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Drug developers regularly exploit the natural ability of B cells to create hundreds of thousands of antibodies and select the ones specific to their target (Singh S., 2018). Depending on the site of interaction, antibodies can block or activate the target protein, favor its clearance, or induce an immune response against cells expressing the target. Moreover, antibodies can be conjugated to drugs or toxins to increase their local concentration at specific sites.

As compared to other proteins that have a serum half-life of a few hours, antibodies are very stable in circulation. Immunoglobulin G (IgG), the most commonly used isotype for therapeutic development, has an approximate half-life of 21 days. The mechanism behind IgG’s extended half-life lies in the interaction between IgG and the neonatal Fc receptor (FcRn) (Roopenian et al., 2003; Challa et al., 2014). Cells lining the blood vessels internalize IgG and shuttle it to the lysosome. The low pH in acidified endosomes causes IgG to bind with FcRn. The IgG: FcRn complexes are subsequently recycled away from the lysosomal degradative pathway to the cell membrane, where, at neutral pH, IgG dissociates from FcRn, releasing IgG back into circulation.

There are several challenges facing researchers who are developing therapeutics, including assessing half-life, selecting efficacious variants, and determining dosage for clinical trials. Given these challenges, what solutions does JAX provide to enable researchers to get accurate antibody data, saving both time and resources?

Problem: How do I predict antibody half-life? 

Measuring half-life of therapeutic candidate molecules is often overlooked because, until a few years ago, the available animal models had significant limitations to test the half-life of human antibodies accurately. For instance, standard rodent models are relatively inexpensive and easy to work with to test half-life of IgGs. However, one disadvantage to using standard rodent models is that mouse and rat FcRn do not bind human IgG with the same affinity as human FcRn leading to inaccurate half-life data (Ober et al., 2001). While non-human primates can predict with reasonable accuracy the half-life of human antibodies in patients, their use for screening at early stages in drug development is not a feasible option due to both ethical and practical reasons.


To address the lack of preclinical models to predict human therapeutic half-life accurately, JAX scientist Dr. Derry Roopenian developed mice expressing transgenic human FCGRT, the gene that encodes FcRn and lacking the endogenous murine gene (Proetzel and Roopenian, 2014). These “Tg32” and “Tg276” FcRn humanized mouse models can be used to predict the PK of IgG antibodies in humans with an accuracy comparable to non-human primates (Fig. 1a and Fig. 1b). Furthermore, as shown in figure 2 using the Tg32 model as an example, the variability between the mice is minimal, enabling a small number of animals per study.

The Jackson Labs Revealing Antibody Char Fig1

The Jackson Labs Revealing Antibody Char Fig2

By using these transgenic lines, JAX Therapeutic Evaluation Services routinely compares the half-life of different client-supplied molecules to identify the most promising candidates (Fig 3).

The Jackson Labs Revealing Antibody Char Fig3

Problem: How can I identify the right antibody variant?

Antibodies can be modified in several ways. For example, Fc engineering may result in multiple variants of the same antibody, each with slightly different in vivo half-life and potentially different effector functions. Due to their similar structure, it is often difficult to identify the variant with the most extended half-life (Fig 4a).

The Jackson Labs Revealing Antibody Char Fig4


JAX Therapeutic Antibody Evaluation Services rely on the use of human FcRn Tg276 mice (004919), which ubiquitously express the human gene at high levels in all tissues (Latvala et al., 2017). Although the levels and pattern of expression of the transgene in Tg276 mice do not recapitulate the physiological expression of FCGRT in humans, these animals represent the optimal tool to detect the smallest differences between similar monoclonal antibodies (mAbs) (Fig 4b), allowing researchers to differentiate between variants for further development.

Problem: How do I determine which dose is the best for my antibody?

The use of antibodies has resolved many issues regarding drug toxicity. The inherent specificity of antibodies makes potential off-target effects significantly less likely, and, due to their composition, impossible to be converted to toxic catabolites. The primary sources of antibody-mediated toxicity are linked to the expression of the antigen at non-therapeutic sites, and hyperactivation of the immune system in case of immunomodulatory antibodies (Brennan et al., 2010). In both cases, the appropriate dosing of the therapeutic antibody can help to prevent or limit the adverse reaction. However, if accurate preclinical data predicting the PK of an antibody in humans is not available, it is challenging to set a therapeutic dose when designing a clinical study. In many cases, it is necessary to perform a dose escalation in humans, with the risk of reducing the efficacy of the treatment or increasing the likelihood of side effects.


JAX-executed studies provide an accurate preclinical evaluation of antibody half-life using human FcRn Tg32 mice (014565), which display a physiological human FCGRT expression pattern (Latvala et al., 2017). These data can support decisions regarding the clinical dosing of the therapeutic molecule (Fig 5). Using this model and human PK predictive allometric scaling (Betts et al., 2018), clinicians can estimate the minimum dose to achieve therapeutic serum concentrations, reducing the need for potentially risky dose-escalation treatments during clinical trials.

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