Thermodynamic parameters for salt-induced reversible protein precipitation from automated microscale experiments

High throughput solubility and redissolution screening for antibody purification via combined PEG and zinc chloride precipitation

As upstream product titers increase, the downstream chromatographic capture step has become a significant "downstream bottleneck." Precipitation becomes more attractive under these conditions as the supersaturation driving force increases with the ever-increasing titer. In this study, two precipitating reagents with orthogonal mechanisms, polyethylene glycol (PEG) as a volume excluder and zinc chloride (ZnCl₂ ) as a cross linker, were examined as precipitants for two monoclonal antibodies (mAbs), one stable and the other aggregation-prone, in purified drug substance and harvested cell culture fluid forms. Manual batch solubility and redissolution experiments were performed as scouting experiments. A high throughput (HTP) liquid handling system was used to investigate the design space as fully as possible while reducing time, labor, and material requirements. Precipitation and redissolution were studied by systematically varying the concentrations of PEG and ZnCl₂ to identify combinations that resulted in high yield and good quality for the stable mAb; PEG concentrations in the range 7-7.5 wt/vol% together with 10 mM ZnCl₂ gave a yield of 97% and monomer contents of about 93%. While yield for the unstable mAb was high, quality was not acceptable. Performance at selected conditions was further corroborated for the stable mAb using a continuous tubular precipitation reactor at the laboratory scale. The HTP automation system was a powerful tool for locating desired (customized) conditions for antibodies of different physicochemical properties.

High throughput screening of particle conditioning operations: I. System design and method development

The biotech industry is under increasing pressure to decrease both time to market and development costs. Simultaneously, regulators are expecting increased process understanding. High throughput process development (HTPD) employs small volumes, parallel processing, and high throughput analytics to reduce development costs and speed the development of novel therapeutics. As such, HTPD is increasingly viewed as integral to improving developmental productivity and deepening process understanding. Particle conditioning steps such as precipitation and flocculation may be used to aid the recovery and purification of biological products. In this first part of two articles, we describe an ultra scale-down system (USD) for high throughput particle conditioning (HTPC) composed of off-the-shelf components. The apparatus is comprised of a temperature-controlled microplate with magnetically driven stirrers and integrated with a Tecan liquid handling robot. With this system, 96 individual reaction conditions can be evaluated in parallel, including downstream centrifugal clarification. A comprehensive suite of high throughput analytics enables measurement of product titer, product quality, impurity clearance, clarification efficiency, and particle characterization. HTPC at the 1 mL scale was evaluated with fermentation broth containing a vaccine polysaccharide. The response profile was compared with the Pilot-scale performance of a non-geometrically similar, 3 L reactor. An engineering characterization of the reactors and scale-up context examines theoretical considerations for comparing this USD system with larger scale stirred reactors. In the second paper, we will explore application of this system to industrially relevant vaccines and test different scale-up heuristics.

Discrimination of soluble and aggregation-prone proteins based on sequence information

Understanding the factors governing protein solubility is a key to grasp the mechanisms of protein solubility and may provide insight into protein aggregation and misfolding related diseases such as Alzheimer's disease. In this work, we attempt to identify factors important to protein solubility using feature selection. Firstly, we calculate 1438 features including physicochemical properties and statistics for each protein. Random Forest algorithm is used to select the most informative and the minimal subset of features based on their predictive performance. A predictive model is built based on 17 selected features. Compared with previous models, our model achieves better performance with a sensitivity of 0.82, specificity 0.85, ACC 0.84, AUC 0.91 and MCC 0.67. Furthermore, a model using a redundancy-reduced dataset (sequence identity <= 30%) achieves the same performance as the model without redundancy reduction. Our results provide not only a reliable model for predicting protein solubility but also a list of features important to protein solubility. The predictive model is implemented as a freely available web application at .