RESUMO
Clonal hematopoiesis, a condition in which individual hematopoietic stem cell clones generate a disproportionate fraction of blood leukocytes, correlates with higher risk for cardiovascular disease. The mechanisms behind this association are incompletely understood. Here, we show that hematopoietic stem cell division rates are increased in mice and humans with atherosclerosis. Mathematical analysis demonstrates that increased stem cell proliferation expedites somatic evolution and expansion of clones with driver mutations. The experimentally determined division rate elevation in atherosclerosis patients is sufficient to produce a 3.5-fold increased risk of clonal hematopoiesis by age 70. We confirm the accuracy of our theoretical framework in mouse models of atherosclerosis and sleep fragmentation by showing that expansion of competitively transplanted Tet2-/- cells is accelerated under conditions of chronically elevated hematopoietic activity. Hence, increased hematopoietic stem cell proliferation is an important factor contributing to the association between cardiovascular disease and clonal hematopoiesis.
Assuntos
Aterosclerose/patologia , Hematopoiese Clonal , Células-Tronco Hematopoéticas/patologia , Envelhecimento/patologia , Animais , Apolipoproteínas E/genética , Aterosclerose/genética , Medula Óssea/metabolismo , Proliferação de Células , Evolução Clonal , Modelos Animais de Doenças , Feminino , Humanos , Camundongos , Camundongos Endogâmicos C57BL , Modelos Biológicos , Privação do Sono/patologiaRESUMO
Mathematical modelling applied to preclinical, clinical, and public health research is critical for our understanding of a multitude of biological principles. Biology is fundamentally heterogeneous, and mathematical modelling must meet the challenge of variability head on to ensure the principles of diversity, equity, and inclusion (DEI) are integrated into quantitative analyses. Here we provide a follow-up perspective on the DEI plenary session held at the 2023 Society for Mathematical Biology Annual Meeting to discuss key issues for the increased integration of DEI in mathematical modelling in biology.
Assuntos
Diversidade, Equidade, Inclusão , Saúde Pública , Conceitos Matemáticos , Modelos BiológicosRESUMO
On July 19th, 2023, the National Institute of Allergy and Infectious Diseases co-organized a workshop with the Society of Mathematical Biology, with the authors of this paper as the organizing committee. The workshop, "Bridging multiscale modeling and practical clinical applications in infectious diseases" sought to create an environment for mathematical modelers, statisticians, and infectious disease researchers and clinicians to exchange ideas and perspectives.
Assuntos
Doenças Transmissíveis , Conceitos Matemáticos , Estados Unidos , Humanos , National Institute of Allergy and Infectious Diseases (U.S.) , Modelos BiológicosRESUMO
Glioblastoma is the most common and deadly primary brain tumor in adults. All glioblastoma patients receiving standard-of-care surgery-radiotherapy-chemotherapy (i.e., temozolomide (TMZ)) recur, with an average survival time of only 15 months. New approaches to the treatment of glioblastoma, including immune checkpoint blockade and oncolytic viruses, offer the possibility of improving glioblastoma outcomes and have as such been under intense study. Unfortunately, these treatment modalities have thus far failed to achieve approval. Recently, in an attempt to bolster efficacy and improve patient outcomes, regimens combining chemotherapy and immune checkpoint inhibitors have been tested in trials. Unfortunately, these efforts have not resulted in significant increases to patient survival. To better understand the various factors impacting treatment outcomes of combined TMZ and immune checkpoint blockade, we developed a systems-level, computational model that describes the interplay between glioblastoma, immune, and stromal cells with this combination treatment. Initializing our model to spatial resection patient samples labeled using imaging mass cytometry, our model's predictions show how the localization of glioblastoma cells, influence therapeutic success. We further validated these predictions in samples of brain metastases from patients given they generally respond better to checkpoint blockade compared with primary glioblastoma. Ultimately, our model provides novel insights into the mechanisms of therapeutic success of immune checkpoint inhibitors in brain tumors and delineates strategies to translate combination immunotherapy regimens more effectively into the clinic. SIGNIFICANCE STATEMENT: Extending survival times for glioblastoma patients remains a critical challenge. Although immunotherapies in combination with chemotherapy hold promise, clinical trials have not shown much success. Here, systems models calibrated to and validated against patient samples can improve preclinical and clinical studies by shedding light on the factors distinguishing responses/failures. By initializing our model with imaging mass cytometry visualization of patient samples, we elucidate how factors such as localization of glioblastoma cells and CD8+ T cell infiltration impact treatment outcomes.
Assuntos
Antineoplásicos , Neoplasias Encefálicas , Glioblastoma , Adulto , Humanos , Temozolomida/uso terapêutico , Glioblastoma/tratamento farmacológico , Inibidores de Checkpoint Imunológico/uso terapêutico , Microambiente Tumoral , Recidiva Local de Neoplasia/tratamento farmacológico , Antineoplásicos/uso terapêutico , Neoplasias Encefálicas/tratamento farmacológico , Imunoterapia/métodos , Análise de SistemasRESUMO
To understand the diversity of immune responses to SARS-CoV-2 and distinguish features that predispose individuals to severe COVID-19, we developed a mechanistic, within-host mathematical model and virtual patient cohort. Our results suggest that virtual patients with low production rates of infected cell derived IFN subsequently experienced highly inflammatory disease phenotypes, compared to those with early and robust IFN responses. In these in silico patients, the maximum concentration of IL-6 was also a major predictor of CD8+ T cell depletion. Our analyses predicted that individuals with severe COVID-19 also have accelerated monocyte-to-macrophage differentiation mediated by increased IL-6 and reduced type I IFN signalling. Together, these findings suggest biomarkers driving the development of severe COVID-19 and support early interventions aimed at reducing inflammation.
Assuntos
COVID-19/imunologia , Modelos Imunológicos , SARS-CoV-2 , Biomarcadores/metabolismo , Linfócitos T CD8-Positivos/imunologia , COVID-19/virologia , Estudos de Coortes , Biologia Computacional , Simulação por Computador , Suscetibilidade a Doenças/imunologia , Interações entre Hospedeiro e Microrganismos/imunologia , Humanos , Imunidade Inata , Terapia de Imunossupressão , Interferons/metabolismo , Interleucina-6/metabolismo , Macrófagos/imunologia , Pandemias , SARS-CoV-2/imunologia , Índice de Gravidade de Doença , Interface Usuário-ComputadorRESUMO
Drug resistance has profoundly limited the success of cancer treatment, driving relapse, metastasis, and mortality. Nearly all anticancer drugs and even novel immunotherapies, which recalibrate the immune system for tumor recognition and destruction, have succumbed to resistance development. Engineers have emerged across mechanical, physical, chemical, mathematical, and biological disciplines to address the challenge of drug resistance using a combination of interdisciplinary tools and skill sets. This review explores the developing, complex, and under-recognized role of engineering in medicine to address the multitude of challenges in cancer drug resistance. Looking through the "lens" of intrinsic, extrinsic, and drug-induced resistance (also referred to as "tolerance"), we will discuss three specific areas where active innovation is driving novel treatment paradigms: (1) nanotechnology, which has revolutionized drug delivery in desmoplastic tissues, harnessing physiochemical characteristics to destroy tumors through photothermal therapy and rationally designed nanostructures to circumvent cancer immunotherapy failures, (2) bioengineered tumor models, which have benefitted from microfluidics and mechanical engineering, creating a paradigm shift in physiologically relevant environments to predict clinical refractoriness and enabling platforms for screening drug combinations to thwart resistance at the individual patient level, and (3) computational and mathematical modeling, which blends in silico simulations with molecular and evolutionary principles to map mutational patterns and model interactions between cells that promote resistance. On the basis that engineering in medicine has resulted in discoveries in resistance biology and successfully translated to clinical strategies that improve outcomes, we suggest the proliferation of multidisciplinary science that embraces engineering.
Assuntos
Antineoplásicos/química , Antineoplásicos/farmacologia , Preparações Farmacêuticas/química , Animais , Antineoplásicos/metabolismo , Simulação por Computador , Composição de Medicamentos , Liberação Controlada de Fármacos , Resistencia a Medicamentos Antineoplásicos , Humanos , Imunoterapia/métodos , Microfluídica , Nanocápsulas/química , Nanotecnologia/métodos , Medicina de PrecisãoRESUMO
BACKGROUND: Renin-angiotensin aldosterone system inhibitors (RAASi) are commonly used among patients hospitalized with a severe acute respiratory syndrome coronavirus 2 infection coronavirus disease 2019 (COVID-19). We evaluated whether continuation versus discontinuation of RAASi were associated with short term clinical or biochemical outcomes. METHODS: The RAAS-COVID-19 trial was a randomized, open label study in adult patients previously treated with RAASi who are hospitalized with COVID-19 (NCT04508985). Participants were randomized 1:1 to discontinue or continue RAASi. The primary outcome was a global rank score calculated from baseline to day 7 (or discharge) incorporating clinical events and biomarker changes. Global rank scores were compared between groups using the Wilcoxon test statistic and the negative binomial test (using incident rate ratio [IRR]) and the intention-to-treat principle. RESULTS: Overall, 46 participants were enrolled; 21 participants were randomized to discontinue RAASi and 25 to continue. Patients' mean age was 71.5 years and 43.5% were female. Discontinuation of RAASi, versus continuation, resulted in a non-statistically different mean global rank score (discontinuation 6 [standard deviation [SD] 6.3] vs continuation 3.8 (SD 2.5); P = .60). The negative binomial analysis identified that discontinuation increased the risk of adverse outcomes (IRR 1.67 [95% CI 1.06-2.62]; P = .027); RAASi discontinuation increased brain natriuretic peptide levels (% change from baseline: +16.7% vs -27.5%; P = .024) and the incidence of acute heart failure (33% vs 4.2%, P = .016). CONCLUSION: RAASi continuation in participants hospitalized with COVID-19 appears safe; discontinuation increased brain natriuretic peptide levels and may increase risk of acute heart failure; where possible, RAASi should be continued.
Assuntos
COVID-19 , Insuficiência Cardíaca , Adulto , Idoso , Aldosterona , Antagonistas de Receptores de Angiotensina/efeitos adversos , Inibidores da Enzima Conversora de Angiotensina/efeitos adversos , Anti-Hipertensivos/uso terapêutico , Feminino , Insuficiência Cardíaca/tratamento farmacológico , Hospitais , Humanos , Peptídeo Natriurético Encefálico , Sistema Renina-AngiotensinaRESUMO
Intra-tumour heterogeneity is a leading cause of treatment failure and disease progression in cancer. While genetic mutations have long been accepted as a primary mechanism of generating this heterogeneity, the role of phenotypic plasticity is becoming increasingly apparent as a driver of intra-tumour heterogeneity. Consequently, understanding the role of this plasticity in treatment resistance and failure is a key component of improving cancer therapy. We develop a mathematical model of stochastic phenotype switching that tracks the evolution of drug-sensitive and drug-tolerant subpopulations to clarify the role of phenotype switching on population growth rates and tumour persistence. By including cytotoxic therapy in the model, we show that, depending on the strategy of the drug-tolerant subpopulation, stochastic phenotype switching can lead to either transient or permanent drug resistance. We study the role of phenotypic heterogeneity in a drug-resistant, genetically homogeneous population of non-small cell lung cancer cells to derive a rational treatment schedule that drives population extinction and avoids competitive release of the drug-tolerant sub-population. This model-informed therapeutic schedule results in increased treatment efficacy when compared against periodic therapy, and, most importantly, sustained tumour decay without the development of resistance.
Assuntos
Antineoplásicos/uso terapêutico , Resistencia a Medicamentos Antineoplásicos/genética , Neoplasias/tratamento farmacológico , Neoplasias/genética , Humanos , Modelos Biológicos , Processos EstocásticosRESUMO
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.
Assuntos
Neutropenia , Leucemia-Linfoma Linfoblástico de Células Precursoras , Adulto , Protocolos de Quimioterapia Combinada Antineoplásica/efeitos adversos , Fator Estimulador de Colônias de Granulócitos , Humanos , Neutropenia/induzido quimicamente , Neutrófilos , Leucemia-Linfoma Linfoblástico de Células Precursoras/tratamento farmacológicoRESUMO
INTRODUCTION: To mitigate the risk of neutropenia during chemotherapy treatment of triple-negative breast cancer, prophylactic and supportive therapy with granulocyte colony-stimulating factor (G-CSF) is administered concomitant to chemotherapy. The proper timing of combined chemotherapy and G-CSF is crucial for treatment outcomes. METHODS: Leveraging our established mathematical model of neutrophil production by G-CSF, we developed quantitative systems pharmacology (QSP) framework to investigate how modulating chemotherapy dose frequency and intensity can maximize antitumour effects. To establish schedules that best control tumour size while minimizing neutropenia, we combined Gompertzian tumour growth with pharmacokinetic/pharmacodynamic models of doxorubicin and G-CSF, and our QSP model of neutrophil production. RESULTS: We optimized a range of chemotherapeutic cycle lengths and dose sizes to establish regimens that simultaneously reduced tumour burden while minimizing neutropenia. Our results suggest that cytotoxic chemotherapy with doxorubicin 45 mg/m2 every 14 days provides effective control of tumour growth while mitigating neutropenic risks. CONCLUSION: This work suggests future avenues for optimal regimens of chemotherapy with prophylactic G-CSF support. Importantly, the algorithmic approach that we developed can aid in balancing the anticancer and the neutropenic effects of both drugs, and therefore contributes to rational considerations in clinical decision-making in triple-negative breast cancer.
Assuntos
Antibióticos Antineoplásicos/uso terapêutico , Doxorrubicina/uso terapêutico , Fator Estimulador de Colônias de Granulócitos/uso terapêutico , Neutropenia/prevenção & controle , Neoplasias de Mama Triplo Negativas/tratamento farmacológico , Antibióticos Antineoplásicos/administração & dosagem , Antibióticos Antineoplásicos/efeitos adversos , Antibióticos Antineoplásicos/farmacocinética , Relação Dose-Resposta a Droga , Doxorrubicina/administração & dosagem , Doxorrubicina/efeitos adversos , Doxorrubicina/farmacocinética , Esquema de Medicação , Fator Estimulador de Colônias de Granulócitos/administração & dosagem , Humanos , Modelos Biológicos , Neutropenia/induzido quimicamente , Carga TumoralRESUMO
Granulosa cell tumors (GCT) constitute only ~5% of ovarian neoplasms yet have significant consequences, as up to 80% of women with recurrent GCT will die of the disease. This study investigated the effectiveness of procaspase-activating compound 1 (PAC-1), an activator of procaspase-3, in treating adult GCT (AGCT) in combination with selected apoptosis-inducing agents. Sensitivity of the AGCT cell line KGN to these drugs, alone or in combination with PAC-1, was tested using a viability assay. Our results show a wide range in cytotoxic activity among the agents tested. Synergy with PAC-1 was most pronounced, both empirically and by mathematical modelling, when combined with tumor necrosis factor-related apoptosis-inducing ligand (TRAIL). This combination showed rapid kinetics of apoptosis induction as determined by caspase-3 activity, and strongly synergistic killing of both KGN as well as patient samples of primary and recurrent AGCT. We have demonstrated that the novel combination of two pro-apoptotic agents, TRAIL and PAC-1, significantly amplified the induction of apoptosis in AGCT cells, warranting further investigation of this combination as a potential therapy for AGCT.
Assuntos
Tumor de Células da Granulosa/tratamento farmacológico , Hidrazonas/administração & dosagem , Neoplasias Ovarianas/tratamento farmacológico , Piperazinas/administração & dosagem , Ligante Indutor de Apoptose Relacionado a TNF/administração & dosagem , Protocolos de Quimioterapia Combinada Antineoplásica , Apoptose/efeitos dos fármacos , Benzoquinonas/administração & dosagem , Carboplatina/administração & dosagem , Caspase 3/metabolismo , Linhagem Celular Tumoral , Desoxicitidina/administração & dosagem , Desoxicitidina/análogos & derivados , Relação Dose-Resposta a Droga , Sinergismo Farmacológico , Ativação Enzimática/efeitos dos fármacos , Feminino , Tumor de Células da Granulosa/enzimologia , Tumor de Células da Granulosa/patologia , Humanos , Técnicas In Vitro , Conceitos Matemáticos , Modelos Biológicos , Neoplasias Ovarianas/enzimologia , Neoplasias Ovarianas/patologia , GencitabinaRESUMO
Oncolytic virotherapies, including the modified herpes simplex virus talimogene laherparepvec (T-VEC), have shown great promise as potent instigators of anti-tumour immune effects. The OPTiM trial, in particular, demonstrated the superior anti-cancer effects of T-VEC as compared to systemic immunotherapy treatment using exogenous administration of granulocyte-macrophage colony-stimulating factor (GM-CSF). Theoretically, a combined approach leveraging exogenous cytokine immunotherapy and oncolytic virotherapy would elicit an even greater immune response and improve patient outcomes. However, regimen scheduling of combination immunostimulation and T-VEC therapy has yet to be established. Here, we calibrate a computational biology model of sensitive and resistant tumour cells and immune interactions for implementation into an in silico clinical trial to test and individualize combination immuno- and virotherapy. By personalizing and optimizing combination oncolytic virotherapy and immunostimulatory therapy, we show improved simulated patient outcomes for individuals with late-stage melanoma. More crucially, through evaluation of individualized regimens, we identified determinants of combination GM-CSF and T-VEC therapy that can be translated into clinically-actionable dosing strategies without further personalization. Our results serve as a proof-of-concept for interdisciplinary approaches to determining combination therapy, and suggest promising avenues of investigation towards tailored combination immunotherapy/oncolytic virotherapy.
Assuntos
Terapia Combinada/métodos , Biologia Computacional/métodos , Fator Estimulador de Colônias de Granulócitos e Macrófagos/farmacologia , Simulação por Computador , Fator Estimulador de Colônias de Granulócitos e Macrófagos/metabolismo , Humanos , Imunoterapia/métodos , Melanoma/patologia , Modelos Teóricos , Terapia Viral Oncolítica/métodos , Vírus Oncolíticos/patogenicidade , Medicina de Precisão/métodos , Estudo de Prova de ConceitoRESUMO
Understanding intrinsic and acquired resistance is crucial to overcoming cancer chemotherapy failure. While it is well-established that intratumor, subclonal genetic and phenotypic heterogeneity significantly contribute to resistance, it is not fully understood how tumor sub-clones interact with each other to withstand therapy pressure. Here, we report a previously unrecognized behavior in heterogeneous tumors: cooperative adaptation to therapy (CAT), in which cancer cells induce co-resistant phenotypes in neighboring cancer cells when exposed to cancer therapy. Using a CRISPR/Cas9 toolkit we engineered phenotypically diverse non-small cell lung cancer (NSCLC) cells by conferring mutations in Dicer1, a type III cytoplasmic endoribonuclease involved in small non-coding RNA genesis. We monitored three-dimensional growth dynamics of fluorescently-labeled mutant and/or wild-type cells individually or in co-culture using a substrate-free NanoCulture system under unstimulated or drug pressure conditions. By integrating mathematical modeling with flow cytometry, we characterized the growth patterns of mono- and co-cultures using a mathematical model of intra- and interspecies competition. Leveraging the flow cytometry data, we estimated the model's parameters to reveal that the combination of WT and mutants in co-cultures allowed for beneficial growth in previously drug sensitive cells despite drug pressure via induction of cell state transitions described by a cooperative game theoretic change in the fitness values. Finally, we used an ex vivo human tumor model that predicts clinical response through drug sensitivity analyses and determined that cellular and morphologic heterogeneity correlates to prognostic failure of multiple clinically-approved and off-label drugs in individual NSCLC patient samples. Together, these findings present a new paradox in drug resistance implicating non-genetic cooperation among tumor cells to thwart drug pressure, suggesting that profiling for druggable targets (i.e. mutations) alone may be insufficient to assign effective therapy.
Assuntos
Carcinoma Pulmonar de Células não Pequenas/tratamento farmacológico , Carcinoma Pulmonar de Células não Pequenas/fisiopatologia , Neoplasias Pulmonares/tratamento farmacológico , Neoplasias Pulmonares/fisiopatologia , Adaptação Fisiológica/genética , Sistemas CRISPR-Cas , Carcinoma Pulmonar de Células não Pequenas/genética , Linhagem Celular Tumoral , Proliferação de Células/efeitos dos fármacos , Proliferação de Células/genética , Proliferação de Células/fisiologia , Técnicas de Cocultura , Biologia Computacional , Simulação por Computador , RNA Helicases DEAD-box/genética , Resistência a Múltiplos Medicamentos/genética , Resistencia a Medicamentos Antineoplásicos/genética , Humanos , Neoplasias Pulmonares/genética , Modelos Biológicos , Mutação , Ribonuclease III/genéticaRESUMO
In spite of the recent focus on the development of novel targeted drugs to treat cancer, cytotoxic chemotherapy remains the standard treatment for the vast majority of patients. Unfortunately, chemotherapy is associated with high hematopoietic toxicity that may limit its efficacy. We have previously established potential strategies to mitigate chemotherapy-induced neutropenia (a lack of circulating neutrophils) using a mechanistic model of granulopoiesis to predict the interactions defining the neutrophil response to chemotherapy and to define optimal strategies for concurrent chemotherapy/prophylactic granulocyte colony-stimulating factor (G-CSF). Here, we extend our analyses to include monocyte production by constructing and parameterizing a model of monocytopoiesis. Using data for neutrophil and monocyte concentrations during chemotherapy in a large cohort of childhood acute lymphoblastic leukemia patients, we leveraged our model to determine the relationship between the monocyte and neutrophil nadirs during cyclic chemotherapy. We show that monocytopenia precedes neutropenia by 3 days, and rationalize the use of G-CSF during chemotherapy by establishing that the onset of monocytopenia can be used as a clinical marker for G-CSF dosing post-chemotherapy. This work therefore has important clinical applications as a comprehensive approach to understanding the relationship between monocyte and neutrophils after cyclic chemotherapy with or without G-CSF support.
Assuntos
Modelos Biológicos , Neutropenia , Leucemia-Linfoma Linfoblástico de Células Precursoras , Antineoplásicos/efeitos adversos , Fator Estimulador de Colônias de Granulócitos/uso terapêutico , Humanos , Neutropenia/induzido quimicamente , Neutropenia/diagnóstico , Neutropenia/patologia , Leucemia-Linfoma Linfoblástico de Células Precursoras/tratamento farmacológico , Leucemia-Linfoma Linfoblástico de Células Precursoras/patologiaRESUMO
The primary goal of drug developers is to establish efficient and effective therapeutic protocols. Multifactorial pathologies, including dynamical diseases and complex disorders, can be difficult to treat, given the high degree of inter- and intra-patient variability and nonlinear physiological relationships. Quantitative approaches combining mechanistic disease modeling and computational strategies are increasingly leveraged to rationalize pre-clinical and clinical studies and to establish effective treatment strategies. The development of clinical trials has led to new computational methods that allow for large clinical data sets to be combined with pharmacokinetic and pharmacodynamic models of diseases. Here, we discuss recent progress using in silico clinical trials to explore treatments for a variety of complex diseases, ultimately demonstrating the immense utility of quantitative methods in drug development and medicine.
Assuntos
Simulação por Computador , HumanosRESUMO
A comparison of the transit compartment ordinary differential equation modelling approach to distributed and discrete delay differential equation models is studied by focusing on Quartino's extension to the Friberg transit compartment model of myelosuppression, widely relied upon in the pharmaceutical sciences to predict the neutrophil response after chemotherapy, and on a QSP delay differential equation model of granulopoiesis. An extension to the Quartino model is provided by considering a general number of transit compartments and introducing an extra parameter that allows for the decoupling of the maturation time from the production rate of cells. An overview of the well established linear chain technique, used to reformulate transit compartment models with constant transit rates as distributed delay differential equations (DDEs), is then given. A state-dependent time rescaling of the Quartino model is performed to apply the linear chain technique and rewrite the Quartino model as a distributed DDE, yielding a discrete DDE model in a certain parameter limit. Next, stability and bifurcation analyses are undertaken in an effort to situate such studies in a mathematical pharmacology context. We show that both the original Friberg and the Quartino extension models incorrectly define the mean maturation time, essentially treating the proliferative pool as an additional maturation compartment. This misspecification can have far reaching consequences on the development of future models of myelosuppression in PK/PD.
Assuntos
Hematopoese/efeitos dos fármacos , Modelos Biológicos , Neutrófilos/fisiologia , Farmacologia/métodos , Antineoplásicos/farmacologia , Medula Óssea/efeitos dos fármacos , Medula Óssea/fisiologia , Proliferação de Células/efeitos dos fármacos , Proliferação de Células/fisiologia , Simulação por Computador , Humanos , Neutrófilos/efeitos dos fármacosRESUMO
We develop a mathematical model of platelet, megakaryocyte, and thrombopoietin dynamics in humans. We show that there is a single stationary solution that can undergo a Hopf bifurcation, and use this information to investigate both normal and pathological platelet production, specifically cyclic thrombocytopenia. Carefully estimating model parameters from laboratory and clinical data, we then argue that a subset of parameters are involved in the genesis of cyclic thrombocytopenia based on clinical information. We provide model fits to the existing data for both platelet counts and thrombopoietin levels by changing four parameters that have physiological correlates. Our results indicate that the primary change in cyclic thrombocytopenia is an interference with, or destruction of, the thrombopoietin receptor with secondary changes in other processes, including immune-mediated destruction of platelets and megakaryocyte deficiency and failure in platelet production. This study contributes to the understanding of the origin of cyclic thrombocytopenia as well as extending the modeling of thrombopoiesis.
Assuntos
Plaquetas/patologia , Plaquetas/fisiologia , Modelos Biológicos , Trombopoese/fisiologia , Algoritmos , Simulação por Computador , Voluntários Saudáveis , Humanos , Conceitos Matemáticos , Megacariócitos/patologia , Megacariócitos/fisiologia , Mitose , Contagem de Plaquetas , Trombocitopenia/sangue , Trombocitopenia/etiologia , Trombopoetina/fisiologiaRESUMO
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.
Assuntos
Antineoplásicos/farmacologia , Filgrastim/farmacologia , Neutrófilos/efeitos dos fármacos , Tetra-Hidroisoquinolinas/farmacologia , Antineoplásicos/administração & dosagem , Antineoplásicos/efeitos adversos , Relação Dose-Resposta a Droga , Esquema de Medicação , Filgrastim/administração & dosagem , Fármacos Hematológicos/administração & dosagem , Fármacos Hematológicos/farmacologia , Humanos , Modelos Biológicos , Mielopoese/efeitos dos fármacos , Tetra-Hidroisoquinolinas/administração & dosagem , Tetra-Hidroisoquinolinas/efeitos adversosRESUMO
Over the years, the overall survival of older patients diagnosed with acute myeloid leukemia (AML) has not significantly increased. Although standard cytotoxic therapies that rapidly eliminate dividing myeloblasts are used to induce remission, relapse can occur due to surviving therapy-resistant leukemic stem cells (LSCs). Hence, anti-LSC strategies have become a key target to cure AML. We have recently shown that previously approved cardiac glycosides and glucocorticoids target LSC-enriched CD34+ cells in the primary human AML 8227 model with more efficacy than normal hematopoietic stem cells (HSCs). To translate these in vitro findings into humans, we developed a mathematical model of stem cell dynamics that describes the stochastic evolution of LSCs in AML post-standard-of-care. To this, we integrated population pharmacokinetic-pharmacodynamic (PKPD) models to investigate the clonal reduction potential of several promising candidate drugs in comparison to cytarabine, which is commonly used in high doses for consolidation therapy in AML patients. Our results suggest that cardiac glycosides (proscillaridin A, digoxin and ouabain) and glucocorticoids (budesonide and mometasone) reduce the expansion of LSCs through a decrease in their viability. While our model predicts that effective doses of cardiac glycosides are potentially too toxic to use in patients, simulations show the possibility of mometasone to prevent relapse through the glucocorticoid's ability to drastically reduce LSC population size. This work therefore highlights the prospect of these treatments for anti-LSC strategies and underlines the use of quantitative approaches to preclinical drug translation in AML.
Assuntos
Leucemia Mieloide Aguda , Células-Tronco Neoplásicas , Humanos , Leucemia Mieloide Aguda/tratamento farmacológico , Leucemia Mieloide Aguda/patologia , Células-Tronco Neoplásicas/efeitos dos fármacos , Células-Tronco Neoplásicas/patologia , Modelos Teóricos , Citarabina/uso terapêutico , Citarabina/farmacologiaRESUMO
The COVID-19 pandemic caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) resulted in millions of deaths globally. Adults with immunosuppression (e.g., solid organ transplant recipients) and those undergoing active cancer treatments experience worse infections and more severe COVID-19. It is difficult to conduct clinical studies in these populations, resulting in a restricted amount of data that can be used to relate mechanisms of immune dysfunction to COVID-19 outcomes in these vulnerable groups. To study immune dynamics after infection with SARS-CoV-2 and to investigate drivers of COVID-19 severity in individuals with cancer and immunosuppression, we adapted our mathematical model of the immune response during COVID-19 and generated virtual patient cohorts of cancer and immunosuppressed patients. The cohorts of plausible patients recapitulated available longitudinal clinical data collected from patients in Montréal, Canada area hospitals. Our model predicted that both cancer and immunosuppressed virtual patients with severe COVID-19 had decreased CD8+ T cells, elevated interleukin-6 concentrations, and delayed type I interferon peaks compared to those with mild COVID-19 outcomes. Additionally, our results suggest that cancer patients experience higher viral loads (however, with no direct relation with severity), likely because of decreased initial neutrophil counts (i.e., neutropenia), a frequent toxic side effect of anti-cancer therapy. Furthermore, severe cancer and immunosuppressed virtual patients suffered a high degree of tissue damage associated with elevated neutrophils. Lastly, parameter values associated with monocyte recruitment by infected cells were found to be elevated in severe cancer and immunosuppressed patients with respect to the COVID-19 reference group. Together, our study highlights that dysfunction in type I interferon and CD8+ T cells are key drivers of immune dysregulation in COVID-19, particularly in cancer patients and immunosuppressed individuals.