Rukmini Kumar

Bio: Rukmini Kumar is an academic researcher from University of Pittsburgh. The author has contributed to research in topics: Sepsis & Pembrolizumab. The author has an hindex of 6, co-authored 14 publications receiving 970 citations.

The acute inflammatory response in diverse shock states.

Abstract: A poorly controlled acute inflammatory response can lead to organ dysfunction and death. Severe systemic inflammation can be induced and perpetuated by diverse insults such as the administration of toxic bacterial products (e.g., endotoxin), traumatic injury, and hemorrhage. Here, we probe whether these varied shock states can be explained by a universal inflammatory system that is initiated through different means and, once initiated, follows a course specified by the cellular and molecular mechanisms of the immune and endocrine systems. To examine this question, we developed a mathematical model incorporating major elements of the acute inflammatory response in C57Bl/6 mice, using input from experimental data. We found that a single model with different initiators including the autonomic system could describe the response to various insults. This model was able to predict a dose range of endotoxin at which mice would die despite having been calibrated only in nonlethal inflammatory paradigms. These results show that the complex biology of inflammation can be modeled and supports the hypothesis that shock states induced by a range of physiologic challenges could arise from a universal response that is differently initiated and modulated.

In silico design of clinical trials: a method coming of age.

Abstract: Objective:To determine the feasibility and potential usefulness of mathematical models in evaluating immunomodulatory strategies in clinical trials of severe sepsis.Design:Mathematical modeling of immunomodulation in simulated patients.Setting:Computer laboratory.Measurements and Main Results:We int

In silico models of acute inflammation in animals

Abstract: Trauma and hemorrhagic shock elicit an acute inflammatory response, predisposing patients to sepsis, organ dysfunction, and death. Few approved therapies exist for these acute inflammatory states, mainly due to the complex interplay of interacting inflammatory and physiological elements working at multiple levels. Various animal models have been used to simulate these phenomena, but these models often do not replicate the clinical setting of multiple overlapping insults. Mathematical modeling of complex systems is an approach for understanding the interplay among biological interactions. We constructed a mathematical model using ordinary differential equations that encompass the dynamics of cells and cytokines of the acute inflammatory response, as well as global tissue dysfunction. The model was calibrated in C57Bl/6 mice subjected to (1) various doses of lipopolysaccharide (LPS) alone, (2) surgical trauma, and (3) surgery + hemorrhagic shock. We tested the model's predictive ability in scenarios on which it had not been trained, namely, (1) surgery +/- hemorrhagic shock + LPS given at times after the beginning of surgical instrumentation, and (2) surgery + hemorrhagic shock + bilateral femoral fracture. Software was created that facilitated fitting of the mathematical model to experimental data, as well as for simulation of experiments with various inflammatory challenges and associated variations (gene knockouts, inhibition of specific cytokines, etc.). Using this software, the C57Bl/6-specific model was recalibrated for inflammatory analyte data in CD14-/- mice and was used to elucidate altered features of inflammation in these animals. In other experiments, rats were subjected to surgical trauma +/- LPS or to bacterial infection via fibrin clots impregnated with various inocula of Escherichia coli. Mathematical modeling may provide insights into the complex dynamics of acute inflammation in a manner that can be tested in vivo using many fewer animals than has been possible previously.

Inflammation and tumor progression: signaling pathways and targeted intervention.

Abstract: Cancer development and its response to therapy are regulated by inflammation, which either promotes or suppresses tumor progression, potentially displaying opposing effects on therapeutic outcomes. Chronic inflammation facilitates tumor progression and treatment resistance, whereas induction of acute inflammatory reactions often stimulates the maturation of dendritic cells (DCs) and antigen presentation, leading to anti-tumor immune responses. In addition, multiple signaling pathways, such as nuclear factor kappa B (NF-kB), Janus kinase/signal transducers and activators of transcription (JAK-STAT), toll-like receptor (TLR) pathways, cGAS/STING, and mitogen-activated protein kinase (MAPK); inflammatory factors, including cytokines (e.g., interleukin (IL), interferon (IFN), and tumor necrosis factor (TNF)-α), chemokines (e.g., C-C motif chemokine ligands (CCLs) and C-X-C motif chemokine ligands (CXCLs)), growth factors (e.g., vascular endothelial growth factor (VEGF), transforming growth factor (TGF)-β), and inflammasome; as well as inflammatory metabolites including prostaglandins, leukotrienes, thromboxane, and specialized proresolving mediators (SPM), have been identified as pivotal regulators of the initiation and resolution of inflammation. Nowadays, local irradiation, recombinant cytokines, neutralizing antibodies, small-molecule inhibitors, DC vaccines, oncolytic viruses, TLR agonists, and SPM have been developed to specifically modulate inflammation in cancer therapy, with some of these factors already undergoing clinical trials. Herein, we discuss the initiation and resolution of inflammation, the crosstalk between tumor development and inflammatory processes. We also highlight potential targets for harnessing inflammation in the treatment of cancer.

Sepsis: Multiple Abnormalities, Heterogeneous Responses, and Evolving Understanding

Abstract: Sepsis represents the host's systemic inflammatory response to a severe infection. It causes substantial human morbidity resulting in hundreds of thousands of deaths each year. Despite decades of intense research, the basic mechanisms still remain elusive. In either experimental animal models of sepsis or human patients, there are substantial physiological changes, many of which may result in subsequent organ injury. Variations in age, gender, and medical comorbidities including diabetes and renal failure create additional complexity that influence the outcomes in septic patients. Specific system-based alterations, such as the coagulopathy observed in sepsis, offer both potential insight and possible therapeutic targets. Intracellular stress induces changes in the endoplasmic reticulum yielding misfolded proteins that contribute to the underlying pathophysiological changes. With these multiple changes it is difficult to precisely classify an individual's response in sepsis as proinflammatory or immunosuppressed. This heterogeneity also may explain why most therapeutic interventions have not improved survival. Given the complexity of sepsis, biomarkers and mathematical models offer potential guidance once they have been carefully validated. This review discusses each of these important factors to provide a framework for understanding the complex and current challenges of managing the septic patient. Clinical trial failures and the therapeutic interventions that have proven successful are also discussed.

Conceptualizing a Model A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force–2

Abstract: The appropriate development of a model begins with understanding the problem that is being represented. The aim of this article is to provide a series of consensus-based best practices regarding the process of model conceptualization. For the purpose of this series of papers, the authors consider the development of models whose purpose is to inform medical decisions and health-related resource allocation questions. They specifically divide the conceptualization process into two distinct components: the conceptualization of the problem, which converts knowledge of the health care process or decision into a representation of the problem, followed by the conceptualization of the model itself, which matches the attributes and characteristics of a particular modeling type to the needs of the problem being represented. Recommendations are made regarding the structure of the modeling team, agreement on the statement of the problem, the structure, perspective and target population of the model, and the interventions and outcomes represented. Best practices relating to the specific characteristics of model structure, and which characteristics of the problem might be most easily represented in a specific modeling method, are presented. Each section contains a number of recommendations that were iterated among the authors, as well as the wider modeling taskforce, jointly set up by the International Society for Pharmacoeconomics and Outcomes Research and the Society for Medical Decision Making.