Droplet-Based Evaporative System for the Estimation of Protein Crystallization Kinetics

Abstract

Crystallization is a potential cost-effective alternative to chromatography for the purification of biotherapeutic proteins. Crystallization kinetics are required for the design and control of such processes, but only a limited quantity of proteins is available during the initial stage of process development. This article describes the design of a droplet-based evaporative system for the evaluation of candidate crystallization conditions and the estimation of kinetics using only a droplet (on the order of μL) of protein solution. The temperature and humidity of air fed to a flow cell containing the droplet are controlled for evaporation and rehydration of the droplet, which are used for manipulating supersaturation. Dual-angle images of the droplet are taken and analyzed on-line to obtain the droplet volume and crystal sizes. Crystallization kinetics are estimated based on a first-principles process model and experimental data. Tight control of temperature and humidity of the air, fast and accurate image analysis, and accurate estimation of crystallization kinetics are experimentally demonstrated for a model protein lysozyme. The estimated kinetics are suitable for the model-based design and control of protein crystallization processes.

Digital Design of an Agrochemical Crystallization Process via Two-Dimensional Population Balance Modeling

“…10,11 There are numerous other novel ways to improve the data quantity and quality methods in the model building process such as the development of a droplet-based evaporation crystallization system or utilization of a continuous in situ dynamic light scattering measurements to capture and model the growth kinetics of protein crystals. 12,13 Despite the improvement in data scarcity with the practice of incorporating in situ measurement, smart model-based experimental design (MBED) is needed to minimize experimental time while ensuring the trained model is predictive and accurate. Each experiment can be designed to isolate or ensure dominance of a mechanism to improve the accuracy and confidence level in the kinetic parameters.…”

“…To better describe high-aspect-ratio particles or describe new particle features, image-based measurements can be used in the parameter estimation process to describe not only multidimensional growth and dissolution kinetics but also the morphology and shape of the crystals . Image analysis methods and machine learning can generate quantitative trends from video microscopy images to aid in the parameter estimation of a morphological population balance model. , There are numerous other novel ways to improve the data quantity and quality methods in the model building process such as the development of a droplet-based evaporation crystallization system or utilization of a continuous in situ dynamic light scattering measurements to capture and model the growth kinetics of protein crystals. , …”

Strategy for Patterning Titania Dendrites by Gas–Solution Interaction at Droplet Surfaces

“…Studying the processes that occur in a drop and on its surface is necessary to understand the physicochemical features of self-organization of matter and to use them to create new materials. Recent works have focused on the investigation of colloidal droplet evaporation on a solid substrate and on the resulting formation of ordered patterns. , Evaporation-induced crystallization is observed with decreasing volume of a single or multicomponent solution droplet on the surface of a solid substrate. , The effect of surfactant on evaporation in a droplet is being studied . The presence of a temperature gradient in a drop contributes to the occurrence of complicated, even fractal-like, deposit patterns .…”

“…2,3 Evaporationinduced crystallization is observed with decreasing volume of a single or multicomponent solution droplet on the surface of a solid substrate. 4,5 The effect of surfactant on evaporation in a droplet is being studied. 6 The presence of a temperature gradient in a drop contributes to the occurrence of complicated, even fractal-like, deposit patterns.…”

Teaching Process Data Analytics and Machine Learning at MIT

“…[9] Applications of real-time imaging include controlling the amount of seasoning with images of snack foods, [10] realtime process control of flexible systems, [11] and measuring the crystal size distribution. [12] Teaching chemical engineers to be effective using data analytics is as important today as in any time in the history of the discipline, and advances in sensor technologies, machine learning, and associated software have enabled applications that were not formerly possible. [1][2][3][4] This article describes experiences with teaching process data analytics and machine learning over the last few years, including in (1) a new chemical engineering elective course in which the students come from chemical engineering, mechanical engineering, and engineering management, and (2) an undergraduate chemical engineering concentration in process data analytics with courses coming from a mixture of multiple disciplines including chemical engineering, computer science, economics, and management.…”