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What Makes A Good Target For Antibody-Drug Conjugates (ADCs)?

Updated: Oct 2

Cancer treatment has come a long way, but the quest for more precise, effective therapies continues. Among the most exciting advancements in cancer therapeutics are Antibody-Drug Conjugates (ADCs). What are they, how do they work, and what makes a good target for ADCs? In this blog, let’s find out.


What is an Antibody-Drug Conjugate (ADC)?

An Antibody-Drug Conjugate (ADC) is a cancer treatment that combines targeted therapy and chemotherapy. With an aim to deliver a drug to the tumor cells while sparing the normal healthy cells, ADCs combine a monoclonal antibody (mAb), which binds to a protein that are specific to the cancer cells, with a drug, typically a cytotoxic chemotherapy agent, via a chemical linker [1].


Therefore, Antibody-Drug Conjugate works like a “biological missile”, offering a novel approach to delivering highly potent drugs directly to cancer cells. By targeting specific antigens present on tumor cells, ADCs promise to improve efficacy and reduce off-target effects.


The method and structure of an ADC

An ADC contains a monoclonal antibody (mAb), linked with an anticancer drug (or ‘payloads’) via a chemical linker. The antibody binds to a surface protein specific to the cancer cells (tumor-specific antigens or tumor-associated antigens), after which the ADC is internalized into the cancer cells where it releases the drug.

Schematic of an ADC (Source: De Cecco et al., 2021)


‘Fully human’ mAbs are often an ideal delivery platform for ADCs. They have been engineered to contain protein sequences encoded by human immunoglobulin genes. These mAbs are designed to target specific cells with precision, exhibit a long circulating half-life, and have low immunogenicity.


The chemical ‘linkers’ that link the mAb with the cytotoxic drug should be highly stable to prevent cleaving (splitting) before the ADC enters the tumor. The anticancer drugs (or ‘payloads’) have an ability to cause cell death through a variety of mechanisms [2].


What makes a good target for ADCs?

Given the structure above, clinical efficacy of the ADCs is subject to the following selection: (1) the tumor-specific antigens (the targets), (2) the antibody, (3) the cytotoxic payload and (4) the method of antibody linkage to the payload [3]. In this article, we’ll just focus on the first parameter of success: finding the right tumor-specific antigens to be targeted by ADCs.


So, what makes a good target for ADCs?


1. High or unique expression on the tumor cells

The target should be overexpressed on the tumor cells and have limited or no expression on normal cells to minimize off-target effects and reduce toxicity.


We need to be cautious with the commonly used term ‘target overexpression’, because it can refer to two different contexts: overexpression in cancer cells compared to adjacent normal tissue or compared to all normal tissues. While the former may suggest biological relevance, the latter is crucial for evaluating therapeutic potential. Target expression in normal tissues can reduce ADC exposure in the tumor through target-mediated drug disposition (TMDD), which can affect both efficacy and safety. Additionally, many gene expression databases focus only on solid tissues, but expression of the target in blood cells must also be considered as it could greatly influence drug exposure and elevate the risk of immunogenicity.[4]


Also, pay attention to uniform expression: Choose targets that are uniformly expressed across the tumor population. Heterogeneous expression can reduce ADC efficacy, as some cells may escape treatment.


2. Accessibility

ADCs often rely on binding to antigens on the cell surface [5]. The antigens should be accessible on the surface of tumor cells, ensuring that the ADC can reach and bind to its target efficiently. They can be membrane-bound or transmembrane, allowing the antibody component of the ADC to bind effectively and initiate internalization.


The target antigen should be non-secreted since secreted antigen in the circulation would cause the undesirable ADC binding outside tumor sites, resulting in the decreased tumor targeting and elevated safety concerns [6].


3. Applicability to multiple tumor types

Ideally, target antigens should be overexpressed in the cancer cells of various cancer types and subtypes because this provides broader therapeutic opportunities and increases clinical applicability. However, this is quite challenging to achieve since cancers are notoriously known for their heterogeneity. Different cancer types and subtypes can exhibit different expression patterns.


4. Efficient internalization and recycling kinetics

The target antigen should efficiently internalize once bound by the antibody, allowing the payload to enter the cell and exert its cytotoxic effect. Ideal targets are those that are rapidly internalized upon antibody binding. [5,7]


Ensure that once internalized, the antigen facilitates efficient payload release inside the cell, particularly within lysosomal compartments where ADCs are designed to release their cytotoxic drug.


It’s also good to ensure that the kinetics of internalization support the ADC’s mechanism of action [8]. Some targets undergo fast recycling to the cell surface after internalization, allowing multiple rounds of ADC binding and internalization, which can enhance therapeutic efficacy.


5. Target stability

The antigen should have stable expression over the course of the disease to ensure consistent target engagement. Also, choose antigens that are non-shedding or not rapidly cleaved from the cell surface [9]. Antigen shedding into the bloodstream can lead to off-target binding of the ADC and decreased efficacy.


Tackle the quest for ADC targets through state-of-the-art multi-omics analysis with Pythia Biosciences

Finding an optimal target for ADCs is tricky. How to know if a candidate target is specific to the tumor cells?


Single-cell RNA-seq, with its capabilities to dissect the expression profiles of different cell populations, has been actively used for finding cell-type specific markers, including tumor-specific or tumor-associated antigens.


However, challenges remain as we lack a platform with accessible and custom-made analytics to easily identify tumor markers for ADCs. Also, few platforms enable cross-omics and cross-study comparison, especially among proteomics and transcriptomics data, to validate the expression at protein level and across different experiments.


In terms of data mining, most publicly available single-cell datasets lack healthy/normal control groups that facilitate the comparison of tumor cells versus healthy tissues. Meanwhile, experiment planning is time consuming.


At Pythia Biosciences, we offer tools and services designed to address these challenges:


  • Cross-omics analysis dashboards: Our dashboards in the C-DIAM Multi-omics Studio enable easy exploration and summary of differentially expressed genes/proteins or cell type markers across many types of omics data (e.g. bulk RNA-seq, single-cell RNA-seq, proteomics, spatial transcriptomics,…)



  • Interactive access to curated public omics data for insight validation: C-DIAM Multi-omics Studio enables interactive access to many public omics databases such as 10x Genomics Visium spatial transcriptomics, CPTAC, TCGA, GTEx, and Cellxgene which support validation of target expression across different cancer types and healthy tissues.



  • Data integration services: We support data integration services to integrate data from various cancer studies to create a comprehensive atlas for identifying and validating potential targets for ADCs.


Intrigued? Take a look at our case study report where we present a use case of building a cell type specificity target expression report and dashboard supporting cancer Antibody-Drug Conjugate research. Click here for more information or to request a trial of our platform!



References

[1] Beck, Alain, et al. "Strategies and challenges for the next generation of antibody–drug conjugates." Nature reviews Drug discovery 16.5 (2017): 315-337.

[2] De Cecco, Martin, Daniel N. Galbraith, and Lisa L. McDermott. "What makes a good antibody–drug conjugate?." Expert Opinion on Biological Therapy 21.7 (2021): 841-847.

[3] Nejadmoghaddam, Mohammad-Reza, et al. "Antibody-drug conjugates: possibilities and challenges." Avicenna journal of medical biotechnology 11.1 (2019): 3.

[4] Damelin, Marc, et al. "Evolving strategies for target selection for antibody-drug conjugates." Pharmaceutical research 32 (2015): 3494-3507.

[5] Fu, Zhiwen, et al. "Antibody drug conjugate: the “biological missile” for targeted cancer therapy." Signal transduction and targeted therapy 7.1 (2022): 93.

[6] Ritchie, Michael, Lioudmila Tchistiakova, and Nathan Scott. "Implications of receptor-mediated endocytosis and intracellular trafficking dynamics in the development of antibody drug conjugates." MAbs. Vol. 5. No. 1. Taylor & Francis, 2013.

[7] Donaghy, Heather. "Effects of antibody, drug and linker on the preclinical and clinical toxicities of antibody-drug conjugates." MAbs. Vol. 8. No. 4. Taylor & Francis, 2016.

[8] Ritchie, Michael, Lioudmila Tchistiakova, and Nathan Scott. "Implications of receptor-mediated endocytosis and intracellular trafficking dynamics in the development of antibody drug conjugates." MAbs. Vol. 5. No. 1. Taylor & Francis, 2013.



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