Biology subjects

Cassidy, T.

Publications and source records attributed to Cassidy, T..

8 recordsLinked to original sources

bioRxiv2026

Linking Systemic Endotoxin Exposure to Retinal Microglia Migration Through Mathematical Modeling

Microglia are the resident immune cells of the central nervous system (CNS), including the brain, spinal cord, and retina, where they serve as the first line of defense against infection and inflammation. Dys-regulated microglia activity has been implicated in vision-threatening diseases, highlighting the need to understand how retinal microglia respond to inflammatory stimuli. Importantly, acute inflammation induces substantial redistribution of microglia across retinal layers, yet the mechanisms governing this migration remain poorly understood. Here, we develop the first mathematical model of retinal microglia migration during inflammation to determine how inflammatory exposure, administration route, and species-specific pharmacokinetics shape redistribution dynamics across the retina. The model couples lipopolysaccharide (LPS) pharmacokinetics with microglia migration between the outer plexiform layer (OPL), inner plexiform layer (IPL), and ganglion cell layer/nerve fiber layer (GCL/NFL). Model parameters are calibrated to retinal microglia density measurements from mice following LPS (bacterial endotoxin) challenge, before extending the framework to rats and rhesus macaques to investigate species-specific responses. Simulations also compare how administration route, i.e. intravenous or intraperitoneal injections, alter retinal LPS exposure and subsequent microglia redistribution. Our results suggest that redistribution patterns are driven primarily by LPS delivery route and species-specific pharmacokinetics, rather than the initial microglia distribution across retinal layers. Together, these findings provide new insight into immune cell reorganization in the inflamed retina and demonstrate how mechanistic mathematical modeling can be adapted across experimental designs, administration routes, and animal species.

immunology↗

bioRxiv2024

Identifiability of heterogeneous phenotype adaptation from low-cell-count experiments and a stochastic model

Phenotypic plasticity contributes significantly to treatment failure in many cancers. Despite the increased prevalence of experimental studies that interrogate this phenomenon, there remains a lack of applicable quantitative tools to characterise data, and importantly to distinguish between resistance as a discrete phenotype and a continuous distribution of phenotypes. To address this, we develop a stochastic individual-based model of plastic phenotype adaptation through a continuously-structured phenotype space in low-cell-count proliferation assays. That our model corresponds probabilistically to common partial differential equation models of resistance allows us to formulate a likelihood that captures the intrinsic noise ubiquitous to such experiments. We apply our framework to assess the identifiability of key model parameters in several population-level data collection regimes; in particular, parameters relating to the adaptation velocity and cell-to-cell heterogeneity. Significantly, we find that cell-to-cell heterogeneity is practically non-identifiable from both cell count and proliferation marker data, implying that population-level behaviours may be well characterised by homogeneous ordinary differential equation models. Additionally, we demonstrate that population-level data are insufficient to distinguish resistance as a discrete phenotype from a continuous distribution of phenotypes. Our results inform the design of both future experiments and future quantitative analyses that probe phenotypic plasticity in cancer. Author SummaryMany cancers adaptively and reversibly develop resistance to treatment, adding complexity to predictive model development and, by extension, treatment design. While so-called drug challenge experiments are now commonly employed to interrogate phenotypic plasticity, there are very few quantitative tools available to interpret the biological data that arises. In particular, it remains unclear what is needed from drug challenge experiments in order to identify the phenotypic structure of a population that responds adaptively to treatment. In this work, we develop a new individual-level mathematical model of phenotypic plasticity in parallel with a structured model calibration process. Applying our framework to various existing and potential experimental designs reveals that experiments that yield only population-level data cannot distinguish between drug resistance as a distinct cell state, or drug resistance as a continuum of cell states. Consequentially, at the population-level, we demonstrate that common mathematical models that assume a set of distinct cell states can characterise the behaviour of cell populations that, in actuality, respond through a continuum of states. Importantly, our results shed light on both the mathematical models and experiments required to capture phenotypic plasticity in cancer.

cancer biology↗

bioRxiv2024

Growth rate-driven modelling reveals how phenotypic adaptation drives drug resistance in BRAFV600E-mutant melanoma

Phenotypic adaptation, the ability of cells to change phenotype in response to external pressures, has been identified as a driver of drug resistance in cancer. To quantify phenotypic adaptation in BRAFV600E-mutant melanoma, we develop a theoretical model informed by growthrate data of WM239A-BRAFV600E cells challenged with the BRAF-inhibitor encorafenib. We use an individual-based model (IBM) in which each cell is described by one of multiple discrete and plastic phenotype states that are directly linked to drug-dependent net growth rates and, by extension, drug resistance. To describe how cells transition between phenotype states, we explore a gamut of candidate models common in the mathematical biology literature. Comparing these on their ability to reproduce in vitro growth curves, data-matched simulations suggest that phenotypic adaptation is directed towards states of high net growth rates, enabling the evasion of drug-effects. The model subsequently provides an explanation for when and why intermittent treatments outperform continuous treatments in the studied system, and demonstrates the benefits of not only targeting, but also leveraging, phenotypic adaptation in treatment protocols. Building on the IBM, we present a flexible mathematical methodology based on ordinary differential equations to compare responses to continuous and intermittent treatments through long-term effective net growth rates.

cancer biology↗

bioRxiv2024

A multiscale model of the action of a capsid assembly modulator for the treatment of chronic hepatitis B

Chronic hepatitis B virus (HBV) infection is strongly associated with increased risk of liver cancer and cirrhosis. While existing treatments effectively inhibit the HBV life cycle, viral rebound occurs rapidly following treatment interruption. Consequently, functional cure rates of chronic HBV infection remain low and there is increased interest in a novel treatment modality, capsid assembly modulators (CAMs). Here, we develop a multiscale mathematical model of CAM treatment in chronic HBV infection. By fitting the model to participant data from a phase I trial of the first-generation CAM vebicorvir, we estimate the drugs dose-dependent effectiveness and identify the physiological mechanisms that drive the observed biphasic decline in HBV DNA and RNA, and mechanistic differences between HBeAg-positive and negative infection. Finally, we demonstrate analytically and numerically that HBV RNA is more sensitive than HBV DNA to increases in CAM effectiveness. Author summaryCapsid assembly modulators (CAMs) are a novel class of anti-hepatitis B virus (HBV) treatments in clinical trials. These CAMs have a distinct mechanism of action from nucleos(t)ide analogues and thus represent an attractive option for the treatment of chronic HBV infection. We developed a multiscale model of the intracellular HBV lifecycle and extracellular dynamics using a time-since-infection structured partial differential equation. We fit the model to participant data from a recent phase I trial, performed a detailed parameter sensitivity analysis, identified key mechanisms driving viral response to first-generation CAM treatment, and demonstrated that HBV RNA is more sensitive than HBV DNA to changes in CAM efficacy, highlighting the potential role of HBV RNA as a biomarker for CAM effectiveness.

microbiology↗

bioRxiv2021

The role of memory in non-genetic inheritance and its impact on cancer treatment resistance

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 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. Author summaryWe propose a simple mathematical model to understand the role of phenotypic plasticity and non-genetic inheritance in driving therapy resistance in cancer. We identify the role of non-genetic inheritance on treatment resistance and use a variety of analytical and numerical techniques to understand the impact of phenotypic plasticity on population fitness and dynamics. We further use our model to study the role of phenotypic heterogeneity in therapeutic resistance in a genetically identical non-small cell lung cancer population. Finally, we combine analytical perspectives and techniques from the theory of structured populations, renewal equations and infinite dimensional dynamical systems to derive a model informed therapeutic strategy that both drives tumour eradication while avoiding competitive release of a drug-tolerant subpopulation. These results exemplify the potential of using mathematical techniques to identify therapeutic strategies to guide the evolution of a heterogeneous tumour.

cancer biology↗

bioRxiv2020

Characterizing chemotherapy-induced neutropenia and monocytopenia through mathematical modelling

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

systems biology↗

bioRxiv2019

Optimal Individualized Combination Immunotherapy/Oncolytic Virotherapy Determined Through In Silico Clinical Trials Improves Late Stage Melanoma Patient Outcomes

Oncolytic virothcrapics, 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 more traditional immunotherapy treatment using exogenous administration of granulocyte-macrophage colony-stimulating factor (GM-CSF). Theoretically, a combined approach leveraging immunotherapies: like exogenous cytokine administration and oncolytic virotherapv would elicit an even greater immune response and improve patient outcomes, but given that their efficacy and safety must be tested in large clinical trials, combination therapeutic regimens have yet to be established. By adopting computational biology and in silico clinical trial approaches, here we show significantly improved patient outcomes for individuals with late-stage melanoma by personalizing and optimizing combination oncolytic, virotherapv and GM-CSF therapy. 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.

cancer biology↗

bioRxiv2018

A mathematical model of viral oncology as a instigator of immuno-oncology

We develop and analyse a mathematical model of tumour-immune interaction that explicitly incorporates heterogeneity in tumour cell cycle duration by using a distributed delay differential equation. Our necessary and sufficient conditions for local stability of the cancer free equilibrium completely characterise the importance of tumour-immune interaction in disease progression. Consistent with the immunoediting hypothesis, we show that decreasing tumour-immune interaction leads to tumour expansion. Finally, we show that immune involvement is crucial in determining the long-term response to viral therapy.

systems biology↗