{"id":336,"date":"2026-07-31T10:44:13","date_gmt":"2026-07-31T10:44:13","guid":{"rendered":"https:\/\/omivera.jppwebdesign.nl\/?p=336"},"modified":"2026-10-23T07:13:02","modified_gmt":"2026-10-23T07:13:02","slug":"chemical-proteomics-in-drug-discovery","status":"publish","type":"post","link":"https:\/\/omivera.jppwebdesign.nl\/?p=336","title":{"rendered":"Chemical proteomics in drug discovery: Better target engagement decisions"},"content":{"rendered":"\n

Chemical proteomics helps drug discovery teams understand how small molecules interact with proteins in biologically relevant systems. By measuring cellular target engagement and selectivity across many endogenous proteins simultaneously, it can reveal whether a candidate reaches its intended target, which additional proteins it engages and how its target engagement profile changes with biological context.<\/strong>
Target engagement evidence is valuable when prioritizing candidates in hit-to-lead, lead optimization, and preclinical development. Chemical proteomics complements other profiling methods by providing data on how drugs engage with their targets and how selective they are within disease-relevant biological environments. It also enables comparison of target engagement across different environments, making it a valuable translational tool throughout the drug discovery process.<\/p>\n\n\n\n


What is chemical proteomics?<\/h2>\n\n\n\n

Chemical proteomics, a sub-branch of proteomics, utilizes chemical probes to study protein function, interaction, and target engagement. In this approach, bioactive small molecules are designed to specifically bind to the protein(s) of interest, enabling these proteins to be pulled out from cell extracts. This allows the identification of both the main drug targets and any possible off-target effects.<\/p>\n\n\n\n


The core of chemical proteomics: the probes<\/h2>\n\n\n\n

The general chemical proteomics technique is similar across different approaches, with the main difference being the bioactive small molecule used, also known as the probe. The choice of probe depends on the protein family studied and the specific questions that chemical proteomics aims to answer. Currently, Omivera offers three types of services based on differentiation in probe:<\/p>\n\n\n\n

    \n
  1. CellEKT: the probe is a competitive broad-spectrum ATP-binding pocket binder used for profiling kinase inhibitors. As the probe binds >90% of the ATP-binding pockets of expressed kinases, this enables measurement of not only the target of the kinase inhibitor, but also potential off-target kinases in unmodified cellular model systems.<\/li>\n\n\n\n
  2. Activity-based protein profiling of serine hydrolases: a cocktail of probes specifically binds active serine hydrolases, as these probes are reliant on the mechanism of action of serine hydrolases to bind. Both probes are broad-spectrum probes, enabling measurement of target engagement of the intended target and selectivity across the serine hydrolase enzyme class.<\/li>\n\n\n\n
  3. Tailored chemical proteomics: a probe is specifically designed based on the covalent inhibitor. This involves adding a biorthogonal ligation (\u2018click\u2019) handle to the molecular structure of the inhibitor, without structurally hindering the binding moiety.
    For more specific details, please visit the page of the specific service.<\/li>\n<\/ol>\n\n\n\n

    The general workflow of chemical proteomics<\/h2>\n\n\n\n

    Regardless of the precise choice of probe, the chemical proteomics workflow is mostly the same. A competitive experiment to profile an inhibitor works as follows:<\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n
      \n
    1. Cells are treated with the inhibitor of interest.<\/li>\n\n\n\n
    2. After treatment, the probe is added. It binds to any available binding sites but cannot bind to sites already occupied by the inhibitor.<\/li>\n\n\n\n
    3. The probe-bound proteins are then extracted from the cells and analyzed using LC\/MS. For broad-spectrum probes, this provides an indirect measurement of how well the inhibitor engages its target by comparing it to an untreated positive control where only the probe is added to the cell. In tailored chemical proteomics, this measurement is direct read-out as the probe is based on the inhibitor, and it is not in binding competition with the inhibitor.<\/li>\n\n\n\n
    4. Finally, tailored data analysis is performed to identify, quantify, and visualize target engagement, either of a whole enzyme class or of specific target(s).<\/li>\n<\/ol>\n\n\n\n

      Why use chemical proteomics in the drug discovery pipeline?<\/h2>\n\n\n\n

      Chemical proteomics can provide a more in-depth view of target engagement of a specific subset of proteins compared to broad proteomics applications. This creates a focused view of the proteins most relevant to a particular inhibitor.<\/p>\n\n\n\n

      Chemical proteomics compared to biochemical assays<\/h3>\n\n\n\n

      The first step in developing inhibitors usually is to evaluate target engagement and off-target effects using isolated proteins in biochemical assays. These assays are high-throughput, relatively inexpensive, and essential for hit identification and validation. The results from biochemical assays often serve as the starting point in the drug development pipeline and provide the basis for assumptions. For example, one common assumption is that cellular target engagement is lower than what is observed in biochemical assays. However, assumptions are less reliable than validation, as they do not reveal how much lower target engagement might be, which can be influenced by factors like permeability, cellular distribution, and efflux. If the difference is significant, drug efficacy may not be seen in more complex biological systems due to insufficient cellular target engagement. Chemical proteomics is an effective method to directly measure target engagement within cells or other complex systems.<\/p>\n\n\n\n

      Chemical proteomics compared to cellular assays<\/h3>\n\n\n\n

      Compared to other cellular assays, chemical proteomics provides a few advantages. Take for example NanoBRET, which is a technique that enables measuring target engagement of a specific target within cells. However, the target is not in its native form, as it has been altered with a luciferase to give the signal. Also, it uses an artificial construct expressed in a specific cell line. The probes of chemical proteomics are cell line agnostic, meaning it can measure target engagement, with of course a bit of tweaking the experiment, in any cell line, and works on natively present proteins.<\/p>\n\n\n\n

      Another powerful cellular assay is CETSA. And while this enables a good view across the proteome, chemical proteomics enables to focus more on a subset of highly similar proteins, without having the background of the whole proteome.<\/p>\n\n\n\n

      For each application, the advantages of chemical proteomics compared to other assays are different, also depending on what is already developed within this enzyme class. For kinases, the focus of our CellEKT platform, the advantages of the different assays are given in Table 1 below.<\/p>\n\n\n\n


      Table 1. Comparison of assays that measure target engagement of kinases
      <\/p>\n\n\n\n

      <\/td>CellEKT<\/strong><\/td>KinomeScan<\/strong><\/td>KinoBeads<\/strong><\/td>NanoBRET<\/strong><\/td>CETSA<\/strong><\/td><\/tr>
      Kinome coverage<\/td>High<\/td>High<\/td>High<\/td>Moderate<\/td>Low<\/td><\/tr>
      Throughput<\/td>Moderate<\/td>High<\/td>Moderate<\/td>High<\/td>Moderate<\/td><\/tr>
      Native kinases<\/td>Yes<\/td>No<\/td>Yes<\/td>No<\/td>Yes<\/td><\/tr>
      Native environment<\/td>Yes<\/td>No<\/td>No<\/td>Yes<\/td>Yes<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

      <\/p>\n\n\n\n

      When can chemical proteomics be used?<\/h2>\n\n\n\n

      Cellular screening<\/h3>\n\n\n\n

      One way to use chemical proteomics is in living cells during the lead optimization stage of the drug discovery pipeline. It enables more confident decision-making when choosing the best lead candidate by addressing efficacy and safety concerns and provides superior validation when compared to other methodologies. As chemical proteomics is based on the probes, it is cell line agnostic. If the target protein or potential off-targets are only expressed in specific cells, chemical proteomics can validate the drugs reacting with its target within those particular cell types.<\/p>\n\n\n\n

      In vivo<\/em> models (validation ongoing)<\/h3>\n\n\n\n

      The next step in chemical proteomics is to use the probes to measure target engagement in in vivo models such as xenografts, organoids, and mice. This approach provides target engagement data in increasingly complex and relevant systems compared to cells and reveals whether target engagement observed in cells aligns or differs from that in in vivo models.<\/p>\n\n\n\n

      Measuring target engagement in vivo answers important questions that phenotypic readouts alone cannot. For example, consider a mouse model with a tumor treated with a molecule of interest. If the tumor does not shrink, what is happening? Chemical proteomics allows you to measure target engagement directly. If the molecule does not reach the tumor, the problem is entirely different than if the molecule reaches the tumor, but inhibiting the target has no effect.<\/p>\n\n\n\n

      Additionally, target engagement of the drug can vary across different tissues. Detecting these differences early helps identify tissue-specific safety concerns, which can then be addressed and mitigated.<\/p>\n\n\n\n

      PBMCs (validation ongoing)<\/h3>\n\n\n\n

      Since increasing biological complexity does not end with in vivo models, the subsequent step is to measure target engagement in human white bloods cells, specifically PBMCs. As PBMCs are just cells, the workflow does not differ from cultured cells, except for obtaining the cells.
      The PBMCs can either come from untreated volunteers, patients, or from people that are treated with an inhibitor. This makes it possible to measure target engagement from lead optimization to clinical trials, knowing exactly how target engagement changes at each step of increasing biological complexity.<\/p>\n\n\n\n

      Limitations<\/h2>\n\n\n\n

      Chemical proteomics is a powerful technology but also comes with its limitations that have to be considered when designing the experiment.<\/p>\n\n\n\n

      Limitations are:<\/p>\n\n\n\n

        \n
      • The coverage (the number of proteins picked up) is highly model and probe dependent.<\/li>\n\n\n\n
      • Target engagement is not the same as functional inhibition.<\/li>\n\n\n\n
      • Not all probes are equally cell permeable or metabolically stable. Specific optimization might be required to unlock all chemical proteomics capabilities.<\/li>\n<\/ul>\n\n\n\n

        Thus, chemical proteomics uses either broad-spectrum probes, such as for kinases or serine hydrolases, or probes tailored to a specific inhibitor. It measures target engagement, selectivity, and off-targets within biologically relevant, native environments. As candidates move through lead optimization and into preclinical development, understanding whether a molecule truly reaches and engages its target in a relevant biological system becomes essential. By evaluating target engagement in those systems, chemical proteomics helps drug discovery teams make more data-driven choices and advance programs with greater confidence.
        Want to know if your candidate is truly engaging its target? Talk to our team about designing a chemical proteomics study built around your program, from kinome-wide profiling to fully tailored probes.<\/p>\n","protected":false},"excerpt":{"rendered":"

        Learn how chemical proteomics measures target engagement, selectivity and off-targets in biologically relevant systems to support better drug discovery decisions.<\/p>\n","protected":false},"author":1,"featured_media":493,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"off","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[10],"tags":[],"class_list":["post-336","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog"],"_links":{"self":[{"href":"https:\/\/omivera.jppwebdesign.nl\/index.php?rest_route=\/wp\/v2\/posts\/336","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/omivera.jppwebdesign.nl\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/omivera.jppwebdesign.nl\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/omivera.jppwebdesign.nl\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/omivera.jppwebdesign.nl\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=336"}],"version-history":[{"count":4,"href":"https:\/\/omivera.jppwebdesign.nl\/index.php?rest_route=\/wp\/v2\/posts\/336\/revisions"}],"predecessor-version":[{"id":550,"href":"https:\/\/omivera.jppwebdesign.nl\/index.php?rest_route=\/wp\/v2\/posts\/336\/revisions\/550"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/omivera.jppwebdesign.nl\/index.php?rest_route=\/wp\/v2\/media\/493"}],"wp:attachment":[{"href":"https:\/\/omivera.jppwebdesign.nl\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=336"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/omivera.jppwebdesign.nl\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=336"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/omivera.jppwebdesign.nl\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=336"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}