Quantitative modeling of immune responses

In Silico Studies to Support Vaccine Development

The progress that has been made in computer science positioned in silico studies as an important and well-recognized methodology in the drug discovery and development process. It has numerous advantages in terms of costs and also plays a huge impact on the way the research is conducted since it can limit the use of animal models leading to more sustainable research. Currently, human trials are already being partly replaced by in silico trials. EMA and FDA are both endorsing these studies and have been providing webinars and guidance to support them. For instance, PBPK modeling studies are being used to gather data on drug interactions with other drugs and are also being used to support clinical and regulatory requirements for the pediatric population, pregnant women, and personalized medicine. This trend evokes the need to understand the role of in silico studies in vaccines, considering the importance that these products achieved during the pandemic and their promising hope in oncology. Vaccines are safer than other current oncology treatments. There is a huge variety of strategies for developing a cancer vaccine, and some of the points that should be considered when designing the vaccine technology are the following: delivery platforms (peptides, lipid-based carriers, polymers, dendritic cells, viral vectors, etc.), adjuvants (to boost and promote inflammation at the delivery site, facilitating immune cell recruitment and activation), choice of the targeted antigen, the timing of vaccination, the manipulation of the tumor environment, and the combination with other treatments that might cause additive or even synergistic anti-tumor effects. These and many other points should be put together to outline the best vaccine design. The aim of this article is to perform a review and comprehensive analysis of the role of in silico studies to support the development of and design of vaccines in the field of oncology and infectious diseases. The authors intend to perform a literature review of all the studies that have been conducted so far in preparing in silico models and methods to support the development of vaccines. From this point, it was possible to conclude that there are few in silico studies on vaccines. Despite this, an overview of how the existing work could support the design of vaccines is described.

Novel methods for quantifying individual host response to infectious pathogens for genetic analyses

We propose two novel approaches for describing and quantifying the response of individual hosts to pathogen challenge in terms of infection severity and impact on host performance. The first approach is a direct extension of the methodology for estimating group tolerance (the change in performance with respect to changes in pathogen burden in a host population) to the level of individuals. The second approach aims to capture the dynamic aspects of individual resistance and tolerance over the entire time course of infections. In contrast to the first approach, which provides a means to disentangle host resistance from tolerance, the second approach focuses on the combined effects of both characteristics. Both approaches provide new individual phenotypes for subsequent genetic analyses and come with specific data requirements. In particular, both approaches rely on the availability of repeated performance and pathogen burden measurements of individuals over the time course of one or several episodes of infection. Consideration of individual tolerance also highlights some of the assumptions hidden within the concept of group tolerance, indicating where care needs to be taken in trait definition and measurement.

Systems biology in vaccine design

Vaccines are the most effective tools to prevent infectious diseases and to minimize their impact on humans or animals. Despite the successful development of vaccines that are able to elicit potent and protective immune responses, the majority of vaccines have been so far developed empirically and mechanistic events leading to protective immune responses are often poorly understood. This hampers the development of new prophylactic as well as therapeutic vaccines for infectious diseases and cancer. Biological correlates of immune‐mediated protection are currently based on standard readout such as antibody titres and ELISPOT assays. The development of successful vaccines for difficult settings, such as infectious agents leading to chronic infection (HIV, HCV. . .) or cancer, calls for novel ‘readout systems’ or ‘correlates’ of immune‐mediated protection that would reliably predict immune responses to novel vaccines in vivo. Systems biology offers a new approach to vaccine design that is based upon understanding the molecular network mobilized by vaccination. Systems vaccinology approaches investigate more global correlates of successful vaccination, beyond the specific immune response to the antigens administered, providing new methods for measuring early vaccine efficacy and ultimately generating hypotheses for understanding the mechanisms that underlie successful immunogenicity. Using functional genomics, specific molecular signatures of individual vaccine can be identified and used as predictors of vaccination efficiency. The immune response to vaccination involves the coordinated induction of master transcription factors that leads to the development of a broad, polyfunctional and persistent immune response integrating all effector cells of the immune systems.

Dynamic models of immune responses: what is the ideal level of detail?

Background

One of the goals of computational immunology is to facilitate the study of infectious diseases. Dynamic modeling is a powerful tool to integrate empirical data from independent sources, make novel predictions, and to foresee the gaps in the current knowledge. Dynamic models constructed to study the interactions between pathogens and hosts' immune responses have revealed key regulatory processes in the infection.

Optimum complexity and dynamic modeling

We discuss the usability of various deterministic dynamic modeling approaches to study the progression of infectious diseases. The complexity of these models is dependent on the number of components and the temporal resolution in the model. We comment on the specific use of simple and complex models in the study of the progression of infectious diseases.

Conclusions

Models of sub-systems or simplified immune response can be used to hypothesize phenomena of host-pathogen interactions and to estimate rates and parameters. Nevertheless, to study the pathogenesis of an infection we need to develop models describing the dynamics of the immune components involved in the progression of the disease. Incorporation of the large number and variety of immune processes involved in pathogenesis requires tradeoffs in modeling.

A rationalized set of default postulates that permit a coherent description of the immune system amenable to computer modeling

This discussion delineates and rationalizes a set of postulates that permit a coherent understanding of immune function. Although analytical tools such as mathematics and computer modeling have become very popular, simulation and data mining in the absence of a conceptual framework cannot increase understanding. The goal of this essay is to provide the foundation for a discussion that has as its goal the formulation of an agreed upon set of default postulates. Such a set is required to guide the algorithms needed to analyze complex immune behavior.