Quantitative Systems Pharmacology Model Research Articles

Impact of estrogen population pharmacokinetics on a QSP model of mammary stem cell differentiation into myoepithelial cells

<abstract> <p>Stem cell differentiation cascades are critical components of healthy tissue maintenance. Dysregulation in these systems can lead to serious diseases, including cancer. Myoepithelial mammary cells are produced from differentiated mammary stem cells in processes regulated, in part, by estrogen signalling and concentrations. To quantify and predict the production of mammary myoepithelial cell production by estrogen, we developed a mechanistic, quantitative systems pharmacology (QSP) model that includes the explicit characterization of free and unbound estrogen concentrations in circulation. Linking this model to a previously developed population pharmacokinetics model for ethinyl estradiol, a synthetic form of estrogen included in oral contraceptives, we predicted the effects of estrogen on myoepithelial cell development. Interestingly, pharmacokinetic intraindividual variability alone did not significantly impact on our modelos predictions, suggesting that combinations of physiological and pharmacokinetic variability drive heterogeneity in mechanistic QSP models. Our model is one component of an improved understanding of mammary myoepithelial cell production and development, and our results support the call for mechanistically constructed systems models for disease and pharmaceutical modelling.</p> </abstract>

A quantitative systems pharmacology model for acute viral hepatitis B

Hepatitis B liver infection is caused by hepatitis B virus (HBV) and represents a major global disease problem when it becomes chronic, as is the case for 80–90% of vertical or early life infections. However, in the vast majority (>95%) of adult exposures, the infected individuals are capable of mounting an effective immune response leading to infection resolution. A good understanding of HBV dynamics and the interaction between the virus and immune system during acute infection represents an essential step to characterize and understand the key biological processes involved in disease resolution, which may help to identify potential interventions to prevent chronic hepatitis B.In this work, a quantitative systems pharmacology model for acute hepatitis B characterizing viral dynamics and the main components of the innate, adaptive, and tolerant immune response has been successfully developed. To do so, information from multiple sources and across different organization levels has been integrated in a common mechanistic framework. The final model adequately describes the chronology and plausibility of an HBV-triggered immune response, as well as clinical data from acute patients reported in the literature. Given the holistic nature of the framework, the model can be used to illustrate the relevance of the different immune pathways and biological processes to ultimate response, observing the negligible contribution of the innate response and the key contribution of the cellular response on viral clearance. More specifically, moderate reductions of the proliferation of activated cytotoxic CD8+ lymphocytes or increased immunoregulatory effects can drive the system towards chronicity.

A Prototype QSP Model of the Immune Response to SARS-CoV-2 for Community Development.

The severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) pandemic requires the rapid development of efficacious treatments for patients with life‐threatening coronavirus disease 2019 (COVID‐19). Quantitative systems pharmacology (QSP) models are mathematical representations of pathophysiology for simulating and predicting the effects of existing or putative therapies. The application of model‐based approaches, including QSP, have accelerated the development of some novel therapeutics. Nevertheless, the development of disease‐scale mechanistic models can be a slow process, often taking years to be validated and considered mature. Furthermore, emerging data may make any QSP model quickly obsolete. We present a prototype QSP model to facilitate further development by the scientific community. The model accounts for the interactions between viral dynamics, the major host immune response mediators and tissue damage and regeneration. The immune response is determined by viral activation of innate and adaptive immune processes that regulate viral clearance and cell damage. The prototype model captures two physiologically relevant outcomes following infection: a “healthy” immune response that appropriately defends against the virus, and an uncontrolled alveolar inflammatory response that is characteristic of acute respiratory distress syndrome. We aim to significantly shorten the typical QSP model development and validation timeline by encouraging community use, testing, and refinement of this prototype model. It is our expectation that the model will be further advanced in an open science approach (i.e., by multiple contributions toward a validated quantitative platform in an open forum), with the ultimate goal of informing and accelerating the development of safe and effective treatment options for patients.

Development of a quantitative systems pharmacology model of chronic kidney disease: metabolic bone disorder.

Chronic kidney disease mineral bone disorder (CKD-MBD) is a virtually universal complication of kidney diseases, starting early in the course of disease and resulting in devastating clinical consequences ranging from bone fragility to accelerated atherosclerosis and early cardiovascular death. Guidelines for therapeutic goals for CKD-MBD have been published, and achievement of these guidelines is associated with improved survival. However, the incomplete understanding of CKD-MBD and the individual variability in the manifestations of CKD-MBD have made it difficult to achieve these guidelines. We hypothesized that the progression of MBD through all stages of CKD, including end-stage kidney disease, could be represented by a quantitative systems pharmacology/systems biology (QSP) model. To address this hypothesis, we constructed a QSP model of CKD-MBD, building on an open-source model of calcium and phosphorus metabolism. Specifically, we estimated and validated the model using data from 5,496 patients with CKD enrolled in the Chronic Renal Insufficiency Cohort study. Our model accurately predicted changes in markers of mineral metabolism related to progressing CKD. We demonstrated that the incorporation of fibroblast growth factor 23 and the soft tissue compartment is essential for accurate modeling of the changes in calcium, phosphorus, intact parathyroid hormone, and calcitriol in CKD-MBD. We conclude that our systems biology model accurately represents CKD-MBD disease progression and can be used as a test bench for improving therapeutic interventions.

Quantitative Systems Pharmacology Modeling of PBMC-Humanized Mouse to Facilitate Preclinical Immuno-oncology Drug Development.

Progress in immunotherapy has resulted in explosively increased new therapeutic interventions and they have shown promising results in the treatment of cancer. Animal testing is performed to provide preliminary efficacy and safety data for drugs under development prior to clinical trials. However, translational challenges remain for preclinical studies such as study design and the relevance of animal models to humans. Hence, only a small fraction of cancer patients showed response. The explosion of drug candidates and therapies makes preclinical assessment of every plausible option impossible, but it can be easily tested using Quantitative System Pharmacology (QSP) models. Here, we developed a QSP model for humanized mice. Tumor growth dynamics, T cell dynamics, cytokine release, immune checkpoint expression, and drug administration were modeled and calibrated using experimental data. Tumor growth inhibition data were used for model validation. Pharmacokinetics of T cell engager (TCE), tumor growth profile, T cell expansion in the blood and infiltration into tumor, T cell dissemination from primary tumor, cytokine release profile, and expression of additional PD-L1 induced by IFN-γ were modeled and calibrated using a variety of experimental data and showed good consistency. Mouse-specific response to T cell engager monotherapy also showed the key features of in vivo efficacy of TCE. This novel QSP model, designed for human peripheral blood mononuclear cells (PBMC) engrafted xenograft mice, incorporating the most critical components of the mouse model with key cancer and immune cells, can become an integral part of preclinical drug development.

Description of amyloid beta influence on synaptic plasticity and tau hyperphosphorylation by mechanistic quantitative systems pharmacology model

AbstractBackgroundLong‐term potentiation (LTP) is a dependent increase in synaptic plasticity that may be an appropriate experimental and theoretical model for learning and memory. In animal models of Alzheimer’s disease (AD), amyloid beta (Aβ) impacts the LTP decline and tau hyperphosphorylation. Increase of the intracellular calcium level, mediated by NMDA and nACh receptors, is responsible for appearance of LTP, which depends on increased in AMPA phosphorylation. Quantitative systems pharmacology (QSP) model of synaptic plasticity was developed to investigate influence of Aβ on impairment of LTP and tau hyperphosphorylation.MethodThe model describes a calcium influx in postsynapse through acetylcholine and glutamate receptors, activation of efflux from intracellular stores, activation of kinase/phosphatase cascade, tau phosphorylation and level of phosphorylation of AMPA receptors, which is associated with LTP. AD pathology is modeled by Aβ impact on receptors (inhibits nACh) and glial glutamate uptake. Model is developed step by step from simpler variants by consecutive addition of new mechanistic details, using in vitro data. In vitro data from the literature on synaptic plasticity in presence of different effectors, are used for verification.ResultThe model qualitatively describes impairment of LTP and tau hyperphosphorylation by Aβ influence and effect of receptor blockers, kinase and phosphatase inhibitors. It demonstrates that: 1) cholinergic and glutamate stimulation leads to LTP, depending on the timing of cholinergic input relative to glutamate input; 2) impaired LTP may recover due to extrasynaptic NMDA receptor blockers (e.g. memantine) or agonists of nACh receptors (e.g. SSR180711); 3) inhibitors of calcineurin decreased tau phosphorylation and recover LTPConclusionThe QSP model describes an influence Aβ on different postsynaptic mechanisms (receptors activation and kinase/phosphatase cascade, phosphorylation and dephosphorylation of AMPA receptors), which seems to underlie LTP and tau hyperphosphorylation.

Quantitative systems pharmacology model of Alzheimer’s disease to study efficacy of combinatorial therapy on multiple pathology components

AbstractBackgroundImmunotherapies targeting tau or Aβ remain the key focus for neurologists. But low brain penetration and limited scope of targeting may restrict efficacy. This restriction may be overcome by combinations with other treatments targeting protein degradation systems. Quantitative systems pharmacology model allows for integration of available information from multiple preclinical studies to simulate clinical studies. The goal was to develop the translational model of key AD markers, verified and validated on the literature data and to simulate effects of combinations.MethodPredeveloped Ab and tau pathology submodels have been merged through the model of intracellular protein degradation systems (proteasome, autophagy), which is known to fail progressively in AD. Literature data (concentration baselines and dynamics, SUVR) from mice and human have been used for calibration. Multiple in vitro data for intracellular dynamics perturbations (rapamycin, proteasome inhibitors, vinblastine) available from the literature were used to estimate the intracellular regulation parameters. Immunotherapy mechanism is described through 1) binding of antibodies and prevention of seeding and growth of aggregates, 2) activation of microglia for Ab degradation. Data from clinical studies for immunotherapy (BAN2401) and BACE inhibitor (verubecestat) have been used for retrospective model validation, together with data for treatment by rapamycin, calpain inhibitors, proteasome activation in mouse AD models.ResultModel correctly describes the Aβ and tau accumulation and mutual exacerbation in mice and humans. It correctly predicts effect of rapamycin, calpain inhibitors, proteasome activation on tau tangles in tau‐transgenic mouse and absence of effect of Aβ targeting on tau pathology markers in humans. Simulation of Aβ immunotherapy starting from 65 years (Braak stage 3‐4), predicts 80% Aβ plaque difference vs placebo within 2 years, without effect on tau markers. Rapamycin is predicted to clear tau pathology, with modest efficacy (up to 30‐40%) of Aβ clearing. An example of combination, anti‐Aβ immunotherapy with rapamycin is most effective in retarding both pathologies (up to 80‐90%) in hippocampus.ConclusionQSP modeling allows for choice of combinations of newly emerging biologics combined with targeting cell metabolism, to lead optimization and increase of treatment efficacy.

Which tau target to select for drug discovery? Insights from an advanced quantitative systems pharmacology model

AbstractBackgroundAlthough tau pathology is a major driver of Alzheimer’s Disease (AD) pathology and remains a very interesting target, the mere complexity of tau biology significantly hampers the identification and validation of the most impactful target.MethodWe developed a Quantitative Systems Pharmacology (QSP) platform integrating diverse preclinical and clinical knowledge about tau biology processes in an actionable computer model. The computer model includes tau species diversity, activity‐dependent tau secretion, tau binding to HSPG at the synaptic cleft and along the axonal membrane following a diffusion gradient and subsequent internalization, slow axonal transport over microtubules, intracellular degradation and templating of monomeric tau by seed‐competent tau into aggregates and insoluble Neurofibrillary Tangles. Polysynaptic progression is implemented by modeling secretion into the second synapse and subsequent uptake in the second axon.ResultThe platform was calibrated with preclinical experiments from in‐vitro cellular assays and injected brain extract in transgene mouse models. The model reproduced experimentally determined in vivo monomeric tau progression speeds at 0.25 mm/day; the rate of oligomerization was estimated to not less than 58 nM/year and the presence of each additional synapse reduces the peak of oligomeric tau about tenfold. We modified the platform to reproduce clinical AD pathology, based on stationary levels of seed‐competent tau from experimental measures in post‐mortem brains at diverse Braak stages. Using sensitivity analysis on this in silico platform we rank ordered various possible therapeutic interventions on the accumulation of seed‐competent tau in distal regions such as passive immunization using antibodies with different epitopes on the tau protein, reduced binding to HSPG, reduction of oligomerization, decreased secretion and increased degradation and identified interesting synergistic interactions. Interestingly this order was not the same as for the simulated conditions of injected brain extract in mouse models, possible identifying another problem of translationability.ConclusionAdvanced computer modeling allows to ‘humanize’ preclinical experiments and can be an additional tool to support innovative disease modifying tauopathy R&D projects.

Salvaging COVID‐19 interrupted Alzheimer clinical trials using virtual patient simulations

BackgroundThe COVID‐19 pandemic has halted many Alzheimer’s Disease clinical trials, with some forced to re‐start at different points along the trial time‐line with substantial protocol amendments and patients dropping out with substantial partial assessments. To align the functional outcomes of these protocol amendments with the original trial design at the individual patient level, we propose to use Physiology‐Based Pharmacokinetic (PBPK) and Quantitative Systems Pharmacology (QSP) Modeling to ‘correct’ the cognitive trajectory of mirror virtual patient population back to the original trail design.MethodThe Virtual Twin approach creates a PBPK computer‐simulated model of each patient with a virtual twin QSP model of trial subjects, with the same co‐medications, common genotype variants affecting metabolism and cognitive outcome; β‐amyloid and tau biomarkers. The QSP platform is a previously ADAS‐Cog calibrated model of key neuronal circuits involved in cognition, allowing to model the effects of CNS active co‐medications based on their pharmacology and genotypes based on imaging studies. In this Virtual Twin approach, the platform will be extensively validated against the actual clinical data from the completer set and the fragmented outcomes of the restarters with their individual protocol amendments, before simulating the cognitive trajectory with the original trial design for those whose trial was interrupted.ResultDifferent interruption scenarios in a 24‐month AD study of a bi‐weekly amyloid antibody infusion are simulated with increased anxiolytics and anti‐depressants use after restart. The impact of changes is dependent upon the genotype combination with the 5‐HTTLPR rs 23351 > APOE > COMTVal158Met and different for placebo and active treatment. Anti‐depressants and benzo also differentially substantially affect the cognitive trajectory. We illustrate how to reconstruct the cognitive trajectory of 600 subjects (out of 1200) affected by the interruption.ConclusionCombining QSP modeling of the biology with PBPK modeling and extensive validation with the fragmented clinical data available, in principle allows to reconstruct the original cognitive trajectory in patients affected by the COVID‐19 interruption. In this way, the original trial design and statistical analysis plan can be applied to achieve a fair evaluation of the clinical effect of the investigative new drug.

The Influence of Haemostatic System Maturation on the Dose-Response Relationship of Unfractionated Heparin.

Unfractionated heparin (UFH) dosing and monitoring guidelines for children are often extrapolated from adult data. This practice is suboptimal given the inherent differences in haemostatic maturation and drug handling in children compared with adults. The aim of this work was to investigate the impact of haemostatic system maturation on the dose-response relationship of UFH in children. A quantitative model for haemostasis in adults was adapted to account for maturation in UFH pharmacokinetic (PK) parameters with and without age-related changes in coagulation factor concentrations. The adult and adapted models were used to predict the time courses of anti-factor Xa activity (aXa) and activated partial thromboplastin time (aPTT) in patients receiving UFH infusion. Predictions from both models were compared with observed aXa and aPTT measurements from 31 paediatric patients receiving UFH during extracorporeal membrane oxygenation (ECMO). The model with maturation for both UFH PK and the haemostatic system had an improved aXa and aPTT predictive performance compared with maturation in UFH PK only and the original adult model. Despite the minor effect of haemostatic system maturation on baseline aPTT, it led to substantial changes in the time course of aPTT sensitivity to UFH. This finding suggests that between-subject variability in clotting factors concentrations is potentially a major contributor to the overall variability of aPTT response to UFH. In addition, time-varying clotting factors concentrations may explain within-subject changes in aPTT sensitivity to UFH. We developed the first quantitative systems pharmacology (QSP) model that provides a mechanistic and quantitative basis for linking physiological and pharmacological maturation to UFH effect and response biomarkers. After appropriate clinical validation, the model could be useful for the development of paediatric-specific individualised UFH dosing recommendations.

Using a Quantitative Systems Pharmacology Model of CKD-MBD to Guide Therapy Minimizing Calcium Flux from Bone and into Vasculature

Using a Quantitative Systems Pharmacology Model of CKD-MBD to Guide Therapy Minimizing Calcium Flux from Bone and into Vasculature

Potential Issues With Virtual Populations When Applied to Nonlinear Quantitative Systems Pharmacology Models.

Potential Issues With Virtual Populations When Applied to Nonlinear Quantitative Systems Pharmacology Models.

QSP-IO: A Quantitative Systems Pharmacology Toolbox for Mechanistic Multiscale Modeling for Immuno-Oncology Applications.

Immunotherapy has shown great potential in the treatment of cancer; however, only a fraction of patients respond to treatment, and many experience autoimmune‐related side effects. The pharmaceutical industry has relied on mathematical models to study the behavior of candidate drugs and more recently, complex, whole‐body, quantitative systems pharmacology (QSP) models have become increasingly popular for discovery and development. QSP modeling has the potential to discover novel predictive biomarkers as well as test the efficacy of treatment plans and combination therapies through virtual clinical trials. In this work, we present a QSP modeling platform for immuno‐oncology (IO) that incorporates detailed mechanisms for important immune interactions. This modular platform allows for the construction of QSP models of IO with varying degrees of complexity based on the research questions. Finally, we demonstrate the use of the platform through two example applications of immune checkpoint therapy.

Model‐Informed Drug Development of the Masked Anti‑PD‑L1 Antibody CX‐072

CX-072 is an anti-PD-L1 (programmed death ligand 1) Probody therapeutic (Pb-Tx) designed to be preferentially activated by proteases in the tumor microenvironment and not in healthy tissue. Here, we report the model-informed drug development of CX-072. A quantitative systems pharmacology (QSP) model that captured known mechanisms of Pb-Tx activation, biodistribution, elimination, and target engagement was used to inform clinical translation. The QSP model predicted that a trough level of masked CX-072 (intact CX-072) of 13-99nM would correspond to a targeted, 95% receptor occupancy in the tumor. The QSP model predictions appeared consistent with preliminary human single-dose pharmacokinetic (PK) data following CX-072 0.03-30.0mg/kg as monotherapy: CX-072 circulated predominantly as intact CX-072 with minimal evidence of target-mediated drug disposition. A preliminary population PK (POPPK) analysis based upon 130 subjects receiving 0.03-30.0mg/kg as monotherapy included a provision for a putative time-dependent and dose-dependent antidrug antibody (ADA) effect on clearance (CL) with a mixture model. Preliminary POPPK estimates for intact CX-072 time-invariant CL and volume of distribution were 0.306L/day and 4.84 L, respectively. Exposure-response analyses did not identify statistically significant relationships with best change from baseline sum of measurements and either adverse events of grade ≥3 or of special interest. Simulations suggested that >95% of patients receiving CX-072 10mg/kg every two weeks would exceed the targeted trough level regardless of ADA, and that dose adjustment by body weight was not necessary, supporting a fixed 800mg dose for evaluation in phase II.

A Dynamic Quantitative Systems Pharmacology Model of Inflammatory Bowel Disease: Part 2 - Application to Current Therapies in Crohn's Disease.

Inflammatory bowel disease (IBD) is a heterogeneic disease with a variety of treatments targeting different mechanisms. A multistate, mechanistic, mathematical model of IBD was developed in part 1 of this two‐part article series. In this paper, application of the model to predict response of key clinical biomarkers following different treatment options for Crohn’s disease was explored. Five therapies, representing four different mechanisms of action, were simulated in the model and longitudinal profiles of key clinical markers, C‐reactive protein and fecal calprotectin were compared with clinical observations. Model simulations provided an accurate match with both central tendency and variability observed in biomarker profiles. We also applied the model to predict biomarker and clinical response in an experimental, combination therapy of existing therapeutic options and provide possible mechanistic basis for the increased response. Overall, we present a validated, modular, mechanistic model construct, which can be applied to explore key biomarkers and clinical outcomes in IBD.

Combination therapy with T cell engager and PD-L1 blockade enhances the antitumor potency of T cells as predicted by a QSP model

BackgroundT cells have been recognized as core effectors for cancer immunotherapy. How to restore the anti-tumor ability of suppressed T cells or improve the lethality of cytotoxic T cells has...

An in vitro quantitative systems pharmacology approach for deconvolving mechanisms of drug-induced, multilineage cytopenias.

Myelosuppression is one of the most common and severe adverse events associated with anti-cancer therapies and can be a source of drug attrition. Current mathematical modeling methods for assessing cytopenia risk rely on indirect measurements of drug effects and primarily focus on single lineage responses to drugs. However, anti-cancer therapies have diverse mechanisms with varying degrees of effect across hematopoietic lineages. To improve predictive understanding of drug-induced myelosuppression, we developed a quantitative systems pharmacology (QSP) model of hematopoiesis in vitro for quantifying the effects of anti-cancer agents on multiple hematopoietic cell lineages. We calibrated the system parameters of the model to cell kinetics data without treatment and then validated the model by showing that the inferred mechanisms of anti-proliferation and/or cell-killing are consistent with the published mechanisms for three classes of drugs with different mechanisms of action. Using a set of compounds as a reference set, we then analyzed novel compounds to predict their mechanisms and magnitude of myelosuppression. Further, these quantitative mechanisms are valuable for the development of translational in vivo models to predict clinical cytopenia effects.

Quantitative Systems Pharmacology Model-Based Predictions of Clinical Endpoints to Optimize Warfarin and Rivaroxaban Anti-Thrombosis Therapy.

BackgroundTight monitoring of efficacy and safety of anticoagulants such as warfarin is imperative to optimize the benefit-risk ratio of anticoagulants in patients. The standard tests used are measurements of prothrombin time (PT), usually expressed as international normalized ratio (INR), and activated partial thromboplastin time (aPTT).ObjectiveTo leverage a previously developed quantitative systems pharmacology (QSP) model of the human coagulation network to predict INR and aPTT for warfarin and rivaroxaban, respectively.MethodsA modeling and simulation approach was used to predict INR and aPTT measurements of patients receiving steady-state anticoagulation therapy. A previously developed QSP model was leveraged for the present analysis. The effect of genetic polymorphisms known to influence dose response of warfarin (CYP2C9, VKORC1) were implemented into the model by modifying warfarin clearance (CYP2C9 *1: 0.2 L/h; *2: 0.14 L/h, *3: 0.04 L/h) and the concentration of available vitamin K (VKORC1 GA: −22% from normal vitamin K concentration; AA: −44% from normal vitamin K concentration). Virtual patient populations were used to assess the ability of the model to accurately predict routine INR and aPTT measurements from patients under long-term anticoagulant therapy.ResultsThe introduced model accurately described the observed INR of patients receiving long-term warfarin treatment. The model was able to demonstrate the influence of genetic polymorphisms of CYP2C9 and VKORC1 on the INR measurements. Additionally, the model was successfully used to predict aPTT measurements for patients receiving long-term rivaroxaban therapy.ConclusionThe QSP model accurately predicted INR and aPTT measurements observed during routine therapeutic drug monitoring. This is an exemplar of how a QSP model can be adapted and used as a model-based precision dosing tool during clinical practice and drug development to predict efficacy and safety of anticoagulants to ultimately help optimize anti-thrombotic therapy.

A framework for simplification of quantitative systems pharmacology models in clinical pharmacology.

Quantitative systems pharmacology (QSP) is a relatively new discipline within modelling and simulation that has gained wide attention over the past few years. The application of QSP models spans drug-target identification and validation, through all drug development phases as well as clinical applications. Due to their detailed mechanistic nature, QSP models are capable of extrapolating knowledge to predict outcomes in scenarios that have not been tested experimentally, making them an important resource in experimental and clinical pharmacology. However, these models are complicated to work with due to their size and inherent complexity. This makes many applications of QSP models for simulation, parameter estimation and trial design computationally intractable. A number of techniques have been developed to simplify QSP models into smaller models that are more amenable to further analyses while retaining their accurate predictive capabilities. Different simplification techniques have different strengths and weaknesses and hence different utilities. Understanding the utilities of different methods is essential for selection of the best method for a particular situation. In this paper, we have created an overall framework for model simplification techniques that allows a natural categorisation of methods based on their utility. We provide a brief description of the concept underpinning the different methods and example applications. A summary of the utilities of methods is intended to provide a guide to modellers in their model endeavours to simplify these complicated models.

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.