Biology subjects

Kareva, I.

Publications and source records attributed to Kareva, I..

13 recordsLinked to original sources

bioRxiv2026

Model-supported patient stratification using multi-objective synergy optimization in combination therapy

The challenge of stratifying patients for combination therapy is both technically demanding and clinically crucial. In previous work, we introduced a multi-objective optimization framework for identifying optimally synergistic combination protocols that are robust to competing definitions of additivity. This manuscript extends this methodology to quantify how inter-individual variability in drug sensitivity influences the combination doses that optimally balance the competing objectives of synergy of efficacy and synergy of potency (a proxy measure of toxicity). For this methodology, we introduce a voxel-based stratification approach to characterize individuals (model parameterizations) into subgroups based on sensitivity to each drug as a monotherapy and in combination. As a case study, we apply the method to a preclinical dataset of murine response to the combination of an immune checkpoint inhibitor and an antiangiogenic agent. We demonstrate that the algorithm can quantify how the robustly optimal combination therapies vary across different treatment response subgroups and how the algorithm can identify subpopulations for which no meaningfully efficacious combination exists. As applying the methodology requires knowledge of specific parameter values for which measurable biomarkers may be unavailable, we also propose an initiation protocol that permits identification of the parameters necessary to place an individual in a subgroup. This methodology is a step in the direction of determining the right combination therapy for a subgroup and finding the right subgroup for an existing therapy.

pharmacology and toxicology↗

bioRxiv2026

Adaptive receptor expression and the emergence of disease as loss of signaling homeostasis

Our bodies have evolved to maintain homeostasis through regulatory systems that continuously adapt to keep physiological processes within a normal range. From this perspective, complex chronic disease can be understood as a breakdown of compensatory mechanisms, resulting in loss of homeostasis. Here we propose that adaptive receptor expression dynamics may serve as one such compensatory mechanism, increasing receptor surface expression when external ligand is insufficient, and clearing it when signaling is excessive. To explore this, we adapt a previously published agent-based model and simulate it under a range of scenarios. We find that the system of adaptive receptor expression is robust to oscillatory perturbations but not to chronic stress. We propose that receptor turnover dynamics may be better understood as an adaptive, environmentally responsive process rather than a fixed biological property, and that in some cases, disease manifests only after compensatory mechanisms have been pushed past their limits. We conclude with a discussion of implications for understanding complex chronic diseases, for thinking about epigenetic and mutational change as escalating layers of adaptation, and for how we model receptor dynamics in the context of receptor-mediated drug activity.

evolutionary biology↗

bioRxiv2025

Energy Constraints and Neural Strategy Transitions in Alzheimer's: A Game-Theoretic Model

While many mechanisms have been proposed to drive Alzheimers disease, particularly the accumulation of amyloid plaques and hyperphosphorylation of tau proteins, emerging evidence suggests that they may be the byproducts of earlier damage rather than initiating events. Instead, metabolic dysfunction and the inability of neural cells to support their energetic demands may be a more plausible trigger for subsequent pathological cascade (the neuron energy crisis hypothesis). Here we highlight how type 2 diabetes (T2D) can contribute to neurodegeneration by impairing brain energy metabolism. We present a game-theoretic framework, where neurons face trade-offs between energy efficiency and information fidelity. We show that under metabolic stress, neural networks can evolve toward smaller group sizes that prioritize energy efficiency over information quality, which may underlie the observed collapse of cognitive capacity during neurodegeneration. We conclude with a discussion of interventions, ranging from antidiabetic drugs to cognitive engagement and sensory stimulation, aimed at reducing metabolic stress and preserving cognitive function.

neuroscience↗

bioRxiv2025

Using Virtual Patients to Evaluate Dosing Strategies for Bispecifics with a Bell-Shaped Efficacy Curve

Bispecific antibodies can be broadly divided into two categories: those that are pharmacologically active as either dimers bound to a single target or as trimers bound to both targets, and those that are only active as trimers. Dose selection of trimer-based bispecifics poses a unique challenge, as toxicity increases with dose, but efficacy does not. Instead, trimer-driven bispecifics have a bell-shaped efficacy curve, for which both under- and over-dosing can cause a decrease in efficacy. To address the challenge of dose selection for trimer-based bispecifics, we develop a semi-mechanistic pharmacokinetic/pharmacodynamic model of one such bispecific, teclistamab. By introducing variability in key patient-specific parameters, we find that the currently selected phase II recommended dose of 1.5 mg/kg administered subcutaneously weekly falls within the calculated optimal range for maximizing concentration of the pharmacologically active trimer for a broad population. We next explore different strategies for patient stratification based on pre-treatment levels of measurable biomarkers. We discover that significantly more variability across subpopulations is predicted when the drug is administered every two weeks as compared to weekly administration, and that higher doses generally result in more interpatient variability. Further, the pharmacologically active trimers are predicted to be maximized at different doses for different subpopulations. These findings underscore the potential for model-supported patient stratification based on measurable biomarkers, offering a middle ground between population-level approaches and fully personalized medicine.

systems biology↗

bioRxiv2025

Delaying Cancer Progression by Integrating Toxicity Constraints in a Model of Adaptive Therapy

Cancer therapies often fail when intolerable toxicity or drug-resistant cancer cells undermine otherwise effective treatment strategies. Over the past decade, adaptive therapy has emerged as a promising approach to postpone emergence of resistance by altering dose timing based on tumor burden thresholds. Despite encouraging results, these protocols often overlook the crucial role of toxicity-induced treatment breaks, which may permit tumor regrowth. Herein, we explore the following question: would incorporating toxicity feedback improve or hinder the efficacy of adaptive therapy? To address this question, we propose a mathematical framework for incorporating toxic feedback into treatment design. We find that the degree of competition between sensitive and resistant populations, along with the growth rate of resistant cells, critically modulates the impact of toxicity feedback on time to progression. Further, our model identifies circumstances where strategic treatment breaks, which may be based on either tumor size or toxicity, can mitigate overtreatment and extend time to progression, both at the baseline parameterization and across a heterogeneous virtual population. Taken together, these findings highlight the importance of integrating toxicity considerations into the design of adaptive therapy.

systems biology↗

bioRxiv2025

Quantitative pharmacology methods for bispecific T cell engagers

T Cell Engager (TCE)s are an exciting therapeutic modality in immuno-oncology that acts to bypass antigen presentation and forms a direct link between cancer and immune cells in the Tumor Microenvironment (TME). TCEs are efficacious only when the drug is bound to both immune and cancer cell targets. Therefore, approaches that maximize the formation of the drug-target trimer in the TME are expected to increase the drugs efficacy. In this study, we quantitatively investigate how the concentration of ternary complex and its biodistribution depend on both the targets specific properties and the design characteristics of the TCE, and specifically on the binding kinetics of the drug to its targets. A simplified mathematical model of drug-target interactions is considered here, with insights from the "three-body" problem applied to the model. Parameter identifiability analysis performed on the model demonstrates that steady state data, which is often available at the early pre-clinical stages, is sufficient to estimate the binding affinity of the TCE molecule to both targets. We used the model to analyze several existing antibodies, both clinically approved and under development, to explore their common kinetic features. The manuscript concludes with an assessment of a full quantitative pharmacology model that accounts for drug disposition into the peripheral compartment.

systems biology↗

bioRxiv2024

From hyperinsulinemia to cancer progression: how diminishing glucose storage capacity fuels insulin resistance

Type 2 diabetes (T2D) is a complex metabolic disorder characterized by insulin resistance, hyperglycemia and hyperinsulinemia, with a quarter to half of people with T2D unaware of their diagnosis until the disease has reached advanced stages. T2D is associated with increased risk and worse prognosis of cardiovascular disease, cognitive decline, and cancer. Here we propose an updated framework for describing emergence of insulin resistance that precedes development of T2D. We show that diminishing capacity to store excess glucose can qualitatively capture the transition from normal to diabetic phenotype as captured by responses to oral glucose tolerance tests (OGTTs). We then show that an emerging tumor can either progress or regress depending on the metabolic environment of the host, consistent with experimental results of Hopkins et al. (2018), who showed that drug-induced transient diabetic phenotype, and specifically hyperinsulinemia, resulted in loss of therapeutic efficacy, and its reversal restored drug sensitivity and response to therapy. Given the prevalence of hyperinsulinemia in individuals with normoglycemia, addressing this condition emerges as a promising avenue to augment cancer therapy outcomes.

cancer biology↗

bioRxiv2023

Minimally Sufficient Experimental Design using Identifiability Analysis

Mathematical models are increasingly being developed and calibrated in tandem with data collection, empowering scientists to intervene in real time based on quantitative model predictions. Well-designed experiments can help augment the predictive power of a mathematical model but the question of when to collect data to maximize its utility for a model is non-trivial. Here we define data as model-informative if it results in a unique parametrization, assessed through the lens of practical identifiability. The framework we propose identifies an optimal experimental design (how much data to collect and when to collect it) that ensures parameter identifiability (permitting confidence in model predictions), while minimizing experimental time and costs. We demonstrate the power of the method by applying it to a modified version of a classic site-of-action pharmacokinetic/pharmacodynamic model that describes distribution of a drug into the tumor microenvironment (TME), where its efficacy is dependent on the level of target occupancy in the TME. In this context, we identify a minimal set of time points when data needs to be collected that robustly ensures practical identifiability of model parameters. The proposed methodology can be applied broadly to any mathematical model, allowing for the identification of a minimally sufficient experimental design that collects the most informative data.

systems biology↗

bioRxiv2023

Cytokine storm mitigation for exogenous immune agonists

Cytokine storm is a life-threatening inflammatory response characterized by hyperactivation of the immune system. It can be caused by various therapies, auto-immune conditions, or pathogens, such as respiratory syndrome coronavirus 2 (SARS-CoV-2) which causes coronavirus disease COVID-19. Here we propose a conceptual mathematical model describing the phenomenology of cytokine-immune interactions when a tumor is treated by an exogenous immune cell agonist which has the potential to cause a cytokine storm, such as CAR T cell therapy. Numerical simulations reveal that as a function of just two model parameters, the same drug dose and regimen could result in one of four outcomes: treatment success without a storm, treatment success with a storm, treatment failure without a storm, and treatment failure with a storm. We then explore a scenario in which tumor control is accompanied by a storm and ask if it is possible to modulate the duration and frequency of drug administration (without changing the cumulative dose) in order to preserve efficacy while preventing the storm. Simulations reveal existence of a "sweet spot" in protocol space (number versus spacing of doses) for which tumor control is achieved without inducing a cytokine storm. This theoretical model, which contains a number of parameters that can be estimated experimentally, contributes to our understanding of what triggers a cytokine storm, and how the likelihood of its occurrence can be mitigated.

cancer biology↗

bioRxiv2022

Serotonin signaling in the enteric nervous system and connection to autism spectrum disorder: a translational mathematical model

While the causes of autism spectrum disorder (ASD) remain unclear, some studies have shown that serotonin-mediated effects on enteric nervous system (ENT) correlate with ASD-like behavioral phenotype in mice. Introduced here is a mathematical model of interactions between gut serotonin and its impact on ENT. The model was used to identify three key factors that affect ENT size, namely, serotonin production, its clearance, and its ability to act as a growth factor on ENT. The model was used to reproduce experimentally reported results from a mouse model by Margolis et al. (2016), which connected serotonin-mediated ENT hypoplasia to an ASD phenotype. The proposed mathematical model was used to scale the quantified relationship from mice to humans to show how the combination of these three factors can translate to a quantifiable metric that could potentially be correlated to ASD spectrum. A detailed discussion of how ENT hypoplasia could mechanistically affect CNS activity concludes this paper.

neuroscience↗

bioRxiv2022

Practical guide to calculating impact of avidity in models of bispecific antibodies with two membrane-bound targets

Here we present details of the calculation necessary to estimate the impact of avidity in a mathematical model of a bispecific antibody with two membrane-bound targets. The calculation is used to reproduce the results reported in Rhoden et al. (2016) and implemented in Kareva et al. (2018). We reproduce the impact of difference in relative concentration of the two targets on projections of free and bound concentrations of both targets and the antibody and highlight the applicability of this approach for supporting model informed decision making particularly in the early stages of drug discovery and development.

pharmacology and toxicology↗

bioRxiv2022

Mathematical model of a cytokine storm

Cytokine storm is a life-threatening inflammatory response that is characterized by hyperactivation of the immune system, and which can be caused by various therapies, auto-immune conditions, or pathogens, such as respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease COVID-19. While initial causes of cytokine storms can vary, late-stage clinical manifestations of cytokine storm converge and often overlap, and therefore a better understanding of how normal immune response turns pathological is warranted. Here we propose a theoretical framework, where cytokine storm phenomenology is captured using a conceptual mathematical model, where cytokines can both activate and regulate the immune system. We simulate normal immune response to infection, and through variation of system parameters identify conditions where, within the frameworks of this model, cytokine storm can arise. We demonstrate that cytokine storm is a transitional regime, and identify three main factors that must converge to result in storm-like dynamics, two of which represent individual-specific characteristics, thereby providing a possible explanation for why some people develop CRS, while others may not. We also discuss possible ecological insights into cytokine-immune interactions and provide mathematical analysis for the underlying regimes. We conclude with a discussion of how results of this analysis can be used in future research.

immunology↗

bioRxiv2019

Delicate balances in cancer chemotherapy: modeling immune recruitment and emergence of systemic drug resistance

Metronomic chemotherapy can drastically enhance immunogenic tumor cell death. However, the responsible mechanisms are still incompletely understood. Here, we develop a mathematical model to elucidate the underlying complex interactions between tumor growth, immune system activation, and therapy-mediated immunogenic cell death. Our model is conceptually simple, yet it provides a surprisingly excellent fit to empirical data obtained from a GL261 mouse glioma model treated with cyclophosphamide on a metronomic schedule. The model includes terms representing immune recruitment as well as the emergence of drug resistance during prolonged metronomic treatments. Strikingly, a fixed set of parameters, not adjusted for individuals nor for drug schedule, excellently recapitulates experimental data across various drug regimens, including treatments administered at intervals ranging from 6 to 12 days. Additionally, the model predicts peak immune activation times, rediscovering experimental data that had not been used in parameter fitting or in model construction. The validated model was then used to make predictions about expected tumor-immune dynamics for novel drug administration schedules. Notably, the validated model suggests that immunostimulatory and immunosuppressive intermediates are responsible for the observed phenomena of resistance and immune cell recruitment, and thus for variation of responses with respect to different schedules of drug administration.

systems biology↗