Computational toxicology in drug development

Muster, Wolfgang; Breidenbach, Alexander; Fischer, Holger; Kirchner, Stephan; Müller, Lutz; Pähler, Axel

doi:10.1016/j.drudis.2007.12.007

Cited by 194 publications

(108 citation statements)

References 53 publications

Supporting

Mentioning

108

Contrasting

Order By: Relevance

“…Although in silico models provide a rapid return of results and do not require chemical substances (Muster et al 2008), these models alone are insufficient for a reliable prediction of clinically relevant human hepatotoxicity. Liu et al identified 13 side effects that collectively provided an indication for DILI and that were further translated via an in silico approach to develop a DILI prediction system .…”

Section: Discussionmentioning

confidence: 99%

A testing strategy to predict risk for drug-induced liver injury in humans using high-content screen assays and the ‘rule-of-two’ model

et al. 2014

View full text Add to dashboard Cite

Drug-induced liver injury (DILI) is a major cause of drug failures in both the preclinical and clinical phase. Consequently, improving prediction of DILI at an early stage of drug discovery will Correspondence to: Weida Tong. Jürgen Borlak and Weida Tong have contributed equally to the manuscript.Disclaimer: The views presented in this article do not necessarily reflect current or future opinion or policy of the US Food and Drug Administration. Any mention of commercial products is for clarification and not intended as endorsement. Electronic supplementary materialThe online version of this article (doi:10.1007/s00204-014-1276-9) contains supplementary material, which is available to authorized users. HHS Public AccessAuthor manuscript Arch Toxicol. Author manuscript; available in PMC 2018 January 04.Published in final edited form as:Arch Toxicol. 2014 July ; 88(7): 1439-1449. doi:10.1007/s00204-014-1276-9. Author Manuscript Author ManuscriptAuthor ManuscriptAuthor Manuscript reduce the potential failures in the subsequent drug development program. In this regard, highcontent screening (HCS) assays are considered as a promising strategy for the study of DILI; however, the predictive performance of HCS assays is frequently insufficient. In the present study, a new testing strategy was developed to improve DILI prediction by employing in vitro assays that was combined with the RO2 model (i.e., 'rule-of-two' defined by daily dose ≥100 mg/day & logP ≥3). The RO2 model was derived from the observation that high daily doses and lipophilicity of an oral medication were associated with significant DILI risk in humans. In the developed testing strategy, the RO2 model was used for the rational selection of candidates for HCS assays, and only the negatives predicted by the RO2 model were further investigated by HCS. Subsequently, the effects of drug treatment on cell loss, nuclear size, DNA damage/fragmentation, apoptosis, lysosomal mass, mitochondrial membrane potential, and steatosis were studied in cultures of primary rat hepatocytes. Using a set of 70 drugs with clear evidence of clinically relevant DILI, the testing strategy improved the accuracies by 10 % and reduced the number of drugs requiring experimental assessment by approximately 20 %, as compared to the HCS assay alone. Moreover, the testing strategy was further validated by including published data (Cosgrove et al. in Toxicol Appl Pharmacol 237:317-330, 2009) on drug-cytokine-induced hepatotoxicity, which improved the accuracies by 7 %. Taken collectively, the proposed testing strategy can significantly improve the prediction of in vitro assays for detecting DILI liability in an early drug discovery phase.

show abstract

Section: Discussionmentioning

confidence: 99%

A testing strategy to predict risk for drug-induced liver injury in humans using high-content screen assays and the ‘rule-of-two’ model

et al. 2014

View full text Add to dashboard Cite

show abstract

“…SciQSAR is a comprehensive QSAR modelling system of good behaviour in hERG toxicity prediction. This system enables researchers to establish reliable QSARs and QSPRs, create new calculators for in silico screening, and generate new compound libraries based on results (Contrera et al 2003;Muster et al 2008). In practice, it is not typically necessary to know the toxicity of a compound.…”

Section: Herg Toxicitymentioning

confidence: 99%

In silicoADME/T modelling for rational drug design

Wang

Xing

Yue

et al. 2015

Quart. Rev. Biophys.

279

155

View full text Add to dashboard Cite

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.

show abstract

“…Published online 16 Drug discovery and development process aims to make available medications that are safe and effective in improving the length and quality of life and relieving pain and suffering. However, the process is very complex, time consuming, resource intensive, requiring multi-disciplinary expertise and innovative approaches.…”

Section: Publisher Intechmentioning

confidence: 99%

“…This in silico approach is thought to obviate some disadvantages of the more traditional approaches (need for large amounts of test agent for in vivo testing, poor predictability of in vivo animal and in vitro models for human toxicity and efficacy, lack of reliable high-throughput in vitro assays and a lack of animal models for some common adverse events seen in humans, e.g. headache, nausea, dizziness) [16]. There are also increasing legal requirements, especially in Europe, for use of alternative, nonanimal models in the regulatory safety assessment of chemicals and urging development, independent assessment and application of computational methods.…”

mentioning

confidence: 99%

Overview of Current Drug Discovery and Development with an Eye towards the Future

Kapetanović¹

2011

Drug Discovery and Development - Present and Future

View full text Add to dashboard Cite

Computational toxicology in drug development

Cited by 194 publications

References 53 publications

A testing strategy to predict risk for drug-induced liver injury in humans using high-content screen assays and the ‘rule-of-two’ model

A testing strategy to predict risk for drug-induced liver injury in humans using high-content screen assays and the ‘rule-of-two’ model

In silicoADME/T modelling for rational drug design

Overview of Current Drug Discovery and Development with an Eye towards the Future

Contact Info

Product

Resources

About