Integrative and Emerging Models in Antibody Research: A Comprehensive Review

Abstract

Antibody research has advanced through the integration of in vivo, in vitro, and in silico models, each offering distinct advantages and limitations. In vivo models, such as traditional animal models and humanized mouse models, provide critical insights into antibody efficacy and pharmacokinetics but face ethical and translational challenges. In vitro techniques, including hybridoma technology, phage display, and B-cell culture, enable efficient screening and optimization but often lack physiological relevance. In silico approaches, powered by computational biology and machine learning, accelerate antibody design and prediction, addressing challenges in cost and scalability. Emerging technologies like CRISPR-based engineering, single-cell sequencing, microfluidics, and organ-on-chip platforms are reshaping antibody discovery and therapeutic development. This review critically evaluates these models, emphasizing their integration to overcome existing challenges such as reproducibility, immunogenicity prediction, and scalability. As innovations continue, a multidisciplinary approach promises to enhance antibody research, driving next-generation therapeutics for cancer, autoimmune diseases, and infectious conditions.

Statement of Significance This review highlights how integrating in vivo models, in vitro, and in silico platforms advances antibody research, addressing challenges in reproducibility, scalability, and immunogenicity prediction to accelerate the development of next-generation therapeutics.

Introduction

Antibodies, also known as immunoglobulins, have long been the body’s natural defensive agents, designed specifically to recognize and kill outside invaders such as viruses and toxins [1]. Their therapeutic potential was first recognized in the late 19th century, when Emil von Behring and Shibasaburo Kitasato devised serum therapy to treat diphtheria, marking one of the earliest uses of antibodies in medicine [2]. Since then, antibodies have evolved into important tools in immunology and medicine. These proteins, shaped like a “Y,” work precisely, attaching to specific antigens while causing little harm to healthy tissues [3]. Because of their specificity, antibodies have become crucial not just in immunological defense but also in current therapeutic interventions [4].

The development of hybridoma technology in the 1970s by Georges Köhler and César Milstein, which allowed for the production of monoclonal antibodies, revolutionized the field. This innovation paved the way for the creation of highly specific antibody therapies. Antibodies are now effective treatments for cancer, autoimmune disorders, and infections because of their capacity to target unhealthy cells while sparing healthy ones [5, 6].

The study and development of antibodies rely significantly on experimental models, which provide an important foundation for understanding how antibodies function and how they might be harnessed for therapeutic applications [7, 8]. Because direct testing in humans is both expensive and complex, these models serve as an important preclinical stage in which scientists investigate antibody production, antigen interaction, and efficacy in a controlled setting [9, 10]. Historically, antibody research has relied heavily on in vivo models, notably animal systems such as mice and rabbits. These models enable researchers to investigate complex biological interactions, immunological responses, and pharmacokinetics in real organisms [11, 12].

In vitro models have developed as effective methods for investigating antibody behavior in a more regulated environment. These models emphasize features including binding affinity, specificity, and functional tests. Furthermore, the introduction of technologies such as phage display in the 1990s transformed monoclonal antibody production, allowing for the efficient creation of high-affinity antibodies without extensive use of animal models [13]. Recently, in silico models have ushered antibody research into a new age. With the development of computational tools, molecular modeling, and docking studies, scientists can now predict antibody–antigen interactions and improve antibody structures prior to synthesis, saving time and money on experimental work [14]. Researchers are accelerating antibody discovery and development by combining in vivo, in vitro, and in silico techniques. Together, these models improve our understanding of antibodies and promote the development of next-generation therapies with greater specificity and efficacy, with the potential to transform treatment across a wide range of medical applications.

This review provides a comprehensive overview of the experimental models and platforms pivotal to contemporary antibody research. We delve into various approaches, encompassing in vivo animal models essential for immunization and evaluating antibody functional properties, in vitro platform technologies that enable high-throughput screening and detailed characterization, and in silico techniques for the computational screening and identification of lead antibody candidates. Furthermore, the article highlights the emergence of novel and unique models in antibody research, discusses the inherent limitations associated with current experimental systems, and explores future directions that could revolutionize antibody discovery and development.

Animal models for immunization

Traditional animal models for immunization

‘Animal models’ have long been indispensable in antibody research, particularly for their role in ‘immunization strategies’ to generate both monoclonal and polyclonal antibodies. Rodents such as ‘mice’, ‘rats’, and ‘rabbits’ are widely favored due to their ease of maintenance, well-characterized immune systems, and their capacity to partially mimic human immunological responses. These models provide a robust platform for understanding immune mechanisms and for the practical production of antibodies.

• Mice: Mice are the foundational model for ‘monoclonal antibody production’ via ‘hybridoma technology’. Specifically, ‘BALB/c mice’ are a preferred strain, renowned for their reproducible immune responses, which is crucial for efficient hybridoma development. ‘C57BL/6 mice’ are also commonly employed, particularly in studies focused on immune regulation. Immunization of mice with target antigens reliably elicits an immune response, leading to the production of diverse antibodies, which can then be harnessed for various applications [15, 16].

• Rats: Rats serve as a valuable alternative when the immune response in mice is suboptimal, particularly for generating antibodies against mouse antigens. They offer a robust system for producing antibodies with specificities that may be challenging to achieve in mice. Commonly utilized strains like ‘Wistar rats’ and ‘Sprague–Dawley rats’ are known for their consistent and reliable antibody production following immunization [17, 18].

• Rabbits: Rabbits are extensively used for ‘polyclonal antibody production’ due to their strong immune responses and ability to generate a wide range of high-affinity, antigen-specific antibodies. ‘New Zealand White rabbits’ are frequently chosen for this purpose. Antibodies derived from rabbits often exhibit higher affinity and specificity compared to those from rodent models, making them highly valuable for various diagnostic and research applications after appropriate immunization protocols [19, 20].

• Hamsters: Hamsters, especially ‘Syrian golden hamsters’, are occasionally utilized in antibody research for generating monoclonal antibodies. Their unique immune system can offer specific advantages in certain research contexts, particularly in the study of ‘cancer’ and ‘infectious diseases’, where their immune responses to specific antigens after immunization might be particularly relevant [21].

• Guinea pigs: Guinea pigs are employed when a more robust or distinct immune response is required compared to smaller rodents. They are often integrated into immunological studies where specific antigen recognition pathways differ from those observed in mice or rats, making them useful for particular antibody production applications after targeted immunization [22].

• Chickens: While less common, chickens are a valuable model for producing antibodies, particularly ‘IgY’, which is abundantly found in egg yolk. A key advantage of chicken-derived antibodies is their lack of cross-reactivity with mammalian antibodies, making them an excellent choice for generating highly specific polyclonal antibodies for diverse diagnostic and therapeutic applications, especially when immunized with mammalian antigens [23, 24].

Humanized mouse models for immunization

Traditional mouse models have been invaluable in immunological research; however, their murine immune systems produce murine antibodies that can trigger immunogenic responses when introduced into humans. To address these limitations, humanized mouse models have been developed. These genetically engineered models carry components of the human immunoglobulin loci, enabling the production of fully human antibodies and reducing the need for subsequent humanization. The primary objective of these models is to enhance the clinical relevance of preclinical antibody studies and minimize the immunogenicity commonly associated with murine-derived antibodies [25].

Humanized mouse models vary in the methods used for inserting human immunoglobulin genes, their efficiency in antibody generation, and the diversity of their repertoires. Table 1 summarizes notable humanized mouse platforms, outlining their genetic engineering strategies, advantages, and therapeutic antibodies developed from each model. Understanding these distinctions is critical for selecting the appropriate system for specific research or clinical applications.

Table 1.

Overview of notable humanized mouse models in therapeutic antibody development.

Mouse platform/company	Construction technology	Differentiation from other humanized antibody mice	FDA-approved biologics discovered (examples)	References
XenoMouse® (Amgen, formerly Abgenix)	Large-scale ‘transgenesis’ by introducing mega-base human immunoglobulin (Ig) heavy and light chain loci into mice with inactivated endogenous mouse Ig loci. This allows the mouse to produce a full repertoire of human antibodies.	Key difference: These mice directly produce fully human antibodies from their B cells upon immunization, rather than being recipients of human immune cells. They possess a complete and diverse human antibody gene repertoire that undergoes in vivo somatic hypermutation and affinity maturation in the mouse, mimicking natural human antibody selection.	Panitumumab (Vectibix), denosumab (Prolia, Xgeva), secukinumab (Cosentyx), durvalumab (Imfinzi), erenumab (Aimovig)	[26–28]
HuMAn Mouse® (Bristol-Myers Squibb, formerly Medarex)	Similar to XenoMouse, involves ‘transgenesis’ with human Ig heavy and light chain loci replacing mouse Ig loci.	Similar differentiation as XenoMouse. Offers a diverse repertoire of fully human antibodies.	Ustekinumab (Stelara), canakinumab (Ilaris), golimumab (Simponi), ipilimumab (Yervoy), nivolumab (Opdivo), daratumumab (Darzalex), olaratumab (Lartruvo)	[29–34]
VelocImmune® (Regeneron Pharmaceuticals)	Utilizes ‘VelociGene® technology’ for precise, large-scale in situ replacement of mouse Ig loci with human Ig loci via homologous recombination in embryonic stem (ES) cells.	Similar differentiation as XenoMouse/HuMAb. Emphasizes precise genomic integration and a highly diverse, naturally matured human antibody repertoire.	Alirocumab (Praluent), dupilumab (Dupixent), sarilumab (Kevzara), cemiplimab (Libtayo), evinacumab (Evkeeza), pozelimab (Veopoz), casirivimab/imdevimab (REGEN-COV—EUA, not full FDA approval)	[35–42]
KyMouse® (Sanofi, formerly Kymab)	Advanced ‘transgenesis’ technology for generating a broad and diverse repertoire of fully human antibodies.	Focuses on generating antibodies with high diversity and "drug-like" properties due to optimized human gene integration.	No drugs developed using this technology have been approved by the FDA.	[25]
H2L2 Harbour Mice® (Harbour BioMed)	A ‘transgenesis’ platform that generates fully human antibodies with two heavy and two light chains (H2L2 format). Also has a unique HCAb (heavy chain only) platform.	Provides options for both conventional human antibodies and novel heavy-chain-only antibodies, which have unique therapeutic potential (e.g., for bispecifics).	No drugs developed using this technology have been approved by the FDA.	[43]
Trianni Mouse® (Trianni)	Leverages advances in ‘DNA synthesis and genomic modification’ to create a transgenic platform with a complete human antibody repertoire.	Aims for a comprehensive human antibody repertoire combined with wild-type mouse immune responses for robust antibody discovery.	No drugs developed using this technology have been approved by the FDA.	[44]
OmniAb® (Ligand Pharmaceuticals—various strains: OmniRat®, OmniMouse®, OmniFlic®, OmniClic®, OmniTaur®)	‘Transgenic animals’ (rats, mice, chickens, cows) genetically modified to produce antibodies with human sequences, often involving extensive genomic engineering.	Offers a diverse suite of animal hosts beyond mice (rats, chickens, cows), providing unique antibody characteristics and diverse repertoires, optimized for different therapeutic modalities.	Teclistamab	[45, 46]
ATX-Gx™ Mouse (Alloy Therapeutics)	Proprietary ‘transgenic mice’ platform with various strains.	Focuses on providing broad, royalty-free access to its platforms for partners, emphasizing flexibility in business models. Offers hyperimmune strains for challenging targets.	No drugs developed using this technology have been approved by the FDA.	[47]

(Continued)

Table 1.

Continued.

Mouse platform/company	Construction technology	Differentiation from other humanized antibody mice	FDA-approved biologics discovered (examples)	References
CAMouse™ (CAMAB)	Transgenic mice platform developed with ‘independent intellectual property’, often involving in-situ gene replacement.	Aims to provide a robust fully human antibody discovery platform, particularly for the Chinese biopharmaceutical market.	No drugs developed using this technology have been approved by the FDA.	[48]
HUGO-Ab™ (Cyagen)	Utilizes ‘TurboKnockout® ES technology’ for in situ gene replacement of mouse Ig genes with human Ig genes.	Emphasizes faster model construction and robust expression by ensuring humanized antibody genes are under native regulatory elements, similar to wild-type mouse expression.	No drugs developed using this technology have been approved by the FDA.	[49]
RenMab™ (Biocytogen)	In situ ‘replacement of mouse variable regions with human variable regions’ for both heavy and light chains, keeping mouse constant regions for normal B cell development and function.	Designed to overcome limitations of traditional humanized mice by maintaining mouse constant regions to ensure robust B cell development and immune responses while producing diverse human variable regions.	No drugs developed using this technology have been approved by the FDA.	[148]
RenLite™ (Biocytogen)	In situ ‘replacement of mouse variable regions with human variable regions’ for heavy chains, and a single common human light chain (e.g., Lambda or Kappa) to simplify bispecific antibody generation.	Unique for its single common light chain strategy (e.g., RenLite Mouse contains a single human λ chain and a silenced endogenous mouse κ chain), which significantly simplifies the screening and manufacturing of bispecific antibodies.	No drugs developed using this technology have been approved by the FDA.	[149]
THX Mouse (Thousand Oaks Biotherapeutics)	Utilizes ‘large fragment chromosome engineering’ to introduce complete human immunoglobulin loci into mice with knocked-out endogenous Ig genes.	Focuses on generating a full and diverse human antibody repertoire with efficient in vivo maturation.	No drugs developed using this technology have been approved by the FDA.	[150]

In vitro platform technologies for antibody discovery

Following immunization in traditional or humanized animal models, the next critical step in antibody discovery involves the application of ‘in vitro platform technologies’. These platforms enable the isolation, screening, and optimization of antibody-producing cells or fragments in a controlled laboratory setting, independent of whole-organism systems. In vitro models are essential for characterizing the antibody response generated postimmunization, including assessing binding specificity, affinity, and functional activity against target antigens. They also significantly reduce the reliance on animal models during early-stage research by enabling high-throughput selection and refinement of candidate antibodies. These systems provide a reproducible and scalable framework for antibody generation, allowing researchers to dissect and manipulate specific molecular interactions involved in antigen recognition. The most widely used in vitro platforms include ‘hybridoma technology’ [50], ‘B-cell culture systems’ [55], and various ‘display technologies’ such as phage, yeast, bacterial, ribosome, and mammalian display. These tools have revolutionized antibody discovery by streamlining the identification of high-affinity candidates and accelerating therapeutic development. Figure 1 offers a visual overview of the key in vitro platforms used in antibody research, highlighting their roles in the postimmunization phase of antibody generation.

Figure 1.

Diagrammatic representation of in vitro antibody discovery approaches: hybridoma technology, B-cell culturing, and display technology.

Hybridoma technology

Hybridoma technology is a fundamental approach for producing monoclonal antibodies (mAbs) and is still frequently utilized due to its ability to manufacture highly specific antibodies of constant quality. This method involves the fusion of antibody-producing B cells taken from the spleen of an immunized animal, often a mouse, with myeloma cells, which are malignant plasma cells capable of unlimited proliferation. Agents such as polyethylene glycol (PEG) accelerate the fusion process by promoting the production of hybrid cells, also known as hybridomas. These hybridomas combine the antibody-producing capabilities of B cells with myeloma cells’ limitless growth potential, allowing them to continually manufacture monoclonal antibodies against a particular antigen. Once hybridomas have been created, they are selected using a specific medium, such as HAT (hypoxanthine–aminopterin–thymidine), which preferentially permits hybridomas to proliferate while removing unfused myeloma or B cells. After that, hybridoma clones are tested for their capacity to make antibodies with the necessary specificity, usually using enzyme-linked immunosorbent assay (ELISA) or flow cytometry. The chosen clones are subsequently grown to create a large number of monoclonal antibodies [5, 50]. While this approach is very efficient, it needs animal vaccination and can take months to provide results, prompting researchers to investigate new, quicker antibody-finding methods [51].

B-cell culture systems

B-cell culture techniques, which involve the direct isolation and in vitro cultivation of B cells from an immunized animal or a human donor, offer a compelling alternative to traditional hybridoma technology for antibody production. These cells are typically obtained from lymphoid organs such as the spleen, blood, or lymph nodes. Once isolated, they can be stimulated in vitro to differentiate into antibody-secreting plasma cells. A key advantage of B-cell culture over hybridoma technology is the elimination of the cell fusion step, which often makes the procedure less labor-intensive and more direct.

In recent years, innovative approaches have been developed to extend B-cell viability and enhance antibody production efficiency within these culture systems. One primary strategy involves B-cell immortalization, frequently achieved through the introduction of specific genes or viral vectors that bypass cellular senescence. This allows the B cells to proliferate indefinitely in culture while continuously producing antibodies [52]. A prominent method for immortalization is the use of the Epstein–Barr virus, which can transform primary B cells into immortalized lymphoblastoid cell lines capable of indefinite antibody secretion [53, 54]. These advanced techniques are particularly valuable for producing fully human antibodies directly from donor B cells, offering significant advantages for therapeutic antibody generation by minimizing potential immunogenicity compared to animal-derived antibodies.

Display technologies

Display methods, including phage display, yeast display, bacterial display, ribosome display, and mammalian display have revolutionized antibody discovery by allowing antibodies to be generated in vitro without the requirement for animal vaccination each time. These techniques show vast libraries of antibody fragments, often single-chain variable fragments (scFv) or Fab fragments, on the surface of a carrier. These antibody libraries are exposed to the target antigen, and those with high binding affinity are chosen using a technique known as biopanning. Phage display, one of the most popular display systems, employs bacteriophages (viruses that infect bacteria) as carriers. Antibody fragments are produced on the bacteriophage’s surface, and, following rounds of antigen binding selection, the top candidates are amplified and tuned for increased affinity or stability [55]. Similarly, yeast [56, 57] and bacterial display [58] methods use living cells to exhibit antibodies on their surfaces, enabling high-throughput screening of vast libraries. Ribosome display, on the other hand, employs a cell-free approach in which ribosomes directly show antibody fragments, allowing greater library sizes to be screened [59]. Mammalian display involves genetically engineering mammalian cells to express antibodies on their surface, allowing for high-throughput screening of large antibody libraries based on their binding affinity [60]. These approaches are extremely efficient and cost-effective, and they avoid the ethical problems relating to animal models.

In vivo models for functional studies

Traditional animal models

Traditional animal models are extensively utilized to evaluate the functional properties of antibodies, including their pharmacokinetics (PK), pharmacodynamics (PD), half-life, and clearance before human clinical trials. Rodents (mice and rats) are widely favored due to their manageable size, cost-effectiveness, and availability of diverse genetic strains, providing initial data on antibody absorption, distribution, metabolism, and excretion profiles and on-target effects [61]. However, differences in immune systems and physiology between rodents and humans can limit direct translation of findings [62]. For the most translationally relevant data, nonhuman primates (NHPs), particularly cynomolgus macaques, are considered superior due to their close physiological and immunological similarities to humans. They are frequently used to assess antibody half-life, clearance rates, tissue distribution, and long-term pharmacodynamic effects, including immune cell modulation and target engagement [63].

Xenograft models

Xenograft models, including cell line–derived or patient-derived xenografts (CDX/PDX) and zebrafish models, are critical tools in studying antibody-based therapies, particularly in oncology research [64, 65]. These models involve the transplantation of human tumors or tissues into immunocompromised animals, such as nude or SCID (severe combined immunodeficiency) mice (NOD SCID IL-2rg mice), which lack a functional immune system, allowing for the engraftment and growth of human cancer cells without rejection [66–68].

CDX/PDX models employ established human cancer cell lines that are injected into these immunodeficient mice to recreate a human-like tumor microenvironment [69, 70]. This approach facilitates the evaluation of therapeutic antibodies, including monoclonal antibodies and antibody–drug conjugates, by assessing their efficacy in targeting and inhibiting tumor growth [71]. Zebrafish models provide an additional platform for high-throughput drug screening and in vivo imaging, offering unique insights into tumor biology and drug interactions due to their transparency and rapid tumor growth [72, 73]. Together, xenograft models, including CDX and zebrafish models, enable a robust preclinical assessment of antibody-based therapies.

Transgenic animal models

Transgenic animal models, genetically engineered to express human proteins, provide a powerful platform for studying antibody responses, particularly in the context of cancer research [74]. These models are often designed to express human disease-related antigens or receptors, enabling the testing of therapeutic antibodies targeting these specific molecules [75, 76]. Their utility extends beyond cancer to autoimmune and infectious disease research, offering crucial insights into therapeutic development. Transgenic mouse models expressing human PD-1 and PD-L1 have been instrumental in evaluating checkpoint inhibitor antibodies in vivo. Burova et al. (2017) employed VelociGene technology to generate human PD-1 knock-in mice. These transgenic mice were specifically developed to test the efficacy of the anti-PD-1 antibody REGN2810 [77].

Similarly, transgenic models expressing human HER2 have been developed to test antibodies and vaccines targeting this oncogene. Finkle et al. developed transgenic mice overexpressing human HER2 under the murine mammary tumor virus promoter, which resulted in spontaneous development of mammary adenocarcinomas and Harderian gland neoplasms [78]. Transgenic mice expressing human angiotensin-converting enzyme 2 (hACE2) have also emerged as key tools for studying SARS-CoV-2 infection and developing COVID-19 therapeutics. McCray Jr. et al. (2007) developed transgenic mice expressing human angiotensin-converting enzyme 2 (hACE2) to study SARS-CoV infection. These mice were used to test the efficacy of the anti-S neutralizing human monoclonal antibody (MAb) MBL SARS-201, providing a valuable model for evaluating potential treatments targeting the viral spike (S) protein [79].

In addition, transgenic models expressing human CD20 have been developed for testing monoclonal antibodies. In a preclinical study by Natarajan et al., a transgenic mouse model expressing human CD20 was used to evaluate the potential clinical application of 64Cu-DOTA-rituximab (PETRIT), facilitating investigations into rituximab’s biodistribution and imaging capabilities in vivo [80]. Further advancing this area, Sun et al. created transgenic mice expressing both human CD20 and human CD3 for testing bispecific antibodies targeting these proteins. By generating huCD20-huCD3 double-transgenic mice through crossing single transgenic mice, they provided a controlled system for assessing the therapeutic efficacy of anti-CD20/CD3 bispecific antibodies [81].

In silico models for antibody research

In silico models are computer techniques that have transformed antibody research by allowing for the creation, optimization, and prediction of antibody characteristics without the use of manual experimentation [82]. These models combine bioinformatics, structural modeling, and machine learning to mimic antibody behavior and interactions with antigens, providing a cost-effective and time-saving alternative to standard in vitro and in vivo methods [83]. In silico models are very effective for predicting antibody structure, improving binding affinity, and directing the development of new therapeutic antibodies.

Antibody structure prediction

In silico models for antibody structure prediction play a crucial role in understanding the function of antibodies, particularly their complementarity-determining regions (CDRs), which are key to antigen binding [84]. Several computational tools have been developed for this purpose. Emerging tools like ‘ABodyBuilder3’ (improved version of ‘ABodyBuilder2’) [89], ‘AlphaFold 3’ (improved version of ‘AlphaFold 2’) [90], ‘RosettaFold’ [91], ‘tfold – Ab’ [92], and ‘ESMFold’ [93] offer high-accuracy predictions of 3D structures by leveraging deep learning approaches. Further, tools like ‘Ablooper’ [94], ‘IgFold’ [95], and ‘EquiFold’ [96] specialize in refining and predicting CDR loop configurations, providing more detailed insights into antibody–antigen interactions. These advanced computational platforms significantly enhance the efficiency and accuracy of antibody structure modeling, reducing time and cost in therapeutic antibody development.

Molecular docking

Molecular docking replicates the interaction between an antibody and its target antigen, providing valuable information on binding affinity and orientation. It predicts the ideal configurations of the antibody–antigen complex using energy minimization, allowing researchers to discover the best “pose” for binding. This method is critical for understanding how antibody CDRs interact with the antigen’s epitope, since it highlights essential amino acid residues involved in the interaction [93].

A variety of molecular docking techniques are available, including AutoDock [94], a popular tool for predicting binding modes, and HADDOCK (High Ambiguity Driven Protein–Protein Docking), which incorporates experimental data for more precise modeling [95]. ZDOCK ranks binding poses based on interaction energy and structural complementarity [96], whereas ClusPro [97], HDock [98], and DLAb [99] provide additional features for protein–protein docking. DockGPT [100], DyMEAN [101], GeoDock [102], and HERN [103] improve the capabilities of molecular docking by adding machine learning techniques and structural dynamics. Together, these technologies form a solid foundation for prescreening antibody–antigen interactions, expediting the design and optimization process prior to more sophisticated in vitro or in vivo research.

Molecular dynamics simulations

Molecular dynamics (MD) simulations give a dynamic perspective of antibody–antigen interactions by simulating the time-dependent behavior of atoms within the complex. Unlike static molecular docking, MD simulations capture the flexibility of antibody binding and follow conformational changes during antigen recognition, providing more insight into how antibodies behave in physiological conditions [104, 105]. These simulations are critical for assessing the stability, binding effectiveness, and possible off-target consequences of antibodies [106].

Several tools are available for MD simulations, including GROMACS, a flexible program commonly used for evaluating the dynamic features of antibody–antigen complexes [107], and AMBER, which is recognized for producing accurate force fields in protein simulations [108]. NAMD is designed for high-performance computation, which enables the simulation of massive complexes over long timescales [109]. Additional tools like as CHARMM [110], Desmond [111], OpenMM [112], and YASARA [113] improve MD simulations by offering adaptable, high-precision settings for simulating antibody–antigen interactions.

Immunogenicity prediction

A key challenge in developing therapeutic antibodies is minimizing immunogenicity, the unwanted immune responses they can provoke [114]. In silico models are invaluable for pinpointing immunogenic regions, especially T-cell epitopes, early in development to reduce these responses [115].

Various tools exist for B-cell epitope prediction, such as IEDB [116], COBEpro [117], BCpred [118], FBCpred [119], Bepipred 3.0 [120], and Discotope 3.0 [121]. For T-cell epitope prediction and identifying immunogenic areas, TEPITOPEpan [122] is a prominent method. These predictive tools guide antibody modifications in early development, enhancing their safety and efficacy for therapeutic use.

Emerging trends in antibody research

As antibody research progresses, new experimental models and technologies emerge that improve antibody discovery, characterization, and application. These cutting-edge models attempt to overcome the constraints of old approaches, increase prediction accuracy, and speed up the discovery of therapeutic antibodies. These unique techniques bring together breakthroughs in genetics, bioengineering, computational biology, and immunology to provide more efficient, scalable, and precise antibody research tools.

Single-cell sequencing and antibody discovery

Single-cell sequencing has changed antibody research by allowing for high-throughput investigation of B-cell repertoires at the individual cell level. This method can identify antigen-specific B cells and sequence their antibody genes directly from tissues or blood samples. Individual B-cell analysis allows researchers to acquire a better understanding of antibody diversity, clonality, and evolution in response to specific antigens like infections or tumor cells [123, 124].

CRISPR-based models for antibody engineering

CRISPR-Cas9 technology has transformed genetic engineering, and its use in antibody research offers new techniques to manipulate B cells and immune responses. Researchers can use CRISPR to alter antibody genes directly in B cells to develop unique antibodies with improved characteristics or examine the genetic underpinnings of antibody production [125, 126].

Microfluidic platforms for antibody screening

Microfluidics is an innovative method that combines the manipulation of small amounts of fluids with chip-based devices, providing a strong platform for high-throughput antibody screening and selection. Microfluidic devices can divide individual cells or molecules into small droplets, allowing for quick and efficient screening of millions of antibody–antigen interactions [127, 128].

Organs-on-chips and 3D tissue models

Organs-on-chips and 3D tissue models are sophisticated in vitro systems that simulate human organ function and disease states in a controlled setting. These systems are meant to replicate the complex architecture and milieu of human tissues, making them useful for researching antibody interactions in more physiologically realistic circumstances [129, 130].

Artificial intelligence and machine learning in antibody design

Artificial intelligence (AI) and machine learning (ML) are rapidly being used in antibody research, notably for antibody creation and optimization. AI algorithms can assess massive volumes of data, such as antibody sequences, structural details, and binding affinities, and forecast how changes in sequence or structure would alter antibody function [131, 132].

Nanobodies/Single-domain antibodies

Nanobodies, also known as single-domain antibodies (sdAbs), are generated from camelid heavy-chain-only antibodies and are made up of a single variable domain (VHH) with complete antigen-binding capacity. Their tiny size (~15 kDa) provides advantages over traditional antibodies, including greater tissue penetration, stability under harsh circumstances, and simplicity of synthesis in microbial systems such as Escherichia coli or yeast [133]. Nanobodies have potential in treatments, particularly where traditional antibodies struggle with tissue access or complicated targets. Their strength makes them suitable for diagnostics, medication administration, and imaging [134]. Key approved nanobodies include:

• Caplacizumab (U.S. Food and Drug Administration (FDA)/European Medicines Agency (EMA) approved): Targets von Willebrand factor (vWF) for treating acquired thrombotic thrombocytopenic purpura (aTTP) [135, 136].

• Vobarilizumab (investigational): Targets the IL-6 receptor for autoimmune diseases like rheumatoid arthritis [137].

• Ozoralizumab (Japan approved, 2022): Targets TNF-alpha for rheumatoid arthritis [138].

• Envafolimab (China approved, 2021): PD-L1-targeting nanobody for cancer immunotherapy, offering subcutaneous administration [139].

Emerging models enhancing translational success and reducing clinical trial failures

The integration of emerging experimental models and technologies is significantly addressing long-standing challenges in antibody research, leading to improved translational success rates and a reduction in costly clinical trial failures. Traditional preclinical models often fall short in accurately predicting human responses, contributing to high failure rates in clinical development, with a significant percentage due to lack of efficacy or toxicity. Emerging models aim to bridge this translational gap by offering more physiologically relevant systems, higher throughput screening capabilities, and predictive power.

Organs-on-chips and 3D tissue models

These sophisticated in vitro systems are designed to mimic human organ function and disease states, replicating the complex architecture and microenvironment of human tissues. By providing a more accurate representation of human physiology than traditional 2D cell cultures or even some animal models, organs-on-chips can:

• Improve efficacy and toxicity prediction: They can more precisely predict the safety and efficacy of investigational drugs in humans. For example, a liver chip accurately detected 87% of drugs known to cause drug-induced liver injury in patients, which had often passed animal testing. This capability allows for the early identification of potentially toxic compounds, preventing them from advancing to human trials [151].

• Reduce reliance on animal testing: Organs-on-chips offer a potential replacement for animal testing in preclinical trials, reducing ethical concerns and the financial burden associated with animal models. The FDA Modernization Act 2.0 now permits the use of organoid models as alternatives to animal testing, encouraging their advancement [152].

• Accelerate drug development: By providing rapid, cost-effective, and more accurate information on human diseases and drug interactions, organs-on-chips can significantly accelerate the early-stage experimental phase, potentially saving time and money in drug development [153].

Artificial intelligence and machine learning

AI and ML are revolutionizing drug discovery by enhancing data analysis and predictive capabilities, thereby contributing to faster and more effective treatments. Their impact on translational success includes:

• Enhanced target identification and drug design: AI algorithms can analyze vast amounts of biological data (genomics, proteomics) to identify potential therapeutic targets and design novel drug-like chemical structures more efficiently. This targeted approach increases the likelihood of successful drug approvals.

• Optimized lead compound selection: AI enables virtual screening of vast chemical libraries, predicting binding affinities and optimizing drug candidates for desired features like potency, selectivity, and favorable pharmacokinetic profiles. This reduces the need for extensive and costly experimental testing, allowing researchers to "fail faster and cheaper with unpromising candidates" [154].

• Improved clinical trial design and efficiency: AI is increasingly integrated into clinical trial design, digital health technologies, and real-world data analytics. It can analyze large clinical trial and observational study datasets to make inferences regarding drug safety and effectiveness, inform trial design, and improve recruitment and retention of participants. This streamlines the trial process and can reduce delays and inefficiencies [155].

Single-cell sequencing

Single-cell sequencing provides unprecedented resolution in understanding cellular diversity and responses, which is crucial for personalized medicine and identifying biomarkers that can predict treatment outcomes. Its contributions to translational success include:

• Dissecting tumor heterogeneity: This technology allows researchers to investigate B-cell repertoires and tumor microenvironments at the individual cell level, providing insights into resistance mechanisms and identifying prognostically relevant tumor clones.

• Understanding drug mechanisms and immune responses: Single-cell RNA sequencing (scRNA-seq) is used in Phase II clinical trials to understand an authorized drug’s mechanism of action, linking molecular changes directly to positive or negative disease outcomes in patients. It can map immune cell clonality, characterizing therapeutic or immunotoxicity responses, and observe gene expression changes indicative of activated immune programs linked to improved patient survival [156].

CRISPR-based models

CRISPR-Cas technology has transformative potential for gene editing, with direct implications for therapeutic development and disease modeling. While still advancing, its role in improving translational success includes:

• Precise genetic manipulation: CRISPR allows for the precise editing of genes in B cells to engineer antibodies with improved characteristics or to model disease-relevant antigens, streamlining the development of targeted therapies.

• Development of advanced disease models: CRISPR has been instrumental in generating new cellular and animal models (e.g. for Duchenne muscular dystrophy) that accelerate preclinical development of therapeutic solutions by providing more accurate representations of human disease [157]. The FDA approval of Casgevy, the first CRISPR/Cas9-based drug, marks a significant milestone in clinical translation [158].

Microfluidic platforms

Microfluidics offers advantages in high-throughput screening and creating more physiologically relevant in vitro environments:

• High-throughput screening: These platforms enable rapid and efficient screening of millions of antibody–antigen interactions, accelerating the identification of high-affinity candidates and reducing the time required for lead discovery.

• Enhanced control over microenvironment: Microfluidic devices can produce 3D cell culture scaffolds and control parameters like oxygen levels, temperature, and pH more accurately than traditional methods, mimicking in vivo microenvironments for drug screening and tissue engineering. This improved control can lead to more reliable preclinical data [159].

Challenges in antibody experimental models

Antibody research has made considerable advances, particularly with the creation of several experimental models, such as in vivo, in vitro, and in silico systems. However, each of these techniques has significant problems that might affect the efficiency, dependability, and scalability of antibody identification, optimization, and application. Understanding these limitations is critical for moving research forward and creating more effective antibody-based therapeutics.

Animal welfare and ethical concerns in in vivo models

In vivo models are crucial for studying antibody responses and pharmacokinetics, but they raise ethical issues concerning animal welfare. Researchers face pressure to reduce, refine, and replace animal use, especially in light of societal concerns about animal suffering. Although alternatives are emerging, in vivo models remain irreplaceable for studying complex immune interactions not easily replicated in vitro or in silico [63, 140].

Reproducibility and scalability in in vitro platform technologies

In vitro platform technologies, such as hybridoma technology and phage display, are essential for antibody production but face challenges in reproducibility and scalability. Variability in experimental conditions can lead to inconsistent results, while scaling these models for industrial-level production is resource-intensive and laborious, complicating large-scale antibody discovery [141, 142].

Structural prediction accuracy in in silico models

In silico approaches are transforming antibody design, but they struggle with accurately predicting antibody–antigen interactions, especially in the CDRs. Additionally, limited structural data for novel antibodies hampers the reliability of these predictions, often necessitating experimental validation to confirm the models’ accuracy [143].

Immunogenicity and human relevance in preclinical models

Predicting immunogenicity in therapeutic antibodies is challenging, as preclinical models often fail to fully replicate the human immune system. Species differences in animal models and the structural complexity of antibodies can result in unforeseen immune responses in humans, making it difficult to predict antibody behavior in clinical settings [144, 145].

High costs and technical expertise for advanced models

Advanced models like single-cell sequencing, microfluidic platforms, and organs-on-chips offer deeper insights into antibody research but are costly and require specialized expertise. The high costs and technical demands of these models limit their widespread use, especially in resource-constrained labs, and require highly trained personnel to operate effectively [146, 147].

To facilitate a comprehensive understanding and critical comparison of the diverse experimental models and platforms currently employed in antibody research, we present a detailed comparative analysis in Table 2. This table synthesizes the key advantages, inherent limitations, scalability, and physiological relevance of in vivo, in vitro, and in silico approaches, alongside emerging technologies. It aims to provide readers with a clear, side-by-side evaluation, highlighting how each platform contributes to and challenges the advancements in antibody discovery and therapeutic development.

Table 2.

Comparative analysis of experimental models and platforms in antibody research.

Category	Platform/Technology	Key advantage(s)	Key limitation(s)	Scalability (inferred/stated)	Physiological relevance (inferred/stated)
In vivo Models for Immunization	Traditional Animal Models (Mice, Rats, Rabbits, Hamsters, Guinea Pigs, Chickens)	Robust platform for understanding immune mechanisms and practical antibody production; foundational for hybridoma technology; reproducible immune responses in BALB/c mice; rats valuable when mouse responses suboptimal; rabbits yield high-affinity, antigen-specific polyclonal antibodies; chickens produce IgY lacking mammalian cross-reactivity. Enable investigation of complex biological interactions, immunological responses, and pharmacokinetics.	Ethical concerns and animal welfare issues; species differences limit direct translation to humans; murine antibodies can trigger immunogenic responses in humans; direct human testing is expensive and complex.	Generally, less scalable than in vitro or in silico methods due to animal maintenance and ethical considerations.	High physiological relevance as they are whole organisms, but species differences can limit direct human translation.
	Humanized Mouse Models (e.g., XenoMouse®, HuMAb Mouse®, VelocImmune®, KyMouse®, H2L2 Harbour Mice®, Trianni Mouse®, OmniAb®, ATX-Gx™ Mouse, CAMouse™, HUGO-Ab™, RenMab™, RenLite™, THX Mouse)	Genetically engineered to produce fully human antibodies, reducing human immunogenicity. Mimic natural human antibody selection with in vivo somatic hypermutation and affinity maturation. Offer diverse human antibody repertoires.	Still involves animal use and associated ethical concerns.	Improved for human antibody production compared to traditional immunization but still tied to animal breeding and maintenance.	High physiological relevance for human antibody production due to human immunoglobulin loci.
In vitro Platforms for Antibody Discovery	Hybridoma Technology	Fundamental for producing monoclonal antibodies (mAbs) of high specificity and constant quality.	Requires animal immunization. Can take months to provide results. Resource-intensive and laborious for large-scale production.	Challenging for industrial-level production; resource-intensive and laborious.	Lacks full physiological relevance as it is performed in a controlled laboratory setting.
	B-Cell Culture Systems	Direct isolation and in vitro cultivation of B cells from immunized animals or human donors. Eliminates the cell fusion step, making it less labor-intensive and more direct than hybridoma technology. Valuable for producing fully human antibodies directly from donor B cells, minimizing potential immunogenicity.	Can be challenging to extend B-cell viability and enhance antibody production efficiency without immortalization techniques.	Improved potential for scalability compared to hybridoma due to elimination of cell fusion.	Improved over hybridomas for human relevance when using human donor B cells but still an isolated system.
	Display Technologies (Phage, Yeast, Bacterial, Ribosome, Mammalian Display)	Allow in vitro generation of antibodies without animal vaccination. Efficient and cost-effective. Avoid ethical problems related to animal models. Enable high-throughput screening of vast libraries of antibody fragments. Ribosome display allows for greater library sizes.	Often display fragments (scFv or Fab) rather than full antibodies, which may require further engineering. Variability in experimental conditions can lead to inconsistent results.	High-throughput, enabling efficient screening of millions of interactions, implying good scalability for discovery.	Low physiological relevance as they are cell-free or microbial systems, lacking the complex environment of a living organism.

(Continued)

Table 2.

Continued.

Category	Platform/Technology	Key advantage(s)	Key limitation(s)	Scalability (inferred/stated)	Physiological relevance (inferred/stated)
In vivo Models for Functional Studies	Traditional Animal Models (Rodents, Nonhuman Primates)	Provide initial data on antibody absorption, distribution, metabolism, and excretion profiles and on-target effects. NHPs are considered superior for translational data due to close physiological and immunological similarities to humans.	Differences in immune systems and physiology between rodents and humans limit direct translation of findings. Ethical concerns and high costs, especially for NHPs.	Limited scalability due to animal use and associated costs/logistics.	High, particularly NHPs, for assessing pharmacokinetics, pharmacodynamics, half-life, and clearance.
	Xenograft Models (CDX/PDX, zebrafish)	Critical tools for studying antibody-based therapies, particularly in oncology research. Allow engraftment and growth of human cancer cells in immunocompromised animals without rejection. Facilitate evaluation of therapeutic antibodies. Zebrafish offer high-throughput drug screening and in vivo imaging.	Relies on immunocompromised animals, which may not fully replicate the human immune response to therapies.	Generally scalable for tumor studies, with zebrafish offering high-throughput capabilities.	Moderate to high for human tumor microenvironment studies, especially PDX models. Zebrafish offer unique insights but are less direct human mimics.
	Transgenic Animal Models	Genetically engineered to express human proteins, enabling testing of therapeutic antibodies targeting specific human disease-related molecules. Offer crucial insights into therapeutic development for cancer, autoimmune, and infectious diseases. Examples include models for human PD-1, HER2, hACE2, and CD20.	Still involve animal use and ethical considerations.	Moderate; creating and maintaining specific transgenic lines can be resource-intensive, but once established, they can be scaled for studies.	High for specific human protein interactions and targeted therapies, as they express human antigens/receptors.
In silico Models for Antibody Research	Antibody Structure Prediction (ABodyBuilder3, AlphaFold 3, RosettaFold, tfold – Ab, ESMFold, Ablooper, IgFold, EquiFold)	Crucial for understanding antibody function, particularly CDRs. Offer high-accuracy predictions of 3D structures using deep learning. Significantly enhance efficiency and accuracy, reducing time and cost in therapeutic antibody development.	Struggle with accurately predicting antibody–antigen interactions, especially in CDRs. Limited structural data for novel antibodies can hamper reliability. Often necessitates experimental validation.	High; computational methods can process large datasets and predict structures rapidly.	Indirect; provides structural insights but does not replicate biological complexity or in vivo conditions.
	Molecular Docking (AutoDock, HADDOCK, ZDOCK, ClusPro, HDock, DLAb, DockGPT, DyMEAN, GeoDock, HERN)	Predicts ideal configurations of the antibody–antigen complex and binding affinity. Critical for understanding CDR-epitope interactions. Expedites design and optimization prior to experimental work.	Provides a static view of interactions; does not capture dynamic conformational changes.	High; can prescreen numerous antibody–antigen interactions computationally.	Indirect; provides interaction predictions but lacks the dynamic and complex biological environment.
	Molecular Dynamics (MD) Simulations (GROMACS, AMBER, NAMD, CHARMM, Desmond, OpenMM, YASARA)	Gives a dynamic perspective of antibody–antigen interactions by simulating time-dependent behavior. Captures flexibility and conformational changes during antigen recognition. Critical for assessing stability, binding effectiveness, and possible off-target consequences.	Computationally intensive, especially for massive complexes or long timescales. Requires accurate force fields.	Moderate to high, but performance depends on computational resources and complexity of the system.	Improved over docking for simulating physiological conditions by capturing dynamic behavior.
	Immunogenicity Prediction (IEDB, COBEpro, BCpred, FBCpred, Bepipred 3.0, Discotope 3.0, TEPITOPEpan)	Extremely useful in identifying immunogenic areas, particularly T-cell epitopes, that may trigger an immunological response. Allows researchers to lower immunogenicity by identifying sites early in the production process.	Preclinical models often fail to fully replicate the human immune system. Species differences and structural complexity can result in unforeseen immune responses in humans.	High; computational prediction can be applied to many sequences.	Indirect; predicts potential immunogenicity but requires experimental validation for human relevance.

(Continued)

Table 2.

Continued.

Category	Platform/Technology	Key advantage(s)	Key limitation(s)	Scalability (inferred/stated)	Physiological relevance (inferred/stated)
Emerging Trends in Antibody Research	Single-Cell Sequencing	Changed antibody research by allowing high-throughput investigation of B-cell repertoires at the individual cell level. Identifies antigen-specific B cells and sequences their antibody genes directly from tissues or blood samples. Provides a better understanding of antibody diversity, clonality, and evolution.	High costs and requires specialized expertise.	High-throughput, capable of analyzing numerous individual cells.	High; directly analyzes B cells from biological samples, reflecting in vivo responses.
	CRISPR-Based Models for Antibody Engineering	Transformed genetic engineering and offers new techniques to manipulate B cells and immune responses. Allows direct alteration of antibody genes in B cells to develop unique antibodies or examine genetic underpinnings.	High costs and requires specialized expertise.	Moderate; while genetic manipulation can be precise, scaling up for large-scale antibody production may still have complexities.	High; allows for precise genetic manipulation within biological systems (B cells).
	Microfluidic Platforms for Antibody Screening	Innovative method for high-throughput antibody screening and selection. Can divide individual cells or molecules into small droplets for quick and efficient screening of millions of antibody–antigen interactions.	High costs and requires specialized expertise.	High-throughput, designed for rapid and efficient screening of large numbers of interactions.	Improved over traditional in vitro methods by enabling more controlled and miniaturized environments but still ex vivo.
	Organs-on-Chips and 3D Tissue Models	Sophisticated in vitro systems that simulate human organ function and disease states in a controlled setting. Meant to replicate the complex architecture and milieu of human tissues, useful for physiologically realistic antibody interactions.	High costs and requires specialized expertise. Still evolving and may not fully replicate all aspects of complex organ systems.	Moderate; setting up and maintaining these complex systems can be challenging for large-scale use.	High; designed to closely mimic human tissues and organ functions, offering a bridge between in vitro and in vivo studies.
	Artificial Intelligence (AI) and Machine Learning (ML) in Antibody Design	Rapidly being used for antibody creation and optimization. AI algorithms can assess massive volumes of data (sequences, structural details, binding affinities) and forecast how changes in sequence or structure would alter antibody function. Cost-effective and time-saving.	Limited by the accuracy and availability of data for training models.	High; highly scalable for analyzing and predicting properties of large datasets of antibodies.	Indirect; a computational tool that guides design but does not directly simulate biological systems.
	Nanobodies/Single-Domain Antibodies (sdAbs)	Tiny size (~15 kDa) provides advantages over traditional antibodies, including greater tissue penetration, stability under harsh circumstances, and simplicity of synthesis in microbial systems. Have potential in treatments where traditional antibodies struggle with tissue access or complicated targets. Suitable for diagnostics, medication administration, and imaging.	Their smaller size might affect their in vivo half-life compared to full-sized antibodies.	Good scalability for synthesis in microbial systems.	High for therapeutic applications due to unique properties like tissue penetration.

Case studies of successful FDA-approved antibodies

The evolution of therapeutic antibodies has been marked by a transition from wholly foreign (murine) proteins to increasingly human-like constructs, driven by the need to reduce immunogenicity and improve efficacy. This progression is evident in the FDA-approved landscape, showcasing various platforms for antibody discovery and the crucial role of preclinical and human studies.

Murine antibodies

Early success stories in antibody therapy often involved murine mAbs, derived directly from immunized mice using hybridoma technology. These antibodies, while revolutionary, carried the inherent challenge of eliciting a human anti-mouse antibody (HAMA) response, limiting their efficacy and leading to adverse reactions. An illustrative example, though less common in recent approvals due to immunogenicity concerns, is ‘Muromonab-CD3 (Orthoclone OKT3)’. Approved in 1986, it was a murine IgG2a antibody used for the treatment of acute organ transplant rejection. The platform involved immunizing mice with human T cells to generate hybridomas, with BALB/cJ mice being the specific strain used for immunization [160]. Preclinical testing would have utilized in vitro assays for binding affinity and functional activity, alongside in vivo studies in animal models, such as NHPs (cynomolgus monkeys), to assess its ability to deplete T cells and prevent rejection [161]. Human studies not only demonstrated its efficacy in reversing acute rejection episodes but also highlighted the significant HAMA response, often necessitating premedication and limiting long-term use [162].

Chimeric antibodies

To mitigate the HAMA response, chimeric antibodies were developed, combining the antigen-binding variable regions from a murine antibody with the constant regions of a human antibody. This approach significantly reduced immunogenicity while retaining the desired antigen specificity. A prominent example is ‘rituximab (Rituxan)’, a chimeric antibody approved in 1997 for non-Hodgkin’s lymphoma, and later for various autoimmune diseases. Its development involved immunizing ‘BALB/c’ mice with human lymphoblastoid cell line SB, followed by the generation of hybridomas. The variable regions from the murine antibody were then fused with human IgG1 constant regions. Preclinical testing involved in vitro assays for CD20 binding, complement-dependent cytotoxicity (CDC), and antibody-dependent cellular cytotoxicity (ADCC) against CD20-positive cells. Studies in cynomolgus monkeys were also important to assess B-cell depletion and pharmacokinetics, given the similarity of their antibody constant domains to humans [163]. Human studies in clinical trials confirmed its efficacy in inducing remission in lymphoma patients, with a reduced incidence and severity of immunogenic reactions compared to fully murine antibodies, although some patients still developed antichimeric antibodies [164].

Humanized antibodies

Further refinement led to humanized antibodies, which contain only the CDRs from the murine antibody grafted onto a human antibody framework. This design minimizes the nonhuman content, typically to <10%, further reducing immunogenicity. ‘Trastuzumab (Herceptin)’, a humanized IgG1 antibody approved in 1998 for HER2-positive breast cancer, is a classic illustration. The discovery involved immunizing ‘BALB/c’ mice with NIH 3 T3/HER2–3400 cells to obtain murine anti-HER2 antibodies, from which the CDRs were identified. These CDRs were then grafted onto a human IgG1 framework. Preclinical validation involved extensive in vitro characterization of HER2 binding, inhibition of HER2 signaling, and ADCC activity against HER2-overexpressing cancer cells [165, 166]. It is important to note that trastuzumab does not bind to rodent ErbB2/neu, the rodent equivalent of human HER2, meaning that common murine models could not be used for direct target-dependent efficacy or safety studies. Therefore, animal models, particularly xenograft models using HER2-positive human breast cancer cells in nude mice, were crucial for demonstrating its antitumor efficacy, focusing on the human tumor xenograft rather than direct antibody-rodent HER2 interaction [167]. Human clinical trials established trastuzumab as a cornerstone therapy for HER2-positive breast cancer, significantly improving patient outcomes with a low rate of significant immunogenicity [168].

Fully Human antibodies

The ultimate goal of antibody engineering is to produce fully human antibodies, which theoretically should exhibit the lowest immunogenicity and longest half-life in patients. These can be generated through various advanced platforms, including phage display libraries, yeast display, or immunization of transgenic mice engineered to produce human antibodies. ‘Adalimumab (Humira)’, a fully human IgG1 antibody approved in 2002 for rheumatoid arthritis and numerous other inflammatory conditions, exemplifies this category. Adalimumab was discovered using phage display technology, where human antibody gene libraries were screened for binding to human TNF-alpha. This in vitro selection bypassed the need for animal immunization to generate the initial antibody repertoire [169]. Preclinical studies involved detailed in vitro analyses of TNF-alpha binding and neutralization, as well as in vivo efficacy in animal models of inflammatory diseases, such as collagen-induced arthritis in mice (e.g., ‘DBA/1 mice’). Human clinical trials established adalimumab as a highly effective therapy, with a remarkably low incidence of antidrug antibodies, contributing to its widespread and long-term use in chronic inflammatory conditions [170]. Another prominent example is ‘nivolumab (Opdivo)’, a human IgG4 anti-PD-1 antibody approved for various cancers, which was developed from ‘HuMAn Mouse®’, developed by Medarex, Inc. These mice are immunized with human PD-1 protein or cells expressing human PD-1. This platform directly generates fully human antibodies through an in vivo immune response, followed by selection and optimization. Preclinical testing demonstrated its ability to block the PD-1/PD-L1 pathway and enhance anti-tumor immunity in relevant in vitro assays and NHPs (cynomolgus monkeys) [171]. Clinical trials showed significant and durable responses in various cancer types, with a favorable safety profile attributed to its fully human nature [172].

Conclusion

The landscape of antibody research is rapidly evolving, driven by technological advancements and novel experimental models. While traditional in vivo and in vitro platforms have been instrumental in advancing our understanding of antibody biology, emerging approaches such as in silico modeling, AI-driven design, microfluidics, and synthetic biology are reshaping the future of antibody discovery and development. Each model/platform—whether in vivo, in vitro, or in silico—offers unique strengths and limitations. In vivo models provide valuable insights into the complex immune responses within whole organisms but face ethical and scalability issues. In vitro platforms offer controlled environments for antibody selection and production but may lack physiological relevance. In silico models promise to accelerate antibody design and screening through computational predictions but are limited by the accuracy of available data. Overcoming the challenges of reproducibility, immunogenicity prediction, scalability, and ethical considerations will be essential to harness the full potential of these models. The future of antibody research lies in the integration of these various approaches, using advanced computational tools, human-on-chip models, and personalized therapies to enhance the precision and efficiency of antibody development.

Ultimately, as new models and technologies emerge, they will not only address existing limitations but also unlock new therapeutic possibilities. Antibody-based treatments will become more specific, effective, and accessible, offering new hope for patients with cancer, autoimmune diseases, infectious diseases, and other challenging conditions. Through continued innovation and interdisciplinary collaboration, the next generation of antibodies will revolutionize medicine and bring us closer to realizing the full potential of immunotherapy and personalized healthcare.

Ethics and consent statement

Not Applicable.

Animal research statement

Not Applicable.

Consent for publication

All authors have read and understood the publishing policy, and this manuscript is submitted in accordance with this policy.

Author contributions

Jagadeeswarareddy Devasani (Conceptualization, Data curation, Funding acquisition, Methodology, Project administration, Resources, Software, Visualization, Writing—original draft [equal]), Girijasankar Guntuku (Formal analysis, Project administration, Supervision, Writing—review & editing [equal]), Prathyusha Sarabu (Data curation, Formal analysis, Software, Writing—review & editing [equal]), Murali Krishna Kumar Muthyala (Data curation, Formal analysis, Project administration, Validation, Writing—review & editing [equal]), Mary Sulakshana Palla (Conceptualization, Data curation, Funding acquisition, Supervision, Writing—review & editing [equal]), and Mallikarjuna Subrahmanyam Volety (Formal analysis, Supervision [equal])

Conflict of interest statement

None declared.

Funding

Jagadeeswara Reddy Devasani is a PhD student, and Prathyusha Sarabu is a Masters student at Andhra University. Girijasankar Gutuku and M. Murali Krishna Kumar are employees of Andhra University. Mary Sulakshna Palla is an employee of GITAM (Deemed to be University), and Volety Mallikarjuna Subrahmanyam is an employee of Manipal Academy of Higher Education. This work received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Data availability

No data generated in this work.