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

Early Identification of Patients at Risk of Cabazitaxel-induced Severe Neutropenia

BACKGROUND

Cabazitaxel frequently causes severe neutropenia. A higher cabazitaxel systemic exposure is related to a lower nadir absolute neutrophil count (ANC).

OBJECTIVE

To describe the effect of cabazitaxel systemic exposure on ANC by a population pharmacokinetic/pharmacodynamic (POP-PK/PD) model, and to identify patients at risk of severe neutropenia early in their treatment course using a PK threshold.

DESIGN, SETTING, AND PARTICIPANTS

Data from five clinical studies were pooled to develop a POP-PK/PD model using NONMEM, linking both patient characteristics and cabazitaxel systemic exposure directly to ANC.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

A PK threshold, predictive of severe neutropenia (grade ≥3), was determined using a receiver operating characteristic curve.

RESULTS AND LIMITATIONS

Ninety-six patients were included with a total of 1726 PK samples and 1081 ANCs. The POP-PK/PD model described both cabazitaxel PK and ANC accurately. A cabazitaxel plasma concentration of >4.96 ng/ml at 6 h after the start of infusion was found to be predictive of severe neutropenia, with a sensitivity of 76% and a specificity of 65%.

CONCLUSIONS

Early cabazitaxel plasma levels are predictive of severe neutropenia. Implementation of the proposed PK threshold results in early identification of almost 76% of all severe neutropenias. If prospectively validated, patients at risk could benefit from prophylactic administration of granulocyte colony stimulating factors, preventing severe neutropenia in an early phase of treatment. Implementation of this threshold permits a less restricted use of the 25 mg/m2 dose, potentially increasing the therapeutic benefit.

PATIENT SUMMARY

Treatment with cabazitaxel chemotherapy often causes neutropenia, leading to susceptibility to infections, which might be life threatening. We found that a systemic cabazitaxel concentration above 4.96 ng/ml 6 h after the start of infusion is predictive of the occurrence of severe neutropenia. Measurement of systemic cabazitaxel levels provides clinicians with the opportunity to prophylactically stimulate neutrophil growth.

Combined Intraperitoneal Paclitaxel and Systemic Chemotherapy for Patients with Massive Malignant Ascites Secondary to Pancreatic Cancer: A Report of Two Patients

The prognosis of patients with peritoneal metastases from pancreatic cancer is poor, largely due to massive ascites, which precludes systemic treatment. Two patients with a poor performance status and malignant ascites were treated with cell-free and concentrated ascites reinfusion therapy followed by combined chemotherapy with intraperitoneal paclitaxel, intravenous gemcitabine, and nab-paclitaxel. These patients achieved a survival of 19 and 36 weeks with a relatively good quality of life. Combined intraperitoneal paclitaxel and systemic chemotherapy may provide effective palliative management for some patients with peritoneal metastases from pancreatic cancer.

Clinical decision support for chemotherapy-induced neutropenia using a hybrid pharmacodynamic/machine learning model

Consensus guidelines recommend use of granulocyte colony stimulating factor in patients deemed at risk of chemotherapy-induced neutropenia, however, these risk models are limited in the factors they consider and miss some cases of neutropenia. Clinical decision making could be supported using models that better tailor their predictions to the individual patient using the wealth of data available in electronic health records (EHRs). Here, we present a hybrid pharmacokinetic/pharmacodynamic (PKPD)/machine learning (ML) approach that uses predictions and individual Bayesian parameter estimates from a PKPD model to enrich an ML model built on her data. We demonstrate this approach using models developed on a large real-world data set of 9121 patients treated for lymphoma, breast, or thoracic cancer. We also investigate the benefits of augmenting the training data using synthetic data simulated with the PKPD model. We find that PKPD-enrichment of ML models improves prediction of grade 3-4 neutropenia, as measured by higher precision (61%) and recall (39%) compared to PKPD model predictions (47%, 33%) or base ML model predictions (51%, 31%). PKPD augmentation of ML models showed minor improvements in recall (44%) but not precision (56%), and data augmentation required careful tuning to control overfitting its predictions to the PKPD model. PKPD enrichment of ML shows promise for leveraging both the physiology-informed predictions of PKPD and the ability of ML to learn predictor-outcome relationships from large data sets to predict patient response to drugs in a clinical precision dosing context.

Do female and male patients derive similar benefits from approved systemic oncology therapies? A systematic review and meta-analysis

PURPOSE

The National Institutes of Health's policy for the inclusion of females in clinical research was a pivotal step towards the consideration of sex as a biological variable, which is of particular importance in oncology, given differential incidence and outcomes of cancer between the sexes, and known pharmacodynamic, pharmacokinetic, and immunological differences. Therefore, we aim to investigate if such biological sex-based differences translate to clinically meaningful outcome differences from recently approved systemic oncology therapies.

METHODS

A systematic review of randomized control trials (RCTs) cited in Food and Drug Administration, European Medicines Agency, and Health Canada approvals was conducted. Chemotherapy, targeted agents, and immunotherapy RCTs reporting sex-based sub-group analyses for overall/progression-free survival (OS/PFS) were considered. Hazard ratios (HRs) and 95% confidence intervals (CIs) were utilized. Sensitivity analyses for survival endpoints, drug type, and cancer site were conducted.

RESULTS

Ninety-nine RCTs were included, representing 62,384 patients (23,574 (38%) female). Pooled OS HRs [95% CIs] were 0.77 [0.72-0.81] and 0.76 [0.72-0.79] for females and males, respectively (P = 0.73), and 0.51 [0.47-0.56] and 0.57 [0.53-0.61] (P = 0.08) for PFS. Sensitivity analyses yielded similar results. No RCTs reported sex-based toxicity or quality-of-life (QOL) data.

CONCLUSION

Female and male patients appear to derive comparable benefits from recently approved systemic oncology therapies. Future RCTs are encouraged to report sex-based toxicity and QOL data.

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

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

Lurbinectedin-induced thrombocytopenia: the role of body surface area

Lurbinectedin is an alkylating agent approved for the second-line treatment of small cell lung cancer. Although initial studies showed no association between body surface area (BSA) and drug clearance, the recommended dose is 3.2 mg/m² every 3 weeks. This recommendation was based on an exposure-response study, which demonstrated that patients with lower BSA had a higher incidence of thrombocytopenia. Herein we present the factors associated with BSA and thrombopoiesis, which may have contributed to the observed relationship.

A continued learning approach for model-informed precision dosing: updating models in clinical practice

Model-informed precision dosing (MIPD) is a quantitative dosing framework that combines prior knowledge on the drug-disease-patient system with patient data from therapeutic drug/biomarker monitoring (TDM) to support individualized dosing in ongoing treatment. Structural models and prior parameter distributions used in MIPD approaches typically build on prior clinical trials that involve only a limited number of patients selected according to some exclusion/inclusion criteria. Compared to the prior clinical trial population, the patient population in clinical practice can be expected to include also altered behavior and/or increased interindividual variability, the extent of which, however, is typically unknown. Here, we address the question of how to adapt and refine models on the level of the model parameters to better reflect this real-world diversity. We propose an approach for continued learning across patients during MIPD using a sequential hierarchical Bayesian framework. The approach builds on two stages to separate the update of the individual patient parameters from updating the population parameters. Consequently, it enables continued learning across hospitals or study centers, since only summary patient data (on the level of model parameters) need to be shared, but no individual TDM data. We illustrate this continued learning approach with neutrophil-guided dosing of paclitaxel. The present study constitutes an important step towards building confidence in MIPD and eventually establishing MIPD increasingly in everyday therapeutic use.

Effectiveness of Cell-Free and Concentrated Ascites Reinfusion Therapy in the Treatment of Malignancy-Related Ascites: A Systematic Review and Meta-Analysis

Simple Summary

Cell-free and concentrated ascites reinfusion therapy (CART) was a safe and effective palliative therapy in malignancy-related ascites. Abdominal distension, dyspnea, and fatigue were alleviated significantly after CART. The mean time to the next paracentesis was 20.7 days. In total, 17% of patients had improved performance status after CART.

Abstract

Background: Malignancy-related ascites (MRA) is one of the symptoms causing discomfort in advanced cancer patients. Cell-free and concentrated ascites reinfusion therapy (CART) is one of the palliative treatments widely conducted in Japan only. Methods: A systematic review following a meta-analysis of CART was performed. The efficiency and adverse events were evaluated. Results: A total of 2567 patients and 6013 procedures of CART were identified in this study. The mean volume of MRA collected was 4.29 (95% confidence interval (CI) 3.47–5.11) L, and the volume reinfused after concentrating was 0.49 (95% CI 0.39–0.60) L. A total of 86.1 (95% CI 77.1–95.2) g protein and 42.9 (95% CI 36.0–50.0) g albumin was reinfused. The mean time to the next paracentesis was 20.7 (95% CI 15.6–25.8) days. The body weight was reduced by 3.38 (95% CI 1.90–4.86; p < 0.01) kg, and abdominal circumference was reduced by 7.86 (95% CI 6.58–9.14; p < 0.001) cm. Serum albumin increased an average of 0.14 (95% CI −0.01–0.28; p = 0.07) mg/dL the day after CART. Abdominal distension, dyspnea, and fatigue were alleviated by 6.0 (95% CI 5.59–6.51), 2.66 (95% CI 2.05–3.28), and 2.64 (95% CI 1.86–3.42) points using a numerical rating scale system ranging from 0 to 10. Overall, 17% (95% CI 0.03–0.31%) of patients had improved performance status after CART. Significant body temperature elevation was observed, at an average of 0.4 °C (95% CI 0.18–0.62 °C). Conclusions: CART might be a safe and effective palliative therapy in MRA and further clinical trials are necessary.

Assessing taxane-associated adverse events using the FDA adverse event reporting system database

Background:

Taxanes are an essential class of antineoplastic agents used to treat various cancers and are a fundamental cause of hypersensitivity reactions. In addition, other adverse events, such as bone marrow toxicity and peripheral neuropathy, can lead to chemotherapy discontinuation. This study aimed to evaluate the safety of taxanes in the real world.

Methods:

Taxane-associated adverse events were identified by the Medical Dictionary for Regulatory Activities Preferred Terms and analyzed and compared by mining the US Food and Drug Administration Adverse Event Reporting System pharmacovigilance database from January 2004 to December 2019. Reported adverse events, such as hypersensitivity reaction, bone marrow toxicity, and peripheral neuropathy, were analyzed with the following signal detection algorithms: reporting odds ratio (ROR), proportional reporting ratio (PRR), multi-item gamma Poisson shrinker (MGPS), Bayesian confidence propagation neural network (BCPNN), and logistic regression methods. Adverse outcome events and death outcome rates were compared between different taxane groups using Pearson's χ² test, whereas significance was determined at P < 0.05 with a 95% confidence interval (CI).

Results:

A total of 966 reports of hypersensitivity reactions, 1109 reports of bone marrow toxicity, and 1374 reports of peripheral neuropathy were analyzed. Compared with paclitaxel and docetaxel, bone marrow toxicity following the use of nab-paclitaxel had the highest ROR of 6.45 (95% two-sided CI, 6.05–6.88), PRR of 5.66, (χ² = 4342.98), information component of 2.50 (95% one-sided CI = 2.34), and empirical Bayes geometric mean of 5.64 (95% one-sided CI = 5.34). Peripheral neuropathy following the use of nab-paclitaxel showed a higher ROR of 12.78 (95% two-sided CI, 11.55–14.14), PRR of 12.16 (χ² = 4060.88), information component of 3.59 (95% one-sided CI = 3.25), and empirical Bayes geometric mean of 12.07 (95% one-sided CI = 11.09).

Conclusions:

The results showed that bone marrow toxicity and peripheral neuropathy were the major adverse events induced by taxanes. Nab-paclitaxel exhibited the highest potential for taxane-associated adverse events. Further research in the future is warranted to explain taxane-associated adverse effects in real-world circumstances.

Clinical usefulness of cell-free and concentrated ascites reinfusion therapy (CART) in combination with chemotherapy for malignant ascites: a post-marketing surveillance study

BACKGROUND

Cell-free and concentrated ascites reinfusion therapy (CART) has been suggested to be able to treat malignant ascites more safely and effectively with chemotherapy because of its ability to retain serum protein and albumin. Although the characteristics of cancer types and CART and the clinical implications of combination therapy with antitumor agents are becoming widespread, there are limited reports on its efficacy and complications.

METHODS

In this prospective observational national post-marketing study, 128 patients with malignancies received 300 CART sessions at 22 centers. After excluding other malignancies, the patients were divided into four groups: gynecological malignancies with chemotherapy (GYC+; 18 cases and 36 times) and without chemotherapy (GYC-; 35 cases and 52 times), and gastrointestinal malignancies with chemotherapy (GIC+; 8 cases and 16 times) and without chemotherapy (20 cases and 58 times).

RESULTS

There were significant reductions in the body weight in all groups and significant reductions in abdominal circumference and significant improvements in the diet and Eastern Cooperative Oncology Group performance status only in the GYC+ group. The total serum protein and albumin increased significantly in all groups, except for the GIC+ group, before and after CART. There was no significant difference in the presence or absence of antitumor medication.

CONCLUSION

With CART, there were differences in the improvement of the clinical symptoms between malignancy groups. The combination of CART and antineoplastic agents may be as safe as CART alone in cases of exudative malignant ascites.

Model-Based Optimal AML Consolidation Treatment

OBJECTIVE

Neutropenia is an adverse event commonly arising during intensive chemotherapy of acute myeloid leukemia (AML). It is often associated with infectious complications. Mathematical modeling, simulation, and optimization of the treatment process would be a valuable tool to support clinical decision making, potentially resulting in less severe side effects and deeper remissions. However, until now, there has been no validated mathematical model available to simulate the effect of chemotherapy treatment on white blood cell (WBC) counts and leukemic cells simultaneously.

METHODS

We developed a population pharmacokinetic/pharmacodynamic (PK/PD) model combining a myelosuppression model considering endogenous granulocyte-colony stimulating factor (G-CSF), a PK model for cytarabine (Ara-C), a subcutaneous absorption model for exogenous G-CSF, and a two-compartment model for leukemic blasts. This model was fitted to data of 44 AML patients during consolidation therapy with a novel Ara-C plus G-CSF schedule from a phase II controlled clinical trial. Additionally, we were able to optimize treatment schedules with respect to disease progression, WBC nadirs, and the amount of Ara-C and G-CSF.

RESULTS

The developed PK/PD model provided good prediction accuracies and an interpretation of the interaction between WBCs, G-CSF, and blasts. For 14 patients (those with available bone marrow blast counts), we achieved a median 4.2-fold higher WBC count at nadir, which is the most critical time during consolidation therapy. The simulation results showed that relative bone marrow blast counts remained below the clinically important threshold of 5%, with a median of 60% reduction in Ara-C.

CONCLUSION

These in silico findings demonstrate the benefits of optimized treatment schedules for AML patients.

SIGNIFICANCE

Until 2017, no new drug had been approved for the treatment of AML, fostering the optimal use of currently available drugs.

Using machine learning to optimize antibiotic combinations: dosing strategies for meropenem and polymyxin B against carbapenem-resistant Acinetobacter baumannii

OBJECTIVES

Increased rates of carbapenem-resistant strains of Acinetobacter baumannii have forced clinicians to rely upon last-line agents, such as the polymyxins, or empirical, unoptimized combination therapy. Therefore, the objectives of this study were: (a) to evaluate the in vitro pharmacodynamics of meropenem and polymyxin B (PMB) combinations against A. baumannii; (b) to utilize a mechanism-based mathematical model to quantify bacterial killing; and (c) to develop a genetic algorithm (GA) to define optimal dosing strategies for meropenem and PMB.

METHODS

A. baumannii (N16870; MIC_meropenem = 16 mg/L, MIC_PMB = 0.5 mg/L) was studied in the hollow-fibre infection model (initial inoculum 10⁸ cfu/mL) over 14 days against meropenem and PMB combinations. A mechanism-based model of the data and population pharmacokinetics of each drug were used to develop a GA to define the optimal regimen parameters.

RESULTS

Monotherapies resulted in regrowth to ~10¹⁰ cfu/mL by 24 h, while combination regimens employing high-intensity PMB exposure achieved complete bacterial eradication (0 cfu/mL) by 336 h. The mechanism-based model demonstrated an SC₅₀ (PMB concentration for 50% of maximum synergy on meropenem killing) of 0.0927 mg/L for PMB-susceptible subpopulations versus 3.40 mg/L for PMB-resistant subpopulations. The GA had a preference for meropenem regimens that improved the %T > MIC via longer infusion times and shorter dosing intervals. The GA predicted that treating 90% of simulated subjects harbouring a 10⁸ cfu/mL starting inoculum to a point of 10⁰ cfu/mL would require a regimen of meropenem 19.6 g/day 2 h prolonged infusion (2 hPI) q5h + PMB 5.17 mg/kg/day 2 hPI q6h (where the 0 h meropenem and PMB doses should be 'loaded' with 80.5% and 42.2% of the daily dose, respectively).

CONCLUSION

This study provides a methodology leveraging in vitro experimental data, a mathematical pharmacodynamic model, and population pharmacokinetics provide a possible avenue to optimize treatment regimens beyond the use of the 'traditional' indices of antibiotic action.

Bayesian Data Assimilation to Support Informed Decision Making in Individualized Chemotherapy

An essential component of therapeutic drug/biomarker monitoring (TDM) is to combine patient data with prior knowledge for model‐based predictions of therapy outcomes. Current Bayesian forecasting tools typically rely only on the most probable model parameters (maximum a posteriori (MAP) estimate). This MAP‐based approach, however, does neither necessarily predict the most probable outcome nor does it quantify the risks of treatment inefficacy or toxicity. Bayesian data assimilation (DA) methods overcome these limitations by providing a comprehensive uncertainty quantification. We compare DA methods with MAP‐based approaches and show how probabilistic statements about key markers related to chemotherapy‐induced neutropenia can be leveraged for more informative decision support in individualized chemotherapy. Sequential Bayesian DA proved to be most computationally efficient for handling interoccasion variability and integrating TDM data. For new digital monitoring devices enabling more frequent data collection, these features will be of critical importance to improve patient care decisions in various therapeutic areas.

Mathematical models for cytarabine-derived myelosuppression in acute myeloid leukaemia

We investigate the personalisation and prediction accuracy of mathematical models for white blood cell (WBC) count dynamics during consolidation treatment using intermediate or high-dose cytarabine (Ara-C) in acute myeloid leukaemia (AML). Ara-C is the clinically most relevant cytotoxic agent for AML treatment. We extend a mathematical model of myelosuppression and a pharmacokinetic model of Ara-C with different hypotheses of Ara-C's pharmacodynamic effects. We cross-validate the 12 model variations using dense WBC count measurements from 23 AML patients. Surprisingly, the prediction accuracy remains satisfactory in each of the models despite different modelling hypotheses. Therefore, we compare average clinical and calculated WBC recovery times for different Ara-C schedules as a successful methodology for model discrimination. As a result, a new hypothesis of a secondary pharmacodynamic effect on the proliferation rate seems plausible. Furthermore, we demonstrate the impact of treatment timing on subsequent nadir values based on personalised predictions as a possibility for influencing/controlling myelosuppression.

Significance of Earlier Initiation of Chemotherapy for Lung Cancer Complicated with Primary or Secondary Nephrotic Syndrome following Its Appropriate Differential Diagnosis

We encountered a case of primary lung cancer complicated with membranous nephropathy as primary nephrotic syndrome. Because treatment approaches vary greatly for primary and secondary nephrotic syndrome, a renal biopsy was performed for diagnosis. Much time was required to make a definitive diagnosis of primary nephrotic syndrome, as opposed to paraneoplastic nephrotic syndrome. Consequently, the subsequent chemotherapy was ineffective and caused significant toxicity due to reduced performance status (PS) and progression of hypoalbuminemia. Therefore, it is imperative that a diagnosis be made and treatment be initiated without delay before PS declines and hypoalbuminemia progresses.

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.

Technologies for Measuring Pharmacokinetic Profiles

The creation of a pharmacokinetic (PK) curve, which follows the plasma concentration of an administered drug as a function of time, is a critical aspect of the drug development process and includes such information as the drug's bioavailability, clearance, and elimination half-life. Prior to a drug of interest gaining clearance for use in human clinical trials, research is performed during the preclinical stages to establish drug safety and dosing metrics from data obtained from the PK studies. Both in vivo animal models and in vitro platforms have limitations in predicting human reaction to a drug due to differences in species and associated simplifications, respectively. As a result, in silico experiments using computer simulation have been implemented to accurately predict PK parameters in human studies. This review assesses these three approaches (in vitro, in vivo, and in silico) when establishing PK parameters and evaluates the potential for in silico studies to be the future gold standard of PK preclinical studies.

Quantitative Relationship Between AUEC of Absolute Neutrophil Count and Duration of Severe Neutropenia for G-CSF in Breast Cancer Patients

The aim of the study was to evaluate the quantitative relationship between duration of severe neutropenia (DSN, the efficacy endpoint) and area under effect curve of absolute neutrophil counts (ANC-AUEC, the pharmacodynamic endpoint), based on data from filgrastim products, a human granulocyte colony-stimulating factor (G-CSF). Clinical data from filgrastim product comparator and test arms of two randomized, parallel-group, phase III studies in breast cancer patients treated with myelosuppressive chemotherapy were utilized. A zero-inflated Poisson regression model best described the negative correlation between DSN and ANC-AUEC. The models predicted that with 10 × 10⁹ day/L of increase in ANC-AUEC, the mean DSN would decrease from 1.1 days to 0.93 day in Trial 1 and from 1.2 days to 1.0 day in Trial 2. The findings of the analysis provide useful information regarding the relationship between ANC and DSN that can be used for dose selection and optimization of clinical trial design for G-CSF.

Model-based optimization of G-CSF treatment during cytotoxic chemotherapy

PURPOSE

Although G-CSF is widely used to prevent or ameliorate leukopenia during cytotoxic chemotherapies, its optimal use is still under debate and depends on many therapy parameters such as dosing and timing of cytotoxic drugs and G-CSF, G-CSF pharmaceuticals used and individual risk factors of patients.

METHODS

We integrate available biological knowledge and clinical data regarding cell kinetics of bone marrow granulopoiesis, the cytotoxic effects of chemotherapy and pharmacokinetics and pharmacodynamics of G-CSF applications (filgrastim or pegfilgrastim) into a comprehensive model. The model explains leukocyte time courses of more than 70 therapy scenarios comprising 10 different cytotoxic drugs. It is applied to develop optimized G-CSF schedules for a variety of clinical scenarios.

RESULTS

Clinical trial results showed validity of model predictions regarding alternative G-CSF schedules. We propose modifications of G-CSF treatment for the chemotherapies 'BEACOPP escalated' (Hodgkin's disease), 'ETC' (breast cancer), and risk-adapted schedules for 'CHOP-14' (aggressive non-Hodgkin's lymphoma in elderly patients).

CONCLUSIONS

We conclude that we established a model of human granulopoiesis under chemotherapy which allows predictions of yet untested G-CSF schedules, comparisons between them, and optimization of filgrastim and pegfilgrastim treatment. As a general rule of thumb, G-CSF treatment should not be started too early and patients could profit from filgrastim treatment continued until the end of the chemotherapy cycle.

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.

Towards Quantitative Systems Pharmacology Models of Chemotherapy-Induced Neutropenia

Neutropenia is a serious toxic complication of chemotherapeutic treatment. For years, mathematical models have been developed to better predict hematological outcomes during chemotherapy in both the traditional pharmaceutical sciences and mathematical biology disciplines. An increasing number of quantitative systems pharmacology (QSP) models that combine systems approaches, physiology, and pharmacokinetics/pharmacodynamics have been successfully developed. Here, I detail the shift towards QSP efforts, emphasizing the importance of incorporating systems-level physiological considerations in pharmacometrics.

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.

Dose schedule optimization and the pharmacokinetic driver of neutropenia

Toxicity often limits the utility of oncology drugs, and optimization of dose schedule represents one option for mitigation of this toxicity. Here we explore the schedule-dependency of neutropenia, a common dose-limiting toxicity. To this end, we analyze previously published mathematical models of neutropenia to identify a pharmacokinetic (PK) predictor of the neutrophil nadir, and confirm this PK predictor in an in vivo experimental system. Specifically, we find total AUC and Cmax are poor predictors of the neutrophil nadir, while a PK measure based on the moving average of the drug concentration correlates highly with neutropenia. Further, we confirm this PK parameter for its ability to predict neutropenia in vivo following treatment with different doses and schedules. This work represents an attempt at mechanistically deriving a fundamental understanding of the underlying pharmacokinetic drivers of neutropenia, and provides insights that can be leveraged in a translational setting during schedule selection.

Population pharmacokinetic-pharmacodynamic analysis for eribulin mesilate-associated neutropenia

AIMS

Eribulin mesilate is an inhibitor of microtubule dynamics that is approved for the treatment of late-stage metastatic breast cancer. Neutropenia is one of the major dose-limiting adverse effects of eribulin. The objective of this analysis was to develop a population pharmacokinetic-pharmacodynamic model for eribulin-associated neutropenia.

METHODS

A combined data set of 12 phase I, II and III studies for eribulin mesilate was analysed. The population pharmacokinetics of eribulin was described using a previously developed model. The relationship between eribulin pharmacokinetic and neutropenia was described using a semi-physiological lifespan model for haematological toxicity. Patient characteristics predictive of increased sensitivity to develop neutropenia were evaluated using a simulation framework.

RESULTS

Absolute neutrophil counts were available from 1579 patients. In the final covariate model, the baseline neutrophil count (ANC0) was estimated to be 4.03 × 10(9) neutrophils l(-1) [relative standard error (RSE) 1.2%], with interindividual variability (IIV, 37.3 coefficient of variation % [CV%]). The mean transition time was estimated to be 109 h (RSE 1.8%, IIV 13.9CV%), the feedback constant (γ) was estimated to be 0.216 (RSE 1.4%, IIV 12.2CV%), and the linear drug effect coefficient (SLOPE) was estimated to be 0.0451 μg l(-1) (RSE 3.2%, IIV 54CV%). Albumin, aspartate transaminase and receival of granulocyte colony-stimulating factor (G-CSF) were identified as significant covariates on SLOPE, and albumin, bilirubin, G-CSF, alkaline phosphatase and lactate dehydrogenase were identified as significant covariates on mean transition time.

CONCLUSIONS

The developed model can be applied to investigate optimal treatment strategies quantitatively across different patient groups with respect to neutropenia. Albumin was identified as the most clinically important covariate predictive of interindividual variability in the neutropenia time course.

Use of a linearization approximation facilitating stochastic model building

The objective of this work was to facilitate the development of nonlinear mixed effects models by establishing a diagnostic method for evaluation of stochastic model components. The random effects investigated were between subject, between occasion and residual variability. The method was based on a first-order conditional estimates linear approximation and evaluated on three real datasets with previously developed population pharmacokinetic models. The results were assessed based on the agreement in difference in objective function value between a basic model and extended models for the standard nonlinear and linearized approach respectively. The linearization was found to accurately identify significant extensions of the model's stochastic components with notably decreased runtimes as compared to the standard nonlinear analysis. The observed gain in runtimes varied between four to more than 50-fold and the largest gains were seen for models with originally long runtimes. This method may be especially useful as a screening tool to detect correlations between random effects since it substantially quickens the estimation of large variance-covariance blocks. To expedite the application of this diagnostic tool, the linearization procedure has been automated and implemented in the software package PsN.

Pharmaceutical care for patients undergoing s-1 plus Cisplatin therapy for unresectable recurrent gastric cancer

We examined the adverse gastrointestinal events associated with tegafur/gimeracil/oteracil potassium (S-1) plus cisplatin therapy for unresectable recurrent gastric cancer and risk factors for discontinuing therapy due to adverse events. A total of 65 subjects who had received S-1 plus cisplatin therapy for gastric cancer at Ogaki Municipal Hospital were examined. We found that the risk factors for discontinuation of the therapy due to adverse events were serum albumin (Alb) level less than 3.5 g/dL (odds ratio [OR]: 321.14, P = .0015), creatinine clearance (CrCl) rate less than 78 mL/min (OR: 35.23, P = .0123), and performance status (PS) more than 1 (OR:12.62, P = .0243). Moreover, grade 3 or 4 nonhematological toxicities (including malaise and anorexia) were significantly higher in subjects with Alb less than 3.5 g/dL and CrCl less than 78 mL/min (P < .01). In conclusion, we should pay attention to the safety and continuity of S-1 plus cisplatin therapy in cases where the Alb level is <3.5 g/dL, CrCl level is <78 mL/min, and PS level is >1. Pharmacists should consider reducing the treatment dosage and providing nutritional support in such cases.

Mechanism-based model characterizing bidirectional interaction between PEGylated liposomal CKD-602 (S-CKD602) and monocytes in cancer patients

S-CKD602 is a PEGylated liposomal formulation of CKD-602, a potent topoisomerase I inhibitor. The objective of this study was to characterize the bidirectional pharmacokinetic-pharmacodynamic (PK-PD) interaction between S-CKD602 and monocytes. Plasma concentrations of encapsulated CKD-602 and monocytes counts from 45 patients with solid tumors were collected following intravenous administration of S-CKD602 in the phase I study. The PK-PD models were developed and fit simultaneously to the PK-PD data, using NONMEM(®). The monocytopenia after administration of S-CKD602 was described by direct toxicity to monocytes in a mechanism-based model, and by direct toxicity to progenitor cells in bone marrow in a myelosuppression-based model. The nonlinear PK disposition of S-CKD602 was described by linear degradation and irreversible binding to monocytes in the mechanism-based model, and Michaelis-Menten kinetics in the myelosuppression-based model. The mechanism-based PK-PD model characterized the nonlinear PK disposition, and the bidirectional PK-PD interaction between S-CKD602 and monocytes.

Analysis of the risk factors for myelosuppression after concurrent chemoradiotherapy for patients with advanced non-small cell lung cancer

PURPOSE

Myelosuppression induced by concurrent chemotherapy and radiotherapy can be a significant problem in patients with non-small cell lung cancer (NSCLC), but its risk factors remain largely unknown. The objective of this study was to retrospectively evaluate clinical data obtained before chemoradiotherapy (CRT) to identify the risk factors for myelosuppression in patients with advanced NSCLC.

METHODS

Between January 2007 and January 2012, 141 patients with advanced NSCLC were treated with curative intent according to the CRT protocol (50-70 Gy at 2 Gy/day with paclitaxel 135-175 mg/m(2) and carboplatin 100 mg/m(2) on days 1, 22, and 43). The endpoint of this survey was the occurrence of grade 3 or higher myelosuppression (neutropenia, leukopenia, thrombocytopenia, or anemia). Risk factors significantly related to myelosuppression were extracted using logistic regression analysis.

RESULTS

Grade 3 or higher neutropenia, leukopenia, thrombocytopenia, or anemia occurred in 19.9, 16.3, 14.9, and 0% of the patients, respectively. According to the multivariate analysis, the risk factors included age, albumin, and body surface area (BSA) for neutropenia; performance status and bone metastases for leukopenia; and age, gender, and serum creatinine concentration for thrombocytopenia (p < 0.05).

CONCLUSIONS

It was found that age, BSA, creatinine level, and female gender were the most important factors for CRT-induced myelosuppression in advanced NSCLC. By identifying these risk factors, medical staff can improve application of appropriate medical care to reduce the myelosuppression in advanced NSCLC patients treated by CRT.

Comparison of different semi-mechanistic models for chemotherapy-related neutropenia: application to BI 2536 a Plk-1 inhibitor

PURPOSE

The aim of this investigation was to compare the performance of a commonly used semi-mechanistic model for drug-related neutropenia with other semi-mechanistic models published in the literature.

METHODS

After their implementation in NONMEM VI, five semi-mechanistic models were assessed using the pharmacokinetic and absolute neutrophil count data obtained from 95 patients with non-small cell lung cancer receiving either 200 mg on day 1 or 50 or 60 mg on days 1, 2 and 3 of a 21-day treatment course with the new Plk-1 inhibitor BI 2536. The model performance was compared by means of predictive (visual and numerical) checks, precision in the parameter estimates and objective function-based measures. Details of model parameterization, model stability and run times are also provided.

RESULTS

The time course of the drug plasma concentrations was described by a three compartment model with a first-order elimination rate. With respect to neutropenia, all models were successfully implemented in NONMEM and provided reasonable fits for the median (although not all models described all percentiles of the data well), and in general precise parameter estimates.

CONCLUSION

In the current evaluation performed in a single drug, none of the models showed superior performance compared to the most commonly used model first described by Friberg et al. (J Clin Oncol 20:4713-4721, 2002).

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.

A fast method for testing covariates in population PK/PD Models

The development of covariate models within the population modeling program like NONMEM is generally a time-consuming and non-trivial task. In this study, a fast procedure to approximate the change in objective function values of covariate-parameter models is presented and evaluated. The proposed method is a first-order conditional estimation (FOCE)-based linear approximation of the influence of covariates on the model predictions. Simulated and real datasets were used to compare this method with the conventional nonlinear mixed effect model using both first-order (FO) and FOCE approximations. The methods were mainly assessed in terms of difference in objective function values (ΔOFV) between base and covariate models. The FOCE linearization was superior to the FO linearization and showed a high degree of concordance with corresponding nonlinear models in ΔOFV. The linear and nonlinear FOCE models provided similar coefficient estimates and identified the same covariate-parameter relations as statistically significant or non-significant for the real and simulated datasets. The time required to fit tesaglitazar and docetaxel datasets with 4 and 15 parameter-covariate relations using the linearization method was 5.1 and 0.5 min compared with 152 and 34 h, respectively, with the nonlinear models. The FOCE linearization method allows for a fast estimation of covariate-parameter relations models with good concordance with the nonlinear models. This allows a more efficient model building and may allow the utilization of model building techniques that would otherwise be too time-consuming.

Two-stage model-based design of cancer phase I dose escalation trials: evaluation using the phase I program of barasertib (AZD1152)

Introduction Modeling and simulation of pharmacokinetics and pharmacodynamics has previously been shown to be potentially useful in designing Phase I programs of novel anti-cancer agents that show hematological toxicity. In this analysis, a two-stage model-based trial design was evaluated retrospectively using data from the Phase I program with the aurora kinase inhibitor barasertib. Methods Data from two Phase I trials and four regimens were used (n = 79). Using barasertib-hydroxy QPA plasma concentrations and neutrophil count data from only study 1A, a PKPD model was developed and subsequently used to predict the MTD and a safe starting dose for the other trials. Results The PKPD model based on data from the first study adequately described the time course of neutrophil count fluctuation. The two-stage model-based design provided safe starting doses for subsequent phase I trials for barasertib. Predicted safe starting dose levels were higher than those used in two subsequent trials, but lower than used in the other trial. Discussion The two-stage approach could have been applied safely to define starting doses for alternative dosing strategies with barasertib. The limited improvement in efficiency for the phase I program of barasertib may have been due to the fact that starting doses for the studied phase I trials were already nearly optimal. Conclusion Application of the two-stage model-based trial design in Phase I programs with novel anti-cancer drugs that cause haematological toxicity is feasible, safe, and may lead to a reduction in the number of patient treated at sub-therapeutic dose-levels.

Association of polymorphisms in the TLR4 gene with the risk of developing neutropenia in children with leukemia

Infections are a major cause of morbidity and mortality in children with acute lymphoblastic leukemia (ALL). Susceptibility to infections increases as the neutrophil count decreases. Despite identical treatment patients vary considerably in the number of neutropenic episodes. Toll-like receptor 4 (TLR4) has been shown to have a role in inhibiting apoptosis of neutrophils. Therefore, we hypothesized that polymorphisms in the TLR4 gene may influence the number of chemotherapy-induced neutropenic episodes. Eight single-nucleotide polymorphisms (SNPs) of the TLR4 gene were determined in 194 children aged 0-17 years, who were diagnosed with ALL. We compared the genotype distributions of the SNPs with the frequency of neutropenic episodes during treatment with chemotherapeutic regimens. The number of neutropenic episodes varied from 0 to 17, with a median of four neutropenic episodes. Four SNPs in the TLR4 gene (rs10759931, rs11536889, rs1927911 and rs6478317) were associated with an increased risk of developing chemotherapy-induced neutropenia, each sustaining correction for multiple testing. Further studies are required to elucidate whether pediatric patients with ALL with the particular SNPs in the TLR4 gene also experience more infections and would benefit from prophylactic antibiotic treatment, by a reduction of morbidity and mortality due to infections.

Controlling the Evolution of Resistance

Evolution has long been understood as the driving force for many problems of medical interest. The evolution of drug resistance in HIV and bacterial infections is recognized as one of the most significant emerging problems in medicine. In cancer therapy, the evolution of resistance to chemotherapeutic agents is often the differentiating factor between effective therapy and disease progression or death. Interventions to manage the evolution of resistance have, up to this point, been based on steady-state analysis of mutation and selection models. In this paper, we review the mathematical methods applied to studying evolution of resistance in disease. We present a broad review of several classical applications of mathematical modeling of evolution, and review in depth two recent problems which demonstrate the potential for interventions which exploit the dynamic behavior of resistance evolution models. The first problem addresses the problem of sequential treatment failures in HIV; we present a review of our recent publications addressing this problem. The second problem addresses a novel approach to gene therapy for pancreatic cancer treatment, where selection is used to encourage optimal spread of susceptibility genes through a target tumor, which is then eradicated during a second treatment phase. We review the recent in Vitro laboratory work on this topic, present a new mathematical model to describe the treatment process, and show why model-based approaches will be necessary to successfully implement this novel and promising approach.

Semi-mechanistic population pharmacokinetic/pharmacodynamic model for neutropenia following therapy with the Plk-1 inhibitor BI 2536 and its application in clinical development

PURPOSE

(1) To describe the neutropenic response of BI 2536 a polo-like kinase 1 inhibitor in patients with cancer using a semi-mechanistic model. (2) To explore by simulations (a) the neutropenic effects for the maximum tolerated dose (MTD) and the dose at which dose-limiting toxicity occurred, (b) the possibility to reduce the cycle duration without increasing neutropenia substantially, and (c) the impact of the initial absolute neutrophil count (ANC) on the degree of neutropenia for different doses.

EXPERIMENTAL DESIGN

BI 2536 was administered as intravenous infusion over 60 min in the dose range from 25 to 250 mg. Three different administration schedules were explored: (a) day 1, (b) days 1, 2, and 3 or (c) days 1 and 8 within a 3 week treatment cycle. BI 2536 plasma concentrations and ANC obtained during the first treatment cycle from 104 patients were analysed using the population approach with NONMEM VI.

RESULTS

Neutropenia was described by a semi-mechanistic model resembling proliferation at the stem cell compartment, maturation, degradation, and homeostatic regulation. BI 2536 acts decreasing proliferation rate. Simulations showed that (1) all MTD doses showed an acceptable risk of neutropenia, (2) when BI 2536 is given as 200 mg single administration, cycle duration can be reduced from 3 to 2 weeks, and (3) baseline ANC might be considered to individualise the dose of BI 2536.

CONCLUSIONS

A semi-mechanistic population model was applied to describe the neutropenic effects of BI 2536. The model was used for simulations to support further clinical development.