Information Extraction From FDA Drug Labeling to Enhance Product-Specific Guidance Assessment Using Natural Language Processing

Towards the objectives of the UnitedStates Food and Drug Administration (FDA) generic drug science and research program, it is of vital importance in developing product-specific guidances (PSGs) with recommendations that can facilitate and guide generic product development. To generate a PSG, the assessor needs to retrieve supportive information about the drug product of interest, including from the drug labeling, which contain comprehensive information about drug products and instructions to physicians on how to use the products for treatment. Currently, although there are many drug labeling data resources, none of them including those developed by the FDA (e.g., Drugs@FDA) can cover all the FDA-approved drug products. Furthermore, these resources, housed in various locations, are often in forms that are not compatible or interoperable with each other. Therefore, there is a great demand for retrieving useful information from a large number of textual documents from different data resources to support an effective PSG development. To meet the needs, we developed a Natural Language Processing (NLP) pipeline by integrating multiple disparate publicly available data resources to extract drug product information with minimal human intervention. We provided a case study for identifying food effect information to illustrate how a machine learning model is employed to achieve accurate paragraph labeling. We showed that the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model is able to outperform the traditional machine learning techniques, setting a new state-of-the-art for labelling food effect paragraphs from drug labeling and approved drug products datasets

A multi-module artificial neural network approach to pattern recognition with optimized nanostructured sensor array

Abstract The selection of appropriate sensing array nanomaterials and the pattern recognition of sensing signals are two challenges for the development of sensitive, selective, and cost-effective sensor array systems. To tackle both challenges, the work described in this paper focuses on the development of a new hybrid method which couples multi-module method with artificial neural networks (ANNs) for the optimization-optimized multi-module ANN classifier (OMAC) to enhance the correct detection rate for multiple volatile organic compounds (VOCs). In this OMAC method, each module is dedicated to a group of VOCs with specific inputs. Each sensor element's selectivity is quantitatively evaluated to assist the selection of sensing array materials, which also facilitates the selection of inputs to each dedicated neural network module. This OMAC method is shown to be useful for achieving a high overall recognition rate for a selected set of vapor analytes. The results are discussed, along with the implications to the better design of ANN pattern classifiers in chemical sensor applications

Sensing Arrays Constructed from Nanoparticle Thin Films and Interdigitated Microelectrodes

This paper describes the results of a study of a few design parameters influencingthe performance of sensor arrays constructed from nanostructured thin films andinterdigitated microelectrodes (IMEs). The nanostructured thin films on the IME deviceswere prepared from nonanedithiol (NDT) and mercaptoundecanoic acid (MUA) linkedassemblies of 2-nm sized gold nanoparticles. The sensor array data in response to volatileorganic compounds were collected and analyzed using fractional factorial experimentaldesign and analysis of variance for understanding effects of the design parameters on thesensitivity. While the smaller value for the microelectrode space, width, and lengthgenerally led to higher response sensitivity, a strong dependence on the nature of thenanostructured thin films was found. The microelectrode space was the most importantdesign parameter for NDT-based thin films. However, the microelectrode space, width, andlength were found to play almost equally important roles for MUA-based thin films. Theprincipal component analysis results for classification performances of the arrays consistingof a set of thin films have demonstrated the possibility of optimizing sensor arrays byappropriate selections of microelectrode parameters and nanostructured sensing films