Artificial Intelligence in Vaccine and Drug Design

Next-Generation Vaccine Development with Nanomaterials: Recent Advances, Possibilities, and Challenges

Nanomaterials are becoming important tools for vaccine development owing to their tunable and adaptable nature. Unique properties of nanomaterials afford opportunities to modulate trafficking through various tissues, complement or augment adjuvant activities, and specify antigen valency and display. This versatility has enabled recent work designing nanomaterial vaccines for a broad range of diseases, including cancer, inflammatory diseases, and various infectious diseases. Recent successes of nanoparticle vaccines during the coronavirus disease 2019 (COVID-19) pandemic have fueled enthusiasm further. In this review, the most recent developments in nanovaccines for infectious disease, cancer, inflammatory diseases, allergic diseases, and nanoadjuvants are summarized. Additionally, challenges and opportunities for clinical translation of this unique class of materials are discussed.

The dawn of a new Era: mRNA vaccines in colorectal cancer immunotherapy

Colorectal cancer (CRC) is a typical cancer that accounts for 10% of all new cancer cases annually and nearly 10% of all cancer deaths. Despite significant progress in current classical interventions for CRC, these traditional strategies could be invasive and with numerous adverse effects. The poor prognosis of CRC patients highlights the evident and pressing need for more efficient and targeted treatment. Novel strategies regarding mRNA vaccines for anti-tumor therapy have also been well-developed since the successful application for the prevention of COVID-19. mRNA vaccine technology won the 2023 Nobel Prize in Physiology or Medicine, signaling a new direction in human anti-cancer treatment: mRNA medicine. As a promising new immunotherapy in CRC and other multiple cancer treatments, the mRNA vaccine has higher specificity, better efficacy, and fewer side effects than traditional strategies. The present review outlines the basics of mRNA vaccines and their advantages over other vaccines and informs an available strategy for developing efficient mRNA vaccines for CRC precise treatment. In the future, more exploration of mRNA vaccines for CRC shall be attached, fostering innovation to address existing limitations.

Designing, cloning and simulation studies of cancer/testis antigens based multi-epitope vaccine candidates against cutaneous melanoma: An immunoinformatics approach

Background

Melanoma is the most fatal kind of skin cancer. Among its various types, cutaneous melanoma is the most prevalent one. Melanoma cells are thought to be highly immunogenic due to the presence of distinct tumor-associated antigens (TAAs), which includes carcinoembryonic antigen (CEA), cancer/testis antigens (CTAs) and neo-antigens. The CTA family is a group of antigens that are only expressed in malignancies and testicular germ cells.

Methods

We used integrative framework and systems-level analysis to predict potential vaccine candidates for cutaneous melanoma involving epitopes prediction, molecular modeling and molecular docking to cross-validate the binding affinity and interaction between potential vaccine agents and major histocompatibility molecules (MHCs) followed by molecular dynamics simulation, immune simulation and in silico cloning.

Results

In this study, three cancer/testis antigens were targeted for immunotherapy of cutaneous melanoma. Among many CTAs that were studied for their expression in primary and malignant melanoma, NY-ESO-1, MAGE1 and SSX2 antigens are most prevalent in cutaneous melanoma. Cytotoxic and Helper epitopes were predicted, and the finest epitopes were shortlisted based on binding score. The vaccine construct was composed of the four epitope-rich domains of antigenic proteins, an appropriate adjuvant, His tag and linkers. This potential multi-epitope vaccine was further evaluated in terms of antigenicity, allergencity, toxicity and other physicochemical properties. Molecular interaction estimated through protein-protein docking unveiled good interactions characterized by favorable binding energies. Molecular dynamics simulation ensured the stability of docked complex and the predicted immune response through immune simulation revealed elevated levels of antibodies titer, cytokines, interleukins and immune cells (NK, DC and MA) population.

Conclusion

The findings indicate that the potential vaccine candidates could be effective immunotherapeutic agents that modify the treatment strategies of cutaneous melanoma.

A Predictive Model of Vaccine Reactogenicity Using Data from an In Vitro Human Innate Immunity Assay System

A primary concern in vaccine development is safety, particularly avoiding an excessive immune reaction in an otherwise healthy individual. An accurate prediction of vaccine reactogenicity using in vitro assays and computational models would facilitate screening and prioritization of novel candidates early in the vaccine development process. Using the modular in vitro immune construct model of human innate immunity, PBMCs from 40 healthy donors were treated with 10 different vaccines of varying reactogenicity profiles and then cell culture supernatants were analyzed via flow cytometry and a multichemokine/cytokine assay. Differential response profiles of innate activity and cell viability were observed in the system. In parallel, an extensive adverse event (AE) dataset for the vaccines was assembled from clinical trial data. A novel reactogenicity scoring framework accounting for the frequency and severity of local and systemic AEs was applied to the clinical data, and a machine learning approach was employed to predict the incidence of clinical AEs from the in vitro assay data. Biomarker analysis suggested that the relative levels of IL-1B, IL-6, IL-10, and CCL4 have higher predictive importance for AE risk. Predictive models were developed for local reactogenicity, systemic reactogenicity, and specific individual AEs. A forward-validation study was performed with a vaccine not used in model development, Trumenba (meningococcal group B vaccine). The clinically observed Trumenba local and systemic reactogenicity fell on the 26th and 93rd percentiles of the ranges predicted by the respective models. Models predicting specific AEs were less accurate. Our study presents a useful framework for the further development of vaccine reactogenicity predictive models.

Role of vaccination in patients with human monkeypox virus and its cardiovascular manifestations

Human monkeypox, caused by the monkeypox virus (MPXV), is an emerging infectious disease with the potential for human-to-human transmission and diverse clinical presentations. While generally considered milder than smallpox, it can lead to severe cardiovascular complications. The virus primarily spreads through contact with infected animals or through human-to-human transmission. Cardiovascular involvement in human monkeypox is rare but has been associated with myocarditis, pericarditis, arrhythmias, and even fulminant myocardial infarction. Vaccination plays a crucial role in preventing and controlling monkeypox, but the eradication of smallpox has left global populations vulnerable. This review explores the cardiovascular manifestations of human monkeypox, the role of vaccination in disease prevention, and the importance of continued research and development of effective vaccines to protect against this emerging infectious threat. The global impact of monkeypox outbreaks, particularly on vulnerable populations, further highlights the importance of understanding and addressing this disease.

WT1 Cancer Vaccine in Advanced Pancreatic Cancer: A Systematic Review

Advanced pancreatic cancer is one of the prominent contributors to cancer-related mortality globally. Chemotherapy, especially gemcitabine, is generally used for the treatment of advanced pancreatic cancer. Despite the treatment, the fatality rate for advanced pancreatic cancer is alarmingly high. Thus, the dire need for better treatment alternatives has drawn focus to cancer vaccinations. The Wilms tumor gene (WT1), typically associated with Wilms tumor, is found to be excessively expressed in some cancers, such as pancreatic cancer. This characteristic feature is harvested to develop cancer vaccines against WT1. This review aims to systematically summarize the clinical trials investigating the efficacy and safety of WT1 vaccines in patients with advanced pancreatic cancer. An extensive literature search was conducted on databases Medline, Web of Science, ScienceDirect, and Google Scholar using the keywords "Advanced pancreatic cancer," "Cancer vaccines," "WT1 vaccines," and "Pulsed DC vaccines," and the results were exclusively studied to construct this review. WT1 vaccines work by introducing peptides from the WT1 protein to trigger an immune response involving cytotoxic T lymphocytes via antigen-presenting cells. Upon activation, these lymphocytes induce apoptosis in cancer cells by specifically targeting those with increased WT1 levels. WT1 vaccinations, which are usually given in addition to chemotherapy, have demonstrated clinically positive results and minimal side effects. However, there are several challenges to their widespread use, such as the immunosuppressive nature of tumors and heterogeneity in expression. Despite these limitations, the risk-benefit profile of cancer vaccines is encouraging, especially for the WT1 vaccine in the treatment of advanced pancreatic cancer. Considering the fledgling status of their development, large multicentric, variables-matched, extensive analysis across diverse demographics is considered essential.

Development and use of machine learning algorithms in vaccine target selection

Computer-aided discovery of vaccine targets has become a cornerstone of rational vaccine design. In this article, I discuss how Machine Learning (ML) can inform and guide key computational steps in rational vaccine design concerned with the identification of B and T cell epitopes and correlates of protection. I provide examples of ML models, as well as types of data and predictions for which they are built. I argue that interpretable ML has the potential to improve the identification of immunogens also as a tool for scientific discovery, by helping elucidate the molecular processes underlying vaccine-induced immune responses. I outline the limitations and challenges in terms of data availability and method development that need to be addressed to bridge the gap between advances in ML predictions and their translational application to vaccine design.

Combating COVID-19 Crisis using Artificial Intelligence (AI) Based Approach: Systematic Review

BACKGROUND

SARS-CoV-2, the unique coronavirus that causes COVID-19, has wreaked damage around the globe, with victims displaying a wide range of difficulties that have encouraged medical professionals to look for innovative technical solutions and therapeutic approaches. Artificial intelligence-based methods have contributed a significant part in tackling complicated issues, and some institutions have been quick to embrace and tailor these solutions in response to the COVID-19 pandemic's obstacles. Here, in this review article, we have covered a few DL techniques for COVID-19 detection and diagnosis, as well as ML techniques for COVID-19 identification, severity classification, vaccine and drug development, mortality rate prediction, contact tracing, risk assessment, and public distancing. This review illustrates the overall impact of AI/ML tools on tackling and managing the outbreak.

PURPOSE

The focus of this research was to undertake a thorough evaluation of the literature on the part of Artificial Intelligence (AI) as a complete and efficient solution in the battle against the COVID-19 epidemic in the domains of detection and diagnostics of disease, mortality prediction and vaccine as well as drug development.

METHODS

A comprehensive exploration of PubMed, Web of Science, and Science Direct was conducted using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) regulations to find all possibly suitable papers conducted and made publicly available between December 1, 2019, and August 2023. COVID-19, along with AI-specific words, was used to create the query syntax.

RESULTS

During the period covered by the search strategy, 961 articles were published and released online. Out of these, a total of 135 papers were chosen for additional investigation. Mortality rate prediction, early detection and diagnosis, vaccine as well as drug development, and lastly, incorporation of AI for supervising and controlling the COVID-19 pandemic were the four main topics focused entirely on AI applications used to tackle the COVID-19 crisis. Out of 135, 60 research papers focused on the detection and diagnosis of the COVID-19 pandemic. Next, 19 of the 135 studies applied a machine-learning approach for mortality rate prediction. Another 22 research publications emphasized the vaccine as well as drug development. Finally, the remaining studies were concentrated on controlling the COVID-19 pandemic by applying AI AI-based approach to it.

CONCLUSION

We compiled papers from the available COVID-19 literature that used AI-based methodologies to impart insights into various COVID-19 topics in this comprehensive study. Our results suggest crucial characteristics, data types, and COVID-19 tools that can aid in medical and translational research facilitation.

Recent advances in mRNA cancer vaccines: meeting challenges and embracing opportunities

Since the successful application of messenger RNA (mRNA) vaccines in preventing COVID-19, researchers have been striving to develop mRNA vaccines for clinical use, including those exploited for anti-tumor therapy. mRNA cancer vaccines have emerged as a promising novel approach to cancer immunotherapy, offering high specificity, better efficacy, and fewer side effects compared to traditional treatments. Multiple therapeutic mRNA cancer vaccines are being evaluated in preclinical and clinical trials, with promising early-phase results. However, the development of these vaccines faces various challenges, such as tumor heterogeneity, an immunosuppressive tumor microenvironment, and practical obstacles like vaccine administration methods and evaluation systems for clinical application. To address these challenges, we highlight recent advances from preclinical studies and clinical trials that provide insight into identifying obstacles associated with mRNA cancer vaccines and discuss potential strategies to overcome them. In the future, it is crucial to approach the development of mRNA cancer vaccines with caution and diligence while promoting innovation to overcome existing barriers. A delicate balance between opportunities and challenges will help guide the progress of this promising field towards its full potential.

mRNA therapeutics: New vaccination and beyond

The idea of mRNA therapy had been conceived for decades before it came into reality during the Covid-19 pandemic. The mRNA vaccine emerges as a powerful and general tool against new viral infections, largely due to its versatility and rapid development. In addition to prophylactic vaccines, mRNA technology also offers great promise for new applications as a versatile drug modality. However, realizing the conceptual potential faces considerable challenges, such as minimal immune stimulation, high and long-term expression, and efficient delivery to target cells and tissues. Here we review the applications of mRNA-based therapeutics, with emphasis on the innovative design and future challenges/solutions. In addition, we also discuss the next generation of mRNA therapy, including circular mRNA and self-amplifying RNAs. We aim to provide a conceptual overview and outlook on mRNA therapeutics beyond prophylactic vaccines.

Sharing Data With Shared Benefits: Artificial Intelligence Perspective

Artificial intelligence (AI) and data sharing go hand in hand. In order to develop powerful AI models for medical and health applications, data need to be collected and brought together over multiple centers. However, due to various reasons, including data privacy, not all data can be made publicly available or shared with other parties. Federated and swarm learning can help in these scenarios. However, in the private sector, such as between companies, the incentive is limited, as the resulting AI models would be available for all partners irrespective of their individual contribution, including the amount of data provided by each party. Here, we explore a potential solution to this challenge as a viewpoint, aiming to establish a fairer approach that encourages companies to engage in collaborative data analysis and AI modeling. Within the proposed approach, each individual participant could gain a model commensurate with their respective data contribution, ultimately leading to better diagnostic tools for all participants in a fair manner.

Hybrid neural network approaches to predict drug-target binding affinity for drug repurposing: screening for potential leads for Alzheimer's disease

Alzheimer's disease (AD) is a neurodegenerative disease that primarily affects elderly individuals. Recent studies have found that sigma-1 receptor (S1R) agonists can maintain endoplasmic reticulum stress homeostasis, reduce neuronal apoptosis, and enhance mitochondrial function and autophagy, making S1R a target for AD therapy. Traditional experimental methods are costly and inefficient, and rapid and accurate prediction methods need to be developed, while drug repurposing provides new ways and options for AD treatment. In this paper, we propose HNNDTA, a hybrid neural network for drug-target affinity (DTA) prediction, to facilitate drug repurposing for AD treatment. The study combines protein-protein interaction (PPI) network analysis, the HNNDTA model, and molecular docking to identify potential leads for AD. The HNNDTA model was constructed using 13 drug encoding networks and 9 target encoding networks with 2506 FDA-approved drugs as the candidate drug library for S1R and related proteins. Seven potential drugs were identified using network pharmacology and DTA prediction results of the HNNDTA model. Molecular docking simulations were further performed using the AutoDock Vina tool to screen haloperidol and bromperidol as lead compounds for AD treatment. Absorption, distribution, metabolism, excretion, and toxicity (ADMET) evaluation results indicated that both compounds had good pharmacokinetic properties and were virtually non-toxic. The study proposes a new approach to computer-aided drug design that is faster and more economical, and can improve hit rates for new drug compounds. The results of this study provide new lead compounds for AD treatment, which may be effective due to their multi-target action. HNNDTA is freely available at https://github.com/lizhj39/HNNDTA.

Viral informatics: bioinformatics-based solution for managing viral infections

Several new viral infections have emerged in the human population and establishing as global pandemics. With advancements in translation research, the scientific community has developed potential therapeutics to eradicate or control certain viral infections, such as smallpox and polio, responsible for billions of disabilities and deaths in the past. Unfortunately, some viral infections, such as dengue virus (DENV) and human immunodeficiency virus-1 (HIV-1), are still prevailing due to a lack of specific therapeutics, while new pathogenic viral strains or variants are emerging because of high genetic recombination or cross-species transmission. Consequently, to combat the emerging viral infections, bioinformatics-based potential strategies have been developed for viral characterization and developing new effective therapeutics for their eradication or management. This review attempts to provide a single platform for the available wide range of bioinformatics-based approaches, including bioinformatics methods for the identification and management of emerging or evolved viral strains, genome analysis concerning the pathogenicity and epidemiological analysis, computational methods for designing the viral therapeutics, and consolidated information in the form of databases against the known pathogenic viruses. This enriched review of the generally applicable viral informatics approaches aims to provide an overview of available resources capable of carrying out the desired task and may be utilized to expand additional strategies to improve the quality of translation viral informatics research.

Toll-like receptor (TLR) agonists as a driving force behind next-generation vaccine adjuvants and cancer therapeutics

Until recently, the development of new human adjuvants was held back by a poor understanding of their mechanisms of action. The field was revolutionized by the discovery of the toll-like receptors (TLRs), innate immune receptors that directly or indirectly are responsible for detecting pathogen-associated molecular patterns (PAMPs) and respond to them by activating innate and adaptive immune pathways. Hundreds of ligands targeting various TLRs have since been identified and characterized as vaccine adjuvants. This work has important implications not only for the development of vaccines against infectious diseases but also for immuno-therapies against cancer, allergy, Alzheimer's disease, drug addiction and other diseases. Each TLR has its own specific tissue localization and downstream gene signalling pathways, providing researchers the opportunity to precisely tailor adjuvants with specific immune effects. TLR agonists can be combined with other TLR or alternative adjuvants to create combination adjuvants with synergistic or modulatory effects. This review provides an introduction to the various classes of TLR adjuvants and their respective signalling pathways. It provides an overview of recent advancements in the TLR field in the past 2-3 years and discusses criteria for selecting specific TLR adjuvants based on considerations, such as disease mechanisms and correlates of protection, TLR immune biasing capabilities, route of administration, antigen compatibility, new vaccine technology platforms, and age- and species-specific effects.