About this Research Topic
Attrition due to safety attrition remains a problem for the pharmaceutical industry. Traditionally, in vivo animal studies have been used to derisk new drugs and years of data are available for most large pharma, often though in multiple formats and kept in a variety of systems/places. In the meantime, the debate on the use of animals has moved on for assessing chemical risk under 3Rs principle, especially in the European Union (EU) where animal testing for cosmetics was phased out from 2004 with a total ban in 2013. These animal-free approaches have tremendous value to support drug discovery and development, particularly for drug safety.
In recent years many large pharma have adopted the discovery toxicology paradigms which use in silico methodology and simple in vitro testing to predict potential adverse human outcomes. The challenge here is the extrapolation from in silico and in vitro to animal if not human outcome. Often in vivo animal data are inconsistent with respect to pathology nomenclature and study duration. In vitro data often lack reproducibility and statistical relevance. Data storage and retrieval for both in vivo and in vitro data is often challenging. The desire to accurately predict using one’s own data requires an infrastructure that utilizes data storage, machine learning, a variety of software applications for in silico modeling and the ability to process and model high throughput high content data. Some the 3Rs approaches such as Adverse Outcome Pathways (AOPs), in vitro-in vivo extrapolations (IVIVE) could play a role through linking and integrating diverse datasets for drug safety.
This special volume will discuss the new data streams and approaches from emerging methodologies to support drug safety assessment involving the entire drug discovery and development process. Topics will be include, but are not limited to,
(1) how in vitro approaches, toxicogenomics and in silico approaches impact drug safety assessment,
(2) how reproducibility of these new tools and methodologies are critical in supporting drug safety assessment;
(3) how artificial intelligence and deep learning algorithms could play a role in predicting drug safety;
(4) how adverse outcome pathways (AOP) development and read-across strategy can be used for drug safety; finally
(5) how these new methodologies are relevant to regulatory decision-making.
The special volume welcome a broad spectrum of article remits / types.
Keywords: Drug Safety, biomarkers, pharma, risk, 3Rs, animal testing, drug development, toxicology, data storage, machine learning, deep learning, Adverse Outcome Pathways, in vitro-in vivo, toxicogenomics, artificial intelligence, read-across
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