Navigating the landscape: A comprehensive overview of computational approaches in therapeutic antibody design and analysis.

The design and development of therapeutic monoclonal antibodies (mAbs) through optimizing their pharmacokinetic (PK) and pharmacodynamic (PD) properties is crucial to improve efficacy while minimizing adverse events. Many of these properties are interdependent, which highlights the inherent challenges in therapeutic antibody design, where improving one antibody property can sometimes lead to changes in others. Here, we discuss optimization approaches for PK/PD properties of therapeutic mAbs.

The tumor acidic microenvironment is a fundamental characteristic of solid tumors which distinguishes the tumor from adjacent normal tissues. This unique tumor microenvironment (TME) feature directly contributes to various aspect of tumor progression including gene expression, immune suppression, drug resistance, invasion and metastasis. Acidic TME poses great challenges for the efficacy of therapeutics; extracellular acidity of cancer cells provides less permissive conditions for optimal target engagement and drug efficacy. In addition targeting the effectors of acid-base balance enriched in hypoxia and acidic regions of the tumor provide valuable tools for normalizing the extracellular acidity and delivery of therapeutics agents to areas of the tumor less malleable to conventional therapies. Thus in the case of therapeutic antibodies, strategies that specifically integrates this TME specific feature in the design could improve binding to the target under low pH conditions and serve as delivery moiety that improve tumor specificity and functional efficacy. Here we describe two examples of these therapeutic strategies that leverage the acidic and hypoxic microenvironment to overcome the challenges posed by this hostile environment with the aim of generating improved therapeutic antibodies. A. Characterization of a set of function blocking therapeutic antibodies against key target expressed in response to hypoxia which contributes to tumor acidification, we demonstrate functional efficacy of this antibody to block the activity of the target in vitro and consequently reduce tumor spheroid growth. This and similar antibodies provide tools for specific targeting of the tumor areas that are often inaccessible to most therapeutics. B. Identification of targets enriched in acidic tumor microenvironment and design of pH selective therapeutic antibodies that preferentially bind to the target and exert their function under tumor acidic conditions. We demonstrate the functional selectivity of a variant of antibody targeting HER 2, with optimized binding under low pH conditions, on the growth of BT474 spheroids. The pH selective antibody variant blocked the tumor spheroid growth when spheroids were grown in TME-relevant- low pH conditions but it remained ineffective under normal physiological pH conditions. Suggesting increased selectivity of this antibody variant towards the acidic TME that in turn lower the on-target -off-tumor toxicity. In conclusion here we provide two examples of the strategies which exploit the targeting of hypoxia induced genes and pH selective design of the therapeutic antibodies in order to improve tumor targeting capacity and reduce systemic toxicity of conventional therapeutics. Citation Format: Nazanin Rohani Larijani, Traian Sulea, Mehdi Arbabi Ghahroudi, Beatrice Paul Roc, Mylene Gosselin, Joey Sheff, John C. Zwaagstra, Anne E.G. Lenferink. Exploiting tumor acidic microenvironment for improved therapeutics [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-049.

Molecular engineering and design of therapeutic antibodies

Developing therapeutic antibodies is a challenging endeavor, often requiring large-scale screening to produce initial binders, that still often require optimization for developability. We present a computational pipeline for the discovery and design of therapeutic antibody candidates, which incorporates physics- and AI-based methods for the generation, assessment, and validation of candidate antibodies with improved developability against diverse epitopes, via efficient few-shot experimental screens. We demonstrate that these orthogonal methods can lead to promising designs. We evaluated our approach by experimentally testing a small number of candidates against multiple SARS-CoV-2 variants in three different tasks: (i) traversing sequence landscapes of binders, we identify highly sequence dissimilar antibodies that retain binding to the Wuhan strain, (ii) rescuing binding from escape mutations, we show up to 54% of designs gain binding affinity to a new subvariant and (iii) improving developability characteristics of antibodies while retaining binding properties. These results together demonstrate an end-to-end antibody design pipeline with applicability across a wide range of antibody design tasks. We experimentally characterized binding against different antigen targets, developability profiles, and cryo-EM structures of designed antibodies. Our work demonstrates how combined AI and physics computational methods improve productivity and viability of antibody designs.

Human IgG comprises four subclasses with different biological functions. The IgG3 subclass has a unique character, exhibiting high effector function and Fab arm flexibility. However, it is not used as a therapeutic drug owing to an enhanced susceptibility to proteolysis. Antibody aggregation control is also important for therapeutic antibody development. To date, there have been few reports of IgG3 aggregation during protein expression and the low pH conditions needed for purification and virus inactivation. This study explored the potential of IgG3 antibody for therapeutics using anti‐CD20 IgG3 as a model to investigate aggregate formation. Initially, anti‐CD20 IgG3 antibody showed substantial aggregate formation during expression and low pH treatment. To circumvent this phenomenon, we systematically exchanged IgG3 constant domains with those of IgG1, a stable IgG. IgG3 antibody with the IgG1 CH3 domain exhibited reduced aggregate formation during expression. Differential scanning calorimetric analysis of individual amino acid substitutions revealed that two amino acid mutations in the CH3 domain, N392K and M397V, reduced aggregation and increased CH3 transition temperature. The engineered human IgG3 antibody was further improved by additional mutations of R435H to obtain IgG3KVH to achieve protein A binding and showed similar antigen binding as wild‐type IgG3. IgG3KVH also exhibited high binding activity for FcγRIIIa and C1q. In summary, we have successfully established an engineered human IgG3 antibody with reduced aggregation during bioprocessing, which will contribute to the better design of therapeutic antibodies with high effector function and Fab arm flexibility.

Since the first monoclonal antibody drug, muromonab-CD3, was approved for marketing in 1986, 165 antibody drugs have been approved or are under regulatory review worldwide. With the approval of new drugs for treating a wide range of diseases, including cancer and autoimmune and metabolic disorders, the therapeutic antibody drug market has experienced explosive growth. Monoclonal antibodies have been sought after by many biopharmaceutical companies and scientific research institutes due to their high specificity, strong targeting abilities, low toxicity, side effects, and high development success rate. The related industries and markets are growing rapidly, and therapeutic antibodies are one of the most important research and development areas in the field of biology and medicine. In recent years, great progress has been made in the key technologies and theoretical innovations provided by therapeutic antibodies, including antibody–drug conjugates, antibody-conjugated nuclides, bispecific antibodies, nanobodies, and other antibody analogs. Additionally, therapeutic antibodies can be combined with technologies used in other fields to create new cross-fields, such as chimeric antigen receptor T cells (CAR-T), CAR-natural killer cells (CAR-NK), and other cell therapy. This review summarizes the latest approved or in regulatory review therapeutic antibodies that have been approved or that are under regulatory review worldwide, as well as clinical research on these approaches and their development, and outlines antibody discovery strategies that have emerged during the development of therapeutic antibodies, such as hybridoma technology, phage display, preparation of fully human antibody from transgenic mice, single B-cell antibody technology, and artificial intelligence-assisted antibody discovery.

Since the establishment of monoclonal antibody production using hybridoma technology in the mid-1970s, there has been expanding progress and continuous technological improvement in the development of therapeutic antibodies. The initial technological breakthroughs involved reduction of immunogenicity and thus enabled repeated administration. The establishment of chimeric, humanized, and fully human antibodies has led to the great success of several ‘second-generation’ therapeutic antibodies, such as rituximab, trastuzumab, cetuximab, and bevacizumab. However, there still exists an urgent demand for improvement in the efficacy of the current antibody therapeutics, which is not yet fully satisfactory for patients. Based on the current understanding of the clinical mechanisms of several therapeutic antibodies, many now believe that Fc-mediated functions (e.g. antibody-dependent cellular cytotoxicity, complement-dependent cytotoxicity, and neonatal Fc receptor [FcRn]-mediated storage) will improve the clinical outcomes of therapeutic antibodies. The present review focuses on the recent progress in the development of ‘Fc engineering,’ which dramatically improves (and sometimes silences) Fc-mediated functions. These achievements can be classified into two technological approaches: (i) introducing amino acid mutations and (ii) modifying Fc-linked oligosaccharide structures. The effectiveness of multiple third-generation therapeutic antibodies armed with various engineered Fcs is now ready to be tested in clinical trials.

Antibodies 2.0

Characterisation and structural analysis of B-cell epitopes for vaccine and therapeutic antibody design

Medical treatments using potent neutralizing SARS-CoV-2 antibodies have achieved remarkable improvements in clinical symptoms, changing the situation for the severity of COVID-19 patients. We previously reported an antibody, NT-108 with potent neutralizing activity. However, the structural and functional basis for the neutralizing activity of NT-108 has not yet been understood. Here, we demonstrated the therapeutic effects of NT-108 in a hamster model and its protective effects at low doses. Furthermore, we determined the cryo-EM structure of NT-108 in complex with SARS-CoV-2 spike. The single-chain Fv construction of NT-108 improved the cryo-EM maps because of the prevention of preferred orientations induced by Fab orientation. The footprints of NT-108 illuminated how escape mutations such as E484K evade from class 2 antibody recognition without ACE2 affinity attenuation. The functional and structural basis for the potent neutralizing activity of NT-108 provides insights into the rational design of therapeutic antibodies.

The need to consider an antibody's "developability" (immunogenicity, solubility, specificity, stability, manufacturability, and storability) is now well understood in therapeutic antibody design. Predicting these properties rapidly and inexpensively is critical to industrial workflows, to avoid devoting resources to non-productive candidates. Here, we describe a high-throughput computational developability assessment tool, the Therapeutic Antibody Profiler (TAP), which assesses the physicochemical "druglikeness" of an antibody candidate. Input variable domain sequences are converted to three-dimensional structural models, and then five developability-linked molecular surface descriptors are calculated and compared to advanced-stage clinical therapeutics. Values at the extremes of/outside of the distributions seen in therapeutics imply an increased risk of developability issues. Therefore, TAP, starting only from sequence information, provides a route to rapidly identifying drug candidate antibodies that are likely to have poor developability. Our web application ( opig.stats.ox.ac.uk/webapps/tap ) profiles input antibody sequences against a continually updated reference set of clinical therapeutics.

ABSTRACTA central dogma in immunology is that an antibody's in vivo functionality is mediated by 2 independent events: antigen binding by the variable (V) region, followed by effector activation by the constant (C) region. However, this view has recently been challenged by reports suggesting allostery exists between the 2 regions, triggered by conformational changes or configurational differences. The possibility of allosteric signals propagating through the IgG domains complicates our understanding of the antibody structure-function relationship, and challenges the current subclass selection process in therapeutic antibody design. Here we review the types of cooperativity in IgG molecules by examining evidence for and against allosteric cooperativity in both Fab and Fc domains and the characteristics of associative cooperativity in effector system activation. We investigate the origin and the mechanism of allostery with an emphasis on the C-region-mediated effects on both V and C region interactions, and discuss its implications in biological functions. While available research does not support the existence of antigen-induced conformational allosteric cooperativity in IgGs, there is substantial evidence for configurational allostery due to glycosylation and sequence variations.

Artificial intelligence in therapeutic antibody design: Advances and future prospects.

Despite the advent of many new therapies, therapeutic monoclonal antibodies remain a prominent biologics product, with a market value of billions of dollars annually. A variety of downstream processing technological advances have led to a paradigm shift in how therapeutic antibodies are developed and manufactured. A key driver of change has been the increased adoption of single-use technologies for process development and manufacturing. An early-stage developability assessment of potential lead antibodies, using both in silico and high-throughput experimental approaches, is critical to de-risk development and identify molecules amenable to manufacturing. Both statistical and mechanistic modelling approaches are being increasingly applied to downstream process development, allowing for deeper process understanding of chromatographic unit operations. Given the greater adoption of perfusion processes for antibody production, continuous and semi-continuous downstream processes are being increasingly explored as alternatives to batch processes. As part of the Quality by Design (QbD) paradigm, ever more sophisticated process analytical technologies play a key role in understanding antibody product quality in real-time. We should expect that computational prediction and modelling approaches will continue to be advanced and exploited, given the increasing sophistication and robustness of predictive methods compared to the costs, time, and resources required for experimental studies.

Natural products with polypharmacological profiles have demonstrated promise as novel therapeutics for various complex diseases, including cancer. Currently, many gaps exist in our knowledge of which compounds interact with which targets, and experimentally testing all possible interactions is infeasible. Recent advances and developments of systems pharmacology and computational (in silico) approaches provide powerful tools for exploring the polypharmacological profiles of natural products. In this review, we introduce recent progresses and advances of computational tools and systems pharmacology approaches for identifying drug targets of natural products by focusing on the development of targeted cancer therapy. We survey the polypharmacological and systems immunology profiles of five representative natural products that are being considered as cancer therapies. We summarize various chemoinformatics, bioinformatics and systems biology resources for reconstructing drug-target networks of natural products. We then review currently available computational approaches and tools for prediction of drug-target interactions by focusing on five domains: target-based, ligand-based, chemogenomics-based, network-based and omics-based systems biology approaches. In addition, we describe a practical example of the application of systems pharmacology approaches by integrating the polypharmacology of natural products and large-scale cancer genomics data for the development of precision oncology under the systems biology framework. Finally, we highlight the promise of cancer immunotherapies and combination therapies that target tumor ecosystems (e.g. clones or 'selfish' sub-clones) via exploiting the immunological and inflammatory 'side' effects of natural products in the cancer post-genomics era.