Leveraging deep learning to improve vaccine design

Application of in-silico approaches in subunit vaccines: Overcoming the challenges of antigen and adjuvant development

Subunit vaccines are crucial in preventing modern diseases due to their safety, stability, and ability to elicit targeted immune responses. However, challenges in antigen and adjuvant design hinder their development. Recent advancements in in-silico approaches, including reverse vaccinology, structural vaccinology, and machine learning, have revolutionized vaccine development from empirical practices to rational design approaches. This review summarizes the transformative impact of in-silico approaches on subunit vaccine development. We address the challenges of antigen identification and designation, highlighting how advanced computational techniques are employed to accelerate antigen acquisition. We also examine the challenges in adjuvant discovery and illustrate how machine learning helps overcome these barriers. Finally, we explore potential future directions for subunit vaccines, highlighting the importance of combining computational methods with other technologies to tackle the challenges associated with subunit vaccine development.

Using machine learning for personalized prediction of longitudinal coronavirus disease 2019 vaccine responses in transplant recipients

The coronavirus disease 2019 pandemic has underscored the importance of vaccines, especially for immunocompromised populations like solid organ transplant recipients, who often have weaker immune responses. The purpose of this study was to compare deep learning architectures for predicting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine responses 12 months postvaccination in this high-risk group. Using data from 303 solid organ transplant recipients from a Canadian multicenter cohort, models were developed to forecast anti-receptor-binding domain antibody levels. The study compared traditional machine learning models-logistic regression, epsilon-support vector regression, random forest regressor, and gradient boosting regressor-and deep learning architectures, including long short-term memory (LSTM), recurrent neural networks, and a novel model, routed LSTM. This new model combines capsule networks with LSTM to reduce the need for large data sets. Demographic, clinical, and transplant-specific data, along with longitudinal antibody measurements, were incorporated into the models. The routed LSTM performed best, achieving a mean square error of 0.02 ± 0.02 and a Pearson correlation coefficient of 0.79 ± 0.24, outperforming all other models. Key factors influencing vaccine response included age, immunosuppression, breakthrough infection, body mass index, sex, and transplant type. These findings suggest that artificial intelligence could be a valuable tool in tailoring vaccine strategies, improving health outcomes for vulnerable transplant recipients.

Deep Learning-Assisted Diagnosis of Placenta Accreta Spectrum Using the DenseNet-121 Model: A Multicenter, Retrospective Study

To explore the diagnostic value of deep learning (DL) imaging based on MRI in predicting placenta accreta spectrum (PAS) in high-risk pregnant women. A total of 263 patients with suspected placenta accreta from Institution I and Institution II were retrospectively analyzed and divided into training (n = 170) and external verification sets (n = 93). Through imaging acquisition, feature extraction, and radiomic data processing, 15 radiomic features were used to train support vector machine (SVM), K-nearest neighbor (KNN), random forest (RF), light gradient boosting machine (LGBM), and DL models to predict PAS. The diagnostic performances of the models were evaluated in the training set using the area under the curve (AUC) and accuracy and further validated in the external verification set. Univariate and multivariate logistic regression analysis revealed that a history of cesarean section, placental thickness, and placenta previa were independent clinical risk factors for predicting PAS. Among machine learning (ML) models, SVM demonstrated the highest diagnostic power (AUC = 0.944), with an accuracy of 0.876. The diagnostic efficiency of the DL model was significantly better than that of other models, with an AUC of 0.956 (95% CI 0.931-0.981) in the training set and 0.863 (95% CI 0.816-0.910) in the external verification set. In terms of specificity, the DL model outperformed the ML models. The DL model based on MRI may demonstrate better performance in the diagnosis of PAS compared to traditional clinical models or ML radiomics models, as further confirmed by the external verification set.

Computational tools and data integration to accelerate vaccine development: challenges, opportunities, and future directions

The development of effective vaccines is crucial for combating current and emerging pathogens. Despite significant advances in the field of vaccine development there remain numerous challenges including the lack of standardized data reporting and curation practices, making it difficult to determine correlates of protection from experimental and clinical studies. Significant gaps in data and knowledge integration can hinder vaccine development which relies on a comprehensive understanding of the interplay between pathogens and the host immune system. In this review, we explore the current landscape of vaccine development, highlighting the computational challenges, limitations, and opportunities associated with integrating diverse data types for leveraging artificial intelligence (AI) and machine learning (ML) techniques in vaccine design. We discuss the role of natural language processing, semantic integration, and causal inference in extracting valuable insights from published literature and unstructured data sources, as well as the computational modeling of immune responses. Furthermore, we highlight specific challenges associated with uncertainty quantification in vaccine development and emphasize the importance of establishing standardized data formats and ontologies to facilitate the integration and analysis of heterogeneous data. Through data harmonization and integration, the development of safe and effective vaccines can be accelerated to improve public health outcomes. Looking to the future, we highlight the need for collaborative efforts among researchers, data scientists, and public health experts to realize the full potential of AI-assisted vaccine design and streamline the vaccine development process.

RosettaHDX: Predicting antibody-antigen interaction from hydrogen-deuterium exchange mass spectrometry data

High-throughput characterization of antibody-antigen complexes at the atomic level is critical for understanding antibody function and enabling therapeutic development. Hydrogen-deuterium exchange mass spectrometry (HDX-MS) enables rapid epitope mapping, but its data are too sparse for independent structure determination. In this study, we introduce RosettaHDX, a hybrid method that combines computational docking with differential HDX-MS data to enhance the accuracy of antibody-antigen complex models beyond what either method can achieve individually. By incorporating HDX data as both distance restraints and a scoring term in the RosettaDock algorithm, RosettaHDX successfully generated near-native models (interface root-mean square deviation ≤ 4 Å) for all 9 benchmark complexes examined, averaging 3.6 times more near-native models than Rosetta alone. Near-native models among the top 10 scoring were identified in 3/9 cases, compared to 1/9 with Rosetta alone. Additionally, we developed a predictive metric based on docking results with HDX restraints to identify allosteric peptides in HDX datasets.

Artificial Intelligence in Medical Affairs: A New Paradigm with Novel Opportunities

The advent of artificial intelligence (AI) revolutionizes the ways of working in many areas of business and life science. In Medical Affairs (MA) departments of the pharmaceutical industry AI holds great potential for positively influencing the medical mission of identifying and addressing unmet medical needs and care gaps, and fostering solutions that improve the egalitarian and unbiased access of patients to treatments worldwide. Given the essential position of MA in corporate interactions with various healthcare stakeholders, AI offers broad possibilities to support strategic decision-making and to pioneer novel approaches in medical stakeholder interactions. By analyzing data derived from the healthcare environment and by streamlining operations in medical content generation, AI advances data-based prioritization and strategy execution. In this review, we discuss promising AI-based solutions in MA that support the effective use of heterogenous information from observations of the healthcare environment, the enhancement of medical education, and the analysis of real-world data. For a successful implementation of such solutions, specific considerations partly unique to healthcare must be taken care of, for example, transparency, data privacy, healthcare regulations, and in predictive applications, explainability.

Designing intelligent bioorthogonal nanozymes: Recent advances of stimuli-responsive catalytic systems for biomedical applications

Bioorthogonal nanozymes have emerged as a potent tool in biomedicine due to their unique ability to perform enzymatic reactions that do not interfere with native biochemical processes. The integration of stimuli-responsive mechanisms into these nanozymes has further expanded their potential, allowing for controlled activation and targeted delivery. As such, intelligent bioorthogonal nanozymes have received more and more attention in developing therapeutic approaches. This review provides a comprehensive overview of the recent advances in the development and application of stimuli-responsive bioorthogonal nanozymes. By summarizing the design outlines for anchoring bioorthogonal nanozymes with stimuli-responsive capability, this review seeks to offer valuable insights and guidance for the rational design of these remarkable materials. This review highlights the significant progress made in this exciting field with different types of stimuli and the various applications. Additionally, it also examines the current challenges and limitations in the design, synthesis, and application of these systems, and proposes potential solutions and research directions. This review aims to stimulate further research toward the development of more efficient and versatile stimuli-responsive bioorthogonal nanozymes for biomedical applications.

Fostering Tomorrow: Uniting Artificial Intelligence and Social Pediatrics for Comprehensive Child Well-being

This comprehensive review explores the integration of artificial intelligence (AI) in the field of social pediatrics, emphasizing its potential to revolutionize child healthcare. Social pediatrics, a specialized branch within the discipline, focuses on the significant influence of societal, environmental, and economic factors on children's health and development. This field adopts a holistic approach, integrating medical, psychological, and environmental considerations. This review aims to explore the potential of AI in revolutionizing child healthcare from social pediatrics perspective. To achieve that, we explored AI applications in preventive care, growth monitoring, nutritional guidance, environmental risk factor prediction, and early detection of child abuse. The findings highlight AI's significant contributions in various areas of social pediatrics. Artificial intelligence's proficiency in handling large datasets is shown to enhance diagnostic processes, personalize treatments, and improve overall healthcare management. Notable advancements are observed in preventive care, growth monitoring, nutritional counseling, predicting environmental risks, and early child abuse detection. We find that integrating AI into social pediatric healthcare aims to enhance the effectiveness, accessibility, and equity of pediatric health services. This integration ensures high-quality care for every child, regardless of their social background. The study elucidates AI's multifaceted applications in social pediatrics, including natural language processing, machine learning algorithms for health outcome predictions, and AI-driven tools for health and environmental monitoring, collectively fostering a more efficient, informed, and responsive pediatric healthcare system.

A new era of antibody discovery: an in-depth review of AI-driven approaches

Given their high affinity and specificity for a range of macromolecules, antibodies are widely used in the treatment of autoimmune diseases, cancers, inflammatory diseases, and Alzheimer's disease (AD). Traditional experimental methods are time-consuming, expensive, and labor-intensive. Recent advances in artificial intelligence (AI) technologies provide complementary methods that can reduce the time and costs required for antibody design by minimizing failures and increasing the success rate of experimental tests. In this review, we scrutinize the plethora of AI-driven methodologies that have been deployed over the past 4 years for modeling antibody structures, predicting antibody-antigen interactions, optimizing antibody affinity, and generating novel antibody candidates. We also briefly address the challenges faced in integrating AI-based models with traditional antibody discovery pipelines and highlight the potential future directions in this burgeoning field.

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.

Next-Generation TB Vaccines: Progress, Challenges, and Prospects

Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), is a prevalent global infectious disease and a leading cause of mortality worldwide. Currently, the only available vaccine for TB prevention is Bacillus Calmette-Guérin (BCG). However, BCG demonstrates limited efficacy, particularly in adults. Efforts to develop effective TB vaccines have been ongoing for nearly a century. In this review, we have examined the current obstacles in TB vaccine research and emphasized the significance of understanding the interaction mechanism between MTB and hosts in order to provide new avenues for research and establish a solid foundation for the development of novel vaccines. We have also assessed various TB vaccine candidates, including inactivated vaccines, attenuated live vaccines, subunit vaccines, viral vector vaccines, DNA vaccines, and the emerging mRNA vaccines as well as virus-like particle (VLP)-based vaccines, which are currently in preclinical stages or clinical trials. Furthermore, we have discussed the challenges and opportunities associated with developing different types of TB vaccines and outlined future directions for TB vaccine research, aiming to expedite the development of effective vaccines. This comprehensive review offers a summary of the progress made in the field of novel TB vaccines.

Permafrost viremia and immune tweening

The immune system, an exquisitely regulated physiological system, utilizes a wide spectrum of soluble factors and multiple cell populations and subpopulations at diverse states of maturation to monitor and protect the organism against foreign organisms. Immune surveillance is ensured by distinguishing self-antigens from self-associated with non-self (e.g., viral) peptides presented by major histocompatibility complexes (MHC). Pathology is often identified as unregulated inflammatory responses (e.g., cytokine storm), or recognizing self as a non-self entity (i.e., auto-immunity). Artificial intelligence (AI), and in particular specific machine learning (ML) paradigms (e.g., Deep Learning [DL]) proffer powerful algorithms to better understand and more accurately predict immune responses, immune regulation and homeostasis, and immune reactivity to challenges (i.e., immune allostasis) by their intrinsic ability to interpret immune parameters, pathways and events by analyzing large amounts of complex data and drawing predictive inferences (i.e., immune tweening). We propose here that DL models play an increasingly significant role in better defining and characterizing immunological surveillance to ancient and novel virus species released by thawing permafrost.

Advances and perspectives in the development of vaccines against highly pathogenic bunyaviruses

Increased human activities around the globe and the rapid development of once rural regions have increased the probability of contact between humans and wild animals. A majority of bunyaviruses are of zoonotic origin, and outbreaks may result in the substantial loss of lives, economy contraction, and social instability. Many bunyaviruses require manipulation in the highest levels of biocontainment, such as Biosafety Level 4 (BSL-4) laboratories, and the scarcity of this resource has limited the development speed of vaccines for these pathogens. Meanwhile, new technologies have been created, and used to innovate vaccines, like the mRNA vaccine platform and bioinformatics-based antigen design. Here, we summarize current vaccine developments for three different bunyaviruses requiring work in the highest levels of biocontainment: Crimean-Congo Hemorrhagic Fever Virus (CCHFV), Rift Valley Fever Virus (RVFV), and Hantaan virus (HTNV), and provide perspectives and potential future directions that can be further explored to advance specific vaccines for humans and livestock.