Predicting the Severity of Disease Progression in COVID-19 at the Individual and Population Level: A Mathematical Model.

The impact of COVID-19 disease on health and economy has been global, and the magnitude of devastation is unparalleled in modern history. Any potential course of action to manage this complex disease requires the systematic and efficient analysis of data that can delineate the underlying pathogenesis. We have developed a mathematical model of disease progression to predict the clinical outcome, utilizing a set of causal factors known to contribute to COVID-19 pathology such as age, comorbidities, and certain viral and immunological parameters. Viral load and selected indicators of a dysfunctional immune response, such as cytokines IL-6 and IFNα which contribute to the cytokine storm and fever, parameters of inflammation D-Dimer and Ferritin, aberrations in lymphocyte number, lymphopenia, and neutralizing antibodies were included for the analysis. The model provides a framework to unravel the multi-factorial complexities of the immune response manifested in SARS-CoV-2 infected individuals. Further, this model can be valuable to predict clinical outcome at an individual level, and to develop strategies for allocating appropriate resources to manage severe cases at a population level.

Predicting the severity of disease progression in COVID-19 at the individual and population level: A mathematical model.

The impact of COVID-19 disease on health and economy has been global, and the magnitude of devastation is unparalleled in modern history. Any potential course of action to manage this complex disease requires the systematic and efficient analysis of data that can delineate the underlying pathogenesis. We have developed a mathematical model of disease progression to predict the clinical outcome, utilizing a set of causal factors known to contribute to COVID-19 pathology such as age, comorbidities, and certain viral and immunological parameters. Viral load and selected indicators of a dysfunctional immune response, such as cytokines IL-6 and IFNα, which contribute to the cytokine storm and fever, parameters of inflammation d-dimer and ferritin, aberrations in lymphocyte number, lymphopenia, and neutralizing antibodies were included for the analysis. The model provides a framework to unravel the multi-factorial complexities of the immune response manifested in SARS-CoV-2 infected individuals. Further, this model can be valuable to predict clinical outcome at an individual level, and to develop strategies for allocating appropriate resources to mitigate severe cases at a population level.

Failure Mode and Effects Analysis (FMEA) for Immunogenicity of Therapeutic Proteins.

Biotherapeutic drugs made by cell-based systems are revolutionizing the practice of medicine. The next generation of biotherapeutics include recombinant proteins, monoclonal antibodies, viral vector expressed proteins, and cell therapies. Immunogenicity associated adverse events is one of the major risks for these biologics. Accurate and precise measurement of the immunogenicity of biologics is a critical component during all phases of drug development. We have utilized the principles of Failure Mode and Effects Analysis (FMEA) in performing assessment of risk of immunogenicity. The multi-dimensional approach involves: i) listing all the potential risks by likelihood of occurrence and severity as part of quality target product profile. ii) ascribing the causes by identifying the risks at each stage of development. iii) predicting the effects. iv) determining the risk mitigation strategy. v) implementing a monitoring process. vi) developing templates for data collection. vii) timely reporting and. viii) life cycle management. FMEA is a continuous process that works throughout the lifecycle of the product or the process and keeps on getting updated with new insights and knowledge.