DDW Editor Reece Armstrong speaks to Dr. Zachary Gurard-Levin, CEO and Founder of Peapod Bio about challenges in small molecule drug discovery and what technologies are advancing this sector.   

RA: What are the biggest challenges facing small molecule drug discovery and development? 

ZG: The challenge that we continue to hear is attrition rate, where roughly 90% of drug candidates still fail in clinical development. And this is particularly important as we pursue more challenging targets that have been previously termed undruggable. These targets demand a far more sophisticated understanding of their biology, how to interrogate them in a meaningful way, and how to identify the patients that will benefit most.  

While clinical phases receive significant attention, the reality is that there are many opportunities in the earliest phases of drug discovery to improve the successful translation of results from research to clinical settings, including for challenging targets. Firstly, it’s important to clearly define therapeutic goals and then tailor your screening and validation assays to address them. When choosing assays, aim to eliminate sources of false-positive results and other artifacts that delay progress and increase costs. Screening libraries are critical components of any screening effort. Ensure the quality and purity of those libraries meet standards that will instill confidence when deciding which compounds to pursue. Consider screening compounds with favorable ADMET properties and integrate orthogonal validation assays that span biology and biophysics earlier.   

Finally, we’ve all heard how AI is going to solve many of these challenges and there are areas where large language models can accelerate and even improve drug discovery programs. For early drug discovery, the challenge we are seeing is that many models are built on problematic datasets rich with assay artifacts, poor target characterization, and an overreliance on indirect readouts. Generating clean, comprehensive datasets with sufficient metadata to train models is arguably where this has the most to gain and make a significant impact.  

RA: How can compound libraries advance and reduce time to market for drug development? 

ZG: The industry has largely moved past the era of testing millions of poorly characterized, aggregation-prone compounds and hoping for the best. Curated libraries now feature pharmacophore-like diversity sets, fragments, and those built around specific chemotypes relevant to a distinct target class. Starting with high-quality, well-behaved compounds including high purity and favorable ADMET properties will dramatically improve hit rates and hit tractability. Thinking beyond initial screening, utilizing compounds that can be modified in a straightforward manner will improve characterizing structure activity relationships (SAR) and can feed into computational models to shorten the feedback loop between experimental results and predictive models.  

RA: How have developments in high-throughput screening (HTS) helped enable better drug discovery? 

ZG: HTS has evolved from a numbers game to maximizing quality, which is achieved through several strategies including selection of appropriate libraries. One common strategy focuses on miniaturization into lower volumes, often in 1536-well formats, to reduce compound consumption and reagent burden. Another is incorporating more automation to improve accuracy, and liquid handlers have dramatically improved throughput and consistency including in nL and even smaller volumes. Importantly, label-free and high-throughput detection platforms like mass spectrometry (MS) increase confidence in results by directly measuring specific analytes rather than a signal that is left up to interpretation. We’re also seeing more direct-to-biology platforms and the utilization of new approach methodologies (NAMs) in screening workflows. Balancing complexity versus practicality is important and ensuring assays are selected based on the needs of the target, biology, timelines, budgets, and again, whether the data enables confident decision making.  

RA: Are more organisations looking to outsource services such as compound screening to save costs and reduce timelines? 

ZG: Absolutely. The current economic environment supports lean biotech companies, many operating in virtual models, that rely on outsourcing to reach inflection points and milestones rapidly and efficiently. Even large pharmaceutical companies are externalizing portions of their screening workflows, particularly to access cutting-edge technologies and niche expertise to achieve their aggressive timelines and drug discovery goals.  

The barrier to access world-class screening infrastructures without having to build it is shrinking and companies are embracing the CRO ecosystem to drive programs forward in the most time and cost-effective manner.  

On the other side, CROs need to respond appropriately and be sufficiently agile and flexible to truly support these programs. Many of these new challenging target classes and modalities will not be solved by one-size-fits-all assay solutions, meaning CROs need to adapt and focus on innovation and scientific rigor. 

RA: If so, what are the IP considerations they must take when choosing to do this? 

ZG: The fundamental question is who owns the data and under what conditions? When working with niche technology providers, a CRO may bring proprietary technologies as Background IP to the engagement. It is critical for these teams to protect their Background IP. In this case, the protection does not prevent or restrict the sponsor from owning the deliverables.  

Another question to consider is what data is included in deliverables. Some CROs deliver only hits resulting from screens rather than full raw datasets. As companies continue to implement predictive models in early discovery, feeding the models comprehensive datasets with true positives and true negatives is critical for their success. In this manner, screening data is transitioning from a list of hits to tractable assets. Companies should ensure that all data generated for a project is shared without limitation on its use and how hits are pursued.  

RA: What are the major considerations companies have to take when choosing a CRO to partner with? 

ZG: It’s interesting to hear the term partner because collaborations with CROs can seem like a partnership, where the CRO acts as an extension of your team. In contract, engagements with some CROs may be more transactional, where requests come in and data goes out. Choosing a model that works for your team and project may aid in selecting the appropriate CRO.   

Technically speaking, scientific credibility and capability are foundational. Companies need CRO teams and platforms that they can trust to answer specific biological questions with appropriate sensitivity and selectivity. Communication transparency and proactivity also matter enormously. A CRO that raises problems early, shares raw data readily, and engages honestly about assay limitations is worth considerably more than one that presents only polished summaries and takes on all projects without hesitation. A willingness to say “this assay isn’t working as expected, here’s how we can address it” or “our platform may not be best suited for your question” can erase months to years of delays and frustration, save millions of dollars, and build trust.  

Finally, many CROs promise accelerated discovery paths, yet capacity and queue are frequently overlooked. Remember that timelines for processing legal paperwork, drafting statements of work (SOWs), the queue to initiate a project once all materials and reagents are in hand, and providing and communicating reports all factor into the overall timeline.  

RA: How are advances in mass spectrometry, such as acoustic ejection mass spectrometry, helping to drive research into protein targets? 

ZG: Acoustic ejection mass spectrometry is just one example where innovation with MS has fundamentally changed early drug discovery and HTS. The ability to perform direct, label-free quantification of binding interactions and enzymatic activity at rates competitive with optical methods, without the associated interference artifacts, has opened avenues to target classes previously deemed intractable. Yet, many MS approaches still require complex sample preparation workflows or restrict assay conditions for researchers to benefit from the speed and label-free readout. If it takes a year to optimize an assay to utilize the MS instrument, a throughput of over 100,000 compounds per day becomes less impactful. Innovation in sample preparation that enables researchers to benefit from the speed, resolution, and sensitivity of MS instruments, whether using electrospray or matrix assisted laser desorption ionization (MALDI) sources, without limitation is how confidence increases in MS-generated HTS data.  

Beyond traditional screening, native mass spectrometry is providing extraordinary mechanistic resolution. Observing intact protein-ligand complexes, resolving stoichiometry, characterizing allosteric effects, and detecting ternary complex formation with a single technique gives medicinal chemists information that previously required multiple orthogonal experiments. For challenging targets like intrinsically disordered proteins, multi-subunit complexes, and membrane-bound proteins, this is truly enabling new avenues for drug discovery. Covalent drug discovery has also been revitalized by MS sensitivity. Characterizing warhead reactivity, selectivity across the proteome, and residence time at a target is possible in a screening context rather than being reserved for late-stage characterization. As the industry continues pressing into harder biology, mass spectrometry’s unique ability to interrogate molecular reality without labels will continue to become more central to early discovery. 

Biography

Dr. Zachary Gurard-Levin is the CEO and Founder of Peapod Bio, Inc., a contract research organization dedicated to rapidly delivering assay development and HTS solutions through innovative screening platforms including integrating surface chemistry and MALDI MS for small molecule and peptide drug discovery.