Abstract
Tackling antimicrobial resistance requires the development of new drugs and vaccines. Artificial intelligence (AI) assisted computational approaches offer an alternative to the traditionally empirical drug and vaccine discovery pipelines. In this mini review, we focus on the increasingly important role that AI now plays in the development of vaccines and provide the reader with the methods used to identify candidate vaccine candidates for selected multi-drug resistant bacteria.
1 Introduction
Increased life expectancy in the developed world in the 20th century can be attributed to several factors – sanitation infrastructure and clean water, increased food security, population-based healthcare systems, mass vaccination programs for the most prevalent infectious diseases of childhood and the use of antibiotics. The discovery of penicillin in 1928 by Alexander Fleming and its purification by Howard Florey and Ernst Chain in 1940 ushered in the ‘antibiotic age’ and laid the foundation for exploring the potential of a plethora of novel antimicrobials (Hutchings et al., 2019; Lima et al., 2020; Vila et al., 2020). Currently, it takes about 12 years to get a drug to the market for public use and the process is hugely expensive, with “median development costs for a new antibiotic exceeding $1 billion and sponsor costs of ~$350m to complete post-approval work and manufacture of the compound during its first 10 years on the market” (Wouters et al., 2020). However, not long after the first clinical use of penicillin, micro-organisms were observed to acquire antibiotic resistance through several different mechanisms (Christaki et al., 2020; Huemer et al., 2020; Larsen et al., 2022). The World Health Organization (WHO) has highlighted the priorities for tackling drug resistance in bacteria, viruses, parasites, and fungi, which requires a globally coordinated multi-sector approach (Tacconelli et al., 2018). Antimicrobial resistance has undeniably been aggravated by i) the extensive use of broad-spectrum antibiotics in agriculture, veterinary and medical practices, ii) in self-medication, iii) with poor diagnostics, and iv) with a heavy patient load on healthcare systems (Sulis et al., 2022). Antibiotic resistance is also spreading rapidly in the environment due to contamination of water bodies through the discharge of untreated domestic sewage, hospital wastewater, effluents from the antibiotic manufacturing units, open defecation, and mass bathing. These contaminated water bodies may lead to the massive growth of resistant bacteria and act as a pool for antibiotic resistance transfer (Davies and Davies, 2010; Perry et al., 2016; Hutchings et al., 2019; Christaki et al., 2020). Prevailing antibiotic resistance has led to a rapidly diminishing pool of bioactive compounds and plausible solutions to combat the challenges posed by pathogen resistance include i) the modification of existing antibiotic classes (repurposing), ii) exploring new structural classes, and iii) the development of prophylactic vaccines. Recent antimicrobial development regimes use resistance gene(s) screening, whole genome(s) sequencing, and correct pathogen detection approaches (Vila et al., 2020; Wang et al., 2022).
Increased antibiotic resistance in micro-organisms, specifically amongst bacteria, has stimulated the development of novel antimicrobials and vaccines using Artificial Intelligence (AI), Machine Learning (ML) and Neural Networks (NN) (Dalsass et al., 2019; Stokes et al., 2020; David et al., 2021; Sahayasheela et al., 2022). The AI-based learning techniques can be categorized into three classes: 1) supervised learning, where the prediction model learns from the previous examples of classification, 2) unsupervised learning, where the prediction model learns by exploring different patterns in the training data, and 3) reinforcement learning, where the prediction model learns by implementing a scheme of operations depending upon reward sequences and penalty. In general, development of an AI-based prediction model requires data pre-processing followed by encoding, feature extraction, training/learning from the features, and testing, validation and evaluation on unseen data with the help of different AI platforms available to the developer. Some of the widely used AI platforms include TensorFlow, Google AI, Microsoft Azure, OpenAI, amongst many. TensorFlow is an open-source ML platform that offers a broad range of AI algorithms to build and design deep learning models from training data of interest. Google AI is a division of Google, focusing on artificial intelligence through the Google Cloud AI platform that hosts several AI tools armored through generative AI (for prototyping and testing generative AI models), vertex AI (to create, train, test, monitor, tune, and deploy ML and AI models), natural language AI (derive insights from unstructured text using Google machine learning), translation AI (multilingual with fast, dynamic machine translation), vision and video AI (detect objects, understand text, videos and more), and so on. The Google AI platform is another extensive suite of open-source AI algorithms with a fast and easy implementation to build used defined prediction models. Microsoft Azure is a comprehensive set of cloud services that offers solutions for AI analytics through its AI tools and frameworks, equipped with multi-layered data protection. OpenAI is an AI research and deployment driven company focusing on benefiting humanity through artificial general intelligence, controlled through a unique capped-profit model, and it delivers very powerful models (e.g. Generative Pre-trained Transformer 4, GPT-4) that are trained to generate ‘human-like’ text and to enable multi-domain applications. ChatGPT and DALL-E2 are among the famous models developed by OpenAI. OpenAI is a potential game-changing state-of-the-art AI platform with the potential to translate languages, write articles and answer operator questions. An AI platform can enable users to develop their own deep leaning models and to utilize pre-trained models for their specific research problems. Trained prediction models can be evaluated with different evaluation matrices that include mean absolute error (MSE), root mean square error (RMSE), various statistics derived from confusion matrix, accuracy, precision, area under curve (AUC), receiver operating (ROC) curve, etc. The prediction model can be implemented for practical purposes after the desired accuracy - as defined by the different evaluation matrices - is achieved (Wang et al., 2019; Yang et al., 2019; Jiang et al., 2020).
The use of AI computational approaches validated fast-tracked novel drugs, drug repurposing and vaccine designs during the COVID-19 pandemic, along with developing infection diagnostics (Shrock et al., 2020; DeGrace et al., 2022; Thomas et al., 2022). AI assisted prediction of B-cell and T-cell epitopes and novel vaccine candidates is rigorously driving immuno-informatics-based approaches towards developing refined AI- and ML-based prediction servers. AI systems reason to find the microbial components that are unlikely to mutate or alter, to guarantee that a vaccine remains effective for a long duration. Computational analysis aided with ML algorithms have played a pivotal role in vaccine development. For example, AI-based approaches can provide structural and molecular insights on SARS-CoV2 viruses and predict the viral components that can trigger potentially protective immune responses and interpret experimental findings. By combining data from multiple experimental and real-world sources, AI can monitor the genetic changes (mutations) in the SARS-CoV2 genome over time to maximize future vaccine efficacy (Waltz, 2020). Different AI and ML techniques have already demonstrated their potential in diverse healthcare and biomedicine related fields, for example by accelerating the discovery of novel antimicrobials in a cost-effective manner, reducing expenses on equipment, synthesis, and human resources. Some well-known AI/ML techniques include support vector machine (SVM), logistic regression (LR), random forest (RF), and different types of neural networks, including multi-layer perceptron (MLP), recurrent neural networks (RNN), convolutional neural networks (CNN) and deep neural networks (DNN), amongst others (Wang et al., 2019; Jiang et al., 2020; Stokes et al., 2020; Vila et al., 2020; Sahayasheela et al., 2022; Thomas et al., 2022). The SVM algorithms implement a supervised statistical leaning approach for linear and non-linear data classification and regression analysis. The LR algorithms utilize standard logistic (sigmoid), and probability (log-odds) functions derived from the explanatory variables to predict the outcome variables. The RF algorithms implement an extensive multiple decision tree approach to learn from the training data and perform the prediction. These algorithms utilize bagging and feature randomness to generate a random forest of decision trees. The MLP algorithms implement a completely connected feed-forward artificial neural network with at least three hidden layers along with an input layer and an output layer. The flow of information passes from input layer to output layers through hidden layers, controlled by an activation function. The RNN algorithms are the advanced version of the MLP algorithms where the flow of information can also occur in a cyclical manner in the hidden layer to impart recurrent learning from the input layer. Likewise, the CNN algorithms are regularized MLP algorithms. The regularization nullifies the possibility of over fitting of data during training due to fully connected layers in the MLP. The CNN algorithms implement convolutions functions instead of matrix multiplication functions. The DNN algorithms broadly cover MLP, RNN, CNN, and several other neural network algorithms. The neural network that implements multiple hidden layers can be categorized as DNN. It should be noted that the list of different AI techniques mentioned above is not exhaustive and providing their fundamental details are beyond the scope of this mini-review.
It is worth mentioning also that AI and ML are intricately related (as both are supervised learning), but conceptually different. AI is a broad field of computer science focused on creating machines or systems that can perform tasks requiring human intelligence, such as problem-solving, reasoning, and understanding natural language. ML is a subset of AI that focuses on the development (training, fine tuning, testing, and deployment) of task-orientated prediction models. ML involves training machines to learn from data and make predictions or decisions without explicit programming. In essence, ML is a technique used within the broader field of AI to achieve ‘intelligent behaviour’. Hereon in, we will use the broader term AI to embrace the diverse subfields of AI and avoid any ambiguity and inconsistency. The field of AI is anticipated to flourish in coming years to become a vital tool to combat microbial infectious diseases. Considering the principles of AI based techniques, the focus of this mini-review is to provide an overview of current AI practices in the discovery and development of vaccines.
2 AI in vaccine development
The 21st century has seen unparalleled and innovative growth and development of cutting-edge technologies to benefit society (Jaffee et al., 2017; Vetrano et al., 2022). AI and ML technologies have made significant contributions to the healthcare and medicine sectors, with the market expected to expand to $45.2 billion by 2025 (Kruk et al., 2018; Ahmed et al., 2020). Recent advances in high throughput experimental techniques have generated a considerable amount of ‘big data’ in the healthcare and biomedical sectors. These data sets fuel AI to deliver highly accurate projections and predictions in the fields of vaccine development and drug discovery. Notably, ~40% of companies engaged in drug discovery are using various AI techniques to identify drug targets and for novel drug design (Lee et al., 2022; Zeng et al., 2022; Blanco-González et al., 2023).
The emergence of new infectious pathogens and increased antimicrobial resistance amongst existing pathogens has accelerated AI-assisted vaccine development in recent years (Thomas et al., 2022). The availability of high throughput genomic and proteomic data garnered from various infectious diseases could serve as a catalyst for developing reliable AI driven predictive models. Vaccine design benefits from an AI-assisted better understanding of the pathogen infection cycle at the genetic, molecular, and cellular levels (Ong and He, 2022; Goodswen et al., 2023).
Identification of potential antigen(s) is the important step for vaccine development. Conventional experimental approaches for antigen detection were laborious and time intensive and the arrival of Reverse Technology (RV) has revolutionized the field of vaccine development. RV is a computationally assisted genome-based approach of vaccine design that circumvents the necessity of developing bacterial cultures to prioritize vaccine targets. The analysis of pathogen genomes with computational bioinformatics has benefitted the screening of potential vaccine candidates significantly. Recent advances in RV techniques are assisted by the incorporation of modern AI techniques at various stages of vaccine development, and these AI based models are beginning to expedite the discovery and optimization of potential vaccine candidates. The contribution of AI computational tools to the development of SARS-CoV-2 vaccines has demonstrated the potential of AI for vaccine development against different microbial pathogens. One of the major challenges for AI is to accurately identify the potential antigens that can trigger host immune responses from amongst the thousands of pathogen components. In the following section we discuss the relevance of AI in vaccine development, its checkpoints, and potential solutions.
2.1 Reverse vaccinology and newer AI-based methods
In general, most vaccines in use today induce antibody-mediated immunity, and they include live-attenuated vaccines, inactivated vaccines, messenger RNA vaccines, toxoid vaccines, recombinant and conjugate vaccines, viral vector vaccines, etc. Identification of the protein/peptide antigen that stimulates immunity is the first step in vaccine design (Schubert-Unkmeir and Christodoulides, 2013; Guimaraes et al., 2015; Hardt et al., 2016; Vetter et al., 2018) and AI methods can deliver reliable antigen identification in a time efficient manner and with improved accuracy. Vaxijen was one of the first AI driven prediction methods for antigen identification, and it assumed that antigenicity was inherently encoded in the protein sequence and could be captured directly through the chemical properties of the amino acid residues. The prediction model was trained on the chemical properties of known bacterial antigens to deliver highly reliable and robust results for antigen identification and quantification (Doytchinova and Flower, 2007).
Affordable and fast sequencing technologies have made microbial whole genome sequences available widely and publicly. The first successful implementation of RV resulted in the eventual development of the meningitis vaccine Bexsero/4CMenB, which includes the antigens Neisseria Heparin Binding Antigen (NHBA, previously known as Genome-derived Neisseria Antigen (GNA) 2132), factor H binding protein, (fHBP or lipoprotein (LP)2086, previously known as GNA1870) and Neisserial adhesin A (NadA, previously known as GNA1994). In addition, GNA1030 and GNA2091 were selected because they also induced protective immunity and these were fused to NHBA and fHBP, respectively, which enhanced immune responses to the individual antigens (Pizza et al., 2000; Masignani et al., 2019; Deghmane and Taha, 2022). Bexsero is currently the only RV-developed bacterial vaccine that has been introduced into the routine immunization schedules for children. It should be stressed that the development of Bexsero vaccine did not include direct assistance of modern-day AI techniques but laid the foundation of incorporating various in silico tools into a RV based vaccine development pipeline. The development of Bexsero is a noteworthy achievement in vaccinology, independent from contemporary AI technologies.
Computational analysis and screening of several different pathogen genomes and proteomes is being used broadly to identify potential vaccine candidates using RV (Ong and He, 2022; Thomas et al., 2022). Exploring host-pathogen interactions at the molecular level and implementation of genomic and proteomics approaches should provide novel insights into the mechanisms of acquiring protective immunity and assist in developing next-generation vaccines. Several computational methods/pipelines have been developed since the introduction of RV and these use various AI techniques (Hardt et al., 2016; Vetter et al., 2018; Bradley et al., 2019; Yang et al., 2019; Ong and He, 2022; Thomas et al., 2022) (Table 1).
Table 1
| Method | AI Approach | Availability | Remarks/Availability/Pros and Cons | Reference |
|---|---|---|---|---|
| VaxiJen3 | XGB, RF, kNN | Webserver | Implemented auto-cross covariance transformation of protein sequences and utilizes physicochemical properties of proteins without using alignment for prediction of candidate vaccine subunits. Pros: fast and easy to use; option to select target organism Cons: batch processing is limited to 100 sequences https://www.ddg-pharmfac.net/vaxijen3 | (Ong et al., 2021) |
| PanRV | LR, SVM, RF | Standalone | Integrative computational pipeline that utilizes microbial pangenome to identify potential vaccine candidates. Pros: available as standalone; pangenome approach; broad-spectrum and species-specific target identification. Cons: computationally intensive; difficult to implement for non-experts. https://sourceforge.net/projects/panrv2 | (Naz et al., 2019) |
| ReVac | LR, SVM, NN, | Standalone | Implement several tools for protein feature prediction and scoring by a redundancy-based approach and perform Pangenome analysis for vaccine design. Pros: standalone with possibility of parallel implementation; time efficient Cons: dependence on several third-party tools makes it difficult to have a smoothly running installed version of ReVac. https://github.com/admelloGithub/ReVac-package | (D’Mello et al., 2019) |
| Vaxign-ML | SVM, LR, RF, kNN, XGB | Standalone/Webserver | Prediction framework for rational vaccine design, and epitope prediction and analysis. Pros: time efficient and easy to use; available as standalone as well as server; batch processing on standalone version. Cons: trained on limited dataset of bacterial antigens. https://github.com/VIOLINet/Vaxign2-django | (Ong et al., 2020) |
| Antigenic | RF | Standalone/Webserver | Utilizes physico-chemical properties of proteins to predict protective antigen and antibody responses. Pros: available as standalone as well as server; batch processing on standalone version; extensive benchmarking with state-of-the-art methods. Cons: very limited documentation and user manual. https://github.com/srautonu/AntigenPredictor | (Rahman et al., 2019) |
| iVAX | LR, SVM | Webserver | Predicts epitope-based candidate vaccines from genome sequences by integrating molecular interactions, T-cell receptor, conserved regions, etc. Pros: integrates 6 different algorithms for reliable prediction; demonstrated application on several pathogens; easy to use. Cons: commercial tool https://epivax.com/ivax-vaccine-design | (Moise et al., 2015) |
A summary of AI-based state of the art methods/pipelines for identifying potential vaccine candidates.
LR, logistic regression; SVM, support vector machine; NN, neural networks; RF, random forest; kNN, k-nearest neighbor; XGB, extreme gradient boost; DA-PLS, discriminant analysis by partial least square.
The pace of development in vaccine-informatics requires that emerging novel technologies are integrated with the existing state-of-the-art methods/pipelines for improved accuracy in antigen selection and optimization. However, this integration is ongoing and yet to be achieved.
2.2 Success stories of AI assisted vaccine development against multidrug resistant bacteria
Recent advances of AI techniques, especially several variants of ML with an ability to rationally learn from the features derived from large volumes of data, have resulted in several applications in medicine and healthcare (Yang et al., 2019; Stokes et al., 2020; Melo et al., 2021; Sahayasheela et al., 2022; Thomas et al., 2022). We are potentially entering a technologically advanced, efficient, and productive era of vaccine development, supported with generous global finance. The fast-tracked development of various COVID-19 mRNA vaccines is irrefutable proof-of-concept for AI-assisted vaccine development against different pathogens (Polack et al., 2020; Feikin et al., 2022; Zheng et al., 2022). Several studies have used the AI driven methods described in Table 1 to try and develop bacterial vaccines. For example, in silico identification of 22 membrane proteins as potential antigens in the Helicobacter pylori proteome was done using AI approaches (Rahman et al., 2020). Likewise, Acinetobacter baumannii was studied with the help of AI based methods, and an outer membrane protein named FilF (a putative pilus assembly protein), was proposed and experimentally validated as a potential vaccine candidate (Singh et al., 2016). In a separate study of 33 A. baumannii genomes, AI driven RV methods identified candidates to develop vaccines to protect against infection with antibiotic resistance strains (Chiang et al., 2015). Computational identification and characterization of T-cell epitopes in Mycobacterium spp has also been reported. The immunoinformatics of Mycobacterium tuberculosis (Mtb) aided by several AI based methods/tools, identified immunogenic epitopes with the potential for inclusion in candidate vaccines for testing in follow up in vitro studies (Panigada et al., 2002; Hossain et al., 2017; Das et al., 2021). In Table 2, we summarize studies that have used AI-RV platforms to develop vaccines against major multidrug resistant bacteria.
Table 2
| Bacteria | Description | Experimental laboratory validation | Reference |
|---|---|---|---|
| Pseudomonas aeruginosa | 52 antigens (31 known + 21 unknown) identified from genome-wide screening; known proteins vital for pathogenicity and maintenance. Individual modules from different AI-assisted pipelines were used for initial screening. | Yes | (Bianconi et al., 2018) |
| Proposed recombinant OprF (maintenance of cell structure, membrane permeability, adhesion, virulence, and biofilm formation) as a vaccine candidate. Minimal implementation of AI-assisted computational methods. | Yes | (Bahey-El-Din et al., 2020) | |
| Assessed combined PcrV, Opr1, Hcp1 vaccine; capable of inducing better protection. Several statistical and bioinformatics tools were used at different stages. | Yes | (Yang et al., 2017) | |
| Proposed chimeric vaccine with recombinant PcrV; induce a Th17 immune response and broad protection. Mainly experimental study with minor assistance of computational tools at initial stages for sequence screening. | Yes | (Wang et al., 2020) | |
| Streptococcus pneumoniae | Analysis of 151 proteins from clinical pneumococcal strains to identify 13 promising vaccine candidates (highly conserved); possibility for a broad-spectrum vaccine. AI-assisted computational methods were used to analyze protein sequence divergence and convergence. | Yes | (Argondizzo et al., 2015) |
| PCV20 (Pfizer) includes 13-valent pneumococcal conjugate vaccine + 7 new serotypes; well tolerated in adults between 18 and 49 years old. Different regression models were used for serotype-specificity. | Yes | (Klein et al., 2021) | |
| Combination of pneumococcal histidine triad protein D (PhtD) and Pneumolysin (dPly) by GlaxoSmithKline, for 6–12 weeks-old infants, immunogenic and well tolerated in clinical trials. Various regression models were used for statistical analysis at different stages of the study. | Yes | (Hammitt et al., 2019) | |
| Klebsiella pneumoniae | Selected 15 outer membrane proteins through computational methods, 4 vaccine constructs, one selected for molecular docking analysis with different (HLA) alleles. The candidate proteins were screened through different AI-assisted tools (e.g. LIPOP, TatP, SignalP) for transmembrane domains (HMMTOP and TMHMM), and for cellular localization (PSORTb and CELLO). | No | (Solanki et al., 2021a) |
| Vaccine constructed based on MHC binding capabilities; CusC (copper silver efflux system) and FepB (transport of siderophore enterobactin) targeted. Different AI-assisted bioinformatics tools (e.g. Bacterial Pan Genome Analysis Tool, protein BLAST) were used to analyze proteome of K. pneumoniae. PanRV platform was used for candidate prioritizing. | No | (Mehmood et al., 2020) | |
| Escherichia coli | Uro-Vaxom – a lyophilized mixture of membrane proteins from 18 uropathogenic E. coli; safe, well tolerated, and effective; used in over 40 countries globally. Minimal implementation of AI-assisted computational methods at different stages. | Yes | (Brodie et al., 2020) |
| OmpA identified as promising vaccine candidate; characteristics and function in localization and pathogenesis; confers protection in adult and neonatal mice. AI-assisted computational methods were used to screen candidate sequences. | Yes | (Gu et al., 2018) | |
| EatA + EtpA + Yghj antigens induced serum and mucosal immune responses in a human experimental Enterotoxigenic E. coli challenge. Various machine learning based tools were used for comparative genomics, and identification of potential surface molecules. | Yes | (Chakraborty et al., 2018) | |
| Staphylococcus aureus | VSA30 – a multi-epitope vaccine based on 30 epitopes; initial screening of 7290 proteomes; high antigenicity and low allergenicity. Various machine learning based tools were used for identification of potential surface molecules, lipoprotein signal peptide detection, and protein localization. Vaxign platform was used adhesion analysis. | No | (Solanki et al., 2021b) |
| SA4Ag – a multi-epitope vaccine targeting capsular polysaccharides (CPs) and virulence-associated surface proteins; safe, tolerable, and efficient among human participants in trials. Mainly experimental study with minor assistance of computational tools at initial stages for sequence screening. | Yes | (Begier et al., 2017) | |
| Recombinant 5-antigen (ClfA + MntC + Hla + SpA + SEB) vaccine; formulated and assessed for its immunogenicity in animal models. Implementation of AI-assisted computational methods at different stages. | Yes | (Zeng et al., 2020) | |
| Acinetobacter baumannii | Recombinant OmpA vaccine; 4200 A. baumannii genomes; capable of producing high levels of IgG; enhances Type 2 immune responses. AI-assisted computational methods were used for detection of exposed proteins, epitope prediction, and virulence and interatomic analysis. | No | (McConnell and Martin-Galiano, 2021) |
| Analysis of 21 strains to identify conserved antigens; 15 proteins selected; LptE with best antigenicity LptE involved in generation of lipopolysaccharides and pathogenesis. Several AI-assisted computational methods were implemented for conserved antigen identification, prediction of sub-cellular localization and protein solubility, B-cell epitope prediction, etc. Vaxign was used for evaluation of antigenicity. | No | (Beiranvand et al., 2021) | |
| Small protein A (SmpA) and phospholipase D (PLD); immunogenic for a subunit vaccine formulation, producing positive responses in protective efficacy. Limited implementation of AI-assisted computational methods at different stages. | Yes | (Li et al., 2017) |
A summary of AI powered RV platforms to develop vaccines against multidrug resistant bacteria.
The journey of vaccine development using traditional approaches has been very successful against several viral and bacterial pathogens. However, efficacious vaccines are yet to be developed for several infectious diseases, owing to the high rate of mutagenesis and sequence variability, antigenic complexity, and pathogen persistence. The lack of efficient vaccines against tuberculosis, and several viral pathogens including herpes simplex virus, respiratory syncytial virus, and human immunodeficiency virus, present some serious challenges to AI-ML models applied to vaccine development. Implementation of novel AI techniques along with the advances in nucleic acid and viral vectors could revolutionize vaccine development programs.
3 Conclusions and prospectus
The WHO asserts that antimicrobial resistance (AMR) is one of the top 10 global threats to human health and development. In 2022, at least 1.27 million deaths per year are directly attributable to AMR (Murray et al., 2022) and by 2050, 10 million deaths are estimated per annum from infections caused by AMR bacteria, with economic healthcare-associated costs in the trillions of dollars (USD). Vaccines represent a prophylactic strategy to combat AMR, although it is worth mentioning that vaccine development against certain pathogens is hampered by a lack of understanding of the mechanisms of infection and of how immunity develops. The accelerated development of COVID-19 vaccines has shown how state-of-the-art biomedical technologies can serve society during pandemic crises. It is inevitable that new and developing AI and ML techniques will be used to develop vaccines for infections caused by many viruses, bacteria and parasites that have proved particularly intractable. Implementation of advanced AI techniques in research dates to the start of 21st century. Undoubtedly, the development of AlphaFold (Higgins, 2021; Jumper et al., 2021; Binder et al., 2022; Varadi et al., 2022), a deep ML-based method for protein structure prediction, and the discovery of halicin, a potent antibiotic compound identified with a deep learning approach (Stokes et al., 2020) has imparted a belief amongst scientific communities of the potential of AI driven methods and technologies.
In summary, the integration of AI in vaccine development represents a transformative leap forward, expediting the process, enhancing precision, and broadening our understanding of infectious diseases. Whilst AI will significantly streamline the development of vaccines, we emphasize that these technologies are not a panacea. The integration of AI should always complement and not replace, rigorous ‘wet’ laboratory experimentation and animal and human trials, which remain indispensable steps in ensuring vaccine safety, efficacy, and are essential for regulatory approval. Nevertheless, as we continue to harness the power of AI-driven tools, the future holds great promise for the rapid creation of effective vaccines, particularly for emerging biological threats. We predict that AI assisted research and innovation in antimicrobial and vaccine development programs will ultimately improve global human health and increase life expectancy.
Statements
Author contributions
RahulK: Writing – original draft, Writing – review & editing. RK: Writing – review & editing. MC: Writing – review & editing.
Funding
The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.
Acknowledgments
RahulK is thankful to Biotechnology Research Center, Technology Innovation Institute, Abu Dhabi, UAE for its support and resources.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The author MC declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.
Publisher’s note
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Summary
Keywords
artificial intelligence, machine learning, vaccine, bacteria, antimicrobial resistance
Citation
Kaushik R, Kant R and Christodoulides M (2023) Artificial intelligence in accelerating vaccine development - current and future perspectives. Front. Bacteriol. 2:1258159. doi: 10.3389/fbrio.2023.1258159
Received
13 July 2023
Accepted
25 September 2023
Published
09 October 2023
Volume
2 - 2023
Edited by
Sandra Sousa, Universidade do Porto, Portugal
Reviewed by
Daniel Alford Powell, University of Arizona, United States
Updates
Copyright
© 2023 Kaushik, Kant and Christodoulides.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Myron Christodoulides, mc4@soton.ac.uk
Disclaimer
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