Leveraging Computational Modeling to Understand Infectious Diseases

Optimal control strategies for mitigating antibiotic resistance: Integrating virus dynamics for enhanced intervention design

Given the global increase in antibiotic resistance, new effective strategies must be developed to treat bacteria that do not respond to first or second line antibiotics. One novel method uses bacterial phage therapy to control bacterial populations. Phage viruses replicate and infect bacterial cells and are regarded as the most prevalent biological agent on earth. This paper presents a comprehensive model capturing the dynamics of wild-type bacteria (S), antibiotic-resistant bacteria (R), and virus-infected (I) bacteria population, incorporating virus inclusion. Our model integrates biologically relevant parameters governing bacterial birth rates, death rates, mutation probabilities and incorporates infection dynamics via contact with a virus. We employ an optimal control approach to study the influence of virus inclusion on bacterial population dynamics. Through numerical simulations, we establish insights into the stability of various system equilibria and bacterial population responses to varying infection rates. By examining the equilibria, we reveal the impact of virus inclusion on population trajectories, describe a medical intervention for antibiotic-resistant bacterial infections through the lense of optimal control theory, and discuss how to implement it in a clinical setting. Our findings underscore the necessity of considering virus inclusion in antibiotic resistance studies, shedding light on subtle yet influential dynamics in bacterial ecosystems.

New nicotinamide-thiadiazol hybrids as VEGFR-2 inhibitors for breast cancer therapy: design, synthesis and in silico and in vitro evaluation

Vascular endothelial growth factor receptor-2 (VEGFR-2) is a key regulator of tumor angiogenesis and has become an important target in anticancer drug development. In this study, novel nicotinamide-thiadiazol hybrids were synthesized and evaluated for their anti-breast cancer potential through VEGFR-2 inhibition. The compounds were assessed in vitro for their cytotoxicity against MDA-MB-231 and MCF-7 cell lines. Among the nicotinamide-thiadiazol hybrids, 7a exhibited the most potent anticancer activity, with IC50 values of 4.64 ± 0.3 μM in MDA-MB-231 and 7.09 ± 0.5 μM in MCF-7, showing comparable efficacy to sorafenib. VEGFR-2 inhibition assays confirmed strong inhibitory potential with an IC50 of 0.095 ± 0.05 μM. In vitro cell cycle analysis indicated that 7a induced S-phase arrest, while apoptosis assays demonstrated a substantial increase in late apoptotic cells (44.01%). Other in vitro mechanistic studies further confirmed the activation of the intrinsic apoptotic pathway, as evidenced by caspase-3 activation (8.2-fold), Bax upregulation (6.9-fold), and Bcl-2 downregulation (3.68-fold). Computational studies, including molecular docking and 200 ns molecular dynamics (MD) simulations, confirmed the stable interaction of 7a with VEGFR-2, showing binding affinities comparable to sorafenib. Further validation through MM-GBSA, ProLIF, PCAT, and FEL analyses reinforced its strong binding capability. Additionally, ADMET predictions suggested favorable pharmacokinetic properties, including good absorption, high plasma protein binding, and non-CYP2D6 inhibition. Moreover, toxicity analysis classified 7a as non-mutagenic and non-carcinogenic, with a lower predicted toxicity than sorafenib. Finally, density functional theory (DFT) calculations highlighted the structural stability and reactivity of 7a, further supporting its potential as a VEGFR-2 inhibitor. These findings suggest that 7a is a promising VEGFR-2 inhibitor with significant anticancer potential, favorable pharmacokinetics, and an improved safety profile. Further preclinical studies and structural modifications are warranted to optimize its therapeutic potential.

Precognition of Known And Unknown Biothreats: A Risk-Based Approach

Data mining and artificial intelligence algorithms can estimate the probability of future occurrences with defined precision. Yet, the prediction of infectious disease outbreaks remains a complex and difficult task. This is demonstrated by the limited accuracy and sensitivity of current models in predicting the emergence of previously unknown pathogens such as Zika, Chikungunya, and SARS-CoV-2, and the resurgence of Mpox, along with their impacts on global health, trade, and security. Comprehensive analysis of infectious disease risk profiles, vulnerabilities, and mitigation capacities, along with their spatiotemporal dynamics at the international level, is essential for preventing their transnational propagation. However, annual indexes about the impact of infectious diseases provide a low level of granularity to allow stakeholders to craft better mitigation strategies. A quantitative risk assessment by analytical platforms requires billions of near real-time data points from heterogeneous sources, integrating and analyzing univariable or multivariable data with different levels of complexity and latency that, in most cases, overwhelm human cognitive capabilities. Autonomous biosurveillance can open the possibility for near real-time, risk- and evidence-based policymaking and operational decision support.

Bioinformatics and molecular biology tools for diagnosis, prevention, treatment and prognosis of COVID-19

Since December 2019, a new form of Severe Acute Respiratory Syndrome (SARS) has emerged worldwide, caused by SARS coronavirus 2 (SARS-CoV-2). This disease was called COVID-19 and was declared a pandemic by the World Health Organization in March 2020. Symptoms can vary from a common cold to severe pneumonia, hypoxemia, respiratory distress, and death. During this period of world stress, the medical and scientific community were able to acquire information and generate scientific data at unprecedented speed, to better understand the disease and facilitate vaccines and therapeutics development. Notably, bioinformatics tools were instrumental in decoding the viral genome and identifying critical targets for COVID-19 diagnosis and therapeutics. Through the integration of omics data, bioinformatics has also improved our understanding of disease pathogenesis and virus-host interactions, facilitating the development of targeted treatments and vaccines. Furthermore, molecular biology techniques have accelerated the design of sensitive diagnostic tests and the characterization of immune responses, paving the way for precision medicine approaches in treating COVID-19. Our analysis highlights the indispensable contributions of bioinformatics and molecular biology to the global effort against COVID-19. In this review, we aim to revise the COVID-19 features, diagnostic, prevention, treatment options, and how molecular biology, modern bioinformatic tools, and collaborations have helped combat this pandemic. An integrative literature review was performed, searching articles on several sites, including PUBMED and Google Scholar indexed in referenced databases, prioritizing articles from the last 3 years. The lessons learned from this COVID-19 pandemic will place the world in a much better position to respond to future pandemics.

Digital Twins of Biological Systems: A Narrative Review

The concept of Digital Twins (DTs), software models that mimic the behavior and interactions of physical or conceptual objects within their environments, has gained traction in recent years, particularly in medicine and healthcare research. DTs technology emerges as a pivotal tool in disease modeling, integrating diverse data sources to computationally model dynamic biological systems. This narrative review explores potential DT applications in medicine, from defining DTs and their history to constructing DTs, modeling biologically relevant systems, as well as discussing the benefits, risks, and challenges in their application. The influence of DTs extends beyond healthcare and can revolutionize healthcare management, drug development, clinical trials, and various biomedical research fields.

NIAID/SMB Workshop on Multiscale Modeling of Infectious and Immune-Mediated Diseases

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.

Determining the rate of infectious disease testing through contagion potential

The emergence of new strains, varying in transmissibility, virulence, and presentation, makes the existing epidemiological statistics an inadequate representation of COVID-19 contagion. Asymptomatic individuals continue to act as carriers for the elderly and immunocompromised, making the timing and extent of vaccination and testing extremely critical in curbing contagion. In our earlier work, we proposed contagion potential (CP) as a measure of the infectivity of an individual in terms of their contact with other infectious individuals. Here we extend the idea of CP at the level of a geographical region (termed a zone). We estimate CP in a spatiotemporal model based on infection spread through social mixing as well as SIR epidemic model optimization, under varying conditions of virus strains, reinfection, and superspreader events. We perform experiments on the real daily infection dataset at the country level (Italy and Germany) and state level (New York City, USA). Our analysis shows that CP can effectively assess the number of untested (and asymptomatic) infected and inform the necessary testing rates. Finally, we show through simulations that CP can trace the evolution of the infectivity profiles of zones due to the combination of inter-zonal mobility, vaccination policy, and testing rates in real-world scenarios.

Qualitative analysis of generalized multistage epidemic model with immigration

A model with multiple disease stages is discussed; its main feature is that it considers a general incidence rate, functions for death and immigration rates in all populations. We show via a suitable Lyapunov function that the unique endemic equilibrium is globally asymptotically stable. We conclude that, in order to obtain the existence and global stability of the equilibrium point of general models, conditions must be imposed on the functions present in the model. In addition, the model has no basic reproduction number due to the constant flow of infected people, which makes its eradication impossible; therefore, there is no equilibrium point free of infection.

Tensor-based insights into systems immunity and infectious disease

Profiling immune responses across several dimensions, including time, patients, molecular features, and tissue sites, can deepen our understanding of immunity as an integrated system. These studies require new analytical approaches to realize their full potential. We highlight recent applications of tensor methods and discuss several future opportunities.

Natural Products as a Potential Source of Promising Therapeutics for COVID-19 and Viral Diseases

Background

A global pandemic has recently been observed due to the new coronavirus disease, caused by SARS-CoV-2. Since there are currently no antiviral medicines to combat the highly contagious and lethal COVID-19 infection, identifying natural sources that can either be viricidal or boost the immune system and aid in the fight against the disease can be an essential therapeutic support.

Methods

This review was conducted based on published papers related to the herbal therapy of COVID-19 by search on databases including PubMed and Scopus with herbal, COVID-19, SARS-CoV-2, and therapy keywords.

Results

To combat this condition, people may benefit from the therapeutic properties of medicinal plants, such as increasing their immune system or providing an antiviral impact. As a result, SARS-CoV-2 infection death rates can be reduced. Various traditional medicinal plants and their bioactive components, such as COVID-19, are summarized in this article to assist in gathering and debating techniques for combating microbial diseases in general and boosting our immune system in particular.

Conclusion

The immune system benefits from natural products and many of these play a role in activating antibody creation, maturation of immune cells, and stimulation of innate and adaptive immune responses. The lack of particular antivirals for SARS-CoV-2 means that apitherapy might be a viable option for reducing the hazards associated with COVID-19 in the absence of specific antivirals.

Analytical and Numerical Investigation of the SIR Mathematical Model

This is a theoretical study of the SIR model — a popular mathematical model of the propagation of infectious diseases. We construct a solution of the Cauchy problem for a system of two ordinary differential equations describing in integral form the concentration dynamics of infected and recovered individuals in an immune population. A qualitative analysis is carried out of the stationary system states using the Lyapunov function. An expression is obtained for the coordinates of the equilibrium points in terms of the Lambert W-function for arbitrary initial values. The application of the SIR model for the description of COVID-19 propagation dynamic is demonstrated.

Systems biology approaches to investigate the role of granulomas in TB-HIV coinfection

The risk of active tuberculosis disease is 15-21 times higher in those coinfected with human immunodeficiency virus-1 (HIV) compared to tuberculosis alone, and tuberculosis is the leading cause of death in HIV+ individuals. Mechanisms driving synergy between Mycobacterium tuberculosis (Mtb) and HIV during coinfection include: disruption of cytokine balances, impairment of innate and adaptive immune cell functionality, and Mtb-induced increase in HIV viral loads. Tuberculosis granulomas are the interface of host-pathogen interactions. Thus, granuloma-based research elucidating the role and relative impact of coinfection mechanisms within Mtb granulomas could inform cohesive treatments that target both pathogens simultaneously. We review known interactions between Mtb and HIV, and discuss how the structure, function and development of the granuloma microenvironment create a positive feedback loop favoring pathogen expansion and interaction. We also identify key outstanding questions and highlight how coupling computational modeling with in vitro and in vivo efforts could accelerate Mtb-HIV coinfection discoveries.

Analytical mapping of information and communication technology in emerging infectious diseases using CiteSpace

The prevalence of severe infectious diseases has become a major global health concern. Currently, the COVID-19 outbreak has spread across the world and has created an unprecedented humanitarian crisis. The proliferation of novel viruses has put traditional health systems under immense pressure and posed several serious issues. Henceforth, early detection, identification, rapid testing, and advanced surveillance systems are required to address public health emergencies. However, Information and Communication Technology (ICT) tackles several issues raised by this pandemic and significantly improves the quality of services in the health care sector. This paper presents an ICT-assisted scientometric analysis of infectious diseases, namely, airborne, food & waterborne, fomite-borne, sexually transmitted illnesses, and vector-borne illnesses. It assesses the international research status of this field in terms of citation structure, prolific journals, and country contributions. It has used the CiteSpace tool to address the visualization needs and in-depth insights of scientific literature to pinpoint core hotspots, research frontiers, emerging research areas, and ICT trends. The research finding reveals that mobile apps, telemedicine, and artificial intelligence technologies have greater scope to reduce the threats of infectious diseases. COVID-19, influenza, HIV, and malaria viruses have been identified as research hotspots whereas COVID-19, contact tracing applications, security and privacy concerns about users' data are the recent challenges in this field that need to address. The United States has produced higher research output in all domains of infectious diseases. Furthermore, it explores the co-occurrence network analysis and intellectual landscape of each domain of infectious diseases. It provides potential research directions and insightful clues to researchers and the academic fraternity for further research.

Multiscale Model of Antiviral Timing, Potency, and Heterogeneity Effects on an Epithelial Tissue Patch Infected by SARS-CoV-2

We extend our established agent-based multiscale computational model of infection of lung tissue by SARS-CoV-2 to include pharmacokinetic and pharmacodynamic models of remdesivir. We model remdesivir treatment for COVID-19; however, our methods are general to other viral infections and antiviral therapies. We investigate the effects of drug potency, drug dosing frequency, treatment initiation delay, antiviral half-life, and variability in cellular uptake and metabolism of remdesivir and its active metabolite on treatment outcomes in a simulated patch of infected epithelial tissue. Non-spatial deterministic population models which treat all cells of a given class as identical can clarify how treatment dosage and timing influence treatment efficacy. However, they do not reveal how cell-to-cell variability affects treatment outcomes. Our simulations suggest that for a given treatment regime, including cell-to-cell variation in drug uptake, permeability and metabolism increase the likelihood of uncontrolled infection as the cells with the lowest internal levels of antiviral act as super-spreaders within the tissue. The model predicts substantial variability in infection outcomes between similar tissue patches for different treatment options. In models with cellular metabolic variability, antiviral doses have to be increased significantly (>50% depending on simulation parameters) to achieve the same treatment results as with the homogeneous cellular metabolism.

Integrative lymph node-mimicking models created with biomaterials and computational tools to study the immune system

The lymph node (LN) is a vital organ of the lymphatic and immune system that enables timely detection, response, and clearance of harmful substances from the body. Each LN comprises of distinct substructures, which host a plethora of immune cell types working in tandem to coordinate complex innate and adaptive immune responses. An improved understanding of LN biology could facilitate treatment in LN-associated pathologies and immunotherapeutic interventions, yet at present, animal models, which often have poor physiological relevance, are the most popular experimental platforms. Emerging biomaterial engineering offers powerful alternatives, with the potential to circumvent limitations of animal models, for in-depth characterization and engineering of the lymphatic and adaptive immune system. In addition, mathematical and computational approaches, particularly in the current age of big data research, are reliable tools to verify and complement biomaterial works. In this review, we first discuss the importance of lymph node in immunity protection followed by recent advances using biomaterials to create in vitro/vivo LN-mimicking models to recreate the lymphoid tissue microstructure and microenvironment, as well as to describe the related immuno-functionality for biological investigation. We also explore the great potential of mathematical and computational models to serve as in silico supports. Furthermore, we suggest how both in vitro/vivo and in silico approaches can be integrated to strengthen basic patho-biological research, translational drug screening and clinical personalized therapies. We hope that this review will promote synergistic collaborations to accelerate progress of LN-mimicking systems to enhance understanding of immuno-complexity.

Modeling transmission of pathogens in healthcare settings

PURPOSE OF REVIEW

Mathematical, statistical, and computational models provide insight into the transmission mechanisms and optimal control of healthcare-associated infections. To contextualize recent findings, we offer a summative review of recent literature focused on modeling transmission of pathogens in healthcare settings.

RECENT FINDINGS

The COVID-19 pandemic has led to a dramatic shift in the modeling landscape as the healthcare community has raced to characterize the transmission dynamics of SARS-CoV-2 and develop effective interventions. Inequities in COVID-19 outcomes have inspired new efforts to quantify how structural bias impacts both health outcomes and model parameterization. Meanwhile, developments in the modeling of methicillin-resistant Staphylococcus aureus, Clostridioides difficile, and other nosocomial infections continue to advance. Machine learning continues to be applied in novel ways, and genomic data is being increasingly incorporated into modeling efforts.

SUMMARY

As the type and amount of data continues to grow, mathematical, statistical, and computational modeling will play an increasing role in healthcare epidemiology. Gaps remain in producing models that are generalizable to a variety of time periods, geographic locations, and populations. However, with effective communication of findings and interdisciplinary collaboration, opportunities for implementing models for clinical decision-making and public health decision-making are bound to increase.

A multi-method approach to modeling COVID-19 disease dynamics in the United States

In this paper, we proposed a multi-method modeling approach to community-level spreading of COVID-19 disease. Our methodology was composed of interconnected age-stratified system dynamics models in an agent-based modeling framework that allowed for a granular examination of the scale and severity of disease spread, including metrics such as infection cases, deaths, hospitalizations, and ICU usage. Model parameters were calibrated using an optimization technique with an objective function to minimize error associated with the cumulative cases of COVID-19 during a training period between March 15 and October 31, 2020. We outlined several case studies to demonstrate the model's state- and local-level projection capabilities. We further demonstrated how model outcomes could be used to evaluate perceived levels of COVID-19 risk across different localities using a multi-criteria decision analysis framework. The model's two, three, and four week out-of-sample projection errors varied on a state-by-state basis, and generally increased as the out-of-sample projection period was extended. Additionally, the prediction error in the state-level projections was generally due to an underestimation of cases and an overestimation of deaths. The proposed modeling approach can be used as a virtual laboratory to investigate a wide range of what-if scenarios and easily adapted to future high-consequence public health threats.

COVID-19 virtual patient cohort reveals immune mechanisms driving disease outcomes

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 indicate 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 that was mediated by increased IL-6 and reduced type I IFN signalling. Together, these findings identify biomarkers driving the development of severe COVID-19 and support early interventions aimed at reducing inflammation.

AUTHOR SUMMARY

Understanding of how the immune system responds to SARS-CoV-2 infections is critical for improving diagnostic and treatment approaches. Identifying which immune mechanisms lead to divergent outcomes can be clinically difficult, and experimental models and longitudinal data are only beginning to emerge. In response, we developed a mechanistic, mathematical and computational model of the immunopathology of COVID-19 calibrated to and validated against a broad set of experimental and clinical immunological data. To study the drivers of severe COVID-19, we used our model to expand a cohort of virtual patients, each with realistic disease dynamics. Our results identify key processes that regulate the immune response to SARS-CoV-2 infection in virtual patients and suggest viable therapeutic targets, underlining the importance of a rational approach to studying novel pathogens using intra-host models.