N-Nitrosodimethylamine investigations in Muta™Mouse define point-of-departure values and demonstrate less-than-additive somatic mutant frequency accumulations.

The N-nitrosamine, N-nitrosodimethylamine (NDMA), is an environmental mutagen and rodent carcinogen. Small levels of NDMA have been identified as an impurity in some commonly used drugs, resulting in several product recalls. In this study, NDMA was evaluated in an OECD TG-488 compliant Muta™Mouse gene mutation assay (28-day oral dosing across seven daily doses of 0.02-4 mg/kg/day) using an integrated design that assessed mutation at the transgenic lacZ locus in various tissues and at the endogenous Pig-a gene-locus, along with micronucleus frequencies in peripheral blood. Liver pathology was determined together with NDMA exposure in blood and liver. The additivity of mutation induction was assessed by including two acute single-dose treatment groups (i.e. 5 and 10 mg/kg dose on Day 1), which represented the same total dose as two of the repeat dose treatment groups. NDMA did not induce statistically significant increases in mean lacZ mutant frequency (MF) in bone marrow, spleen, bladder, or stomach, nor in peripheral blood (Pig-a mutation or micronucleus induction) when tested up to 4 mg/kg/day. There were dose-dependent increases in mean lacZ MF in the liver, lung, and kidney following 28-day repeat dosing or in the liver and kidney after a single dose (10 mg/kg). No observed genotoxic effect levels (NOGEL) were determined for the positive repeat dose-response relationships. Mutagenicity did not exhibit simple additivity in the liver since there was a reduction in MF following NDMA repeat dosing compared with acute dosing for the same total dose. Benchmark dose modelling was used to estimate point of departure doses for NDMA mutagenicity in Muta™Mouse and rank order target organ tissue sensitivity (liver > kidney or lung). The BMD50 value for liver was 0.32 mg/kg/day following repeat dosing (confidence interval 0.21-0.46 mg/kg/day). In addition, liver toxicity was observed at doses of ≥ 1.1 mg/kg/day NDMA and correlated with systemic and target organ exposure. The integration of these results and their implications for risk assessment are discussed.

Prediction of inotropic effect based on calcium transients in human iPSC-derived cardiomyocytes and machine learning.

Functional changes to cardiomyocytes are undesirable during drug discovery and identifying the inotropic effects of compounds is hence necessary to decrease the risk of cardiovascular adverse effects in the clinic. Recently, approaches leveraging calcium transients in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) have been developed to detect contractility changes, induced by a variety of mechanisms early during drug discovery projects. Although these approaches have been able to provide some predictive ability, we hypothesised that using additional waveform parameters could offer improved insights, as well as predictivity. In this study, we derived 25 parameters from each calcium transient waveform and developed a modified Random Forest method to predict the inotropic effects of the compounds. In total annotated data for 48 compounds were available for modelling, out of which 31 were inotropes. The results show that the Random Forest model with a modified purity criterion performed slightly better than an unmodified algorithm in terms of the Area Under the Curve, giving values of 0.84 vs 0.81 in a cross-validation, and outperformed the ToxCast Pipeline model, for which the highest value was 0.76 when using the best-performing parameter, PW10. Our study hence demonstrates that more advanced parameters derived from waveforms, in combination with additional machine learning methods, provide improved predictivity of cardiovascular risk associated with inotropic effects.

Deriving waveform parameters from calcium transients in human iPSC-derived cardiomyocytes to predict cardiac activity with machine learning.

Human induced pluripotent stem cell-derived cardiomyocytes have been established to detect dynamic calcium transients by fast kinetic fluorescence assays that provide insights into specific aspects of clinical cardiac activity. However, the precise derivation and use of waveform parameters to predict cardiac activity merit deeper investigation. In this study, we derived, evaluated, and applied 38 waveform parameters in a novel Python framework, including (among others) peak frequency, peak amplitude, peak widths, and a novel parameter, shoulder-tail ratio. We then trained a random forest model to predict cardiac activity based on the 25 parameters selected by correlation analysis. The area under the curve (AUC) obtained for leave-one-compound-out cross-validation was 0.86, thereby replicating the predictions of conventional methods and outperforming fingerprint-based methods by a large margin. This work demonstrates that machine learning is able to automate the assessment of cardiovascular liability from waveform data, reducing any risk of user-to-user variability and bias.

Discovery of small-molecule positive allosteric modulators of Parkin E3 ligase.

Pharmacological activation of the E3 ligase Parkin represents a rational therapeutic intervention for the treatment of Parkinson's disease. Here we identify several compounds that enhance the activity of wildtype Parkin in the presence of phospho-ubiquitin and act as positive allosteric modulators (PAMs). While these compounds activate Parkin in a series of biochemical assays, they do not act by thermally destabilizing Parkin and fail to enhance the Parkin translocation rate to mitochondria or to enact mitophagy in cell-based assays. We conclude that in the context of the cellular milieu the therapeutic window to pharmacologically activate Parkin is very narrow.

Genetic toxicology in silico protocol.

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.

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.

Information-Derived Mechanistic Hypotheses for Structural Cardiotoxicity.

Adverse events resulting from drug therapy can be a cause of drug withdrawal, reduced and or restricted clinical use, as well as a major economic burden for society. To increase the safety of new drugs, there is a need to better understand the mechanisms causing the adverse events. One way to derive new mechanistic hypotheses is by linking data on drug adverse events with the drugs' biological targets. In this study, we have used data mining techniques and mutual information statistical approaches to find associations between reported adverse events collected from the FDA Adverse Event Reporting System and assay outcomes from ToxCast, with the aim to generate mechanistic hypotheses related to structural cardiotoxicity (morphological damage to cardiomyocytes and/or loss of viability). Our workflow identified 22 adverse event-assay outcome associations. From these associations, 10 implicated targets could be substantiated with evidence from previous studies reported in the literature. For two of the identified targets, we also describe a more detailed mechanism, forming putative adverse outcome pathways associated with structural cardiotoxicity. Our study also highlights the difficulties deriving these type of associations from the very limited amount of data available.

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.

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.

Cell transformation assays for prediction of carcinogenic potential: state of the science and future research needs.

Cell transformation assays (CTAs) have long been proposed as in vitro methods for the identification of potential chemical carcinogens. Despite showing good correlation with rodent bioassay data, concerns over the subjective nature of using morphological criteria for identifying transformed cells and a lack of understanding of the mechanistic basis of the assays has limited their acceptance for regulatory purposes. However, recent drivers to find alternative carcinogenicity assessment methodologies, such as the Seventh Amendment to the EU Cosmetics Directive, have fuelled renewed interest in CTAs. Research is currently ongoing to improve the objectivity of the assays, reveal the underlying molecular changes leading to transformation and explore the use of novel cell types. The UK NC3Rs held an international workshop in November 2010 to review the current state of the art in this field and provide directions for future research. This paper outlines the key points highlighted at this meeting.

Enantioselective α-arylation of carbonyls via Cu(I)-bisoxazoline catalysis.

The enantioselective α-arylation of both lactones and acyl oxazolidones has been accomplished using a combination of diaryliodonium salts and copper catalysis. These mild catalytic conditions provide a new strategy for the enantioselective construction and retention of enolizable α-carbonyl benzylic stereocenters, a valuable synthon for the production of medicinal agents.

Follow-up actions from positive results of in vitro genetic toxicity testing.

Appropriate follow-up actions and decisions are needed when evaluating and interpreting clear positive results obtained in the in vitro assays used in the initial genotoxicity screening battery (i.e., the battery of tests generally required by regulatory authorities) to assist in overall risk-based decision making concerning the potential effects of human exposure to the agent under test. Over the past few years, the International Life Sciences Institute (ILSI) Health and Environmental Sciences Institute (HESI) Project Committee on the Relevance and Follow-up of Positive Results in In Vitro Genetic Toxicity (IVGT) Testing developed a decision process flow chart to be applied in case of clear positive results in vitro. It provides for a variety of different possibilities and allows flexibility in choosing follow-up action(s), depending on the results obtained in the initial battery of assays and available information. The intent of the Review Subgroup was not to provide a prescriptive testing strategy, but rather to reinforce the concept of weighing the totality of the evidence. The Review Subgroup of the IVGT committee highlighted the importance of properly analyzing the existing data, and considering potential confounding factors (e.g., possible interactions with the test systems, presence of impurities, irrelevant metabolism), and chemical modes of action when analyzing and interpreting positive results in the in vitro genotoxicity assays and determining appropriate follow-up testing. The Review Subgroup also examined the characteristics, strengths, and limitations of each of the existing in vitro and in vivo genotoxicity assays to determine their usefulness in any follow-up testing.

Analysis of 75 marketed pharmaceuticals using the GADD45a-GFP 'GreenScreen HC' genotoxicity assay.

The GADD45a-GFP (GreenScreen HC) reporter assay detects genotoxic damage in the human lymphoblastoid TK6 cell line and gives positive results for all classes of genotoxin, including mutagens, aneugens and clastogens. In this study, a collection of 75 marketed pharmaceuticals were tested in the assay. Compounds in the collection represent a broad range of chemical structures, pharmacologies and therapeutic indications, including neoplasia and viral infection where positive genotoxicity results are often associated with the pharmacological activity. Based on the results of this study, two main conclusions can be drawn: (i) the GreenScreen HC is more predictive of in vivo genotoxicity (88%) and genotoxic carcinogenicity (93%) data than the any of the other regulatory in vitro genotoxicity assay and (ii) no compounds were uniquely positive in the GADD45a-GFP assay. This analysis therefore provides additional evidence to support the use of the GADD45a-GFP assay as an effective tool either in early genotoxic liability identification or non-clinical safety assessment of candidate pharmaceuticals during development.

High-specificity and high-sensitivity genotoxicity assessment in a human cell line: validation of the GreenScreen HC GADD45a-GFP genotoxicity assay.

The battery of genetic toxicity tests required by most regulatory authorities includes both bacterial and mammalian cell assays and identifies practically all genotoxic carcinogens. However, the relatively high specificity of the Salmonella mutagenicity assay (Ames test) is offset by the low specificity of the established mammalian cell assays, which leads to difficulties in the interpretation of the biological relevance of results. This paper describes a new high-throughput assay that links the regulation of the human GADD45a gene to the production of Green Fluorescent Protein (GFP). A study of 75 well-characterised genotoxic and non-genotoxic compounds with diverse mechanisms of DNA-damage induction (including aneugens) reveals that the assay responds positively to all classes of genotoxic damage with both high specificity and high sensitivity. The current micro-well assay format does not include metabolic activation, but a separate low-throughput protocol demonstrates a successful proof-of-principle for an S9 metabolic activation assay with the model pro-mutagen cyclophosphamide. The test should be of value both as a tool in the selection of candidate compounds for further development, where additional data may be required because of conflicting information from the in vitro test battery, or in product development areas where the use of animals is to be discontinued. As a microplate assay however, it has the qualities of high throughput and low compound use that will facilitate its application in early screening for genotoxic liability.

The results of five coded compounds: genistein, metaproterenol, rotenone, p-anisidine and resorcinol tested in the pH 6.7 Syrian hamster embryo cell morphological transformation assay.

The pH 6.7 Syrian hamster embryo (SHE) cell morphological transformation assay is a short-term in vitro test that has been used to predict rodent carcinogenicity. Previous reports have indicated that the SHE assay has an overall concordance of approximately 80% with the 2 year rodent bioassay. We selected five compounds, genistein, metaproterenol, rotenone, p-anisidine and resorcinol, that had extensive genotoxicity and carcinogenicity data and tested them in the standard 7 day exposure SHE assay. Somewhat surprisingly, the SHE assay misclassified the actual rodent carcinogenicity of four out of the five test compounds. It is difficult to explain these findings as the actual mechanisms of SHE cell morphological transformation are currently unknown. However, it is obvious that in these studies there was no simple correlation between in vitro genotoxicity, morphological transformation in SHE cells and rodent carcinogenicity. Clearly, further research is required to accurately assess the role of the SHE assay in the carcinogenic risk assessment of new chemical entities.