How Computational Immunology has Redefined Drug Development

Raisa karnik

As developments in machine learning and mathematical modeling have transformed finance, agriculture, and manufacturing, healthcare has faced similar changes. Thanks to new practices of applying statistical modeling to artificial intelligence via computers, disease detection has become more efficient and accurate. This has given rise to the field of computational immunology, defined as a discipline that uses mathematical models and statistical techniques to understand patterns behind immune systems and effectively develop drugs. New methods of sequencing genomes and understanding proteomics –the study of protein structures and functions– have increased the amount of data available for research, which scientists can study to glean more information about immune responses. By analyzing the patterns between disease progression and drug responses, scientists can develop vaccines that efficiently combat a variety of viruses while maintaining their efficacy and minimizing harmful side effects. Computational immunology has allowed for more accurate mapping of diseases, leading to a better understanding of the biology of the immune system and the subsequent vaccine development.  

Diseases and Drug Development

While vaccines have successfully curbed the fatality of infectious diseases, the mutability of viruses prevent vaccines from being completely effective, as vaccines are specific to the pathogens they seek to target. Take influenza, whose high mutability means that immune systems struggle to recognize each new version of the virus, requires an annual shot. Thus, highly mutable viruses require the development of new vaccines to combat new pathogens.

However, in an article published by the National Academy of Engineering, Chakraborty and Trout state that machine learning can be used to model areas of a virus’s protein structure that are especially vulnerable to immune attacks, therefore killing the virus while preventing it from mutating. Additionally, combining an understanding of the immune system, as an environment that interacts with other biological structures, with machine learning, which utilizes data from animal models, can help us create molecular designs that effectively induce antibodies and T cells to extract reliable reactions from the immune system. Using mathematical modeling in this way also has the potential to address the different immune responses based on differing human genotypes as well as predict “pandemic-causing viruses” and act accordingly.

Even the manufacturing of vaccines is seeing advancements thanks to machine learning. Developing vaccines “at large scale in a reliable, robust, and cost-effective way” is crucial to success, and automated systems lend themselves to contemporary manufacturing processes. Currently, the individual steps of vaccine development are done separately with little integration. With autonomous sensing and the ability to leverage reinforcement learning, robots can perform complicated tasks with greater accuracy. Through robots and automated manufacturing pieces, machine learning can assist robotics to develop vaccines substantially.

Neural Networks and Immunology

Using machine learning to model a network of genes and proteins within a biological system can help determine commonalities in drug responses and predict effective medicine. In an article discussing computational systems biology, Yue and Dutta explain that static network models “predict the potential interactions among drug molecules and target proteins through the shared components,” allowing for connections to be found between diseases and suggest the repurposing of drugs. Here, machine learning is used to predict drug target interaction (DTI). DTI networks identify uniquely valuable combinations of drugs and their potential side effects.

Chart describes the interactions between drugs and proteins. Defining drug similarity with DTI aids in repurposing the drug to similar proteins. Image sourced from https://www.nature.com/articles/s41540-022-00247-4

Supervised machine learning is able to take in known molecular interactions, drug chemical structures, and larger scale data to predict DTIs. Taken a step further, machine learning models that group instances into three or more classes can use drug similarities to predict the therapeutic class of a drug. Additionally, deep learning neural networks that incorporate multiple training layers are effective in extracting and analyzing patterns. 

While supervised machine learning uses labeled input to classify data into labeled output, unsupervised learning uses models to find patterns in unlabeled input data. Combining both into semi-supervised machine learning can identify patterns in interactions with very little known labeled data, resulting in a model based on the entire dataset as opposed to just a portion of data used to train the model.

 Dynamic modeling, on the contrary, analyzes the “organism’s response” to their environment and changing factors. Differential equations are used to understand the behavior of gene networks, metabolization rate, transcription, and other processes under drug administration. For example, differential equations have been used to understand the process of infection from HIV by modeling the interactions between HIV and the immune response to optimize existing and active T cells in treatment. Dynamic modeling recognizes patterns in processes, allowing for the identification of abnormal events. 

Algorithms are also used to monitor drug dosage. Approximated with linear models, simulations show how to maintain drug concentration at a certain level and balance the side effects. By capitalizing on clinical data, dynamic modeling uses mathematical algorithms to monitor disease progression and develop drugs with optimized individual performance and minimal drawbacks. 

Applications

Most recently, computational immunology has been used to better understand COVID-19, as mathematical models were best posed to parse the mass of data scientists received. In a feature from Nature Medicine, the authors explained how a “set of equations to model the evolution of seasonal influenza virus from year to year” became the basis for developing universal vaccines against viruses with high mutability. Later, scientists began using agent based models, a computational practice that involves simulations to study the interactions between autonomous agents. In healthcare, agent based modeling simulates the actions of entities in a system to determine how they are influenced by their environment. The cells become the agents, allowing scientists to understand the average behavior of T-cells when responding to infections. With the use of computational models, the time it takes to process the necessary data to develop vaccines has shortened considerably. In terms of COVID-19, this process allowed scientists to narrow down the antibodies that combated COVID-19 in a recovering patient and identify a blocking epitope. 

Since the boom of artificial intelligence in recent years, the field of medicine has successfully benefitted from incorporating machine learning into healthcare to identify diseases and extract medical data from records. Computational immunology has likewise used mathematical modeling to redefine disease analysis and drug development. By using machine learning and neural networks, scientists can identify patterns in disease progression and drug molecules, paving the way for improved therapeutic function. While in silico research, performed on a computer or via computer simulation, cannot– and should not– replace clinical experiments, modeling can supplement trials and organize large amounts of data to better highlight notable patterns in immune responses.   

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