Team Consulting is a medical device design and development consultancy. The company works with pharmaceutical companies and medtech businesses globally.The company is located on the outskirts of Cambridge, UK, and is part of Silicon Fen, the cluster of high-tech businesses which has made the Cambridge area one of the most important technology centres in Europe. The majority of the company's workforce are consultants who offer expertise in industrial design, human factors and ergonomics, electrical, mechanical and software engineering, and in the physical sciences.
Background & Aims Achieving speed and cost efficiency in cell and gene therapy (CGT) development requires improved analytics as the foundation for automation and digitalisation. Smart tools such as AI/ML, digital twins and bioinformatics depend on rapid, reliable and actionable data. Current reliance on offline measurements delays critical in process decisions, while most at line instruments are stand alone, requiring manual intervention and offering limited integration.We conducted a study to demonstrate that simple, low-cost devices can be rapidly designed and integrated to deliver valuable data. We illustrate this through microfluidic cell handling, multiplexed analysis techniques and ML algorithms. The examples represent selected ‘tools’ from a broader toolbox, many options exist depending on specific measurement needs and process objectives. Success lies in choosing the right tool. Methodology We assembled a low cost, integrated prototype device for real time, in process analytics for cell characterisation, from commercially available components. Fluid was driven by a syringe pump and controlled by a pair of rocker valves. Mixed cell solutions passed through a custom microfluidic flow cell where they were focused and analysed by a multiplexed system incorporating optics and spectroscopic measurements, label free. An ML algorithm quantified the critical characteristics of the distinct cell types and directed the valves to sort them into the correct channel. Results Cells differing in morphology, chemistry and/or impedance were identified without extrinsic labelling by the multiplexed system. No pre or post processing was required to add or remove fluorescent or magnetic labels, avoiding added time and complexity. The microfluidic flow cell is compatible with small sample volumes and allows easy integration into the manufacturing process, compatible with closed, aseptic automation. This combination of techniques treated cells gently and avoided the destruction of valuable samples. Once trained, the algorithm distinguished cell properties and applied the method to subsequent mixtures, providing real time process insight. Conclusion Enhanced analytics are critical to advancing automation and digitalisation in CGT manufacturing. Adaptable systems capable of delivering real time, in process data, reduce complexity, shorten production times and lower costs. Leveraging and tailoring existing technologies can enhance outcomes for both patients and developers.
Background & Aims We will explore the development journey that led to the NANOme® system, which enables rapid back-to-back aseptic production with no cleaning required.Team Consulting partnered with LEON Nanodrugs to translate their proprietary nanoencapsulation technology, the FR JET into a commercial device tailored to small batch manufacturing. Together, we developed NANOme®, an easy-to-use aseptic nanoencapsulation system that lowers changeover times and operational costs compared with conventional platforms.Personalised medicine requires flexible manufacturing at small scales, but conventional pharmaceutical systems remain poorly suited. Lipid nanoparticle vectors offer tailored payloads and precise targeting potential, but current approaches impose significant cost and time burdens due to cleaning at batch changeover.We will outline the journey from idea to market ready GMP instrument, highlighting key technical challenges, lessons learned and how a user centred design (UCD) approach created an intuitive device for healthcare professionals (HCPs). Methodology Early computational fluid dynamics (CFD) simulations evaluated the feasibility of a single use version of the FR JET bioreactor. We created a novel pumpless, non contacting fluid flow architecture, enabling a fully closed aseptic system with no cleaning requirements. Integrated system engineering spanned hardware, firmware and software. This ensured precise product quality control while achieving orders of magnitude faster processing times than market leaders. A UCD process mapped physical inputs, screen interactions and background processes, directly shaping system design decisions. Results The final design elevates sterility standards for personalised nanoformulations, mitigates cross contamination risks and reduces batch run time, changeover effort and manual handling. NANOme® earned an iF Design Award for its novel design and potential patient impact. Conclusion The NANOme® journey shows how combining fluid mechanics, embedded systems and user experience design led to a system that sets a new standard for nanoencapsulation in personalised medicine, to empower HCPs and enhance patient outcomes.The poster will detail CFD evaluation of single use feasibility, engineering of a novel fluid flow approach, systems integration across disciplines and UCD that drives adoption and safer operations.
Nature-based solutions (NBS) are becoming increasingly popular for mitigating flood risk while providing multiple benefits such as enhanced biodiversity, and improved water quality. Hydraulic modelling is an essential tool for evaluating and optimising the flood risk regulating service of NBS. Yet, there is no standardised approach to represent NBS interventions in hydraulic models, leading to inconsistent implementations. This study reports a systematic review of 1,080 publications from Scopus and Web of Science. A final selection of 30 key-studies is identified utilising HEC-RAS to model NBS interventions. By collating and synthesising the various approaches documented in these studies, a consolidated resource for researchers and practitioners is provided. This synthesis offers practical recommendations for the application of different NBS interventions in hydraulic modelling and outlines the corresponding parameter ranges to consider. A critical assessment is undertaken on how these interventions map to specific parameters in the governing hydraulic equations, elucidating the physical mechanisms of flow attenuation and water storage that are broadly applicable to all hydraulic models. To demonstrate the real-world utility of these recommendations, a case study of the River Chew catchment (England) is presented, detailing pre- and post-intervention configurations wherein multiple candidate areas are converted to wetland and woodland using a HEC-RAS 2D model; this section is available in the Supplementary Material.
The temporal clustering of storms can present successive natural hazards for coastal areas in the form of extreme sea levels, storm surges and waves. Studies have investigated the prevalence of the temporal clustering of such hazards but are hindered by the rarity of the phenomena combined with short records and a lack of data availability around the coastline. This has made it difficult to determine if the levels of clustering reported were typical for the location or were being masked by natural variability or climate change over different timescales. In this study, we assess a near 500-year model simulation of extreme sea levels and storm surges forced with pre-industrial meteorological conditions to quantify the levels of temporal clustering seen from natural variability around Great Britain. We then utilise a 50-year rolling window to see how clustering statistics can change through time when dealing with time periods that are representative of the average length of a record in the United Kingdom National Tide Gauge Network. When using near 500-year timeseries, we highlight that many clustering statistics return values close to their statistical expectancies. However, when analysing discrete 50-year windows, results can vary dramatically. The percentage of years with an extreme sea level or surge exceedance at a given location at the 1 in 1-, 5-, and 10-year return level, can vary by up to ~ 33%, ~ 24%, and ~ 18%, the mean number of days between consecutive sea level or surge exceedances can vary by ~ 231, ~14,780, and ~ 17,793 days, and the extremal index can vary by ~ 0.37, ~ 0.64, and ~ 0.79, respectively. Although these results represent the best estimate of the levels of clustering to be expected under natural variability, a comparison of the longest records in the tide gauge network and their nearest model grid nodes shows a tendency for the model to underestimate the clustering statistics that are calculated from the measured data (apart from the extremal index). As such, these can be considered to represent the minimum levels of temporal clustering around Great Britain, as the potential underestimation of clustering, combined with climatic change and sea level rise, means that the temporal clustering of sea levels and storm surges are likely to be far greater over the next 500 years.
The metabolic and lipid profiles of horses treated with sodium-glucose cotransporter 2 inhibitors are not well understood. This retrospective study evaluated blood parameters in hyperinsulinemic horses treated with either ertugliflozin (0.05 mg/kg) or dapagliflozin (0.02 mg/kg) orally once daily. Blood samples were collected at baseline (day 0) and after 7 and/or 30 days of treatment. Statistical analyses were conducted using Wilcoxon signed-rank, Mann-Whitney and Spearman's rank correlation tests. Thirty-four horses received dapagliflozin and 24 received ertugliflozin. Significant (p<0.05) within-horse changes between day 0 and day 30 included [median, inter-quartile range (IQR)]: basal serum [Insulin] (uU/ml) reduced 170 (92-280) to 28.7 (14.5-90); [triglycerides] (mmol/l) increased 0.5 (0.3-0.6) to 1.0 (0.6-1.56), [β-hydroxybutyrate] (umol/l) increased 0.22 (0.17-2.7) to 0.30 (0.24-0.35); [total cholesterol] (mmol/l) increased 2.36 (2-2.6) to 2.84 (2.4-3.7); and GGT (IU/ml) increased 21 (16-32) to 25 (18-38). As a percentage of total serum lipids, high-density lipoprotein (HDL) reduced 52.4% (47.9%-61.0%) to 50% (41%-54.8%) and very-low density lipoprotein (VLDL) increased 10.4% (6.4%-14.4%) to 12.3% (9.9%-16.8%) (all p<0.05). Differences between ertugliflozin and dapagliflozin groups were not significant in any of these parameters at days 0, 7 or 30. At day 30, 10/48 (21%) cases had [triglycerides] > 2.0mmol/l (maximum = 10.8mmol/l). Day 30 [triglyceride] correlated with day 0: basal insulin (rho=0.47); [triglyceride] (rho=0.42); %VLDL (rho=0.34) day 30: [total cholesterol] (rho=0.67), %HDL (rho=-0.432) and %VLDL (rho=0.708). Our findings suggest that SGLT2 inhibitors induce minor changes in lipid profiles, with occasional cases of marked hypertriglyceridemia, and that dapagliflozin and ertugliflozin exhibit similar biochemical effects.
