The ICH M7 guidelines for the assessment and control of DNA reactive (mutagenic) impurities in pharmaceuticals allows for the consideration of in silico predictions in place of in vitro studies. This ...represents a significant advance in the acceptance of (Q)SAR models and has resulted from positive interactions between modellers, regulatory agencies and industry with a shared purpose of developing effective processes to minimise risk. This paper discusses key scientific principles that should be applied when evaluating in silico predictions with a focus on accuracy and scientific rigour that will support a consistent and practical route to regulatory submission.
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•In silico predictions of genotoxicity may be submitted under ICH M7 guidelines.•Expert review is a critical step to ensure accurate and defensible assessments.•This paper establishes best practise including how to challenge a prediction.
There is a general consensus that supports the need for standardized reporting of metadata or information describing large-scale metabolomics and other functional genomics data sets. Reporting of ...standard metadata provides a biological and empirical context for the data, facilitates experimental replication, and enables the re-interrogation and comparison of data by others. Accordingly, the Metabolomics Standards Initiative is building a general consensus concerning the minimum reporting standards for metabolomics experiments of which the Chemical Analysis Working Group (CAWG) is a member of this community effort. This article proposes the minimum reporting standards related to the chemical analysis aspects of metabolomics experiments including: sample preparation, experimental analysis, quality control, metabolite identification, and data pre-processing. These minimum standards currently focus mostly upon mass spectrometry and nuclear magnetic resonance spectroscopy due to the popularity of these techniques in metabolomics. However, additional input concerning other techniques is welcomed and can be provided via the CAWG on-line discussion forum at http://msi-workgroups.sourceforge.net/ or http://Msi-workgroups-feedback@lists.sourceforge.net. Further, community input related to this document can also be provided via this electronic forum.
The toxicokinetics and biotransformation of methyl-
tert.butyl ether (MTBE), ethyl-
tert.butyl ether (ETBE) and
tert.amyl-methyl ether (TAME) in rats and humans are summarized. These ethers are used ...as gasoline additives in large amounts, and thus, a considerable potential for human exposure exists. After inhalation exposure MTBE, ETBE and TAME are rapidly taken up by both rats and humans; after termination of exposure, clearance by exhalation and biotransformation to urinary metabolites is rapid in rats. In humans, clearance by exhalation is slower in comparison to rats. Biotransformation of MTBE and ETBE is both qualitatively and quantitatively similar in humans and rats after inhalation exposure under identical conditions. The extent of biotransformation of TAME is also quantitatively similar in rats and humans; the metabolic pathways, however, are different. The results suggest that reactive and potentially toxic metabolites are not formed during biotransformation of these ethers and that toxic effects of these compounds initiated by covalent binding to cellular macromolecules are unlikely.
In silico toxicology (IST) approaches to rapidly assess chemical hazard, and usage of such methods is increasing in all applications but especially for regulatory submissions, such as for assessing ...chemicals under REACH as well as the ICH M7 guideline for drug impurities. There are a number of obstacles to performing an IST assessment, including uncertainty in how such an assessment and associated expert review should be performed or what is fit for purpose, as well as a lack of confidence that the results will be accepted by colleagues, collaborators and regulatory authorities. To address this, a project to develop a series of IST protocols for different hazard endpoints has been initiated and this paper describes the genetic toxicity in silico (GIST) protocol. The protocol outlines a hazard assessment framework including key effects/mechanisms and their relationships to endpoints such as gene mutation and clastogenicity. IST models and data are reviewed that support the assessment of these effects/mechanisms along with defined approaches for combining the information and evaluating the confidence in the assessment. This protocol has been developed through a consortium of toxicologists, computational scientists, and regulatory scientists across several industries to support the implementation and acceptance of in silico approaches.
The sharing of legacy preclinical safety data among pharmaceutical companies and its integration with other information sources offers unprecedented opportunities to improve the early assessment of ...drug safety. Here, we discuss the experience of the eTOX project, which was established through the Innovative Medicines Initiative to explore this possibility.
A substantial amount of mutagenicity data on acyl/sulfonyl halides is available in the public domain, and these data are the basis for many in silico models of mutagenicity (e.g., Derek Nexus and ...Leadscope). A review of these data indicates that the perceived mutagenic potential of this class of compounds is based on a number of nonreproducible positive findings in the bacterial mutagenicity assay and positive bacterial mutagenicity data on a series of compounds where formation of reactive halodimethyl sulfides (HDMSs) in DMSO may have compromised the interpretation of the Ames data (HDMSs are typically mutagenic). The only genuine mutagenic, genotoxic, and carcinogenic compound within the 50+ acyl/sulfonyl halides described herein is dimethylcarbamic chloride, which is appropriately considered to be a potential human carcinogen. Some in silico systems, such as Derek Nexus, contain rules detailing that the activity of this class should be considered a false positive flag for mutagenicity, and ideally, any in silico structure–activity rules for mutagenicity in other systems should likewise be addressed. The data presented here support the view that these alerts should currently be interpreted as a false positive flag for mutagenicity and that the entire class should be viewed as a low concern from a mutagenicity perspective. The formation of these reactive HDMSs is an example of the classical Pummerer rearrangement. The chemical reactivity of this class of compounds also supports the contention that they are of limited concern from a mutagenic and carcinogenic impurity risk perspective when used in the synthesis of drug products. They can be expected to rapidly purge from any reaction sequence with generic predicted purge factors in the range from 1 × 103 to 3 × 105 per stage and hence should be effectively eliminated at the stage of introduction. We would therefore recommend avoiding the use of DMSO as the solvent for mutagenicity tests with acyl/sulfonyl halides because of the potential for false positive results arising from DMSO reaction products, which are not relevant under aqueous, physiological conditions. Furthermore, as indicated by the Ames test data for mesyl chloride/2-fluorobenzoyl chloride, even non-DMSO organic solvents may not be appropriate for certain members of this class (acyl/sulfonyl halides), suggesting that they may not be amenable to adequate testing in the Ames assay.
The present study applies a systems biology approach for the in silico predictive modeling of drug toxicity on the basis of high-quality preclinical drug toxicity data with the aim of increasing the ...mechanistic understanding of toxic effects of compounds at different levels (pathway, cell, tissue, organ). The model development was carried out using 77 compounds for which gene expression data for treated primary human hepatocytes is available in the LINCS database and for which rodent in vivo hepatotoxicity information is available in the eTOX database. The data from LINCS were used to determine the type and number of pathways disturbed by each compound and to estimate the extent of disturbance (network perturbation elasticity), and were used to analyze the correspondence with the in vivo information from eTOX. Predictive models were developed through this integrative analysis, and their specificity and sensitivity were assessed. The quality of the predictions was determined on the basis of the area under the curve (AUC) of plots of true positive vs. false positive rates (ROC curves). The ROC AUC reached values of up to 0.9 (out of 1.0) for some hepatotoxicity endpoints. Moreover, the most frequently disturbed metabolic pathways were determined across the studied toxicants. They included, e.g., mitochondrial beta-oxidation of fatty acids and amino acid metabolism. The process was exemplified by successful predictions on various statins. In conclusion, an entirely new approach linking gene expression alterations to the prediction of complex organ toxicity was developed and evaluated.
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