer drugs

BACKGROUND

Because both the nadir count and the duration of leukopenia after chemotherapy with anticancer drugs are important, a pharmacodynamic model describing the entire time course of leukopenia is valuable. In this study, a pharmacodynamic model was developed to simulate leukopenia.

METHODS

The model was developed with the 3-hour infusion data of paclitaxel. A concentration-time curve of paclitaxel for each patient estimated by a 3-compartment pharmacokinetic model was used as input to the model, which had 2 compartments corresponding to leukocytes in bone marrow and peripheral blood, respectively. Differentiation stages of myeloid cells sensitive to anticancer drugs were assumed, and exposure to a drug during their sensitive period as a function of time was used to inhibit the production of leukocytes in bone marrow. The model was validated by fitting the data of 24-hour infusion of paclitaxel or 14-day infusion of etoposide.

RESULTS

Successful fitting of the leukopenia after a 3-hour infusion of paclitaxel was achieved. The following parameters were estimated: lag-time, 58 +/- 38 (mean +/- SD) hours before the leukocyte count started to decline; exposure giving 50% inhibition of leukocyte production (IC), 12.1 +/- 6.1 microg x h/mL; and sensitive period, 288 +/- 64 hours. These estimations were within physiologic ranges. In validation, leukopenia after 24-hour infusion of paclitaxel or 14-day infusion of etoposide was also explained by the model. Age was significantly negatively correlated with IC of paclitaxel (P = .039).

CONCLUSIONS

This mechanistic model describes the time course of leukopenia and may provide a platform for pharmacodynamic analysis of anticancer drugs.

Expanding clinical applications of population pharmacodynamic modelling

Population pharmacokinetics or pharmacodynamics is the study of the variability in drug concentration or pharmacological effect between individuals when standard dosage regimens are administered. We provide an overview of pharmacokinetic models, pharmacodynamic models, population models and residual error models. We outline how population modelling approaches seek to explain interpatient variability with covariate analysis, and, in some approaches, to characterize the unexplained interindividual variability. The interpretation of the results of population modelling approaches is facilitated by shifting the emphasis from the perspective of the modeller to the perspective of the clinician. Both the explained and unexplained interpatient variability should be presented in terms of their impact on the dose-response relationship. Clinically relevant questions relating to the explained and unexplained variability in the population can be posed to the model, and confidence intervals can be obtained for the fraction of the population that is estimated to fall within a specific therapeutic range given a certain dosing regimen. Such forecasting can be used to develop optimal initial dosing guidelines. The development of population models (with random effects) permits the application of Bayes's formula to obtain improved estimates of an individual's pharmacokinetic and pharmacodynamic parameters in the light of observed responses. An important challenge to clinical pharmacology is to identify the drugs that might benefit from such adaptive-control-with-feedback dosing strategies. Drugs used for life threatening diseases with a proven pharmacokinetic-pharmacodynamic relationship, a small therapeutic range, large interindividual variability, small interoccasion variability and severe adverse effects are likely to be good candidates. Rapidly evolving changes in health care economics and consumer expectations make it unlikely that traditional drug development approaches will succeed in the future. A shift away from the narrow focus on rejecting the null hypothesis towards a broader focus on seeking to understand the factors that influence the dose-response relationship--together with the development of the next generation of software based on population models--should permit a more efficient and rational drug development programme.

Overview of recent topics in clinical pharmacology of anticancer agents

The rationale for studying the clinical pharmacology of antineoplastic agents is that the information obtained will result in enhanced drug development and enhanced or improved clinical use. A great deal of effort has been expended in studying the pharmacokinetics and pharmacodynamics of investigational and noninvestigational antineoplastic agents. More recently, a deeper appreciation has developed regarding the importance of the metabolism of antineoplastic agents and the potential role of metabolites in their activity or toxicity, as well as the potential for drug-drug interactions. Investigators studying the clinical pharmacology of antineoplastic agents face an increasingly challenging task as new agents continue to be developed. Some of these challenges arise from the enhanced potency of new agents, resulting in increased difficulty in measuring such agents in biological matrices. Furthermore, as agents have been developed to affect specific biological targets, the necessity of assessing pharmacodynamics at the biochemical or molecular level has become increasingly important. In addition, development of agents with cytostatic, as opposed to cytotoxic, properties poses a further challenge to assessment of pharmacologic effect. In addressing these challenges, a great deal of effort has been expended to develop increasingly sensitive analytical chemical techniques, in evaluating alternative biological matrices, such as saliva, in which to monitor drug concentrations in a less invasive fashion, and in developing limited sampling strategies to assess both the pharmacokinetics and pharmacodynamics of antineoplastic agents. Similarly, a great deal of effort has been expended in providing suitable means for assessing the numerous novel targets for which antineoplastic agents are being developed. These include the assessment of cell cycle kinetics and specific oncoproteins, definition of cell damage such as cleavable complexes, and formation of drug-macromolecular adducts in suitable target cells. Additional effort is being expended to explore nontraditional means of drug delivery. In this regard, the increasing importance of orally administered agents reflects a fundamental change in the approach to antineoplastic drug delivery. Finally, the increased computational power made available by faster personal computers has facilitated a number of innovative modeling techniques involving population modeling, modeling of combination chemotherapy, and assessment of drug-drug interactions.

Automated covariate model building within NONMEM

PURPOSE

One important task in population pharmacokinetic/pharmacodynamic model building is to identify the relationships between the parameters and demographic factors (covariates). The purpose of this study is to present an automated procedure that accomplishes this. The benefits of the proposed procedure over other commonly used methods are (i) the covariate model is built for all parameters simultaneously, (ii) the covariate model is built within the population modeling program (NONMEM) giving familiar meaning to the significance levels used, (iii) it can appropriately handle covariates that varies over time and (iv) it is not dependent on the quality of the posterior Bayes estimates of the individual parameter values. For situations in which the computer run-times are a limiting factor, a linearization of the non-linear mixed effects model is proposed and evaluated.

METHODS

The covariate model is built in a stepwise fashion in which both linear and non-linear relationships between the parameters and covariates are considered. The linearization is basically a linear mixed effects model in which the population predictions and their derivatives with respect to the parameters are fixed from a model without covariates. The stepwise procedure as well as the linearization was evaluated using simulations in which the covariates were taken from a real data set.

RESULTS

The covariate models identified agreed well with what could be expected based on the covariates that were actually supported in each of the simulated data sets. The predictive performance of the linearized model was close to that of the non-linearized model.

CONCLUSIONS

The proposed procedure identifies covariate models that are close to the model supported by the data set as well as being useful in the prediction of new data. The linearized model performs nearly as well as the non-linearized model.

Assumption testing in population pharmacokinetic models: illustrated with an analysis of moxonidine data from congestive heart failure patients

Deriving a population pharmacokinetic model from real data is always associated with numerous assumptions. Violations of these assumptions, especially if undetected, may lead to inappropriate conclusions being made from the analysis. Routinely, only a few of the assumptions are explicitly stated and justified in the reporting of a population model. Here, we attempt to be exhaustive in the presentation of the assumptions made in the course of an analysis of moxonidine pharmacokinetics. The different ways that assumptions were justified, through experience, graphical examination, or additional modeling, are outlined. Models for relaxing assumptions regarding the covariate and statistical submodels, not previously reported in the area of population pharmacokinetic modeling, are also described.

Human variability and noncancer risk assessment- An analysis of the default uncertainty factor

A 10-fold uncertainty factor is used for noncancer risk assessments to allow for possible interindividual differences between humans in the fate of the chemical in the body (kinetics) and target organ sensitivity (dynamics). Analysis of a database on the variability in each of these aspects is consistent with an even subdivision of the 10-fold factor into 10(0.5) (3.16) for kinetics and 10(0.5) (3.16) for dynamics. Analysis of the number of subjects in a normally and log-normally distributed population which would not be covered by factors of 3.16 supports this subdivision and also the use of a 10-fold factor to allow for both aspects. Analysis of kinetic data for subgroups of the population indicates that the standard default value of 3.16 for kinetics will not be adequate for all routes of elimination and all groups of the population. A scheme is proposed which would allow the selection of appropriate default uncertainty factors based on knowledge of the biological fate and effects of the chemical under review. Copyright 1998 Academic Press.

A general model for time-dissociated pharmacokinetic-pharmacodynamic relationship exemplified by paclitaxel myelosuppression

BACKGROUND

Hematologic toxicity after cancer chemotherapy and other drug effects that occur late compared to the exposure are usually modeled with use of some summary exposure variable such as the area under the concentration-time curve (AUC model) or the time of exposure above a threshold concentration (threshold model). An underlying assumption for both of these models is that the drug exerts a direct effect while present in the body and that it is the time integral of this direct effect that is related to the ultimate observed effect, either linearly (AUC model) or by a step function (threshold model). We propose a more general model that allows this relationship to be characterized by a nonlinear continuous function.

METHODS

Data on survival fraction of neutrophiles and time course of leukopenia from 92 courses of paclitaxel therapy in 21 patients with breast or ovarian cancer was related to paclitaxel concentration-time profiles with the AUC, threshold, and general models. The properties of the general model were also investigated with use of simulations.

RESULTS

For both pharmacodynamic end points, the general model described the data significantly better than the AUC or threshold models.

CONCLUSION

The general model is an extension to the present way of relating concentration-time profiles to late-effect measures, and it may provide an improved description of the concentration-response relationship and more accurate predictions of the ultimate effect when doses and schedules are varied. It can explain complex relationships between concentration-time profiles and the observed effect, and predictions from it lack some of the counterintuitive properties that the AUC or threshold model have when extrapolations are made.

Therapeutic drug monitoring opportunities in cancer therapy

Cancer is a common cause of death, and improvements in treatment are desperately needed. The high degree of variation in systemic exposure for a given dose and the relationships between blood concentrations and either toxic or antitumor effects would suggest that therapeutic drug monitoring is a potential mechanism for improving the treatment of individual patients. In this review, the case for therapeutic drug monitoring is made in a select number of commonly used cancer drugs and areas that require more concerted effort are highlighted.