Understanding the mechanism of action of a compound is one of the most important goals in drug discovery. However, it can be extremely challenging to reveal the full complexity of a compound’s pharmacological effects on the entire disease pathway, including interactions with the primary target and any associated proteins. Fortunately, unbiased approaches to target identification, such as mass spectrometry (MS)-based proteomics, are helping to advance our understanding of MoA, including the identification of protein targets and off-targets.
But that’s not the full story. Although there are various unbiased approaches available, they each require careful consideration before being adopted as part of your workflows. As we discuss in our new eBook, choosing the most efficient and reliable approach from the start of your target identification studies can help maximize productivity and success rates in your discovery efforts.
If you think your target identification studies could benefit from a relevant unbiased approach, then read on to find out more below.
Unbiased methods for target identification in drug discovery
Although target-based (hypothesis-driven) approaches to drug discovery have greatly enhanced discovery efforts, traditionally more common phenotypic based (empirically driven) methods are regaining momentum. These unbiased methods fall into two main categories: functional genomics and MS-based proteomics. Below we take a closer look at what these two different approaches involve, and which could be the most suitable one for your target identification studies.
Functional genomics typically involves genetic modification through downregulation or knockout to identify genes and intergenic regions that contribute to pathological processes. Combined with our growing understanding of the human genome, the recent introduction of next-generation genome sequencing and advanced genome-wide functional methods has helped to identify novel drug candidates and clarify the role of genomic and molecular changes in disease. For example, gene silencing via RNA interference (RNAi) is expediting the identification and validation of new targets and patient selection biomarkers for various diseases, and CRISPR/Cas9 gene-editing is showing great promise in identifying high-value cancer drug targets.
Despite the value of functional genomics in drug discovery, the approach has some pitfalls. One major problem is that genetic modification creates a highly artificial system that cannot recapitulate physiological processes within the patient. Moreover, as downregulation/knockout can affect more than one protein in the disease pathway, the selectivity and activity of the genetic modification can be difficult to predict.
As such, functional genomics experiments could produce highly misleading results in target identification that can impact your success in downstream discovery and development stages. Faced with these problems, pursuing a reliable approach to target identification might seem like an uphill struggle. Fortunately, MS-based proteomics methods offer a promising solution.
Mass spectrometry (MS)-based proteomics
MS-based proteomics allows the direct quantification of thousands of compound-protein interactions simultaneously, in unmodified and complex systems. The approach has had a huge impact on preclinical drug discovery by enabling the identification of protein targets and off-targets, as well as advancing understanding about how a compound exerts its pharmacological effect (i.e., the mechanism of action, or MoA).
There are currently two main MS-based methods that can directly quantify proteome-wide compound-target binding: chemoproteomics and a proteome-wide format of the Cellular Thermal Shift Assay (CETSA® MS), which is also known as ‘thermal proteome profiling’ or TPP. Although each of these methods can be valuable for your target identification studies, choosing the most efficient approach from the start is essential to reduce risk and boost your success rates.
Chemoproteomics: A traditional target
Chemoproteomics is the more traditional MS-based proteomics approach to target identification. The method typically involves synthesizing a chemical probe that functionally replicates the parent compound. The probe is linked to an agent for separation (such as an analytically detectable bead), after which mass spectrometry is used to identify the protein targets that are captured. However, chemoproteomics has several issues that could limit your drug discovery success.
For example, the artificial modification of the ligand or protein target limits physiological relevance, which impairs insights into how the drug will modulate the target within the patient. Also, building a probe can take 6-12 months, but even after investing all that time and funds, it’s uncertain that it will accurately replicate the function of the parent compound. This could mean that the targets captured by the probe might not be the same ones that the actual drug would bind to. Moreover, chemoproteomics can only reveal how the probe affects the primary targets, so you could miss protein targets along the rest of the disease pathway. Therefore, your compound’s MoA is unlikely to be fully elucidated.
Overcoming the limitations of
chemoproteomics in target identification
One effective solution to address the limitations of traditional chemoproteomics methods is to adopt CETSA MS from an early stage. Having been successfully applied in both target-based drug discovery and phenotypic approaches, CETSA MS combines quantitative proteomics with CETSA to simultaneously measure proteome-wide drug-target binding in live cells and tissue.
A label-free and physiologically relevant method that is immediately ready to use, CETSA MS offers several advantages over chemoproteomics, including greater efficiency and richer insights into the entire disease pathway (not just the primary target). Indeed, leading research groups are gaining various benefits from adopting CETSA MS from the early stages of their target identification studies. This includes more reliable insights for off-target monitoring and gaining a better understanding of the biological impact of the candidate when more traditional methods have failed.
Overall, adopting CETSA MS can enhance predictions about the efficacy and safety of your compounds and ultimately drive the successful development of new medicines for patients.