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1 in 3 Antibody Drug Candidates Bind Off-Targets. Here’s What That Means.

In this article:

• How common is off-target binding, really?
• Does polyspecificity persist into late-stage clinical development?
• Why can’t you predict off-targets from sequence analysis alone?
• What is TCR missing?

This article is part 5 of a series about specificity testing. Be sure to read part 3, about the limitations of tissue cross-reactivity studies. 

Summary

Off-target binding in antibody therapeutics is far more common than most people expect. Our analysis of hundreds of antibody-based drug candidates found that roughly 1 in 3 lead candidates display polyspecific off-target binding. And the problem persists as drugs advance through development. Even among FDA-approved MAbs, about 15% show off-target binding. Side-by-side comparisons of MPA and tissue cross-reactivity (TCR) data reveal that many of these off-targets went undetected by traditional methods. Spotting these interactions early, and identifying the specific off-target proteins, is the first step toward addressing them.

How common is off-target binding, really?

In an earlier article, we mentioned that 1 in 3 antibody lead candidates has off-target binding. Here, we’ll dig into that number.

To quantify the prevalence of polyspecificity across the industry, we conducted a retrospective analysis of all antibody-based therapeutics submitted by customers for specificity testing on the Membrane Proteome Array (MPA) over a defined time period. We analyzed 254 samples in total, including MAbs, scFv-Fcs, and VHH-Fcs. These samples primarily represent lead candidates at biopharmaceutical companies throughout the industry. To be included in the analysis, each sample had to have successfully completed all three steps of the MPA process: Assay Setup, MPA Screen, and Validation (Norden et al., 2024).

The results were striking:

• 83 of 254 samples (32.7%) displayed polyspecific off-target binding.
• Among the polyspecific antibodies, about half had a single off-target, while the rest had two or more.
• Off-target binding was almost always to completely unpredictable membrane proteins with no significant sequence homology to the intended target.

33% of 254 antibody-based lead candidates screened on the MPA demonstrated validated off-target binding. Data from Norden et al., 2024.

The 33% figure reflects true, confirmed off-target binding interactions. Off-target binding was detected during MPA screening on ~6,000 native membrane proteins, then validated by antibody titration studies to confirm each hit. Note that these results include only CDR-mediated interactions. Non-CDR-mediated interactions, such as binding to Fc receptors or lectins, would push the off-target rate even higher if counted.

Perhaps most importantly, the off-targets were almost never related to the intended targets. In part, that’s because most MAbs screened on the MPA have already been tested against members of the same protein family. The study results represent genuinely unexpected interactions that conventional approaches would miss entirely.

The findings support our recommendation to conduct specificity testing using cell-based protein arrays early in drug development, ideally during lead selection, when potential toxicity issues can be identified and addressed with relatively little impact on a drug program.

Does polyspecificity persist into late-stage development?

Given the high off-target rate among lead candidates, we wanted to know whether polyspecificity persists among MAbs that have gone into humans. To find out, we produced biosimilars of 83 clinical-stage, FDA-approved, and withdrawn MAbs and screened them on the Membrane Proteome Array (MPA).

The answer is yes: polyspecificity persists at every stage of clinical development.

• 18.1% of clinical MAbs overall showed off-target binding.
• Off-target rates were slightly higher for withdrawn MAbs (22.2%) and those in Phase 2/3 (20.0%) compared to approved MAbs (15.0%).

Off-target binding was found in MAbs at all stages of clinical development, with higher rates among withdrawn and clinical-stage MAbs than approved ones. Based on Norden et al., 2024.

The lower rate among clinical-stage MAbs compared to lead candidates (18% vs. 33%) suggests that polyspecificity contributes to drug attrition; candidates with off-target binding appear to drop out of the pipeline at higher rates. But an off-target binding rate of 15% in FDA-approved drugs makes clear that current screening methods are not catching everything.

An independent analysis published in 2026 reached a similar conclusion. Dai et al. used a different platform to screen 174 FDA-approved and clinical-stage MAbs against 6,172 human extracellular proteins, finding that 28% had at least one off-target. The Dai et al. and Norden et al. studies used different technologies and somewhat different protein sets, but both point to the same conclusion: off-target binding among clinical antibodies is not rare, and it is not an artifact of any single platform.

Why can’t you predict off-target binding from sequence analysis alone?

One of the most consistent findings across our Membrane Proteome Array (MPA) data is that off-target binding is almost never to a related protein. So why is it happening?

Three mechanisms have been identified:

Molecular mimicry is likely the most common. Critical epitope residues in the intended target are mirrored, by chance, in a completely unrelated protein. In one well-documented example, a MAb we isolated against the glucose transporter SLC2A4 (GLUT4) also bound to Notch1, a signaling protein with less than 7% sequence identity and no structural similarity to GLUT4. Epitope mapping traced the cross-reactivity to a shared LGXXGP motif present in both proteins: one in a loop on GLUT4, the other in a disulfide-constrained loop on Notch1 (Tucker et al., 2018).

Molecular mimicry explains how a MAb against SLC2A4 (GLUT4) also bound Notch1—despite less than 7% sequence identity. Epitope mapping revealed that both proteins share an LGXXGP epitope motif. Graphic based on Norden et al., 2024, Fig. 6.

CDR plasticity is a second mechanism, in which conformational flexibility in the antibody’s complementarity-determining regions (CDRs) allows the paratope to adapt to more than one antigen.
Differential CDR engagement is a third possibility: off-target binding may occur through entirely different CDR residues than those used to bind the primary target.

The practical implication is the same regardless of mechanism: off-target binding cannot be reliably predicted from sequence or structural analysis. Proteome-wide empirical screening is the only reliable way to detect it.

What is TCR missing?

As discussed in an earlier article, tissue cross-reactivity (TCR) studies have significant limitations. Our own data provides direct evidence of one of the most consequential: TCR is missing off-target interactions that the Membrane Proteome Array (MPA) detects.

To compare the two approaches, we reproduced FDA-approved MAbs and screened them on the MPA, then compared our results to TCR data from the corresponding biologics license applications (BLAs). In several cases, the MPA identified off-target binding that was not mentioned in the TCR summaries.

In three side-by-side comparisons, MPA screening of biosimilars of FDA-approved MAbs identified off-targets that were not detected or reported in available TCR data from BLA applications. Dotted line represents 3 SD above calculated background. Adapted from Norden et al., 2024.

Three examples illustrate the pattern:

• Case Study 1. This antibody targets a plasma membrane protein expressed on lymphocytes. The MPA correctly identified the intended target—and also detected two off-target membrane proteins that bound even more strongly than the target. The BLA summary indicated TCR staining consistent with known target expression on lymphocytes, with no off-target binding reported.
• Case Study 2. This antibody targets a membrane protein expressed on the plasma membrane and intracellularly in myeloid cells. The MPA identified the target and an additional off-target expressed on the same cell type. The BLA summary noted staining consistent with known target expression and did not mention off-target binding. When the target and off-target are co-expressed on the same cell, tissue-based methods cannot distinguish between them.
• Case Study 3. This antibody targets a membrane protein with low, widespread expression across most normal tissues that is upregulated in certain disease states. The MPA identified the target and an off-target membrane protein. The BLA summary reported primarily cytoplasmic staining across many tissues, with no significant off-target concerns—likely because identifying an off-target signal against a background of widespread positive staining is extremely difficult using TCR.

In none of these cases were the off-targets members of the same protein family as the intended target. These represent genuinely unexpected interactions that TCR was not designed to detect.

Looking ahead

The data clearly show that off-target binding is a widespread problem that traditional methods are failing to catch. But what actually happens when those interactions go undetected—and make it into the clinic? In the next article, we examine case studies where undetected off-target binding led to serious adverse events in patients, and what those examples tell us about the need for better specificity testing. 

What Tools Are Available for Specificity Testing During Drug Development?

In this article:

• Why is specificity testing important during biotherapeutic drug development?

• What is off-target binding?

• What off-target specificity testing methods are currently available?

• How has FDA guidance for specificity testing changed over time?

This article is part 2 of a series about specificity testing. Be sure to read part 1, an introduction to the series. 

Summary 

Even though monoclonal antibodies are prized for their specificity, 1 in 3 lead candidates exhibit off-target binding. Because off-target interactions are so common and their safety consequences so large, we need accurate methods for identifying them. Specificity testing during preclinical development typically involves one or both of two technologies: tissue cross-reactivity (TCR) studies, which have been in use since the 1980s, and cell-based protein arrays, which have emerged in the last decade or so. Other methods, including tissue microarrays and spotted protein microarrays, have limitations that make them inappropriate for preclinical safety testing. While early FDA guidance focused on TCR, recent documents have named protein arrays as an appropriate alternative. 

Why is specificity testing important?

Monoclonal antibodies (Mabs) are prized for their exquisite specificity—especially compared to their small-molecule counterparts. Yet a study of therapeutic MAbs in development and on the market showed that 1 in 3 lead candidates exhibited off-target binding (Norden et al., 2024). 

Given the potential consequences of off-target interactions—on patient safety, drug efficacy, pharmacokinetics—it’s essential to have accurate methods for identifying them during preclinical development. That way, we can advance only the safest, most-selective drugs to the clinic. 

In this article, we’ll break down the methods that are currently available for preclinical specificity testing and describe how FDA guidance in this realm has changed over time. 

What is off-target binding?

Off-target binding, also known as cross-reactivity or polyspecificity, is CDR-mediated binding to one or more proteins other than the intended target. Sometimes the off-target is a member of the same protein family, but most often it’s a completely unrelated protein and thus extremely difficult to predict.  

That’s in contrast to polyreactivity, which refers to a more general type of antibody stickiness—an antibody developability challenge in its own right. There are well-known methods for detecting polyreactivity, though we won’t discuss them here.  

For a deeper discussion of polyreactivity and polyspecificity, see Cunningham et al., 2021. 

What off-target specificity testing methods are currently available?

While the FDA has yet to qualify any tools for evaluating biotherapeutic specificity, several technologies exist for this purpose. Specificity testing during preclinical development typically involves tissue cross-reactivity studies, cell-based protein arrays, or both. Other methods, including tissue microarrays and spotted protein microarrays, have limitations that make them inappropriate for regulatory submissions.  

Tissue cross-reactivity (TCR) studies were developed in the early 1980s to meet the need for specificity testing for biologics. This in vitro assay uses immunohistochemistry (IHC) to reveal antibody binding across a panel of human tissue samples from three individual donors. Results are interpreted by a pathologist, who looks for unexpected off-target binding and previously unknown sites of on-target binding. While TCR data is still widely used in regulatory submissions, no validation studies have been published to quantify its sensitivity, false-negative or false-positive rate, or outcome-based results. 

Tissue microarrays (TMAs) consist of up to 1,000 small tissue samples attached to a slide. As with TCR, IHC reveals binding locations within tissues. TMAs are available for purchase, making testing easier, faster, and less costly than TCR studies. However, because the small samples do not faithfully represent all elements of the original tissue, and because TMAs typically are not interpreted by a pathologist, they are less rigorous than TCR. TMAs are best suited as a research tool for investigating target expression profiles and screening for specificity earlier in development. 

Spotted protein arrays consist of recombinant proteins from bacteria, yeast, or eukaryotic cell lysates spotted onto a solid surface. While these tools typically represent a high percentage of human proteins and come at an affordable price point, they sacrifice protein integrity. Limitations include a lack of post-translational modifications due to non-mammalian cell production, altered protein conformations, protein denaturation, and unnatural surface interactions. Most importantly, spotted protein arrays do not accurately represent multi-pass membrane proteins (the targets of most biotherapeutics), which typically require a lipid bilayer to maintain their native structure.  

Cell-based protein arrays (CBPAs) have emerged in the last decade or so as a faster, less costly, and more precise alternative to TCR studies. CBPAs, such as the Membrane Proteome Array, are a higher-throughput in vitro technology using vertebrate (usually human) cells that overexpress thousands of individual proteins in their natural conformation with post-translational modifications. Binding is evaluated by either IHC or flow cytometry. While TCR reveals only the locations of off-target binding, CBPAs identify the off-target proteins across the human membrane proteome, enabling detailed follow-up study. CBPAs are commonly used early in drug development for lead selection and later for regulatory submissions. 

How has FDA guidance for specificity testing changed over time?

When it comes to selecting specificity testing tools for preclinical safety studies, drug developers generally follow guidance for regulatory submission provided by the FDA and similar agencies around the globe.  

Regulatory documents first described TCR in 1983. In the mid-1990s, regulatory agencies, including the EMA and FDA, began formally recognizing the need to assess specificity testing. The FDA formally recommended TCR in 1997 as the best available tool at the time.  

Regulatory agencies have long anticipated that new specificity testing technologies would emerge, potentially enhancing or even replacing TCR data. In its 1997 guidance the FDA stated, “appropriate newer technologies should be employed as they become available and validated.” Similarly, in 2011, ICH biotherapeutic guidance stated, “other technologies can be employed in place of IHC techniques to demonstrate target/binding site distribution.” 

In January 2024, the FDA for the first time delineated specific alternative methods to TCR studies. In their new guidance document for CAR-T therapeutic development, they named “protein arrays” as an appropriate specificity testing method. This brief mention formalized a shift that had already begun. Because TCR lacks the sensitivity required for CAR-T testing, drug developers had already been using cell-based protein arrays for this purpose, and the FDA had already accepted this data in dozens of IND submissions. 

1FDA, 1997. Points to Consider in the Manufacture and Testing of Monoclonal Antibody Products for Human Use   

2FDA, 2024. Considerations for the Development of Chimeric Antigen Receptor (CAR) T Cell Products  

 Most recently, the FDA signaled an even greater shift toward advancing human-relevant new-approach methodologies (NAMs). In April of 2025, the FDA released a “Roadmap to reduced animal testing in preclinical safety studies,” outlining its plan to leverage NAMs to replace, reduce, and refining animal testing. Essential to this vision, the FDA has plans to advance human-relevant methods like organoid systems, computational modeling, and advanced in vitro assays.  

The Membrane Proteome Array is a high-throughput in vitro cell-based screening assay that embodies the FDA’s vision. It screens the full human membrane proteome with objective, quantitative results rather than the subjective tissue analysis methods that have defined the field for 40 years. In the coming articles, we’ll explore what that means in practice.

Looking ahead 

For the next article in this series, we’ll dig deeper into TCR studies, their limitations, and how much they influence drug development decisions (spoiler: not much).