2015
DOI: 10.1016/j.chroma.2015.09.038
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Use of MiniColumns for linear isotherm parameter estimation and prediction of benchtop column performance

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Cited by 41 publications
(35 citation statements)
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“…For example, the used system to perform a chromatographic step is usually customized engineered for the needs of the process and differs between the processes between pumps, piping and thus dead volumes, detectors, automation platforms, and data transfer rates are used, which is less true for laboratory equipment which is usually built in a standardized way. SDD in column chromatography for resin and process window determination are commonly miniaturized robotic columns of 0.2-1 ml bed volume 1,23,7 ; in addition, these SDDs can offer a first appreciation of dynamic effects. 20,24,26 However, fluid dynamics abbreviation as well as wall and mass transfer effects are known challenges between existing SDDs and lab-scale columns.…”
mentioning
confidence: 99%
“…For example, the used system to perform a chromatographic step is usually customized engineered for the needs of the process and differs between the processes between pumps, piping and thus dead volumes, detectors, automation platforms, and data transfer rates are used, which is less true for laboratory equipment which is usually built in a standardized way. SDD in column chromatography for resin and process window determination are commonly miniaturized robotic columns of 0.2-1 ml bed volume 1,23,7 ; in addition, these SDDs can offer a first appreciation of dynamic effects. 20,24,26 However, fluid dynamics abbreviation as well as wall and mass transfer effects are known challenges between existing SDDs and lab-scale columns.…”
mentioning
confidence: 99%
“…Although this approach was shown to quickly guide the selection of resins and conditions for experimental process development, we believe that this approach can be further improved to explicitly account for product yield as well as other important preparative issues such as column binding capacity and process robustness by combining state‐of‐the‐art HTS and column modelling techniques (Coffman et al, ; Keller, Evans, Ferreira, Robbins, & Cramer, ; Welsh et al, ; Welsh et al, ). Iteratively expanding the breadth and types of data underlying the algorithmic approach, as well as the classes of molecules and hosts characterized, may enable substantially reduced experimentation for designing and optimizing purification processes, and ultimately the time required to transition new products to the clinic.…”
Section: Discussionmentioning
confidence: 99%
“…The product characterization experiments could be made both faster and more informative through the use of high-throughput screening to determine resin binding conditions and capacities, to evaluate product elution conditions, and to estimate model parameters for in silico process optimization (Coffman et al, 2008;Keller et al, 2015;Welsh et al, 2014;Welsh et al, 2016). Furthermore, while the method described in the current work focused on processrelated impurities, we acknowledge that product-related variants must often be mitigated and purification processes may require inclusion of specific steps to address these challenges, which is beyond the scope of this work.…”
Section: Discussionmentioning
confidence: 99%
“…Although benchtop linear gradient screens were used in the current work, it would be straightforward to use high-throughput systems to rapidly determine this bioproduct gradient elution data (Coffman, Kramarczyk, & Kelley, 2008;Keller, Evans, Ferreira, Robbins, & Cramer, 2015;Welsh et al, 2014;Welsh et al, 2016). The databases of product retention times for both biologics were then exported for use in the in silico tool.…”
Section: Chromatographic Screening Of Product Materialsmentioning
confidence: 99%
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