Automated Sample Preparation Systems

Did you know that with technologies like 96-deep-well plates, biotech labs can now run high-throughput experiments, sometimes handling up to 60 plates in parallel for massive screens? In a recent episode of the Smart Biotech Scientist Podcast, Tom Valentin, Group Leader at CSEM, shared that this setup can mean managing 5,760 individual samples at once (since each plate has 96 wells). That’s a game changer for process development: it speeds up optimization, enables robust data collection, and brings true automation to early-stage perfusion and fed-batch studies. For anyone in CMC or bioprocessing, this scale of miniaturization is opening doors to new efficiencies and more predictive results than ever before. Tom is pioneer in automated sample handling and miniaturized perfusion systems. With a unique blend of biomedical and mechanical engineering expertise, Tom is helping redefine what’s possible for small‑scale cell‑culture automation. Top 3 takeaways from our conversation: 1. Automation meets miniaturization: 96-deep-well plates offer high-throughput, low-volume experimentation that integrates seamlessly with liquid handling robots - paving the way for fully automated workflows in both fed-batch and perfusion processes. 2. Current limitations: Despite advances, replicating true bioreactor conditions at this small scale is still hampered by challenges in sensor miniaturization, precise liquid handling, and especially real-time viable cell density monitoring. 3. Next-gen solutions on the horizon: Tom and his team are working toward customizable perfusion systems compatible with existing platforms and exploring advanced sensor integration for real-time analytics, bringing fully automated, scalable biotech process development ever closer. Curious about how these insights could accelerate your own cell culture pipeline? Check out the full podcast episode or drop your questions/thoughts in the comments below. #HighThroughputScreening, #SmallScalePerfusion, #CellCultureAutomation, #BioprocessDevelopment, #ContinuousPerfusion, #FedBatchProcess, #MiniaturizedPerfusionSystems, #BioreactorConditions, #ProcessMonitoring, #CMCDevelopment

🧪𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗦𝗮𝗺𝗽𝗹𝗲-𝗽𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻, 𝗠𝗮𝘀𝘀 𝗦𝗽𝗲𝗰𝘁𝗿𝗼𝗺𝗲𝘁𝗿𝘆-𝗯𝗮𝘀𝗲𝗱 𝗔𝘀𝘀𝗮𝘆𝘀 𝗙𝗼𝗿 𝗙𝗿𝗲𝗲 𝗔𝗻𝗱 𝗧𝗼𝘁𝗮𝗹 𝗜𝗻𝗳𝗹𝗶𝘅𝗶𝗺𝗮𝗯: 𝗔 𝗠𝗼𝗱𝗲𝗹 𝗙𝗼𝗿 𝗧𝗵𝗲𝗿𝗮𝗽𝗲𝘂𝘁𝗶𝗰 𝗗𝗿𝘂𝗴 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗢𝗳 𝗠𝗼𝗻𝗼𝗰𝗹𝗼𝗻𝗮𝗹 𝗔𝗻𝘁𝗶𝗯𝗼𝗱𝗶𝗲𝘀🧪 🛎 Overview: Therapeutic drug monitoring (TDM) for biologic therapies is central to managing inflammatory diseases — but standard testing methods can miss clinically meaningful signals. When anti-drug antibodies (ADAs) bind to biologics, they can interfere with measurement and distort the true picture of drug exposure. 📌 Analytical challenge: Most ELISA-based approaches are reliable and widely used, yet they have well-recognized blind spots. ADA–drug complexes can mask antibody presence and circulating drug levels, with studies suggesting that up to 25% of ADAs may go undetected. Despite growing awareness of this limitation, few high-throughput, clinic-ready alternatives have been available. 🔎 Sam et al work presents a high-throughput LC-MS/MS workflow designed to quantify both free drug and ADA-bound drug improving visibility into true exposure and immunogenicity risk. 🎯 Summary: Sam et al method introduces a dual LC-MS/MS assay using a 96-well plate format to measure both free and bound infliximab directly from patient serum. By pairing targeted mass spectrometry with on-plate digestion and robotic sample handling, Sam et al workflow is built not only for analytical rigor but also for routine laboratory scalability. Validation results show that free-drug measurements align well with ELISA while extending the quantitation range. In parallel, the total-drug assay captures infliximab regardless of ADA presence, helping uncover masked immunogenicity signals that conventional assays may overlook. In patient samples, Sam et al observed that nearly half showed substantially higher total versus free drug levels consistent with previously undetected anti-infliximab antibodies. This kind of differential measurement provides a more nuanced and clinically actionable view of biologic therapy response. 💡 Why this matters: Because the workflow is automated, cost-effective, and adaptable, it offers a practical path toward broader clinical adoption. The same analytical framework could be extended to other therapeutic antibodies affected by ADA interference, strengthening precision monitoring across biologic treatments. 📌 Check out the full publication below 🔗 https://lnkd.in/ddxw9FpV #TherapeuticDrugMonitoring #LCMSMS #Biologics #ClinicalDiagnostics #MassSpectrometry #Immunogenicity #Infliximab

Is lab automation a luxury—or a scientific imperative? In the evolving landscape of translational research and high-throughput experimentation, automation has become indispensable for ensuring analytical fidelity, reproducibility, and process control. In domains such as: Genomics – Automation ensures uniform pipetting, temperature control, and precise incubation across critical steps like DNA/RNA extraction, normalization, and NGS library prep. This directly reduces inter-sample variability and cross-contamination—two major threats to data integrity. Biobanking – Automated platforms offer barcode-driven sample tracking, robotic handling at ultra-low temperatures, and integration with LIMS, which are vital for maintaining chain-of-custody and compliance with standards like ISO 20387. Microbiology workflows – From automated colony picking and microbial ID to liquid handling for antimicrobial susceptibility testing, automation enables reproducible inoculations, growth monitoring, and endpoint quantification with minimal manual variation. Solid-phase extraction (SPE) – Automated SPE systems precisely regulate flow rates, drying times, and elution volumes—critical parameters that impact analyte recovery, matrix effect, and LC-MS/MS reproducibility. Across these applications, automation is not merely a throughput enabler—it is a quality enabler. It aligns with the scientific method by minimizing uncontrolled variables, enabling longitudinal comparability, and ensuring compliance with rigorous QC/QA frameworks. The future of lab science hinges not on how many samples we can process, but on how consistently, accurately, and transparently we can process them. Automation isn't replacing science. It's refining it. Leader Life Sciences Leader Healthcare Tecan #ScientificAutomation #LabInnovation #Genomics #Biobanking #Microbiology #SPE #Reproducibility #QualityByDesign #PrecisionScience #HighThroughputResearch #MolecularWorkflows #SampleIntegrity #FutureOfResearch

Thrilled to share our latest work, "A Smart Centrifuge for Automated Sample Processing with Liquid Handling Robot," just published in the Journal of Open Hardware! In collaboration with my talented colleagues Yueyang Gao, Andrew Redfearn, Simon Dawes, Jialei Shi, Alaric Taylor-Roffey, and Helge A Wurdemann, we've developed a centrifuge with infrared sensor-derived positional control, designed with open-source hardware principles. This device not only supports precise, adaptable centrifugation parameters but also integrates seamlessly with modular systems like the OT-2 pipetting robot from Opentrons Labworks Inc.. Why this matters: - It reduces manual effort and speeds up material synthesis and diagnostic assays. - It improves reproducibility by eliminating experimenter variability. - It’s modular and accessible, making it versatile for different applications in life sciences, physical sciences, and engineering. A huge thanks to the team for their dedication and intellectual input! We hope this platform makes automated workflows more accessible for researchers everywhere. Check out the full paper here: [Link to article: https://lnkd.in/eipZ6Rct] #OpenSource #Automation #LabInnovation #LiquidHandling #Research #LifeSciences #Engineering

Tips for Success with Dissolution Autosamplers Manual Sampling and filtration is the most labor-intensive, time consuming, and error prone step in the dissolution process. The USP has tight criteria for sampling location and time, and this can be difficult for an analyst to do correctly for 6-8 samples. Some dissolution runs are longer than your workday too, which can be challenging to deal with. For these reasons, many dissolution labs use autosamplers to automate this process. These can be amazing tools, but it is important to use them properly! An autosampler only works when you can get same results you would with a manual sample. So, how do we make sure you're successful? First off, make sure your pump settings are correct for your method. Different autosamplers have different ways of programming. On the Agilent 850-DS, we have prime and purge cycles. The prime pumps just before the timepoint to wet all of the lines and ensure a proper volume collection. The purge pumps air through the system to return media after the timepoint back to your vessel. Your prime and purge may vary a bit depending on filter type, media viscosity, amount of surfactant, etc. so you could check these settings when working on a new method. The 850-DS also has a waste drop volume which cleans the needles between timepoints and this is the only volume which is sent to waste on the 850-DS (typically 0.5mL). Pump speed can also be changed between 6-12mL/min if needed to help with a successful sample. Cleaning methods are key to develop with each method. Different formulations and media may have different cleaning requirements. Some may only need to rinse with 20-30mL DI Water, others may need multiple cycles of water/alcohol mixes, or something else. Proper cleaning will not only prevent carryover but will keep your instrument running well with fewer repairs. If your autosampler uses resident probes, meaning that sampling cannulas are in the vessel the entire experiment, you should check to ensure there is not a bias in the results. Probes left in the media alter the hydrodynamics in the vessel, which can lead to higher and more variable results. Not all products are sensitive to this, but you must check. Generally, performing n=12 dissolution with manual sampling and a separate n=12 with the autosampler is done. These results are compared, and if they're equivalent you are good to go. If your system has a motorized sampling manifold like the Agilent 708-DS, then this is typically not needed. You may also want to check that there is no adsorbance of your product in the tubing. This can be checked easily by putting a dilute standard into one or more of your vessels and taking a sample and assessing recovery. If using different filters on automation, make sure those are validated as well. A good guide is here: https://lnkd.in/ehiiZZUb

Self-driving labs (SDLs) are hot. But how can they make an impact in industrial R&D? A recent paper by Sijie Fu et al., from a collaboration between Carnegie Mellon University and Covestro, offers a glimpse of what’s possible. The team presents #autoHSP, an autonomous, closed-loop workflow for determining Hansen Solubility Parameters (HSPs), demonstrated on Covestro’s coating resins. 🔹Closed-loop self-driving lab: A centralized automated facility prepares samples, analyzes them, and reports results remotely, with minimal manual intervention. 🔹Computer vision analysis: An edge-detection pipeline pinpoints phase boundaries in vials to automatically HSP determination. 🔹Batch-mode active learning: An algorithm selects the most informative and diverse solvent or mixture candidates in batches, to accelerate screening. For coatings, adhesives, and other applications, HSPs are essential for guiding formulation design. AutoHSP is a great example of how industrial R&D can gain directly from autonomous experimentation. 📄 Autonomous Determination of Hansen Solubility Parameters via Active Learning, ChemRxiv, Aug 6, 2025 🔗 https://lnkd.in/eeKrbhUt

Fully automated development platforms are being increasingly used for applications across disciplines including pharmaceuticals and materials science. Self-driving chemistry labs in drug discovery may automate the process of synthesizing compounds and testing their efficacy, significantly reducing the time and cost associated with bringing new therapies to market. These platforms employ robotics to conduct experiments and fully integrated analytical platforms to collect and analyse data with minimal to no human intervention.   Dispensing small quantities of liquids can be relatively straightforward, but efficiently dosing solids below the milligram level poses significant challenges. For the current EPFL project Swiss Cat+, a data-driven infrastructure for catalysts discovery under the supervision of Pascal Miéville and promoted by Innosuisse, Dec Group developed an automated single-use micro-sampling system capable of automatically preparing microgram quantities of powder samples needed for experimentation.   #LabAutomation #Robotics #MachineLearning #AIInPharma #Biotech #LifeSciences #Innovation #TechinPharma #Pharma #DataAnalytics #DrugDiscovery #DecGroup

               ⚛️𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗳𝗼𝗰𝘂𝘀: 𝗔𝗗𝗠𝗘/𝗗𝗠𝗣𝗞 One of the most interesting parts of drug discovery is understanding how compounds behave in the body, and that’s where ADME/DMPK studies come in. As part of the 60+ new application pages on the redesigned Hamilton Company website, the ADME/DMPK page highlights how automation can make these studies faster, more consistent, and more reproducible. Hamilton Robotics’ automated liquid handling solutions can be customized to: • Deliver precise, reproducible sample prep before LC-MS analysis • Run multiple assay types — plasma protein binding, hepatocyte metabolic stability, partition coefficient studies — all on one instrument • Enable complex sample handling and precise timepoint harvesting • Support low-volume dispensing and miniaturized assays to conserve valuable material You’ll also find great real-world references, including work with Chris Chantler at Vertex, showing how automation drives reliable ADME workflows in practice. Curious to see how it all comes together? 🔗 Explore the page here: https://lnkd.in/gZDN3N-D #LaboratoryAutomation #ADME #DMPK #Pharma #Bioanalytics

About five years ago my Conceivable Life Sciences—and TMRW Life Sciences—co-founder Joshua Abram and I were visiting the lab of Reproductive Medicine Associates (RMA Network) J. Glenn Proctor, a TMRW client and a brilliant embryologist. I remember Glenn pointing to a member of his team preparing dishes, turning to us and saying: “If you guys are so smart, why don’t you automate dish prep?” That moment inspired us to not just automate dish prep, it inspired us to begin thinking about reimagining the entire IVF lab and led to what is today our AI-powered automated AURA lab. It is also the reason we were able to present the following research at this year’s American Society for Reproductive Medicine - ASRM in San Antonio: “Can an automated dish preparation system deliver consistent dish characteristics, maintain acceptable media pH, and avoid embryotoxic effects?" Dish prep at first glance seems so basic. But research from Columbia University's Fertility Center demonstrates that significant clinical improvements can be tied to getting this protocol done right every time. A paper published in American Society for Reproductive Medicine - ASRM 𝘍𝘦𝘳𝘵𝘪𝘭𝘪𝘵𝘺 𝘢𝘯𝘥 𝘚𝘵𝘦𝘳𝘪𝘭𝘪𝘵𝘺 stated: “Embryo culture dishes prepared using [automation] demonstrated a greater than 10-fold improvement in consistency…and had a greater mouse embryo blastocyst rate (100% vs. 90%-91%). Human embryos cultured in dishes prepared by [automation] had a higher rate of development on days 3 (92.4% vs. 82.6%) and 5 (19.75% vs. 15.57%), and a total number of usable embryos (50.3% vs. 46.1%) compared with manually prepared dishes….” Conceivable’s C:DISH prepares hundreds of dishes a day for all IVF functions with exacting precision and repeatability. It also laser inscribes patient information on every dish for smart track and trace of each specimen. I’m so proud of our team! Special shout out to the paper’s authors:  Adolfo Flores Saiffe Farías, Ph.D. Fátima Acosta-Gómez, Carla Patricia Barragán Álvarez, Karen Aleriano, Gerardo Mendizabal Ruiz, Sofia Anahi Real Covarrubias, Nuno Costa Borges, César Millán Castillo, Jacques Cohen