Integrating high-throughput experimentation with advanced decision-support tools for chromatography process development

The development and commercialisation of a new therapeutic drug is a lengthy and expensive process hindered with uncertainties and high attrition rates. Monoclonal antibodies are a major contributor to the continuous growth of the global biopharmaceutical industry. Chromatography remains the workhorse in antibody purification despite its complex process development and the high operating cost. The research here presents the establishment of an integrated and data-driven decision-support framework in early-stage protein chromatography process development. The key focus of the research is the development of a systematic and rational methodology to automate and accelerate data analysis and decision-making. A novel workflow was developed that combined high-throughput experimentation (HTE) at micro-scale with design of experiments (DoE), multi-variate data analysis, multi-attribute decision-making and a robustness analysis technique to screen and optimise chromatography resins. DoE was linked with an advanced chromatogram analysis method to cope with the large datasets resulting from HTE by automating raw data manipulation. Additionally, the approach offers the ability to correlate the trade-offs between purity and yield with process parameters through a regression analysis. High-throughput purification data were further leveraged using a decision-support tool for the chromatographic purification train linked with a bioprocess economics spreadsheet model. The bioprocess economics model was also used to provide insights regarding the cost-effectiveness of pre-packed chromatography columns as an alternative to conventional self-packed columns for clinical and commercial manufacture. The implementation of the framework demonstrated the synergy of different decision-support tools and allowed for the rapid evaluation of multiple chromatographic purification trains in order to determine the most cost-effective resin sequence and column type considering the whole manufacturing process. Additionally, it is demonstrated that chromatography process development activities could be accelerated by defining platform purification processes and identifying manufacturing bottlenecks fast and with limited feedstock material

Reuse of By-Products from Ready-Mixed Concrete Plants for the Production of Cement Mortars

This study was motivated by the necessity to recycle sludge water resulting from washing out concrete mixing trucks - a problem of both environmental and economic importance for the ready-mixed concrete industry. Sludge water from ready-mixed concrete plants as well as dry sludge, which is derived from the settling of the water, are hazardous for disposal due to their high pH value (pH>11.5). In this work, cement mortars were composed using either sludge water after various treatment, or dry sludge in several ratios. The cement mortars were tested for their workability and strength development. The purpose of this experimental design was to prove that sludge water, as well as sludge in a wet or dry form, can be used in the production of mortars without degrading any of their properties

OVeNet: Offset Vector Network for Semantic Segmentation

Semantic segmentation is a fundamental task in visual scene understanding. We focus on the supervised setting, where ground-truth semantic annotations are available. Based on knowledge about the high regularity of real-world scenes, we propose a method for improving class predictions by learning to selectively exploit information from neighboring pixels. In particular, our method is based on the prior that for each pixel, there is a seed pixel in its close neighborhood sharing the same prediction with the former. Motivated by this prior, we design a novel two-head network, named Offset Vector Network (OVeNet), which generates both standard semantic predictions and a dense 2D offset vector field indicating the offset from each pixel to the respective seed pixel, which is used to compute an alternative, seed-based semantic prediction. The two predictions are adaptively fused at each pixel using a learnt dense confidence map for the predicted offset vector field. We supervise offset vectors indirectly via optimizing the seed-based prediction and via a novel loss on the confidence map. Compared to the baseline state-of-the-art architectures HRNet and HRNet+OCR on which OVeNet is built, the latter achieves significant performance gains on three prominent benchmarks for semantic segmentation, namely Cityscapes, ACDC and ADE20K. Code is available at https://github.com/stamatisalex/OVeNetComment: Accepted at WACV 202

Transformation of a university building into a zero energy building in Mediterranean climate.

In the context of environmental policy, the EU has launched a series of initiatives aimed at increasing the use of energy efficiency, as it has pledged to reduce energy consumption by 20%, compared with projected levels of growth of CO2 emissions into the atmosphere by 2020. In Greece CO2 emission levels in the atmosphere have risen significantly over the past two decades [1]. For the year 2011, CO2 emissions per person in Greece correspond to 7.56 t. According to the data, this increase in emissions is reflected to a 151.2% above from the levels of 1980 and a 756% increase from 1960 levels. The building sector consumes the largest amount of energy in Greece, therefore constitutes the most important source of CO2 emissions. The energy upgrade of the building sector produces multiple benefits such as reduced energy consumption, which is consistent with the reduction of air pollution. Additionally, there is a significant improvement at the interior comfort conditions of the building, which promotes productivity and occupant health. Moreover, because of the large number of educational buildings in the country, the energy consumption of them present a significant amount of the country's total energy consumption and simultaneously has the effect of increasing the costs paid by the state budget for the operation and maintenance of public buildings. The investigation of alternative methods to reduce energy consumption in educational buildings is an important approach for sustainability and economic development of the country over time. The purpose of this paper is to study and evaluate the energy saving methods of a university building in Mediterranean climate with significant energy consumption. Additionally, through Building Information Modeling (BIM) and Computational Fluid Dynamics (CFD) software, studies considering the contribution of passive heating and cooling techniques were conducted, in order to minimize energy consumption in pursuit of desirable interior thermal comfort conditions.N/

Integrated continuous bioprocessing: Costs of goods versus cost of development

A significant benefit of continuous manufacture is the potential to provide higher productivities compared to traditional batch processes. Smaller facilities with single-use technology could become preferable offering reductions in the capital expenditure. Hence, continuous bioprocessing could offer savings in the cost of goods (COG). However there are other cost factors that need to be considered when evaluating bioprocess facilities in addition to the COG. The cost of development (COD) is a key cost driver that could affect the decision to adopt new manufacturing methods. This study aims to carry out a holistic financial assessment of introducing continuous bioprocessing strategies by considering both the COG and the COD. To be able to perform this level of analysis a decisional tool was developed at University College London to evaluate the cost of implementing traditional batch or continuous bioprocessing (end-to-end and hybrid) at various stages of the drug development pathway. A range of scenarios investigated the economics of different manufacturing strategies at various demands, company sizes and stages of manufacture (pre-clinical, clinical and commercial). Therefore, through the analysis it was possible to determine whether the apparent benefits of continuous bioprocessing translate into cost savings, focusing on the development and commercialisation of monoclonal antibodies

Use of Steel Slag as Coarse Aggregate for the Production of Pervious Concrete

Pervious concrete is a type of concrete with significantly increased water permeability, ensuring increased rates of drainage of rainfall. The high porosity is achieved by removing a large percentage of fine aggregates from the mix. The present paper is an approach for the addition of steel slag as a substitute for coarse aggregates in pervious concrete. More specifically, three types of aggregates have been used: steel slag, construction and demolition wastes and conventional limestone aggregates. The produced pervious concretes are compared for their properties, such as water permeability, compressive strength and abrasion behaviour. Also this paper contains the study of the porosity analysis of these pervious concrete mix designs by using porosity profiles produced from X-ray CT Scanning. According to the results of this paper, it is observed that the incorporation of industry by-products or of Construction and Demolition (C&D) wastes leads to better abrasion behaviour, and to the increase, in some cases, of the compressive strength and of the water permeability

Decisional tools for successful commercialisation of novel vaccine technologies

With the emergence of personalised cancer vaccines and more recently with the COVID-19 pandemic, the increasing need for novel and disruptive manufacturing and supply chain strategies to deliver affordable vaccines has been highlighted. Decision-support tools are essential to accelerate and enhance decision-making during the development, commercialisation and distribution of prophylactic or therapeutic vaccines across the globe. This presentation will share our most recent insights from UCL’s Bioprocess Decisional Tools research on modelling the economics of integrated and intensified manufacturing technologies for viral vectors and mRNA vaccines. On the mRNA front, UCL collaborated with Univercells Technologies and Quantoom Biosciences to explore novel identified, integrated and automated platforms for the production of personalised cancer vaccines, and we evaluated the benefits and limitations of the technology across a range of demands and dose sizes. Furthermore, we simulated the same manufacturing technology for the production of a prophylactic mRNA vaccine against an infectious disease at a pandemic pace, focusing on the adequate and rapid supply of vaccines in developing countries. The case study used the COVID-19 pandemic as a real-world example to determine the necessary infrastructure and manufacturing capacity in Africa to support a rapid response across the continent. In the analysis, we considered two vaccine technologies; an adenoviral vector and an mRNA vaccine, aiming to determine the required facility footprint, the capital investment and the cost of goods. Moreover, for each vaccine technology we compared an integrated with a conventional manufacturing platform for a centralised and a regional manufacturing and supply chain network. These case studies have highlighted the importance of utilising decision-support tools in bioprocessing to gain an in-depth understanding of the necessary infrastructure and the associated cost to manufacture and supply affordable vaccines

Towards the representation of groundwater in the Joint UK Land Environment Simulator

Groundwater is an important component of the hydrological cycle with significant interactions with soil hydrological processes. Recent studies have demonstrated that incorporating groundwater hydrology in land surface models (LSMs) considerably improves the prediction of the partitioning of water components (e.g., runoff and evapotranspiration) at the land surface. However, the Joint UK Land Environment Simulator (JULES), an LSM developed in the United Kingdom, does not yet have an explicit representation of groundwater. We propose an implementation of a simplified groundwater flow boundary parameterization (JULES‐GFB), which replaces the original free drainage assumption in the default model (JULES‐FD). We tested the two approaches under a controlled environment for various soil types using two synthetic experiments: (1) single‐column and (2) tilted‐V catchment, using a three‐dimensional (3‐D) hydrological model (ParFlow) as a benchmark for JULES’ performance. In addition, we applied our new JULES‐GFB model to a regional domain in the UK, where groundwater is the key element for runoff generation. In the single‐column infiltration experiment, JULES‐GFB showed improved soil moisture dynamics in comparison with JULES‐FD, for almost all soil types (except coarse soils) under a variety of initial water table depths. In the tilted‐V catchment experiment, JULES‐GFB successfully represented the dynamics and the magnitude of saturated and unsaturated storage against the benchmark. The lateral water flow produced by JULES‐GFB was about 50% of what was produced by the benchmark, while JULES‐FD completely ignores this process. In the regional domain application, the Kling‐Gupta efficiency (KGE) for the total runoff simulation showed an average improvement from 0.25 for JULES‐FD to 0.75 for JULES‐GFB. The mean bias of actual evapotranspiration relative to the Global Land Evaporation Amsterdam Model (GLEAM) product was improved from −0.22 to −0.01 mm day−1. Our new JULES‐GFB implementation provides an opportunity to better understand the interactions between the subsurface and land surface processes that are dominated by groundwater hydrology