Simultaneous prediction of 16 quality attributes during protein A chromatography using machine learning based Raman spectroscopy models

Several key technologies for advancing biopharmaceutical manufacturing depend on the successful implementation of process analytical technologies that can monitor multiple product quality attributes in a continuous in-line setting. Raman spectroscopy is an emerging technology in the biopharma industry that promises to fit this strategic need, yet its application is not widespread due to limited success for predicting a meaningful number of quality attributes. In this study, we addressed this very problem by demonstrating new capabilities for preprocessing Raman spectra using a series of Butterworth filters. The resulting increase in the number of spectral features is paired with a machine learning algorithm and laboratory automation hardware to drive the automated collection and training of a calibration model that allows for the prediction of 16 different product quality attributes in an in-line mode. The demonstrated ability to generate these Raman-based models for in-process product quality monitoring is the breakthrough to increase process understanding by delivering product quality data in a continuous manner. The implementation of this multiattribute in-line technology will create new workflows within process development, characterization, validation, and control.

Application of Raman spectroscopy during pharmaceutical process development for determination of critical quality attributes in Protein A chromatography

Raman spectroscopy is considered a Process Analytical Technology (PAT) tool in biopharmaceutical downstream processes. In the past decade, researchers have shown Raman spectroscopy's feasibility in determining Critical Quality Attributes (CQAs) in bioprocessing. This study verifies the feasibility of implementing a Raman-based PAT tool in Protein A chromatography as a CQA monitoring technique, for the purpose of accelerating process development and achieving real-time release in manufacturing. A system connecting Raman to a Tecan liquid handling station enables high-throughput model calibration. One calibration experiment collects Raman spectra of 183 samples with 8 CQAs within 25 h. After applying Butterworth high-pass filters and k-nearest neighbor (KNN) regression for model training, the model showed high predictive accuracy for fragments (Q2 = 0.965) and strong predictability for target protein concentration, aggregates, as well as charge variants (Q2≥ 0.922). The model's robustness was confirmed by varying the elution pH, load density, and residence time using 19 external validation preparative Protein A chromatography runs. The model can deliver elution profiles of multiple CQAs within a set point ± 0.3 pH range. The CQA readouts were presented as continuous chromatograms with a resolution of every 28 s for enhanced process understanding. In external validation datasets, the model maintained strong predictability especially for target protein concentration (Q2 = 0.956) and basic charge variants (Q2 = 0.943), except for overpredicted HCP (Q2 = 0.539). This study demonstrates a rapid, effective method for implementing Raman spectroscopy for in-line CQA monitoring in process development and biomanufacturing, eliminating the need for labor-intensive sample pooling and handling.

Automated calibration and in-line measurement of product quality during therapeutic monoclonal antibody purification using Raman spectroscopy

Current manufacturing and development processes for therapeutic monoclonal antibodies demand increasing volumes of analytical testing for both real-time process controls and high-throughput process development. The feasibility of using Raman spectroscopy as an in-line product quality measuring tool has been recently demonstrated and promises to relieve this analytical bottleneck. Here, we resolve time-consuming calibration process that requires fractionation and preparative experiments covering variations of product quality attributes (PQAs) by engineering an automation system capable of collecting Raman spectra on the order of hundreds of calibration points from two to three stock seed solutions differing in protein concentration and aggregate level using controlled mixing. We used this automated system to calibrate multi-PQA models that accurately measured product concentration and aggregation every 9.3 s using an in-line flow-cell. We demonstrate the application of a nonlinear calibration model for monitoring product quality in real-time during a biopharmaceutical purification process intended for clinical and commercial manufacturing. These results demonstrate potential feasibility to implement quality monitoring during GGMP manufacturing as well as to increase chemistry, manufacturing, and controls understanding during process development, ultimately leading to more robust and controlled manufacturing processes.

A multiscale modeling method for therapeutic antibodies in ion exchange chromatography

The development of biopharmaceutical downstream processes relies on exhaustive experimental studies. The root cause is the poorly understood relationship between the protein structure of monoclonal antibodies (mAbs) and their macroscopic process behavior. Especially the development of preparative chromatography processes is challenged by the increasing structural complexity of novel antibody formats and accelerated development timelines. This study introduces a multiscale in silico model consisting of homology modeling, quantitative structure-property relationships (QSPR), and mechanistic chromatography modeling leading from the amino acid sequence of a mAb to the digital representation of its cation exchange chromatography (CEX) process. The model leverages the mAbs' structural characteristics and experimental data of a diverse set of 21 therapeutic antibodies to predict elution profiles of two mAbs that were removed from the training data set. QSPR modeling identified mAb-specific protein descriptors relevant for the prediction of the thermodynamic equilibrium and the stoichiometric coefficient of the adsorption reaction. The consideration of two discrete conformational states of IgG4 mAbs enabled prediction of split-peak elution profiles. Starting from the sequence, the presented multiscale model allows in silico development of chromatography processes before protein material is available for experimental studies.

In-line product quality monitoring during biopharmaceutical manufacturing using computational Raman spectroscopy

The implementation of process analytical technologies is positioned to play a critical role in advancing biopharmaceutical manufacturing by simultaneously resolving clinical, regulatory, and cost challenges. Raman spectroscopy is emerging as a key technology enabling in-line product quality monitoring, but laborious calibration and computational modeling efforts limit the widespread application of this promising technology. In this study, we demonstrate new capabilities for measuring product aggregation and fragmentation in real-time during a bioprocess intended for clinical manufacturing by applying hardware automation and machine learning data analysis methods. We reduced the effort needed to calibrate and validate multiple critical quality attribute models by integrating existing workflows into one robotic system. The increased data throughput resulting from this system allowed us to train calibration models that demonstrate accurate product quality measurements every 38 s. In-process analytics enable advanced process understanding in the short-term and will lead ultimately to controlled bioprocesses that can both safeguard and take necessary actions that guarantee consistent product quality.

Specification-driven acceptance criteria for validation of biopharmaceutical processes

Intermediate acceptance criteria are the foundation for developing control strategies in process validation stage 1 in the pharmaceutical industry. At drug substance or product level such intermediate acceptance criteria for quality are available and referred to as specification limits. However, it often remains a challenge to define acceptance criteria for intermediate process steps. Available guidelines underpin the importance of intermediate acceptance criteria, because they are an integral part for setting up a control strategy for the manufacturing process. The guidelines recommend to base the definition of acceptance criteria on the entirety of process knowledge. Nevertheless, the guidelines remain unclear on how to derive such limits. Within this contribution we aim to present a sound data science methodology for the definition of intermediate acceptance criteria by putting the guidelines recommendations into practice (ICH Q6B, 1999). By using an integrated process model approach, we leverage manufacturing data and experimental data from small scale to derive intermediate acceptance criteria. The novelty of this approach is that the acceptance criteria are based on pre-defined out-of-specification probabilities, while also considering manufacturing variability in process parameters. In a case study we compare this methodology to a conventional +/- 3 standard deviations (3SD) approach and demonstrate that the presented methodology is superior to conventional approaches and provides a solid line of reasoning for justifying them in audits and regulatory submission.

In silico process characterization for biopharmaceutical development following the quality by design concept

With the quality by design (QbD) initiative, regulatory authorities demand a consistent drug quality originating from a well-understood manufacturing process. This study demonstrates the application of a previously published mechanistic chromatography model to the in silico process characterization (PCS) of a monoclonal antibody polishing step. The proposed modeling workflow covered the main tasks of traditional PCS studies following the QbD principles, including criticality assessment of 11 process parameters and establishment of their proven acceptable ranges of operation. Analyzing effects of multi-variate sampling of process parameters on the purification outcome allowed identification of the edge-of-failure. Experimental validation of in silico results demanded approximately 75% less experiments compared to a purely wet-lab based PCS study. Stochastic simulation, considering the measured variances of process parameters and loading material composition, was used to estimate the capability of the process to meet the acceptance criteria for critical quality attributes and key performance indicators. The proposed workflow enables the implementation of digital process twins as QbD tool for improved development of biopharmaceutical manufacturing processes.

Analysis of complex protein elution behavior in preparative ion exchange processes using a colloidal particle adsorption model

A fundamental understanding of the protein retention mechanism in preparative ion exchange (IEX) chromatography columns is essential for a model-based process development approach. For the past three decades, the mechanistic description of protein retention has been based predominantly on the steric mass action (SMA) model. In recent years, however, retention profiles of proteins have been reported more frequently for preparative processes that are not consistent with the mechanistic understanding relying on the SMA model. In this work, complex elution behavior of proteins in preparative IEX processes is analyzed using a colloidal particle adsorption (CPA) model. The CPA model is found to be capable of reproducing elution profiles that cannot be described by the traditional SMA model. According to the CPA model, the reported complex behavior can be ascribed to a strong compression and concentration of the elution front in the lower unsaturated part of the chromatography column. As the unsaturated part of the column decreases with increasing protein load density, exceeding a critical load density can lead to the formation of a shoulder in the peak front. The general applicability of the model in describing preparative IEX processes is demonstrated using several industrial case studies including multiple monoclonal antibodies on different IEX adsorber systems. In this context, the work covers both salt controlled and pH-controlled protein elution.

Modeling the impact of amino acid substitution in a monoclonal antibody on cation exchange chromatography

A vital part of biopharmaceutical research is decision making around which lead candidate should be progressed in early-phase development. When multiple antibody candidates show similar biological activity, developability aspects are taken into account to ease the challenges of manufacturing the potential drug candidate. While current strategies for developability assessment mainly focus on drug product stability, only limited information is available on how antibody candidates with minimal differences in their primary structure behave during downstream processing. With increasing time-to-market pressure and an abundance of monoclonal antibodies (mAbs) in development pipelines, developability assessments should also consider the ability of mAbs to integrate into the downstream platform. This study investigates the influence of amino acid substitutions in the complementarity-determining region (CDR) of a full-length IgG1 mAb on the elution behavior in preparative cation exchange chromatography. Single amino acid substitutions within the investigated mAb resulted in an additional positive charge in the light chain (L) and heavy chain (H) CDR, respectively. The mAb variants showed an increased retention volume in linear gradient elution compared with the wild-type antibody. Furthermore, the substitution of tryptophan with lysine in the H-CDR3 increased charge heterogeneity of the product. A multiscale in silico analysis, consisting of homology modeling, protein surface analysis, and mechanistic chromatography modeling increased understanding of the adsorption mechanism. The results reveal the potential effects of lead optimization during antibody drug discovery on downstream processing.

Introduction and clearance of beta-glucan in the downstream processing of monoclonal antibodies

β‐Glucan process‐related impurities can be introduced into biopharmaceutical products via upstream or downstream processing or via excipients. This study obtained a comprehensive process‐mapping dataset for five monoclonal antibodies to assess β‐glucan introduction and clearance during development and production runs at various scales. Overall, 198 data points were available for analysis. The greatest β‐glucan concentrations were found in the depth‐filtration filtrate (37–2,745 pg/ml). Load volume correlated with β‐glucan concentration in the filtrate, whereas flush volume was of secondary importance. Cation‐exchange chromatography significantly cleared β‐glucans. Furthermore, β‐glucan leaching from the Planova 20N virus removal filter was reduced by increasing the flush volume (1 vs. 10 L/m²). β‐glucan concentrations after filter flush with 10 L/m² were consistently <10 pg/ml. No or only limited β‐glucan clearance was attained via ultrafiltration/diafiltration (UF/DF). However, during the first run with monoclonal antibody (mAb) 4, β‐glucan concentration in the UF/DF retentate was 10.8 pg/mg, potentially due to β‐glucan leaching from the first run with a regenerated cellulose membrane. Overall, β‐glucan levels in the final mAb drug substance were 1–12 pg/mg. Assuming high doses of 1,000–5,000 mg, a β‐glucan contamination at 20 pg/mg would translate to 20–100 ng/dose, which is below the previously suggested threshold for product safety (≤500 ng/dose).

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

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.

Development of a High-Throughput Assay to Measure Histidine Decarboxylase Activity

Histamine is a well-known mediator of allergic, inflammatory, and neurological responses. More recent studies suggest a role for histamine and its receptors in a wide range of biological processes, including T-cell maturation and bone remodeling. Histamine serum levels are regulated mainly by the activity of the histamine-synthesizing enzyme histidine decarboxylase (HDC). Despite the importance of this enzyme in many physiological processes, very few potent HDC inhibitors have been identified. HDC assays suitable for high-throughput screening have not been reported. The authors describe the development of a fluorescence polarization assay to measure HDC enzymatic activity. They used a fluorescein-histamine probe that binds with high affinity to an antihistamine antibody for detection. Importantly, they show that probe binding is fully competed by histamine, but no competition by the HDC substrate histidine was observed. The automated assay was performed in a total volume of 60 μL, had an assay window of 80 to 100 mP, and had a Z′ factor of 0.6 to 0.7. This assay provides new tools to study HDC activity and pharmacological modulation of histamine levels.

Expression, purification, crystallization and preliminary crystallographic analysis of human Pim-1 kinase

Pim kinases, including Pim-1, Pim-2 and Pim-3, belong to a distinctive serine/threonine protein-kinase family. They are involved in cytokine-induced signal transduction and the development of lymphoid malignancies. Their kinase domains are highly homologous to one another, but share low sequence identity to other kinases. Specifically, there are two proline residues in the conserved hinge-region sequence ERPXPX separated by a residue that is non-conserved among Pim kinases. Full-length human Pim-1 kinase (1-313) was cloned and expressed in Escherichia coli as a GST-fusion protein and truncated to Pim-1 (14-313) by thrombin digestion during purification. The Pim-1 (14-313) protein was purified to high homogeneity and monodispersity. This protein preparation yielded small crystals in the initial screening and large crystals after optimization. The large crystals of apo Pim-1 enzyme diffracted to 2.1 A resolution and belong to space group P6(5), with unit-cell parameters a = b = 95.9, c = 80.0 A, beta = 120 degrees and one molecule per asymmetric unit.

Structural basis of constitutive activity and a unique nucleotide binding mode of human Pim-1 kinase

Pim-1 kinase is a member of a distinct class of serine/threonine kinases consisting of Pim-1, Pim-2, and Pim-3. Pim kinases are highly homologous to one another and share a unique consensus hinge region sequence, ER-PXPX, with its two proline residues separated by a non-conserved residue, but they (Pim kinases) have <30% sequence identity with other kinases. Pim-1 has been implicated in both cytokine-induced signal transduction and the development of lymphoid malignancies. We have determined the crystal structures of apo Pim-1 kinase and its AMP-PNP (5'-adenylyl-beta,gamma-imidodiphosphate) complex to 2.1-angstroms resolutions. The structures reveal the following. 1) The kinase adopts a constitutively active conformation, and extensive hydrophobic and hydrogen bond interactions between the activation loop and the catalytic loop might be the structural basis for maintaining such a conformation. 2) The hinge region has a novel architecture and hydrogen-bonding pattern, which not only expand the ATP pocket but also serve to establish unambiguously the alignment of the Pim-1 hinge region with that of other kinases. 3) The binding mode of AMP-PNP to Pim-1 kinase is unique and does not involve a critical hinge region hydrogen bond interaction. Analysis of the reported Pim-1 kinase-domain structures leads to a hypothesis as to how Pim kinase activity might be regulated in vivo.

Hot flashes in postmenopausal women treated for breast carcinoma: prevalence, severity, correlates, management, and relation to quality of life

BACKGROUND

Research on hot flashes (HFs) after the diagnosis and treatment of breast carcinoma (BC) is scarce. To our knowledge, this research represents the second study of HF prevalence and severity in women with BC and the first study of 1) correlates of HF prevalence and severity, 2) use of HF management strategies, and 3) the relation between HFs and quality of life (QOL) among women with BC.

METHODS

Eligible women (n = 136) participated in structured telephone interviews.

RESULTS

Of the 114 postmenopausal women interviewed, 65% reported HFs, with 59% of women with HFs (n = 74) rating the symptom as severe. Multivariate analysis revealed that 1) HFs were most common in women with a high school education or less and those who were younger at diagnosis and 2) HFs were most severe in women with a higher body mass index, those who were younger at diagnosis, and those receiving tamoxifen. Among women with HFs, 37% were not using any HF management strategies and 63% expressed interest in learning more regarding > or = 1 strategy. HFs marginally were related to decreased mental and physical QOL using the SF-12 Health Survey (P < 0.10).

CONCLUSIONS

The results of the current study significantly contribute to knowledge regarding HFs in women with BC and support the need for carefully controlled clinical trials evaluating interventions for relieving HFs in this population.