Mechanistic model of <scp>pH</scp> and excipient concentration during ultrafiltration and diafiltration processes of therapeutic antibodies

Ladwig, Justin E.; Zhu, Xiaoxiang; Rolandi, Pablo; Hart, Roger A.; Robinson, John W.; Rydholm, Amber E.

doi:10.1002/btpr.2993

Cited by 14 publications

(14 citation statements)

References 31 publications

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“…Equivalence between a process model and its respective full‐scale unit operation is the fundamental requirement for applying the model to process characterization studies in late stage DSP development. The TOST is currently the state of the art equivalence testing approach for SDM qualification 15,23 . In Figure 5, the in silico model was qualified against manufacturing‐scale data using the TOST qualification scheme described in section 3.2.…”

Section: Resultsmentioning

confidence: 99%

“…In contrast, mechanistic model simulations considered the variability in the input material, loading density and mobile phase properties for each of the chromatographic cycles. Experimental SDM and mechanistic model were qualified against large‐scale data using the identical equivalence testing procedure 15,23 Step 1‐ Calculation of the equivalence acceptance criteria (EAC) based on the sample mean ± 3 standard deviations ( SD ) of historical large‐scale runs. Step 2‐ Equivalence test: A TOST was performed according to Schuirmann 48 using Python 3.8.2.…”

Section: Methodsmentioning

confidence: 99%

“…The TOST is currently the state of the art equivalence testing approach for SDM qualification. 15,23 In Figure 5, the in silico model was qualified against manufacturing-scale data using the TOST qualification scheme This work presents a systematic framework for qualification of mechanistic chromatography models prior to their applications to late stage biopharmaceutical process development. Rules provided allow a more objective and gradual decision-making.…”

Section: Comparison Of Mechanistic Model With Experimental Sdmmentioning

confidence: 99%

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Cross‐scale quality assessment of a mechanistic cation exchange chromatography model

Saleh

Wang

Mueller

et al. 2020

Biotechnology Progress

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Cation exchange chromatography (CEX) is an essential part of most monoclonal antibody (mAb) purification platforms. Process characterization and root cause investigation of chromatographic unit operations are performed using scale down models (SDM). SDM chromatography columns typically have the identical bed height as the respective manufacturing-scale, but a significantly reduced inner diameter. While SDMs enable process development demanding less material and time, their comparability to manufacturing-scale can be affected by variability in feed composition, mobile phase and resin properties, or dispersion effects depending on the chromatography system at hand. Mechanistic models can help to close gaps between scales and reduce experimental efforts compared to experimental SDM applications. In this study, a multicomponent steric mass-action (SMA) adsorption model was applied to the scale-up of a CEX polishing step. Based on chromatograms and elution pool data ranging from laboratory-to manufacturing-scale, the proposed modeling workflow enabled early identification of differences between scales, for example, system dispersion effects or ionic capacity variability. A multistage model qualification approach was introduced to measure the model quality and to understand the model's limitations across scales. The experimental SDM and the in silico model were qualified against large-scale data using the identical state of the art equivalence testing procedure. The mechanistic chromatography model avoided limitations of the SDM by capturing effects of bed height, loading density, feed composition, and mobile phase properties. The results demonstrate the applicability of mechanistic chromatography models as a possible alternative to conventional SDM approaches.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Comparison Of Mechanistic Model With Experimental Sdmmentioning

confidence: 99%

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Cross‐scale quality assessment of a mechanistic cation exchange chromatography model

Saleh

Wang

Mueller

et al. 2020

Biotechnology Progress

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“…Process and product‐related impurities are removed during downstream purification followed by the ultrafiltration and diafiltration (UF/DF) step to generate the drug substance. During a UF/DF operation, diafiltration (DF) is used to buffer exchange the protein from its initial composition into the final formulation composition and pH, while ultrafiltration (UF) is used to concentrate the solution to the desired protein concentration (Ladwig, Zhu, Rolandi, Hart, & Rydholm, 2020; Stoner et al, 2004). The design of UF/DF operations to achieve the target final formulation conditions often presents unique challenges at high concentrations as proteins and excipients tend to interact significantly to cause exclusion effects (Bolton et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

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

Wasalathanthri

Feroz

Puri

et al. 2020

Biotech & Bioengineering

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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 midinfrared (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.

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“…More recently, dynamic mathematical UF/DF models based on Nernst-Planck and Poisson-Boltzmann equations were developed (Hebbi et al, 2020;Ladwig et al, 2020). Hebbi et al (2020) focused on the interaction between mAb and monovalent ions on a membrane, by assuming constant feed tank formulation, while Ladwig et al (2020) focused on the simulation of pH and excipient conditions in retentate pools. Although all later studies showed promising industrial potential, they do not consider the effect of pH and salt concentration on a protein net charge.…”

Section: Introductionmentioning

confidence: 99%

Designing scalable ultrafiltration/diafiltration process of monoclonal antibodies via mathematical modeling by coupling mass balances and Poisson–Boltzmann equation

Ambrožič

Arzenšek²,

Podgornik

2020

Biotech & Bioengineering

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Ultrafiltration/diafiltration (UF/DF) operations are employed for achieving the desired therapeutic monoclonal antibody (mAb) formulations. Due to electrostatic interactions between the charged proteins, solute ions, and uncharged excipients, the final pH and concentration values are not always equal to those in the DF buffer. At high protein concentrations, typical for industrial formulations, this effect becomes predominant. To account for challenges occurring in industrial environments, a robust mathematical framework enabling the prediction of pH and concentration profiles throughout the UF/DF process is provided. The proposed mechanistic model combines a macroscopic mass balance approach with a molecular approach based on a Poisson–Boltzmann equation dealing with electrostatic interactions and accounting for protein exclusion volume effect. The mathematical model was validated with experimental data of two commercially relevant mAbs obtained from an industrial UF/DF process using scalable laboratory equipment. The robustness and flexibility of the model were tested by using proteins with different isoelectric points and net charges. The latter was determined via a titration curve, enabling realistic protein charge‐pH evaluation. In addition, the model was tested for different DF buffer types containing both monovalent and polyvalent ions, with various types of uncharged excipients. The model generality enables its implementation for the UF/DF processes of other protein varieties.

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Mechanistic model of pH and excipient concentration during ultrafiltration and diafiltration processes of therapeutic antibodies

Abstract: Ultrafiltration and diafiltration (UF/DF) unit operations are widely used for the manufacture of therapeutic antibodies to control drug substance protein concentration, pH, and excipient properties. During UF/DF, molecular interactions and volume

Cited by 14 publications

References 31 publications

Cross‐scale quality assessment of a mechanistic cation exchange chromatography model

Cross‐scale quality assessment of a mechanistic cation exchange chromatography model

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

Designing scalable ultrafiltration/diafiltration process of monoclonal antibodies via mathematical modeling by coupling mass balances and Poisson–Boltzmann equation

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