Computer systems for the prediction of toxicity: an update

Greene, Nigel

doi:10.1016/s0169-409x(02)00012-1

Cited by 180 publications

(107 citation statements)

References 27 publications

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“…this is a major difference to experimental approaches. An important consideration will thus be whether in silico methods are limited by the limitations of their input and whether we have any hope of overcoming their weaknesses or can only approximate them… there are some excellent introductions to and reviews of computational toxicology (Durham and Pearl, 2001;van de Waterbeemd, 2002;Greene, 2002;Veith, 2004, Helma, 2005Simon-Hettich et al, 2006;Kavlock et al, 2008;Merlot, 2008;Nigsch et al, 2009;Greene and Naven, 2009). In addition, the ex-eCB website (http://ecb.jrc.ec.europa.eu/qsar/), hosted by Andrew Worth and his team (chronically understaffed given the high expectations) who act as key promoters of computational toxicology, is an excellent resource.…”

Section: Food For Thought … On In Silico Methods In Toxicologymentioning

confidence: 99%

Food for thought … on in silico methods in toxicology

Hartung

2009

ALTEX

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this series of articles has already addressed in vitro and in vivo tools (Hartung, 2007;Hartung, 2008b). With this contribution addressing computational toxicology we now try to complete considerations on the main methodological approaches in toxicology. However, we do still plan to address specific aspects, such as endpoints (omics, image technologies), high-throughput testing or physiology-based pharmaco-(toxico-)kinetic modelling (PBPK), in later issues. All of these aspects have major in silico components, which already shows how difficult it is to discuss in silico methods on their own. Indeed, their integration and interplay with in vivo and in vitro approaches is critical, at least in the way their development often depends critically on the input of either in vitro or in vivo data. this is a major difference to experimental approaches. An important consideration will thus be whether in silico methods are limited by the limitations of their input and whether we have any hope of overcoming their weaknesses or can only approximate them… there are some excellent introductions to and reviews of

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Section: Food For Thought … On In Silico Methods In Toxicologymentioning

confidence: 99%

Food for thought … on in silico methods in toxicology

Hartung

2009

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“…For the Ames test, the estimated inter-laboratory reproducibility is only 85% due to the limitation of the in vitro test itself (Greene, 2002). Setting up good computational models to replace the repeating in vitro Ames test is valuable (Xu et al 2012).…”

Section: Genotoxicitymentioning

confidence: 99%

In silicoADME/T modelling for rational drug design

Wang

Xing

Yue

et al. 2015

Quart. Rev. Biophys.

247

155

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Abstract. In recent decades, in silico absorption, distribution, metabolism, excretion (ADME), and toxicity (T) modelling as a tool for rational drug design has received considerable attention from pharmaceutical scientists, and various ADME/T-related prediction models have been reported. The high-throughput and low-cost nature of these models permits a more streamlined drug development process in which the identification of hits or their structural optimization can be guided based on a parallel investigation of bioavailability and safety, along with activity. However, the effectiveness of these tools is highly dependent on their capacity to cope with needs at different stages, e.g. their use in candidate selection has been limited due to their lack of the required predictability. For some events or endpoints involving more complex mechanisms, the current in silico approaches still need further improvement. In this review, we will briefly introduce the development of in silico models for some physicochemical parameters, ADME properties and toxicity evaluation, with an emphasis on the modelling approaches thereof, their application in drug discovery, and the potential merits or deficiencies of these models. Finally, the outlook for future ADME/T modelling based on big data analysis and systems sciences will be discussed.

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“…The current approaches for in silico prediction of toxicity can be divided into two basic categories: knowledgebased and statistically-based [174,175]. Knowledge-based approaches use rules about generalized relationships between structure and biological activity that are derived from human expert opinion and interpretation of toxicological data to predict the potential toxicity of novel structure.…”

Section: In Silico Prediction Of Toxicitymentioning

confidence: 99%

“…accelrys.com). The features, main strength and limitations of these programs have recently been reviewed [174]. The primary emphasis of the current software packages is carcinogenicity and mutagenicity, although some packages do also include models and/or knowledge bases for other endpoints, such as teratogenicity, irritation, sensitization, immunotoxicology and neurotoxicity.…”

Section: In Silico Prediction Of Toxicitymentioning

confidence: 99%

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Recent Development and Application of Virtual Screening in Drug Discovery: An Overview

Hou¹,

Xu²

2012

Frontiers in Medicinal Chemistry - (Volume 3)

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Virtual screening, especially the structure-based virtual screening, has emerged as a reliable, cost-effective and time-saving technique for the discovery of lead compounds. Here, the basic ideas and computational tools for virtual screening have been briefly introduced, and emphasis is placed on aspects of recent development of docking-based virtual screening, scoring functions in molecular docking and ADME/Tox-based virtual screening in the past three years (2000 to 2003). Moreover, successful examples are provided to further demonstrate the effectiveness of virtual screening in drug discovery.

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Computer systems for the prediction of toxicity: an update

Cited by 180 publications

References 27 publications

Food for thought … on in silico methods in toxicology

Food for thought … on in silico methods in toxicology

In silicoADME/T modelling for rational drug design

Recent Development and Application of Virtual Screening in Drug Discovery: An Overview

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