Computational and artificial intelligence-based methods for antibody development

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.

Structure-aware machine learning strategies for antimicrobial peptide discovery

Machine learning models are revolutionizing our approaches to discovering and designing bioactive peptides. These models often need protein structure awareness, as they heavily rely on sequential data. The models excel at identifying sequences of a particular biological nature or activity, but they frequently fail to comprehend their intricate mechanism(s) of action. To solve two problems at once, we studied the mechanisms of action and structural landscape of antimicrobial peptides as (i) membrane-disrupting peptides, (ii) membrane-penetrating peptides, and (iii) protein-binding peptides. By analyzing critical features such as dipeptides and physicochemical descriptors, we developed models with high accuracy (86-88%) in predicting these categories. However, our initial models (1.0 and 2.0) exhibited a bias towards α-helical and coiled structures, influencing predictions. To address this structural bias, we implemented subset selection and data reduction strategies. The former gave three structure-specific models for peptides likely to fold into α-helices (models 1.1 and 2.1), coils (1.3 and 2.3), or mixed structures (1.4 and 2.4). The latter depleted over-represented structures, leading to structure-agnostic predictors 1.5 and 2.5. Additionally, our research highlights the sensitivity of important features to different structure classes across models.

Emerging trends and therapeutic applications of monoclonal antibodies

Monoclonal antibodies (mAbs) are being used to prevent, detect, and treat a broad spectrum of malignancies and infectious and autoimmune diseases. Over the past few years, the market for mAbs has grown exponentially. They have become a significant part of many pharmaceutical product lines, and more than 250 therapeutic mAbs are undergoing clinical trials. Ever since the advent of hybridoma technology, antibody-based therapeutics were realized using murine antibodies which further progressed into humanized and fully human antibodies, reducing the risk of immunogenicity. Some of the benefits of using mAbs over conventional drugs include a drastic reduction in the chances of adverse reactions, interactions between drugs, and targeting specific proteins. While antibodies are very efficient, their higher production costs impede the process of commercialization. However, their cost factor has been improved by developing biosimilar antibodies, which are affordable versions of therapeutic antibodies. Along with biosimilars, innovations in antibody engineering have helped to design bio-better antibodies with improved efficacy than the conventional ones. These novel mAb-based therapeutics are set to revolutionize existing drug therapies targeting a wide spectrum of diseases, thereby meeting several unmet medical needs. In the future, mAbs generated by applying next-generation sequencing (NGS) are expected to become a powerful tool in clinical therapeutics. This article describes the methods of mAb production, pre-clinical and clinical development of mAbs, approved indications targeted by mAbs, and novel developments in the field of mAb research.

Prospects for the computational humanization of antibodies and nanobodies

To be viable therapeutics, antibodies must be tolerated by the human immune system. Rational approaches to reduce the risk of unwanted immunogenicity involve maximizing the 'humanness' of the candidate drug. However, despite the emergence of new discovery technologies, many of which start from entirely human gene fragments, most antibody therapeutics continue to be derived from non-human sources with concomitant humanization to increase their human compatibility. Early experimental humanization strategies that focus on CDR loop grafting onto human frameworks have been critical to the dominance of this discovery route but do not consider the context of each antibody sequence, impacting their success rate. Other challenges include the simultaneous optimization of other drug-like properties alongside humanness and the humanization of fundamentally non-human modalities such as nanobodies. Significant efforts have been made to develop in silico methodologies able to address these issues, most recently incorporating machine learning techniques. Here, we outline these recent advancements in antibody and nanobody humanization, focusing on computational strategies that make use of the increasing volume of sequence and structural data available and the validation of these tools. We highlight that structural distinctions between antibodies and nanobodies make the application of antibody-focused in silico tools to nanobody humanization non-trivial. Furthermore, we discuss the effects of humanizing mutations on other essential drug-like properties such as binding affinity and developability, and methods that aim to tackle this multi-parameter optimization problem.

Exploring the Potential of Artificial Intelligence as a Facilitating Tool for Formulation Development in Fluidized Bed Processor: a Comprehensive Review

This in-depth study looks into how artificial intelligence (AI) could be used to make formulation development easier in fluidized bed processes (FBP). FBP is complex and involves numerous variables, making optimization challenging. Various AI techniques have addressed this challenge, including machine learning, neural networks, genetic algorithms, and fuzzy logic. By integrating AI with experimental design, process modeling, and optimization strategies, intelligent systems for FBP can be developed. The advantages of AI in this context include improved process understanding, reduced time and cost, enhanced product quality, and robust formulation optimization. However, data availability, model interpretability, and regulatory compliance challenges must be addressed. Case studies demonstrate successful applications of AI in decision-making, process outcome prediction, and scale-up. AI can improve efficiency, quality, and cost-effectiveness in significant ways. Still, it is important to think carefully about data quality, how easy it is to understand, and how to follow the rules. Future research should focus on fully harnessing the potential of AI to advance formulation development in FBP.

Alrobaian M. Strengths and weaknesses of current and future prospects of artificial intelligence-mounted technologies applied in the development of pharmaceutical products and services

Starting from drug discovery, through research and development, to clinical trials and FDA approval, artificial intelligence (AI) plays a vital role in planning, developing, assessing modelling, and optimization of product attributes. In recent decades, machine-learning algorithms integrated into artificial neural networks, neuro-fuzzy logic and decision trees have been applied to tremendous domains related to drug formulation development. Optimized formulations were transformed from lab to market based on optimized properties derived from AI Technologies. Research and development in pharmaceutical industry rely upon computer-driven equipment and machine learning technology to extract data, perform simulations, modelling, and optimization to get optimum solutions. Merging AI technologies in various steps of pharmaceutical manufacture is a major challenge due to lack of in-house technologies. In silico studies based on artificial intelligence are widely applied as effective tools to screen the market needs of medications and pharmaceutical services through inspecting scientific literature and prioritizing medicines for specific illnesses or a particular patient. Specialized personnel who excel in scientific and data science with analytical knowledge are essential for transformation to smart manufacturing and offering services. However, privacy, cybersecurity, AI-dependent unemployment, and ownership rights of AI technologies require proper regulations to gain the benefits and minimize the drawbacks.

Tribulations and future opportunities for artificial intelligence in precision medicine

Upon a diagnosis, the clinical team faces two main questions: what treatment, and at what dose? Clinical trials' results provide the basis for guidance and support for official protocols that clinicians use to base their decisions. However, individuals do not consistently demonstrate the reported response from relevant clinical trials. The decision complexity increases with combination treatments where drugs administered together can interact with each other, which is often the case. Additionally, the individual's response to the treatment varies with the changes in their condition. In practice, the drug and the dose selection depend significantly on the medical protocol and the medical team's experience. As such, the results are inherently varied and often suboptimal. Big data and Artificial Intelligence (AI) approaches have emerged as excellent decision-making tools, but multiple challenges limit their application. AI is a rapidly evolving and dynamic field with the potential to revolutionize various aspects of human life. AI has become increasingly crucial in drug discovery and development. AI enhances decision-making across different disciplines, such as medicinal chemistry, molecular and cell biology, pharmacology, pathology, and clinical practice. In addition to these, AI contributes to patient population selection and stratification. The need for AI in healthcare is evident as it aids in enhancing data accuracy and ensuring the quality care necessary for effective patient treatment. AI is pivotal in improving success rates in clinical practice. The increasing significance of AI in drug discovery, development, and clinical trials is underscored by many scientific publications. Despite the numerous advantages of AI, such as enhancing and advancing Precision Medicine (PM) and remote patient monitoring, unlocking its full potential in healthcare requires addressing fundamental concerns. These concerns include data quality, the lack of well-annotated large datasets, data privacy and safety issues, biases in AI algorithms, legal and ethical challenges, and obstacles related to cost and implementation. Nevertheless, integrating AI in clinical medicine will improve diagnostic accuracy and treatment outcomes, contribute to more efficient healthcare delivery, reduce costs, and facilitate better patient experiences, making healthcare more sustainable. This article reviews AI applications in drug development and clinical practice, making healthcare more sustainable, and highlights concerns and limitations in applying AI.

Beyond Natural Immune Repertoires: Synthetic Antibodies

Synthetic antibody libraries, in which the antigen-binding sites are precisely designed, offer unparalleled precision in antibody engineering, exceeding the potential of natural immune repertoires and constituting a novel generation of research tools and therapeutics. Recent advances in artificial intelligence-driven technologies and their integration into synthetic antibody discovery campaigns hold the promise to further streamline and effectively develop antibodies. Here, we provide an overview of synthetic antibodies. Our associated protocol describes how to develop highly diverse and functional synthetic antibody phage display libraries.

Atomically accurate de novo design of single-domain antibodies

Despite the central role that antibodies play in modern medicine, there is currently no way to rationally design novel antibodies to bind a specific epitope on a target. Instead, antibody discovery currently involves time-consuming immunization of an animal or library screening approaches. Here we demonstrate that a fine-tuned RFdiffusion network is capable of designing de novo antibody variable heavy chains (VHH's) that bind user-specified epitopes. We experimentally confirm binders to four disease-relevant epitopes, and the cryo-EM structure of a designed VHH bound to influenza hemagglutinin is nearly identical to the design model both in the configuration of the CDR loops and the overall binding pose.

An integrative approach to protein sequence design through multiobjective optimization

With recent methodological advances in the field of computational protein design, in particular those based on deep learning, there is an increasing need for frameworks that allow for coherent, direct integration of different models and objective functions into the generative design process. Here we demonstrate how evolutionary multiobjective optimization techniques can be adapted to provide such an approach. With the established Non-dominated Sorting Genetic Algorithm II (NSGA-II) as the optimization framework, we use AlphaFold2 and ProteinMPNN confidence metrics to define the objective space, and a mutation operator composed of ESM-1v and ProteinMPNN to rank and then redesign the least favorable positions. Using the multistate design problem of the foldswitching protein RfaH as an in-depth case study, we show that the evolutionary multiobjective optimization approach leads to significant reduction in the bias and variance in RfaH native sequence recovery, compared to a direct application of ProteinMPNN. We suggest that this improvement is due to three factors: (i) the use of an informative mutation operator that accelerates the sequence space exploration, (ii) the parallel, iterative design process inherent to the genetic algorithm that improves upon the ProteinMPNN autoregressive sequence decoding scheme, and (iii) the explicit approximation of the Pareto front that leads to optimal design candidates representing diverse tradeoff conditions. We anticipate this approach to be readily adaptable to different models and broadly relevant for protein design tasks with complex specifications.

Physics-driven structural docking and protein language models accelerate antibody screening and design for broad-spectrum antiviral therapy

Therapeutic antibodies have become one of the most influential therapeutics in modern medicine to fight against infectious pathogens, cancer, and many other diseases. However, experimental screening for highly efficacious targeting antibodies is labor-intensive and of high cost, which is exacerbated by evolving antigen targets under selective pressure such as fast-mutating viral variants. As a proof-of-concept, we developed a machine learning-assisted antibody generation pipeline that greatly accelerates the screening and re-design of immunoglobulins G (IgGs) against a broad spectrum of SARS-CoV-2 coronavirus variant strains. These viruses infect human host cells via the viral spike protein binding to the host cell receptor angiotensin-converting enzyme 2 (ACE2). Using over 1300 IgG sequences derived from convalescent patient B cells that bind with spike's receptor binding domain (RBD), we first established protein structural docking models in assessing the RBD-IgG-ACE2 interaction interfaces and predicting the virus-neutralizing activity of each IgG with a confidence score. Additionally, employing Gaussian process regression (also known as Kriging) in a latent space of an antibody language model, we predicted the landscape of IgGs' activity profiles against individual coronaviral variants of concern. With functional analyses and experimental validations, we efficiently prioritized IgG candidates for neutralizing a broad spectrum of viral variants (wildtype, Delta, and Omicron) to prevent the infection of host cells in vitro and hACE2 transgenic mice in vivo. Furthermore, the computational analyses enabled rational redesigns of selective IgG clones with single amino acid substitutions at the RBD-binding interface to improve the IgG blockade efficacy for one of the severe, therapy-resistant strains - Delta (B.1.617). Our work expedites applications of artificial intelligence in antibody screening and re-design even in low-data regimes combining protein language models and Kriging for antibody sequence analysis, activity prediction, and efficacy improvement, in synergy with physics-driven protein docking models for antibody-antigen interface structure analyses and functional optimization.

Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody-Antigen Interactions

This perspective sheds light on the transformative impact of recent computational advancements in the field of protein therapeutics, with a particular focus on the design and development of antibodies. Cutting-edge computational methods have revolutionized our understanding of protein-protein interactions (PPIs), enhancing the efficacy of protein therapeutics in preclinical and clinical settings. Central to these advancements is the application of machine learning and deep learning, which offers unprecedented insights into the intricate mechanisms of PPIs and facilitates precise control over protein functions. Despite these advancements, the complex structural nuances of antibodies pose ongoing challenges in their design and optimization. Our review provides a comprehensive exploration of the latest deep learning approaches, including language models and diffusion techniques, and their role in surmounting these challenges. We also present a critical analysis of these methods, offering insights to drive further progress in this rapidly evolving field. The paper includes practical recommendations for the application of these computational techniques, supplemented with independent benchmark studies. These studies focus on key performance metrics such as accuracy and the ease of program execution, providing a valuable resource for researchers engaged in antibody design and development. Through this detailed perspective, we aim to contribute to the advancement of antibody design, equipping researchers with the tools and knowledge to navigate the complexities of this field.

Sparks of function by de novo protein design

Information in proteins flows from sequence to structure to function, with each step causally driven by the preceding one. Protein design is founded on inverting this process: specify a desired function, design a structure executing this function, and find a sequence that folds into this structure. This 'central dogma' underlies nearly all de novo protein-design efforts. Our ability to accomplish these tasks depends on our understanding of protein folding and function and our ability to capture this understanding in computational methods. In recent years, deep learning-derived approaches for efficient and accurate structure modeling and enrichment of successful designs have enabled progression beyond the design of protein structures and towards the design of functional proteins. We examine these advances in the broader context of classical de novo protein design and consider implications for future challenges to come, including fundamental capabilities such as sequence and structure co-design and conformational control considering flexibility, and functional objectives such as antibody and enzyme design.

Artificial Intelligence for Drug Discovery: Are We There Yet?

Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development while reducing costs and animal experiments. AI is transforming drug discovery, as indicated by increasing interest from investors, industrial and academic scientists, and legislators. Successful drug discovery requires optimizing properties related to pharmacodynamics, pharmacokinetics, and clinical outcomes. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small-molecule drugs. AI technologies, such as generative chemistry, machine learning, and multiproperty optimization, have enabled several compounds to enter clinical trials. The scientific community must carefully vet known information to address the reproducibility crisis. The full potential of AI in drug discovery can only be realized with sufficient ground truth and appropriate human intervention at later pipeline stages.

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.

Computational simulations of bispecific T cell engagers by a multiscale model

The use of bispecific antibodies as T cell engagers can bypass the normal T cell receptor-major histocompatibility class interaction, redirect the cytotoxic activity of T cells, and lead to highly efficient tumor cell killing. However, this immunotherapy also causes significant on-target off-tumor toxicologic effects, especially when it is used to treat solid tumors. To avoid these adverse events, it is necessary to understand the fundamental mechanisms involved in the physical process of T cell engagement. We developed a multiscale computational framework to reach this goal. The framework combines simulations on the intercellular and multicellular levels. On the intercellular level, we simulated the spatial-temporal dynamics of three-body interactions among bispecific antibodies, CD3 and tumor-associated antigens (TAAs). The derived number of intercellular bonds formed between CD3 and TAAs was further transferred to the multicellular simulations as the input parameter of adhesive density between cells. Through the simulations under various molecular and cellular conditions, we were able to gain new insights into how to adopt the most appropriate strategy to maximize the drug efficacy and avoid the off-target effect. For instance, we discovered that the low antibody-binding affinity resulted in the formation of large clusters at the cell-cell interface, which could be important to control the downstream signaling pathways. We also tested different molecular architectures of the bispecific antibody and suggested the existence of an optimal length in regulating the T cell engagement. Overall, the current multiscale simulations serve as a proof-of-concept study to help in the future design of new biological therapeutics.

Advancing Subcutaneous Dosing Regimens for Biotherapeutics: Clinical Strategies for Expedited Market Access

In recent years, subcutaneous administration of biotherapeutics has made significant progress. The self-administration market for rheumatoid arthritis has witnessed the introduction of additional follow-on biologics, while the first subcutaneous dosing options for monoclonal antibodies have become available for multiple sclerosis. Oncology has also seen advancements with the authorization of high-volume subcutaneous formulations, facilitated by the development of high-concentration formulations and innovative delivery systems. Regulatory and Health Technology Assessment bodies increasingly consider preference data in filing dossiers, particularly in evaluating novel drug delivery methods. The adoption of a pharmacokinetic-based clinical bridging approach has become standard for transitioning from intravenous to subcutaneous administration. Non-inferiority studies with pharmacokinetics as the only primary endpoint have started deviating from traditional randomization schemes, favoring the subcutaneous route and comparing with historical intravenous data. While nonclinical and computational models made progress in predicting safety and immunogenicity for subcutaneously dosed antibodies, clinical trial evidence remains essential due to inter-individual variations and the impact of formulation parameters on anti-drug antibody formation. Ongoing technological advancements and the expanding knowledge base on pharmacokinetic-pharmacodynamic correlation across specialty areas are expected to further accelerate clinical development of subcutaneous biologics.

Next generation of multispecific antibody engineering

Multispecific antibodies recognize two or more epitopes located on the same or distinct targets. This added capability through protein design allows these man-made molecules to address unmet medical needs that are no longer possible with single targeting such as with monoclonal antibodies or cytokines alone. However, the approach to the development of these multispecific molecules has been met with numerous road bumps, which suggests that a new workflow for multispecific molecules is required. The investigation of the molecular basis that mediates the successful assembly of the building blocks into non-native quaternary structures will lead to the writing of a playbook for multispecifics. This is a must do if we are to design workflows that we can control and in turn predict success. Here, we reflect on the current state-of-the-art of therapeutic biologics and look at the building blocks, in terms of proteins, and tools that can be used to build the foundations of such a next-generation workflow.

Evaluating the Efficacy of a Thermoresponsive Hydrogel for Delivering Anti-Collagen Antibodies to Reduce Posttraumatic Scarring in Orthopedic Tissues

Excessive posttraumatic scarring in orthopedic tissues, such as joint capsules, ligaments, tendons, muscles, and peripheral nerves, presents a significant medical problem, resulting in pain, restricted joint mobility, and impaired musculoskeletal function. Current treatments for excessive scarring are often ineffective and require the surgical removal of fibrotic tissue, which can aggravate the problem. The primary component of orthopedic scars is collagen I-rich fibrils. Our research team has developed a monoclonal anti-collagen antibody (ACA) that alleviates posttraumatic scarring by inhibiting collagen fibril formation. We previously established the safety and efficacy of ACA in a rabbit-based arthrofibrosis model. In this study, we evaluate the utility of a well-characterized thermoresponsive hydrogel (THG) as a delivery vehicle for ACA to injury sites. Crucial components of the hydrogel included N-isopropylacrylamide, poly(ethylene glycol) diacrylate, and hyaluronic acid. Our investigation focused on in vitro ACA release kinetics, stability, and activity. Additionally, we examined the antigen-binding characteristics of ACA post-release from the THG in an in vivo context. Our preliminary findings suggest that the THG construct exhibits promise as a delivery platform for antibody-based therapeutics to reduce excessive scarring in orthopedic tissues.

Target-dependent RNA polymerase as universal platform for gene expression control in response to intracellular molecules

Controlling gene expression in response to specific molecules is an essential technique for regulating cellular functions. However, current platforms with transcription and translation regulators have a limited number of detectable molecules to induce gene expression. Here to address these issues, we present a Target-dependent RNA polymerase (TdRNAP) that can induce RNA transcription in response to the intracellular target specifically recognized by single antibody. By substituting the fused antibody, we demonstrate that TdRNAPs respond to a wide variety of molecules, including peptides, proteins, RNA, and small molecules, and produce desired transcripts in human cells. Furthermore, we show that multiple TdRNAPs can construct orthogonal and multilayer genetic circuits. Finally, we apply TdRNAP to achieve cell-specific genome editing that is autonomously triggered by detecting the target gene product. TdRNAP can expand the molecular variety for controlling gene expression and provide the genetic toolbox for bioengineering and future therapeutic applications.

Accelerating antibody discovery and design with artificial intelligence: Recent advances and prospects

Therapeutic antibodies are the largest class of biotherapeutics and have been successful in treating human diseases. However, the design and discovery of antibody drugs remains challenging and time-consuming. Recently, artificial intelligence technology has had an incredible impact on antibody design and discovery, resulting in significant advances in antibody discovery, optimization, and developability. This review summarizes major machine learning (ML) methods and their applications for computational predictors of antibody structure and antigen interface/interaction, as well as the evaluation of antibody developability. Additionally, this review addresses the current status of ML-based therapeutic antibodies under preclinical and clinical phases. While many challenges remain, ML may offer a new therapeutic option for the future direction of fully computational antibody design.

Evaluation of Molecular Simulations and Deep Learning Prediction of Antibodies' Recognition of TRBC1 and TRBC2

T cell receptor β-chain constant (TRBC) is a promising class of cancer targets consisting of two highly homologous proteins, TRBC1 and TRBC2. Developing targeted antibody therapeutics against TRBC1 or TRBC2 is expected to eradicate the malignant T cells and preserve half of the normal T cells. Recently, several antibody engineering strategies have been used to modulate the TRBC1 and TRBC2 specificity of antibodies. Here, we used molecular simulation and artificial intelligence methods to quantify the affinity difference in antibodies with various mutations for TRBC1 and TRBC2. The affinity of the existing mutants was verified by FEP calculations aided by the AI. We also performed long-time molecular dynamics simulations to reveal the dynamical antigen recognition mechanisms of the TRBC antibodies.

Therapeutic Antibodies in Medicine

Antibody engineering has developed into a wide-reaching field, impacting a multitude of industries, most notably healthcare and diagnostics. The seminal work on developing the first monoclonal antibody four decades ago has witnessed exponential growth in the last 10-15 years, where regulators have approved monoclonal antibodies as therapeutics and for several diagnostic applications, including the remarkable attention it garnered during the pandemic. In recent years, antibodies have become the fastest-growing class of biological drugs approved for the treatment of a wide range of diseases, from cancer to autoimmune conditions. This review discusses the field of therapeutic antibodies as it stands today. It summarizes and outlines the clinical relevance and application of therapeutic antibodies in treating a landscape of diseases in different disciplines of medicine. It discusses the nomenclature, various approaches to antibody therapies, and the evolution of antibody therapeutics. It also discusses the risk profile and adverse immune reactions associated with the antibodies and sheds light on future applications and perspectives in antibody drug discovery.

Computational Methods in Immunology and Vaccinology: Design and Development of Antibodies and Immunogens

The design of new biomolecules able to harness immune mechanisms for the treatment of diseases is a prime challenge for computational and simulative approaches. For instance, in recent years, antibodies have emerged as an important class of therapeutics against a spectrum of pathologies. In cancer, immune-inspired approaches are witnessing a surge thanks to a better understanding of tumor-associated antigens and the mechanisms of their engagement or evasion from the human immune system. Here, we provide a summary of the main state-of-the-art computational approaches that are used to design antibodies and antigens, and in parallel, we review key methodologies for epitope identification for both B- and T-cell mediated responses. A special focus is devoted to the description of structure- and physics-based models, privileged over purely sequence-based approaches. We discuss the implications of novel methods in engineering biomolecules with tailored immunological properties for possible therapeutic uses. Finally, we highlight the extraordinary challenges and opportunities presented by the possible integration of structure- and physics-based methods with emerging Artificial Intelligence technologies for the prediction and design of novel antigens, epitopes, and antibodies.

Understanding the General Principles of T Cell Engagement by Multiscale Computational Simulations

The use of bispecific antibodies as T cell engagers can bypass the normal TCR-MHC interaction, redirect the cytotoxic activity of T-cells, and lead to highly efficient tumor cell killing. However, this immunotherapy also causes significant on-target off-tumor toxicologic effects, especially when they were used to treat solid tumors. In order to avoid these adverse events, it is necessary to understand the fundamental mechanisms during the physical process of T cell engagement. We developed a multiscale computational framework to reach this goal. The framework combines simulations on the intercellular and multicellular levels. On the intercellular level, we simulated the spatial-temporal dynamics of three-body interactions among bispecific antibodies, CD3 and TAA. The derived number of intercellular bonds formed between CD3 and TAA were further transferred into the multicellular simulations as the input parameter of adhesive density between cells. Through the simulations under various molecular and cellular conditions, we were able to gain new insights of how to adopt the most appropriate strategy to maximize the drug efficacy and avoid the off-target effect. For instance, we discovered that the low antibody binding affinity resulted in the formation of large clusters at the cell-cell interface, which could be important to control the downstream signaling pathways. We also tested different molecular architectures of the bispecific antibody and suggested the existence of an optimal length in regulating the T cell engagement. Overall, the current multiscale simulations serve as a prove-of-concept study to help the future design of new biological therapeutics.

SIGNIFICANCE

T-cell engagers are a class of anti-cancer drugs that can directly kill tumor cells by bringing T cells next to them. However, current treatments using T-cell engagers can cause serious side-effects. In order to reduce these effects, it is necessary to understand how T cells and tumor cells interact together through the connection of T-cell engagers. Unfortunately, this process is not well studied due to the limitations in current experimental techniques. We developed computational models on two different scales to simulate the physical process of T cell engagement. Our simulation results provide new insights into the general properties of T cell engagers. The new simulation methods can therefore serve as a useful tool to design novel antibodies for cancer immunotherapy.