Identification and validation of a monoclonal antibody off-target

Background

Off-target toxicity can present a major problem in drug development. Early knowledge of a potential off-target interaction can allow for strategies to manage and mitigate risks, or even for development of a drug candidate to be halted altogether prior to costly clinical studies that may fail to achieve endpoints or could even endanger the safety of patients and other trial participants. Tissue cross reactivity is a standard tool in safety assessment that can indicate a potential off-target issue. However, the technique does not reveal the identity of any specific off-target receptor or point to a potential mechanism of action that would assist with evaluating the potential threat posed.

Retrogenix’s cell microarray technology was originally developed to identify the primary receptors that bind specifically to ligands of interest, ranging from small molecules, peptide and protein ligands and antibodies right through to complex ligands such as engineered cell lines and viruses. More recently, this sensitive technology has become widely adopted across the pharmaceutical industry for screening candidate biotherapeutics for potential off-target binding as part of their pre-clinical safety assessment. Here we present details of a typical cell microarray off-target screen, which was followed by flow cytometry analysis to validate/invalidate observed interactions, which provided highly valuable insight into the receptor binding profile of an antibody candidate.

Materials and methods

A single monoclonal test antibody – developed against a key GPCR target – was provided by the study sponsors along with an isotype-matched negative control antibody.

An initial pre-screen was undertaken to determine the levels of background binding of the test antibody to untransfected HEK293 cells as well as its binding to cells over-expressing the known primary receptor. Binding was assessed using a well-validated AlexaFluor647-labelled anti-human IgG Fc detection antibody, followed by imaging for fluorescence. The pre-screen assessed the suitability of the test antibody for onward screening and helped Retrogenix scientists to select the optimal dose to use throughout the study.

The test antibody was then screened for binding against >4,500 full-length human plasma membrane proteins, each individually over-expressed in HEK293 cells. Imaging for fluorescence identified ‘primary hits’.

The vectors encoding these hits were sequenced to confirm their identities before a ‘confirmation/specificity’ screen was conducted. All primary hits were re-expressed, and probed with the test antibody and with suitable positive and negative controls to isolate those hits that were reproducible and specific to the test antibody.

Validation: Confirmed hits were carried forward for additional validation using flow cytometry. Standard transfections with relevant vectors were performed on human HEK293 cells and each live transfectant incubated with the test antibody and the isotype-matched negative control. Cells were then incubated with the detection antibody and analysed by flow cytometry.

Results

Cell microarray screening: 13 ‘hits’ were identified out of the >4,500 plasma membrane proteins screened. Vectors encoding all 13 hits, plus expression vectors encoding CD20 (positive control) and EGFR (transfection and negative control receptor), were taken into the confirmation/specificity screen. As expected, a strong intensity interaction was seen between the positive control Rituximab biosimilar and over-expressed CD20, validating the screening and detection conditions.

All 13 hits were reproducibly seen with the test antibody in the confirmation/specificity screens. However, five of these were also seen with at least one of the control treatments, and were therefore classed as non-specific. These included Fc gamma receptor 1A, 2A, and IGHG3, which are the result of a direct interaction of the Fc domain of the primary antibodies and/or the detection antibody.

The remaining hits appeared to be specific to the test antibody although two of these were very weak intensity. Hits that are so close to background carry little/no confidence that they are real and/or specific so were disregarded.

After filtering out the above, this left 6 specific hits (Figure 1). All of these were versions of the known primary target (2 cDNA clones) or the two previously unknown, putative off-target receptors (3 clones and 1 clone respectively).

Figure 1

Figure 1. Images from the Confirmation/Specificity screens. Vectors encoding all hits from the Primary screens, and control vectors encoding CD20 (positive control) and EGFR (negative and transfection control), were re-arrayed/re-expressed in duplicate and probed with: (A) the test antibody, (B) a rituximab biosimilar (hIgG1, positive control) and (C) the isotype-matched control. The positive control interaction is shown in orange; hits specific to the test antibody are shown in green (primary target), purple (putative off-target 1) and blue (putative off-target 2 – weak intensity).

Validation: The follow-on flow cytometry study on live HEK293 cells expressing each of the three receptor hits validated the interaction between the test antibody and its primary target as well as with a second, functionally related receptor (putative off-target 1). The interaction between the test antibody and the third receptor (putative off-target 2) was not seen on live cells by flow cytometry and could be disregarded (Figure 2). Dose-response relationship analysis shows higher affinity binding of the test antibody to transfectants expressing the primary receptor compared to off-target-expressing cells (Figure 3).

figure-2-case-study


Figure 2: Follow-on flow cytometry analysis of binding of the test antibody, the isotype-matched negative control antibody and Rituximab biosimilar control to unfixed transfected cells expressing (A) CD20 (negative control for antibody binding), and the specific targets identified by cell microarray screening: (B) primary target, (C) putative off-target 1 and (D) putative off-target 2.

figure-3-case-studyFigure 3: Dose response curve shows higher affinity binding of the test antibody to transfectants expressing the primary receptor compared to off-target-expressing cells.

Impact

This study provides clear evidence of a secondary target receptor interaction for this test antibody. The cell microarray screening followed by flow cytometry allowed for interactions to be observed and then validated or discarded providing very clean results for the study sponsors to rely on. The crucial information uncovered by this project will assist in determining the future strategy for development of this drug lead.

Flow Cytometry Validation

Our flow cytometry service routinely follows on from a full study. This allows clients to opt into the service dependent on results rather than commit upfront. We are frequently able to use materials that are already available to us at the end of the main screen, avoiding the need to produce and ship further samples to our UK labs.

Find out more

FAQ: “Can I request a follow-on validation study after results have been delivered?”

Absolutely. We don’t require commitment to a follow-on validation study up front when you book your project, giving you the flexibility to appraise your initial results first before deciding if validation is required or beneficial.

See more FAQs

Talk to us about a project
Please leave this field empty.
Pfizer
The University of Sheffield
Aveo Oncology - The Human Response
Theraclone Sciences
BioInvent
AstraZeneca
Bluebird Bio
glycotope_logo
The Center for Infectious Disease Research
Compugen Logo
The University of Copenhagen
Lund University
MedImmune
NIH - National Institutes of Health
The University of Pennsylvania
Scripps Florida - The Scripps Research Institute
Peptinnovate Ltd - Unlocking Nature's Potential
Retrogenix helped us to crack a very challenging problem, the result of which has significantly advanced our research. A collaborative approach, alongside an in-depth scientific and technical knowledge of their technology turned an unsuccessful initial screen into an extremely successful secondary screen. We have already committed to further studies.
Dr. Nicky Cooper, CSO, Peptinnovate