In silico dynamics of COVID-19 phenotypes for optimizing clinical management.

Understanding the underlying mechanisms of COVID-19 progression and the impact of various pharmaceutical interventions is crucial for the clinical management of the disease. We developed a comprehensive mathematical framework based on the known mechanisms of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, incorporating the renin-angiotensin system and ACE2, which the virus exploits for cellular entry, key elements of the innate and adaptive immune responses, the role of inflammatory cytokines, and the coagulation cascade for thrombus formation. The model predicts the evolution of viral load, immune cells, cytokines, thrombosis, and oxygen saturation based on patient baseline condition and the presence of comorbidities. Model predictions were validated with clinical data from healthy people and COVID-19 patients, and the results were used to gain insight into identified risk factors of disease progression including older age; comorbidities such as obesity, diabetes, and hypertension; and dysregulated immune response. We then simulated treatment with various drug classes to identify optimal therapeutic protocols. We found that the outcome of any treatment depends on the sustained response rate of activated CD8+ T cells and sufficient control of the innate immune response. Furthermore, the best treatment-or combination of treatments-depends on the preinfection health status of the patient. Our mathematical framework provides important insight into SARS-CoV-2 pathogenesis and could be used as the basis for personalized, optimal management of COVID-19.

Strategies to minimize heterogeneity and optimize clinical trials in Acute Respiratory Distress Syndrome (ARDS): Insights from mathematical modelling.

BACKGROUND: Mathematical modelling may aid in understanding the complex interactions between injury and immune response in critical illness. METHODS: We utilize a system biology model of COVID-19 to analyze the effect of altering baseline patient characteristics on the outcome of immunomodulatory therapies. We create example parameter sets meant to mimic diverse patient types. For each patient type, we define the optimal treatment, identify biologic programs responsible for clinical responses, and predict biomarkers of those programs. FINDINGS: Model states representing older and hyperinflamed patients respond better to immunomodulation than those representing obese and diabetic patients. The disparate clinical responses are driven by distinct biologic programs. Optimal treatment initiation time is determined by neutrophil recruitment, systemic cytokine expression, systemic microthrombosis and the renin-angiotensin system (RAS) in older patients, and by RAS, systemic microthrombosis and trans IL6 signalling for hyperinflamed patients. For older and hyperinflamed patients, IL6 modulating therapy is predicted to be optimal when initiated very early (<4th day of infection) and broad immunosuppression therapy (corticosteroids) is predicted to be optimally initiated later in the disease (7th - 9th day of infection). We show that markers of biologic programs identified by the model correspond to clinically identified markers of disease severity. INTERPRETATION: We demonstrate that modelling of COVID-19 pathobiology can suggest biomarkers that predict optimal response to a given immunomodulatory treatment. Mathematical modelling thus constitutes a novel adjunct to predictive enrichment and may aid in the reduction of heterogeneity in critical care trials. FUNDING: C.V. received a Marie Sklodowska Curie Actions Individual Fellowship (MSCA-IF-GF-2020-101028945). R.K.J.'s research is supported by R01-CA208205, and U01-CA 224348, R35-CA197743 and grants from the National Foundation for Cancer Research, Jane's Trust Foundation, Advanced Medical Research Foundation and Harvard Ludwig Cancer Center. No funder had a role in production or approval of this manuscript.

A multi-scale model for determining the effects of pathophysiology and metabolic disorders on tumor growth.

The search for efficient chemotherapy drugs and other anti-cancer treatments would benefit from a deeper understanding of the tumor microenvironment (TME) and its role in tumor progression. Because in vivo experimental methods are unable to isolate or control individual factors of the TME and in vitro models often do not include all the contributing factors, some questions are best addressed with systems biology mathematical models. In this work, we present a new fully-coupled, agent-based, multi-scale mathematical model of tumor growth, angiogenesis and metabolism that includes important aspects of the TME spanning subcellular-, cellular- and tissue-level scales. The mathematical model is computationally implemented for a three-dimensional TME, and a double hybrid continuous-discrete (DHCD) method is applied to solve the governing equations. The model recapitulates the distinct morphological and metabolic stages of a solid tumor, starting with an avascular tumor and progressing through angiogenesis and vascularized tumor growth. To examine the robustness of the model, we simulated normal and abnormal blood conditions, including hyperglycemia/hypoglycemia, hyperoxemia/hypoxemia, and hypercarbia/hypocarbia - conditions common in cancer patients. The results demonstrate that tumor progression is accelerated by hyperoxemia, hyperglycemia and hypercarbia but inhibited by hypoxemia and hypoglycemia; hypocarbia had no appreciable effect. Because of the importance of interstitial fluid flow in tumor physiology, we also examined the effects of hypo- or hypertension, and the impact of decreased hydraulic conductivity common in desmoplastic tumors. The simulations show that chemotherapy-increased blood pressure, or reduction of interstitial hydraulic conductivity increase tumor growth rate and contribute to tumor malignancy.

High precision invasive FFR, low-cost invasive iFR, or non-invasive CFR?: optimum assessment of coronary artery stenosis based on the patient-specific computational models.

The objective of this paper is to apply computational fluid dynamic (CFD) as a complementary tool for clinical tests to not only predict the present and future status of left coronary artery stenosis but also to evaluate some clinical hypotheses. In order to assess the present status of the coronary artery stenosis severity, and thereby selecting the most appropriate type of treatment for each patient, fractional flow reserve (FFR), instantaneous wave free-ratio (iFR), and coronary flow reserve (CFR) are calculated. To examine FFR, iFR, and CFR results, the effect of geometric features of stenoses, including diameter reduction (%), lesion length (LL), and minimum lumen diameter (MLD), is studied on them. It is observed that FFR is a more conservative index than iFR and CFR to assess the severity of coronary stenosis. In addition, it is seen that FFR, iFR, and CFR decrease by increasing LL and decreasing MLD. Therefore, the morphological indices, LL/MLD and LL/MLD̂4, with the calculated conservative cut-off values equal to 5.5 and 3.6, are considered. Next, some controversial clinical hypotheses about the assessment of the severity of coronary stenosis are evaluated numerically. These include the examination of FFR, iFR, and CFR accuracies, investigating the effect of coronary hyperemia on iFR, as well as the reliability of the hybrid iFR-FFR decision-making strategy. The presented numerical model can also be used as a predictive tool to identify the atherosuseptible sites of arteries by calculating the time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), and relative residence time (RRT).

In silico dynamics of COVID-19 phenotypes for optimizing clinical management.

Understanding the underlying mechanisms of COVID-19 progression and the impact of various pharmaceutical interventions is crucial for the clinical management of the disease. We developed a comprehensive mathematical framework based on the known mechanisms of the SARS-CoV-2 virus infection, incorporating the renin-angiotensin system and ACE2, which the virus exploits for cellular entry, key elements of the innate and adaptive immune responses, the role of inflammatory cytokines and the coagulation cascade for thrombus formation. The model predicts the evolution of viral load, immune cells, cytokines, thrombosis, and oxygen saturation based on patient baseline condition and the presence of co-morbidities. Model predictions were validated with clinical data from healthy people and COVID-19 patients, and the results were used to gain insight into identified risk factors of disease progression including older age, co-morbidities such as obesity, diabetes, and hypertension, and dysregulated immune response 1,2 . We then simulated treatment with various drug classes to identify optimal therapeutic protocols. We found that the outcome of any treatment depends on the sustained response rate of activated CD8 + T cells and sufficient control of the innate immune response. Furthermore, the best treatment -or combination of treatments - depends on the pre-infection health status of the patient. Our mathematical framework provides important insight into SARS-CoV-2 pathogenesis and could be used as the basis for personalized, optimal management of COVID-19.

Simulation of Low Density Lipoprotein (LDL) permeation into multilayer coronary arterial wall: Interactive effects of wall shear stress and fluid-structure interaction in hypertension.

Due to increased atherosclerosis-caused mortality, identification of its genesis and development is of great importance. Although, key factors of the origin of the disease is still unknown, it is widely believed that cholesterol particle penetration and accumulation in arterial wall is mainly responsible for further wall thickening and decreased rate of blood flow during a gradual progression. To date, various effective components are recognized whose simultaneous consideration would lead to a more accurate approximation of Low Density Lipoprotein (LDL) distribution within the wall. In this research, a multilayer Fluid-Structure Interaction (FSI) model is studied to simulate the penetration of LDL into the arterial wall. Distention impact on wall properties is taken into account by considering FSI and Wall Shear Stress (WSS) dependent endothelium properties. The results show intensified permeation of LDL whilst the FSI approach is applied. In addition, luminal distension prompted by FSI reduces WSS along lumen/wall interface, especially in hypertension. This effect leads to a lowered endothelial resistance against LDL permeation, comparing to the case in which WSS effect is overlooked. The results are in an acceptable consistency with the clinical researches on WSS effect on atherosclerosis development.

Organ-Tumor-on-a-Chip for Chemosensitivity Assay: A Critical Review.

With a mortality rate over 580,000 per year, cancer is still one of the leading causes of death worldwide. However, the emerging field of microfluidics can potentially shed light on this puzzling disease. Unique characteristics of microfluidic chips (also known as micro-total analysis system) make them excellent candidates for biological applications. The ex vivo approach of tumor-on-a-chip is becoming an indispensable part of personalized medicine and can replace in vivo animal testing as well as conventional in vitro methods. In tumor-on-a-chip, the complex three-dimensional (3D) nature of malignant tumor is co-cultured on a microfluidic chip and high throughput screening tools to evaluate the efficacy of anticancer drugs are integrated on the same chip. In this article, we critically review the cutting edge advances in this field and mainly categorize each tumor-on-a-chip work based on its primary organ. Specifically, design, fabrication and characterization of tumor microenvironment; cell culture technique; transferring mechanism of cultured cells into the microchip; concentration gradient generators for drug delivery; in vitro screening assays of drug efficacy; and pros and cons of each microfluidic platform used in the recent literature will be discussed separately for the tumor of following organs: (1) Lung; (2) Bone marrow; (3) Brain; (4) Breast; (5) Urinary system (kidney, bladder and prostate); (6) Intestine; and (7) Liver. By comparing these microchips, we intend to demonstrate the unique design considerations of each tumor-on-a-chip based on primary organ, e.g., how microfluidic platform of lung-tumor-on-a-chip may differ from liver-tumor-on-a-chip. In addition, the importance of heart⁻liver⁻intestine co-culture with microvasculature in tumor-on-a-chip devices for in vitro chemosensitivity assay will be discussed. Such system would be able to completely evaluate the absorption, distribution, metabolism, excretion and toxicity (ADMET) of anticancer drugs and more realistically recapitulate tumor in vivo-like microenvironment.