A simultaneous analysis of the time-course of leukocytes and neutrophils following docetaxel administration using a semi-mechanistic myelosuppression model

Predicting the Long-Term Effects of Therapeutic Neutralization of Oncostatin M on Human Hematopoiesis

Therapeutic neutralization of Oncostatin M (OSM) causes mechanism-driven anemia and thrombocytopenia, which narrows the therapeutic window complicating the selection of doses (and dosing intervals) that optimize efficacy and safety. We utilized clinical data from studies of an anti-OSM monoclonal antibody (GSK2330811) in healthy volunteers (n = 49) and systemic sclerosis patients (n = 35), to quantitatively determine the link between OSM and alterations in red blood cell (RBC) and platelet production. Longitudinal changes in hematopoietic variables (including RBCs, reticulocytes, platelets, erythropoietin, and thrombopoietin) were linked in a physiology-based model, to capture the long-term effects and variability of therapeutic OSM neutralization on human hematopoiesis. Free serum OSM stimulated precursor cell production through sigmoidal relations, with higher maximum suppression (Imax ) and OSM concentration for 50% suppression (IC50 ) for platelets (89.1% [95% confidence interval: 83.4-93.0], 6.03 pg/mL [4.41-8.26]) than RBCs (57.0% [49.7-64.0], 2.93 pg/mL [2.55-3.36]). Reduction in hemoglobin and platelets increased erythro- and thrombopoietin, respectively, prompting reticulocytosis and (partially) alleviating OSM-restricted hematopoiesis. The physiology-based model was substantiated by preclinical data and utilized in exploration of once-weekly or every other week dosing regimens. Predictions revealed an (for the indication) unacceptable occurrence of grade 2 (67% [58-76], 29% [20-38]) and grade 3 (17% [10-25], 3% [0-7]) anemias, with limited thrombocytopenia. Individual extent of RBC precursor modulation was moderately correlated to skin mRNA gene expression changes. The physiological basis and consideration of interplay among hematopoietic variables makes the model generalizable to other drug and nondrug scenarios, with adaptations for patient populations, diseases, and therapeutics that modulate hematopoiesis or exhibit risk of anemia and/or thrombocytopenia.

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.

Immune-related adverse events associated with nab-paclitaxel/paclitaxel combined with immune checkpoint inhibitors: a systematic review and network meta-analysis

Objective

The combination of nanoparticle albumin-bound paclitaxel (nab-PTX)/paclitaxel (PTX) with immune checkpoint inhibitors (ICIs) has demonstrated significant efficacy in cancer patients. However, the safety of these combination regimens remains conflicting in former researches. Therefore, in order to address this issue, we performed a systematic review and network meta-analysis (NMA) to evaluate and compare the safety profile.

Methods

We performed a systematic review by searching randomized controlled trials (RCTs) from PubMed, EMBASE, Cochrane Library, ClinicalTrials.gov, and Web of Science up to August 15, 2022. The primary outcomes were all-grade (grade 1-5) and high-grade (grade 3-5) immune-related adverse events (irAEs). Secondary outcomes were all-grade (grade 1-5) and high-grade (grade 3-5) irAEs of subgroups of ICIs.

Results

There were 22 RCTs included in the NMA, involving a total of 15 963 patients diagnosed with any type of cancer. ICIs+nab-PTX was associated with a noticeably decreased risk of grade 3-5 pneumonitis (odds ratio [OR]=0.28, 95% credible interval [CrI]: 0.09,0.90) compared to ICI monotherapy; ICIs+PTX showed a lower risk of grade 1-5 hyperthyroidism (OR=0.46, 95% CrI: 0.22-0.96) and grade 1-5 hypothyroidism (OR=0.49, 95% CrI: 0.26-0.93) than ICIs. Compared with PD-1, PD-1+PTX was associated with a statistically significantly lower risk of grade 1-5 pneumonitis (OR=0.32, 95% CrI: 0.11-0.92). PD-L1 resulted in a noticeably lower risk of grade 1-5 hypothyroidism (OR=0.34, 95% CrI: 0.12-1.00) than PD-L1+PTX. Nearly all treatment regimens containing ICIs demonstrated significantly higher risks of irAEs compared to the standard chemotherapy groups.

Conclusion

Nab-PTX/PTX+ICIs demonstrated an approach leading to decreased risk of irAEs compared with ICI monotherapy. This finding supports that ICIs+nab-PTX/PTX may be a safer treatment strategy. Moreover, we also found that the combination regimens containing ICIs had a higher risk of irAEs than standard chemotherapy. Additionally, ICIs+nab-PTX demonstrated a decreased risk of irAEs compared to ICIs+PTX. PD-1 inhibitors were associated with a higher risk of irAEs than PD-L1 inhibitors.

Chemo-Immunotherapy: A New Trend in Cancer Treatment

Chemotherapy has been the basis of advanced cancer treatment for decades. This therapy has largely been considered immunosuppressive, yet accumulated preclinical and clinical evidence shows that certain chemotherapeutic drugs, under defined conditions, may stimulate antitumor immunity and potentiate immune checkpoint inhibitor (ICI)-based therapy. Its effectiveness has been highlighted by recent regulatory approvals of various combinations of chemotherapy with ICIs in several tumors, particularly in some difficult-to-treat cancers. This review discusses the immune modulatory properties of chemotherapy and how they may be harnessed to develop novel chemo-immunotherapy combinations. It also highlights the key determinants of the success of chemo-immunotherapy and provides an overview of the combined chemo-immunotherapies that have been clinically approved.

Development of a Machine Learning-Based Prediction Model for Chemotherapy-Induced Myelosuppression in Children with Wilms' Tumor

Purpose: Develop and validate an accessible prediction model using machine learning (ML) to predict the risk of chemotherapy-induced myelosuppression (CIM) in children with Wilms' tumor (WT) before chemotherapy is administered, enabling early preventive management. Methods: A total of 1433 chemotherapy cycles in 437 children with WT who received chemotherapy in our hospital from January 2009 to March 2022 were retrospectively analyzed. Demographic data, clinicopathological characteristics, hematology and blood biochemistry baseline results, and medication information were collected. Six ML algorithms were used to construct prediction models, and the predictive efficacy of these models was evaluated to select the best model to predict the risk of grade ≥ 2 CIM in children with WT. A series of methods, such as the area under the receiver operating characteristic curve (AUROC), the calibration curve, and the decision curve analysis (DCA) were used to test the model's accuracy, discrimination, and clinical practicability. Results: Grade ≥ 2 CIM occurred in 58.5% (839/1433) of chemotherapy cycles. Based on the results of the training and validation cohorts, we finally identified that the extreme gradient boosting (XGB) model has the best predictive efficiency and stability, with an AUROC of up to 0.981 in the training set and up to 0.896 in the test set. In addition, the calibration curve and the DCA showed that the XGB model had the best discrimination and clinical practicability. The variables were ranked according to the feature importance, and the five variables contributing the most to the model were hemoglobin (Hgb), white blood cell count (WBC), alkaline phosphatase, coadministration of highly toxic chemotherapy drugs, and albumin. Conclusions: The incidence of grade ≥ 2 CIM was not low in children with WT, which needs attention. The XGB model was developed to predict the risk of grade ≥ 2 CIM in children with WT for the first time. The model has good predictive performance and stability and has the potential to be translated into clinical applications. Based on this modeling and application approach, the extension of CIM prediction models to other pediatric malignancies could be expected.

Should personalised dosing have a role in cancer treatment?

Almost all pharmaceutical products are approved on the basis of their effect in patients representing the "average" of the population studied in registrational trials, with most drug labels allowing, at most, for empirical dose reduction in the case of toxicity. In this perspective article we explore some of the evidence that supports the use of personalised dosing in cancer treatment and show how we have been able to build on existing models linking dose, exposure and toxicity to demonstrate how dose optimisation, including increasing the dose, has the potential to significantly improve efficacy outcomes. We also explore, through the lens of our own experience of developing a personalised dosing platform, some of the hurdles that stand in the way of implementing a personalised approach to dosing in real world settings. In particular, our experience is illustrated by the application of a dosing platform for docetaxel treatment in prostate cancer.

Moving Immune Checkpoint Inhibitors to Early Non-Small Cell Lung Cancer: A Narrative Review

Lung cancer is the leading cause of cancer-related death worldwide. Since prognosis of early-stage non-small cell lung cancer (NSCLC) remains dismal for common relapses after curative surgery, considerable efforts are currently focused on bringing immunotherapy into neoadjuvant and adjuvant settings. Previously, perioperative chemotherapy showed only a modest but significative improvement in overall survival. The presence of broad tumor neoantigens load at primary tumor prior to surgery as well as the known immunosuppressive status following resection represent the main rationale for immunotherapy in early disease. Several trials have been conducted in recent years, leading to atezolizumab and nivolumab approval in the adjuvant and neoadjuvant setting, respectively, and perioperative immunotherapy in NSCLC remains a field of active clinical and preclinical investigation. Unanswered questions in perioperative therapy in NSCLC include the optimal sequence and timing of chemotherapy and immunotherapy, the potential of combination strategies, the role of predictive biomarkers for patient selection and the choice of useful endpoints in clinical investigation.

Chemotherapy reinforces anti-tumor immune response and enhances clinical efficacy of immune checkpoint inhibitors

New evidence suggests that the clinical success of chemotherapy is not merely due to tumor cell toxicity but also arises from the restoration of immunosurveillance, which has been immensely neglected in previous preclinical and clinical researches. There is an urgent need for novel insights into molecular mechanisms and regimens that uplift the efficacy of immunotherapy since only a minority of cancer patients are responsive to immune checkpoint inhibitors (ICIs). Recent findings on combination therapy of chemotherapy and ICIs have shown promising results. This strategy increases tumor recognition and elimination by the host immune system while reducing immunosuppression by the tumor microenvironment. Currently, several preclinical studies are investigating molecular mechanisms that give rise to the immunomodulation by chemotherapeutic agents and exploit them in combination therapy with ICIs in order to achieve a synergistic clinical activity. In this review, we summarize studies that exhibit the capacity of conventional chemotherapeutics to elicit anti-tumor immune responses, thereby facilitating anti-tumor activities of the ICIs. In conclusion, combining chemotherapeutics with ICIs appears to be a promising approach for improving cancer treatment outcomes.

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

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

Non-Linear Rituximab Pharmacokinetics and Complex Relationship between Rituximab Concentrations and Anti-Neutrophil Cytoplasmic Antibodies (ANCA) in ANCA-Associated Vasculitis: The RAVE Trial Revisited

BACKGROUND AND OBJECTIVES

Rituximab is approved in patients with anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) and leads to a decrease of ANCA levels. The objectives of this study were to investigate the non-linear pharmacokinetics of rituximab and the relationship between its concentrations and ANCA levels in AAV patients.

METHODS

Ninety-two AAV patients from the RAVE (Rituximab in ANCA-Associated Vasculitis) trial were assessed. Both ANCA anti-myeloperoxidase (MPO-ANCA) and anti-proteinase 3 (PR3-ANCA) levels were used as biomarkers. The pharmacokinetics of rituximab were described using a semi-mechanistic two-compartment model that included a latent target antigen turnover and allowed the estimation of specific target-mediated elimination in addition to its non-specific elimination of rituximab. The effect of rituximab on the ANCA level was described using a semi-mechanistic compartment model with a negative feedback (Friberg) model with no transit compartment. A population modeling approach was used.

RESULTS

Our pharmacokinetic and pharmacokinetic-pharmacodynamic (PK-PD) models satisfactorily described both concentration-time and concentration-effect relationship data. The mean (inter-individual standard deviation) estimated non-specific clearance was 0.15 L/day (0.30%) and the target-mediated elimination rate constant was 2.4 × 10^-5 nmol/day. The elimination half-lives for MPO-ANCA and PR3-ANCA were 24 and 18 days, respectively.

CONCLUSIONS

A non-linear target-mediated elimination of rituximab was detected in AAV patients. Our PK-PD model allowed quantification of the association between rituximab concentrations and ANCA levels. This decrease was deep but delayed, and more sustained in patients with MPO-ANCA than in those with PR3-ANCA. Our results suggest that repeating courses of rituximab might improve the clinical response to rituximab.

Systems Modeling to Quantify Safety Risks in Early Drug Development: Using Bifurcation Analysis and Agent-Based Modeling as Examples

Quantitative Systems Toxicology (QST) models, recapitulating pharmacokinetics and mechanism of action together with the organic response at multiple levels of biological organization, can provide predictions on the magnitude of injury and recovery dynamics to support study design and decision-making during drug development. Here, we highlight the application of QST models to predict toxicities of cancer treatments, such as cytopenia(s) and gastrointestinal adverse effects, where narrow therapeutic indexes need to be actively managed. The importance of bifurcation analysis is demonstrated in QST models of hematologic toxicity to understand how different regions of the parameter space generate different behaviors following cancer treatment, which results in asymptotically stable predictions, yet highly irregular for specific schedules, or oscillating predictions of blood cell levels. In addition, an agent-based model of the intestinal crypt was used to simulate how the spatial location of the injury within the crypt affects the villus disruption severity. We discuss the value of QST modeling approaches to support drug development and how they align with technological advances impacting trial design including patient selection, dose/regimen selection, and ultimately patient safety.

A mathematical model of white blood cell dynamics during maintenance therapy of childhood acute lymphoblastic leukemia

Acute lymphoblastic leukemia is the most common malignancy in childhood and requires prolonged oral maintenance chemotherapy to prevent disease relapse after remission induction with intensive intravenous chemotherapy. In maintenance therapy, drug doses of 6-mercaptopurine (6-MP) and methotrexate (MTX) are adjusted to achieve sustained antileukemic activity without excessive myelosuppression. However, uncertainty exists regarding timing and extent of drug dose responses and optimal dose adaptation strategies. We propose a novel comprehensive mathematical model for 6-MP and MTX pharmacokinetics, pharmacodynamics and myelosuppression in acute lymphoblastic maintenance therapy. We personalize and cross-validate the mathematical model using clinical data and propose a real-time algorithm to predict chemotherapy responses with a clinical decision support system as a potential future application.

Characterizing Chemotherapy-Induced Neutropenia and Monocytopenia Through Mathematical Modelling

In spite of the recent focus on the development of novel targeted drugs to treat cancer, cytotoxic chemotherapy remains the standard treatment for the vast majority of patients. Unfortunately, chemotherapy is associated with high hematopoietic toxicity that may limit its efficacy. We have previously established potential strategies to mitigate chemotherapy-induced neutropenia (a lack of circulating neutrophils) using a mechanistic model of granulopoiesis to predict the interactions defining the neutrophil response to chemotherapy and to define optimal strategies for concurrent chemotherapy/prophylactic granulocyte colony-stimulating factor (G-CSF). Here, we extend our analyses to include monocyte production by constructing and parameterizing a model of monocytopoiesis. Using data for neutrophil and monocyte concentrations during chemotherapy in a large cohort of childhood acute lymphoblastic leukemia patients, we leveraged our model to determine the relationship between the monocyte and neutrophil nadirs during cyclic chemotherapy. We show that monocytopenia precedes neutropenia by 3 days, and rationalize the use of G-CSF during chemotherapy by establishing that the onset of monocytopenia can be used as a clinical marker for G-CSF dosing post-chemotherapy. This work therefore has important clinical applications as a comprehensive approach to understanding the relationship between monocyte and neutrophils after cyclic chemotherapy with or without G-CSF support.

Model-Based Simulation of Maintenance Therapy of Childhood Acute Lymphoblastic Leukemia

Acute lymphoblastic leukemia is the most common malignancy in childhood. Successful treatment requires initial high-intensity chemotherapy, followed by low-intensity oral maintenance therapy with oral 6-mercaptopurine (6MP) and methotrexate (MTX) until 2–3 years after disease onset. However, intra- and inter-individual variability in the pharmacokinetics (PK) and pharmacodynamics (PD) of 6MP and MTX make it challenging to balance the desired antileukemic effects with undesired excessive myelosuppression during maintenance therapy. A model to simulate the dynamics of different cell types, especially neutrophils, would be a valuable contribution to improving treatment protocols (6MP and MTX dosing regimens) and a further step to understanding the heterogeneity in treatment efficacy and toxicity. We applied and modified a recently developed semi-mechanistic PK/PD model to neutrophils and analyzed their behavior using a non-linear mixed-effects modeling approach and clinical data obtained from 116 patients. The PK model of 6MP influenced the accuracy of absolute neutrophil count (ANC) predictions, whereas the PD effect of MTX did not. Predictions based on ANC were more accurate than those based on white blood cell counts. Using the new cross-validated mathematical model, simulations of different treatment protocols showed a linear dose-effect relationship and reduced ANC variability for constant dosages. Advanced modeling allows the identification of optimized control criteria and the weighting of specific influencing factors for protocol design and individually adapted therapy to exploit the optimal effect of maintenance therapy on survival.

Individual optimal dose of amrubicin to prevent severe neutropenia in Japanese patients with lung cancer

This study determined individual optimal amrubicin doses for Japanese patients with lung cancer after platinum‐based treatment. We carried out population pharmacokinetic and pharmacodynamic modeling incorporating gene polymorphisms of metabolizing enzymes and transporters. Fifty patients with lung cancer, who were given 35‐40 mg/m² amrubicin on days 1‐3 every 3‐4 weeks, were enrolled. Mechanism‐based modeling described relationships between the pharmacokinetics of amrubicin and absolute neutrophil counts. A population pharmacokinetic and pharmacodynamic model was developed for amrubicin and amrubicinol (active metabolite), connected by a delay compartment. The final model incorporated body surface area as a covariate of amrubicin and amrubicinol clearance and distribution volume. SLC28A3 single nucleotide polymorphism (rs7853758) was also incorporated as a constant covariate of the delay compartment of amrubicinol. Performance status was considered a covariate of pharmacokinetic (amrubicinol clearance) and pharmacodynamic (mean maturation time) parameters. Twenty‐nine patients with grade 4 neutropenia showed higher amrubicinol area under the plasma concentration‐time curve from 0 to 72 hours (AUC_0‐72, P = .01) and shorter overall survival periods than other patients did (P = .01). Using the final population pharmacokinetic and pharmacodynamic model, median optimal dose to prevent grade 4 neutropenia aggravation was estimated at 22 (range, 8−40) mg/m² for these 29 patients. We clarified correlations between area under the plasma concentration‐time curve from 0 to 72 hours of amrubicinol and severity of neutropenia and survival of patients given amrubicin after platinum chemotherapy. This analysis revealed important amrubicin pharmacokinetic‐pharmacodynamic covariates and provided useful information to predict patients who would require prophylactic granulocyte colony stimulating factor.

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.

Enhancing antitumor response by combining immune checkpoint inhibitors with chemotherapy in solid tumors

BACKGROUND

Cancer immunotherapy has changed the standard of care for a subgroup of patients with advanced disease. Immune checkpoint blockade (ICB) in particular has shown improved survival compared with previous standards of care for several tumor types. Although proven to be successful in more immunogenic tumors, ICB is still largely ineffective in patients with tumors that are not infiltrated by immune cells, the so-called cold tumors.

PATIENTS AND METHODS

This review describes the effects of different chemotherapeutic agents on the immune system and the potential value of these different types of chemotherapy as combination partners with ICB in patients with solid tumors. Both preclinical data and currently ongoing clinical trials were evaluated. In addition, we reviewed findings regarding different dosing schedules, including the effects of an induction phase and applying metronomic doses of chemotherapy.

RESULTS

Combining ICB with other treatment modalities may lead to improved immunological conditions in the tumor microenvironment and could thereby enhance the antitumor immune response, even in tumor types that are so far unresponsive to ICB monotherapy. Chemotherapy, that was originally thought to be solely immunosuppressive, can exert immunomodulatory effects which may be beneficial in combination with immunotherapy. Each chemotherapeutic drug impacts the tumor microenvironment differently, and in order to determine the most suitable combination partners for ICB it is crucial to understand these mechanisms.

CONCLUSION

Preclinical studies demonstrate that the majority of chemotherapeutic drugs has been shown to exert immunostimulatory effects, either by inhibiting immunosuppressive cells and/or activating effector cells, or by increasing immunogenicity and increasing T-cell infiltration. However, for certain chemotherapeutic agents timing, dose and sequence of administration of chemotherapeutic agents and ICB is important. Further studies should focus on determining the optimal drug combinations, sequence effects and optimal concentration-time profiles in representative preclinical models.

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.

Population Pharmacokinetic-Pharmacodynamic Modeling of 5-Fluorouracil for Toxicities in Rats

BACKGROUND AND OBJECTIVES

Myelosuppression is a dose-limiting toxicity of 5-fluorouracil (5-FU). Predicting the inter- and intra-patient variability in pharmacokinetics and toxicities of 5-FU may contribute to the individualized medicine. This study aimed to establish a population pharmacokinetic-pharmacodynamic model that could evaluate the inter- and intra-individual variability in the plasma 5-FU concentration, 5-FU-induced body weight loss and myelosuppression in rats.

METHOD

Plasma 5-FU concentrations, body weight loss, and blood cell counts in rats following the intravenous administration of various doses of 5-FU for 4 days were used to develop the population pharmacokinetic-pharmacodynamic model.

RESULTS

The population pharmacokinetic model consisting of a two-compartment model with Michaelis-Menten elimination kinetics successfully characterized the individual and population predictions of the plasma concentration of 5-FU and provided credible parameter estimates. The estimates of inter-individual variability in maximal rate of saturable metabolism and residual variability were 8.1 and 22.0%, respectively. The population pharmacokinetic-pharmacodynamic model adequately described the individual complete time-course of alterations in body weight loss, erythrocyte, leukocyte, and lymphocyte counts in rats treated with various doses of 5-FU. The inter-individual variability of the drug effects in the pharmacodynamic model for body weight loss was 82.6%, which was relatively high. The results of the present study suggest that not only individual fluctuations in the 5-FU concentration but also the cell sensitivity would affect the onset and degree of 5-FU-induced toxicity.

CONCLUSION

This population pharmacokinetic-pharmacodynamic model could evaluate the inter- and intra-individual variability in drug-induced toxicity and guide the assessments of novel anticancer agents in drug development.

Pharmacokinetic-pharmacodynamic modelling of neutrophil response to G-CSF in healthy subjects and patients with chemotherapy-induced neutropenia

AIM

The objective of the present study was to use pharmacokinetic-pharmacodynamic modelling to characterize the effects of chemotherapy on the granulopoietic system and to predict the absolute neutrophil counts (ANCs) for patients with chemotherapy-induced neutropenia treated with filgrastim and pegfilgrastim.

METHODS

Data were extracted from 10 phase I-III studies conducted in 110 healthy adults, and 618 adult and 52 paediatric patients on chemotherapy following administration of filgrastim or pegfilgrastim. The structural model accounted for ANC dynamics and the effects of filgrastim and pegfilgrastim, chemotherapy and corticosteroids. The impact of neutrophils on drug disposition was based on a drug receptor-binding model that assumed quasi-equilibrium and stimulation of the production and maturation of neutrophils upon treatment. The chemotherapy and corticosteroid effects were represented by kinetic-pharmacodynamic-type models, where chemotherapy stimulated elimination of neutrophil precursors at the mitotic stage, and corticosteroids stimulated neutrophil production.

RESULTS

The systemic half-lives of filgrastim (2.6 h) and pegfilgrastim (10.1 h) were as expected. The effective half-life of chemotherapy was 9.6 h, with a 2-day killing effect. The rate of receptor elimination from mitotic compartments exhibited extreme interindividual variability (% coefficient of variation >200), suggesting marked differences in sensitivity to chemotherapy effects on ANCs. The stimulatory effects of pegfilgrastim were significantly greater than those of filgrastim. Model qualification confirmed the predictive capability of this model.

CONCLUSION

This qualified model simulates the time course of ANC in the absence or presence of chemotherapy and predicts nadir, time to nadir and time of recovery from different grades of neutropenia upon treatment with filgrastim and pegfilgrastim.

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.

Advanced Methods for Dose and Regimen Finding During Drug Development: Summary of the EMA/EFPIA Workshop on Dose Finding (London 4-5 December 2014)

Inadequate dose selection for confirmatory trials is currently still one of the most challenging issues in drug development, as illustrated by high rates of late‐stage attritions in clinical development and postmarketing commitments required by regulatory institutions. In an effort to shift the current paradigm in dose and regimen selection and highlight the availability and usefulness of well‐established and regulatory‐acceptable methods, the European Medicines Agency (EMA) in collaboration with the European Federation of Pharmaceutical Industries Association (EFPIA) hosted a multistakeholder workshop on dose finding (London 4–5 December 2014). Some methodologies that could constitute a toolkit for drug developers and regulators were presented. These methods are described in the present report: they include five advanced methods for data analysis (empirical regression models, pharmacometrics models, quantitative systems pharmacology models, MCP‐Mod, and model averaging) and three methods for study design optimization (Fisher information matrix (FIM)‐based methods, clinical trial simulations, and adaptive studies). Pairwise comparisons were also discussed during the workshop; however, mostly for historical reasons. This paper discusses the added value and limitations of these methods as well as challenges for their implementation. Some applications in different therapeutic areas are also summarized, in line with the discussions at the workshop. There was agreement at the workshop on the fact that selection of dose for phase III is an estimation problem and should not be addressed via hypothesis testing. Dose selection for phase III trials should be informed by well‐designed dose‐finding studies; however, the specific choice of method(s) will depend on several aspects and it is not possible to recommend a generalized decision tree. There are many valuable methods available, the methods are not mutually exclusive, and they should be used in conjunction to ensure a scientifically rigorous understanding of the dosing rationale.

Activation of Nrf2-ARE signaling mitigates cyclophosphamide-induced myelosuppression

Myelosuppression is the most common dose-limiting adverse effect of chemotherapies. In the present study, we investigated the involvement of nuclear erythroid 2-related factor 2 (Nrf2) in cyclophosphamide-induced myelosuppression in mice, and evaluated the potential of activating Nrf2 signaling as a preventive strategy. The whole blood from Nrf2^-/- mice exhibited decreased antioxidant capacities, while the bone marrow cells, peripheral blood mononuclear cells and granulocytes from Nrf2^-/- mice were more susceptible to acrolein-induced cytotoxicity than those from wild type mice. Single dosage of cyclophosphamide induced significantly severer acute myelosuppression in Nrf2^-/- mice than in wild type mice. Furthermore, Nrf2^-/- mice exhibited greater loss of peripheral blood nucleated cells and recovered slower from myelosuppression nadir upon multiple consecutive dosages of cyclophosphamide than wild type mice did. This was accompanied with decreased antioxidant and detoxifying gene expressions and impaired colony formation ability of Nrf2^-/- bone marrow cells. More importantly, activation of Nrf2 signaling by CDDO-Me significantly alleviated cyclophosphamide-induced myelosuppression, while this alleviation was diminished in Nrf2^-/- mice. In conclusion, the present study shows that Nrf2 plays a protective role in cyclophosphamide-induced myelosuppression and activation of Nrf2 is a promising strategy to prevent or treat chemotherapy-induced myelosuppression.

A Mathematical Model of Granulopoiesis Incorporating the Negative Feedback Dynamics and Kinetics of G-CSF/Neutrophil Binding and Internalization

We develop a physiological model of granulopoiesis which includes explicit modelling of the kinetics of the cytokine granulocyte colony-stimulating factor (G-CSF) incorporating both the freely circulating concentration and the concentration of the cytokine bound to mature neutrophils. G-CSF concentrations are used to directly regulate neutrophil production, with the rate of differentiation of stem cells to neutrophil precursors, the effective proliferation rate in mitosis, the maturation time, and the release rate from the mature marrow reservoir into circulation all dependent on the level of G-CSF in the system. The dependence of the maturation time on the cytokine concentration introduces a state-dependent delay into our differential equation model, and we show how this is derived from an age-structured partial differential equation model of the mitosis and maturation and also detail the derivation of the rest of our model. The model and its estimated parameters are shown to successfully predict the neutrophil and G-CSF responses to a variety of treatment scenarios, including the combined administration of chemotherapy and exogenous G-CSF. This concomitant treatment was reproduced without any additional fitting to characterize drug-drug interactions.

Human neutrophil kinetics: modeling of stable isotope labeling data supports short blood neutrophil half-lives

Human neutrophils have traditionally been thought to have a short half-life in blood; estimates vary from 4 to 18 hours. This dogma was recently challenged by stable isotope labeling studies with heavy water, which yielded estimates in excess of 3 days. To investigate this disparity, we generated new stable isotope labeling data in healthy adult subjects using both heavy water (n = 4) and deuterium-labeled glucose (n = 9), a compound with more rapid labeling kinetics. To interpret results, we developed a novel mechanistic model and applied it to previously published (n = 5) and newly generated data. We initially constrained the ratio of the blood neutrophil pool to the marrow precursor pool (ratio = 0.26; from published values). Analysis of heavy water data sets yielded turnover rates consistent with a short blood half-life, but parameters, particularly marrow transit time, were poorly defined. Analysis of glucose-labeling data yielded more precise estimates of half-life (0.79 ± 0.25 days; 19 hours) and marrow transit time (5.80 ± 0.42 days). Substitution of this marrow transit time in the heavy water analysis gave a better-defined blood half-life of 0.77 ± 0.14 days (18.5 hours), close to glucose-derived values. Allowing the ratio of blood neutrophils to mitotic neutrophil precursors (R) to vary yielded a best-fit value of 0.19. Reanalysis of the previously published model and data also revealed the origin of their long estimates for neutrophil half-life: an implicit assumption that R is very large, which is physiologically untenable. We conclude that stable isotope labeling in healthy humans is consistent with a blood neutrophil half-life of less than 1 day.

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.

Neutrophil dynamics during concurrent chemotherapy and G-CSF administration: Mathematical modelling guides dose optimisation to minimise neutropenia

The choice of chemotherapy regimens is often constrained by the patient's tolerance to the side effects of chemotherapeutic agents. This dose-limiting issue is a major concern in dose regimen design, which is typically focused on maximising drug benefits. Chemotherapy-induced neutropenia is one of the most prevalent toxic effects patients experience and frequently threatens the efficient use of chemotherapy. In response, granulocyte colony-stimulating factor (G-CSF) is co-administered during chemotherapy to stimulate neutrophil production, increase neutrophil counts, and hopefully avoid neutropenia. Its clinical use is, however, largely dictated by trial and error processes. Based on up-to-date knowledge and rational considerations, we develop a physiologically realistic model to mathematically characterise the neutrophil production in the bone marrow which we then integrate with pharmacokinetic and pharmacodynamic (PKPD) models of a chemotherapeutic agent and an exogenous form of G-CSF (recombinant human G-CSF, or rhG-CSF). In this work, model parameters represent the average values for a general patient and are extracted from the literature or estimated from available data. The dose effect predicted by the model is confirmed through previously published data. Using our model, we were able to determine clinically relevant dosing regimens that advantageously reduce the number of rhG-CSF administrations compared to original studies while significantly improving the neutropenia status. More particularly, we determine that it could be beneficial to delay the first administration of rhG-CSF to day seven post-chemotherapy and reduce the number of administrations from ten to three or four for a patient undergoing 14-day periodic chemotherapy.

Complex pattern of interleukin-11-induced inflammation revealed by mathematically modeling the dynamics of C-reactive protein

Inflammation underlies many diseases and is an undesired effect of several therapy modalities. Biomathematical modeling can help unravel the complex inflammatory processes and the mechanisms triggering their emergence. We developed a model for induction of C-reactive protein (CRP), a clinically reliable marker of inflammation, by interleukin (IL)-11, an approved cytokine for treatment of chemotherapy-induced thrombocytopenia. Due to paucity of information on the mechanisms underlying inflammation-induced CRP dynamics, our model was developed by systematically evaluating several models for their ability to retrieve variable CRP profiles observed in IL-11-treated breast cancer patients. The preliminary semi-mechanistic models were designed by non-linear mixed-effects modeling, and were evaluated by various performance criteria, which test goodness-of-fit, parsimony and uniqueness. The best-performing model, a robust population model with minimal inter-individual variability, uncovers new aspects of inflammation dynamics. It shows that CRP clearance is a nonlinear self-controlled process, indicating an adaptive anti-inflammatory reaction in humans. The model also reveals a dual IL-11 effect on CRP elevation, whereby the drug has not only a potent immediate influence on CRP incline, but also a long-term influence inducing elevated CRP levels for several months. Consistent with this, model simulations suggest that periodic IL-11 therapy may result in prolonged low-grade (chronic) inflammation post treatment. Future application of the model can therefore help design improved IL-11 regimens with minimized long-term CRP toxicity. Our study illuminates the dynamics of inflammation and its control, and provides a prototype for progressive modeling of complex biological processes in the medical realm and beyond.

Mechanism-based pharmacokinetic/pharmacodynamic meta-analysis of navitoclax (ABT-263) induced thrombocytopenia

OBJECTIVE

Navitoclax is a first-in-class, orally bioavailable, targeted Bcl-2 family protein inhibitor and promotes apoptosis. Thrombocytopenia is a primary dose-limiting toxicity of navitoclax which exhibited a distinct time profile in circulating platelets from that caused by traditional chemotherapies. A population pharmacokinetic/pharmacodynamic (PK/PD) model was developed to describe the pharmacokinetic of navitoclax as well as the time course of the platelet counts in cancer patients receiving navitoclax.

METHODS

Data from 256 patients who received oral navitoclax (dose range 10-475 mg) as a 14/21-day schedule or a continuous once daily (QD) schedule were used to construct the model using NONMEM. The PK model was a two-compartmental model with a lag-time and a transit compartment in absorption. The PD model was a semi-physiological model that comprised a progenitor cell compartment, three transition compartments representing the maturation chain in the bone marrow and a peripheral blood compartment. Compared with the previously published models, the model established in this analysis applied a different feedback mechanism and introduced a new concept of progenitor cell "pool", which describes a large pool of platelet progenitor cells at the beginning of navitoclax treatment.

RESULTS

The PD model was able to describe a slight downward trend of platelet counts over the long-term navitoclax treatment as observed in around 8 % of the patients and the initial drop in platelets seen in our Phase 1/2a studies.

CONCLUSIONS

We have developed a new semi-physiological platelet model for describing fast drop of platelets after initial navitoclax administration and long-term decline of platelets after continuous administration of navitoclax.

Semi-physiological pharmacokinetic-pharmacodynamic (PK-PD) modeling and simulation of 5-fluorouracil for thrombocytopenia in rats

1. The aim of this study was to develop a simple pharmacokinetic-pharmacodynamic (PK-PD) model that could characterize the complete time-course of alterations in platelet counts to predict the onset and degree of thrombocytopenia, which severely limits the use of the anticancer agent 5-fluorouracil (5-FU), in rats. 2. Platelet counts were measured in rats following the intravenous administration of various doses of 5-FU for 4 days to obtain data for an analysis of the PK-PD model. Our PK-PD model consisted of a two-compartment PK model, with three compartments for the PD model and 10 structural PK-PD model parameters. 3. After the 5-FU treatment, platelet counts transiently decreased to a nadir level, showed a rebound to above the baseline level before recovering to baseline levels. Nadir platelet counts and rebounds varied with the AUC0-∞ level. The final PK-PD model effectively characterized platelet count data and final PD parameters were estimated with high certainty. 4. This PK-PD model and simulation may represent a valuable tool for quantifying and predicting the complete time-course of alterations in blood cell counts, and could contribute to the development of therapeutic strategies with 5-FU and assessments of various novel anticancer agents that are difficult to examine in humans.

Pharmacokinetic/Pharmacodynamic modeling of abexinostat-induced thrombocytopenia across different patient populations: application for the determination of the maximum tolerated doses in both lymphoma and solid tumour patients

BACKGROUND

In the clinical development of oncology drugs, the recommended dose is usually determined using a 3 + 3 dose-escalation study design. However, this phase I design does not always adequately describe dose-toxicity relationships.

METHODS

125 patients, with either solid tumours or lymphoma, were included in the study and 1217 platelet counts were available over three treatment cycles. The data was used to build a population pharmacokinetic/pharmacodynamic (PKPD) model using a sequential modeling approach. Model-derived Recommended Doses (MDRD) of abexinostat (a Histone Deacetylase Inhibitor) were determined from simulations of different administration schedules, and the higher bound for the probability of reaching these MDRD with a 3 + 3 design were obtained.

RESULTS

The PKPD model developed adequately described platelet kinetics in both patient populations with the inclusion of two platelet baseline counts and a disease progression component for patients with lymphoma. Simulation results demonstrated that abexinostat administration during the first 4 days of each week in a 3-week cycle led to a higher MDRD compared to the other administration schedules tested, with a maximum probability of 40 % of reaching these MDRDs using a 3 + 3 design.

CONCLUSIONS

The PKPD model was able to predict thrombocytopenia following abexinostat administration in both patient populations. A model-based approach to determine the recommended dose in phase I trials is preferable due to the imprecision of the 3 + 3 design.

Application of hematological toxicity modeling in clinical development of abexinostat (S-78454, PCI-24781), a new histone deacetylase inhibitor

PURPOSE

A population pharmacokinetic/pharmacodynamic (PK/PD) model was developed to describe the thrombocytopenia (dose-limiting toxicity) of abexinostat, a new histone deacetylase inhibitor. An optimal administration schedule of the drug was determined using a simulation-based approach.

METHODS

Early PK and PK/PD data were analysed using a sequential population modeling approach (NONMEM 7), allowing for the description of a PK profile and platelet-count decrease after abexinostat administration with various administration schedules. Simulations of platelet count with several administration schedules over 3-week treatment cycles (ASC) and over a day (ASD) were computed to define the optimal schedule that limits the depth of thrombocytopenia.

RESULTS

An intermediate PK/PD model accurately described the data. The administration of abexinostat during the first 4 days of each week in a 3-week cycle resulted in fewer adverse events (with no influence of ASD on platelet count profiles), and corresponded to the optimal treatment schedule. This administration schedule was clinically evaluated in a phase I clinical trial and allowed for the definition of a new maximum tolerated dose (MTD), leading to a nearly 30% higher dose-intensity than that of another previously tested schedule. Lastly, a final model was built using all of the available data.

CONCLUSIONS

The final model, characterizing the dose-effect and the dose-toxicity relationships, provides a useful modeling tool for clinical drug development.

Polysaccharide-K augments docetaxel-induced tumor suppression and antitumor immune response in an immunocompetent murine model of human prostate cancer

Advanced castration-resistant prostate cancer has high mortality rates and limited treatment options. Novel therapies are needed to better contend with this disease. Polysaccharide-K^® (PSK), an extract of the mushroom Trametes versicolor, has immunomodulatory and tumor suppressive activities. PSK is used in Asia as a cancer immunotherapy. However, its benefit in combination with taxanes for prostate cancer is unknown. We examined whether PSK would enhance docetaxel-induced apoptosis and augment anti-tumor immune responses in orthotopic tumors using transgenic adenocarcinoma of the mouse prostate (TRAMP)-C2-bearing mice. Combining PSK with docetaxel induced significantly higher tumor suppression than either treatment alone (p<0.05), including a reduction in tumor proliferation and enhanced apoptosis. Combined PSK and docetaxel treatment led to a lower decrease in number of white blood cells than docetaxel alone, an effect accompanied by increased numbers of tumor-infiltrating CD4⁺ and CD8⁺ T cells. PSK with or without docetaxel significantly enhanced mRNA expression of IFN-γ compared to control, but did not significantly alter T-regulatory FoxP3 mRNA expression in tumors. PSK also augmented docetaxel-induced splenic natural killer cell cytolytic activity against YAC-1 target cells (p=0.045). This study is the first to show that PSK enhances docetaxel-induced prostate cancer tumor suppression, apoptosis and antitumor responses.