Safety and efficacy of pegunigalsidase alfa in patients with Fabry disease who were previously treated with agalsidase alfa: results from BRIDGE, a phase 3 open-label study

BACKGROUND: Pegunigalsidase alfa is a novel, PEGylated α-galactosidase-A enzyme-replacement therapy approved in the EU and US to treat patients with Fabry disease (FD). OBJECTIVE/METHODS: BRIDGE is a phase 3 open-label, switch-over study designed to assess safety and efficacy of 12 months of pegunigalsidase alfa (1 mg/kg every 2 weeks) treatment in adults with FD who had been previously treated with agalsidase alfa (0.2 mg/kg every 2 weeks) for ≥ 2 years. RESULTS: Twenty-seven patients were screened; 22 met eligibility criteria; and 20 (13 men, 7 women) completed the study. Pegunigalsidase alfa was well-tolerated, with 97% of treatment-emergent adverse events (TEAEs) being of mild or moderate severity. The incidence of treatment-related TEAEs was low, with 2 (9%) discontinuations due to TEAEs. Five patients (23%) reported infusion-related reactions. Overall mean (SD; n = 22) baseline estimated glomerular filtration rate (eGFR) was 82.5 (23.4) mL/min/1.73 m2 and plasma lyso-Gb3 level was 38.3 (41.2) nmol/L (men: 49.7 [45.8] nmol/L; women: 13.8 [6.1] nmol/L). Before switching to pegunigalsidase alfa, mean (standard error [SE]) annualized eGFR slope was − 5.90 (1.34) mL/min/1.73 m2/year; 12 months post-switch, the mean eGFR slope was − 1.19 (1.77) mL/min/1.73 m2/year; and mean plasma lyso-Gb3 reduced by 31%. Seven (35%) out of 20 patients were positive for pegunigalsidase alfa antidrug antibodies (ADAs) at ≥ 1 study timepoint, two of whom had pre-existing ADAs at baseline. Mean (SE) changes in eGFR slope for ADA-positive and ADA-negative patients were + 5.47 (3.03) and + 4.29 (3.15) mL/min/1.73 m2/year, respectively, suggesting no negative impact of anti-pegunigalsidase alfa ADAs on eGFR slope. CONCLUSION: Pegunigalsidase alfa may offer a safe and effective treatment option for patients with FD, including those previously treated with agalsidase alfa. TRN: NCT03018730. Date of registration: January 2017

Characterizing the optimal flux space of genome-scale metabolic reconstructions through modified latin-hypercube sampling

Genome-Scale Metabolic Reconstructions (GSMRs), along with optimization-based methods, predominantly Flux Balance Analysis (FBA) and its derivatives, are widely applied for assessing and predicting the behavior of metabolic networks upon perturbation, thereby enabling identification of potential novel drug targets and biotechnologically relevant pathways. The abundance of alternate flux profiles has led to the evolution of methods to explore the complete solution space aiming to increase the accuracy of predictions. Herein we present a novel, generic algorithm to characterize the entire flux space of GSMR upon application of FBA, leading to the optimal value of the objective (the optimal flux space). Our method employs Modified Latin-Hypercube Sampling (LHS) to effectively border the optimal space, followed by Principal Component Analysis (PCA) to identify and explain the major sources of variability within it. The approach was validated with the elementary mode analysis of a smaller network of Saccharomyces cerevisiae and applied to the GSMR of Pseudomonas aeruginosa PAO1 (iMO1086). It is shown to surpass the commonly used Monte Carlo Sampling (MCS) in providing a more uniform coverage for a much larger network in less number of samples. Results show that although many fluxes are identified as variable upon fixing the objective value, majority of the variability can be reduced to several main patterns arising from a few alternative pathways. In iMO1086, initial variability of 211 reactions could almost entirely be explained by 7 alternative pathway groups. These findings imply that the possibilities to reroute greater portions of flux may be limited within metabolic networks of bacteria. Furthermore, the optimal flux space is subject to change with environmental conditions. Our method may be a useful device to validate the predictions made by FBA-based tools, by describing the optimal flux space associated with these predictions, thus to improve the

How to Integrate Security Compliance Requirements with Agile Software Engineering at Scale?

Integrating security into agile software development is an open issue for research and practice. Especially in strongly regulated industries, complexity increases not only when scaling agile practices but also when aiming for compliance with security standards. To achieve security compliance in a large-scale agile context, we developed S2C-SAFe: An extension of the Scaled Agile Framework that is compliant to the security standard IEC 62443-4-1 for secure product development. In this paper, we present the framework and its evaluation by agile and security experts within Siemens’ large-scale project ecosystem. We discuss benefits and limitations as well as challenges from a practitioners’ perspective. Our results indicate that S2C-SAFe contributes to successfully integrating security compliance with lean and agile development in regulated environments. We also hope to raise awareness for the importance and challenges of integrating security in the scope of Continuous Software Engineering. © 2020, Springer Nature Switzerland AG

Metamodeling of the Electrical Conditions in Submerged Arc Furnaces

Physics-based Finite Element Methods models can be used to investigate the electrical conditions in submerged arc furnaces (SAFs). However, their explicit solution may be very demanding in terms of time and computational resources. This makes these models difficult to employ during control operations and in fast prototyping. To obviate these inconveniences, we developed metamodels that are grounded on the physics-based model. In this context, a metamodel is a surrogate of an original model obtained using statistical analysis tools to determine approximate input–output relationships in a database of simulations from the original model. The metamodels for the SAF electrical conditions are shown to retain the same generalization capabilities as the original model while being computationally lightweight