Data Standardization, Pharmaceutical Drug Development, and the 3Rs.

ILAR J

Laura Kaufman, PhD, DABT, is an independent consultant based out of New Jersey. Katrina Gore, BSc, MSc, is the founder of KDL Gore Solutions Ltd, Hertfordshire, England. Joyce Chandler Zandee, MS, is Chief Operating Officer at Integrated Nonclinical Development Solutions, Inc, Ann Arbor, Michigan.

Published: December 2016

Despite the efforts, cost, and extensive use of animals for nonclinical research, only a small number of studies have methodically compared findings from animal toxicology studies to those from human clinical trials. Impediments to understanding the translation of nonclinical safety have included the lack of easy access to data and the need for extensive data curation given the diverse terminologies, formats, and data platforms in use. SEND and SDTM study data standards, developed by CDISC and about to become mandated by FDA, can address this and other drug development issues by facilitating access to data in ways that are not currently feasible. A consistent data standard across clinical and nonclinical will discourage the development of data silos, which easily become obstacles to data sharing and maximizing the value of animal and human data. The confluence of rapid scientific advances, increasingly larger quantities of diverse data, technological advances in data mining, and the FDA's requirements for standardized study data create new opportunities for the advancement of drug development and for refinement in the way we use animals.

Download full-text PDF

Source
http://dx.doi.org/10.1093/ilar/ilw030DOI Listing

Publication Analysis

Top Keywords

data
13
drug development
12
access data
8
study data
8
data standardization
4
standardization pharmaceutical
4
pharmaceutical drug
4
development
4
development 3rs
4
3rs despite
4

Similar Publications

Computer simulation was utilized to characterize the electrophoretic processes occurring during the enantioselective capillary electrophoresis-mass spectrometry (CE-MS) analysis of ketamine, norketamine, and hydroxynorketamine in a system with partial filling of the capillary with 19 mM (equals 5%) of highly sulfated γ-cyclodextrin (HS-γ-CD) and analyte detection on the cathodic side. Provided that the sample is applied without or with a small amount of the chiral selector, analytes become quickly focused and separated in the thereby formed HS-γ-CD gradient at the cathodic end of the sample compartment. This gradient broadens with time, remains stationary, and gradually reduces its span from the lower side due to diffusion such that analytes with high affinity to the anionic selector become released onto the other side of the focusing gradient where anionic migration and defocusing occur concomitantly.

View Article and Find Full Text PDF

Concomitant Waldenström Macroglobulinemia/Lymphoplasmacytic Lymphoma and Non-Immunoglobulin M Plasma Cell Neoplasm.

Arch Pathol Lab Med

January 2025

the Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles (Petersen, Stuart, He, Ju, Ghezavati, Siddiqi, Wang).

Context.—: The co-occurrence of plasma cell neoplasm (PCN) and lymphoplasmacytic lymphoma (LPL) is rare, and their clonal relationship remains unclear.

Objective.

View Article and Find Full Text PDF

TRIAGE: an R package for regulatory gene analysis.

Brief Bioinform

November 2024

Institute for Molecular Bioscience, The University of Queensland, 306 Carmody Road, St Lucia, Brisbane, QLD 4072, Australia.

Regulatory genes are critical determinants of cellular responses in development and disease, but standard RNA sequencing (RNA-seq) analysis workflows, such as differential expression analysis, have significant limitations in revealing the regulatory basis of cell identity and function. To address this challenge, we present the TRIAGE R package, a toolkit specifically designed to analyze regulatory elements in both bulk and single-cell RNA-seq datasets. The package is built upon TRIAGE methods, which leverage consortium-level H3K27me3 data to enrich for cell-type-specific regulatory regions.

View Article and Find Full Text PDF

Deep learning in integrating spatial transcriptomics with other modalities.

Brief Bioinform

November 2024

State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, 2 Sipailou, Xuanwu District, Nanjing 210096, China.

Spatial transcriptomics technologies have been extensively applied in biological research, enabling the study of transcriptome while preserving the spatial context of tissues. Paired with spatial transcriptomics data, platforms often provide histology and (or) chromatin images, which capture cellular morphology and chromatin organization. Additionally, single-cell RNA sequencing (scRNA-seq) data from matching tissues often accompany spatial data, offering a transcriptome-wide gene expression profile of individual cells.

View Article and Find Full Text PDF

CellMsg: graph convolutional networks for ligand-receptor-mediated cell-cell communication analysis.

Brief Bioinform

November 2024

College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China.

The role of cell-cell communications (CCCs) is increasingly recognized as being important to differentiation, invasion, metastasis, and drug resistance in tumoral tissues. Developing CCC inference methods using traditional experimental methods are time-consuming, labor-intensive, cannot handle large amounts of data. To facilitate inference of CCCs, we proposed a computational framework, called CellMsg, which involves two primary steps: identifying ligand-receptor interactions (LRIs) and measuring the strength of LRIs-mediated CCCs.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!