Inhibition of sterol synthesis by delta 5-sterols in a sterol auxotroph of yeast defective in oxidosqualene cyclase and cytochrome P-450

Synthesis of ergosterol is demonstrated in the GL7 mutant of Saccharomyces cerevisiae. This sterol auxotroph has been thought to lack the ability to synthesize sterols due both to the absence of 2,3-oxidosqualene cyclase and to a heme deficiency eliminating cytochrome P-450 which is required in demethylation at C-14. However, when the medium sterol was 5 alpha-cholestan-3 beta-ol, 5 alpha-cholest-8(14)-en-3 beta-ol, or 24 beta-methyl-5 alpha-cholest-8(14)-en-3 beta-ol, sterol synthesis was found to proceed yielding 1-3 fg/cell of ergosterol (24 beta-methylcholesta-5,7,22E-trien-3 beta-ol). Ergosterol was identified by mass spectroscopy, gas and high performance liquid chromatography, ultraviolet spectroscopy, and radioactive labeling from [3H]acetate. Except for some cholest-5-en-3 beta-ol (cholesterol) which was derived from the 5 alpha-cholestan-3 beta-ol, the stanol and the two 8(14)-stenols were not significantly metabolized confirming the absence of an isomerase for migration of the double bond from C-8(14) to C-7. Drastic reduction of ergosterol synthesis to not more than 0.06 fg/cell was observed when the medium sterol either had a double bond at C-5, as in the case of cholesterol, or could be metabolized to a sterol with such a bond. Thus, both 5 alpha-cholest-8(9)-en-3 beta-ol and 5 alpha-cholest-7-en-3 beta-ol (lathosterol) were converted to cholesta-5,7-dien-3 beta-ol (7-dehydrocholesterol), and the presence of the latter dienol depressed the level of ergosterol. The most attractive of the possible explanations for our observations is the assumption of two genetic compartments for synthesis of sterols, one of which has and one of which has not been affected by the two mutations. The ability, despite the mutations, to synthesize small amounts of ergosterol which could act to regulate the cell cycle may also explain why this mutant can grow aerobically with cholesterol (acting in the bulk membrane role) as the sole exogenous sterol.     

Real-time monitoring of quality attributes by in-line Fourier transform infrared spectroscopic sensors at ultrafiltration and diafiltration of bioprocess

Technologies capable of monitoring product quality attributes and process parameters in real time are becoming popular due to the endorsement of regulatory agencies and also to support the agile development of biotherapeutic pipelines. The utility of vibrational spectroscopic techniques such as Fourier transform mid-infrared (Mid-IR) and multivariate data analysis (MVDA) models allows the prediction of multiple critical attributes simultaneously in real time. This study reports the use of Mid-IR and MVDA model sensors for monitoring of multiple attributes (excipients and protein concentrations) in real time (measurement frequency of every 40 s) at ultrafiltration and diafiltration (UF/DF) unit operation of biologics manufacturing. The platform features integration of fiber optic Mid-IR probe sensors to UF/DF set up at the bulk solution and through a flow cell at the retentate line followed by automated Mid-IR data piping into a process monitoring software platform with pre-loaded partial least square regression (PLS) chemometric models. Data visualization infrastructure is also built-in to the platform so that upon automated PLS prediction of excipients and protein concentrations, the results were projected in a graphical or numerical format in real time. The Mid-IR predicted concentrations of excipients and protein show excellent correlation with the offline measurements by traditional analytical methods. Absolute percent difference values between Mid-IR predicted results and offline reference assay results were ≤5% across all the excipients and the protein of interest; which shows a great promise as a reliable process analytical technology tool.     

Predictors of Diabetic Foot and Leg Ulcers in a Developing Country with a Rapid Increase in the Prevalence of Diabetes Mellitus

Objective

To identify the socio demographic, life style and foot examination related predictors of diabetic foot and leg ulcers with a view to develop a screening tool appropriate for the use in an outpatient setting.

Research design and methods

This cross sectional study included type 2 diabetes mellitus (DM) patients; 88 subjects with leg and foot ulcers and 80 non ulcer controls. Socio demographic data and life style factors were documented. Foot was examined for skin changes and structural abnormalities. Distal peripheral neuropathy was assessed by pressure sense, vibration sense and joint position sense. Multivariate analysis by logistic regression was used to determine the significant predictors in screening for foot ulcers.

Results

Education of grade 6 and below (OR - 1.41, 95% CI; 1.03 - 4.68), low income (OR - 23.3, 95% CI; 1.5 - 34.0), impaired vibration sense (OR - 24.79, 95% CI; 9.3 - 66.2), abnormal monofilament test on first (OR - 1.69, 95% CI; 1.36 - 16.6), third (OR - 3.4, 95% CI; 1.1 - 10.6) and fifth (OR - 1.8, 95% CI; 1.61- 12.6) toes are found to be predictors of increased risk whereas incidental diagnosis of DM (OR - 0.03, 95% CI; 0.003 - 0.28), wearing covered shoes (OR - 0.003, 95% CI; 0.00 - 0.28), presence of normal skin color (OR - 0.01, 95% CI; 0.001 - 0.14) and normal monofilament test on first metatarsal head (OR - 0.10, 95% CI; 0.00 - 0.67) are protective factors for ulcers.

Conclusions

Ten independent risk and protective factors identified in this study are proposed as a simple screening tool to predict the risk of developing leg and foot ulcers in patients with DM.     

Technology outlook for real-time quality attribute and process parameter monitoring in biopharmaceutical development—A review

Real-time monitoring of bioprocesses by the integration of analytics at critical unit operations is one of the paramount necessities for quality by design manufacturing and real-time release (RTR) of biopharmaceuticals. A well-defined process analytical technology (PAT) roadmap enables the monitoring of critical process parameters and quality attributes at appropriate unit operations to develop an analytical paradigm that is capable of providing real-time data. We believe a comprehensive PAT roadmap should entail not only integration of analytical tools into the bioprocess but also should address automated-data piping, analysis, aggregation, visualization, and smart utility of data for advanced-data analytics such as machine and deep learning for holistic process understanding. In this review, we discuss a broad spectrum of PAT technologies spanning from vibrational spectroscopy, multivariate data analysis, multiattribute chromatography, mass spectrometry, sensors, and automated-sampling technologies. We also provide insights, based on our experience in clinical and commercial manufacturing, into data automation, data visualization, and smart utility of data for advanced-analytics in PAT. This review is catered for a broad audience, including those new to the field to those well versed in applying these technologies. The article is also intended to give some insight into the strategies we have undertaken to implement PAT tools in biologics process development with the vision of realizing RTR testing in biomanufacturing and to meet regulatory expectations.     

An adaptive modeling approach using spiking-augmentation method to improve chemometric model performance in bioprocess monitoring

Intensified and continuous processes require fast and robust methods and technologies to monitor product titer for faster analytical turnaround time, process monitoring, and process control. The current titer measurements are mostly offline chromatography-based methods which may take hours or even days to get the results back from the analytical labs. Thus, offline methods will not meet the requirement of real time titer measurements for continuous production and capture processes. FTIR and chemometric based multivariate modeling are promising tools for real time titer monitoring in clarified bulk (CB) harvests and perfusate lines. However, empirical models are known to be vulnerable to unseen variability, specifically a FTIR chemometric titer model trained on a given biological molecule and process conditions often fails to provide accurate predictions of titer in another molecule under different process conditions. In this study, we developed an adaptive modeling strategy: the model was initially built using a calibration set of available perfusate and CB samples and then updated by augmenting spiking samples of the new molecules to the calibration set to make the model robust against perfusate or CB harvest of the new molecule. This strategy substantially improved the model performance and significantly reduced the modeling effort for new molecules.     

Process analytics 4.0: A paradigm shift in rapid analytics for biologics development

Analytical testing of product quality attributes and process parameters during the biologics development (Process analytics) has been challenging due to the rapid growth of biomolecules with complex modalities to support unmet therapeutic needs. Thus, the expansion of the process analytics tool box for rapid analytics with the deployment of cutting-edge technologies and cyber-physical systems is a necessity. We introduce the term, Process Analytics 4.0; which entails not only technology aspects such as process analytical technology (PAT), assay automation, and high-throughput analytics, but also cyber-physical systems that enable data management, visualization, augmented reality, and internet of things (IoT) infrastructure for real time analytics in process development environment. This review is exclusively focused on dissecting high-level features of PAT, automation, and data management with some insights into the business aspects of implementing during process analytical testing in biologics process development. Significant technological and business advantages can be gained with the implementation of digitalization, automation, and real time testing. A systematic development and employment of PAT in process development workflows enable real time analytics for better process understanding, agility, and sustainability. Robotics and liquid handling workstations allow rapid assay and sample preparation automation to facilitate high-throughput testing of attributes and molecular properties which are otherwise challenging to monitor with PAT tools due to technological and business constraints. Cyber-physical systems for data management, visualization, and repository must be established as part of Process Analytics 4.0 framework. Furthermore, we review some of the challenges in implementing these technologies based on our expertise in process analytics for biopharmaceutical drug substance development.