In the last several years, the speed at which information and data has become available and instantly accessible is incredible. So why should drug discovery be any different?
The 2022 Society for Laboratory Automation and Screening (SLAS) conference returned as an in-person event and it definitely lived up to the “hype”. An amazing turnout of researchers, engineers, scientists, and more showed up to share and present innovative technologies, instruments, robotics, and software to generate more data faster and bring drug discovery of the future to the labs of today.
Label-free and high-throughput approaches—in particular, those integrating mass spectrometry—remain some of the most powerful tools used in drug discovery today. Novel solutions on the front-end aim to address the inherent limitations of traditional mass spec (MS) while analytical tools, including those using artificial intelligence (AI) and machine learning, speed up processing of large datasets to progress hit identification and validation towards pre-clinical phases. While the application of MS in drug discovery has been a staple at SLAS over the past decade, new diverse tools that expand the application of MS in drug discovery emerge each year.
Here are three trends that echoed across the scientific sessions and exhibition hall at SLAS 2022 with a focus on drug discovery assay development and screening:
Trend 1 – Improving sample preparation to enable MS analysis
One of the biggest challenges with traditional MS is that historical approaches require tedious separation procedures such as solid phase extraction (SEC-MS) and liquid chromatography (LC-MS). These approaches remain critical tools in drug discovery, although the required sample prep limits throughput and are not suitable for large screening campaigns. Eliminating sample preparation altogether is a common theme to promote rapid label-free analysis using MS. For example, approaches such as matrix assisted laser desorption ionization (MALDI) MS and the combination of acoustic dispense with MS continue to gain momentum as methods that eliminate sample prep and are therefore amenable to high-throughput screening. However, without a sample preparation process to clean the reactions, it is critical to ensure that any assay is optimized to minimize interference with buffer components such as salts, detergents, and others that lead to ion suppression and compromised data quality. Alternatively, the use of high-density biochips featuring defined surface chemistries was shown to rapidly immobilize the analytes of interest out of a biochemical or binding reaction prior to MALDI analysis. Without any limitations to the buffer components, this approach encourages the optimization of the assay according to the needs of the target rather than the needs of the MS instrument, contributing to higher data quality.
Another challenge with MS is that compounds ionize differently, and some compounds are significantly more challenging to ionize than others. To address this, one pharma company has developed their own custom-modified instrument in an approach they call MALDESI, which offers an opportunity for ionization at ambient pressure using an infrared laser. Alternatively, a new commercial MALDI instrument integrates a second laser that not only improves the ionization of small molecules, but also offers new opportunities to separate compounds based on surface area to resolve those small molecules that have the same mass.
Trend 2 – Affinity selection mass spectrometry to identify small molecule binders
A hot question in drug discovery today is how to identify small molecules that engage a specific protein or oligonucleotide target. Small molecule binders open avenues for targeted degradation through molecular glues or PROTACs, or through interfering with therapeutically important protein-protein (or other) interacting partners. Historically, label-based binding assays are cumbersome, tedious, and often not very reliable. The use of DNA-encoded libraries (DELs) is attractive for companies looking to screen upwards of billions of small molecules against a particular target. Integrating artificial intelligence (AI) and machine learning can be used to aid in designing the DEL libraries to feature encouraging chemical scaffolds or structural moieties to improve the likelihood of identifying promising hits. While these computational approaches may offer an accelerated path towards hit-finding, it does require that sufficient structural data is available to develop models, which may be lacking for novel or challenging targets. This is where both traditional and innovative approaches can engage to support this new age of drug discovery.
Traditional affinity selection mass spectrometry (also known as ASMS) has aimed to overcome the limitations of conventional label-based binding assays. ASMS informs on the mass ID of the analyte rather than relying on a signal (fluorescence, radioactivity, or DNA sequencing) that leaves much up to interpretation. However, once again the need for sample preparation to isolate the small molecule-target complex limits throughput. Solutions that accelerate sample preparation (for example, through defined surface chemistry) has shined in its ability to rapidly screen compounds, and hits can be validated in orthogonal approaches, increasing confidence when making go/no-go decisions.
Trend 3 – MS solutions for beyond biochemical reactions
Once small molecules have been identified and validated in biochemical or binding assays, the next step often includes analyzing whether the compound exhibits activity in cells and/or whether the compound engages with the target in the cellular context. Target engagement assays have historically relied on fluorescence or radioactivity-based approaches, although SLAS2022 posed the question on whether novel label-free methods including MS could offer unique solutions. One example showcased how to perform a screen directly in cells and utilize MS to analyze distinct cellular activities on a specific substrate. This type of approach is particularly powerful for screening difficult to express enzymes or where targets are not active as a recombinant protein. In a second example, combining MS data with AI or machine learning offers the opportunity to identify fingerprints indicative of a healthy or diseased state. By screening compounds directly in a diseased cell model, one can use MS to analyze the cellular material. Computational analysis on the backend can ascertain key features and determine whether the compound alters the cellular phenotype to a healthier cellular state, even if the specific features are not fully identified. In other words, MS techniques may now be used for label-free high-content screening. It will be very interesting to monitor the evolution of this approach and how it compares with high-content screening using traditional microscopy approaches.
This year’s SLAS conference formulated a strong stamp of support for the key role that MS plays in drug discovery, and more companies continue to integrate this powerful approach internally or through partnerships with contract research organizations (CROs). The wealth of new data and advances has been incredible, and seeing it in-person (and virtually) was worth the wait and lived up to the hype!