As the world waits with bated breath for the COVID-19 vaccine, this article explores what data science holds for the future of vaccinology?
Theobjective of this article is to throw some light on the developments in modern vaccinology under the purview of data science technologies. It tries to answer some of the questions which garner more and more attention as the world looks at scientists and pharmaceutical companies racing to develop the vaccine(s) for Covid-19 pandemic — in record time. For example:
This is a review article with a focus on data science but with the goal of covering the essentials of vaccinology and related concepts. Therefore, before trying to progress towards the objective of reviewing the opportunities/accomplishments for data science in the modern vaccinology, a cursory look at the definitions of a few key terms in this field is necessary.
Vaccinologyis the branch of medicine concerned with the development of vaccines. The basic idea is to introduce a harmless entity into the body which triggers the immune response of the body against the disease-causing pathogen which persists in the body thus preventing future infection. Depending on the entity introduced into the body, there are three typesof vaccines.
Whole-pathogen vaccines (traditional) introduce a dead or weakened pathogen to elicit life long immunity. Subunit vaccines consist of only component(s) (or antigens) of the pathogen along with adjuvants. *Adjuvantsare substances to boost the required immunity response of a vaccine. It must be mentioned that adjuvants are subject to debate with respect to their safety. _Nucleic acid vaccines _are based on the approach which involves introducing the genetic material encoding the antigen against which immunity is desired. In a crude sense, the terms *vaccinology 1.0 and 2.0 could be attributed to the development of the first two types of vaccines, respectively.Vaccinology 3.0 or reverse vaccinology, _which is more recent, involves the process of antigen discovery enabled by the genome information of the pathogen. From the problem-solving point of view, Vaccinology 3.0 is similar to the first principles from Physics. It starts by gaining information about the epidemiology of vaccine candidates and modelling the host-pathogen interactions, effectively reducing the huge list of candidate vaccines.This is made possible by the explosion of the omics data; omics is used to refer to the data from (fancy!) disciplines viz., transcriptomics, proteomics, metabolomics, cytomics, immunomics, secretomics, surfomics, or interactomics. Enabled by this _Big Data, reverse vaccinology essentially becomes a top-down approach compared to the previous bottom-up and hypothesis-driven approaches. Consequently, the latest addition to the omics terms is *vaccinomics *defined as the study of vaccine-induced immune responses.With an understanding of these key terms, albeit at a high level and some background into the evolution of vaccine development, we can delve into the methodologies from data science which enables vaccinology 3.0. In order to do that effectively, let us list out the key challenges in vaccine development; which this new paradigm of reverse vaccinology aims to be effective in solving. And for each of these challenges, highlight how data science and big data technologies are leveraged.
There are a large number of issues concerning vaccine development and its delivery. For example, one of the societal issues being an increase in the anti-vax movements. The ageing population of the world is another problem. A very recent list of the top four current challenges from an article by the scientists from Mayo clinic is being discussed here.
The human body and its immune system are extremely complex with a large number of components. The current understanding of this system is far from complete even after considerable progress in recent decades. It is not possible for scientists to accurately predict the behaviour of the immune system for a particular vaccination and the related infection.This is where the data-based computing approaches of vaccinomics and systems biology provide the opportunity in filling the knowledge gaps. It is worthwhile to quote Donald Knuth, the famous American scientist and ACM Turing Award recipient, who has inspired multiple generations of computer scientists. Knuth is extremely optimistic about the scope of computational biology when he says (courtesy: Computer Literacy Bookshops, Inc):
“I can’t be as confident about computer science as I can about biology. Biology easily has 500 years of exciting problems to work on, it’s at that level.” — _**_Donald Knuth**.
This is an excerpt from his interview in 1993, and things have moved quite exponentially in the direction he anticipated. Computing hardware, cloud technologies and most importantly deep learning have opened up the floodgates of innovation in this area.
One of the principal underlying activities in the immune system is performed by the relatively autonomous and specialized cells called T-cells; which communicate with each other activated through their surface receptors. Recently, technology to measure (en masse) cell state, function and their products along with their gene encoding have become feasible. This essentially generates a large amount of data, which could be used to develop more holistic models of the immune system — something not possible traditionally. Thus it has given rise to the area of _systems immunology — _another playground for the data scientists. It also enables scientists to develop better adjuvants and improve the durability of immune responses.
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