Mechanistic models for myelosuppression

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.

Individual modelling of haematotoxicity with NARX neural networks: A knowledge transfer approach

Cytotoxic cancer therapy often results in dose-limiting haematotoxic side effects. Predicting an individual's risk is a major objective in precision medicine of cancer treatment. In this regard, patient heterogeneity presents a significant challenge. In this paper, we explore the use of hypothesis-free machine learning models based on recurrent nonlinear auto-regressive networks with exogenous inputs (NARX) as an approach to achieve this goal. Also, we propose a knowledge transfer approach to ameliorate the issue of sparse individual data, which typically hampers learning of individual networks. We demonstrate the feasibility of our approach based on a virtual patient population generated using a semi-mechanistic model of haematopoiesis and imposing different cytotoxic therapy scenarios on it. Employing different techniques of model optimisation, we derive robust and parsimonious individual networks with good generalisation performances. Moreover, we analyse in detail possible factors influencing the generalisation performance. Results suggest that our transfer learning approach using NARX networks can provide robust predictions of individual patient's response to treatment. As a practical perspective, we apply our approach to individual time series data of two patients with non-Hodgkin's lymphoma.

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

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

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.

Leveraging modeling and simulation to optimize the therapeutic window for epigenetic modifier drugs

Dysregulated epigenetic processes can lead to altered gene expression and give rise to malignant transformation and tumorigenesis. Epigenetic drugs aim to revert the phenotype of cancer cells to normally functioning cells, and are developed and applied to treat both hematological and solid cancers. Despite this promising therapeutic avenue, the successful development of epigenetic modulators has been challenging. We argue that besides identifying the right responder patient population, the selection of an optimized dosing regimen is equally important. For the majority of epigenetic modulators, hematological adverse effects such as thrombocytopenia, anemia or neutropenia are frequently observed and may limit their therapeutic potential. Therefore, one of the key challenges is to identify a dosing regimen that maximizes drug efficacy and minimizes toxicity. This requires a good understanding of the quantitative relationship between the administered dose, the drug exposure and the magnitude and duration of drug response related to safety and efficacy. With case examples, we highlight how modeling and simulation has been successfully applied to address those questions. As an outlook, we suggest the combination of efficacy and safety prediction models that capture the quantitative, mechanistic relationships governing the balance between their safety and efficacy dynamics. A stepwise approach for its implementation is presented. Utilizing in silico explorations, the impact of dosing regimen on the therapeutic window can be explored. This will serve as a basis to select the most promising dosing regimen that maximizes efficacy while minimizing adverse effects and to increase the probability of success for the given epigenetic drug.

A parallelized, perfused 3D triculture model of leukemia for in vitro drug testing of chemotherapeutics

Leukemia patients undergo chemotherapy to combat the leukemic cells (LCs) in the bone marrow. During therapy not only the LCs, but also the blood-producing hematopoietic stem and progenitor cells (HSPCs) may be destroyed. Chemotherapeutics targeting only the LCs are urgently needed to overcome this problem and minimize life-threatening side-effects. Predictive in vitro drug testing systems allowing simultaneous comparison of various experimental settings would enhance the efficiency of drug development. Here, we present a 3D human leukemic bone marrow model perfused using a magnetic, parallelized culture system to ensure media exchange. Chemotherapeutic treatment of the acute myeloid leukemia cell line KG-1a in 3D magnetic hydrogels seeded with mesenchymal stem/stromal cells (MSCs) revealed a greater resistance of KG-1a compared to 2D culture. In 3D tricultures with HSPCs, MSCs and KG-1a, imitating leukemic bone marrow, HSPC proliferation decreased while KG-1a cells remained unaffected post treatment. Non-invasive metabolic profiling enabled continuous monitoring of the system. Our results highlight the importance of using biomimetic 3D platforms with proper media exchange and co-cultures for creating in vivo-like conditions to enable in vitro drug testing. This system is a step towards drug testing in biomimetic, parallelized in vitro approaches, facilitating the discovery of new anti-leukemic drugs.

Ayurveda-based Botanicals as Therapeutic Adjuvants in Paclitaxel-induced Myelosuppression

Chemotherapy-induced myelosuppression is one of the major challenges in cancer treatment. Ayurveda-based immunomodulatory botanicals Asparagus racemosus Willd (AR/Shatavari) and Withania somnifera (L.). Dunal (WS/Ashwagandha) have potential role to manage myelosuppression. We have developed a method to study the effects of AR and WS as therapeutic adjuvants to counter paclitaxel (PTX)-induced myelosuppression. Sixty female BALB/c mice were divided into six groups—vehicle control (VC), PTX alone, PTX with aqueous and hydroalcoholic extracts of AR (ARA, ARH) and WS (WSA, WSH). The myelosuppression was induced in mice by intraperitoneal administration of PTX at 25 mg/kg dose for three consecutive days. The extracts were orally administered with a dose of 100 mg/kg for 15 days prior to the induction with PTX administration. The mice were observed daily for morbidity parameters and were bled from retro-orbital plexus after 2 days of PTX dosing. The morbidity parameters simulate clinical adverse effects of PTX that include activity (extreme tiredness due to fatigue), behavior (numbness and weakness due to peripheral neuropathy), body posture (pain in muscles and joints), fur aspect and huddling (hair loss). The collected samples were used for blood cell count analysis and cytokine profiling using Bio-Plex assay. The PTX alone group showed a reduction in total leukocyte and neutrophil counts (4,800 ± 606; 893 ± 82) when compared with a VC group (9,183 ± 1,043; 1,612 ± 100) respectively. Pre-administration of ARA, ARH, WSA, and WSH extracts normalized leukocyte counts (10,000 ± 707; 9,166 ± 1,076; 10,333 ± 1,189; 9,066 ± 697) and neutrophil counts (1,482 ± 61; 1,251 ± 71; 1,467 ± 121; 1,219 ± 134) respectively. Additionally, higher morbidity score in PTX group (7.4 ± 0.7) was significantly restricted by ARA (4.8 ± 1.1), ARH (5.1 ± 0.6), WSA (4.5 ± 0.7), and WSH (5 ± 0.8). (Data represented in mean ± SD). The extracts also significantly modulated 20 cytokines to evade PTX-induced leukopenia, neutropenia, and morbidity. The AR and WS extracts significantly prevented PTX-induced myelosuppression (p < 0.0001) and morbidity signs (p < 0.05) by modulating associated cytokines. The results indicate AR and WS as therapeutic adjuvants in cancer management.

Associations between solar and geomagnetic activity and peripheral white blood cells in the Normative Aging Study

It has been hypothesized that solar and geomagnetic activity can affect the function of the autonomic nervous system (ANS) and melatonin secretion, both of which may influence immune response. We investigated the association between solar geomagnetic activity and white blood cell counts in the Normative Aging Study (NAS) Cohort between 2000 and 2013. Linear mixed effects models with moving day averages ranging from 0 to 28 days were used to evaluate the effects of solar activity measures, interplanetary magnetic field (IMF), and sunspot number (SSN), and a measure of geomagnetic activity, K Index (K), on total white blood cell (WBC), neutrophil, monocytes, lymphocyte, eosinophil, and basophil concentrations. After adjusting for demographic and health-related factors, there were consistently significant associations between IMF, SSN, and K_p index, with reductions in total WBC, neutrophils, and basophil counts. These associations were stronger with longer moving averages. The associations were similar after adjusting for ambient air particulate pollution and particle radioactivity. Our findings suggest that periods of increased solar and geomagnetic activity result in lower WBC, neutrophil, and basophil counts that may contribute to mil mild immune suppression.

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).

Antigen Mass May Influence Trastuzumab Concentrations in Cerebrospinal Fluid After Intrathecal Administration

Intravenous administration of monoclonal antibodies leads to low concentrations in the central nervous system, which is a serious concern in neuro-oncology, especially in leptomeningeal carcinomatosis of HER2-overexpressing breast cancer. Case reports of i.t. administrations of trastuzumab have shown promising results in these patients but dosing regimens are empirical in absence of pharmacokinetic (PK) study. With a population PK approach, we described the fate of trastuzumab after i.t. administration in 21 women included in a phase I-II clinical trial. Trastuzumab was administered by i.t. route every week for 8 weeks and both cerebrospinal fluid (CSF) and serum were sampled to measure trough concentrations. Some patients showed noticeable CSF concentration fluctuations predicted using a target-mediated drug disposition. This target was latent and produced with a delayed feedback. Apparent volumes of distribution were close to physiological volumes (V₁  = 3.25 L, V₂  = 0.644 L, for serum and CSF, respectively). Estimated (constant) transfer from serum to CSF was very slow (k₁₂  = 0.264 mg/day) whereas estimated half-life of transfer from CSF to serum was rapid (2.2 days). From the individual parameters of patients, a single i.t. administration of 150 mg of trastuzumab corresponded to median mean residence times of 3.8 days and 15.6 days in CSF and serum, respectively. Survival without neurological relapse was not related to trastuzumab exposure. This study confirms that transfer of trastuzumab from serum to CSF is very limited and that this monoclonal antibody, when administered by i.t. route, is rapidly transferred to the serum.

Individual prediction of thrombocytopenia at next chemotherapy cycle: Evaluation of dynamic model performances

AIMS

Thrombocytopenia is a common major side-effect of cytotoxic cancer therapies. A clinically relevant problem is to predict an individual's thrombotoxicity in the next planned chemotherapy cycle in order to decide on treatment adaptation. To support this task, 2 dynamic mathematical models of thrombopoiesis under chemotherapy were proposed, a simple semimechanistic model and a comprehensive mechanistic model. In this study, we assess the performance of these models with respect to existing thrombocytopenia grading schemes.

METHODS

We consider close-meshed individual time series data of 135 non-Hodgkin's lymphoma patients treated with 6 cycles of CHOP/CHOEP chemotherapies. Individual parameter estimates were derived on the basis of these data considering a varying number of cycles per patient. Parsimony assumptions were applied to optimize parameter identifiability. Models' predictability are assessed by determining deviations of predicted and observed degrees of thrombocytopenia in the next cycles.

RESULTS

The mechanistic model results in better agreement of model prediction and individual time series data. Prediction accuracy of future cycle toxicities by the mechanistic model is higher even if the semimechanistic model is provided with data of more cycles for calibration.

CONCLUSION

We successfully established a quantitative and clinically relevant method for assessing prediction performances of biomathematical models of thrombopoiesis under chemotherapy. We showed that the more comprehensive mechanistic model outperforms the semimechanistic model. We aim at implementing the mechanistic model into clinical practice to assess its utility in real life clinical decision-making.

Efficacy of Plinabulin vs Pegfilgrastim for Prevention of Chemotherapy-Induced Neutropenia in Adults With Non-Small Cell Lung Cancer: A Phase 2 Randomized Clinical Trial

Importance

Plinabulin is a novel, non-granulocyte colony-stimulating factor (GCSF) small molecule with both anticancer and neutropenia-prevention effects.

Objective

To assess the efficacy and safety of plinabulin compared with pegfilgrastim for the prevention of chemotherapy-induced neutropenia following docetaxel chemotherapy in patients with non-small lung cancer.

Design, Setting, and Participants

This was a randomized, open-label, phase 2 clinical trial of 4 treatment arms that was conducted in 19 cancer treatment centers in the United States, China, Russia, and Ukraine. Participants were adult patients with non-small cell lung cancer whose cancer had progressed after platinum-based chemotherapy. Data were collected from April 2017 through March 2018 and analyzed from August 2019 through February 2020.

Interventions

All patients received docetaxel 75 mg/m2 on day 1 and were randomly assigned to 1 of 3 doses of plinabulin (5, 10, or 20 mg/m2) on day 1 or to pegfilgrastim 6 mg on day 2. Patients were treated every 21 days for 4 chemotherapy cycles.

Main Outcomes and Measures

The primary end point was the determination of the recommended phase 3 dose of plinabulin based on the days of severe neutropenia during chemotherapy cycle 1. Daily complete blood cell counts and absolute neutrophil counts were drawn during times of anticipated neutropenia during cycle 1.

Results

Of the 55 patients randomized and evaluated, the mean (SD) age was 61.3 (10.2) years, and 38 (69.1%) were men. With each escalation of the plinabulin dose, the incidence of any grade of neutropenia decreased. There were no significant differences in mean (SD) days of severe neutropenia among those treated with pegfilgrastim (0.15 [0.38] days) when dosed at day 2 vs plinabulin 20 mg/m2 (0.36 [0.93] days; P = .76) when dosed at day 1, and no safety signals were detected.

Conclusions and Relevance

Single dose-per-cycle plinabulin has a similar neutropenia protection benefit as pegfilgrastim. Plinabulin 40 mg fixed dose, which is pharmacologically equivalent to 20 mg/m2, will be compared with pegfilgrastim 6 mg in the phase 3 portion of this trial. Noninferior days of severe neutropenia will be the primary end point, and bone pain reduction, thrombocytopenia reduction, and quality of life maintenance will be secondary end points.

Trial Registration

ClinicalTrials.gov Identifier: NCT03102606.

The timing of cyclic cytotoxic chemotherapy can worsen neutropenia and neutrophilia

Despite recent advances in immunotherapies, cytotoxic chemotherapy continues to be a first-line treatment option for the majority of cancers. Unfortunately, a common side effect in patients undergoing chemotherapy treatment is neutropenia. To mitigate the risk of neutropenia and febrile neutropenia, prophylactic treatment with granulocyte-colony stimulating factor (G-CSF) is administered. Extensive pharmacokinetic/pharmacodynamic modelling of myelosuppression during chemotherapy has suggested avenues for therapy optimization to mitigate this neutropenia. However, the issue of resonance, whereby neutrophil oscillations are induced by the periodic administration of cytotoxic chemotherapy and the coadministration of G-CSF, potentially aggravating a patient's neutropenic/neutrophilic status, is not well-characterized in the clinical literature. Here, through analysis of neutrophil data from young acute lymphoblastic leukaemia patients, we find that resonance is occurring during cyclic chemotherapy treatment in 26% of these patients. Motivated by these data and our previous modelling studies on adult lymphoma patients, we examined resonance during treatment with or without G-CSF. Using our quantitative systems pharmacology model of granulopoiesis, we show that the timing of cyclic chemotherapy can worsen neutropenia or neutrophilia, and suggest clinically-actionable schedules to reduce the resonant effect. We emphasize that delaying supportive G-CSF therapy to 6-7 days after chemotherapy can mitigate myelosuppressive effects. This study therefore highlights the importance of quantitative systems pharmacology for the clinical practice for developing rational therapeutic strategies.

Quantifying Drug-Induced Bone Marrow Toxicity Using a Novel Haematopoiesis Systems Pharmacology Model

Haematological toxicity associated with cancer therapeutics is monitored by changes in blood cell count, and their primary effect is on proliferative progenitors in the bone marrow. Using observations in rat bone marrow and blood, we characterize a mathematical model that comprises cell proliferation and differentiation of the full haematopoietic phylogeny, with interacting feedback loops between lineages in homeostasis as well as following carboplatin exposure. We accurately predicted the temporal dynamics of several mature cell types related to carboplatin-induced bone marrow toxicity and identified novel insights into haematopoiesis. Our model confirms a significant degree of plasticity within bone marrow cells, with the number and type of both early progenitors and circulating cells affecting cell balance, via feedback mechanisms, through fate decisions of the multipotent progenitors. We also demonstrated cross-species translation of our predictions to patients, applying the same core model structure and considering differences in drug-dependent and physiology-dependent parameters.

Pharmacokinetic/Pharmacodynamic Modeling to Support the Re-approval of Gemtuzumab Ozogamicin

Gemtuzumab ozogamicin (Mylotarg; Pfizer, New York, NY) was the first antibody-drug conjugate to be approved for CD33-positive acute myeloid leukemia (AML). However, it was voluntarily withdrawn from the US market due to lack of clinical benefit in the confirmatory phase III trial. In 2012, several investigator cooperative studies using a different dosing regimen showed efficacy, but pharmacokinetic (PK) data were not collected in these trials. Through simulation of expected concentrations for new dosing regimens, PK/pharmacodynamic modeling was able to support the safety and efficacy of these regimens. Significant exposure-response relationships were found for the attainment of complete remission with and without platelet recovery, attainment of blast-free status, the time course of myelosuppression, several grade ≥ 3 hepatic adverse events, and veno-occlusive disease. Gemtuzumab ozogamicin received full approval by the US Food and Drug Administration (FDA) in September 2017 for newly diagnosed and relapsed AML in adult patients and relapsed AML in pediatric patients aged 2-17 years.

Pharmacological and transcriptome profiling analyses of Fufang E'jiao Jiang during chemotherapy-induced myelosuppression in mice

ETHNOPHARMACOLOGICAL RELEVANCE

Fufang E'jiao Jiang (FEJ), a famous traditional Chinese medicine formula from "Liangyi Ointment", consists of five crude drugs, Colla corii asini, Radix Ginseng Rubra, Radix Rehmanniae Preparata, Codonopsis pilosula, and Crataegus pinnatifida Bge. It has pronounced functions of qi-nourishing and blood-activating. Recently, it has been widely used in China as a medication against myelosuppression in cancer treatment.

AIM OF THE STUDY

We aimed to investigate the complex mode of action and underlying mechanisms of Fufang E'jiao Jiang (FEJ) regarding its hematopoietic effect.

MAIN METHODS

Mice were divided into 5 groups of control, model, high dose FEJ (HFEJ), medium dose FEJ (MFEJ) and low dose FEJ (LFEJ). After 10 days from the administration, bone marrow cells (BMCs) were extracted for nucleated cells counts, flow cytometry analysis of hematopoietic stem cells (HSCs) population, as well as hematopoietic progenitor cells (HPCs) colony-forming unit (CFU) assay. A portion of bone marrow nucleated cells (BMNCs) of MFEJ group were prepared for RNA sequencing (RNA-Seq). The transcriptome data were analyzed based on the differentially expressed genes (DEGs). The molecular mechanisms of FEJ were deducted based on the biological processes and protein-protein interaction (PPI) network.

RESULTS

FEJ could significantly increase the percentage of HSCs and the quantities of BFU-E and CFU-GM in BMSCs. FEJ could stimulate the proliferation of HSC and the differentiation of HPC to all lineages, which may thereby accelerate the recovery of hematopoietic function in myelosuppressive mice. By providing transcriptome profile we highlighted several genes and biological processes that might be applicable for FEJ to treat chemotherapy-induced myelosuppression. GO analysis showed that the co-expressed DEGs in FEJ vs model and model vs control group were involved in biological processes including ossification, osteoblast differentiation, bone mineralization and bone development. The KEGG pathway analysis pointed out ECM-receptor interaction and PI3K-AKT signaling pathway as the most relevant pathways to the function of FEJ on myelosuppression. PPI network showed MMP2 and COL1A1 were the relatively large nodes.

CONCLUSION

FEJ has the hematopoietic effect in chemotherapy-induced myelosuppression mice. It might be achieved by improving the proliferative capacity of HSCs and the differentiation ability of HPCs. The molecular mode of action of FEJ might be the improvement of the bone marrow microenvironment via ECM-receptor interaction, the promoted proliferation of HSC through regulation of PI3K-AKT signaling pathway, and the involvement of osteoblasts and osteoclasts. MMP2 and COL1A1 appear to be the key relevant regulatory molecules. These results provide significant insight into the hematopoietic effects of FEJ in myelosuppression and point out novel targets for future validating analyses.

Modeling individual time courses of thrombopoiesis during multi-cyclic chemotherapy

Background

Thrombocytopenia is a major side-effect of cytotoxic cancer therapies. The aim of precision medicine is to develop therapy modifications accounting for the individual’s risk.

Methodology/Principle findings

To solve this task, we develop an individualized bio-mechanistic model of the dynamics of bone marrow thrombopoiesis, circulating platelets and therapy effects thereon. Comprehensive biological knowledge regarding cell differentiation, amplification, apoptosis rates, transition times and corresponding regulations are translated into ordinary differential equations. A model of osteoblast/osteoclast interactions was incorporated to mechanistically describe bone marrow support of quiescent cell stages. Thrombopoietin (TPO) as a major regulator is explicitly modelled including pharmacokinetics and–dynamics of TPO injections. Effects of cytotoxic drugs are modelled by transient depletions of proliferating cells.

To calibrate the model, we used population data from the literature and close-meshed individual data of N = 135 high-grade non-Hodgkin’s lymphoma patients treated with CHOP-like chemotherapies. To limit the number of free parameters, several parsimony assumptions were derived from biological data and tested via Likelihood methods. Heterogeneity of patients was explained by a few model parameters. The over-fitting issue of individual parameter estimation was successfully dealt with a virtual participation of each patient in population-based experiments.

The model qualitatively and quantitatively explains a number of biological observations such as the role of osteoblasts in explaining long-term toxic effects, megakaryocyte-mediated feedback on stem cells, bi-phasic stimulation of thrombopoiesis by TPO, dynamics of megakaryocyte ploidies and non-exponential platelet degradation. Almost all individual time series could be described with high precision. We demonstrated how the model can be used to provide predictions regarding individual therapy adaptations.

Conclusions

We propose a mechanistic thrombopoiesis model of unprecedented comprehensiveness in both, biological mechanisms considered and experimental data sets explained. Our innovative method of parameter estimation allows robust determinations of individual parameter settings facilitating the development of individual treatment adaptations during chemotherapy.

Chemotherapy is ubiquitously used to treat cancer diseases. Due to general toxicity of the drugs, chemotherapy results in a number of side effects especially with respect to blood formation. Here we study the loss of platelets during chemotherapy which is dose limiting in many situations. However, this side-effect greatly varies between patients with respect to both, severity and necessity of clinical countermeasures.We therefore developed a mathematical model to predict the time course of platelets of patients under chemotherapy and to propose possible treatment adaptations in cases of intolerable toxicity. The model is based on available biological knowledge and data of platelet formation and therapeutic effects thereon. As a major result, we could describe individual time series data of 135 patients under chemotherapy. Conversely, the model can be used to make predictions regarding alternative therapy schedules such as postponement of therapy or chemotherapy dose reductions. Our model is intended to support clinical decision making on an individual patient level.

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.

Anticancer and cardio-protective effects of liposomal doxorubicin in the treatment of breast cancer

Breast cancer (BC) is a highly prevalent disease, accounting for the second highest number of cancer-related mortalities worldwide. The anthracycline doxorubicin (DOX), isolated from Streptomyces peucetius var. caesius, is a potent chemotherapeutic drug that is successfully used to treat various forms of liquid and solid tumors and is currently approved to treat BC. DOX exerts its effects by intercalation into DNA and inhibition of topoisomerases I and II, causing damage to DNA and the formation of reactive oxygen species (ROS), resulting in the activation of caspases, which ultimately leads to apoptosis. Unfortunately, DOX also can cause cardiotoxicity, with patients only allowed a cumulative lifetime dose of 550 mg/m². Efforts to decrease cardiotoxicity and to increase the blood circulation time of DOX led to the US Food and Drug Administration (FDA) approval of a PEGylated liposomal formulation (L-DOX), Doxil^® (known internationally as Caelyx^®). Both exhibit better cardiovascular safety profiles; however, they are not currently FDA approved for the treatment of metastatic BC. Here, we provide detailed insights into the mechanism of action of L-DOX and its most common side effects and highlight results of its use in clinical trials for the treatment of BC as single agent and in combination with other commonly used chemotherapeutics.

Studies of the effects and mechanisms of ginsenoside Re and Rk3 on myelosuppression induced by cyclophosphamide

Background

Ginsenoside Re (Re) is one of the major components of Panax ginseng Meyer. Ginsenoside Rk₃ (Rk₃) is a secondary metabolite of Re. The aim of this study was to investigate and compare the effects and underlying mechanisms of Re and Rk₃ on cyclophosphamide-induced myelosuppression.

Methods

The mice myelosuppression model was established by intraperitoneal (i.p.) injection of cyclophosphamide. Peripheral blood cells, bone marrow nucleated cells, and colony yield of hematopoietic progenitor cells in vitro were counted. The levels of erythropoietin, thrombopoietin, and granulocyte macrophage colony-stimulating factor in plasma were measured by enzyme-linked immunosorbent assay. Bone marrow cell cycle was performed by flow cytometry. The expression of apoptotic protein bcl-2, bax, and caspase-3 was detected by Western blotting.

Results

Both Re and Rk₃ could improve peripheral blood cells, bone marrow nucleated cell counts, thymus index, and spleen index. Furthermore, they could enhance the yield of colonies cultured in vitro and make the levels of granulocyte macrophage colony-stimulating factor, erythropoietin, and thrombopoietin normal, reduce the ratio of G₀/G₁ phase cells, and increase the proliferation index. Finally, Re and Rk₃ could upregulate the expression of bcl-2, whereas they could downregulate the expression of bax and caspase-3.

Conclusion

Re and Rk₃ could improve the hematopoietic function of myelosuppressed mice. The effect of Rk₃ was superior to that of Re at any dose. Regulating the levels of cytokines, promoting cells enter the normal cell cycle, regulating the balance of bcl-2/bax, and inhibiting the expression of caspase-3 may be the effects of Re and Rk₃ on myelosuppression.

SEP enhanced the antitumor activity of 5-fluorouracil by up-regulating NKG2D/MICA and reversed immune suppression via inhibiting ROS and caspase-3 in mice

Chemotherapy and immunotherapy are the main remedies used in cancer treatment. Because immunotherapy can not only reduce the toxicity of chemotherapeutics but also enhance antitumor effects in vivo, combining these two therapies is a trend that continues to gain more attention in clinic. SEP, a polysaccharide isolated from Strongylocentrotus nudus egg, has been reported to display antitumor activity by stimulating immune cells, including NK and T cells, via TLR2 and TLR4. In the present study, the synergistic effect between SEP and 5-fluorouracil (5-FU), a traditional cytotoxic drug, in vitro and in vivo was investigated. The results obtained indicated that SEP alone stimulated NK-92 cytotoxicity and coordinated with 5-FU to augment the cytotoxicity of NK-92 cells against HepG-2 or A549 cells in vitro. SEP promoted NK-92 activity by stimulating NKG2D and its downstream DAP10/PI3K/Erk signaling pathway. Additionally, 5-FU could increase MICA expression on HepG-2 or A549 cells and prevent membrane MICA from shedding as soluble MICA, which were abrogated in the tumor cells transfected with ADAM 10 overexpression plasmid. Moreover, in H22- or Lewis lung cancer (LLC)-bearing mouse models, SEP reversed 5-FU-induced atrophy and apoptosis in both the spleen and bone marrow in vivo by suppressing ROS generation and caspase-3 activation. All of these results highlight the potential for the combination of SEP and 5-FU in cancer therapy in the future.

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.

Dose/Exposure-Response Modeling to Support Dosing Recommendation for Phase III Development of Baricitinib in Patients with Rheumatoid Arthritis

Baricitinib is an oral inhibitor of Janus kinases (JAKs), selective for JAK1 and 2. It demonstrated dose‐dependent efficacy in patients with moderate‐to‐severe rheumatoid arthritis (RA) in a phase IIb study up to 24 weeks. Population pharmacokinetic/pharmacodynamic (PopPK/PD) models were developed to characterize concentration‐time profiles and dose/exposure‐response (D/E‐R) relationships for the key efficacy (proportion of patients achieving American College of Rheumatology 20%, 50%, or 70% response rate) and safety endpoints (incidence of anemia) for the phase IIb study. The modeling suggested that 4 mg q.d. was likely to offer the optimum risk/benefit balance, whereas 2 mg q.d. had the potential for adequate efficacy. In addition, at the same total daily dose, a twice‐daily regimen is not expected to provide an advantage over q.d. dosing for the efficacy or safety endpoints. The model‐based simulations formed the rationale for key aspects of dosing, such as dose levels and dosing frequency for phase III development.

3D models of the hematopoietic stem cell niche under steady-state and active conditions

Hematopoietic stem cells (HSCs) in the bone marrow are able to differentiate into all types of blood cells and supply the organism each day with billions of fresh cells. They are applied to cure hematological diseases such as leukemia. The clinical need for HSCs is high and there is a demand for being able to control and multiply HSCs in vitro. The hematopoietic system is highly proliferative and thus sensitive to anti-proliferative drugs such as chemotherapeutics. For many of these drugs suppression of the hematopoietic system is the dose-limiting toxicity. Therefore, biomimetic 3D models of the HSC niche that allow to control HSC behavior in vitro and to test drugs in a human setting are relevant for the clinics and pharmacology. Here, we describe a perfused 3D bone marrow analog that allows mimicking the HSC niche under steady-state and activated conditions that favor either HSC maintenance or differentiation, respectively, and allows for drug testing.

Understanding and applying pharmacometric modelling and simulation in clinical practice and research

Understanding the dose–concentration–effect relationship is a fundamental component of clinical pharmacology. Interpreting data arising from observations of this relationship requires the use of mathematical models; i.e. pharmacokinetic (PK) models to describe the relationship between dose and concentration and pharmacodynamic (PD) models describing the relationship between concentration and effect. Drug development requires several iterations of pharmacometric model‐informed learning and confirming. This includes modelling to understand the dose–response in preclinical studies, deriving a safe dose for first‐in‐man, and the overall analysis of Phase I/II data to optimise the dose for safety and efficacy in Phase III pivotal trials. However, drug development is not the boundary at which PKPD understanding and application stops. PKPD concepts will be useful to anyone involved in the prescribing and administration of medicines for purposes such as determining off‐label dosing in special populations, individualising dosing based on a measured biomarker (personalised medicine) and in determining whether lack of efficacy or unexpected toxicity maybe solved by adjusting the dose rather than the drug. In clinical investigator‐led study design, PKPD can be used to ensure the optimal dose is used, and crucially to define the expected effect size, thereby ensuring power calculations are based on sound prior information. In the clinical setting the most likely people to hold sufficient expertise to advise on PKPD matters will be the pharmacists and clinical pharmacologists. This paper reviews fundamental PKPD principles and provides some real‐world examples of PKPD use in clinical practice and applied clinical research.

A cell-level model of pharmacodynamics-mediated drug disposition

We aimed to develop a cell-level pharmacodynamics-mediated drug disposition (PDMDD) model to analyze in vivo systems where the PD response to a drug has an appreciable effect on the pharmacokinetics (PK). An existing cellular level model of PD stimulation was combined with the standard target-mediated drug disposition (TMDD) model and the resulting model structure was parametrically identifiable from typical in vivo PK and PD data. The PD model of the cell population was controlled by the production rate k in and elimination rate k out which could be stimulated or inhibited by the number of bound receptors on a single cell. Simulations were performed to assess the impact of single and repeated dosing on the total drug clearance. The clinical utility of the cell-level PDMDD model was demonstrated by fitting published data on the stimulatory effects of filgrastim on absolute neutrophil counts in healthy subjects. We postulated repeated dosing as a means of detecting and quantifying PDMDD as a single dose might not be sufficient to elicit the cellular response capable of altering the receptor pool to visibly affect drug disposition. In the absence of any PD effect, the model reduces down to the standard TMDD model. The applications of this model can be readily extended to include chemotherapy-induced cytopenias affecting clearance of endogenous hematopoietic growth factors, different monoclonal antibodies and immunogenicity effects on PK.

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.

A model qualification method for mechanistic physiological QSP models to support model-informed drug development

Mechanistic physiological modeling is a scientific method that combines available data with scientific knowledge and engineering approaches to facilitate better understanding of biological systems, improve decision‐making, reduce risk, and increase efficiency in drug discovery and development. It is a type of quantitative systems pharmacology (QSP) approach that places drug‐specific properties in the context of disease biology. This tutorial provides a broadly applicable model qualification method (MQM) to ensure that mechanistic physiological models are fit for their intended purposes.

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.

Echinacoside improves hematopoietic function in 5-FU-induced myelosuppression mice

AIMS

We aimed to investigate the effects of echinacoside (ECH) on hematopoietic function in 5-FU-induced bone marrow depression mice.

MAIN METHODS

In vitro, after stimulation with ECH, the proliferation ability of bone marrow (BM) cells and bone marrow stromal cells (BMSCs) derived from myelosuppression mice were assessed by CCK8 assay and morphology, respectively. In vivo, 5-FU-induced myelosuppression or control mice were intragastrically administrated with either ECH at 15 mg/kg or the equal volume of normal saline daily for 12 days before BM cells were isolated for colony-forming cell assay. Meanwhile, BMSCs were cultured for 4 weeks before cells were observed for growth pattern, cell culture supernatants were collected for GM-CSF secretion by ELISA, and RNA of the cells were extracted for EPO and GM-CSF RT-PCR. BM cells or BMSCs stimulated with ECH for 24 h or 48 h were collected for protein extraction and Western blotting.

KEY FINDINGS

ECH stimulated the growth of BM cells but not BMSCs derived from 5-FU treated mice. The intragastric administration of ECH in 5-FU treated mice could increase the number of total hematopoietic progenitor cells and GM progenitor cells to healthy control mice level, but not BFU progenitor cells. BMSCs from ECH treated myelosuppression mice grew more vigorously and expressed more GM-CSF, but not EPO. ECH activated the PI3K signaling pathway in 5-FU suppressed BM cells.

SIGNIFICANCE

ECH could improve the hematopoietic function of bone marrow in 5-FU-induced myelosuppression mice. ECH can be considered as an alternative effective therapy for patients during chemotherapy or HSC transplantation.

Lifespan based pharmacokinetic-pharmacodynamic model of tumor growth inhibition by anticancer therapeutics

Accurate prediction of tumor growth is critical in modeling the effects of anti-tumor agents. Popular models of tumor growth inhibition (TGI) generally offer empirical description of tumor growth. We propose a lifespan-based tumor growth inhibition (LS TGI) model that describes tumor growth in a xenograft mouse model, on the basis of cellular lifespan T. At the end of the lifespan, cells divide, and to account for tumor burden on growth, we introduce a cell division efficiency function that is negatively affected by tumor size. The LS TGI model capability to describe dynamic growth characteristics is similar to many empirical TGI models. Our model describes anti-cancer drug effect as a dose-dependent shift of proliferating tumor cells into a non-proliferating population that die after an altered lifespan T_A. Sensitivity analysis indicated that all model parameters are identifiable. The model was validated through case studies of xenograft mouse tumor growth. Data from paclitaxel mediated tumor inhibition was well described by the LS TGI model, and model parameters were estimated with high precision. A study involving a protein casein kinase 2 inhibitor, AZ968, contained tumor growth data that only exhibited linear growth kinetics. The LS TGI model accurately described the linear growth data and estimated the potency of AZ968 that was very similar to the estimate from an established TGI model. In the case study of AZD1208, a pan-Pim inhibitor, the doubling time was not estimable from the control data. By fixing the parameter to the reported in vitro value of the tumor cell doubling time, the model was still able to fit the data well and estimated the remaining parameters with high precision. We have developed a mechanistic model that describes tumor growth based on cell division and has the flexibility to describe tumor data with diverse growth kinetics.

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.

Review on modeling anti-antibody responses to monoclonal antibodies

Monoclonal antibodies (mAbs) represent a therapeutic strategy that has been increasingly used in different diseases. mAbs are highly specific for their targets leading to induce specific effector functions. Despite their therapeutic benefits, the presence of immunogenic reactions is of growing concern. The immunogenicity identified as anti-drug antibodies (ADA) production due to the continuous administration of mAbs may affect the pharmacokinetics (PK) and/or the pharmacodynamics (PD) of mAbs administered to patients. Therefore, the immunogenicity and its clinical impact have been studied by several authors using PK modeling approaches. In this review, the authors try to present all those models under a unique theoretical mechanism-based framework incorporating the main considerations related to ADA formation, and how ADA may affect the efficacy or toxicity profile of some therapeutic biomolecules.

Clinical population pharmacokinetics and toxicodynamics of linezolid

Thrombocytopenia is a common side effect of linezolid, an oxazolidinone antibiotic often used to treat multidrug-resistant Gram-positive bacterial infections. Various risk factors have been suggested, including linezolid dose and duration of therapy, baseline platelet counts, and renal dysfunction; still, the mechanisms behind this potentially treatment-limiting toxicity are largely unknown. A clinical study was conducted to investigate the relationship between linezolid pharmacokinetics and toxicodynamics and inform strategies to prevent and manage linezolid-associated toxicity. Forty-one patients received 42 separate treatment courses of linezolid (600 mg every 12 h). A new mechanism-based, population pharmacokinetic/toxicodynamic model was developed to describe the time course of plasma linezolid concentrations and platelets. A linezolid concentration of 8.06 mg/liter (101% between-patient variability) inhibited the synthesis of platelet precursor cells by 50%. Simulations predicted treatment durations of 5 and 7 days to carry a substantially lower risk than 10- to 28-day therapy for platelet nadirs of <100 ×10(9)/liter. The risk for toxicity did not differ noticeably between 14 and 28 days of therapy and was significantly higher for patients with lower baseline platelet counts. Due to the increased risk of toxicity after longer durations of linezolid therapy and large between-patient variability, close monitoring of patients for development of toxicity is important. Dose individualization based on plasma linezolid concentration profiles and platelet counts should be considered to minimize linezolid-associated thrombocytopenia. Overall, oxazolidinone therapy over 5 to 7 days even at relatively high doses was predicted to be as safe as 10-day therapy of 600 mg linezolid every 12 h.

Model-based drug development applied to oncology

Model-based drug development (MBDD) is an approach that is used to organize the vast and complex data streams that feed the drug development pipelines of small molecule and biotechnology sponsors. Such data streams are ultimately reviewed by the global regulatory community as evidence of a drug's potential to treat and/or harm patients. Some of this information is captured in the scientific literature and prescribing compendiums forming the basis of how new and existing agents will ultimately be administered and further evaluated in the broader patient community. As this data stream evolves, the details of data qualification, the assumptions and/or critical decisions based on these data are lost under conventional drug development paradigms. MBDD relies on the construction of quantitative relationships to connect data from discrete experiments conducted along the drug development pathway. These relationships are then used to ask questions relevant at critical development stages, hopefully, with the understanding that the various scenarios explored represent a path to optimal decision making. Oncology, as a therapeutic area, presents a unique set of challenges and perhaps a different development paradigm as opposed to other disease targets. The poor attrition of development compounds in the recent past attests to these difficulties and provides an incentive for a different approach. In addition, given the reliance on multimodal therapy, oncological disease targets are often treated with both new and older agents spanning several drug classes. As MBDD becomes more integrated into the pharmaceutical research community, a more rational explanation for decisions regarding the development of new oncology agents as well as the proposed treatment regimens that incorporate both new and existing agents can be expected. Hopefully, the end result is a more focussed clinical development programme, which ultimately provides a means to optimize individual patient care.

A population pharmacokinetic/pharmacodynamic model of thrombocytopenia characterizing the effect of trastuzumab emtansine (T-DM1) on platelet counts in patients with HER2-positive metastatic breast cancer

PURPOSE

Trastuzumab emtansine (T-DM1) is an antibody-drug conjugate in the development for the treatment of human epidermal growth factor receptor 2-positive cancers. Thrombocytopenia (TCP) is the dose-limiting toxicity of T-DM1. A semimechanistic population pharmacokinetic/pharmacodynamic (PK/PD) model was developed to characterize the effect of T-DM1 on patient platelet counts.

METHODS

A PK/PD model with transit compartments that mimic platelet development and circulation was fit to concentration-platelet-time course data from two T-DM1 single-agent studies (TDM3569g; N = 52 and TDM4258g; N = 112). NONMEM(®) 7 software was used for model development. Data from a separate phase II study (TDM4374g; N = 110) were used for model evaluation. Patient baseline characteristics were evaluated as covariates of model PD parameters.

RESULTS

The model described the platelet data well and predicted the incidence of grade ≥3 TCP. The model predicted that with T-DM1 3.6 mg/kg given every 3 weeks (q3w), the lowest platelet nadir would occur after the first dose. Also predicted was a patient subgroup (46 %) having variable degrees of downward drifting platelet-time profiles, which were predicted to stabilize by the eighth treatment cycle to platelet counts above grade 3 TCP. Baseline characteristics were not significant covariates of PD parameters in the model.

CONCLUSIONS

This semimechanistic PK/PD model accurately captures the cycle 1 platelet nadir, the downward drift noted in some patient platelet-time profiles, and the ~8 % incidence of grade ≥3 TCP with T-DM1 3.6 mg/kg q3w. This model supports T-DM1 3.6 mg/kg q3w as a well-tolerated dose with minimal dose delays or reductions for TCP.

Lifespan based indirect response models

In the field of hematology, several mechanism-based pharmacokinetic-pharmacodynamic models have been developed to understand the dynamics of several blood cell populations under different clinical conditions while accounting for the essential underlying principles of pharmacology, physiology and pathology. In general, a population of blood cells is basically controlled by two processes: the cell production and cell loss. The assumption that each cell exits the population when its lifespan expires implies that the cell loss rate is equal to the cell production rate delayed by the lifespan and justifies the use of delayed differential equations for compartmental modeling. This review is focused on lifespan models based on delayed differential equations and presents the structure and properties of the basic lifespan indirect response (LIDR) models for drugs affecting cell production or cell lifespan distribution. The LIDR models for drugs affecting the precursor cell production or decreasing the precursor cell population are also presented and their properties are discussed. The interpretation of transit compartment models as LIDR models is reviewed as the basis for introducing a new LIDR for drugs affecting the cell lifespan distribution. Finally, the applications and limitations of the LIDR models are discussed.

BCR-ABL transcript dynamics support the hypothesis that leukemic stem cells are reduced during imatinib treatment

PURPOSE

Imatinib induces a durable response in most patients with Philadelphia chromosome-positive chronic myeloid leukemia, but it is currently unclear whether imatinib reduces the leukemic stem cell (LSC) burden, which may be an important step toward enabling safe discontinuation of therapy. In this article, we use mathematical models of BCR-ABL levels to make inferences on the dynamics of LSCs.

EXPERIMENTAL DESIGN

Patients with at least 1 BCR-ABL transcript measurement on imatinib were included (N = 477). Maximum likelihood methods were used to test 3 potential hypotheses of the dynamics of BCR-ABL transcripts on imatinib therapy: (i) monoexponential, in which there is little, if any, decline in BCR-ABL transcripts; (ii) biexponential, in which patients have a rapid initial decrease in BCR-ABL transcripts followed by a more gradual response; and (iii) triexponential, in which patients first exhibit a biphasic decline but then have a third phase when BCR-ABL transcripts increase rapidly.

RESULTS

We found that most patients treated with imatinib exhibit a biphasic decrease in BCR-ABL transcript levels, with a rapid decrease during the first few months of treatment, followed by a more gradual decrease that often continues over many years.

CONCLUSIONS

We show that the only hypothesis consistent with current data on progenitor cell turnover and with the long-term, gradual decrease in the BCR-ABL levels seen in most patients is that these patients exhibit a continual, gradual reduction of the LSCs. This observation may explain the ability to discontinue imatinib therapy without relapse in some cases.

Interpretation of transit compartments pharmacodynamic models as lifespan based indirect response models

Transit compartments (TC) models are used to describe pharmacodynamic responses that involve drug action on cells undergoing differentiation and maturation. Such pharmacodynamic systems can also be described by lifespan based indirect response (LIDR) models. The purpose of this report is to investigate conditions under which the transit compartments models can be considered a special case of LIDR models. An integral representation of a solution to TC model has been used to determine the lifespan distribution for cell population described by this model. The distribution served as a basis for definition of new LIDRE (lifespan based indirect response with an effect on the lifespan distribution) models. Time courses of responses described by both types of models were simulated for a monoexponential pharmacokinetic function. The limit response was calculated as the number of transit compartments approached infinity. The difference between the limit response and TC responses were evaluated by computer simulations using MATLAB 7.7. TC models are a special case of LIDR models with the lifespan distribution described by the gamma function. If drug affects only the production of cells, then the cell lifespan distribution is time invariant. In this case an increase in the number of compartments results in a basic LIDR model with a point lifespan distribution. When the drug inhibits or stimulates cell aging, the cell lifespan distribution becomes time dependent revealing a new mechanism for drug effect on the gamma probability density function. The TC model with a large number of transit compartments converges to an LIDRE model. The limit LIDR models are approximated by the TC models when the number of compartments is at least 5. A moderate improvement in the approximation is observed if this number exceeds 20. The lifespan distribution for a cell population described by a TC model is described by the gamma probability density function. A drug affects this distribution only if it stimulates or inhibits the rate of cell maturation. If the number of transit compartments increases, then the TC model converges to a new type of LIDR model.

Performance and robustness of the Monte Carlo importance sampling algorithm using parallelized S-ADAPT for basic and complex mechanistic models

The Monte Carlo Parametric Expectation Maximization (MC-PEM) algorithm can approximate the true log-likelihood as precisely as needed and is efficiently parallelizable. Our objectives were to evaluate an importance sampling version of the MC-PEM algorithm for mechanistic models and to qualify the default estimation settings in SADAPT-TRAN. We assessed bias, imprecision and robustness of this algorithm in S-ADAPT for mechanistic models with up to 45 simultaneously estimated structural parameters, 14 differential equations, and 10 dependent variables (one drug concentration and nine pharmacodynamic effects). Simpler models comprising 15 parameters were estimated using three of the ten dependent variables. We set initial estimates to 0.1 or 10 times the true value and evaluated 30 bootstrap replicates with frequent or sparse sampling. Datasets comprised three dose levels with 16 subjects each. For simultaneous estimation of the full model, the ratio of estimated to true values for structural model parameters (median [5-95% percentile] over 45 parameters) was 1.01 [0.94-1.13] for means and 0.99 [0.68-1.39] for between-subject variances for frequent sampling and 1.02 [0.81-1.47] for means and 1.02 [0.47-2.56] for variances for sparse sampling. Imprecision was ≤25% for 43 of 45 means for frequent sampling. Bias and imprecision was well comparable for the full and simpler models. Parallelized estimation was 23-fold (6.9-fold) faster using 48 threads (eight threads) relative to one thread. The MC-PEM algorithm was robust and provided unbiased and adequately precise means and variances during simultaneous estimation of complex, mechanistic models in a 45 dimensional parameter space with rich or sparse data using poor initial estimates.