A New Development Playbook for PROTACs

Shanghao Li
Shanghao Li, PhD
International Marketing Associate Director, La, boratory Testing Division, WuXi AppTec

As proteolysis-targeting chimeras (PROTACs) mature from scientific breakthrough to clinical modality, a candidate’s degradation potency is no longer enough to justify its advancement. A strong degrader is not necessarily a strong drug candidate if liabilities in exposure, safety, selectivity, manufacturability, or dosing strategy emerge later. As the field advances, success will depend on whether sponsors can integrate chemistry, pharmacology, safety, manufacturability, and clinical strategy early enough to translate promising degraders into viable medicines.

PROTACs have helped redefine what may be possible in drug discovery. By harnessing the ubiquitin-proteasome system to eliminate disease-relevant proteins, rather than simply inhibiting their activity, they have expanded the scope of targets that may be therapeutically addressable. For the past several years, much of the excitement around PROTACs has centered on this breakthrough mechanism. The field has been driven by the promise of degrading previously “undruggable” proteins, improving selectivity, and potentially overcoming resistance mechanisms that limit traditional inhibitors. But as clinical programs progress and the modality moves closer to late-stage regulatory milestones, the central question is changing.

The issue is no longer whether targeted protein degradation works. It is whether promising degraders can be translated into clinically and commercially viable therapies.

That transition marks an important phase of maturity for PROTAC development. The next wave of progress will depend on translational discipline and the ability to balance potency with developability, pharmacology, safety, manufacturability, and clinical feasibility from the earliest stages of development.

Entering a new phase of maturity

Early PROTAC innovation was rightly focused on validating the modality itself. Demonstrating that a heterobifunctional molecule could recruit an E3 ligase, drive ubiquitination of a target protein, and induce selective degradation was a foundational scientific achievement. That early work established targeted protein degradation as part of a broader wave of transformational therapeutic modalities, alongside approaches such as RNA interference therapeutics and antibody–drug conjugates, that have expanded what drug developers can target and how they think about translation.

Today, however, the field is operating under a different set of expectations. As more candidates advance through clinical development, sponsors must show not just that a PROTAC can degrade a target, but that it can do so with an exposure profile, safety margin, formulation strategy, and manufacturing pathway appropriate for clinical applications. A potent degrader in vitro may still fail to become a viable development candidate if it cannot achieve sufficient intracellular exposure, is metabolically unstable, if its degradation profile extends beyond the intended target set, or if its chemistry introduces manufacturing and formulation complications that slow advancement.

In other words, the scientific novelty of a modality can carry a program only so far before technical feasibility must be addressed for a candidate to advance. For PROTACs, that moment has arrived.

Potency alone is an incomplete metric

Degradation potency remains important. Maximum degradation, degradation half-life, and related pharmacodynamic measures are essential for understanding whether a molecule is engaging its biology as intended. But potency on its own can be misleading, particularly when it becomes the dominant criterion for candidate selection.

PROTACs are not conventional inhibitors. Their event-driven, catalytic mechanism introduces complexities that make exposure-response relationships less intuitive than those seen with traditional small molecules. Biological effects may persist after plasma concentrations decline, while higher concentrations do not necessarily lead to greater activity. In some cases, excessive exposure may even reduce degradation efficiency because of saturation effects that limit productive ternary complex formation.

This means the “best degrader” in a screening cascade is not always the best drug candidate. A molecule may demonstrate impressive degradation in a cellular assay while carrying liabilities that emerge only later, such as poor permeability, limited oral bioavailability, rapid linker metabolism, high nonspecific binding, unstable analytical performance, or off-target degradation driven by ligase biology or ternary complex behavior. If those issues are not considered early, potency can create a false sense of confidence in a degrader’s potential for clinical use.

Development workflows fall short

One reason translational issues emerge so frequently in PROTAC programs is that many development workflows still reflect assumptions built around traditional small molecules. In those models, discovery, DMPK, bioanalysis, toxicology, and chemistry, manufacturing, and controls (CMC) often proceed in a staged or partially sequential manner, with each function evaluating modality-relevant properties within its own domain before handing it forward.

With targeted protein degraders, however, early chemistry decisions can directly influence permeability, intracellular exposure, metabolic clearance, assay reliability, biodistribution, and manufacturability. Linker design, ligand selection, and overall polarity are not simply medicinal chemistry concerns; they shape how the molecule behaves across the entire development continuum. Likewise, a bioanalytical challenge may obscure the interpretation of PK/PD relationships, complicate dose optimization, or delay confidence in candidate selection.

The same is true for safety. Because PROTACs eliminate proteins rather than transiently inhibiting them, the consequences of target engagement can differ meaningfully from those associated with conventional inhibitors. On-target toxicity may emerge when complete or prolonged degradation is not tolerated, even if partial functional inhibition is acceptable. Off-target effects may arise not only from target promiscuity, but also from E3 ligase recruitment and unintended ternary complex formation. These risks cannot be addressed effectively if safety is considered only after potency and exposure have been optimized.

Traditional workflows can also underweight manufacturability and CMC considerations. PROTACs are generally handled as small molecules, but their structural complexity can create multi-step synthesis challenges, impurity-control difficulties, and formulation constraints much earlier than teams may expect. When these issues are discovered late, promising programs can lose momentum for reasons that have little to do with biology.

The core issue is not organizational design alone. It is that PROTACs expose the limits of linear decision-making. They require earlier integration because the liabilities that determine success are tightly interconnected.

PROTAC-specific development

If PROTACs require a different development model, what would it look like?

First, a successful PROTAC development plan should begin with balanced optimization across parameters rather than sequential, single-parameter optimization. Candidate selection should account not only for degradation potency, but also for permeability, solubility, metabolic stability, intracellular exposure, selectivity, formulation feasibility, and synthetic tractability. Programs that rank candidates holistically are better positioned to recognize which molecules are genuinely translatable.

Second, the PK/PD strategy should be built around the biology of degradation. Because systemic exposure does not fully explain pharmacological effect, teams increasingly need direct measures of target degradation and recovery kinetics, not just plasma concentration data. Mechanistic PK/PD models can help connect degradation durability, protein resynthesis, and dosing schedule in a way that better reflects how PROTACs work in vivo.

Third, bioanalysis should be treated as a strategic enabler rather than a downstream technical function. PROTACs can introduce assay complications, including nonspecific binding, chromatographic artifacts, and instability across matrices. Robust analytical methods are essential not only for quantitation but for making reliable decisions about exposure, disposition, and translation across study systems.

Fourth, safety assessment must expand beyond conventional assumptions. Early proteomic profiling, tissue distribution analysis, and evaluation of degradation selectivity can help identify liabilities before they become entrenched in a program. For PROTACs, understanding where degradation occurs, how long it persists, and what unintended proteins may be affected is central to designing an acceptable therapeutic window.

Finally, CMC and manufacturability should be considered earlier than many teams may be accustomed to. A molecule with compelling pharmacology but limited synthetic scalability, poor solid-state properties, or unstable formulation behavior may not be a strong development candidate. Integrating these realities earlier supports smarter program prioritization and reduces late-stage surprises.

Taken together, these elements define a development playbook centered on translation. They signal a maturation of the field, in which the emphasis shifts from demonstrating biological power to establishing overall developability, recognizing that promising degraders must ultimately succeed as integrated therapeutic candidates, not just mechanistic innovations.

Sponsors can improve the odds

For sponsors advancing PROTAC programs, translation should be a design principle from the beginning. That starts with cross-functional alignment early in discovery. Chemistry, DMPK, bioanalysis, safety, and CMC teams should work together to shape candidate criteria, so that trade-offs are recognized early, and optimization reflects the realities of development rather than the priorities of any one function.

It also means adopting more realistic success metrics. Degradation data should remain central, but it should be interpreted alongside developability, not in isolation from it. Sponsors may also benefit from building translational assays and biomarkers earlier. The ability to directly measure target degradation and connect it to pharmacodynamic effect can strengthen decision-making throughout preclinical and clinical development. In a modality where traditional exposure markers may be incomplete, translational pharmacology can provide strategic direction.

Most importantly, teams should resist the temptation to force PROTACs into a conventional small-molecule framework. These candidates may be classified as small molecules for many regulatory purposes, but functionally, they behave as a distinct modality. Treating them as such allows the development strategy to evolve in step with biology.

The next phase of PROTAC success

PROTACs have already shifted the pharmacological landscape around drugability. Their next contribution may be just as important by forcing the industry to rethink what a good development strategy looks like for complex, mechanism-driven therapeutics. As the field matures, successful drug sponsors will be those who can translate degradation into a developable, manufacturable, safe, and clinically meaningful therapy. That requires a different playbook built on integration, balanced optimization, and translational discipline. For PROTACs, that is no longer a future concern. It is the central challenge of the present.

 

Shanghao Li, PhD, currently serves as international marketing associate director in the Laboratory Testing Division at WuXi AppTec.

The post A New Development Playbook for PROTACs appeared first on GEN – Genetic Engineering and Biotechnology News.

STAT+: ‘Secret shopper’ study probes GLP-1 telehealth sites

You’re reading the web edition of STAT’s Health Tech newsletter, our guide to how technology is transforming the life sciences. Sign up to get it delivered in your inbox every Tuesday and Thursday.

Good morning health tech readers!

If it feels like despite your employer offering health insurance you’re still paying a lot of money for health coverage and care, you’re not wrong. Bob Herman’s new series, Out of Pocket, Out of Reach, explores the impacts of soaring health care costs.

Continue to STAT+ to read the full story…

<![CDATA[Phase 3 data show COMP360 psilocybin delivers rapid, lasting relief in treatment-resistant depression; FDA filing advances toward 2027 launch.]]>

The Download: your stake in OpenAI, and the Treasury’s AI warning

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

Your family’s $300 stake in OpenAI

Sam Altman’s proposal that Americans should share in the wealth created by AI is back in the spotlight, with reports that he is discussing giving the US government a 5% stake in OpenAI. At the company’s current valuation, that stake would be worth roughly $320 per American household.

The idea is meant to address concerns that AI companies are benefiting from human-generated work without compensating creators, while also easing fears that AI will cause a collapse of the labor market by providing a safety net. 

The details, however, remain unclear. Indeed, the offer may be more powerful as a political narrative than as a policy plan.

Read the full story on what the dividend proposal reveals about the future of AI.

—James O’Donnell

This article is from The Algorithm, our weekly AI newsletter. Sign up to receive it in your inbox every Monday.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

 1 A leaked Treasury report compares the AI market to the dotcom bubble
 Which contradicts the administration’s public optimism about AI. (NOTUS)
+ Fears that the market is overinflated are growing. (Reuters $)
+ And AI profits are hiding bigger risks in earnings reports. (FT $)
+ What even is the AI bubble? (MIT Technology Review)

2 Samsung profits have jumped 1,800% on booming AI chip sales
It just reported its third consecutive record quarterly profit. (BBC)
+ But its shares slumped over fears that the AI boom will stall. (Reuters $)
+ That boom has turned Samsung into a $1 trillion company. (CNBC)
 
3 A US cyber agency is using Mythos to audit government code
Sources say CISA is tapping Anthropic’s model to search for bugs. (Reuters $)
+ Agencies are using it despite Anthropic’s feud with the White House. (Axios)
 
4 Illinois’ governor has signed the nation’s strongest frontier AI law
It’s designed to protect citizens from AI risks. (Gizmodo)
+ US lawmakers are clashing over AI rules. (MIT Technology Review)
 
5 A hidden tracker in Claude Code has been exposed and removed
It secretly monitored users in China. (WP $)
+ Critics said it shows Anthropic’s willingness to surveil users. (Ars Technica)
+ The company has also found a hidden “thinking” space in Claude. (Axios)

6 Russia is suspected of flying drones over Europe from a shadow fleet
The flights were reportedly launched from commercial ships. (Ars Technica)
+ Europe has a drone-filled vision for future wars. (MIT Technology Review)
 
7 A controversial AI “actor” is set to star in its first feature film
Tilly Norwood will debut in a comedy-drama called “Misaligned.” (Variety)
+ A major actors union has lambasted the AI creation. (NBC News)

8 AI costs are driving US companies toward Chinese models
Businesses are hunting for cheaper model alternatives. (CNBC)
+ Chinese AI labs are betting big on open source. (MIT Technology Review)
 
9 Researchers have shown quantum proofs can beat classical ones
They found a problem that classical proofs can’t solve. (Quanta)

10 Earth will never be swallowed by the sun, according to new models
But it probably won’t be much fun to live here by that point anyway! (Wired $)

Quote of the day

“The goal might be to make machines in our image. But what I fear is that—perhaps without even quite noticing—we remake ourselves in theirs.” 

—Reporter Sarah O’Connor sounds a note of caution in her new book, We Are Not Machines, the Guardian reports.

One More Thing

mammoth walking on a strand of DNA

KATE DEHLER


Adventures in the genetic time machine

Eske Willerslev, a specialist in recovering DNA from old bones and objects, has made numerous breakthroughs. These include recovering the first more or less complete genome of an ancient human and 2.4-million-year-old genetic material from Greenland, revealing that today’s Arctic desert was once a forest with poplar, birch, and mastodons.

These findings are part of a wave of discoveries from what’s being called an “ancient-DNA revolution.” 

Beyond revealing stories of human migration and vanished ecosystems, scientists believe ancient DNA can unearth clues about modern diseases. It could even lead to a better food supply for our warming world. “And can we get that?” Willerslev asks. “Yes, I believe we can.”

Discover how ancient DNA could rescue the future

—Antonio Regalado

We can still have nice things

A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)

+ Underworld’s electric set from EDC Las Vegas 2026 has been released as a full concert video.
+ This photographic journey through global soccer culture captures the mad passion of fandom around the world.
+ Feeling unhinged? Me too. This playlist of gloriously intense classical music sympathetically captures the mood.
+ If you’re looking for visual inspiration, this collection of graphic design archives from across the web is a goldmine.

Prototypical graph based deep label propagation with semantic augmentation for cross-subject and cross-session EEG emotion recognition

Electroencephalogram (EEG)-based emotion recognition faces significant generalization challenges in cross-subject and cross-session settings, primarily due to the inherent non-stationarity, individual differences, and semantic deficiency of EEG signals. To address these challenges and enhance the universality of affective brain-computer interface systems, this study proposes a novel unsupervised domain adaptation framework named Prototypical Graph-based Deep Label Propagation with Semantic Augmentation (PGDLP). PGDLP seamlessly integrates three core components—prototypical semantic augmentation, prototype-graph deep label propagation, and prototypical alignment—into an end-to-end optimized system. Specifically, class-wise multivariate normal distributions are constructed using source-domain feature statistics to augment target-domain features semantically, bridging the domain gap and mitigating semantic insufficiency. An adaptive similarity graph based on prototype-semantic distances is designed to optimize pseudo-label quality while reducing computational complexity. It is combined with linear projection and an exponential moving average (EMA) for dynamic refinement. Dual intra- and inter-domain alignment losses with an adaptive balance factor are introduced to enhance intra-class compactness and inter-domain transferability, thereby facilitating the learning of discriminative and domain-invariant features. Extensive experiments are conducted on three benchmark datasets (SEED, SEED-IV, DEAP) under four rigorous evaluation protocols (cross-subject cross-session, cross-subject single-session, within-subject cross-session, cross-database). Overall, PGDLP achieves superior recognition accuracy and generalization performance compared with most state-of-the-art methods across the majority of evaluation protocols. PGDLP also presents strong robustness against label noise, stable hyperparameter performance, and fast convergence. The results demonstrated that PGDLP outperforms state-of-the-art methods in recognition accuracy and generalization, with verified robustness to label noise, stable hyperparameters, and efficient convergence. This study provides a promising solution for unsupervised cross-domain EEG emotion recognition and offers valuable insights for domain adaptation research on other physiological signals.

Lower limb rehabilitation in chronic incomplete SCI: a randomized controlled trial protocol comparing combined TMS-tSCS neuromodulation vs. tSCS alone

BackgroundSpinal cord injury (SCI) affects 15.4 million people worldwide, with a substantial proportion of incomplete SCI patients remaining non-ambulatory, highlighting the importance of motor function recovery and mobility in rehabilitation. While transcutaneous spinal cord stimulation (tSCS) has emerged as a promising non-invasive neuromodulation technique for enhancing motor recovery, the therapeutic potential of combining tSCS with transcranial magnetic stimulation (TMS) remains largely unexplored. This combination may leverage the complementary mechanisms of supraspinal and spinal neuromodulation to enhance corticospinal tract plasticity and functional motor outcomes.ObjectiveTo evaluate the efficacy and safety of combined TMS-tSCS intervention compared to tSCS alone for improving lower extremity motor function in individuals with chronic incomplete spinal cord injury.MethodsThis prospective, randomized, controlled, assessor-blinded clinical trial will enroll 60 participants with chronic (>12 months post-injury) incomplete spinal cord injury (AIS C or D) aged 18–65 years from Alexandra Hospital, Singapore. Participants will be randomized 1:1 to receive either combined TMS-tSCS (intervention group) or tSCS with sham TMS (control group) for 16 weeks (32 sessions). The primary outcome is change in Lower Extremity Motor Score (LEMS) from baseline to 16 weeks. Secondary outcomes include walking speed (10-Meter Walk Test), functional independence (Spinal Cord Independence Measure-III), spasticity (Modified Ashworth Scale), electromyography of the lower limb muscles and neurophysiological measures of corticospinal excitability.Expected outcomesWe hypothesize that combined TMS-tSCS will yield superior improvements in LEMS (≥2 points greater improvement) compared to tSCS alone, with enhanced corticospinal tract plasticity as evidenced by neurophysiological measures.Clinical trial registrationClinicalTrials.gov, identifier: NCT07595497.

Development of a transformation model to analyze horizontal saccadic velocity using electrooculography: a pilot feasibility study

IntroductionSaccadic eye movements are established biomarkers in neuroscience and clinical neurology, with video-oculography (VOG) serving as the gold standard for measurement. However, the high cost, bulky equipment, and poor portability of VOG systems restrict their clinical utility. Electrooculography (EOG) provides a practical alternative, but quantitative conversion of EOG-derived measurements into VOG-equivalent values remains insufficiently established. This study aimed to develop and validate a mathematically derived transformation model for estimating VOG-equivalent horizontal saccadic velocities from EOG recordings.MethodsFour healthy adults underwent simultaneous EOG and VOG recordings while performing controlled horizontal gaze shifts. Based on a current-source model of the corneal potential, an analytical relationship between EOG voltage velocity and angular eye velocity was derived. Multiple high-pass filter settings were systematically evaluated to identify optimal signal-processing conditions. Transformation equations were derived from the pooled horizontal-saccade dataset and further evaluated using leave-one-subject-out (LOSO) analysis.ResultsThe theoretical model predicted a linear relationship between EOG- and VOG-derived saccadic velocities. Among the tested filter settings, a 0.3 Hz high-pass combined with a 35 Hz low-pass filter yielded the best overall agreement. Under this condition, the final transformation model produced a common slope coefficient of 0.146 °/μV for both movement directions, with an additional direction-specific intercept of −82.37 °/s for rightward saccades. Converted EOG-derived velocities showed no significant differences from measured VOG-derived velocities. LOSO validation demonstrated stable transformation coefficients (mean slope = 0.147 °/μV, mean intercept = −82.47 °/s) and maintained agreement across individuals.ConclusionA biophysically derived and experimentally validated EOG-to-VOG transformation model can provide accurate estimates of horizontal saccadic velocity under appropriate filtering conditions. These findings support the feasibility of quantitative saccadic analysis using EOG, providing a practical alternative to VOG.

A dual-branch network with brain region-constrained attention for EEG emotion recognition

IntroductionElectroencephalography (EEG)-based emotion recognition provides an objective avenue for affective computing. However, the complexity of EEG signals across temporal, frequency, and spatial domains makes any single dimension inadequate.MethodsTo overcome these limitations, we propose the Brain Region-Constrained Attention Dual-Branch Network (BRAD-Net). This network adopts a parallel Spatio-Temporal and Spectral-Spatial dual-branch architecture to achieve synergistic multi-domain EEG feature learning. Within the spatio-temporal branch, we introduce a novel Brain Region-Constrained Attention mechanism, which strictly confines self-attention computation to channels belonging to the same brain region. This design not only suppresses irrelevant cross-region interference but also incorporates neuroanatomical priors of brain parcellation, thereby enabling effective and interpretable representation learning.ResultsIn subject-dependent experiments using 10-fold cross-validation on DEAP and DREAMER datasets, BRAD-Net achieves high accuracies of 97.44%, 97.70%, and 97.97% for valence, arousal, and dominance on DEAP, and 99.66%, 99.78%, and 99.80% on DREAMER, respectively. Leave-one-subject-out validation on DREAMER dataset achieves accuracies of 72.80% and 75.66% for arousal and dominance, respectively. Additionally, the BRAD-Net demonstrates strong cross-paradigm adaptability, achieving 98.21% accuracy on a depression classification dataset.ConclusionsThese findings confirm that integrating neuroanatomical priors into a dual-branch multi-dimensional learning framework effectively extracts robust and interpretable neural representations. BRAD-Net not only advances high-performance EEG emotion recognition but also provides a novel, biologically-constrained design paradigm for developing more interpretable brain-computer interface models. By demonstrating that restricting attention to within-brain-region interactions suffices for accurate emotion recognition, our work offers a new theoretical perspective on the application of brain parcellation knowledge in classification models.
<![CDATA[Therapeutic drug monitoring clarifies adherence and plasma levels, while long-acting injectables and patches reduce variability and support patient-centered benefit-risk decisions.]]>