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

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

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

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.

Effects of Prophylactic Administration of Granulocyte Colony-Stimulating Factor on Peripheral Leukocyte and Neutrophil Counts Levels After Chemotherapy in Patients With Early-Stage Breast Cancer: A Retrospective Cohort Study

Background

Both chemotherapy-induced neutropenia (CIN) and febrile neutropenia (FN) frequently occur and can lead to dose-limiting toxicity and even fatal chemotherapy side effects. The prophylactic use of recombinant human granulocyte colony-stimulating factor (rhG-CSF), including pegylated rhG-CSF (PEG-rhG-CSF), significantly reduces the risks of CIN and FN during chemotherapy in early-stage breast cancer (ESBC) patients. However, whether the prophylactic use of granulocyte colony-stimulating factor (G-CSF), especially PEG-rhG-CSF, can influence white blood cell (WBC) counts and absolute neutrophil counts (ANCs) after finishing the chemotherapy remains unknown. Therefore, exploring the development and recovery tendency of WBC counts and ANCs during and after chemotherapy is crucial.

Objective

We aimed to investigate the variation tendency and recovery of WBC counts and ANCs during and after chemotherapy and evaluate the independent factors influencing leukopenia and neutropenia lasting longer after chemotherapy. We also aimed to provide individualized prophylactically leukocyte elevation therapy for breast cancer patients.

Methods

This single-center retrospective cohort study evaluated 515 ESBC patients who received rhG-CSF or PEG-G-CSF for prophylaxis after adjuvant or neoadjuvant chemotherapy. Blood test reports were analyzed during chemotherapy, and on a 12-month follow-up period after finishing the chemotherapy. The WBC counts and ANCs were measured to assess their variation tendency characteristics and to identify independent factors that influenced the occurrence of leukopenia and neutropenia lasting longer than 12 months after chemotherapy.

Results

Prophylaxis with rhG-CSF or PEG-rhG-CSF kept the mean values of WBC counts and ANCs within the normal range during chemotherapy, but a significant difference in WBC levels was detected before the end of the last chemotherapy compared to the prechemotherapy period (baseline) (p < 0.001). During the 12-month follow-up after the end of the last chemotherapy, WBC counts and ANCs gradually recovered, but the group that used only PEG-rhG-CSF (long-acting group, p _WBC = 0.012) or rhG-CSF (short-acting group, p _WBC = 0.0005) had better leukocyte elevation effects than the mixed treatment group (PEG-rhG-CSF mixed rhG-CSF). Besides, the short-acting group had a better neutrophil elevation effect than the longer-acting (p _ANC = 0.019) and mixed (p _ANC = 0.002) groups. Leukopenia was still present in 92 (17.9%) patients and neutropenia in 63 (12.2%) 12 months after the end of the last chemotherapy. The duration of leukopenia over 12 months was closely associated with the baseline WBC level (p < 0.001), G-CSF types (p = 0.027), and surgical method (p = 0.041). Moreover, the duration of neutropenia over 12 months was closely related to the baseline ANC (p < 0.001), G-CSF types (p = 0.043), and molecular typing (p = 0.025).

Conclusion

The prophylactic application of G-CSF effectively stabilized the WBC counts and ANCs during chemotherapy in ESBC patients. Nevertheless, the recovery of WBC counts and ANCs after chemotherapy varied between different G-CSF treatment groups. The risk of leukopenia and neutropenia persisting for more than 12 months after chemotherapy was associated with G-CSF types, the baseline level of WBC count/ANCs, surgical method, and molecular typing.

Fever and breast cancer: A critical review of the literature and possible underlying mechanisms

Fever is a common feature in various pathological conditions that manifests a series of molecular events in the internal milieu. Much less attention has been paid to the clinical importance and the management of fever in breast cancer patients. However, several studies have reported an association between postoperative fever and poor treatment outcomes in breast cancer patients. The fever is a side effect of chemotherapy and a manifestation of cancer recurrence. The postmenopausal breast cancer patients experience another body temperature disturbance that is known as a hot flashes. Here, we reviewed the literature regarding postoperative fever and the possible underlying molecular and cellular mechanisms. Then the efficacy of non-steroidal anti-inflammatory drugs was discussed as a therapeutic option to control postoperative fever. Finally, we reviewed the chemotherapy-induced neutropenic fever and cancer vaccination-induced fever.

Modeling approaches for reducing safety-related attrition in drug discovery and development: a review on myelotoxicity, immunotoxicity, cardiovascular toxicity, and liver toxicity

Introduction:Safety and tolerability is a critical area where improvements are needed to decrease the attrition rates during development of new drug candidates. Modeling approaches, when smartly implemented, can contribute to this aim.Areas covered:The focus of this review was on modeling approaches applied to four kinds of drug-induced toxicities: hematological, immunological, cardiovascular (CV) and liver toxicity. Papers, mainly published in the last 10 years, reporting models in three main methodological categories - computational models (e.g., quantitative structure-property relationships, machine learning approaches, neural networks, etc.), pharmacokinetic-pharmacodynamic (PK-PD) models, and quantitative system pharmacology (QSP) models - have been considered.Expert opinion:The picture observed in the four examined toxicity areas appears heterogeneous. Computational models are typically used in all areas as screening tools in the early stages of development for hematological, cardiovascular and liver toxicity, with accuracies in the range of 70-90%. A limited number of computational models, based on the analysis of drug protein sequence, was instead proposed for immunotoxicity. In the later stages of development, toxicities are quantitatively predicted with reasonably good accuracy using either semi-mechanistic PK-PD models (hematological and cardiovascular toxicity), or fully exploited QSP models (immuno-toxicity and liver toxicity).

Quantitative translational modeling to facilitate preclinical to clinical efficacy & toxicity translation in oncology

Significant scientific advances in biomedical research have expanded our knowledge of the molecular basis of carcinogenesis, mechanisms of cancer growth, and the importance of the cancer immunity cycle. However, despite scientific advances in the understanding of cancer biology, the success rate of oncology drug development remains the lowest among all therapeutic areas. In this review, some of the key translational drug development objectives in oncology will be outlined. The literature evidence of how mathematical modeling could be used to build a unifying framework to answer these questions will be summarized with recommendations on the strategies for building such a mathematical framework to facilitate the prediction of clinical efficacy and toxicity of investigational antineoplastic agents. Together, the literature evidence suggests that a rigorous and unifying preclinical to clinical translational framework based on mathematical models is extremely valuable for making go/no-go decisions in preclinical development, and for planning early clinical studies.

The risk of febrile neutropenia in breast cancer patients following adjuvant chemotherapy is predicted by the time course of interleukin-6 and C-reactive protein by modelling

AIMS

Early identification of patients with febrile neutropenia (FN) is desirable for initiation of preventive treatment, such as with antibiotics. In this study, the time courses of two inflammation biomarkers, interleukin (IL)-6 and C-reactive protein (CRP), following adjuvant chemotherapy of breast cancer, were characterized. The potential to predict development of FN by IL-6 and CRP, and other model-derived and clinical variables, was explored.

METHODS

The IL-6 and CRP time courses in cycles 1 and 4 of breast cancer treatment were described by turnover models where the probability for an elevated production following initiation of chemotherapy was estimated. Parametric time-to-event models were developed to describe FN occurrence to assess: (i) predictors available before chemotherapy is initiated; (ii) predictors available before FN occurs; and (iii) predictors available when FN occurs.

RESULTS

The IL-6 and CRP time courses were successfully characterized with peak IL-6 typically occurring 2 days prior to CRP peak. Of all evaluated variables the CRP time course was most closely associated with the occurrence of FN. Since the CRP peak typically occurred at the time of FN diagnosis it will, however, have limited value for identifying the need for preventive treatment. The time course of IL-6 was the predictor that could best forecast FN events. Of the variables available at baseline, age was the best, although in comparison a relatively weak, predictor.

CONCLUSIONS

The developed models add quantitative knowledge about IL-6 and CRP and their relationship to the development of FN. The study suggests that IL-6 may have potential as a clinical predictor of FN if monitored during myelosuppressive chemotherapy.

Disease Progression Modeling: Key Concepts and Recent Developments

Disease modeling involves the use of mathematical functions to describe quantitatively the time course of disease progression. In order to characterize the natural progression of disease, these models generally incorporate longitudinal data for some biomarker(s) of disease severity or can incorporate more direct measures of disease severity. Disease models are also often linked to pharmacokinetic-pharmacodynamic models so that the influence of drug treatment on disease progression can be quantified and evaluated. Regulatory agencies have embraced disease progression models as powerful tools that can be used to improve drug development productivity. This article provides a brief overview of key concepts in disease progression modeling followed by illustrative examples from models for Alzheimer's disease. Finally, recent novel applications in which disease progression models have been linked to cost-effectiveness analysis and genomic analysis are described.

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.

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.

Bringing Model-Based Prediction to Oncology Clinical Practice: A Review of Pharmacometrics Principles and Applications

Despite much investment and progress, oncology is still an area with significant unmet medical needs, with new therapies and more effective use of current therapies needed. The emergent field of pharmacometrics combines principles from pharmacology (pharmacokinetics [PK] and pharmacodynamics [PD]), statistics, and computational modeling to support drug development and optimize the use of already marketed drugs. Although it has gained a role within drug development, its use in clinical practice remains scarce. The aim of the present study was to review the principal pharmacometric concepts and provide some examples of its use in oncology. Integrated population PK/PD/disease progression models as part of the pharmacometrics platform provide a powerful tool to predict outcomes so that the right dose can be given to the right patient to maximize drug efficacy and reduce drug toxicity. Population models often can be developed with routinely collected medical record data; therefore, we encourage the application of such models in the clinical setting by generating close collaborations between physicians and pharmacometricians.

IMPLICATIONS FOR PRACTICE

The present review details how the emerging field of pharmacometrics can integrate medical record data with predictive pharmacological and statistical models of drug response to optimize and individualize therapies. In order to make this routine practice in the clinic, greater awareness of the potential benefits of the field is required among clinicians, together with closer collaboration between pharmacometricians and clinicians to ensure the requisite data are collected in a suitable format for pharmacometrics analysis.

Developing Exposure/Response Models for Anticancer Drug Treatment: Special Considerations

Anticancer agents often have a narrow therapeutic index (TI), requiring precise dosing to ensure sufficient exposure for clinical activity while minimizing toxicity. These agents frequently have complex pharmacology, and combination therapy may cause schedule-specific effects and interactions. We review anticancer drug development, showing how integration of modeling and simulation throughout development can inform anticancer dose selection, potentially improving the late-phase success rate. This article has a companion article in Clinical Pharmacology & Therapeutics with practical examples.

Optimizing oncology therapeutics through quantitative translational and clinical pharmacology: challenges and opportunities

Despite advances in biomedical research that have deepened our understanding of cancer hallmarks, resulting in the discovery and development of targeted therapies, the success rates of oncology drug development remain low. Opportunities remain for objective dose selection informed by exposure-response understanding to optimize the benefit-risk balance of novel therapies for cancer patients. This review article discusses the principles and applications of modeling and simulation approaches across the lifecycle of development of oncology therapeutics. Illustrative examples are used to convey the value gained from integration of quantitative clinical pharmacology strategies from the preclinical-translational phase through confirmatory clinical evaluation of efficacy and safety.

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.

Risk factors for febrile neutropenia among patients with cancer receiving chemotherapy: A systematic review

Neutropenia with fever (febrile neutropenia [FN]) is a serious consequence of myelosuppressive chemotherapy that usually results in hospitalization and the need for intravenous antibiotics. FN may result in dose reductions, delays, or even discontinuation of chemotherapy, which, in turn, may compromise patient outcomes. It is important to identify which patients are at high risk for developing FN so that patients can receive optimal chemotherapy while their risk for FN is appropriately managed. A systematic review of the literature was performed to gain a comprehensive and updated understanding of FN risk factors. Older age, poor performance status, advanced disease, certain comorbidities, low baseline blood cell counts, low body surface area/body mass index, treatment with myelosuppressive chemotherapies, and specific genetic polymorphisms correlated with the risk of developing FN. Albeit many studies have analyzed FN risk factors, there are several limitations, including the retrospective nature and small sample sizes of most studies.