Pharmacokinetic/pharmacodynamic modelling in oncological drug development

Synergistic Pharmacodynamic Effects of Gemcitabine and Fibroblast Growth Factor Receptor Inhibitors on Pancreatic Cancer Cell Cycle Kinetics and Proliferation

Median survival of pancreatic ductal adenocarcinoma cancer (PDAC) is 6 months, with 9% 5-year survival. Standard-of-care gemcitabine (Gem) provides only modest survival benefits, and combination therapies integrating novel targeted agents could improve outcomes. Fibroblast growth factor (FGF) receptors (FGFRs) play important roles in PDAC growth and invasion. Therefore, FGFR inhibitors (FGFRi) merit further investigation. Efficacy of Gem combined with NVP-BGJ398, a pan-FGFRi, was investigated in multiple PDAC cell lines exposed to the drugs alone and combined. Cell cycle distribution and cell numbers were quantified over time. Two pharmacodynamic models were developed to investigate Gem/BGJ398 interactions quantitatively: a drug-mediated cell proliferation/death model, and a drug-perturbed cell cycle progression model. The models captured temporal changes in cell numbers, cell cycle progression, and cell death during drug exposure. Simultaneous fitting of all data provided reasonable parameter estimates. Therapeutic efficacy was then evaluated in a PDAC mouse model. Compared with Gem alone, combined Gem + FGFRi significantly downregulated ribonucleotide-diphosphate reductase large subunit 1 (RRM1), a gemcitabine resistance (GemR) biomarker, suggesting the FGFRi inhibited GemR emergence. The cell proliferation/death pharmacodynamic model estimated the drug interaction coefficient ψ death = 0.798, suggesting synergistic effects. The mechanism-based cell cycle progression model estimated drug interaction coefficient ψ cycle = 0.647, also suggesting synergy. Thus, FGFR inhibition appears to synergize with Gem in PDAC cells and tumors by sensitizing cells to Gem-mediated inhibition of proliferation and cell cycle progression. SIGNIFICANCE STATEMENT: An integrated approach of quantitative modeling and experimentation was employed to investigate the nature of fibroblast growth factor receptor inhibitor (FGFRi)/gemcitabine (Gem) interaction, and to identify mechanisms by which FGFRi exposure reverses Gem resistance in pancreatic cancer cells. The results show that FGFRi interacts synergistically with Gem to sensitize pancreatic cancer cells and tumors to Gem-mediated inhibition of proliferation and cell cycle progression. Thus, addition of FGFRi to standard-of-care Gem treatment could be a clinically deployable approach to enhance therapeutic benefit to pancreatic cancer patients.

circ-Keratin 6c Promotes Malignant Progression and Immune Evasion of Colorectal Cancer through microRNA-485-3p/Programmed Cell Death Receptor Ligand 1 Axis

Recently, circular RNA was reported to be a significant participant in the development of tumorigenesis, including colorectal cancer. Therefore, we aimed to clarify the precise role of circ-keratin 6C (circ-KRT6C) in colorectal cancer progression. The relative expression levels of circ-KRT6C, microRNA-485-3p (miR-485-3p), and programmed cell death receptor ligand 1 (PDL1) were analyzed by real-time quantitative polymerase chain reaction and Western blot assays. The proliferation was assessed by cell count kit 8 and colony-forming assays. The apoptotic cells were determined by flow cytometry assay. The migration and invasion were analyzed by transwell assay. Colorectal cancer cells were cocultured with peripheral blood mononuclear cells or cytokine-induced killer cells to assess immune response. The interaction relationships among circ-KRT6C, miR-485-3p, and PDL1 were examined by dual-luciferase reporter assay. The effects of circ-KRT6C inhibition in vivo were analyzed by an animal experiment. circ-KRT6C was overexpressed in colorectal cancer tissues and cells, and its level was associated with overall survival time of patients with colorectal cancer. The suppression of circ-KRT6C suppressed growth, migration, invasion, and immune escape while stimulating apoptosis in colorectal cancer cells, which was abolished by shortage of miR-485-3p. In addition, overexpression of miR-485-3p repressed malignant progression and immune evasion of colorectal cancer by targeting PDL1, implying that PDL1 was a functional target of miR-485-3p. A xenograft experiment also suggested that circ-KRT6C inhibition could repress tumor growth in vivo. circ-KRT6C could increase PDL1 expression by functioning as an miR-485-3p sponge, which promoted malignant progression and immune evasion of colorectal cancer cells. SIGNIFICANCE STATEMENT: circ-keratin 6c could increase programmed cell death receptor ligand 1 expression by functioning as a microRNA-16-5p sponge, which promoted malignant progression and immune evasion of colorectal cancer.

The Use of Translational Modelling and Simulation to Develop Immunomodulatory Therapy as an Adjunct to Antibiotic Treatment in the Context of Pneumonia

The treatment of respiratory tract infections is threatened by the emergence of bacterial resistance. Immunomodulatory drugs, which enhance airway innate immune defenses, may improve therapeutic outcome. In this concept paper, we aim to highlight the utility of pharmacometrics and Bayesian inference in the development of immunomodulatory therapeutic agents as an adjunct to antibiotics in the context of pneumonia. For this, two case studies of translational modelling and simulation frameworks are introduced for these types of drugs up to clinical use. First, we evaluate the pharmacokinetic/pharmacodynamic relationship of an experimental combination of amoxicillin and a TLR4 agonist, monophosphoryl lipid A, by developing a pharmacometric model accounting for interaction and potential translation to humans. Capitalizing on this knowledge and associating clinical trial extrapolation and statistical modelling approaches, we then investigate the TLR5 agonist flagellin. The resulting workflow combines expert and prior knowledge on the compound with the in vitro and in vivo data generated during exploratory studies in order to construct high-dimensional models considering the pharmacokinetics and pharmacodynamics of the compound. This workflow can be used to refine preclinical experiments, estimate the best doses for human studies, and create an adaptive knowledge-based design for the next phases of clinical development.

A population analysis of the DGAT1 inhibitor GSK3008356 and its effect on endogenous and meal-induced triglyceride turnover in healthy subjects

Non-alcoholic steatohepatitis (NASH) is a liver disease in which fatty infiltration is accompanied by liver inflammation. GSK3008356 is under development as a selective inhibitor of diacylglycerol acyltransferase 1 (DGAT1), a key enzyme involved in the formation of triglyceride (TG). Decreased DGAT1 activity can reduce circulating TG and liver TG, and therefore could potentially prevent or treat NASH. The aim of the current study was to develop a population pharmacokinetic-pharmacodynamic (PKPD) model that characterizes the PK disposition of GSK3008356 and its relation to the changes in blood TG. Drug concentrations were measured in 104 healthy adults receiving various single (SD) and repeat doses (RD) in a first time in human (FiH) study. A 30% fat meal was given at hour 2 postdose, and blood postprandial TG concentrations were measured at various time points. The population PKPD model consists of several parts including a PK model, drug effect model, meal effect model, and a turnover model. The pharmacokinetic data were described using a 3-compartment model. Drug effect was described by an inhibitory sigmoidal Emax model. Since TG levels change with the introduction of a meal, a bi-exponential meal effect model was utilized. The total change in TG was fitted using a turnover model with drug and meal effects on the TG production rate. The current analysis presents a PKPD modeling strategy of time-varying TG data coming from both endogenous and exogenous sources. In general, the presented model could be utilized in the model-based drug development of drugs that influence TG levels in blood.

Understanding Hematological Toxicities Using Mathematical Modeling

Balancing antitumor efficacy with toxicity is a significant challenge, and drug-induced myelosuppression is a common dose-limiting toxicity of cancer treatments. Mathematical modeling has proven to be a powerful ally in this field, scaling results from animal models to humans, and designing optimized treatment regimens. Here we outline existing mathematical approaches for studying bone marrow toxicity, identify gaps in current understanding, and make future recommendations to advance this vital field of safety research further.

Integrated Pharmacokinetic/Pharmacodynamic Model of a Bispecific CD3xCD123 DART Molecule in Nonhuman Primates: Evaluation of Activity and Impact of Immunogenicity

Purpose: Flotetuzumab (MGD006 or S80880) is a bispecific molecule that recognizes CD3 and CD123 membrane proteins, redirecting T cells to kill CD123-expressing cells for the treatment of acute myeloid leukemia. In this study, we developed a mathematical model to characterize MGD006 exposure-response relationships and to assess the impact of its immunogenicity in cynomolgus monkeys.Experimental Design: Thirty-two animals received multiple escalating doses (100-300-600-1,000 ng/kg/day) via intravenous infusion continuously 4 days a week. The model reflects sequential binding of MGD006 to CD3 and CD123 receptors. Formation of the MGD006/CD3 complex was connected to total T cells undergoing trafficking, whereas the formation of the trimolecular complex results in T-cell activation and clonal expansion. Activated T cells were used to drive the peripheral depletion of CD123-positive cells. Anti-drug antibody development was linked to MGD006 disposition as an elimination pathway. Model validation was tested by predicting the activity of MGD006 in eight monkeys receiving continuous 7-day infusions.Results: MGD006 disposition and total T-cell and CD123-positive cell profiles were well characterized. Anti-drug antibody development led to the suppression of T-cell trafficking but did not systematically abolish CD123-positive cell depletion. Target cell depletion could persist after drug elimination owing to the self-proliferation of activated T cells generated during the first cycles. The model was externally validated with the 7-day infusion dosing schedule.Conclusions: A translational model was developed for MGD006 that features T-cell activation and expansion as a key driver of pharmacologic activity and provides a mechanistic quantitative platform to inform dosing strategies in ongoing clinical studies. Clin Cancer Res; 24(11); 2631-41. ©2018 AACR.

Model-based prediction of myelosuppression and recovery based on frequent neutrophil monitoring

Purpose

To investigate whether a more frequent monitoring of the absolute neutrophil counts (ANC) during myelosuppressive chemotherapy, together with model-based predictions, can improve therapy management, compared to the limited clinical monitoring typically applied today.

Methods

Daily ANC in chemotherapy-treated cancer patients were simulated from a previously published population model describing docetaxel-induced myelosuppression. The simulated values were used to generate predictions of the individual ANC time-courses, given the myelosuppression model. The accuracy of the predicted ANC was evaluated under a range of conditions with reduced amount of ANC measurements.

Results

The predictions were most accurate when more data were available for generating the predictions and when making short forecasts. The inaccuracy of ANC predictions was highest around nadir, although a high sensitivity (≥90%) was demonstrated to forecast Grade 4 neutropenia before it occurred. The time for a patient to recover to baseline could be well forecasted 6 days (±1 day) before the typical value occurred on day 17.

Conclusions

Daily monitoring of the ANC, together with model-based predictions, could improve anticancer drug treatment by identifying patients at risk for severe neutropenia and predicting when the next cycle could be initiated.

Electronic supplementary material

The online version of this article (doi:10.1007/s00280-017-3366-x) contains supplementary material, which is available to authorized users.

The multistate tuberculosis pharmacometric model: a semi-mechanistic pharmacokinetic-pharmacodynamic model for studying drug effects in an acute tuberculosis mouse model

The Multistate Tuberculosis Pharmacometric (MTP) model, a pharmacokinetic-pharmacodynamic disease model, has been used to describe the effects of rifampicin on Mycobacterium tuberculosis (M. tuberculosis) in vitro. The aim of this work was to investigate if the MTP model could be used to describe the rifampicin treatment response in an acute tuberculosis mouse model. Sixty C57BL/6 mice were intratracheally infected with M. tuberculosis H37Rv strain on Day 0. Fifteen mice received no treatment and were sacrificed on Days 1, 9 and 18 (5 each day). Twenty-five mice received oral rifampicin (1, 3, 9, 26 or 98 mg·kg⁻¹·day⁻¹; Days 1–8; 5 each dose level) and were sacrificed on Day 9. Twenty mice received oral rifampicin (30 mg·kg⁻¹·day⁻¹; up to 8 days) and were sacrificed on Days 2, 3, 4 and 9 (5 each day). The MTP model was linked to a rifampicin population pharmacokinetic model to describe the change in colony forming units (CFU) in the lungs over time. The transfer rates between the different bacterial states were fixed to estimates from in vitro data. The MTP model described well the change in CFU over time after different exposure levels of rifampicin in an acute tuberculosis mouse model. Rifampicin significantly inhibited the growth of fast-multiplying bacteria and stimulated the death of fast- and slow-multiplying bacteria. The data did not support an effect of rifampicin on non-multiplying bacteria possibly due to the short duration of the study. The pharmacometric modelling framework using the MTP model can be used to perform investigations and predictions of the efficacy of anti-tubercular drugs against different bacterial states.

Development of a mechanism-based pharmacokinetic/pharmacodynamic model to characterize the thermoregulatory effects of serotonergic drugs in mice

We have shown recently that concurrent harmaline, a monoamine oxidase-A inhibitor (MAOI), potentiates serotonin (5-HT) receptor agonist 5-methoxy-N,N-dimethyltryptamine (5-MeO-DMT)-induced hyperthermia. The objective of this study was to develop an integrated pharmacokinetic/pharmacodynamic (PK/PD) model to characterize and predict the thermoregulatory effects of such serotonergic drugs in mice. Physiological thermoregulation was described by a mechanism-based indirect-response model with adaptive feedback control. Harmaline-induced hypothermia and 5-MeO-DMT–elicited hyperthermia were attributable to the loss of heat through the activation of 5-HT_1A receptor and thermogenesis via the stimulation of 5-HT_2A receptor, respectively. Thus serotonergic 5-MeO-DMT–induced hyperthermia was readily distinguished from handling/injection stress-provoked hyperthermic effects. This PK/PD model was able to simultaneously describe all experimental data including the impact of drug-metabolizing enzyme status on 5-MeO-DMT and harmaline PK properties, and drug- and stress-induced simple hypo/hyperthermic and complex biphasic effects. Furthermore, the modeling results revealed a 4-fold decrease of apparent SC₅₀ value (1.88–0.496 µmol/L) for 5-MeO-DMT when harmaline was co-administered, providing a quantitative assessment for the impact of concurrent MAOI harmaline on 5-MeO-DMT–induced hyperthermia. In addition, the hyperpyrexia caused by toxic dose combinations of harmaline and 5-MeO-DMT were linked to the increased systemic exposure to harmaline rather than 5-MeO-DMT, although the body temperature profiles were mispredicted by the model. The results indicate that current PK/PD model may be used as a new conceptual framework to define the impact of serotonergic agents and stress factors on thermoregulation.

Preclinical PK/PD model for combined administration of erlotinib and sunitinib in the treatment of A549 human NSCLC xenograft mice

AIM

Combined therapy of EGFR TKI and VEGFR TKI may produce a greater therapeutic benefit and overcome EGFR TKI-induced resistance. However, a previous study shows that a combination of EGFR TKI erlotinib (ER) with VEGFR TKI sunitinib (SU) did not improve the overall survival in patients with non-small-cell lung cancer (NSCLC). In this study we examined the anticancer effect of ER, SU and their combination in the treatment of A549 human NSCLC xenograft mice, and conducted PK/PD modeling and simulations to optimize the dose regimen.

METHODS

ER (20, 50 mg·kg(-1)·d(-1)) or SU (5, 10, 20 mg·kg(-1)·d(-1)) alone, or their combination were administered to BALB/c nude mice bearing A549 tumors for 22 days. The tumor size and body weight were recorded daily. The experimental data were used to develop PK/PD models describing the quantitative relationship between the plasma concentrations and tumor suppression in different dose regimens. The models were further evaluated and validated, and used to predict the efficacy of different combination regimens and to select the optimal regimen.

RESULTS

The in vivo anticancer efficacy of the combination groups was much stronger than that of either drug administered alone. A PK/PD model was developed with a combination index (φ) of 4.4, revealing a strong synergistic effect between ER and SU. The model simulation predicted the tumor growth in different dosage regimens, and showed that the dose of SU played a decisive role in the combination treatment, and suggested that a lower dose of ER (≤5 mg·kg(-1)·d(-1)) and adjusting the dose of SU might yield a better dosage regimen for clinical research.

CONCLUSION

The experimental data and modeling confirm synergistic anticancer effect of ER and SU in the treatment of A549 xenograft mice. The optimal dosage regimen determined by the PK/PD modeling and simulation can be used in future preclinical study and provide a reference for clinical application.

Population pharmacokinetic-pharmacodynamic modelling in oncology: a tool for predicting clinical response

In oncology trials, overall survival (OS) is considered the most reliable and preferred endpoint to evaluate the benefit of drug treatment. Other relevant variables are also collected from patients for a given drug and its indication, and it is important to characterize the dynamic effects and links between these variables in order to improve the speed and efficiency of clinical oncology drug development. However, the drug-induced effects and causal relationships are often difficult to interpret because of temporal differences. To address this, population pharmacokinetic–pharmacodynamic (PKPD) modelling and parametric time-to-event (TTE) models are becoming more frequently applied. Population PKPD and TTE models allow for exploration towards describing the data, understanding the disease and drug action over time, investigating relevance of biomarkers, quantifying patient variability and in designing successful trials. In addition, development of models characterizing both desired and adverse effects in a modelling framework support exploration of risk-benefit of different dosing schedules. In this review, we have summarized population PKPD modelling analyses describing tumour, tumour marker and biomarker responses, as well as adverse effects, from anticancer drug treatment data. Various model-based metrics used to drive PD response and predict OS for oncology drugs and their indications are also discussed.

Pharmacokinetic-pharmacodynamic modeling of antibacterial drugs

Pharmacokinetic-pharmacodynamic (PKPD) modeling and simulation has evolved as an important tool for rational drug development and drug use, where developed models characterize both the typical trends in the data and quantify the variability in relationships between dose, concentration, and desired effects and side effects. In parallel, rapid emergence of antibiotic-resistant bacteria imposes new challenges on modern health care. Models that can characterize bacterial growth, bacterial killing by antibiotics and immune system, and selection of resistance can provide valuable information on the interactions between antibiotics, bacteria, and host. Simulations from developed models allow for outcome predictions of untested scenarios, improved study designs, and optimized dosing regimens. Today, much quantitative information on antibiotic PKPD is thrown away by summarizing data into variables with limited possibilities for extrapolation to different dosing regimens and study populations. In vitro studies allow for flexible study designs and valuable information on time courses of antibiotic drug action. Such experiments have formed the basis for development of a variety of PKPD models that primarily differ in how antibiotic drug exposure induces amplification of resistant bacteria. The models have shown promise for efficacy predictions in patients, but few PKPD models describe time courses of antibiotic drug effects in animals and patients. We promote more extensive use of modeling and simulation to speed up development of new antibiotics and promising antibiotic drug combinations. This review summarizes the value of PKPD modeling and provides an overview of the characteristics of available PKPD models of antibiotics based on in vitro, animal, and patient data.

Moving from basic toward systems pharmacodynamic models

Building upon many classical foundations of pharmacology, a diverse array of mechanistic pharmacokinetic-pharmacodynamic (PK/PD) models have emerged based on mechanisms of drug action and primary rate-limiting or turnover processes in physiology. An array of basic models can be extended to handle various complexities including tolerance and can readily be employed as building blocks in assembling enhanced PK/PD or small systems models. Our corticosteroid models demonstrate these concepts as well as elements of horizontal and vertical integration of molecular to whole-body processes. The potential advantages and challenges in moving PK/PD toward systems models are described.

Dose selection using a semi-mechanistic integrated glucose-insulin-glucagon model: designing phase 2 trials for a novel oral glucokinase activator

Selecting dosing regimens for phase 2 studies for a novel glucokinase activator LY2599506 is challenging due to the difficulty in modeling and assessing hypoglycemia risk. A semi-mechanistic integrated glucose-insulin-glucagon (GIG) model was developed in NONMEM based on pharmacokinetic, glucose, insulin, glucagon, and meal data obtained from a multiple ascending dose study in patients with Type 2 diabetes mellitus treated with LY2599506 for up to 26 days. The series of differential equations from the NONMEM model was translated into an R script to prospectively predict 24-h glucose profiles following LY2599506 treatment for 3 months for a variety of doses and dosing regimens. The reduction in hemoglobin A1c (HbA1c) at the end of the 3-month treatment was estimated using a transit compartment model based on the simulated fasting glucose values. Two randomized phase 2 studies, one with fixed dosing and the other employing conditional dose titration were conducted. The simulation suggested that (1) Comparable HbA1c lowering with lower hypoglycemia risk occurs with titration compared to fixed-dosing; and (2) A dose range of 50-400 mg BID provides either greater efficacy or lower hypoglycemia incidence or both than glyburide. The predictions were in reasonable agreement with the observed clinical data. The model predicted HbA1c reduction and hypoglycemia risk provided the basis for the decision to focus on the dose-titration trial and for the selection of doses for the demonstration of superiority of LY2599506 to glyburide. The integrated GIG model represented a valuable tool for the evaluation of hypoglycemia incidence.

Integration of in silico and in vitro platforms for pharmacokinetic-pharmacodynamic modeling

IMPORTANCE OF THE FIELD

Pharmacokinetic-pharmacodynamic (PK-PD) modeling enables quantitative prediction of the dose-response relationship. Recent advances in microscale technology enabled researchers to create in vitro systems that mimic biological systems more closely. Combination of mathematical modeling and microscale technology offers the possibility of faster, cheaper and more accurate prediction of the drug's effect with a reduced need for animal or human subjects.

AREAS COVERED IN THIS REVIEW

This article discusses combining in vitro microscale systems and PK-PD models for improved prediction of drug's efficacy and toxicity. First, we describe the concept of PK-PD modeling and its applications. Different classes of PK-PD models are described. Microscale technology offers an opportunity for building physical systems that mimic PK-PD models. Recent progress in this approach during the last decade is summarized.

WHAT THE READER WILL GAIN

This article is intended to review how microscale technology combined with cell cultures, also known as 'cells-on-a-chip', can confer a novel aspect to current PK-PD modeling. Readers will gain a comprehensive knowledge of PK-PD modeling and 'cells-on-a-chip' technology, with the prospect of how they may be combined for synergistic effect.

TAKE HOME MESSAGE

The combination of microscale technology and PK-PD modeling should contribute to the development of a novel in vitro/in silico platform for more physiologically-realistic drug screening.

Model-based neutrophil-guided dose adaptation in chemotherapy: evaluation of predicted outcome with different types and amounts of information

One of the most employed approaches to reduce severe neutropenia following anticancer drug regimens is to reduce the consecutive dose in fixed steps, commonly by 25%. Another approach has been to use pharmacokinetic (PK) sampling to tailor dosing, but only rarely have model-based computer approaches utilizing collected PK and/or pharmacodynamic (PD) data been used. A semi-mechanistic model for myelosuppression that can characterize the interindividual and interoccasion variability in the time-course of neutrophils following administration of a wide range of anticancer drugs may be used in a clinical setting for model-based dose individualization. The aim of this study was to compare current stepwise procedures to model-based dose adaptation by simulations, and investigate if the overall dose intensity in the population could be increased without increasing the risk of severe toxicity. The value of various amounts of PK- and/or PD-information was compared to standard dosing strategies using a maximum a posteriori procedure in NONMEM. The results showed that when information on neutrophil counts was available, the additional improvement from PK sampling was negligible. Using neutrophil sampling at baseline and an observation near the predicted nadir increased the number of patients in the target range by 27% in comparison with a one-sided 25% dose adjustment schedule, while keeping the number of patients experiencing severe toxicity at a comparable low level after five courses of treatment. High interindividual variability did not limit the benefit of model-based dose adaptation, whereas high interoccasion variability was predicted to make any dose adaptation method less successful. This study indicates that for successful model-based dose adaptation clinically, there is no need for drug concentration sampling, and that one extra neutrophil measurement in addition to the pre-treatment value is sufficient to limit severe neutropenia while increasing dose intensity.

Semi-mechanistic model for neutropenia after high dose of chemotherapy in breast cancer patients

PURPOSE

To describe the absolute neutrophil counts (ANC) profile in breast cancer patients receiving high-dose of chemotherapy and peripheral blood stem-cells (PBSC) transplantation.

METHODS

Data from 41 subjects receiving cyclophosphamide, thiotepa and carboplatin were used to develop the ANC model consisting of a drug-sensitive progenitor cell compartment, linked to the peripheral blood compartment, through three transition compartments. PBSC were incorporated into the first transit compartment following a zero-order process, k(in), and the rebound effect was explained by a feedback mechanism. A 'kinetics of drug action' model was used to quantify the HDC effect on the progenitor cells according to a linear function, with a slope (alpha).

RESULTS

The typical of the ANC at baseline (Circ(0)), mean transit time (MTT), feedback parameter (gamma), k(in) and alpha were estimated to be 5,610 x 10(6)/L, 3.25 days, 0.145, 0.954 cell/kg/day and 2.50 h/U, respectively. rHuG-CSF shortens the MTT by 92% and increases the mitotic activity by 120%. Bootstrap analysis, visual predictive check and numerical predictive checks evidenced accurate prediction of the ANC nadir, time to ANC nadir and time to grade 4 neutropenia recovery.

CONCLUSION

The time course of neutropenia following high-dose of chemotherapy and PBSC transplantation was accurately predicted. Higher amount of CD34+ cells in the PBSC transplantation and earlier administration rHuG-CSF were associated with faster haematological recovery.

A tool for neutrophil guided dose adaptation in chemotherapy

Chemotherapy dosing in anticancer treatment is a balancing act between achieving concentrations that are effective towards the malignancy and that result in acceptable side-effects. Neutropenia is one major side-effect of many antitumor agents, and is related to an increased risk of infection. A model capable of describing the time-course of myelosuppression from administered drug could be used in individual dose selection. In this paper we describe the transfer of a previously developed semi-mechanistic model for myelosuppression from NONMEM to a dosing tool in MS Excel, with etoposide as an example. The tool proved capable to solve a differential equation system describing the pharmacokinetics and pharmacodynamics, with estimation performance comparable to NONMEM. In the dosing tool the user provides neutrophil measures from a previous treatment course and request for the dose that results in a desired nadir in the upcoming course through a Bayesian estimation procedure.

Multiple-pool cell lifespan models for neutropenia to assess the population pharmacodynamics of unbound paclitaxel from two formulations in cancer patients

PURPOSE

Our objective was to build a mechanism-based pharmacodynamic model for the time course of neutropenia in cancer patients following paclitaxel treatment with a tocopherol-based Cremophor-free formulation (Tocosol Paclitaxel) and Cremophor EL-formulated paclitaxel (Taxol).

METHODS

A randomized two-way crossover trial was performed with 35 adult patients who received 175 mg/m(2) paclitaxel as either 15 min (Tocosol Paclitaxel) or 3 h (Taxol) intravenous infusions. Paclitaxel concentrations were measured by LC-MS/MS. NONMEM VI was used for population pharmacodynamics.

RESULTS

The cytotoxic effect on neutrophils was described by four mechanism-based models predicated on known properties of paclitaxel that used unbound concentrations in the central, deep peripheral or an intracellular compartment as forcing functions. Tocosol Paclitaxel was estimated to release 9.8% of the dose directly into the deep peripheral compartment (DPC). All models provided reasonable fitting of neutropenic effects. The model with the best predictive performance assumed that this dose fraction was released into 22.5% of the DPC which included the site of toxicity. The second-order cytotoxic rate constant was 0.00211 mL/ng per hour (variability: 52% CV). The relative exposure at the site of toxicity was 2.21 +/- 0.41 times (average +/- SD) larger for Tocosol Paclitaxel compared to Taxol. Lifespan was 11.0 days for progenitor cells, 1.95 days for maturating cells, and 4.38 days for neutrophils. Total drug exposure in blood explained half of the variance in nadir to baseline neutrophil count ratio.

CONCLUSIONS

The relative exposure of unbound paclitaxel at the site of toxicity was twice as large for Tocosol Paclitaxel compared to Taxol. The proposed mechanism-based models explained the extent and time course of neutropenia jointly for both formulations.

Integrated pharmacokinetics and pharmacodynamics in drug development

Drug development is a complex, lengthy and expensive process. Pharmaceutical companies and regulatory authorities have recognised that the drug development process needs optimisation for efficiency in view of the return on investments. Pharmacokinetics and pharmacodynamics are the two main principles determining the relationship between dose and response. This article provides an update on integrated approaches towards drug development by linking pharmacokinetics, pharmacodynamics and disease aspects into mathematical models. Gradually, a transition is taking place from a rather empirical approach towards a modelling- and simulation-based approach to drug development. The main learning phases should be phases 0, I and II, whereas phase III studies should merely have a confirmatory purpose. In model-based drug development, mechanism-based mathematical models, which are iteratively refined along the path of development, incorporate the accumulating knowledge of the investigational drug, the disease and their mutual interference in different subsets of the target population. These models facilitate the design of the next study and improve the probability of achieving the projected efficacy and safety endpoints. In this article, several theoretical and practical aspects of an integrated approach towards drug development are discussed, together with some case studies from different therapeutic areas illustrating the application of pharmacokinetic/pharmacodynamic disease models at different stages of drug development.

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.

Recent developments in the clinical pharmacology of classical cytotoxic chemotherapy

Advances in analytical methods, imaging techniques and an increased understanding of the influence of pharmacogenetic factors have added to our knowledge of the pharmacology of many chemotherapeutic agents. Extending the use of these approaches to pharmacodynamic end-points, together with the application of population-based modelling techniques, offers the potential to develop truly individualized therapy in the future.

A Bayesian population PK-PD model of ispinesib-induced myelosuppression

The goal of the present analysis is to fit a Bayesian population pharmacokinetic pharmacodynomic (PK-PD) model to characterize the relationship between the concentration of ispinesib and changes in absolute neutrophil counts (ANC). Ispinesib, a kinesin spindle protein (KSP) inhibitor, blocks assembly of a functional mitotic spindle, leading to G2/M arrest. A first time in human, phase I open-label, non-randomized, dose-escalating study evaluated ispinesib at doses ranging from 1 to 21 mg/m(2). PK-PD data were collected from 45 patients with solid tumors. The pharmacokinetics of ispinesib were well characterized by a two-compartment model. A semimechanistic model was fit to the ANC. The PK and PD data were successfully modelled simultaneously. This is the first presentation of simultaneously fitting a PK-PD model to ANC using Bayesian methods. Bayesian methods allow for the use of prior information for some system-related parameters. The model may be used to examine different schedules, doses, and infusion times.

Investigation of the effects of herbal medicines on warfarin response in healthy subjects: a population pharmacokinetic-pharmacodynamic modeling approach

Systematic evidence regarding herb-drug interactions is lacking. This study investigated herb-drug interactions with warfarin. S-warfarin concentration and response (prothrombin complex activity) data from healthy subjects (n = 24) who received a single warfarin dose (25 mg) and either St John's wort, Asian ginseng, Ginkgo biloba, or ginger were analyzed using a population pharmacokinetic-pharmacodynamic modeling approach. The ratio of S-warfarin apparent clearance (CL/F) compared to control was 1.39 +/- 0.06 and 1.14 +/- 0.04 after St John's wort and Asian ginseng pretreatment, respectively. Other pharmacokinetic and pharmacodynamic parameters were unaffected. Coadministration of St John's wort significantly increased S-warfarin CL/F, whereas treatment with Asian ginseng produced only a moderate increase in CL/F. Ginkgo and ginger did not affect the pharmacokinetics of warfarin in healthy subjects. None of the herbs studied had a direct effect on warfarin pharmacodynamics. Studies in anticoagulated patients are warranted to assess the clinical significance of these herb-drug interactions.

Model-based drug development: the road to quantitative pharmacology

High development costs and low success rates in bringing new medicines to the market demand more efficient and effective approaches. Identified by the FDA as a valuable prognostic tool for fulfilling such a demand, model-based drug development is a mathematical and statistical approach that constructs, validates, and utilizes disease models, drug exposure-response models, and pharmacometric models to facilitate drug development. Quantitative pharmacology is a discipline that learns and confirms the key characteristics of new molecular entities in a quantitative manner, with goal of providing explicit, reproducible, and predictive evidence for optimizing drug development plans and enabling critical decision making. Model-based drug development serves as an integral part of quantitative pharmacology. This work reviews the general concept, basic elements, and evolving role of model-based drug development in quantitative pharmacology. Two case studies are presented to illustrate how the model-based drug development approach can facilitate knowledge management and decision making during drug development. The case studies also highlight the organizational learning that comes through implementation of quantitative pharmacology as a discipline. Finally, the prospects of quantitative pharmacology as an emerging discipline are discussed. Advances in this discipline will require continued collaboration between academia, industry and regulatory agencies.