KETCHUP: Parameterizing of large-scale kinetic models using multiple datasets with different reference states.

Large-scale kinetic models provide the computational means to dynamically link metabolic reaction fluxes to metabolite concentrations and enzyme levels while also conforming to substrate level regulation. However, the development of broadly applicable frameworks for efficiently and robustly parameterizing models remains a challenge. Challenges arise due to both the heterogeneity, paucity, and difficulty in obtaining flux and/or concentration data but also due to the computational difficulties of the underlying parameter identification problem. Both the computational demands for parameterization, degeneracy of obtained parameter solutions and interpretability of results has so far limited widespread adoption of large-scale kinetic models despite their potential. Herein, we introduce the Kinetic Estimation Tool Capturing Heterogeneous Datasets Using Pyomo (KETCHUP), a flexible parameter estimation tool that leverages a primal-dual interior-point algorithm to solve a nonlinear programming (NLP) problem that identifies a set of parameters capable of recapitulating the (non)steady-state fluxes and concentrations in wild-type and perturbed metabolic networks. KETCHUP is benchmarked against previously parameterized large-scale kinetic models demonstrating an at least an order of magnitude faster convergence than the tool K-FIT while at the same time attaining better data fits. This versatile toolbox accepts different kinetic descriptions, metabolic fluxes, enzyme levels and metabolite concentrations, under either steady-state or instationary conditions to enable robust kinetic model construction and parameterization. KETCHUP supports the SBML format and can be accessed at https://github.com/maranasgroup/KETCHUP.

Comparative study of two Saccharomyces cerevisiae strains with kinetic models at genome-scale.

The parameterization of kinetic models requires measurement of fluxes and/or metabolite levels for a base strain and a few genetic perturbations thereof. Unlike stoichiometric models that are mostly invariant to the specific strain, it remains unclear whether kinetic models constructed for different strains of the same species have similar or significantly different kinetic parameters. This important question underpins the applicability range and prediction limits of kinetic reconstructions. To this end, herein we parameterize two separate large-scale kinetic models using K-FIT with genome-wide coverage corresponding to two distinct strains of Saccharomyces cerevisiae: CEN.PK 113-7D strain (model k-sacce306-CENPK), and growth-deficient BY4741 (isogenic to S288c; model k-sacce306-BY4741). The metabolic network for each model contains 306 reactions, 230 metabolites, and 119 substrate-level regulatory interactions. The two models (for CEN.PK and BY4741) recapitulate, within one standard deviation, 77% and 75% of the fitted dataset fluxes, respectively, determined by 13C metabolic flux analysis for wild-type and eight single-gene knockout mutants of each strain. Strain-specific kinetic parameterization results indicate that key enzymes in the TCA cycle, glycolysis, and arginine and proline metabolism drive the metabolic differences between these two strains of S. cerevisiae. Our results suggest that although kinetic models cannot be readily used across strains as stoichiometric models, they can capture species-specific information through the kinetic parameterization process.

An improved method of using two-dimensional model to evaluate the carrying capacity of regional water resource in Inner Mongolia of China.

The evaluation of regional water resource carrying capacity has been repeatedly conducted to provide a scientific basis for the local water resource management and the sustainable development, in particular in the water-limited regions. However, the definition of regional water resource carrying capacity and its evaluation method are still arguable. Through a case study of Inner Mongolia, located in the arid and semi-arid northern China, this paper developed an improved method to calculate regional water resource carrying capacity by the combination of the water supply-demand analysis and the S-shaped curve threshold analysis. The spatial and temporal patterns of the regional water resource carrying capacity in Inner Mongolia during 2000-2019 was evaluated at three scales, namely the province scale, the basin scale and the city scale. The results showed that the average regional water resource carrying capacity of the whole province was 0.25 (the full mark is 1.00); at the basin scale, the Yellow River Basin had the lowest regional water resource carrying capacity (0.17) among all the basins, showing that the utilization of the water resources was unreasonable; at the city scale, the average regional water resource carrying capacities in Hulunbuir and Xilingol were both over 0.25, while those in Alxa, BayanNur and Wuhai were below 0.1; Hulunbuir had 25.48 billion m3 water surplus, while BayanNur suffered from an average water deficit of 4.51 billion m3 from 2000 to 2019. This paper has provided a reasonable way to measure the regional water resource carrying capacity using an improved method by incorporating S-shaped curve threshold analysis, which may have a wider application for the clustering and optimization of regional water management. In addition, the spatial and temporal patterns of regional water carrying capacity are beneficial for policymakers in the implementation of the effective water usage.

Identification of Differentially Expressed Genes in COVID-19 and Integrated Bioinformatics Analysis of Signaling Pathways.

Coronavirus disease 2019 (COVID-19) is acutely infectious pneumonia. Currently, the specific causes and treatment targets of COVID-19 are still unclear. Herein, comprehensive bioinformatics methods were employed to analyze the hub genes in COVID-19 and tried to reveal its potential mechanisms. First of all, 34 groups of COVID-19 lung tissues and 17 other diseases' lung tissues were selected from the GSE151764 gene expression profile for research. According to the analysis of the DEGs (differentially expressed genes) in the samples using the limma software package, 84 upregulated DEGs and 46 downregulated DEGs were obtained. Later, by the Database for Annotation, Visualization, and Integrated Discovery (DAVID), they were enriched in the Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. It was found that the upregulated DEGs were enriched in the type I interferon signaling pathway, AGE-RAGE signaling pathway in diabetic complications, coronavirus disease, etc. Downregulated DEGs were in cellular response to cytokine stimulus, IL-17 signaling pathway, FoxO signaling pathway, etc. Then, based on GSEA, the enrichment of the gene set in the sample was analyzed in the GO terms, and the gene set was enriched in the positive regulation of myeloid leukocyte cytokine production involved in immune response, programmed necrotic cell death, translesion synthesis, necroptotic process, and condensed nuclear chromosome. Finally, with the help of STRING tools, the PPI (protein-protein interaction) network diagrams of DEGs were constructed. With degree ≥13 as the cutoff degree, 3 upregulated hub genes (ISG15, FN1, and HLA-G) and 4 downregulated hub genes (FOXP3, CXCR4, MMP9, and CD69) were screened out for high degree. All these findings will help us to understand the potential molecular mechanisms of COVID-19, which is also of great significance for its diagnosis and prevention.

Discovery of talmapimod analogues as polypharmacological anti-inflammatory agents.

Twenty novel talmapimod analogues were designed, synthesised and evaluated for the in vivo anti-inflammatory activities. Among them, compound 6n, the most potent one, was selected for exploring the mechanisms underlying its anti-inflammatory efficacy. In RAW264.7 cells, it effectively suppressed lipopolysaccharides-induced (LPS-induced) expressions of iNOS and COX-2. As illustrated by the western blot analysis, 6n downregulated both the NF-κB signalling and p38 MAPK phosphorylation. Further enzymatic assay identified 6n as a potent inhibitor against both p38α MAPK (IC50=1.95 µM) and COX-2 (IC50=0.036 µM). By virtue of the concomitant inhibition of p38α MAPK, its upstream effector, and COX-2, along with its capability to downregulate NF-κB and MAPK-signalling pathways, 6n, a polypharmacological anti-inflammatory agent, deserves further development as a novel anti-inflammatory drug.

Design, synthesis, and biological evaluation of tetrahydroisoquinoline-based diaryl urea derivatives for suppressing VEGFR-2 signaling.

A novel structural series of tetrahydroisoquinoline-based compounds that incorporate the diaryl urea moiety was designed, synthesized, and biologically evaluated as suppressors of VEFGR-2 signaling. As a consequence, compounds 9k and 9s exhibited comparable or superior cytotoxic activity to that of gefitinib against the tested three cell lines, including A549, MCF-7, and PC-3. Importantly, both of them downregulated the expression of VEGFR-2, and inhibited VEGFR-2 phosphorylation at the concentration of 0.5 or 1.0 µmol/l. Besides, they suppressed human umbilical vein endothelial cell tube formation at the concentration of 4.0 µmol/l. Considering their capability of down-regulating VEGFR-2 expression and inhibiting VEGFR-2 phosphorylation, 9k and 9s may serve as suppressors of angiogenesis for further investigation.

A Semi-automated Approach to Improve the Efficiency of Medical Imaging Segmentation for Haptic Rendering.

The Sensimmer platform represents our ongoing research on simultaneous haptics and graphics rendering of 3D models. For simulation of medical and surgical procedures using Sensimmer, 3D models must be obtained from medical imaging data, such as magnetic resonance imaging (MRI) or computed tomography (CT). Image segmentation techniques are used to determine the anatomies of interest from the images. 3D models are obtained from segmentation and their triangle reduction is required for graphics and haptics rendering. This paper focuses on creating 3D models by automating the segmentation of CT images based on the pixel contrast for integrating the interface between Sensimmer and medical imaging devices, using the volumetric approach, Hough transform method, and manual centering method. Hence, automating the process has reduced the segmentation time by 56.35% while maintaining the same accuracy of the output at ±2 voxels.

Tasquinimod enhances the sensitivity of ovarian cancer cells to cisplatin by regulating the Nur77-Bcl-2 apoptotic pathway.

BACKGROUND: Resistance to cisplatin (DDP) in ovarian cancer therapy has been a major clinical barrier. Drug-resistant cancers have been shown to downregulate the proapoptotic protein B-cell lymphoma-2 (Bcl-2) to inhibit apoptosis. Therefore, we explored whether tasquinimod could modulate resistance to DDP through apoptotic pathways. OBJECTIVES: We aimed to explore the relationship between tasquinimod, Nur77-Bcl-2 apoptosis pathway and sensitivity of the ovarian carcinoma cell line SKOV3 and the DDP-resistant strain SKOV3/DDP cells to DDP. MATERIAL AND METHODS: First, SKOV3 and SKOV3/DDP cells were treated with 2 µg/mL DDP or 40 µM tasquinimod. Western blot and quantitative real-time polymerase chain reaction (qPCR) were then used to analyze the expression of histone deacetylase 4 (HDAC4), Nur77, Bcl-2 (BH3 domain-specific), and caspase-3. Flow cytometry, scratch-wound assay and immunofluorescence were used to detect apoptosis, migration rate, and related expression of Nur77 and Bcl-2 (BH3 domain-specific). Subsequently, 5×107 SKOV3 or SKOV3/DDP cells cultured with 2 µg/mL DDP were injected into 4-week-old female BALB/c nude mice. Then, the mice were administered 4 mg/kg DDP and 50 mg/kg tasquinimod every 3 days. Finally, the changes in tumor diameter and weight were measured. RESULTS: After treatment of SKOV3 and SKOV3/DDP cells with tasquinimod, cell migration and HDAC4 expression levels were significantly reduced, while Nur77 expression was increased. Tasquinimod treatment enhanced the expression of Nur77 and caspase-3, and cells transfected with si-Nur77 showed the opposite result. Transfection of si-Nur77 reduced the expression of caspase-3 and Nur77 in the SKOV3/DDP cells that were treated with both DDP and tasquinimod. After injection of SKOV3/DDP cells into the mice, the tumor diameter, mass and in vivo HDAC4 level were significantly decreased by tasquinimod. Meanwhile, the levels of Nur77 and Bcl-2 (BH3 domain-specific) were increased. CONCLUSIONS: Tasquinimod upregulated the Nur77/Bcl-2 pathway to induce apoptosis in SKOV3/DDP cells and enhanced the anti-tumor effect of DDP in SKOV3/DDP xenografts. Therefore, tasquinimod can be expected to find clinical applications in enhancing DDP resistance.

A simple and sensitive differential digestion method to analyze adeno-associated virus residual host cell proteins by LC-MS.

Many methods using liquid chromatography-mass spectrometry (LC-MS) have been established for identifying residual host cell proteins (HCPs) to aid in the process development and quality control of therapeutic proteins. However, the use of MS-based techniques for adeno-associated virus (AAV) is still in its infancy, with few methods reported and minimal information available on potentially problematic HCPs. In this study, we developed a highly sensitive and effective differential digestion method to profile residual HCPs in AAV. Unlike direct digestion, which completely digests both AAV and HCPs, our differential digestion method takes advantage of AAV's unique characteristics to maintain the integrity of AAV while preferentially digesting HCPs under denaturing and reducing conditions. This differential digestion method requires only several micrograms of sample and significantly enhances the identification of HCPs. Furthermore, this method can be applied to all five different AAV serotypes for comprehensive HCP profiling. Our work fills a gap in AAV HCP analysis by providing a sensitive and robust strategy for detecting, monitoring, and measuring HCPs.

Australian researchers' perceptions and experiences with stem cell registration.

The recently issued ISSCR standards in stem cell research recommend registration of human pluripotent stem cell lines (hPSCs). Registration is critical to establishing stem cell provenance and connecting cell lines to data derived on those lines. In this study, we sought to understand common barriers to registration by conducting interviews with forty-eight Australian stem cell stakeholders, including researchers, clinicians, and industry professionals. Australian stem cell researchers do not routinely register their lines, and only a third of those Australian lines captured by an international registry have fully completed the registration process. Most registered Australian cell lines lack complete information about their ethical provenance or key pluripotency characteristics. Incomplete registration is poorly aligned with the goals of open science on which registries are founded. Users also expressed concerns about the quality of the incomplete information provided to the resource. Registration was considered negatively, for instance as a hurdle or barrier to publication, which impacted on user perceptions of usefulness of registration and lowered the likelihood that they would engage with registries to find resources. Broader adoption of registration by journals, and continued advocacy by stem cell societies, will be important levers for awareness and engagement with registration. Although the Australian community represents a small fraction of potential registry users, the results of this study suggest ways for journals, registries, funders, and the international stem cell community to improve registration compliance.

Deep Deterministic Policy Gradient With Compatible Critic Network.

Deep deterministic policy gradient (DDPG) is a powerful reinforcement learning algorithm for large-scale continuous controls. DDPG runs the back-propagation from the state-action value function to the actor network's parameters directly, which raises a big challenge for the compatibility of the critic network. This compatibility emphasizes that the policy evaluation is compatible with the policy improvement. As proved in deterministic policy gradient, the compatible function guarantees the convergence ability but restricts the form of the critic network tightly. The complexities and limitations of the compatible function impede its development in DDPG. This article introduces neural networks' similarity indices with gradients to measure the compatibility concretely. Represented as kernel matrices, we consider the actor network's and the critic network's training dataset, trained parameters, and gradients. With the sketching trick, the calculation time of the similarity index decreases hugely. The centered kernel alignment index and the normalized Bures similarity index provide us with consistent compatibility scores empirically. Moreover, we demonstrate the necessity of the compatible critic network in DDPG from three aspects: 1) analyzing the policy improvement/evaluation steps; 2) conducting the theoretic analysis; and 3) showing the experimental results. Following our research, we remodel the compatible function with an energy function model, enabling it suitable to the sizeable state-action space problem. The critic network has higher compatibility scores and better performance by introducing the policy change information into the critic-network optimization process. Besides, based on our experiment observations, we propose a light-computation overestimation solution. To prove our algorithm's performance and validate the compatibility of the critic network, we compare our algorithm with six state-of-the-art algorithms using seven PyBullet robotics environments.

WRKY transcription factor family in lettuce plant (Lactuca sativa): Genome-wide characterization, chromosome location, phylogeny structures, and expression patterns.

WRKY transcription factors (TF) have been identified in many plant species and play critical roles in multiple stages of growth and development and under various stress conditions. As one of the most popular vegetable crops, asparagus lettuce has important medicinal and nutritional value. However, study of WRKY TFs family in asparagus lettuce is limited. With the lettuce (Lactuca sativa L.) genome publication, we identified 76 WRKY TFs and analyzed structural characteristics, phylogenetic relationships, chromosomal distribution, interaction network, and expression profiles. The 76 LsWRKY TFs were phylogenetically classified as Groups I, II (IIa-IIe), and III. Cis element analysis revealed complex regulatory relationships of LsWRKY genes in response to different biological progresses. Interaction network analysis indicated that LsWRKY TFs could interact with other proteins, such as SIB (sigma factor binding protein), WRKY TFs, and MPK. The WRKYIII subfamily genes showed different expression patterns during the progress of asparagus lettuce stem enlargement. According to qRT-PCR analysis, abiotic stresses (drought, salt, low temperature, and high temperature) and phytohormone treatment could induce specific LsWRKYIII gene expression. These results will provide systematic and comprehensive information on LsWRKY TFs and lay the foundation for further clarification of the regulatory mechanism of LsWRKY, especially LsWRKYIII TFs, involved in stress response and the progress of plant growth and development.

Host cell protein identification in monoclonal antibody high molecular weight species.

High molecular weight (HMW) species are product-related variants that may impact therapeutic product safety and efficacy. Therefore, HMW species and aggregates are considered critical quality attributes and their levels should be closely monitored and controlled during drug development, commercial manufacturing, and shelf-life storage period for therapeutic monoclonal antibody drug products. Various biophysical and analytical methods have been developed to characterize the HMW species to understand their mechanisms of formation and assess potential product risk. However, host cell protein (HCP) analysis has seldom been conducted to characterize the impurities in aggregates. In this work, HCP analysis of enriched HMW species and drug substance (DS) from five different monoclonal antibodies (mAbs) was performed. More HCPs are identified in the enriched HMW than in the DS, thus demonstrating a potential interaction between HCPs and HMW. Certain HCPs, including commonly detected HCPs and problematic HCPs, were enriched in HMW fractions. Especially, the most abundant HCP from mAb1, CC motif chemokine, was 46 times more abundant in enriched HMW than DS. The enriched HMW was further fractionated into enriched dimers and enriched very HMW (vHMW) fractions. The CC motif chemokine was found to interact mainly with mAb1 dimer species rather than vHMW fraction. Removing the HMW species from mAb1 significantly decreased the CC motif chemokine level in the final mAb1 DS. Our findings demonstrate that some HCPs are more preferentially bound to HMW species and this finding may provide a new opportunity for removing HCPs in downstream purification steps.

E2SGAN: EEG-to-SEEG translation with generative adversarial networks.

High-quality brain signal data recorded by Stereoelectroencephalography (SEEG) electrodes provide clinicians with clear guidance for presurgical assessments for epilepsy surgeries. SEEG, however, is limited to selected patients with epilepsy due to its invasive procedure. In this work, a brain signal synthesis framework is presented to synthesize SEEG signals from non-invasive EEG signals. First, a strategy to determine the matching relation between EEG and SEEG channels is presented by considering both signal correlation and spatial distance. Second, the EEG-to-SEEG generative adversarial network (E2SGAN) is proposed to precisely synthesize SEEG data from the simultaneous EEG data. Although the widely adopted magnitude spectra has proved to be informative in EEG tasks, it leaves much to be desired in the setting of signal synthesis. To this end, instantaneous frequency spectra is introduced to further represent the alignment of the signal. Correlative spectral attention (CSA) is proposed to enhance the discriminator of E2SGAN by capturing the correlation between each pair of EEG and SEEG frequencies. The weighted patch prediction (WPP) technique is devised to ensure robust temporal results. Comparison experiments on real-patient data demonstrate that E2SGAN outperforms baseline methods in both temporal and frequency domains. The perturbation experiment reveals that the synthesized results have the potential to capture abnormal discharges in epileptic patients before seizures.

Discovery of novel brain-penetrant GluN2B NMDAR antagonists via pharmacophore-merging strategy as anti-stroke therapeutic agents.

In this work, a novel structural series of brain-penetrant GluN2B NMDAR antagonists were designed, synthesized and biologically evaluated as anti-stroke therapeutic agents via merging the structures of NBP and known GluN2B ligands. Approximately half of them exhibited superior neuroprotective activity to NBP against NMDA-induced neurotoxicity in hippocampal neurons at 10 µM, and compound 45e and 45f exerted equipotent activity to ifenprodil, an approved GluN2B- selective NMDAR antagonist. In particular, 45e, with the most potent neuroprotective activity throughout this series, displayed dramatically enhanced activity (Ki = 3.26 nM) compared to ifenprodil (Ki = 14.80 nM) in Radioligand Competitive Binding Assay, and remarkable inhibition (IC50 = 79.32 nM) against GluN1/GluN2B receptor-mediated current in Patch Clamp Assay. Meanwhile, 45e and its enantiomers exhibited low inhibition rate against the current mediated by other investigated receptors at the concentration of 10 µM, indicating their favorable selectivity for GluN1/GluN2B. In the rat model of middle cerebral artery ischemia (MCAO), 45e exerted comparable therapeutic efficacy to ifenprodil at the same dosage. In addition to the attractive in vitro and in vivo potency, 45e displayed a favorable bioavailability (F = 63.37%) and an excellent brain exposure. In further repeated dose toxicity experiments, compound 45e demonstrated an acceptable safety profile. With the above merits, 45e is worthy of further functional investigation as a novel anti-stroke therapeutic agent.

Informatics in radiology: Efficiency metrics for imaging device productivity.

Acute awareness of the costs associated with medical imaging equipment is an ever-present aspect of the current healthcare debate. However, the monitoring of productivity associated with expensive imaging devices is likely to be labor intensive, relies on summary statistics, and lacks accepted and standardized benchmarks of efficiency. In the context of the general Six Sigma DMAIC (design, measure, analyze, improve, and control) process, a World Wide Web-based productivity tool called the Imaging Exam Time Monitor was developed to accurately and remotely monitor imaging efficiency with use of Digital Imaging and Communications in Medicine (DICOM) combined with a picture archiving and communication system. Five device efficiency metrics-examination duration, table utilization, interpatient time, appointment interval time, and interseries time-were derived from DICOM values. These metrics allow the standardized measurement of productivity, to facilitate the comparative evaluation of imaging equipment use and ongoing efforts to improve efficiency. A relational database was constructed to store patient imaging data, along with device- and examination-related data. The database provides full access to ad hoc queries and can automatically generate detailed reports for administrative and business use, thereby allowing staff to monitor data for trends and to better identify possible changes that could lead to improved productivity and reduced costs in association with imaging services. © RSNA, 2011.

An automated DICOM database capable of arbitrary data mining (including radiation dose indicators) for quality monitoring.

The U.S. National Press has brought to full public discussion concerns regarding the use of medical radiation, specifically x-ray computed tomography (CT), in diagnosis. A need exists for developing methods whereby assurance is given that all diagnostic medical radiation use is properly prescribed, and all patients' radiation exposure is monitored. The "DICOM Index Tracker©" (DIT) transparently captures desired digital imaging and communications in medicine (DICOM) tags from CT, nuclear imaging equipment, and other DICOM devices across an enterprise. Its initial use is recording, monitoring, and providing automatic alerts to medical professionals of excursions beyond internally determined trigger action levels of radiation. A flexible knowledge base, aware of equipment in use, enables automatic alerts to system administrators of newly identified equipment models or software versions so that DIT can be adapted to the new equipment or software. A dosimetry module accepts mammography breast organ dose, skin air kerma values from XA modalities, exposure indices from computed radiography, etc. upon receipt. The American Association of Physicists in Medicine recommended a methodology for effective dose calculations which are performed with CT units having DICOM structured dose reports. Web interface reporting is provided for accessing the database in real-time. DIT is DICOM-compliant and, thus, is standardized for international comparisons. Automatic alerts currently in use include: email, cell phone text message, and internal pager text messaging. This system extends the utility of DICOM for standardizing the capturing and computing of radiation dose as well as other quality measures.

Continuous Model Adaptation Using Online Meta-Learning for Smart Grid Application.

The rapid development of deep learning algorithms provides us an opportunity to better understand the complexity in engineering systems, such as the smart grid. Most of the existing data-driven predictive models are trained using historical data and fixed during the execution stage, which cannot adapt well to real-time data. In this research, we propose a novel online meta-learning (OML) algorithm to continuously adapt pretrained base-learner through efficiently digesting real-time data to adaptively control the base-learner parameters using meta-optimizer. The simulation results show that: 1) both ML and OML can perform significantly better than online base learning. 2) OML can perform better than ML and online base learning when the training data are limited, or the training and real-time data have very different time-variant patterns.

Toward unbiased identification and comparative quantification of host cell protein impurities by automated iterative LC-MS/MS (HCP-AIMS) for therapeutic protein development.

Host cell proteins (HCPs) are process-related impurities expressed by the host cells used for production of therapeutic proteins. Although an extensive purification process removes most of the HCPs, residual HCPs are commonly present in protein therapeutics. If not well-controlled, certain HCPs may be present in the product, impacting drug stability, and potentially affecting product safety leading to safety risks for patients. Therefore, as a critical quality attribute, the levels of HCPs must be closely monitored during drug development and determined in the final drug substance at release. Liquid chromatography-mass spectrometry (LC-MS) as an orthogonal approach to traditional ELISA for HCP analysis has shown tremendous value in drug process development and analytical characterization by providing additional critical information, such as protein identity and relative abundance of individual HCPs. To meet the challenges in HCP analysis during drug development, especially downstream process development, which entails fast turnaround time and robustness while identifying high level of HCPs and their clearance trend for further purification development, we have developed HCP-automated iterative MS (HCP-AIMS): it is a simple, automated and robust HCP analysis workflow with deep and unbiased identification and relative quantification capability. This HCP-AIMS approach only requires easy direct digestion of the samples without enrichment or pre- treatment. With the fully automated precursor ion exclusion in MS/MS mode, low abundance HCP peptides could be selected for MS/MS analysis in iterative replicates, and therefore, the identification of HCPs at low abundance can be achieved. Using an in-house mAb with various levels of spiked-in HCPs as well as the NIST mAb, we were able to achieve unbiased identification and quantitation of HCPs as low as 10 ppm level. Furthermore, robustness of the HCP-AIMS approach was also confirmed for the feasibility of large-scale and high-throughput analysis.

Host cell protein profiling of commercial therapeutic protein drugs as a benchmark for monoclonal antibody-based therapeutic protein development.

Therapeutic proteins including monoclonal antibodies (mAbs) are usually produced in engineered host cell lines that also produce thousands of endogenous proteins at varying levels. A critical aspect of the development of biotherapeutics manufacturing processes is the removal of these host cell proteins (HCP) to appropriate levels in order to minimize risk to patient safety and drug efficacy. During the development process and associated analytical characterization, mass spectrometry (MS) has become an increasingly popular tool for HCP analysis due to its ability to provide both relative abundance and identity of individual HCP and because the method does not rely on polyclonal antibodies, which are used in enzyme-linked immunosorbent assays. In this study, HCP from 29 commercially marketed mAb and mAb-based therapeutics were profiled using liquid chromatography (LC)-MS/MS with the identification and relative quantification of 79 individual HCP in total. Excluding an outlier drug, the relative levels of individual HCP determined in the approved therapeutics were generally low, with an average of 20 ppm (µmol HCP/mol drug) measured by LC-MS/MS, and only a few (<7 in average) HCP were identified in each drug analyzed. From this analysis, we also gained knowledge about which HCP are frequently identified in mAb-based products and their typical levels relative to the drugs for the identified individual HCP. In addition, we examined HCP composition from antibodies produced in house and found our current development process brings HCP to levels that are consistent with marketed drugs. Finally, we described a specific case to demonstrate how the HCP information from commercially marketed drugs could inform future HCP analyses.