Seongkyu Yoon

N-Glycosylation Design and Control of Therapeutic Monoclonal Antibodies

The N-linked glycan profiles on recombinant monoclonal antibody therapeutics significantly affect antibody biological functions and are largely determined by host cell genotypes and culture conditions. A key step in bioprocess development for monoclonal antibodies (mAbs) involves optimization and control of N-glycan profiles. With pressure from pricing and biosimilars looming, more efficient and effective approaches are sought in the field of glycoengineering. Metabolic studies and mathematical modeling are two such approaches that optimize bioprocesses by better understanding and predicting glycosylation. In this review, we summarize a group of strategies currently used for glycan profile modulation and control. Metabolic analysis and mathematical modeling are then explored with an emphasis on how these two techniques can be utilized to advance glycoengineering.

Estimation of raw material performance in mammalian cell culture using near infrared spectra combined with chemometrics approaches

Understanding variability in raw materials and their impacts on product quality is of critical importance in the biopharmaceutical manufacturing processes. For this purpose, several spectroscopic techniques have been studied for raw material characterization, providing fast and nondestructive ways to measure quality of raw materials. However, investigations of correlation between spectra of raw materials and cell culture performance have been scarce due to their complexity and uncertainty. In this study, near‐infrared spectra and bioassays of multiple soy hydrolysate lots manufactured by different vendors were analyzed using chemometrics approaches in order to address variability of raw materials as well as correlation between raw material properties and corresponding cell culture performance. Principal component analysis revealed that near‐infrared spectra of different soy lots contain enough physicochemical information about soy hydrolysates to allow identification of lot‐to‐lot variability as well as vendor‐to‐vendor differences. The identified compositional variability was further analyzed in order to estimate cell growth and protein production of two mammalian cell lines under the condition of varying soy dosages using partial least square regression combined with optimal variable selection. The performance of the resulting models demonstrates the potential of near‐infrared spectroscopy as a robust lot selection tool for raw materials while providing a biological link between chemical composition of raw materials and cell culture performance.

Data fusion‐based assessment of raw materials in mammalian cell culture

In mammalian cell culture producing therapeutic proteins, one of the important challenges is the use of several complex raw materials whose compositional variability is relatively high and their influences on cell culture is poorly understood. Under these circumstances, application of spectroscopic techniques combined with chemometrics can provide fast, simple, and non‐destructive ways to evaluate raw material quality, leading to more consistent cell culture performance. In this study, a comprehensive data fusion strategy of combining multiple spectroscopic techniques is investigated for the prediction of raw material quality in mammalian cell culture. To achieve this purpose, four different spectroscopic techniques of near‐infrared, Raman, 2D fluorescence, and X‐ray fluorescence spectra were employed for comprehensive characterization of soy hydrolysates which are commonly used as supplements in culture media. First, the different spectra were compared separately in terms of their prediction capability. Then, ensemble partial least squares (EPLS) was further employed by combining all of these spectral datasets in order to produce a more accurate estimation of raw material properties, and compared with other data fusion techniques. The results showed that data fusion models based on EPLS always exhibit best prediction accuracy among all the models including individual spectroscopic methods, demonstrating the synergetic effects of data fusion in characterizing the raw material quality. Biotechnol. Bioeng. 2012; 109: 2819–2828.

Quantification of protein mixture in chromatographic separation using multi-wavelength UV spectra

In therapeutic protein production, the protein purification with chromatographic processes is of high importance in separating the qualified proteins from the impurities for consistent product quality. Therefore, to aid real‐time monitoring of the protein purification processes, various kinds of methodologies have been proposed until now. However, the majority of them still rely on the use of a single ultraviolet (UV) absorbance or the utilization of expensive and time‐consuming instruments, thus requiring a simple, fast, and cost‐effective methodology for protein quantification. In this study, the feasibility of using multiwavelength UV spectroscopy was investigated as an alternative tool for the real‐time monitoring of the protein mixtures in protein purification. To this end, three different proteins were selected as a model system for the protein mixture, and the multivariate UV spectra were analyzed to construct the reliable quantification models for different proteins of interest. By using various chemometrics tools, such as partial least squares (PLS), the validity of estimating the protein concentration from the UV spectra of the mixture samples was rigorously analyzed with their prediction performance, and the results indicated that the multiwavelength UV spectra had sufficient sensitivity and accuracy to estimate the protein concentrations in mixture, demonstrating its usefulness for the rapid quantification of the protein mixtures in protein purification.

Reproducibility, complementary measure of predictability for robustness improvement of multivariate calibration models via variable selections.

In multivariate calibration with the spectral dataset, variable selection is often applied to identify relevant subset of variables, leading to improved prediction accuracy and easy interpretation of the selected fingerprint regions. Until now, numerous variable selection methods have been proposed, but a proper choice among them is not trivial. Furthermore, in many cases, a set of variables found by those methods might not be robust due to the irreproducibility and uncertainty issues, posing a great challenge in improving the reliability of the variable selection. In this study, the reproducibility of the 5 variable selection methods was investigated quantitatively for evaluating their performance. The reproducibility of variable selection was quantified by using Monte-Carlo sub-sampling (MCS) techniques together with the quantitative similarity measure designed for the highly collinear spectral dataset. The investigation of reproducibility and prediction accuracy of the several variable selection algorithms with two different near-infrared (NIR) datasets illustrated that the different variable selection methods exhibited wide variability in their performance, especially in their capabilities to identify the consistent subset of variables from the spectral datasets. Thus the thorough assessment of the reproducibility together with the predictive accuracy of the identified variables improved the statistical validity and confidence of the selection outcome, which cannot be addressed by the conventional evaluation schemes.

Free light chain content in culture media reflects recombinant monoclonal antibody productivity and quality

Monoclonal antibodies (mAbs) are currently the dominant class of biopharmaceuticals. Due to the high dosage requirements of most mAb therapeutics, high productivity and low aggregation are prevailing criteria during cell line generation and process development. Given that light chains (LCs) play an important role in antibody folding and assembly, and that most mAb producing cell lines also manufacture free LCs, we sought to investigate whether there was a relationship between free LC levels in cell culture media and mAb productivity/quality. To this end, a series of analytical methods were developed in order to quantify free LC content in cell culture media and assess mAb productivity and aggregation levels. Afterwards, conditioned media samples from different cell lines at identical culturing conditions and a single clone under varying culturing conditions were analyzed. Higher LC expression was found to correlate with higher cell viability, higher mAb productivity, and lower aggregation. While LC expression cannot yet be definitively considered the root cause of these phenomena, these results are consistent with the role of LCs in mAb production, suggesting that free LC expression levels may potentially serve as a parameter for cell line generation and cell culture process optimization.

MicroRNA-146a inhibits epithelial mesenchymal transition in non-small cell lung cancer by targeting insulin receptor substrate 2.

During cancer progression, some tumor cells show changes in their plasticity by morphological and phenotypical conversions, as an expression of mesenchymal markers and loss of epithelial markers, collectively referred to as epithelial-mesenchymal transition (EMT). EMT has been increasingly recognized as a critical phenomenon in lung cancer progression. The goal of this study was to identify microRNAs involved in lung cancer progression. A microarray and qRT-PCR were performed to investigate the miRNA expression profiles in mesenchymal-like lung cancer cells. The role of miR‑146a in lung cancer progression was measured by invasion and migration assays in vitro. Bioinformatics and luciferase report assays were used to identify the target of miR‑146a. The expression of miR‑146a was reduced in mesenchymal-like lung cancer cell lines. The overexpression of miR‑146a induced a marked reduction of the mesenchymal marker and increase the epithelial marker in lung cancer cell lines. Moreover, the overexpression of miR‑146a suppressed lung cancer cell migration and invasion. Co-treatment with miR‑146a and gefitinib treatment showed a significant reduction of invasion in the resistant lung cancer cells induced by EMT. The expression of miR‑146a was downregulated in advanced lung cancer tissues. Insulin receptor substrate 2 (IRS2), an adaptor protein that modulates normal growth, metabolism, survival, and differentiation, was identified as a target of miR‑146a. miR‑146a regulated the expression of IRS2 at the mRNA and protein levels. These data demonstrate for the first time that miR‑146a suppresses lung cancer progression by repressing IRS2 expression. This provides new insight into the post-transcriptional regulation of lung cancer progression by miRNAs, a potential approach for the treatment of lung cancer.

Optimization of critical quality attributes in continuous twin-screw wet granulation via design space validated with pilot scale experimental data

In this study, the influence of key process variables (screw speed, throughput and liquid to solid (L/S) ratio) of a continuous twin screw wet granulation (TSWG) was investigated using a central composite face-centered (CCF) experimental design method. Regression models were developed to predict the process responses (motor torque, granule residence time), granule properties (size distribution, volume average diameter, yield, relative width, flowability) and tablet properties (tensile strength). The effects of the three key process variables were analyzed via contour and interaction plots. The experimental results have demonstrated that all the process responses, granule properties and tablet properties are influenced by changing the screw speed, throughput and L/S ratio. The TSWG process was optimized to produce granules with specific volume average diameter of 150μm and the yield of 95% based on the developed regression models. A design space (DS) was built based on volume average granule diameter between 90 and 200μm and the granule yield larger than 75% with a failure probability analysis using Monte Carlo simulations. Validation experiments successfully validated the robustness and accuracy of the DS generated using the CCF experimental design in optimizing a continuous TSWG process.

Environmentally benign synthesis of vinyl ester resin from biowaste glycerin

We present here for the first time a novel environmentally benign protocol for the synthesis of vinyl ester resin (VER). Our synthetic strategy utilizes a commercial waste material, glycerin, from biodiesel manufacturing and converts it into a widely utilized resin. The VER was synthesized using bisphenol A (BPA) and glycidyl methacrylate (GMA) as precursors. GMA was synthesized via a multistep synthetic protocol using glycerin obtained from a biodiesel manufacturing waste stream. The structure of the intermediates was confirmed by 1H NMR, HPLC and FT-IR spectroscopy.

Robustness of chemometrics-based feature selection methods in early cancer detection and biomarker discovery

Abstract In omics studies aimed at the early detection and diagnosis of cancer, bioinformatics tools play a significant role when analyzing high dimensional, complex datasets, as well as when identifying a small set of biomarkers. However, in many cases, there are ambiguities in the robustness and the consistency of the discovered biomarker sets, since the feature selection methods often lead to irreproducible results. To address this, both the stability and the classification power of several chemometrics-based feature selection algorithms were evaluated using the Monte Carlo sampling technique, aiming at finding the most suitable feature selection methods for early cancer detection and biomarker discovery. To this end, two data sets were analyzed, which comprised of MALDI-TOF-MS and LC/TOF-MS spectra measured on serum samples in order to diagnose ovarian cancer. Using these datasets, the stability and the classification power of multiple feature subsets found by different feature selection methods were quantified by varying either the number of selected features, or the number of samples in the training set, with special emphasis placed on the property of stability. The results show that high consistency does not necessarily guarantee high predictive power. In addition, differences in the stability, as well as agreement in feature lists between several feature selection methods, depend on several factors, such as the number of available samples, feature sizes, quality of the information in the dataset, etc. Among the tested methods, only the variable importance in projection (VIP)-based method shows complementary properties, providing both highly consistent and accurate subsets of features. In addition, successive projection analysis (SPA) was excellent with regards to maintaining high stability over a wide range of experimental conditions. The stability of several feature selection methods is highly variable, stressing the importance of making the proper choice among feature selection methods. Therefore, rather than evaluating the selected features using only classification accuracy, stability measurements should be examined as well to improve the reliability of biomarker discovery.