The Experts below are selected from a list of 16269 Experts worldwide ranked by ideXlab platform
Per Holm - One of the best experts on this subject based on the ideXlab platform.
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Roller compaction scale-up using roll width as scale factor and laser-based determined ribbon porosity as critical Material Attribute
European Journal of Pharmaceutical Sciences, 2016Co-Authors: Morten Alleso, Rene Holm, Per HolmAbstract:Due to the complexity and difficulties associated with the mechanistic modeling of roller compaction process for scale-up, an innovative equipment approach is to keep roll diameter fixed between scales and instead vary the roll width. Assuming a fixed gap and roll force, this approach should create similar conditions for the nip regions of the two compactor scales, and thus result in a scale-reproducible ribbon porosity. In the present work a non-destructive laser-based technique was used to measure the ribbon porosity at-line with high precision and high accuracy as confirmed by an initial comparison to a well-established volume displacement oil intrusion method. The ribbon porosity was found to be scale-independent when comparing the average porosity of a group of ribbon samples (n = 12) from small-scale (Mini-Pactor®) to large-scale (Macro-Pactor®). A higher standard deviation of ribbons fragment porosities from the large-scale roller compactor was Attributed to minor variations in powder densification across the roll width. With the intention to reproduce ribbon porosity from one scale to the other, process settings of roll force and gap size applied to the Mini-Pactor® (and identified during formulation development) were therefore directly transferrable to subsequent commercial scale production on the Macro-Pactor®. This creates a better link between formulation development and tech transfer and decreases the number of batches needed to establish the parameter settings of the commercial process.
E.t. Fox - One of the best experts on this subject based on the ideXlab platform.
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Incorporation of an item/Material Attribute system into PAMTRAK
1994Co-Authors: D.a. Anspach, I.g. Waddoups, E.t. FoxAbstract:The Department of Energy (DOE) mission is changing due to the number of nuclear weapon reductions by the United States and the former Soviet Union with long-term storage requirements for DOE sites increasing. New technology to ensure the integrity of special nuclear Material (SNM) in storage is available to sites to supplement manual physical inventories. This allows them to decrease operating costs while keeping radiation exposure at minimal levels. We have developed a generic, real time, personnel tracking and Material monitoring system named PAMTRAK. Such a system can significantly reduce the number of required, manual physical inventories at DOE sites while increasing assurance that an insider has not diverted or stolen Material. Until recently Pamtrak used only Material monitoring devices that provided location/containment Attributes. However, Westinghouse Electric Corp. and Metrox, Inc. have recently developed hard-wired item/Material Attribute systems that monitor both temperature and weight. We have incorporated both of these systems into PAMTRAK. If a site employed one of these item/Material Attribute systems, it could decrease its manual inventory frequency to three years. This paper describes how a site might implement such a system to meet the DOE`s requirements.
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incorporation of an item Material Attribute system into pamtrak
1994Co-Authors: D.a. Anspach, I.g. Waddoups, E.t. FoxAbstract:The Department of Energy (DOE) mission is changing due to the number of nuclear weapon reductions by the United States and the former Soviet Union with long-term storage requirements for DOE sites increasing. New technology to ensure the integrity of special nuclear Material (SNM) in storage is available to sites to supplement manual physical inventories. This allows them to decrease operating costs while keeping radiation exposure at minimal levels. We have developed a generic, real time, personnel tracking and Material monitoring system named PAMTRAK. Such a system can significantly reduce the number of required, manual physical inventories at DOE sites while increasing assurance that an insider has not diverted or stolen Material. Until recently Pamtrak used only Material monitoring devices that provided location/containment Attributes. However, Westinghouse Electric Corp. and Metrox, Inc. have recently developed hard-wired item/Material Attribute systems that monitor both temperature and weight. We have incorporated both of these systems into PAMTRAK. If a site employed one of these item/Material Attribute systems, it could decrease its manual inventory frequency to three years. This paper describes how a site might implement such a system to meet the DOE`s requirements.
Lawrence X Yu - One of the best experts on this subject based on the ideXlab platform.
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Quality by Design: Concepts for ANDAs
The AAPS Journal, 2008Co-Authors: Robert A. Lionberger, Sau Lawrence Lee, Laiming Lee, Andre Raw, Lawrence X YuAbstract:Quality by design is an essential part of the modern approach to pharmaceutical quality. There is much confusion among pharmaceutical scientists in generic drug industry about the appropriate element and terminology of quality by design. This paper discusses quality by design for generic drugs and presents a summary of the key terminology. The elements of quality by design are examined and a consistent nomenclature for quality by design, critical quality Attribute, critical process parameter, critical Material Attribute, and control strategy is proposed. Agreement on these key concepts will allow discussion of the application of these concepts to abbreviated new drug applications to progress.
Omar Sprockel - One of the best experts on this subject based on the ideXlab platform.
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Attribute-Based Design Space: Materials-Science-Based Quality-By-Design for Operational Flexibility and Process Portability
Journal of Pharmaceutical Innovation, 2011Co-Authors: James N. Michaels, Holly Bonsignore, Buffy L. Hudson-curtis, Steven Laurenz, Thomas Mathai, Girish Pande, Ashlesh Sheth, Omar SprockelAbstract:In April, 2009, the Pharmaceutical Research and Manufacturers of America (PhRMA) Drug Product Technical Group sponsored an industry workshop to explore the practicality and limitations of defining a design space strictly in terms of Material Attributes rather than process variables. This Material-Attribute design space would be independent of scale and configuration of process equipment and the associated process variables. For this reason, it would be portable in the sense that post-approval changes of equipment scale, nameplate, or location would not require regulatory approval. This paper summarizes and expands on the output of the workshop. A key concept that underlies this work is that the performance of a drug product is determined by its structure. The control objective of a manufacturing process is to assemble the components of the product into this structure. This is achieved by controlling the Attributes of raw Materials and process intermediates from each step in the production train within specified ranges, i.e., by operating within a Material-Attribute design space. In this paper, we explore the development, implementation, and limitations of an Attribute-based design space. We show that developing the design space and translating it into process conditions and manufacturing instructions for specific process trains requires the development of thorough process understanding. Thus, this concept is fully consistent with the principles of quality by design. While implementation of the concept developed in this paper is not endorsed by regulatory agencies and would require changes to relevant guidances and regulations, we believe it would provide the quality assurance required by regulators and the operational and process flexibility desired by manufacturers.
Ko Nishino - One of the best experts on this subject based on the ideXlab platform.
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Integrating Local Material Recognition with Large-Scale Perceptual Attribute Discovery
arXiv: Computer Vision and Pattern Recognition, 2016Co-Authors: Gabriel Schwartz, Ko NishinoAbstract:Material Attributes have been shown to provide a discriminative intermediate representation for recognizing Materials, especially for the challenging task of recognition from local Material appearance (i.e., regardless of object and scene context). In the past, however, Material Attributes have been recognized separately preceding category recognition. In contrast, neuroscience studies on Material perception and computer vision research on object and place recognition have shown that Attributes are produced as a by-product during the category recognition process. Does the same hold true for Material Attribute and category recognition? In this paper, we introduce a novel Material category recognition network architecture to show that perceptual Attributes can, in fact, be automatically discovered inside a local Material recognition framework. The novel Material-Attribute-category convolutional neural network (MAC-CNN) produces perceptual Material Attributes from the intermediate pooling layers of an end-to-end trained category recognition network using an auxiliary loss function that encodes human Material perception. To train this model, we introduce a novel large-scale database of local Material appearance organized under a canonical Material category taxonomy and careful image patch extraction that avoids unwanted object and scene context. We show that the discovered Attributes correspond well with semantically-meaningful visual Material traits via Boolean algebra, and enable recognition of previously unseen Material categories given only a few examples. These results have strong implications in how perceptually meaningful Attributes can be learned in other recognition tasks.
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Discovering Perceptual Attributes in a Deep Local Material Recognition Network.
arXiv: Computer Vision and Pattern Recognition, 2016Co-Authors: Gabriel Schwartz, Ko NishinoAbstract:Perceptual Material Attributes, intrinsic visual properties of Materials, are being studied in parallel in computer vision and human vision. In neuroscience, perceptual Attributes are shown to be an integral part of the human neural response during Material recognition. In computer vision, however, they are merely an intermediate representation of Materials and not integrated into the recognition process. In this paper, we show that perceptual Material Attributes can indeed be found inside a framework for local, patch-based, object-independent Material recognition. We introduce a new CNN architecture, the Material Attribute-category CNN (MAC-CNN), that uses deep weak supervision to simultaneously classify Materials and discover per-pixel perceptual Attributes. We show that these Attributes conform with past semantic Material Attributes and enhance recognition of novel Materials. We also introduce an extensive new database for local Material recognition. Our results show that the internal representation of the MAC-CNN generalizes well and agrees with human perception, which has potential implications for our understanding of human Material perception as well as applications in object recognition.