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GLP-1 receptor assay: drug discovery in the metabolic field

Science Spyglass GLP-1 receptor assay: drug discovery in the metabolic disorders field Metabolic disorders, particularly type 2 diabetes and obesity, represent a major global health burden. In the United States alone, over 30 million people live with type 2 diabetes, and one-third of the population is affected by obesity. These conditions are closely linked to increased cardiovascular risk and long-term health complications. As their prevalence continues to rise, there is an urgent need for effective and innovative pharmacological interventions to manage and treat these chronic diseases. In this article, we explore how metabolism is regulated, highlight the key cellular players, and introduce the tools developed by Axxam to study these mechanisms, focusing in particular on the GLP-1 (glucagon-like peptide-1) receptor assay. Interested in GLP-1–based drug discovery? 💡Join our upcoming webinar   “Empowering GLP-1 Drug Discovery: Axxam’s Integrated Assay Platform for Metabolic Research” Learn how Axxam’s complementary GLP-1 receptor and secretion assays enable integrated, mechanistic research in metabolic disorders.  🗓️ 4 December 2025                                                                                         ⏰ 16:00 CET / 10:00 ET / 07:00 PT Register for the webinar Hormonal regulation of metabolism: the role of enteroendocrine cells and incretins Metabolic regulation is tightly controlled by a complex network of hormones, many of which are secreted by enteroendocrine cells (EECs), specialized sensory cells that make up about 1% of the gut epithelium. These cells detect the presence of nutrients in the intestinal lumen via chemosensory G protein-coupled receptors (GPCRs) and taste receptors, and in response, release hormones that influence digestion, appetite, and glucose metabolism. Besides nutrient availability, this process is regulated also by other stimuli, including mechanical stretch, and neural signals. Key hormones released by EECs include GLP-1, GIP, cholecystokinin (CCK), ghrelin, and peptide YY (PYY). Among them, GLP-1 and GIP (gastric inhibitory polypeptide) are known as incretins, gut-derived hormones that enhance glucose-dependent insulin secretion, playing a pivotal role in maintaining glucose homeostasis and serve as important therapeutic targets for type 2 diabetes and obesity. The GLP-1 receptor: a key target in glucose and appetite regulation The glucagon-like peptide-1 receptor (GLP-1R) is a GPCR found primarily in pancreatic β-cells, brain, heart, kidney, and the gastrointestinal tract. It plays a key role in regulating blood sugar by enhancing insulin secretion in response to glucose and reducing glucagon secretion, which helps lower blood glucose levels. GLP-1R is activated by the hormone GLP-1, released from EECs after eating. Activation of this receptor also slows gastric emptying, promotes satiety, and reduces food intake, contributing to weight management and appetite control. From a therapeutic perspective, GLP-1 receptor agonists — drugs that mimic the endogenous hormone GLP-1 — are used to treat type 2 diabetes and obesity by improving insulin secretion, lowering blood sugar, and supporting weight reduction. While peptide-based GLP-1 receptor agonists such as Exenatide, Liraglutide, Dulaglutide, and Semaglutide have demonstrated strong efficacy, there remains significant interest in developing next-generation therapies. One key challenge is that these biologics typically require injection, which can limit patient compliance and convenience. To address this, researchers are exploring alternative routes of administration, including oral, transdermal, and inhalable formulations. In parallel, there is growing focus on small molecule GLP-1R agonists, which have the potential to be administered orally, produced more cost-effectively, and offer improved stability and pharmacokinetic profiles. These advancements aim to expand therapeutic options, enhance accessibility, and improve long-term adherence in the management of metabolic disorders. Finally, beyond their role in controlling metabolic processes, GLP-1 receptor agonists also show promising potential benefits in treating cardiovascular and neurodegenerative diseases. Axxam’s GLP-1 receptor assay: a tool for metabolic drug discovery At Axxam, we have developed and optimized a GLP-1 recombinant assay using a CHO (Chinese Hamster Ovary) cell line. This GLP-1 receptor assay is ideal for studying receptor activation mediated by small molecules. The GLP-1 receptor pathway involves GLP-1 binding to its receptor (GLP-1R), which activates G-proteins, leading to increased cyclic adenosine monophosphate (cAMP) levels and subsequent activation of protein kinase A (PKA). This pathway primarily influences insulin secretion and glucagon inhibition in pancreatic beta and alpha cells, respectively. In our assay, we employ the HTRF® cAMP Gs HiRange detection kit from Revvity as the readout for the measurement of the cAMP levels produced upon GLP1 receptor activation. Talk with our experts The cAMP kit operates on a competitive format involving a specific antibody labeled with cryptate (donor) and cAMP coupled to d2 (acceptor). This setup enables direct characterization of all types of compounds acting on Gs-coupled receptors in either adherent or suspension cells. Native cAMP produced by the cells competes with the d2-labeled cAMP for binding to monoclonal anti-cAMP Eu3+ cryptate. Source: www.revvity.com The assay has been validated with both GLP-1 (7-36), the endogenous physiological full agonist secreted by intestinal L-cells, and its metabolite GLP1 (9-36) which is a weaker partial agonist. The EC50 of GLP1 (7-36) is in the expected 2 digits pM range (25-60pM), while its metabolite shows a potency which is 4 logs lower and falls into the low µM range. We also tested the effect of the known covalent allosteric modulator BETP, which – as expected – significantly potentiated the activity of GLP-1 (9-36) on the GLP-1 receptor. This GLP-1 receptor assay is suitable for compound profiling studies as well as high throughput screening campaigns with proven high sensitivity to the GLP-1 (7-36) reference ligand and robustness. The GLP-1 receptor assay is also suitable for testing natural compounds and extracts, supporting the development of nutraceuticals and functional food products aimed at improving metabolic health. We are also working on optimizing a GLP-1 secretion assay in a suitable cell line, which will be available soon. Axxam’s metabolic platform assays Enteroendocrine cells act as nutrient sensors in the gut, detecting dietary components through receptors located on their luminal surface. These include GPCRs such as GPR40, GPR120, and various taste receptors,

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AXXVirtual for drug discovery

AXXVirtual: a chemistry-driven virtual library for drug discovery

Science Spyglass Building AXXVirtual: a chemistry-driven virtual library for drug discovery In the same way as in high-throughput screening (HTS), the quality of the screened library plays a crucial role in the success of virtual screening. While virtual screening enables the exploration of much broader and more diverse chemical spaces, many virtual libraries are populated with molecules that, while computationally attractive, are difficult — or even impossible — to synthesize. For many drug discovery programs, this stage represents a major bottleneck: synthesis can be slow, unpredictable, and resource-intensive, delaying the confirmation of biological activity and the chemical exploration of the promising compounds. This is precisely the gap that the AXXVirtual library was designed to overcome. Beyond ensuring high-quality, drug-like chemical space, this 19 million non-commercial small molecule library was built with synthesis feasibility at its core. Every AXXVirtual compound can be produced in just 2–3 steps from readily available building blocks. This unique design guarantees that virtual hits are not just theoretical possibilities but tangible molecules, accessible within controlled and predictable timelines. As a result, AXXVirtual enables researchers to reach in vitro confirmation faster, accelerating the path from virtual screening to validated hits. Developed through a structured four-stage process, the compounds were rigorously selected by applying strict rules and filters to guarantee drug-like properties and a high degree of structural diversity, thereby enabling efficient downstream development. This article walks you through the principles behind building a high-quality virtual library for drug discovery and shows how these concepts were applied in the design of the 19 million compound AXXVirtual library. Excitingly, we are currently expanding the AXXVirtual library by approximately an order of magnitude and the expanded collection will be released soon! Want to explore AXXVirtual for your projects? Get in touch Designing for the real lab: the synthetic accessibility Synthetic feasibility has become a key parameter in the design of virtual libraries, ensuring that computational efforts translate into compounds that can be readily produced for downstream testing [1, 2]. The core of this approach lies in relying on synthetic routes based on established reaction classes that have demonstrated their value over decades — including, for instance, amide coupling and the Suzuki–Miyaura reaction. Despite the emergence of new methodologies, these reactions remain the backbone of medicinal chemistry due to their efficiency, reproducibility, scalability, and high yields [3]. Built on the concepts previously described, the AXXVirtual compounds have been designed to be synthesized through eight synthetic routes, each consisting of two to three steps, employing six reliable reactions. The building blocks, more than 3.000 in total, were selected from the inventory of a trusted partner and are immediately available, eliminating delays from external orders. In addition, the reagents have been carefully selected to ensure clean reactions, minimizing side products and regioisomer formation. This thoughtful combination of proven chemistry and readily available reagents enables a fast and efficient synthesis, allowing the preparation of 100 compounds within just two to three weeks. To maintain these standards, the library is regularly updated in line with the partner’s inventory, making AXXVirtual a dynamic and continuously evolving library. Designing smarter: AI-powered properties and synthetic feasibility prediction Artificial intelligence (AI) is playing an increasingly important role in the landscape of virtual libraries for drug discovery by enabling more accurate and efficient predictions of molecular properties and synthetic accessibility. Today, a variety of machine learning models are employed to predict molecular properties with increasing accuracy and speed. These models rely heavily on large and curated training sets – databases of molecules with known experimental properties – to identify patterns and relationships between molecular features, such as size, chemical groups, and shape, and their observed behaviors, such as solubility and toxicity. Unlike simple rule-based methods, machine learning adapts to the complexity and the variability inherent in chemical data and this allows it to capture subtle influences and nonlinear effects that traditional rules often miss. For synthetic accessibility, tools like RAscore (Retrosynthetic Accessibility Score) [8] are widely used. RAscore is a machine learning classifier trained on the outcomes of the retrosynthetic planning software AiZynthFinder. Instead of running a full retrosynthetic analysis for each molecule — which is impractical when dealing with millions of compounds — RAscore provides a rapid estimate of whether a compound is likely to be synthesizable using known building blocks and reaction rules. Applying RAscore to evaluate AXXVirtual compounds, we found that the vast majority (96%) scored above 0.8 on the 0-to-1 scale, confirming their high synthetic accessibility. This result further highlights the robustness of the chemistry underpinning our library. Designing for success: from synthesizable to developable molecules While synthetic accessibility defines what can be built, drug-likeness defines what is worth pursuing. Virtual libraries should not only contain compounds that are synthetically feasible, but also exhibit molecular properties that make them suitable candidates for future development. This includes properties that impact solubility, permeability, metabolic stability, and safety. The concept of drug-likeness is grounded in the empirical observation of properties shared by orally bioavailable drugs. Large-scale analyses of marketed drugs and clinical candidates have revealed that certain molecular properties – such as moderate size and balanced lipophilicity – are associated with favorable pharmacokinetic behavior. These findings led to the formulation of guidelines, with Lipinski’s Rule of Five (Ro5) [4] and Veber’s rules [5] being among the most well-known and widely adopted. In parallel with physicochemical profiling, the quality of chemical libraries, including virtual ones, must be ensured by excluding compounds known to cause assay interference or unreliable readouts. A major class of such problematic molecules is represented by PAINS (Pan-Assay Interference compoundS), which are chemical structures prone to react nonspecifically with numerous biological targets rather than specifically affecting one desired target [6]. Rhodanines exemplify the extent of the problem. More than 2.000 rhodanines have been reported to have biological activity in over 400 papers. However, a publication by Bristol-Myers Squibb points out that these compounds undergo light-induced reactions that irreversibly modify proteins. It is hard to imagine how such a mechanism could be optimized to produce a drug

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Generative AI for PPI modulators

Designing PPI modulators with Generative AI

Science Spyglass Designing a library of PPI modulators with Generative Artificial Intelligence Why Generative AI for PPI Modulators? Protein–protein interactions (PPIs) play a central role in the regulation of cellular signaling, structural organization, and enzymatic function. Dysregulation of specific PPIs has been implicated in a wide range of pathologies, including oncogenesis, neurodegenerative disorders, and infectious diseases. Despite their biological relevance, PPIs have long posed a challenge for small-molecule drug discovery due to their typically large, shallow, and hydrophobic binding interfaces that often involve dispersed hot spots making it difficult to achieve high-affinity and selective binding with conventional small molecules. However, advances in structural biology and computational modeling have begun to redefine the tractability of PPIs. Among the most promising developments in this space is the application of generative artificial intelligence (Generative AI), which offers powerful new capabilities to explore chemical space and maximize the chance of binding at protein-protein interfaces. Figure 1. Example of protein-protein interaction (PPI) molecules binding at the interface of two protein units. Below we describe our application of Generative AI in designing a library of PPI modulators, called the AXXPPI library. Furthermore, important AI concepts are briefly described at the end of the article. Ready to accelerate your drug discovery with the AXXPPI library? Contact us Data curation for PPI modulator design High-quality data is foundational to the success of any machine learning–driven drug discovery project, and this is especially true when targeting complex systems like protein–protein interactions. Accurate, well-curated datasets enable models to learn meaningful structure–activity relationships and generate chemically plausible, target-relevant molecules. For this project, we assembled a diverse and representative dataset of known PPI modulators by integrating compounds from several specialized sources, including 2P2Idb, iPPI-DB, Timbal, ChEMBL, and peer-reviewed literature. Each entry was carefully curated to ensure correct annotation of bioactivity, target interface, and molecular structure, enabling downstream modeling to focus on truly relevant chemical features. This rigorous data preparation step is essential not only for training robust models but also for minimizing bias and improving the translational relevance of the predicted molecules. Finetuning a pretrained drug-like model To explore the chemical space of PPI modulators, we employed de novo molecular generation using state-of-the-art generative modeling frameworks. As a starting point, we utilized a model pretrained on large, drug-like chemical libraries, which had learned the general rules of chemical syntax and structure.  Figure 2. Schematic representation of ideal molecules (yellow spheres) within focused (light grey) and general (dark grey) chemical space. To adapt this model to PPI-relevant scaffolds and molecular features, we applied a transfer learning strategy by fine-tuning it on our curated dataset of known PPI modulators. This domain-specific refinement enabled the generative algorithm to focus on structural motifs, physicochemical properties, and topologies commonly found in PPI-active compounds within the drug-like space. Nevertheless, further optimization of the fine-tuned model is required to directly incorporate additional design constraints such as synthetic tractability without compromising its ability to generate functionally relevant modulators with promising pharmacological profiles. De novo generation of optimized molecules To further guide the de novo generation of PPI modulators toward synthetically tractable and functionally relevant chemical space, we implemented reinforcement learning (RL) with multi-objective optimization. A key component of our reward strategy was a machine learning classifier specifically trained to distinguish PPI modulators from non-PPI compounds, providing a probabilistic measure of PPI-likeness for generated molecules. In addition to this classifier, we incorporated the Synthetic Accessibility Score (SAS) to penalize impractical structures. The reinforcement learning algorithm ranked generated molecules based on Pareto efficiency across multiple objectives, ensuring a balance between synthetic tractability and predicted biological relevance. In this framework, the pretrained generative model served as the agent, i.e., the network being actively optimized, while the fine-tuned model acted as the prior, i.e. a non-trainable reference that contributes mutations and maintains the generation of chemically valid and PPI-relevant scaffolds. This setup allowed us to efficiently navigate the multi-dimensional objective space and converge on high-quality candidate structures with an optimal trade-off between desirability and diversity. Figure 3. Evaluation of the Machine Learning Classifier’s ability to discriminate known PPIs (not used during the training process – light green) from random compounds (light grey). Filtering, evaluation and synthesis Once molecules are generated, they must undergo a rigorous filtering and prioritization process before synthesis. This workflow follows a funnel-like approach, where large numbers of candidate compounds are progressively narrowed down.   Figure 4. Filtering funnel to select a dataset of molecules to be synthesized. Initially, molecules are removed if they violate medicinal chemistry filters such as REOS or contain undesirable reactive groups. Additional filtering ensures that selected compounds are sufficiently distinct from the training set to avoid rediscovery, while also maximizing chemical diversity. Finally, compounds are evaluated for predicted ADMET properties to ensure acceptable pharmacokinetic and safety profiles. Only a small, carefully selected subset of molecules meeting all these criteria proceeds to synthesis and experimental validation. Use of the PPI library in Axxam HTS A focused PPI compound library is a valuable resource for high-throughput screening (HTS) campaigns targeting challenging biological interfaces. Unlike traditional approaches to PPI-focused libraries, which often rely on large and lipophilic chemotypes with limited drug-like properties to disrupt or stabilize PPIs, our strategy was to combine the structural features required for engaging protein–protein interfaces with careful optimization of physicochemical and developability parameters. The result is a collection of compounds that retain the 3D topologies and interaction patterns relevant for PPI modulation, while maintaining chemical tractability and favorable drug-like characteristics. This makes the library uniquely positioned to deliver high-quality starting points for hit discovery campaigns in the PPI space, increasing their chance for success. PPI-focused libraries are particularly valuable in therapeutic areas where dysregulated protein–protein interactions play a central role in pathogenic interactions. Key areas include: Oncology: Numerous cancer pathways are driven by aberrant PPIs, such as p53–MDM2, BCL-2 family interactions, and β-catenin–TCF. These interactions regulate cell cycle progression, apoptosis, and transcription, making them prime targets for small-molecule inhibitors that can restore normal signaling or induce cell death in tumor cells. Neurodegenerative diseases:

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Artificial intelligence in drug discovery

Artificial intelligence in drug discovery

Science Spyglass How artificial intelligence is supporting the small molecule drug discovery process Faster and more efficient The discovery of new small molecule drug candidates has always been a complex, time-consuming, and costly endeavor. For decades, scientists have worked tirelessly to identify new chemical compounds that interact with specific biomolecules in the human body in therapeutically meaningful ways. Today, artificial intelligence in drug discovery is driving a major transformation of the process, providing scientists with powerful new tools to accelerate their work. In simple terms, artificial intelligence (AI) refers to machines or computer programs that can perform tasks that usually require human intelligence, such as recognizing patterns, learning from experience, or making decisions. Machine learning (ML) is a specific type of AI that uses data to learn and improve over time without being explicitly programmed for every scenario. Let’s explore how AI and ML are revolutionizing small molecule drug discovery and why it matters. Let’s talk science and strategy: Schedule a call with our experts Enter artificial intelligence: smarter and faster drug discovery This is where AI and ML come in. These technologies can process vast amounts of data quickly and identify patterns that humans might miss. Artificial intelligence in drug discovery can perform a wide range of tasks, including: Identify novel therapeutic targets: AI can integrate vast biological datasets to uncover new disease-related targets that may be overlooked by traditional methods. Predict the molecular architecture of protein receptors: AI predicts protein structure directly from the amino acid sequence, bypassing time-consuming and costly experiments. Predict how molecules behave: AI models estimate how a particular molecule might interact with a protein in the body, helping researchers decide whether it’s worth testing. Design new molecules: ML can generate entirely new chemical structures that have the potential to become effective drugs. Prioritize candidates for synthesis: AI and ML models assess ADME (absorption, distribution, metabolism, and excretion) properties, toxicity, and stability to help select the most promising compounds for experimental validation. Improve efficiency: By automating early steps in drug discovery, AI reduces the time and cost to bring a new drug to market. Let’s break down each of these contributions. 1. Identifying novel targets AI plays an increasingly important role in target identification, the critical first step in the drug discovery process. By integrating and analyzing large-scale omics datasets, including genomic, transcriptomic, proteomic, and clinical data, AI systems can detect complex biological patterns that point to previously unrecognized disease drivers. These insights can reveal novel, high-potential therapeutic targets and enable researchers to stratify diseases more precisely. This data-driven approach goes beyond what traditional target discovery methods can achieve, particularly in areas like oncology, rare diseases, and multifactorial conditions. AI models can also prioritize targets based on predicted druggability and relevance to patient subgroups, ultimately helping de-risk early discovery efforts. Discover Axxam approach to target identification and validation 2. Predicting protein (receptor) structure AI helps predict protein structures by analyzing amino acid sequences and determining how they fold into a unique 3D shape. This structural information is essential, as a protein’s shape defines its role in the body and forms the receptor site where small molecule drugs can bind. These interactions rely on shape complementarity and non-covalent molecular forces within specific binding pockets. AI-based systems used to predict protein structures are trained on thousands of known examples; one such system is AlphaFold. These systems identify folding patterns and structural motifs, enabling them to predict the 3D conformation of proteins from their amino acid sequences with remarkable speed and accuracy. In essence, by acting like an intelligent puzzle solver, AI dramatically accelerates structural biology. The significance of this breakthrough was recognized by the Nobel Prize in Chemistry awarded to the developers of AlphaFold. 3. Predicting molecular behavior At the heart of many diseases is a faulty protein. Scientists aim to find molecules that can bind to these proteins to inhibit or activate their function. The ability of small molecules to recognize and bind to specific pockets on the protein receptors relies on factors such as shape complementarity, molecular interaction forces, and thermodynamic considerations. Traditionally, this challenge is addressed through High-Throughput Screening (HTS), where hundreds of thousands of compounds are tested experimentally using suitable bioassays, extensive compound libraries, and laboratory automation platforms. In contrast, by digitally simulating these molecular interactions, AI enables the virtual screening of vast compound libraries, including molecules that have not been synthesized yet. This dramatically expands the search space and can potentially save time and resources. At some point, however, predictions need to be confirmed by real experiments for which compounds need to be purchased and prepared for testing or potentially synthesized from scratch.  Importantly, HTS and AI-based virtual screening are not mutually exclusive approaches and can be used in a complementary fashion in modern hit discovery. Insights obtained from virtual screening can guide HTS efforts by focusing experimental resources on the most promising compounds. Conversely, HTS can uncover active compounds that might be missed by current models, providing valuable data that can be fed back into AI algorithms to improve future predictions. When combined strategically, AI and HTS create a powerful workflow that enhances both speed and success rates of identifying high-quality hits. Leverage the best of both worlds: Discover virtual screening and HTS at Axxam 4. Designing new molecules from scratch One of the most exciting applications of artificial intelligence in drug discovery is its ability to design new molecules. Generative AI acts as a virtual chemist, rapidly inventing novel synthesizable molecules and organizing them into virtual libraries to speed up drug discovery. Designing new molecules: AI models extract chemical rules and patterns from large datasets of known compounds and reactions. They use this knowledge to generate new molecular structures that align with specific goals, such as targeting a particular protein or exhibiting desired properties. Ensuring synthetic accessibility: Generative AI can predict whether a proposed molecule can realistically be synthesized using available chemical building blocks and known reactions. Some AI systems, like SynFormer, even generate the synthetic pathways alongside the molecules, ensuring that the designs

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Neuroscience drug screening

Next generation neuroscience drug screening

Science Spyglass Next generation neuroscience drug screening Tessara’s RealBrain® neural micro-tissues validate compound effects in Axxam’s High-Content Imaging workflow Application note Axxam S.p.A. – Milan (Italy) Tessara Therapeutics Pty Ltd. – Melbourne (Australia) Keywords: chemotherapy induced neuropathy (CIPN), induced pluripotent stem cell (iPSC), neurotoxicity, high content imaging, mini brain, organoids, drug discovery, neural micro-tissues, neurodegenerative diseases. Introduction The use of three-dimensional (3D) biology has revolutionized the way to model and study complex biological processes, surpassing the traditional two-dimensional (2D) approaches. While 2D cell cultures have been the standard for many years, they fail to capture the intricate cell-to-cell and cell-to-matrix interactions that occur in vivo. In contrast, 3D biology offers a more physiologically relevant environment, allowing for the study of cellular behaviors in a context that mimics in vivo conditions more closely. The growing need for non-animal models in biomedical research is also driving the shift toward 3D systems. Indeed, in 2022, the U.S. FDA made headlines by endorsing non-animal testing methods for drug discovery and development, emphasizing the importance of human-relevant models for in vitro drug discovery. Whilst the global life science community has begun rapidly expanding and adopting 3D human tissue models such as spheroids and organoids, a number of limitations have arisen. Spheroids for example are reproducible, but like 2D cultures, the structure of their neural networks is markedly different from the human brain. Organoids can mimic brain complexity but have lacked reproducibility and have been difficult and slow to manufacture. In response to this demand and to facilitate the uptake of 3D models that can better meet the requirements of a preclinical drug discovery pipeline, a new technology from Tessara Therapeutics was introduced. Tessara’s 3D models combined with Axxam’s workflow Based in Melbourne Australia, Tessara is a world leader in 3D cell-based models of the brain. In a world-first innovation, Tessara has developed its RealBrain® neural micro-tissue technology and 3D cell-based models that replicate the biological complexity of the human brain in a reproducible manner. Tessara has validated and recently launched two brain models: the ArtiBrain™ model (healthy human brain), and the ADBrain™ model (sporadic Alzheimer’s disease (AD), representing ~98% of all AD cases). Tessara has developed this platform with commercial scale manufacturing processes, delivering models that are predictive, reproducible at scale and cost-effective. Combining these relevant in vitro models with suitable approaches to fully harness their potential could be transformative for the drug discovery field. In this context, high-content screening (HCS) and image-based readouts represent ideal tools for exploring the dynamic behaviours of cells and networks within 3D environments. Through image-based analysis, researchers can explore the complex biology of 3D systems in unprecedented detail across multiplecellular parameters, generating vast datasets from complex biological systems, facilitating the identification of subtle phenotypic changes that would be missed in traditional screening methods. This technology pipeline enables a deeper understanding of drug responses, toxicity, and mechanisms of action in more relevant biological contexts, offering greater translational value for pre-clinical studies. Incorporating these advanced technologies into research pipelines is transforming the drug discovery and disease modeling field. Generation of Tessara’s RealBrain® neural micro-tissues Tessara’s RealBrain® drug screening platform is based on manufactured human mimetic brain micro-tissues that closely recapitulate neural physiology and pathophysiology. RealBrain® neural micro-tissues are generated by encapsulating primary or induced pluripotent stem cell (iPSC)-derived neural precursor cells in a proprietary hydrogel matrix composed of chemically defined biomaterials. The RealBrain® hydrogel provides the optimal micro-environment to activate endogenous cellular programs of neuro-development in vitro. Over three weeks of maturation, the developing neural and glial cells remodel their micro-environment, migrate in 3 dimensions, form a complex network and replace the synthetic hydrogel with their own cell-secreted extracellular matrix (ECM). The resulting RealBrain® neural micro-tissues closely model human neurophysiology – they respond to neurotransmitters, feature a genetic profile representative of human brain tissue, and under controlled conditions can develop the complex pathological hallmarks of Alzheimer’s disease. These neural micro-tissues are also compatible with all standard laboratory workflows, from immunocytochemistry and confocal microscopy that doesn’t require the use of any clearance protocols, through to genomics and proteomics applications and other functional assays such as calcium imaging. Imaging the RealBrain® micro-tissues with Axxam’s High Content Imaging workflow In this study, we employed an automated high-content confocal imaging system (Opera Phenix Plus, Revvity) to acquire detailed 3D images of RealBrain® micro-tissues, stained with fluorescent markers to visualize neuronal cells (β-III Tubulin), glial cells (GFAP) and cell nuclei (DAPI) (Figure 1); these images illustrate the structural organization and neuronal differentiation within the brain micro-tissue. Figure 1 Representative images of a brain micro-tissue acquired by High Content Microscopy (Opera Phenix Plus, Revvity). (A) Brightfield image showing the overall morphology of the microo-tissue. (B) DAPI staining (blue) highlighting cell nuclei distribution. (C) βIII-Tubulin staining (green) marking neuronal structures. (D) Merged image combining DAPI and βIII-Tubulin signals, providing a composite view of the organoid’s neuronal architecture. Using two different acquisition setups and z-stack acquisitions we were able to capture multiple optical slices, enabling comprehensive 3D reconstruction of the neural micro-tissues at different levels of resolution (Figure 2). Figure 2 High-resolution 3D imaging of microtissues acquired through z-stack acquisition, enabling detailed visualization of cellular structures at different depths. Left panel: A volumetric reconstruction of the microtissue, showcasing its overall morphology and spatial distribution of cellular components. Right panel: Cross-sectional view highlighting internal structural organization, revealing intricate cellular networks and fiber orientations. Neuronal cells are stained by βIII-Tubulin (green) whilst glial cells are marked by GFAP staining (red). Key parameters such as z-stack step size, magnification, field of view, and laser intensities were carefully optimized to ensure both signal clarity and high-quality micro-tissue reconstruction. We balanced acquisition time and resolution to maintain both data quality and efficiency. The resulting images were analyzed using Harmony software (Revvity), where we developed a customized pipeline to visualize cellular features and extract multiparametric data. To assess the impact of magnification on data quality, we compared two settings: 20X (high magnification) and 5X (low magnification) (Figure 3). Figure 3 Examples of acquisition parameters

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Potency assays for gene therapy CMC studies

Science Spyglass Development of potency assays for clinical studies on gene therapy candidates Gene therapy has emerged as a promising and powerful treatment modality for numerous human diseases, with Adeno-Associated Virus (AAV) vectors recognized as some of the safest and most widely used delivery systems. For these complex biological medicines, where product testing poses unique challenges, potency assays play a critical role in clinical studies and drug licensing. Leveraging Axxam’s advanced technological platforms, these potency assays can be tailored to meet the specific requirements of various application fields, providing robust solutions to this critical step in the drug development process. AAV vectors have been proven to be particularly successful in ophthalmology. In this field, Axxam has recently developed and validated on behalf of GenSight Biologics a potency assay* to support clinical studies of an AAV-based optogenetic drug, i.e. GS030-DP from GenSight Biologics, for visual restoration in patients with Retinitis Pigmentosa. Explore more about gene therapy and potency assays in the expandable boxes below or scroll down to dive straight into our case study on a potency assay for Retinitis Pigmentosa gene therapy. AAVs as a promising strategy for gene therapy Gene therapy is a therapeutic strategy based on the modification of gene expression within target cells via the employment of viral or non-viral vectors. Up to date, most of the US Food and Drug Administration (FDA)-approved gene therapies are viral based (Wang et al., 2024 Signal Transduct Target Ther). The viral vector approach takes advantage of the natural ability of viruses to infect human cells. Viral pathological genetic sequences are replaced by the desired therapeutic genes and target cells are then infected with the modified viruses, leading to the incorporation of the therapeutic material into the nuclei (Ghoraba et al., 2022 Clin Ophthalmol).   Amongst the viral vectors studied and used for in-vivo gene therapy -which include adenovirus, retrovirus, lentivirus, and herpes simplex virus-, AAV vectors have attracted a significant amount of attention in the field, due to their broad tissue tropism, good safety profile and versatile manufacturing processes. In fact, AAVs are non-pathogenic, do not integrate into the host genome, can sustain long-term gene expression and are often inherently capable of efficient cellular entry thus enhancing transduction efficiency (Wang et al., 2024 Signal Transduct Target Ther).   Notably, the form of AAV used in gene therapy is not the wild-type but a recombinant one (rAAV), which lacks viral DNA and instead contains the recombinant DNA. This will persist as episome in the nucleus of transduced cells and will not be integrated into the host genome, thus it will be diluted over time as cells proliferate and will be eventually lost, which is ideal for some gene therapy applications (Naso et al., 2017 BioDrugs). AAV-based gene therapy for ocular diseases rAAVs have the potential to find several applications in the clinic and are currently being tested in clinical trials for a wide range of human diseases, spamming from ocular and neurological to metabolic, hematological, cardiovascular and oncogenic diseases (Wang et al., 2024 Signal Transduct Target Ther). Amongst them, ophthalmology is certainly the main application field of rAAVs. This is due to several reasons: the eye has special immune features that reduce AAV immunogenicity; the eye is small and compartmentalized, thus being easily accessible and requiring low rAAV doses; many ocular diseases are monogenic and thus are suitable for gene therapy.   Gene therapy for ocular diseases was approved by the FDA in 2017 to treat pediatric patients with an inherited retinal disease. Several studies are currently focusing on other possible gene therapies for a wide range of ocular diseases involving from the cornea to the retina (Wang et al., 2024 Signal Transduct Target Ther; Ghoraba et al., 2022 Clin Ophthalmol).   A specialized approach of gene therapy particularly used in the eye is optogenetics. It consists in delivering genetic information that encodes for light sensitive proteins to non-photoreceptor retinal neurons such as ganglion cells, making them sensitive to light stimulation and bypassing the photoreceptors (Ghoraba et al., 2022 Clin Ophthalmol). This strategy could improve vision in patients with Retinitis Pigmentosa (RP) or other inherited diseases where photoreceptors are damaged. The use of optogenetics to restore vision was initially proposed in 2006 by Pan and colleagues (Bi et al., 2006 Neuron). A decade later, the biopharma company GenSight Biologics developed GS030, an optogenetic treatment candidate combining an AAV2-based gene therapy (GS030-DP) with the use of light-stimulating goggles (GS030-MD). As described in the case study below, Axxam was involved in developing and validating the GS030-DP potency assay, as shown in Gael et al., 2018.Download the poster on GS030-PD Potency assays for assessing biological medicines When developing any kind of pharmaceutical, it is mandatory to establish its potency to comply with authorities’ regulations (e.g. FDA or European Medicines Agency, EMA). A potency assay is a quantitative measure of the biological activity of a drug; more specifically, it measures the ability of the product to elicit a specific response in a disease-relevant context. It is used to evaluate product features associated with its quality and manufacturing controls to assure product identity, purity, stability in all phases of clinical studies.   The first step in potency assay development is the choice of the best experimental method, which depends on the mechanism of action of the candidate product. Then, characteristics to be assessed through the assay are the followings: linearity, precision, accuracy, robustness, repeatability, specificity. In fact, potency assays should be able to detect small variations in drug product potency in a robust and specific manner, suitable to assess batch-to-batch variability and drug stability in long-term storage.   Such evaluations can be particularly challenging in case of biological medicines, such as cell and gene therapy medicinal products and tissue engineered products, which have nucleic acid, viral vectors, viable cells and tissues as starting material (Salmikangas et al., 2023 Front Med). For cells, viability and cell phenotype are important features but alone are not sufficient to address biological activity. For example, if cells are transduced with

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Axxam in oncology drug discovery

Empowering oncology drug discovery

Science Spyglass Empowering oncology drug discovery with Axxam’s tailored solutions At Axxam, we are committed to advancing oncology drug discovery by applying a range of approaches designed to define molecular targets or disease mechanisms and to identify small molecule hits and lead candidates as starting points for drug development in cancer research. We offer tailored assays and screening solutions, addressing many of the biological mechanisms underlying complex cancer hallmarks: Advanced assays for biomolecular interactions: Our assay designs are based on the current structural understanding of crucial molecular mechanisms driving cancer progression, such as protein-protein or protein-DNA/RNA interactions. This enables us to identify even weakly active small molecules. Biophysical methods for drug interaction validation: We utilize techniques such as thermal shift analysis and microscale thermophoresis to confirm and explore the interactions between compounds and their targets, ensuring precise and reliable results in oncology drug discovery. Covalent binder compound collection: We have recently expanded our chemical libraries to include a library of covalent binders named AXXCovalent, with the aim of identifying lead candidates also for notoriously difficult-to-target proteins. Targeted protein degradation: Axxam has developed assays to explore targeted protein degradation, particularly to address so-called “undruggable” targets like transcription factors and intrinsically disordered proteins. We have also partnered with Symeres to generate a PROTAC (proteolysis-targeting chimeras) platform enabling the identification of degraders that act via the proteasome. Cellular platform techniques: Axxam uses advanced cellular assays to monitor pharmacological effects of compounds on epigenetic and transcriptional mechanisms, splicing processes, and signaling pathways. These assays are performed in physiologically relevant cell types as well as within native cellular environments. High-content screening with 2D and 3D models: Axxam employs high-content screening techniques to monitor changes in the cell phenotypes, both in traditional 2D cultures and more complex 3D tumor spheroids to better simulate the tumor environment. Mass spectrometry-based target identification: Through our collaboration with Momentum Biotechnologies, we employ affinity selection mass spectrometry to identify novel small molecule binders testing our AXXDiversity compound collection, targeting both protein and RNA targets. Contact us Back

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Smart cellular assays to study inflammatory skin disorders

Science Spyglass Functional and phenotypic cellular assays to study inflammatory skin disorders Inflammation is a major driver of most chronic skin diseases, causing significant decrease in health-related quality of life for patients. Skin diseases are a heterogenous group of disorders, both acute and chronic, that affect individuals of all ages and are reported to be the most frequent reason for consultation in general practice. The skin is a complex of different cell types providing a physical, chemical and microbiological barrier against external assaults. Keratinocytes represent the major cell type of the epidermis, the outermost layer of skin, and act as the first line of defense of our innate immunity system by sensing pathogens via pattern recognition receptors. Receptor activation, in turn, triggers direct defense mechanisms, like the production of antimicrobial peptides, and release of chemo- and cytokines to recruit and activate additional immune cells. Acute or prolonged dysregulation of keratinocyte function is one of the key steps contributing to the pathogenesis of different types of skin diseases, including atopic dermatitis and psoriasis. Abnormal activation of these cells leads to alterations in their cytoskeleton, expression of cell surface markers (i.e. up-regulation of ICAM-1, reduced expression of E-cadherin and Filaggrin), migration/hyperproliferation of activated cells at the site of inflammation, production and release of pro-inflammatory cytokines and/or chemokines, to maintain a pro-inflammatory state. In fact, impaired resolution of inflammation has been identified as a major culprit in chronic skin diseases. Despite continuous improvement in therapeutic options in recent years, for a many patients affected by chronic skin diseases the response to treatment remains limited. For this reason, there is an urgent need to find novel drugs targeting specific cytokines or receptors implicated in the etiopathogenesis of these disorders. Cell-based models of inflammatory skin diseases With respect to assay development for inflammatory skin disorders, Axxam has now developed and optimized novel cell-based assays to evaluate the activation of human keratinocytes in vitro. These assays are suitable for testing compounds or biologics (antisense oligonucleotides – ASO -, therapeutic antibodies, RNAs) in the early stages of the drug discovery process, as well as to define potential chemical skin-irritants. So far, these validated assays are available in human immortalized (HaCaT) and primary (NHEK) keratinocyte cellular models. These cells are stimulated with a cocktail of pro-inflammatory cytokines, mimicking the inflammatory microenvironment (e.g. TNF-α, IFN-γ) to trigger activation of two major signalling disease-relevant cascades, i.e. the NF-kB (nuclear factor-kappa B) and JAK/STAT (STAT, signal transducer and activator of transcription) pathways. The assays have been miniaturized for 384-well plate formats for compound screening and are designed to analyze 1) cytokine production/release through multiplex measurement; 2) nuclear translocation of inflammation-related transcription factors through immunofluorescence. 1. Cytokine multiplexing assay The cytokine multiplexing assay principle consists of treatment of keratinocytes with a mix of pro-inflammatory cytokines to trigger NF-kB and JAK/STAT pathways, thus leading to production and release of chemokines and cytokines (i.e. IL-6 and CCL5/Rantes), often correlated with the pathogenesis of diseases like atopic dermatitis and psoriasis. The experimental workflow is described in Figure 1. Levels of the two key cytokines CCL5/Rantes and IL-6 released in the medium by activated keratinocyte cellular models are measured by a luminescent readout (AlphaLISA®/AlphaPlexTM, Revvity) which allows simultaneous quantitative determination of two analytes in the same well. Cell culture conditions and pro-inflammatory stimuli are optimized for each cell type to obtain reliable and reproducible CCL5/Rantes and IL-6 levels in physiological conditions. Figure 1: cytokine multiplexing assay workflow. The assay consists of 5 different steps – Step 1: seeding of keratinocytes in 384-well plates; Step 2: stimulation of cells with pro-inflammatory cytokines; Step 3/4: cytokine multiplexing detection in cell culture medium using AlphaLISA®/AlphaPlexTM, Revvity at BMS Labtech Pherastar; Step 5: data analysis. As shown in Figure 2, validation of the assay was performed employing the reference compound Baricitinib, a JAK1/2 inhibitor employed in clinic in the treatment of atopic dermatitis, able to prevent IL-6 and CCL5/Rantes production/release by HaCaT and NHEK cells, stimulated with specific pro-inflammatory cytokine cocktails, in dose-response. Figure 2: Baricitinib treatment inhibits IL-6 and CCL5/Rantes release by HaCaT and NHEK cells stimulated with pro-inflammatory cytokine cocktails. Dose response curves of reference compound Baricitinib for inhibition of production of IL-6 (blu line) and CCL5/Rantes (red line) in cultures of HaCaT (left) and NHEK primary cells (right). Data are presented as Fold Change= Raw values/Central Reference values (referred to stimulated cells not treated with Baricitinib) with a multiplier factor of 100. The developed cytokine multiplexing assay resulted suitable for compound testing in 384-well formats with the aim of identifying novel anti-inflammatory compounds able to inhibit cytokine production/release following keratinocyte activation. 2. Nuclear translocation assay The nuclear translocation assay developed at Axxam allows the detection of the accumulation of NF-kB or Stat1 transcription factors in the nuclei of activated keratinocytes through an imaging-based approach. The experimental workflow is described in Figure 3. Nuclear translocation is measured in HaCaT and NHEK cells stimulated with pro-inflammatory cocktails containing TNF-α and IFN-γ, by immunofluorescent imaging using specific antibodies. Cell culture conditions and stimuli are optimized for each cell type to obtain reliable and reproducible assay signals in physiological conditions. Figure 3: nuclear translocation assay workflow. The assay consists of 5 different steps – Step 1: seeding of keratinocytes in 384-well plates; Step 2: stimulation of cells with pro-inflammatory cytokines; Step 3: immunofluorescence staining with specific antibodies to visualize the targets of interest; Step 4: image acquisition; Step 5: data analysis. As shown in Figure 4, the assay has been validated using the compounds BAY11-7082 (NF-kB inhibitor) and Baricitinib (JAK 1/2 inhibitor), both able to strongly inhibit NF-kB and Stat1 nuclear translocation in HaCaT and NHEK cells, stimulated with a specific pro-inflammatory cytokine cocktail. Figure 4: BAY11-7082 or Baricitinib treatment inhibits respectively NF-kB and Stat1 nuclear translocation in HaCaT and NHEK cells stimulated with a pro-inflammatory cytokine cocktail. A/B) Representative images and dose response curves for BAY11-7082 in HaCaT cells (A) and NHEK cells (B) stimulated with TNF-α and IFN-γ (blue) or treated with vehicle (red, control not stimulated cells); C/D)

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Photo of Lysosomes in HTS

Bringing lysosomal patch clamp recording to HTS

Science Spyglass High throughput organellar electrophysiology of TMEM175 and TPC2 from freshly isolated lysosomes recorded on the SyncroPatch 384 Application note Axxam S.p.A., MilanNanion Technologies GmbH, Munich Summary Intracellular ion channels are known to play an essential role in various signaling pathways for health and disease, considering that over 80% of transport processes occur inside the cells (1). Among the variety of organellar channels and transporters the proton leak channel transmembrane protein 175 (TMEM175) and the lysosomal two-pore channel (TPC) have received increasing attention in the field given their potential roles in connecting lysosomal homeostasis with pathophysiological conditions such as Parkinson’s disease and cancer (2-4). Consequently, the interest to explore intracellular ion channels as therapeutic targets has grown tremendously indicating a need for high-throughput electrophysiology including patch clamp. There has been some progress in alternative approaches such as solid supported membrane electrophysiology (SSME using the SURFE2R 96SE) recently (5), however, until now, HTS patch clamp has lacked the possibility to collect data from native lysosomes. Axxam and Nanion Technologies have now developed assays to investigate the function and pharmacology of lysosomal channels under native conditions, providing groundbreaking tools for the drug discovery industry. This is possible due to the development of special consumables (single- and multi-hole) dedicated to pursuing organellar recordings in combination with the high flexibility of the SyncroPatch 384 utilizing an ultra-low cell density approach that can use as low as 50k cells/ml, and small volumes of 1 ml for the whole of the 384-well plate, without a drastic reduction in success rate. This can be of extreme importance for expensive – as well as for samples of low quantity (cardiomyocytes, iPS cells or organelles) – to reduce costs and save time. Our approaches resulted in the construction of cumulative concentration response curves and even intraluminal solution exchange during the recording from freshly isolated lysosomes highlighting the broad range of applications possible with the SyncroPatch 384. ResultsTMEM175 Enlarged lysosomes incubated with 1 µM Vacuolin-1 were stained with 0.1 µM LysoTracker™ Red DND-99 (Invitrogen), a red fluorescent dye that stains acidic cellular compartments, such as lysosomes. The dye was added to the cells before isolation of the lysosomes and images were acquired at different magnifications and dilutions using the Operetta system (Perkin Elmer), resulting in an average diameter of 2.1 µm (Figure 1). Figure 1 A – Isolated lysosomes stained with LysoTracker™ Red DND-99 (Invitrogen); images at different magnifications were acquired using the Operetta (Perkin Elmer). B – Average diameter calculated at different dilutions: 3.0 ± 2.1 µm (1:10); 2.0 ± 1.3 µm (1:20); 2.4 ± 1.7 µm (1:50); data are presented as mean ± SD. The remaining lysosomes were used on the SyncroPatch 384 for recording TMEM175 channels expressed endogenously in HEK-293 cells. Critical for success was usage of Nanion’s “Organellar Chips”, a specialized consumable that supported and maintained the integrity of lysosomes throughout the recording and supported cumulative concentration response curves of DCPIB, a novel TMEM175 activator, able to mediate H+ and K+ currents (6) as highlighted in Figure 2. Since TMEM175 channels release luminal H+ into the cytosol, we developed assays using luminal solutions with different pH values, to enhance proton conductance, in addition to potassium flux. The seal resistance in “whole-lysosome” configuration was calculated before compound application and shows average values of 1.4 ± 0.2 GΩ and 2.1 ± 0.6 GΩ at pHluminal 4.0 and 7.0, respectively. TMEM175 activation was accompanied by a drop in Rseal, indicative for stimulation of a leak channel (Figure 2 A). We then executed cumulative concentration additions of DCPIB to activate endogenous TMEM175 channels using only part of the NPC-384 chip (32 wells per condition). Our analysis reveals an EC50 of 65.3 ± 17.5 µM (n=5) at pHluminal 4.0 and 21.5 ± 4.1 µM (n=3) at pHluminal 7.0 for outward currents (ion and proton flux from lumen to cytosol), as shown in Figure 2 D. Representative traces (Figure 2 B-C) clearly show a larger TMEM175 current evoked in the presence of the highest DCPIB concentration in an acidic luminal environment, suggesting enhanced proton flux at acidic luminal pH. Given the known pH dependence of TMEM175 activity (7) we also employed intraluminal solution exchange for the first time where we observed a current modulation after changes in luminal pH. During the experiment with pHluminal 7.0, TMEM175 current was first evoked by DCPIB application, then partially blocked by 4-AP (Figure 3 A). In the presence of 4-AP, acidification of the luminal solution, due to the internal exchange from pHluminal 7.0 to 4.0, increases TMEM175 current (Figure 3 B). A similar experiment was repeated by inverting the luminal pH, starting from 4.0 and changing to 7.0, using the internal perfusion feature of the SyncroPatch 384. In the presence of 4-AP, the reduction of H+ in the luminal solution induces a reduction in TMEM175 current due to a lower proton contribution (Figure 3 C-D). Figure 2 A – Bar graph of seal resistance calculated before and after DCPIB application. B – Representative traces recorded in control and in the presence of increasing concentrations of DCPIB, using luminal solution with pH 4.0, and C pH 7.0. D – Concentration response curve of DCPIB application using different luminal solution, with pH 4.0 (red) and 7.0 (black); in both experiments, cytosolic solutionwas pH 7.0. Figure 3 A – Representative TMEM175 traces recorded in control and in the presence of 100 µM DCPIB (light green) and 2 mM 4-AP (dark green); pHluminal 7.0 – pHcytosolic 7.0. B – Effect of luminal solution exchange (from pH 7.0 to pH 4.0) on TMEM175 current in the presence of 4-AP. C – Representative TMEM175 traces recorded in control and in the presence of 100 µM DCPIB (light green) and 2 mM 4-AP (dark green); pHluminal 4.0 – pHcytosolic 7.0. D – Effect of luminal solution exchange (from pH 4.0 to pH 7.0) on TMEM175 current in the presence of 4-AP. ResultsTPC2 Enlarged lysosomes (Vacuolin, 1 µM) were freshly isolated as described in Schieder et al (8-9) from HEK cells either stably

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The dualism of the aging and disease

Science Spyglass Navigating the dualism of the aging and disease landscape with the pertinent tools An interview with Fernanda Ricci, High Content Screening Unit Manager at Axxam. Don’t miss her upcoming webinar: Forever young?Targeting the hallmarks of aging Request link for webinar Read Fernanda’s opinions on the subject of early drug discovery for diseases related to aging: Q. Why do you consider a webinar on aging important? In an era where life expectancy is increasing, the quest to understand aging and promote healthier, longer lives has become more critical than ever. Aging is a complex and multifaceted process that leaves its mark at the molecular, cellular, and systemic levels for all of us, increasing our risk of diseases and disability, and placing demands on health and social care services. However, recent scientific breakthroughs are shedding light on new approaches to unravel the mysteries of aging, such us metabolic regulators, mitochondrial functionality, inflammation pathways. What is truly exciting today is that these discoveries might pave the way for a future where aging is not just a process but a target for intervention; for a future where we may replace the word “aging” with “longevity.” Certainly, we are all committed to reaching a healthier state as much and as fast as possible. The road is still long, with many issues to solve, but starting with the right methods we can speed up the discovery process. For this reason, we are working to establish biological assays relevant to understanding the fundamental processes of aging. Fernanda Ricci, Axxam High Content Screening Unit Manager Q. What tools are you developing to study aging in the laboratory? We are operating on multiple fronts. Aging processes involve several significant molecular pathways, and our commitment extends to miniaturizing all the assays, enabling high-throughput drug screening campaigns to increase the likelihood of finding the right hits for the specific phenotype. Few examples of relevant cell-based assays include DNA damage, mitochondrial dysfunction, autophagy rate readouts, inflammation-based readouts. Q. What are the major relevant pathways or targets involved in aging progression? A real game-changer is certainly inflammation. Chronic inflammation is the troublemaker here, creating the basis for systemic dysfunction that can lead to issues ranging from cancer to heart problems. Some effects at the molecular and cellular levels, for example, involve the mislocalization of transcription factors, consequently altering the genetic script, or a decrease in the stem cell pool despite an increase in senescent cells, and in turn these cells release inflammatory cytokines, adding fuel to the systemic inflammation storm. Remarkably, our vital organelles such as lysosomes and mitochondria get damaged, and they are no longer able to perform their functions, such as clearing cellular toxic products, producing energy, and removing oxidative species. The “Free Radical Theory” of aging comes into play, increasing the probability of protein crosslinking, DNA damage, and a shuffle in gene expression, all contributing to the onset of metabolic and neurodegenerative disorders, as well as cancer in the long term. However, research is progressing rapidly, much like the aging process itself. Several targets, pathways, and phenotypes involved in aging have already been discovered that can be of therapeutic interest and can be addressed selecting the right tools and assays. Today, life science is approaching aging as a Pandora’s box; by treating aging, the incidence of other diseases can also be reduced, achieving a good and longer healthy old age. Aging is inevitable, but how we age could be our decision in the near future. Contact us Back

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