کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
8513361 1556494 2018 27 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Deep Convolutional Neural Network Analysis of Flow Imaging Microscopy Data to Classify Subvisible Particles in Protein Formulations
ترجمه فارسی عنوان
تجزیه و تحلیل شبکه عصبی مصنوعی عمیق داده های میکروسکوپ تصویری جریان برای طبقه بندی ذرات ذاتا در فرمولاسیون پروتئین
کلمات کلیدی
موضوعات مرتبط
علوم پزشکی و سلامت داروسازی، سم شناسی و علوم دارویی اکتشاف دارویی
چکیده انگلیسی
Flow-imaging microscopy (FIM) is commonly used to characterize subvisible particles in therapeutic protein formulations. Although pharmaceutical companies often collect large repositories of FIM images of protein therapeutic products, current state-of-the-art methods for analyzing these images rely on low-dimensional lists of “morphological features” to characterize particles that ignore much of the information encoded in the existing image databases. Deep convolutional neural networks (sometimes referred to as “CNNs or ConvNets”) have demonstrated the ability to extract predictive information from raw macroscopic image data without requiring the selection or specification of “morphological features” in a variety of tasks. However, the inherent heterogeneity of protein therapeutics and optical phenomena associated with subvisible FIM particle measurements introduces new challenges regarding the application of ConvNets to FIM image analysis. We demonstrate a supervised learning technique leveraging ConvNets to extract information from raw images in order to predict the process conditions or stress states (freeze-thawing, mechanical shaking, etc.) that produced a variety of different protein particles. We demonstrate that our new classifier, in combination with a “data pooling” strategy, can nearly perfectly differentiate between protein formulations in a variety of scenarios of relevance to protein therapeutics quality control and process monitoring using as few as 20 particles imaged via FIM.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Pharmaceutical Sciences - Volume 107, Issue 4, April 2018, Pages 999-1008
نویسندگان
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