Assessing the utility of common arguments used in expert review of in silico predictions as part of ICH M7 assessments.

Expert review of two predictions, made by complementary (quantitative) structure-activity relationship models, to an overall conclusion is a key component of using in silico tools to assess the mutagenic potential of impurities as part of the ICH M7 guideline. In lieu of a specified protocol, numerous publications have presented best practise guides, often indicating the occurrence of common prediction scenarios and the evidence required to resolve them. A semi-automated expert review tool has been implemented in Lhasa Limited's Nexus platform following collation of these common arguments and assignment to the associated prediction scenarios made by Derek Nexus and Sarah Nexus. Using datasets primarily donated by pharmaceutical companies, an automated analysis of the frequency these prediction scenarios occur, and the likelihood of the associated arguments assigning the correct resolution, could then be conducted. This article highlights that a relatively small number of common arguments may be used to accurately resolve many prediction scenarios to a single conclusion. The use of a standardised method of argumentation and assessment of evidence for a given impurity is proposed to improve the efficiency and consistency of expert review as part of an ICH M7 submission.

In Silico Approaches In Carcinogenicity Hazard Assessment: Current Status and Future Needs.

Historically, identifying carcinogens has relied primarily on tumor studies in rodents, which require enormous resources in both money and time. In silico models have been developed for predicting rodent carcinogens but have not yet found general regulatory acceptance, in part due to the lack of a generally accepted protocol for performing such an assessment as well as limitations in predictive performance and scope. There remains a need for additional, improved in silico carcinogenicity models, especially ones that are more human-relevant, for use in research and regulatory decision-making. As part of an international effort to develop in silico toxicological protocols, a consortium of toxicologists, computational scientists, and regulatory scientists across several industries and governmental agencies evaluated the extent to which in silico models exist for each of the recently defined 10 key characteristics (KCs) of carcinogens. This position paper summarizes the current status of in silico tools for the assessment of each KC and identifies the data gaps that need to be addressed before a comprehensive in silico carcinogenicity protocol can be developed for regulatory use.

Skin sensitization in silico protocol.

The assessment of skin sensitization has evolved over the past few years to include in vitro assessments of key events along the adverse outcome pathway and opportunistically capitalize on the strengths of in silico methods to support a weight of evidence assessment without conducting a test in animals. While in silico methods vary greatly in their purpose and format; there is a need to standardize the underlying principles on which such models are developed and to make transparent the implications for the uncertainty in the overall assessment. In this contribution, the relationship between skin sensitization relevant effects, mechanisms, and endpoints are built into a hazard assessment framework. Based on the relevance of the mechanisms and effects as well as the strengths and limitations of the experimental systems used to identify them, rules and principles are defined for deriving skin sensitization in silico assessments. Further, the assignments of reliability and confidence scores that reflect the overall strength of the assessment are discussed. This skin sensitization protocol supports the implementation and acceptance of in silico approaches for the prediction of skin sensitization.

Extending (Q)SARs to incorporate proprietary knowledge for regulatory purposes: is aromatic N-oxide a structural alert for predicting DNA-reactive mutagenicity?

(Quantitative) structure-activity relationship or (Q)SAR predictions of DNA-reactive mutagenicity are important to support both the design of new chemicals and the assessment of impurities, degradants, metabolites, extractables and leachables, as well as existing chemicals. Aromatic N-oxides represent a class of compounds that are often considered alerting for mutagenicity yet the scientific rationale of this structural alert is not clear and has been questioned. Because aromatic N-oxide-containing compounds may be encountered as impurities, degradants and metabolites, it is important to accurately predict mutagenicity of this chemical class. This article analysed a series of publicly available aromatic N-oxide data in search of supporting information. The article also used a previously developed structure-activity relationship (SAR) fingerprint methodology where a series of aromatic N-oxide substructures was generated and matched against public and proprietary databases, including pharmaceutical data. An assessment of the number of mutagenic and non-mutagenic compounds matching each substructure across all sources was used to understand whether the general class or any specific subclasses appear to lead to mutagenicity. This analysis resulted in a downgrade of the general aromatic N-oxide alert. However, it was determined there were enough public and proprietary data to assign the quindioxin and related chemicals as well as benzo[c][1,2,5]oxadiazole 1-oxide subclasses as alerts. The overall results of this analysis were incorporated into Leadscope's expert-rule-based model to enhance its predictive accuracy.

Use of in silico systems and expert knowledge for structure-based assessment of potentially mutagenic impurities.

Genotoxicity hazard identification is part of the impurity qualification process for drug substances and products, the first step of which being the prediction of their potential DNA reactivity using in silico (quantitative) structure-activity relationship (Q)SAR models/systems. This white paper provides information relevant to the development of the draft harmonized tripartite guideline ICH M7 on potentially DNA-reactive/mutagenic impurities in pharmaceuticals and their application in practice. It explains relevant (Q)SAR methodologies as well as the added value of expert knowledge. Moreover, the predictive value of the different methodologies analyzed in two surveys conveyed in the US and European pharmaceutical industry is compared: most pharmaceutical companies used a rule-based expert system as their primary methodology, yielding negative predictivity values of ⩾78% in all participating companies. A further increase (>90%) was often achieved by an additional expert review and/or a second QSAR methodology. Also in the latter case, an expert review was mandatory, especially when conflicting results were obtained. Based on the available data, we concluded that a rule-based expert system complemented by either expert knowledge or a second (Q)SAR model is appropriate. A maximal transparency of the assessment process (e.g. methods, results, arguments of weight-of-evidence approach) achieved by e.g. data sharing initiatives and the use of standards for reporting will enable regulators to fully understand the results of the analysis. Overall, the procedures presented here for structure-based assessment are considered appropriate for regulatory submissions in the scope of ICH M7.

In silico methods combined with expert knowledge rule out mutagenic potential of pharmaceutical impurities: an industry survey.

With the increasing emphasis on identification and low level control of potentially genotoxic impurities (GTIs), there has been increased use of structure-based assessments including application of computerized models. To date many publications have focused on the ability of computational models, either individually or in combination, to accurately predict the mutagenic effects of a chemical in the Ames assay. Typically, these investigations take large numbers of compounds and use in silico tools to predict their activity with no human interpretation being made. However, this does not reflect how these assessments are conducted in practice across the pharmaceutical industry. Current guidelines indicate that a structural assessment is sufficient to conclude that an impurity is non-mutagenic. To assess how confident we can be in identifying non-mutagenic structures, eight companies were surveyed for their success rate. The Negative Predictive Value (NPV) of the in silico approaches was 94%. When human interpretation of in silico model predictions was conducted, the NPV increased substantially to 99%. The survey illustrates the importance of expert interpretation of in silico predictions. The survey also suggests the use of multiple computational models is not a significant factor in the success of these approaches with respect to NPV.

Evaluation of the Litron In Vitro MicroFlow Kit for the flow cytometric enumeration of micronuclei (MN) in mammalian cells.

We have evaluated the performance of the prototype In Vitro MicroFlow Kit (Litron Laboratories), which offers a flow cytometric method for scoring micronuclei (MN). This method uses sequential staining to differentiate MN from chromatin fragments derived from apoptotic or necrotic cells. Data were generated using the genotoxins methylmethane sulphonate (MMS), dimethylbenzanthracene (DMBA) and vinblastine, and the non-genotoxins dexamethasone and staurosporine, which are known to induce apoptosis in vitro. The results obtained with these agents were compared with conventional microscopy. For short-duration exposures (3-4h) both manual and flow methodologies demonstrated good concordance, with concentration-related increases in the percentage of MN for MMS, DMBA and vinblastine. Statistically significant increases were observed at > or = 20 and 40 microg/mL, for manual and flow analysis, respectively, for MMS; at 0.5 and 0.75 microg/mL for DMBA; and at 0.035 and 0.04 microg/mL, respectively, for vinblastine. Dexamethasone showed clear negative responses by manual and flow cytometric analysis, with comparable results for both methodologies (all <1.7-fold compared with concurrent vehicle controls). Data for staurosporine, however, were less consistent showing significantly higher flow cytometric MN frequencies compared with those seen after manual analysis. Continuous (24 h) treatments were also conducted with MMS, vinblastine, dexamethasone and staurosporine. There was good concordance between the methodologies for MMS, staurosporine and vinblastine. However, dexamethasone generated discordant results, i.e. microscopic analysis was clearly negative at all doses tested, whereas flow cytometry produced significant increases in MN frequency (up to 8.1-fold at 100 microg/mL compared with the concurrent vehicle control). The inconsistencies observed between flow cytometry and standard microscopy, and the differences in assay sensitivity, particularly for apoptosis-inducing compounds, suggest that the prototype In Vitro MicroFlow Kit requires further refinement. Studies to investigate new parameters to address these issues are now under way and will be reported separately.

Interlaboratory evaluation of a flow cytometric, high content in vitro micronucleus assay.

An international, multi-lab trial was conducted to evaluate a flow cytometry-based method for scoring micronuclei in mouse lymphoma L5178Y cells [S.L. Avlasevich, S.M. Bryce, S.E. Cairns, S.D. Dertinger, In vitro micronucleus scoring by flow cytometry: differential staining of micronuclei versus apoptotic and necrotic chromatin enhances assay reliability, Environ. Mol. Mutagen. 47 (2006) 56-66]. A reference laboratory investigated the potential of six chemicals to induce micronuclei -- the genotoxicants mitomycin C (MMC), etoposide (ETOPO), and vinblastine (VB), and the non-genotoxicants sucrose (SUC), staurosporine (STS), and dexamethasone (DEX). The latter two non-genotoxicants were selected as extreme challenges to the assay because of their potent apoptogenic activity. Three collaborating laboratories were supplied with prototype In Vitro MicroFlow kits, and each was assigned one genotoxicant and one non-genotoxicant. Cells were treated continuously for 24h over a range of concentrations up to 5 mg/ml, or overtly cytotoxic concentrations. Micronuclei were scored via standard microscopy and flow cytometry. In addition to enumerating micronucleus frequencies, a cytotoxicity measurement that is simultaneously acquired with the flow cytometric micronucleus scoring procedure was evaluated (Flow-NBR). With this method, latex particles served as counting beads, and facilitated relative survival measurements that exclude the presence of dead/dying cells. For comparison purposes, additional cytotoxicity endpoints were measured, including several that are based on cell number, and others that reflect compromised membrane integrity, including dye permeability and/or phospholipid distribution. Key findings for this set of compounds include the following: (1) significant discrepancies in top concentration selection were found when cytotoxicity measurements were based on different methods, with the Flow-NBR approach tending to be the most sensitive, (2) both microscopy- and flow cytometry-based scoring methods detected concentration-dependent micronucleus formation for the three genotoxic agents studied, with good agreement between the reference laboratory and the collaborating laboratories, and (3) whereas flow cytometric analyses showed no significant increases for the non-genotoxicants when top concentration selection was based on Flow-NBR, significantly elevated micronucleus frequencies were observed for concentrations that were chosen based on less-sensitive cytotoxicity assays. Collectively, these results indicate that rapid assessment of genotoxicity can be accomplished with a relatively simple flow cytometric technique, and that the scoring system is transferable across laboratories. Furthermore, a concurrent assessment of cytotoxicity, Flow-NBR, may help reduce the occurrence of irrelevant positive results, as it may represent a more appropriate means for choosing top concentration levels. Finally, the data presented herein reinforce concerns about the manner in which cytotoxicity limits are described in guidance documents, since these recommendations tend to cite fixed cut-off values without reference to methodology.

Glutathione depletion modulates gene expression in HepG2 cells via activation of protein kinase C alpha.

Buthionine sulphoximine (BSO; 1mM) resulted in the depletion of glutathione (GSH) in HepG2 cells to 17+/-1.5% within 24h. This was not associated with apoptotic or necrotic cell death over this time period. Use of a human (Phase 1) cDNA custom toxicology-array and a larger scale (>10,000 gene) Affymetrix U95Av2 array identified a total of 48 and 104 genes, respectively, with a statistically significant (and >1.5-fold) change in expression. A total of 64 differentially expressed genes (6 of which were confirmed by real-time polymerase chain reaction) were suggestive of protein kinase C (PKC) activation. Activation of PKC-alpha (but not betaI or delta) was demonstrated at 24 h through activity measurements and through Western blot analysis of membrane-associated PKC-alpha protein. Activation did not occur in the presence of additional gamma-glutamylcysteine to prevent GSH depletion. Activation of PKC-alpha by GSH-depletion may, at least in part, be mediated by thiol oxidation and may contribute to a survival signal. If sustained, the activation may be important in non-genotoxic carcinogenesis.