Multi-attribute Method Research Articles

Automated mass spectrometry multi-attribute method analyses for process development and characterization of mAbs

More than 370 biotherapeutics drug products have been approved by regulatory agencies on the US and EU markets and this industry continues to expand. Process change and optimization is necessary to develop new effective biologics in a cost effective and productive way. Consequently, improvement of analytical techniques is required for better product characterization according to Quality by Design (QbD) approach recommended by regulatory agencies. Recently, multi-attribute method (MAM) has emerged to meet such demands using mass spectrometry coupled to liquid chromatography (LC-MS). However, traditional sample preparation or data processing would not be suitable to guide process development, because one of the common challenges during development of analytical platforms is instrument or method variability which can cause deviation in results. Here, we show a new automated analytical platform for MAM implemented on 3 different sites: the components of MAM platform include automated sample preparation, LC-MS based MAM, and data treatment automation. To our knowledge, this is the first study to show global harmonization on automated MAM platforms and the inter-sites comparability including the automated sample preparation and LC-MS instrument. Also, we demonstrate the applicability of MAM to support cell line development, cell culture process development and downstream process development. We expect that this MAM platform will effectively guide process development across multiple projects.

The Multi-Attribute Method (MAM) for the Characterization of Biopharmaceuticals

Over the last decade or so, biopharmaceuticals have gained market share and become more complex in their composition. Therefore, new analytical workflows are needed to address these advances, and one of those workflows that has gained substantial traction over the last several years is the multi-attribute method (MAM). MAM was originally developed to be used throughout the product life cycle, from process development through quality control, and has become even more popular as quality by design (QbD) has become a more prevalent approach for biopharmaceutical development. MAM is designed to monitor critical quality attributes (CQAs) simultaneously and directly, such as sequence, post-translational modifications, and impurities, making it a more streamlined and productive workflow for biopharmaceutical analysis. In this column, we will discuss the role of liquid chromatography and mass spectrometry in MAM, as well as other new technologies and anticipated advances of MAM that are on the horizon.

Multi-attribute Seismic application for Modeling Static Reservoir In “Athran” Field South Sumatera Basin

“Athran” field is an active field for hydrocarbon exploration with trending northeast-southwest and focus on the reservoir zone is the Talang Akar Formation with A-C layer intervals. The method used in this research is electrofacies, delta deposition system, formation evaluation, static reservoir modeling, and multi-attribute linear regression. The distribution of reservoir modeling is done by using the stochastic variogram method to make modeling lithofacies and depositional facies. Identification of lithofacies in the study area obtained two units, namely sandstone and shale facies. The depositional facies analysis was carried out using the electrofacies approach, showing that the research target was in a transitional environment with a delta deposition system, namely the distributary channel, delta front and prodelta. The deposition process. Sandstones and shales affect the distribution of porosity which functions as a reservoir property. The data integration of the distribution of lithofacies, gamma ray and porosity was carried out using the multi-attribute linear regression method to predict data both vertically and horizontally. The results of this study are used to help determine the distribution direction and direction of sediment deposition as a good reservoir zone for exploration activities.

Optimization of College English Teaching Model Based on the Multi-Attribute Decision Method of Random Variables

Optimization of College English Teaching Model Based on the Multi-Attribute Decision Method of Random Variables

Simultaneous Monitoring and Comparison of Multiple Product Quality Attributes for Cell Culture Processes at Different Scales Using a LC/MS/MS Based Multi-Attribute Method

In response to the FDA's call for applying Quality by Design (QbD) to the manufacturing process, the biopharmaceutical industry has invested extensively into the monitoring and controlling of product quality attributes for bioprocesses. To assure the safety and efficacy of the drug product, defining critical quality attributes (CQA) and understanding their correlation with critical process parameters (CPP) becomes vitally important. In this work, a liquid chromatography-mass spectrometry based multi-attribute method (MAM) has been applied to the monitoring and trending of multiple CQAs of a monoclonal antibody product. To the best of our knowledge, this is the first demonstration of applying MAM to both a 3-liter development mini-bioreactor (3 L bioreactor) and a 2000-liter GMP single use bioreactor (2000L SUB). MAM was proven not only to be a great analytical tool for monitoring product quality attributes throughout the time course of the cell culture process, it could also provide critical product quality information in order to understand any potential process performance differences during scale-up and/or technology transfer. The successful monitoring and trending of the multiple CQAs throughout the 17-day cell culture process lays a solid foundation for possible real time in-process control and release of biotherapeutics using MAM in the future.

Inter-laboratory study of an optimised peptide mapping workflow using automated trypsin digestion for monitoring monoclonal antibody product quality attributes

Peptide mapping analysis is a regulatory expectation to verify the primary structure of a recombinant product sequence and to monitor post-translational modifications (PTMs). Although proteolytic digestion has been used for decades, it remains a labour-intensive procedure that can be challenging to accurately reproduce. Here, we describe a fast and reproducible protocol for protease digestion that is automated using immobilised trypsin on magnetic beads, which has been incorporated into an optimised peptide mapping workflow to show method transferability across laboratories. The complete workflow has the potential for use within a multi-attribute method (MAM) approach in drug development, production and QC laboratories. The sample preparation workflow is simple, ideally suited to inexperienced operators and has been extensively studied to show global applicability and robustness for mAbs by performing sample digestion and LC-MS analysis at four independent sites in Europe. LC-MS/MS along with database searching was used to characterise the protein and determine relevant product quality attributes (PQAs) for further testing. A list of relevant critical quality attributes (CQAs) was then established by creating a peptide workbook containing the specific mass-to-charge (m/z) ratios of the modified and unmodified peptides of the selected CQAs, to be monitored in a subsequent test using LC-MS analysis. Data is provided that shows robust digestion efficiency and low levels of protocol induced PTMs.Graphical abstract

Advancing Mass Spectrometry Technology in cGMP Environments

Mass spectrometry is a powerful analytical tool that has been widely used in the biotech industry. Recent trends of implementing a liquid chromatography-mass spectrometry (LC-MS) based multi-attribute method (MAM) in cGMP environments enables end-to-end monitoring and control of critical quality attributes (CQAs) of therapeutic proteins to ensure drug product quality.

The EPIK multi-attribute method for intrinsic vulnerability assessment of karstic aquifer under semi-arid climatic conditions, case of Cheria Plateau, NE Algeria

Karst aquifers supply an important freshwater resource for MENA (Middle East and North Africa) semi-arid regions. Their functioning conditions reveal often several contaminations. The Cheria carbonate aquifer (NE Algeria) has never had management tools to deal with the pollution of this resource. The outlining and protection of the vulnerable areas are essential to ensure the water purity and the consumer health. Based on the EPIK multicriteria analysis method, we aim in our study to assess the karstic aquifers intrinsic-vulnerability to contamination in the Cheria Plateau. This method has been already applied for contamination problems in Northeast Algeria. It uses a conceptual scheme that considers epikarst, protective cover, infiltration, and karst network as parameters. In addition, it also uses the interpolation of the geophysics data to define the conditioning attributes. The resulting vulnerability map categorizes all the basin vulnerable areas and the groundwater protection zones. Nearly 30% of the study area was judged as highly vulnerable to anthropogenic pollution, whereas around 10% is moderately vulnerable. These results confirm that the karst aquifer of the Cheria Plateau is severely polluted by chemical pesticides resulting from farming activity. In addition to the surrounding agricultural lands, the underground of Cheria city was found as extremely vulnerable to pollution (F < 19). Our study delimited boundaries for the karst aquifer protection zones and for an integrated resource management in this region. It recommends applying this method for comparable problems.

Application Monitoring and Evaluation using SMART (Simple Multi attribute Rating Technique) Method

Montoring is a systematic process of collecting data from that data to be evaluated to find out the quality of the system, along with times the monitoring and evaluation process is done using technology that can be web-based or android mobile, so that the process of monitoring & evaluation is easier and more efficient. In addition to getting an accurate assessment, it is necessary to apply a method that can process data into an objective assessment, the SMART method is a simple multi-attribute method that can be used in processing data into an accurate assessment. Trunojoyo Madura University Dormitory has many activities aimed at realizing the education of the dormitory character, the activity data is monitored and evaluated to provide an assessment of the activeness of students living in the dormitory. However, the process of monitoring & evaluation of the dormitory is still carried out manually, so an application for monitoring & evaluating the activities of the hostel which uses the SMART method is needed to facilitate the monitoring process and obtain an objective assessment result.

Reservoir Characterization with Acoustic Impedance Inversion and Multi attribute Method on “Essen” Field, Talang Akar Formation, Sub-Ciputat Basin, North West Java

Acoustic impedance and multi-attribute seismic inversion seismic are a number of seismic methods that can be used to map sandstone reservoir distribution. Using this method, we can separate the sandstone and shale in the Talang Akar Formation found on the “Essen” Field, Ciputat Sub-Basin. Both of these methods will be compared with each other in order to get more valid results in sandstone reservoir mapping. The inversion method used in this study is a model-based method. While the multi-attribute method used is the neural network in mapping gamma-ray volume, shale volume, and porosity. The inversion results are not able to image the sandstone distribution good enough because the range is too large and there is overlapping on the acoustic impedance values of sandstones with ranges (8000-12000) (m/s)*(g/cc). The results of the gamma-ray multi-attribute, shale volume and porosity, have been shown to consistently show the distribution of sandstones that have a trend in the distribution of NW-SE reservoir zones. From the results of the analysis there are several potential areas that have the potential to become the next development area, namely the distribution of sandstones in the northern part with high effective porosity, and good seals; In the south with a large volume of sandstones; as well as the distribution formed on the channel surrounding the fault.

Rapid Intact mass based multi-attribute method in support of mAb upstream process development

N-linked glycosylation is a critical quality attribute for monoclonal antibodies (mAbs) as it affects product stability, immunogenicity, receptor binding and antibody effector function, clearance and half-life. It has been widely accepted that the glycosylation process is greatly impacted by several factors during bioreactor operations. Therefore, the timely acquisition of N-linked glycosylation information during cell culture process development is critical for the success of the endeavor. In this paper we describe a simple, rapid, and robust Multi-Attribute Method (MAM) based on intact mass analysis. This method, developed for an open access instrument, has been optimized for the analysis of light and heavy chains generated from dithiothreitol (DTT) reduction of intact mAbs sampled directly out of bioreactors. The N-linked glycosylation profile, identity confirmation of light chain and heavy chain, light chain glycation and non-glycosylated heavy chain (NGHC) can all be monitored by this method. Our results confirm that the N-linked glycosylation profile determined using Intact mass based MAM is comparable with a release glycan assay and LC–MS/MS peptide based MAM assay for the most abundant glycoforms. Furthermore, the results for the NGHC attribute determined with our method are comparable to results from capillary gel electrophoresis (CGE) and LC–MS/MS peptide based MAM.

A Stochastic Multi-Attribute Method for Measuring Sustainability Performance of a Supplier Based on a Triple Bottom Line Approach in a Dual Hesitant Fuzzy Linguistic Environment.

China is a developing country and with the speeding up of its industrialization, the environmental problems are becoming more serious, environmental pollution is a major environmental health problem in China. In order to have a more effective management and control of the significant growth issues of environment pollution, green supply chain incentives have started, which is kind of market incentive aiming to moderate the adverse effects of environmental pollution. Proper green chain supply selection and evaluation of companies is becoming very essential in sustainable green supply chain management. Generally speaking, decision-makers (DMs) prefer to provide a set of feasible and quantitative information for making performance evaluation, which motivates us to propose a framework using dual hesitant fuzzy linguistic term set (DHFLTS) and hesitant fuzzy linguistic term set (HFLTS) to select green suppliers. In this paper, group satisfaction and the regret theory are adopted for elicitation of preference information. The DHFLTS and HFLTS provide qualitative preferences of the DMs as well as reflect their hesitancy, inconsistency, and vagueness. Further, two new group satisfaction degrees are defined called the group satisfaction of hesitant fuzzy linguistic term set and dual hesitant fuzzy linguistic term set. Some properties of group satisfaction with DHFLST and HFL are also discussed. Unknown attribute weights are obtained to construct a novel Lagrange function optimization model to maximize the group satisfaction degree, which is an extension of general group satisfaction degree. A novel methodological approach based on two group satisfaction degrees framework and regret theory is developed to rank and select green chain suppliers focusing on specific selection objectives. The proposed model and method of this paper allow the DM to execute different fuzzy scenarios by changing importance weights attached to the triple-bottom-line areas. In the final part, the advantage of the proposed group satisfaction degree under DHFL and HFL background over the existing group satisfaction degree using examples have been presented with different computational combinations.

Automated middle-up approach for the characterization of biotherapeutic products by combining on-line hinge-specific digestion with RPLC-HRMS analysis

The development of biotherapeutic proteins requires the use of efficient analytical methods to support their manufacturing process and quality control (QC). Analytical approaches at intact and middle-up levels emerged as promising alternatives to bottom-up strategies for protein characterization as they require less sample amount and simplified sample handling, thus reducing the overall turn-around time. This study describes, for the first time, the development of an automated liquid chromatography-mass spectrometry (LC–MS) workflow comprised of an immobilized IdeS-HPLC column for on-line sample digestion, followed by a reversed-phase liquid chromatography (RPLC) for protein subunit separation, and a high-resolution MS for molecular weight analysis. A proof of concept study was described here for the characterization of a therapeutic mAb and a bsAb. For the mAb, this automated workflow enabled rapid characterization of post-translational modifications (PTMs) such as N-glycosylation, glycation and N-terminal lysine. For the bsAb, the same workflow was successfully employed to identify product impurities due to chain pairing. The sample analysis using this workflow can be accomplished within less than one day. This workflow demonstrated its potential as a multi-attribute method for characterization of therapeutic proteins.

Seismic multi-attribute analysis for petrophysics reservoir prediction with probabilistic neural network in “FA” field

Oil and gas reserves are increasingly difficult to find due to more complex geological conditions. This complex condition causes difficulties in determining reservoir distribution. Therefore, a better method is needed to overcome these complex geological conditions. In this study, the petrophysics analysis by using the multi-attribute and the Probabilistic Neural Network (PNN) used to make reservoir distribution model on seismic horizontal slice. This multi-attribute method and Probabilistic Neural Network (PNN) that can search for correlation between seismic attributes and the data sought, for the prediction of property values from surrounding rocks. From this method, the distribution of porosity data with a correlation value of 0.52 was generated, water saturation with a correlation value of 0.73, and shale content with a correlation value of 0.58. Where the combination of porosity data, water saturation, shale content, and acoustic impedance (AI) data of inversion results can be a clue to identify reservoir distribution. From the porosity and saturation values, hydrocarbon dispersion can be made, wherein this study values were obtained between 0.01 0.03. This “FA” field has a reservoir between wells F-06, FA-05, FA-15, and FA-18 and spreads westward from wells FA-05, FA-15 &amp; FA-18. The distribution of petrophysical parameters generated from the validation of well data using the multi-attribute method. This thing prove that Multi-attribute and neural network analysis can be used to determine predictions of porosity, water saturation, and shale content well and can be used for reservoir characterization.

Establishment of a highly precise multi-attribute method for the characterization and quality control of therapeutic monoclonal antibodies

ABSTRACT The multi-attribute method (MAM) has garnered attention as a new quality control method of therapeutic monoclonal antibodies (mAbs). MAM analysis allows multiple relative quantifications of several structural attributes of therapeutic mAbs; however, some issues remain to be addressed in its procedures especially for sample preparation. The goal of this study was to optimize the sample preparation method for MAM analysis of mAbs. Using a model mAb, we compared five sample preparation methods based on sequence coverage, peptide redundancy, missed cleavage and chemical deamidation. It was found that low pH buffer and short digestion time reduced artificial deamidation. The desalting process after carboxymethylation was essential to obtaining high sequence coverage by a short digestion time. The generation of missed cleavage peptides was also improved by using a trypsin/lysyl endopeptidase (Lys-C) mixture. Next, we evaluated the usefulness of our method as a part of MAM analysis. Finally, 17 glycopeptides, 2 deamidated peptides and N- and C-terminal peptides of the heavy chain were successfully monitored with acceptable mass accuracy and coefficient of variation (CV, %) of the relative peak area. On the other hand, 4 oxidated peptides indicated the unavoidable slightly higher inter-assay CV (%) of the peak area ratio due to the instability in the MS sample solution. Collectively, we demonstrated that our method was applicable as an easy and reliable sample preparation method for MAM analysis, and the variation in the relative peak area could be influenced by the modification type rather than by the amount of each peptide.

Research of “double desserts” seismic prediction method for tight sandstone - Taking Linxing block on the east edge of Ordos Basin as an example

The determination of the sweet spot of tight sandstone reservoir is the primary problem in the exploration and development of tight sandstone reservoir. Practice has proved that the prediction of reservoir spatial pore characteristics, thickness distribution and oil and gas potential (i.e. geological sweet spot) of tight sandstone reservoir can not meet the demand of exploration and development of tight sandstone reservoir. In view of the requirements of tight sandstone fracturing engineering, it is also necessary to analyze the engineering field of the reservoir. In this paper, based on seismic data preprocessing and petrophysical analysis, thin reservoir quantitative characterization and AVO fluid prediction are used to evaluate the tight reservoir. At the same time, combining with the fracture analysis method of FMI imaging logging, pre stack fracture description and brittleness prediction, the “engineering sweet spot” prediction is carried out. Finally, the multi-attribute RGB digital fusion visualization research method is used to comprehensively evaluate the tight reservoir. The practice shows that the research method has guiding significance for the exploration and development of tight reservoir.

A prospective multi-attribute decision making-based reliability allocation method using fuzzy linguistic approach and minimum effort function

A prospective multi-attribute decision making-based reliability allocation method using fuzzy linguistic approach and minimum effort function

Analysis of preferences of agro-pastoralists for the attributes of traction dromedaries in harness cultivation: A case study of the Koro district of Mali

The aim of this study was to characterize agro-pastoralists’ preferences in dromedary rearing in the Koro district of Mali, which has experienced a decade of spontaneous development in using dromedaries for harnessed cultivation. Five criteria for assessment of dromedary types used for traction were identified through nine focus group discussions. The assessment criteria developed and estimated rates were the morphological characteristics (23.2%), disease resistance (14.1%), work attitude (26.3%), colour of the coat (25.3%) and docility (11.1%). A multi-attribute analysis method was applied with 115 dromedary owners. Conditional logit was used to estimate utility function and willingness to pay (WTP) for different attributes. Work attitude seems to be an important parameter for decision-making with a WTP amounting to 558.94 euros (614.94 USD). Disease resistance comes second position in decision-making for the selection of dromedaries, with a WTP of 348.77 euros (383.40 USD). To a lesser extent, the white coat with a WTP in the order of 54.45 euros (59.86 USD) intervenes in agro-pastoral decision-making. The agro-pastoralists have a strong reluctance for the dark brown coat. In conclusion, the present results showed that further studies should be conducted on materials and traction technologies to promote harnessed cultivation by dromedaries to preserve and strengthen the multi-functional nature of the dromedary.

Multi-Attribute Method for Quality Control of Therapeutic Proteins.

Recent advances in high resolution mass spectrometry (MS) instrumentation and semi-automated software have led to a push toward the use of MS-based methods for quality control (QC) testing of therapeutic proteins in a cGMP environment. The approach that is most commonly being proposed for this purpose is known as the multi-attribute method (MAM). MAM is a promising approach that provides some distinct benefits compared to conventional methods currently used for QC testing of protein therapeutics, such as CEX, HILIC, and CE-SDS. Because MS-based methods have not been regularly used in this context in the past, new scientific and regulatory questions should be addressed prior to the final stages of implementation. We have categorized these questions into four major aspects for MAM implementation in a cGMP environment for both new and existing products: risk assessment, method validation, capabilities and specificities of the New Peak Detection (NPD) feature, and comparisons to conventional methods. This perspective outlines considerations for each of these main points and suggests approaches to help address potential issues.

Reliable LC-MS Multiattribute Method for Biotherapeutics by Run-Time Response Calibration.

A major challenge of a mass-spectrometry-based quantitative multiattribute method (MAM) for biotherapeutics is its high variability between instruments. For reproducible attribute measurements, not only is a similar instrument model required, but the instruments must also be tuned to the same condition. This poses great long-term challenges, considering the rapid development of new instrumentations. In addition, differences in digestion efficiency, peptide recovery, and artificial modifications during sample preparation also contribute to variability between laboratories. To overcome these challenges, new mathematical methods are developed to calculate the attribute abundance in the sample, using the reference standard (RS) material as calibrant. Most quality attributes in the RS remain constant throughout the life of the standard, and therefore, the RS can serve as a calibrant to correct for the difference between instruments or sample preparation procedures. Because RS data are usually collected in a MAM assay, no additional work is required from the analyst. Data from a large number of attributes demonstrated that these methodologies greatly reduced instrument-to-instrument and sample preparation variabilities. With these methodologies, a consistent instrument model and sample preparation procedure is no longer a requirement. As a result, changes in digestion procedure and advances in instrumentations will not significantly affect the assay result.