Angela White

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.

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.

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.

Principles and procedures for implementation of ICH M7 recommended (Q)SAR analyses.

The ICH M7 guideline describes a consistent approach to identify, categorize, and control DNA reactive, mutagenic, impurities in pharmaceutical products to limit the potential carcinogenic risk related to such impurities. This paper outlines a series of principles and procedures to consider when generating (Q)SAR assessments aligned with the ICH M7 guideline to be included in a regulatory submission. In the absence of adequate experimental data, the results from two complementary (Q)SAR methodologies may be combined to support an initial hazard classification. This may be followed by an assessment of additional information that serves as the basis for an expert review to support or refute the predictions. This paper elucidates scenarios where additional expert knowledge may be beneficial, what such an expert review may contain, and how the results and accompanying considerations may be documented. Furthermore, the use of these principles and procedures to yield a consistent and robust (Q)SAR-based argument to support impurity qualification for regulatory purposes is described in this manuscript.

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.

Extending (Q)SARs to incorporate proprietary knowledge for regulatory purposes: A case study using aromatic amine mutagenicity

Statistical-based and expert rule-based models built using public domain mutagenicity knowledge and data are routinely used for computational (Q)SAR assessments of pharmaceutical impurities in line with the approach recommended in the ICH M7 guideline. Knowledge from proprietary corporate mutagenicity databases could be used to increase the predictive performance for selected chemical classes as well as expand the applicability domain of these (Q)SAR models. This paper outlines a mechanism for sharing knowledge without the release of proprietary data. Primary aromatic amine mutagenicity was selected as a case study because this chemical class is often encountered in pharmaceutical impurity analysis and mutagenicity of aromatic amines is currently difficult to predict. As part of this analysis, a series of aromatic amine substructures were defined and the number of mutagenic and non-mutagenic examples for each chemical substructure calculated across a series of public and proprietary mutagenicity databases. This information was pooled across all sources to identify structural classes that activate or deactivate aromatic amine mutagenicity. This structure activity knowledge, in combination with newly released primary aromatic amine data, was incorporated into Leadscopes expert rule-based and statistical-based (Q)SAR models where increased predictive performance was demonstrated.

Differentiation-Associated Reprogramming of the Transforming Growth Factor β Receptor Pathway Establishes the Circuitry for Epithelial Autocrine/Paracrine Repair

Transforming growth factor (TGF) β has diverse and sometimes paradoxical effects on cell proliferation and differentiation, presumably reflecting a fundamental but incompletely-understood role in regulating tissue homeostasis. It is generally considered that downstream activity is modulated at the ligand:receptor axis, but microarray analysis of proliferative versus differentiating normal human bladder epithelial cell cultures identified unexpected transcriptional changes in key components of the canonical TGFβ R/activin signalling pathway associated with cytodifferentiation. Changes included upregulation of the transcriptional modulator SMAD3 and downregulation of inhibitory modulators SMURF2 and SMAD7. Functional analysis of the signalling pathway revealed that non-differentiated normal human urothelial cells responded in paracrine mode to TGFβ by growth inhibition, and that exogenous TGFβ inhibited rather than promoted differentiation. By contrast, in differentiated cell cultures, SMAD3 was activated upon scratch-wounding and was involved in promoting tissue repair. Exogenous TGFβ enhanced the repair and resulted in hyperplastic scarring, indicating a feedback loop implicit in an autocrine pathway. Thus, the machinery for autocrine activation of the SMAD3-mediated TGFβR pathway is established during urothelial differentiation, but signalling occurs only in response to a trigger, such as wounding. Our study demonstrates that the circuitry of the TGFβR pathway is defined transcriptionally within a tissue-specific differentiation programme. The findings provide evidence for re-evaluating the role of TGFβR signalling in epithelial homeostasis as an autocrine-regulated pathway that suppresses differentiation and promotes tissue repair. This provides a new paradigm to help unravel the apparently diverse and paradoxical effect of TGFβ signalling on cell proliferation and differentiation.

In silico toxicology protocols

The present publication surveys several applications of in silico (i.e., computational) toxicology approaches across different industries and institutions. It highlights the need to develop standardized protocols when conducting toxicity-related predictions. This contribution articulates the information needed for protocols to support in silico predictions for major toxicological endpoints of concern (e.g., genetic toxicity, carcinogenicity, acute toxicity, reproductive toxicity, developmental toxicity) across several industries and regulatory bodies. Such novel in silico toxicology (IST) protocols, when fully developed and implemented, will ensure in silico toxicological assessments are performed and evaluated in a consistent, reproducible, and well-documented manner across industries and regulatory bodies to support wider uptake and acceptance of the approaches. The development of IST protocols is an initiative developed through a collaboration among an international consortium to reflect the state-of-the-art in in silico toxicology for hazard identification and characterization. A general outline for describing the development of such protocols is included and it is based on in silico predictions and/or available experimental data for a defined series of relevant toxicological effects or mechanisms. The publication presents a novel approach for determining the reliability of in silico predictions alongside experimental data. In addition, we discuss how to determine the level of confidence in the assessment based on the relevance and reliability of the information.

Potential impurities in drug substances: Compound-specific toxicology limits for 20 synthetic reagents and by-products, and a class-specific toxicology limit for alkyl bromides

ABSTRACT This paper provides compound‐specific toxicology limits for 20 widely used synthetic reagents and common by‐products that are potential impurities in drug substances. In addition, a 15 &mgr;g/day class‐specific limit was developed for monofunctional alkyl bromides, aligning this with the class‐specific limit previously defined for monofunctional alkyl chlorides. Both the compound‐ and class‐specific toxicology limits assume a lifetime chronic exposure for the general population (including sensitive subpopulations) by all routes of exposure for pharmaceuticals. Inhalation‐specific toxicology limits were also derived for acrolein, formaldehyde, and methyl bromide because of their localized toxicity via that route. Mode of action was an important consideration for a compound‐specific toxicology limit. Acceptable intake (AI) calculations for certain mutagenic carcinogens assumed a linear dose‐response for tumor induction, and permissible daily exposure (PDE) determination assumed a non‐linear dose‐response. Several compounds evaluated have been previously incorrectly assumed to be mutagenic, or to be mutagenic carcinogens, but the evidence reported here for such compounds indicates a lack of mutagenicity, and a non‐mutagenic mode of action for tumor induction. For non‐mutagens with insufficient data to develop a toxicology limit, the ICH Q3A qualification thresholds are recommended. The compound‐ and class‐specific toxicology limits described here may be adjusted for an individual drug substance based on treatment duration, dosing schedule, severity of the disease and therapeutic indication. HighlightsCompound‐specific toxicology limits were developed for common potential impurities in drug substances.A class‐specific limit of 15 &mgr;g/day was developed for monofunctional alkyl bromides.All compound‐specific toxicology limits were based on existing data, current regulatory guidance, and scientific knowledge.

A quantitative in silico model for predicting skin sensitization using a nearest neighbours approach within expert‐derived structure–activity alert spaces

Dermal contact with chemicals may lead to an inflammatory reaction known as allergic contact dermatitis. Consequently, it is important to assess new and existing chemicals for their skin sensitizing potential and to mitigate exposure accordingly. There is an urgent need to develop quantitative non‐animal methods to better predict the potency of potential sensitizers, driven largely by European Union (EU) Regulation 1223/2009, which forbids the use of animal tests for cosmetic ingredients sold in the EU. A Nearest Neighbours in silico model was developed using an in‐house dataset of 1096 murine local lymph node (LLNA) studies. The EC3 value (the effective concentration of the test substance producing a threefold increase in the stimulation index compared to controls) of a given chemical was predicted using the weighted average of EC3 values of up to 10 most similar compounds within the same mechanistic space (as defined by activating the same Derek skin sensitization alert). The model was validated using previously unseen internal (n = 45) and external (n = 103) data and accuracy of predictions assessed using a threefold error, fivefold error, European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC) and Globally Harmonized System of Classification and Labelling of Chemicals (GHS) classifications. In particular, the model predicts the GHS skin sensitization category of compounds well, predicting 64% of chemicals in an external test set within the correct category. Of the remaining chemicals in the previously unseen dataset, 25% were over‐predicted (GHS 1A predicted: GHS 1B experimentally) and 11% were under‐predicted (GHS 1B predicted: GHS 1A experimentally). Copyright