Semi-automation of process analytics reduces operator effect

Advances and opportunities in process analytical technologies for viral vector manufacturing

Viral vectors are an emerging, exciting class of biologics whose application in vaccines, oncology, and gene therapy has grown exponentially in recent years. Following first regulatory approval, this class of therapeutics has been vigorously pursued to treat monogenic disorders including orphan diseases, entering hundreds of new products into pipelines. Viral vector manufacturing supporting clinical efforts has spurred the introduction of a broad swath of analytical techniques dedicated to assessing the diverse and evolving panel of Critical Quality Attributes (CQAs) of these products. Herein, we provide an overview of the current state of analytics enabling measurement of CQAs such as capsid and vector identities, product titer, transduction efficiency, impurity clearance etc. We highlight orthogonal methods and discuss the advantages and limitations of these techniques while evaluating their adaptation as process analytical technologies. Finally, we identify gaps and propose opportunities in enabling existing technologies for real-time monitoring from hardware, software, and data analysis viewpoints for technology development within viral vector biomanufacturing.

Automated ELISA for potency measurements of therapeutic antibodies and antibody fragments

Potency assays are essential for the development and quality control of biopharmaceutical drugs, but they are often a time limiting factor due to manual handling steps and consequently low analytical throughput. On the other hand, automation of potency assays can be challenging due to their complexity and the use of biological materials. ELISA (enzyme-linked immunosorbent assay) is widely used for potency determination and is a good candidate for automation as all ELISA types depend on the same basic steps: coating, blocking, sample incubation, detection, and signal measurement. Nevertheless, ELISA for relative potency measurements still require drug-specific development and assay validation thereby complicating automation efforts. To simplify potency testing by ELISA, we first developed a manual protocol generally applicable to different drugs and then adapted this protocol for automated measurements. We identified unexpected critical parameters which had to be adapted to transfer the manual ELISA to an automated liquid handling system and we demonstrated that gravimetric sample dilution is unnecessary with the automated protocol. Both manual and automated protocols were validated and compared using multiple biotherapeutics. The automated protocol showed similar or higher precision and accuracy when compared to the manual method.

Online solid phase extraction high-performance liquid chromatography - Isotope dilution - Tandem mass spectrometry quantification of organophosphate pesticides, synthetic pyrethroids, and selected herbicide metabolites in human urine

Analytical methods to quantify pesticide biomarkers in human population studies are critical for exposure assessment given the widespread use of pesticides for pest and weed control and their potential for affecting human health. We developed a method to quantify, in 0.2 mL of urine, concentrations of 10 pesticide biomarkers: four organophosphate insecticide metabolites (3,5,6-trichloro-2-pyridinol (TCPy), 2-isopropyl-6-methyl-4-pyrimidinol, para-nitrophenol, malathion dicarboxylic acid); five synthetic pyrethroid insecticide metabolites (4-fluoro-3-phenoxybenzoic acid, 3-phenoxybenzoic acid, cis and trans-3-(2,2-dichlorovinyl)-2,2-dimethylcyclopropane carboxylic acid (DCCA), cis-3-(2,2-dibromovinyl)-2,2-dimethylcyclopropane-1-carboxylic acid); and the herbicide 2,4-dichlorophenoxyacetic acid. he method is based on enzymatic hydrolysis of conjugated urinary metabolites, extraction and pre-concentration of the deconjugated metabolites using automated online solid-phase extraction, and separation and quantification using liquid chromatography-isotope dilution tandem mass spectrometry. Depending on the analyte, method detection limits were 0.1-0.6 ng/mL; mean accuracy, calculated as spike recoveries, was 91-102%, and total precision, given as percent variation coefficient, was 5.9-11.5%. Percent differences associated with three freeze-thaw cycles, 24-h benchtop storage, and short-term processed sample stability were <14%. Method suitability was assessed by recurring successful participation in external quality assessment schemes and by analyzing samples from subjects with suspected exposure to pesticides (n = 40) or who self-reported consuming an organic diet (n = 50). Interquartile ranges were considerably lower for people consuming an organic diet than for those potentially exposed for cis-DCCA (0.37 ng/mL vs 0.75 ng/mL), trans-DCCA (0.88 ng/mL vs 1.78 ng/mL) and TCPy (1.81 ng/mL vs 2.48 ng/mL). This method requires one-fifth of the sample used in our previous method and is suitable for assessing background exposures to select pesticides in large human populations and for studies with limited sample volumes.

Design of millidevices to expedite apparent solubility measurements

A fast, automated and accurate millidevice for determination of the apparent solubility of proteins and impurities and different industrially relevant precipitating agents.

Machine learning for coronary artery calcification detection and labeling using only native computer tomography

In recent decades, the World Health Organization has found an increase in the death rate due to cardiovascular disease. Calcifications of the coronary arteries are the main sign of any cardiovascular event. Each individual's calcium score helps estimate the severity of the disease. However, the score for each artery is more significant. This study aims to research the segmentation, the labeling, and then the complete and partial quantification of calcium using only native coronary computed tomography with the help of machine-learning algorithms. Our semi-automatic system limited the region of interest by applying a defined preprocessing step. We then implemented two random forest classifiers; the first separated true coronary artery calcification (CAC) from the noise, and the second labeled CAC into the right coronary artery, left coronary artery, left anterior descending artery, and left circumflex artery using specific features. Agatston score and volume score of each CAC, each artery, and all of the arteries were calculated. This method gave promising results, comparable to those found in the literature, with the accuracy of 99.98% and 100% for CAC detection and labeling respectively.

Technology transfer of a monitoring system to predict product concentration and purity of biopharmaceuticals in real-time during chromatographic separation

Technological developments require the transfer to their location of application to make use of them. We describe the transfer of a real-time monitoring system for lab-scale preparative chromatography to two new sites where it will be used and developed further. Equivalent equipment was used. The capture of a biopharmaceutical model protein, human fibroblast growth factor 2 (FGF-2) was used to evaluate the system transfer. Predictive models for five quality attributes based on partial least squares regression were transferred. Six out of seven online sensors (UV/VIS, pH, conductivity, IR, RI, and MALS) showed comparable signals between the sites while one sensor (fluorescence) showed different signal profiles. A direct transfer of the models for real-time monitoring was not possible, mainly due to differences in sensor signals. Adaptation of the models was necessary. Then, among five prediction models, the prediction errors of the test run at the new sites were on average twice as high as at the training site (model-wise 0.9-5.7 times). Additionally, new prediction models for different products were trained at each new site. These allowed monitoring the critical quality attributes of two new biopharmaceutical products during their purification processes with mean relative deviations between 1% and 33%.

PAT for Continuous Chromatography Integrated into Continuous Manufacturing of Biologics towards Autonomous Operation

This study proposes a reliable inline PAT concept for the simultaneous monitoring of different product components after chromatography. The feed for purification consisted of four main components, IgG monomer, dimer, and two lower molecular weight components of 4.4 kDa and 1 kDa molecular weight. The proposed measurement setup consists of a UV–VIS diode-array detector and a fluorescence detector. Applying this system, a R2 of 0.93 for the target component, a R2 of 0.67 for the dimer, a R2 of 0.91 for the first side component and a R2 of 0.93 for the second side component is achieved. Root mean square error for IgG monomer was 0.027 g/L, for dimer 0.0047 g/L, for side component 1 0.016 g/L and for the side component 2 0.014 g/L. The proposed measurement concept tracked component concentration reliably down to 0.05 g/L. Zero-point fluctuations were kept within a standard deviation of 0.018 g/L for samples with no IgG concentration but with side components present, allowing a reliable detection of the target component. The main reason inline concentration measurements have not been established yet, is the false-positive measurement of target components when side components are present. This problem was eliminated using the combination of fluorescence and UV–VIS data for the test system. The use of this measurement system is simulated for the test system, allowing an automatic fraction cut at 0.05 g/L. In this simulation a consistent yield of >99% was achieved. Process disturbances for processed feed volume, feed purity and feed IgG concentration can be compensated with this setup. Compared to a timed process control, yield can be increased by up to 12.5%, if unexpected process disturbances occur.

Increased of the carotid intima media thickness in preeclampsia

Objectives The aim of the study was to determine carotid intima-media thickness (CIMT) values in patients who developed and did not develop preeclampsia (PE), and to determine whether CIMT values could be predictors of PE development. Methods The study included pregnant women who were examined by regular ultrasound examination at the Materno-Infantil Presidente Vargas Hospital (HMIPV) in Porto Alegre, Brazil, from April 2016 to September 2017. The examinations were performed every three months. Patients were divided into two groups. The first group included patients diagnosed with PE (n=21) and second group included patients who did not have PE (n=199). A high frequency ultrasound device (12 MHz) with a semi-automatic method was used to estimate CIMT. Results CIMT was significantly higher in pregnant women with PE than in women without PE (55±0.11 vs. 0.44±0.06, respectively; p<0.001). Using a cut-off value of 0.51 mm, CIMT had a specificity of 77.9% and sensitivity of 81% in the diagnosis of PE. With CIMT ≥0.6 mm, the probability of a patient developing PE was 44.4%; with CIMT >0.42 mm, the probability was only 4.2%. Conclusions An increase in CIMT was associated with the onset of PE. CIMT values were significantly higher in patients who develop PE.