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VivoScan™: Abundant Whole-Organism Data for AI-driven Drug Discovery Pipelines
Artificial intelligence and machine learning are rapidly reshaping how pharmaceutical companies discover and prioritise new compounds [1]. From virtual screening and generative chemistry to predictive toxicology and target identification, AI-driven workflows are now embedded across early-stage R&D. But AI is only as good as the real biological data it is trained on. As these approaches mature, a key question has emerged: how can AI-first discovery pipelines be grounded in robust, biologically meaningful evidence early enough to reduce downstream risk? This is where whole-organism high-throughput screening (HTS) offers unique and under-utilised value.
VivoScan™, Magnitude Biosciences’ C. elegans-based lifespan and healthspan HTS platform, is designed to integrate seamlessly into AI-enabled drug discovery workflows, providing scalable, early, organism-level data that complements, strengthens, and de-risks machine learning driven decisions. C. elegans is a millimetre-long nematode worm widely used as a model organism, with a fully mapped biology and a decades-long track record in genetics, ageing, and drug discovery [2]. With this tiny organism, Magnitude Biosciences aims to turn in-vivo healthspan and lifespan data into an abundant commodity for the first time ever.
The rapid lifespan, low cost, ease of handling, and minimal ethical burden of C. elegans make it the only well-established model organism suited to true high-throughput in vivo screening – especially for lifespan and healthspan effects. No other organism combines sufficient biological complexity with the speed, scale, and practicality required for early-stage whole-organism testing. As a result, VivoScan™ represents not just a practical solution for whole-organism screening, but the only viable way to generate organism-level training data at scale.
AI excels at pattern recognition, optimisation, and prioritisation. Given sufficient data, models can rank compound libraries, explore chemical space efficiently, elucidate structure–activity relationships, and predict molecular properties at scale. However, AI systems are fundamentally limited by the biological scope of their training data. Most models are trained on in vitro assays, biochemical endpoints, omics datasets, or historical clinical outcomes. While powerful, these data sources often lack integrated organismal responses, which limits their ability to predict longitudinal outcomes or systemic toxicity and pleiotropy. As a result, AI pipelines can confidently prioritise compounds that later fail due to effects that only emerge at the whole-organism level [3]. For example, a generated protein may fold correctly according to AlphaFold but may not express well in cells, could be insoluble, or could be immunogenic.
VivoScan™ addresses this gap by generating high-throughput, quantitative, whole-organism data early in the discovery process. Using C. elegans as a validated model organism [4], VivoScan™ captures lifespan effects, healthspan trajectories, dose-dependent toxicity, and trade-offs between efficacy and organismal fitness. Crucially, this data is scalable, standardised, longitudinal, and machine-readable. Far from competing with AI/ML approaches, VivoScan™ functions as an upstream biological intelligence layer, feeding organism-level reality into model-driven pipelines.
VivoScan™ integrates seamlessly into the closed-loop optimisation pipelines that modern AI-enabled drug discovery increasingly relies on. When generative models propose novel small molecules, repurposing candidates, structure modifications or drug targets, VivoScan™ enables rapid high-throughput evaluation for lifespan, healthspan, and toxicity effects in a whole organism. Hits are separated from false positives, and results are fed back into ML models to retrain predictors. This loop supports both active learning and multi-objective decision-making (e.g. efficacy vs toxicity vs longevity impact). The result is less time wasted on spurious hits, and faster convergence towards biologically accurate models.
One of the central challenges facing AI-led drug discovery is late biological failure. Compounds optimised in silico or in vitro often encounter unexpected toxicity, particularly in whole organisms where complex multi-system interactions may occur. By introducing whole-organism screening at the earliest feasible point in the discovery pipeline, VivoScan™ enables false-positive candidates to be filtered out sooner, while providing AI models with rich, longitudinal in-vivo data that cannot be generated at comparable scale using any other model organism.
This aligns with a core industry goal: fail earlier, fail cheaper, and learn more from every failure.
Importantly, VivoScan™ is model-agnostic. It does not require specific AI architectures, proprietary algorithms, or changes to internal data science teams. Model architectures will evolve, training pipelines will change, but the need for high-quality organismal will persist, and its value will compound. VivoScan™ positions C. elegans not as a legacy model, but as a modern, scalable, and AI-compatible platform for whole-organism insight, bridging the gap between computation and biology. By embedding organism-level evidence upstream, AI-driven pipelines can become not only faster, but smarter, safer, and more predictive of real-world outcomes.
In the era of AI-accelerated drug discovery, high-quality biology remains the limiting factor. The ultimate purpose of all AI in drug development is to predict the safety and efficacy of compounds in whole organisms. This requires training data, which can only come from real biological experiments, and model predictions must be validated at every stage of the development pipeline. VivoScan™ ensures that AI is trained, validated, and guided by data that reflects how compounds behave in living systems – at an earlier stage than any competing approach.
References:
[1] “Applications of machine learning in drug discovery and development”, Vamathevan et al., 2019, Nature Reviews Drug Discovery
[2] “Caenorhabditis elegans as a Model System to Study Human Neurodegenerative Disorders”, Roussos et al., 2023, MDPI Biomolecules
[3] “Artificial intelligence in biologic drug discovery: A review of methodological evolution and therapeutic applications”, Tang et al., 2026, Acta Pharmaceutica Sinica B
[4] “C. elegans in high-throughput drug discovery.”, O’Reilly et al., 2014, Drug Discovery Today
Let the Worm Wiggle into the Pipeline: The Case for C. elegans in Modern Drug Discovery
Between exponential investment into AI technologies, changing global superpowers, and strategic shifts within the field, the current moment could mark a paradigm shift in drug development. Coming off the heels of a decade-long stagnation despite high R&D spending1, the urgency for innovation is heightened to rescue the trajectory of drug development.
Amongst these changing tides arises a small but powerful organism — one that has quietly undergone its own transformation to meet the challenges the industry now faces.
The Worm’s Long Trail to High-throughput Screening
The model nematode Caenorhabditis elegans has a long history in drug screening. It traces back to 1974, when late Nobel laureate Sydney Brenner published the first C. elegans-based drug screen2. Brenner tested 100 readily available compounds and found two that caused strong scorable phenotypes such as paralysis. He then selected for resistant mutants under drug pressure, revealing mutations in cholinergic pathway genes. The work showcased the genetic tractability of C. elegans and established its status as a foundational genetic model.
In 1998, C. elegans became the first animal to have its genome fully sequenced, showing that we share more in common with the simple organism than previously thought. It’s estimated that two-thirds of human genes implicated in disease have worm homologs, with many core pathways involved in development, stress, and neurodegeneration strongly conserved.
Despite this illustrious history, C. elegans was not considered a prime candidate for drug high-throughput screening (HTS), partially due to standard culture conditions that were not easily scalable. This changed in 2006, with the development of an all-liquid workflow3,4. In an early liquid culture screen, 88,000 compounds were tested for their longevity benefits. Among the 115 hits was the antidepressant Mianserin, one of the first demonstrations that a CNS drug could extend lifespan through modulation of serotonergic food-sensing pathways without decreasing food intake5.
C. elegans-based drugs screens have steadily grown over the years6, but the adoption of worms within major pharma pipelines has remained low, due to reservations about translatability and integration with more established methods. However, given the increasing uncertainty and escalating costs in drug development today, perhaps the worm deserves another look.
From Hits to Targets to Mechanisms
The process of identifying hits is inextricably linked to the underlying targets and mechanisms affected. Pharmacological innovation is slowing because we still lack strong predictive methods for identifying effective targets. As the lack of efficacy remains the primary reason for clinical trial failure7, we may have hit a plateau in finding easily druggable targets using solely in vitro assays and readouts.
Phenotypic-based approaches are increasingly being used to identify new targets and have seen success identifying first-in-class medicines8. A key advantage of C. elegans is the ability to interfere with gene function at any stage in its life cycle via RNAi delivered through feeding. This genetic tractability extends to transgenic approaches, enabling phenotypic screens for humanised disease models. In one HTS screen integrating automated imaging and analysis, compounds were screened for their ability to alter protein aggregation in transgenic worms modelling α1-antitrypsin deficiency (ATD). One hit, fluphenazine, was later shown to be effective in mammalian cell-based and mouse models with ATD9.
2D in vitro High-content screens (HCS), which prioritize multiple complex outputs over single readouts, are now routine in pharmaceutical pipelines. By capturing whole-organism responses, C. elegans addresses blind spots that even 3D organoid systems cannot resolve, improving target and biomarker predictions. As the chemical space of drug discovery diversifies into targets once thought to be undruggable10, such as transcription factors that are implicated in complex downstream pathways, whole-organism screening may become a prerequisite for capturing their systemic effects.
One Man’s Treasure is Another Man’s Treasure
C. elegans has seen considerable success in drug repurposing. The most notable case was a campaign focused on treating PMM2-CDG, a rare genetic disorder that causes neurological complications11. Epalrestat, originally used as diabetic treatment, showed a reversal of the disease phenotype in the C. elegans strain carrying the PMM2-CDG patient-specific mutation. This translated as a marked improvement of symptoms when Epalrestat was prescribed off-label to a patient, and the drug is currently in Phase III of clinical testing only five years from initial screening.
Other C. elegans repurposing screens have identified promising candidates in rare mendelian diseases, longevity,
and neurodegeneration, including a hit for ALS currently in phase II testing12. Repurposing bypasses de novo development and safety testing while extending patent life. The strategy is also about three times more likely to gain approval compared to developing new molecular entities from scratch13, making it an increasingly attractive proposition for the industry amidst record high spending and falling approval rates14,15.
The worm’s genetic tractability, in combination with its high-throughput and high-content capacity, integrates particularly powerfully into drug repurposing pipelines where the molecular targets are unclear, such as CNS disorders which experience higher late-stage failure rates than many other disease areas16. Robust hit prioritization schemes that integrate phenotypic readouts from C. elegans before committing to mouse screening are a viable strategy for reducing risk within these pipelines17.
Your Very Own Worm Avatar
Treatment options remain limited for 95% of rare diseases due to a lack of incentive and resources to develop them18. However, collectively these diseases affect more than 300 million people globally and therefore represent a gravely underserved area within medicine.
C. elegans can be engineered with patient-specific mutations using CRISPR and phenotyped systematically using high-throughput imaging. Across dozens of the Mendelian disease models available, nearly every C. elegans strain exhibits clear, quantifiable phenotypes — or can be sensitized to show one. Drug repurposing screens with these avatars identified FDA approved drugs capable of rescuing disease-associated behaviours19,20.
With the precision medicine market projected to reach ~USD 530 billion by 203521, the need for cheap and efficient platforms that can model patients-specific mutations and screen therapies in vivo will only intensify. Personalized worm assays could become a practical component of rare disease pipelines, helping translate genetic insight into actionable treatment, even for the rarest mutations.
C. elegans: Your Reliable C. ompanion
C. elegans boasts an impressive portfolio of contributions to drug development in the short time it has possessed HTS capabilities. The emerging view among researchers is that complex and diverse compounds, targets, and diseases should be met with a combination of diverse screening approaches — each leveraged for what it does best10,16. While C. elegans isn’t here to replace cell or mammalian models, it has the potential to alleviate many bottlenecks in modern drug discovery when used in combination with them. It may be time for the industry to let the worm meaningfully wiggle its way into the pipeline.
References:
1. Sun D, Gao W, Hu H, Zhou S. Why 90% of clinical drug development fails and how to improve it? Acta Pharm Sin B. 2022 Jul;12(7):3049–62.
2. Brenner S. The genetics of Caenorhabditis elegans. Genetics. 1974 May;77(1):71–94.
3. O’Reilly LP, Luke CJ, Perlmutter DH, Silverman GA, Pak SC. C. elegans in high-throughput drug discovery. Adv Drug Deliv Rev. 2014 Apr 20;69–70:247–53.
4. Lehner B, Tischler J, Fraser AG. RNAi screens in Caenorhabditis elegans in a 96-well liquid format and their application to the systematic identification of genetic interactions. Nat Protoc. 2006 Aug;1(3):1617–20.
5. Petrascheck M, Ye X, Buck LB. A high-throughput screen for chemicals that increase the lifespan of Caenorhabditis elegans. Ann N Y Acad Sci. 2009 Jul;1170:698–701.
6. Roy PJ. Drug screens using the nematode Caenorhabditis elegans. Genetics. 2025 Aug 12;231(1):iyaf141.
7. Jain R, Subramanian J, Rathore AS. A review of therapeutic failures in late-stage clinical trials. Expert Opin Pharmacother. 2023 Feb 11;24(3):389–99.
8. Swinney DC. Phenotypic vs. Target-Based Drug Discovery for First-in-Class Medicines. Clin Pharmacol Ther. 2013;93(4):299–301.
9. Gosai SJ, Kwak JH, Luke CJ, Long OS, King DE, Kovatch KJ, et al. Automated High-Content Live Animal Drug Screening Using C. elegans Expressing the Aggregation Prone Serpin α1-antitrypsin Z. PLOS ONE. 2010 Nov 12;5(11):e15460.
10. Lanne A, Usselmann LEJ, Llowarch P, Michaelides IN, Fillmore M, Holdgate GA. A perspective on the changing landscape of HTS. Drug Discov Today. 2023 Aug 1;28(8):103670.
11. Iyer S, Sam FS, DiPrimio N, Preston G, Verheijen J, Murthy K, et al. Repurposing the aldose reductase inhibitor and diabetic neuropathy drug epalrestat for the congenital disorder of glycosylation PMM2-CDG. Dis Model Mech. 2019 Nov 11;12(11):dmm040584.
12. Patten SA, Aggad D, Martinez J, Tremblay E, Petrillo J, Armstrong GAB, et al. Neuroleptics as therapeutic compounds stabilizing neuromuscular transmission in amyotrophic lateral sclerosis. JCI Insight [Internet]. 2017 Nov 16 [cited 2026 Jan 22];2(22). Available from: https://insight.jci.org/articles/view/97152
13. Al Khzem AH, Wali SM. Drug Repurposing as an Effective Drug Discovery Strategy: A Critical Review. Drug Des Devel Ther. 2025 Dec 31;19:12019–34.
14. Schlander M, Hernandez-Villafuerte K, Cheng CY, Mestre-Ferrandiz J, Baumann M. How Much Does It Cost to Research and Develop a New Drug? A Systematic Review and Assessment. Pharmacoeconomics. 2021;39(11):1243–69.
15. Mullin K. Why are clinical development success rates falling? [Internet]. Norstella. 2024 [cited 2026 Jan 16]. Available from: https://www.norstella.com/why-clinical-development-success-rates-falling/
16. Pankevich DE, Altevogt BM, Dunlop J, Gage FH, Hyman SE. Improving and Accelerating Drug Development for Nervous System Disorders. Neuron. 2014 Nov 5;84(3):546–53.
17. Varma H, Lo DC, Stockwell BR. High-Throughput and High-Content Screening for Huntington’s Disease Therapeutics. In: Lo DC, Hughes RE, editors. Neurobiology of Huntington’s Disease: Applications to Drug Discovery [Internet]. Boca Raton (FL): CRC Press/Taylor & Francis; 2011 [cited 2026 Feb 11]. (Frontiers in Neuroscience). Available from: http://www.ncbi.nlm.nih.gov/books/NBK55989/
18. Nguengang Wakap S, Lambert DM, Olry A, Rodwell C, Gueydan C, Lanneau V, et al. Estimating cumulative point prevalence of rare diseases: analysis of the Orphanet database. Eur J Hum Genet. 2020 Feb;28(2):165–73.
19. O’Brien TJ, Barlow IL, Feriani L, Brown AE. Systematic creation and phenotyping of Mendelian disease models in C. elegans: towards large-scale drug repurposing. eLife [Internet]. 2024 Dec 13 [cited 2026 Jan 23];12. Available from: https://elifesciences.org/reviewed-preprints/92491
20. O’Brien TJ, Navarro EP, Barroso C, Menzies L, Martinez-Perez E, Carling D, et al. High-throughput behavioural phenotyping of 25 C. elegans disease models including patient-specific mutations. BMC Biol. 2025 Sep 26;23:281.
21. Precision Medicine Market Size to Hit USD 537.17 Bn by 2035 [Internet]. [cited 2026 Jan 23]. Available from: https://www.precedenceresearch.com/precision-medicine-market
Magnitude Biosciences and BioAscent Discovery Announce Collaboration to Profile Chemogenomics Library Using VivoScan™ Whole-Organism Screening
Magnitude Biosciences and BioAscent Discovery Announce Collaboration to Profile Chemogenomics Library Using VivoScan™ Whole-Organism Screening
Announcement
BioAscent Discovery Ltd. is a leading provider of integrated drug discovery services, including compound management, chemistry, DMPK, biophysics and more.
Magnitude Biosciences and BioAscent Discovery Ltd. are pleased to announce a new research collaboration that will combine Magnitude Biosciences’ VivoScan™ C. elegans whole organism screening platform with BioAscent’s richly annotated Chemogenomics Library to generate high quality phenotypic datasets.
VivoScan™ enables rapid, scalable, and quantitative assessment of compound effects on endpoints like longevity and mobility, in a genetically tractable and evolutionarily conserved whole organism model, C. elegans. By capturing integrated physiological responses across tissues and pathways, the platform allows researchers to detect both subtle and system level phenotypes that are often missed in simplified cell-based systems and provides early insights into compound activity in a complex biological model organism.
At the centre of this collaboration is BioAscent’s 1,600 compound Chemogenomics Library; a uniquely curated, deeply annotated set of selective pharmacological probes assembled to help researchers understand the mechanisms underlying phenotypic responses in complex biological systems. The library spans key target classes including kinase inhibitors, GPCR ligands, and epigenetic modulators, supported by extensive, high-quality pharmacology and selectivity data.
BioAscent is further expanding these annotations through internal profiling campaigns across multiple assay platforms, generating proprietary experimental datasets that complement literature-derived information. Screening this enhanced library using VivoScan™ will allow both partners to correlate activity patterns across model systems, gain mechanistic insights into disease-relevant pathways, and strengthen the translational value of early-stage discovery projects.
“This collaboration highlights the power of whole-organism screening as a complement to traditional drug discovery approaches,” said Dr Chris Saunter, Director of Magnitude Biosciences. “By combining BioAscent’s Chemogenomics library with VivoScan™’s ability to run large-scale screens in a model of healthy aging, we can apply well-annotated, mechanism-informed compounds directly in a physiologically relevant system. This enables the rapid identification of meaningful biological effects and insights that go well beyond what can be learned from in vitro models alone.”
“By providing access to high-quality, curated library collections backed by robust experimental annotation, BioAscent enables researchers to explore biology with far greater confidence and resolution” says Stuart McElroy, Director of Biosciences at BioAscent. “This collaboration with Magnitude Biosciences helps us deepen our understanding of compound behaviour in a whole organism context and further strengthens the quality of data available to our clients as they progress their drug discovery programmes”.
The collaboration will generate detailed phenotypic datasets focused on functional activity, potential toxicity liabilities, and ageing-related effects in C. elegans, supporting more informed and timely decision-making across early drug discovery.
Tackling the Translational Gap with Whole-Organism HTS
We’re screening faster than ever, but are we screening smart?
High-throughput screening (HTS) has revolutionised early-stage drug discovery. It enables teams to screen hundreds of thousands of compounds at speed, helping to identify and generate leads, before high-cost pre-clinical studies. But despite its scale, HTS often hits a wall. Compounds can look undeniably promising in vitro, however, they can underperform or fail completely, down the line during in vivo studies. The root issue? A lack of biological context. This can cause drug development to be extremely costly and time-consuming, with the average cost for developing a drug being $2.6 billion and taking 10-15 years to get to market [1, 2]. As drug discovery teams face growing pressure to de-risk pipelines earlier, and avoid costly downstream failures, there’s a growing need for methods that combine HTS efficiency with physiological relevance, without sacrificing throughput.
Current HTS Models: Strengths and Shortcomings
HTS remains a vital tool in early drug discovery, particularly through two dominant strategies: biochemical (target-based) assays and cell-based phenotypic screens. Biochemical assays allow researchers to assess compound activity against defined molecular targets with high precision. They are designed to detect direct interactions such as enzyme inhibition, receptor binding, or modulation of specific signalling components, and their strength lies in target specificity, quantifiability, and scalability [3.]. When the underlying disease mechanism is well understood, target-based assays offer a streamlined path from screen to lead optimisation, however, the lack of biological context can lead to downstream failure, when tested in more complex environments.
Cell-based models, on the other hand, offer a way of identifying phenotypic effects, adding another layer of biological relevance. They offer the ability to measure compound effects on more complex cellular behaviours within a living cellular context [4]. Both approaches benefit from automation, cost-efficiency, and clear readouts, making them ideal for large-scale compound libraries.
For all their advantages, however, traditional HTS models often fall short when it comes to biological complexity. Isolated targets may not reflect the broader context of human physiology, and even cellular systems fail to capture whole-organism interactions [5].
VivoScan™, our HTS platform, uses widefield video imagine to track motion in liquid culture for the duration of life
The Translational Gap
Despite advances in HTS technologies, a persistent challenge in drug discovery remains: the translational gap. Promising hits identified in biochemical or cell-based assays often fail to demonstrate efficacy or safety in animal models or human trials. This gap stems largely from the lack of systemic context in early-stage models. While these platforms are excellent for understanding molecular interactions or cellular phenotypes, they rarely capture the complex interactions between tissues, organs, and metabolic processes that define disease progression in living organisms [6].
For example, a compound may show potency in isolated cells but be rendered ineffective in vivo due to poor absorption, rapid metabolism, or unintended effects on other systems. This is especially true for diseases involving ageing, neurodegeneration, or metabolic dysfunction, where whole-organism dynamics are crucial. As a result, pharmaceutical teams are often forced to rely on costly, time-consuming in vivo studies later in the pipeline to validate findings, by which point major resources have already been committed.
This disconnect can cause high attrition rates and inflates R&D timelines. The ideal solution would be a screening platform that offers both biological relevance and scalability, enabling earlier identification of candidates with true translational potential, before committing to resource-intensive downstream development.
C. elegans as a Whole-Organism HTS Tool
The nematode Caenorhabditis elegans (C. elegans) has long been a valuable tool in biological research. As one of the first multicellular organisms to have its genome fully sequenced, it has contributed significantly to our understanding of development, neurobiology, and ageing. With a short lifecycle, transparent body, and a fully mapped nervous system, C. elegans offers a rare combination of genetic tractability, experimental speed, and whole-organism insight.
Importantly, around 60–80% of human disease genes have homologues in C. elegans, making it a powerful model for exploring conserved pathways in areas such as neurodegeneration, metabolic dysfunction, mitochondrial disorders, and lifespan regulation [7]. The worm’s simplicity does not come at the expense of biological relevance. It has a nervous system, a well-characterised muscle system that shares features with more complex animals, a digestive tract, and complex behavioural outputs that can be quantified in response to drug exposure.
Historically, however, the C. elegans model has been underutilised in drug discovery pipelines, largely due to perceived limitations in throughput and automation. However, their ability to thrive in liquid culture, paired with recent technological advances, has now made scalable, phenotypic screening in C. elegans not only possible but practical, opening new doors for early-stage in vivo testing [8].
Bridging the Gap with Our Platform
At Magnitude Biosciences, we’ve harnessed the well-established biological advantages of C. elegans to develop a high-throughput screening (HTS) platform tailored for the demands of modern drug discovery. VivoScanTM , our liquid-based C. elegans screening system, enables the efficient testing of large compound libraries at scale, providing whole-organism, functional readouts that go beyond what traditional cell-based or biochemical assays can offer.
By introducing in vivo screening earlier in the pipeline, our platform helps teams identify promising targets and compounds with greater biological context, improving the chances of translational success. Positive hits from VivoScanTM can be prioritised with higher confidence before moving into mammalian models, reducing risk, cost, and time in early-stage development. Because C. elegans is a non-protected species under most regulatory frameworks, this approach also avoids the ethical and logistical challenges associated with vertebrate studies.
Our HTS workflow provides lifespan and healthspan data captured across multiple timepoints, enabling you to monitor treatment effects dynamically over the worm’s lifecycle. This makes it possible to detect both acute and long-term compound effects, further enhancing decision-making during hit selection. For teams looking for lead optimisation, our proprietary WormGazer™ platform adds another level of resolution. WormGazer™ uses advanced imaging and analysis tools to quantify healthspan, locomotion, and cognitive-like behaviours over time, revealing the biological impacts of candidate compounds.
Together, our platforms offer a scalable, biologically rich, and cost-effective solution to streamline early-stage discovery and improve the likelihood of identifying translatable drug candidates faster and with greater confidence.
Rethinking Where in vivo Belongs
Why wait for in vivo testing when early whole-organism screening can give you invaluable insight from the first hurdle? VivoScanTM delivers scalable, biologically rich data, capturing the systemic effects missed by traditional screens. If you’re looking for truly translatable discovery data, at scale, the worm is the way to go.
References
- Deloitte. Global pharma companies’ return on R&D investment increases after record low | Deloitte UK [Internet]. www.deloitte.com. 2024.
- MS Trust. Drug development process [Internet]. MS Trust. 2021.
- Zhu Z, Cuozzo J. Review Article: High-Throughput Affinity-Based Technologies for Small-Molecule Drug Discovery. Journal of Biomolecular Screening. 2009 Dec;14(10):1157–64.
- An WF, Tolliday N. Cell-Based Assays for High-Throughput Screening. Molecular Biotechnology. 2010 Feb 12;45(2):180–6.
- Swinney DC, Anthony J. How were new medicines discovered? Nature Reviews Drug Discovery. 2011 Jun 24;10(7):507–19.
- Seyhan AA. Lost in translation: the valley of death across preclinical and clinical divide – identification of problems and overcoming obstacles. Translational Medicine Communications [Internet]. 2019 Nov 18;4(1).
- O’Reilly LP, Luke CJ, Perlmutter DH, Silverman GA, Pak SC. C. elegans in high-throughput drug discovery. Advanced Drug Delivery Reviews. 2014 Apr;69-70:247–53.
- Kaletta T, Hengartner MO. Finding function in novel targets: C. elegans as a model organism. Nature Reviews Drug Discovery. 2006 Apr 21;5(5):387–99.
The technology developed by Magnitude Biosciences images the movement of worms on several petri dishes at once. On the right you can see a control dish with a single C. elegans worm and another with a worm exposed to the acaricide etoxazole. The white tracks show the movement of the worms, that are the progeny of the original animal.
You will see that in the presence of etoxazole, there are far fewer worms. The graph on the left shows the increase in worm population over time as the experiment progresses. Increasing doses of etoxazole reduce the population size. The bar graph compares the area under the curves to give a quantitative measure of reproductive toxicity. This dataset can be further analysed to measure the rate of population growth and reveal delays in the onset of egg-laying.
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