A cross-industry collaboration to assess if acute oral toxicity (Q)SAR models are fit-for-purpose for GHS classification and labelling.

This study assesses whether currently available acute oral toxicity (AOT) in silico models, provided by the widely employed Leadscope software, are fit-for-purpose for categorization and labelling of chemicals. As part of this study, a large data set of proprietary and marketed compounds from multiple companies (pharmaceutical, plant protection products, and other chemical industries) was assembled to assess the models' performance. The absolute percentage of correct or more conservative predictions, based on a comparison of experimental and predicted GHS categories, was approximately 95%, after excluding a small percentage of inconclusive (indeterminate or out of domain) predictions. Since the frequency distribution across the experimental categories is skewed towards low toxicity chemicals, a balanced assessment was also performed. Across all compounds which could be assigned to a well-defined experimental category, the average percentage of correct or more conservative predictions was around 80%. These results indicate the potential for reliable and broad application of these models across different industrial sectors. This manuscript describes the evaluation of these models, highlights the importance of an expert review, and provides guidance on the use of AOT models to fulfill testing requirements, GHS classification/labelling, and transportation needs.

New phenotypic cytotoxicity assay for ROS-inducing compounds using rat renal epithelial cells.

An important mechanism of chemical toxicity is the induction of oxidative stress through the production of excess reactive oxygen species (ROS). In this study, we show that the level of drug-induced ROS production between NRK52E and HepG2 cells is significantly different for several marketed drugs and a number of Takeda's internal proprietary compounds. Nifedipine, a calcium channel blocker and the initial focus of the study, was demonstrated to promote in vitro ROS production and a decrease in cell viability in NRK52E cells but not HepG2 cells. ROS production after nifedipine treatment was inhibited by a NOX inhibitor (GKT136901) but not the mitochondrial NADH dehydrogenase inhibitor, rotenone, suggesting that nifedipine decreases NRK52E cell viability primarily through a NOX-mediated pathway. To understand the breadth of NOX-mediated ROS production, 12 commercially available compounds that are structurally and/or pharmacologically related to nifedipine as well as 172 internal Takeda candidate drugs, were also evaluated against these two cell types. Over 15 % of compounds not cytotoxic to HepG2 cells (below 50 µM) were cytotoxic to NRK52E cells. Our results suggest that a combination of cell viability data from both NRK52E and HepG2 cells was superior for the prediction of in vivo toxicity findings when compared to use of only one cell line. Further, the NRK52E cell viability assay is a good predictor of NOX-mediated ROS production and can be used as a follow up assay following a negative HepG2 response to aid in the selection of suitable compounds for in vivo toxicity studies.

Utility of Physicochemical Properties for the Prediction of Toxicological Outcomes: Takeda Perspective.

The role that physicochemical properties play toward increasing the likelihood of toxicity findings in in vivo studies has been well reported, albeit sometimes with different conclusions. We decided to understand the role that physicochemical properties play toward the prediction of in vivo toxicological outcomes for Takeda chemistry using 284 internal compounds. In support of the previously reported "3/75 rule", reducing lipophilicity of molecules decreases toxicity odds noticeably; however, we also found that the trend of toxicity odds is different between compounds classified by their ionization state. For basic molecules, the odds of in vivo toxicity outcomes were significantly impacted by both lipophilicity and polar surface area, whereas neutral molecules were impacted less so. Through an analysis of several project-related compounds, we herein demonstrate that the utilization of the 3/75 rule coupled with consideration of ionization state is a rational strategy for medicinal chemistry design of safer drugs.

Small-Scale Panel Comprising Diverse Gene Family Targets To Evaluate Compound Promiscuity.

Despite the recent advances in the life sciences and the remarkable investment in drug discovery research, the success rate of small-molecule drug development remains low. Safety is the second most influential factor of drug attrition in clinical studies; thus, the selection of compounds with fewer toxicity concerns is crucial to increase the success rate of drug discovery. Compounds that promiscuously bind to multiple targets are likely to cause unexpected pharmacological activity that may lead to adverse effects. Therefore, avoiding such compounds during early research stages would contribute to identifying compounds with a higher chance of success in the clinic. To evaluate the interaction profile against a wide variety of targets, we constructed a small-scale promiscuity panel (PP) consisting of eight targets (ROCK1, PDE4D2, GR, PPARγ, 5-HT2B, adenosine A3, M1, and GABAA) that were selected from diverse gene families. The validity of this panel was confirmed by comparison with the promiscuity index evaluated from larger-scale panels. Analysis of data from the PP revealed that both lipophilicity and basicity are likely to increase promiscuity, while the molecular weight does not significantly contribute. Additionally, the promiscuity assessed using our PP correlated with the occurrence of both in vitro cytotoxicity and in vivo toxicity, suggesting that the PP is useful to identify compounds with fewer toxicity concerns. In summary, this small-scale and cost-effective PP can contribute to the identification of safer compounds that would lead to a reduction in drug attrition due to safety issues.

Principles and procedures for handling out-of-domain and indeterminate results as part of ICH M7 recommended (Q)SAR analyses.

The International Council for Harmonization (ICH) M7 guideline describes a hazard assessment process for impurities that have the potential to be present in a drug substance or drug product. In the absence of adequate experimental bacterial mutagenicity data, (Q)SAR analysis may be used as a test to predict impurities' DNA reactive (mutagenic) potential. However, in certain situations, (Q)SAR software is unable to generate a positive or negative prediction either because of conflicting information or because the impurity is outside the applicability domain of the model. Such results present challenges in generating an overall mutagenicity prediction and highlight the importance of performing a thorough expert review. The following paper reviews pharmaceutical and regulatory experiences handling such situations. The paper also presents an analysis of proprietary data to help understand the likelihood of misclassifying a mutagenic impurity as non-mutagenic based on different combinations of (Q)SAR results. This information may be taken into consideration when supporting the (Q)SAR results with an expert review, especially when out-of-domain results are generated during a (Q)SAR evaluation.

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 Leadscope's expert rule-based and statistical-based (Q)SAR models where increased predictive performance was demonstrated.

The development of structure-activity relationships for mitochondrial dysfunction: uncoupling of oxidative phosphorylation.

Mitochondrial dysfunction has been implicated as an important factor in the development of idiosyncratic organ toxicity. An ability to predict mitochondrial dysfunction early in the drug development process enables the deselection of those drug candidates with potential safety liabilities, allowing resources to be focused on those compounds with the highest chance of success to the market. A database of greater than 2000 compounds was analyzed to identify structural and physicochemical features associated with the uncoupling of oxidative phosphorylation (herein defined as an increase in basal respiration). Many toxicophores associated with potent uncoupling activity were identified, and these could be divided into two main mechanistic classes, protonophores and redox cyclers. For the protonophores, potent uncoupling activity was often promoted by high lipophilicity and apparent stabilization of the anionic charge resulting from deprotonation of the protonophore. The potency of redox cyclers did not appear to be prone to variations in lipophilicity. Only 11 toxicophores were of sufficient predictive performance that they could be incorporated into a structural-alert model. Each alert was associated with one of three confidence levels (high, medium, and low) depending upon the lipophilicity-activity profile of the structural class. The final model identified over 68% of those compounds with potent uncoupling activity and with a value for specificity above 99%. We discuss the advantages and limitations of this approach and conclude that although structural alert methodology is useful for identifying toxicophores associated with mitochondrial dysfunction, they are not a replacement for the mitochondrial dysfunction assays in early screening paradigms.

Latest advances in computational genotoxicity prediction.

INTRODUCTION: Computational approaches for genotoxicity prediction have existed for over two decades. Numerous methodologies have been utilized and the results of various evaluations have published. AREAS COVERED: In silico methods are considered mature enough to be part of draft FDA regulatory guidelines for the assessment of genotoxic impurities. However, aspects of how best to use predictive systems remain unresolved: i) methodologies to measure how similar two compounds need to be in order to assume they have the same biological outcome; and ii) defining whether a compound is close enough to the model training set such that a model prediction can be considered reliable. EXPERT OPINION: In silico prediction of genotoxicity is a fundamental part of screening strategies for the assessment genotoxic impurities in drug products. However, the concept of using chemical similarity to infer mutagenic potential from one of known activity to another whose activity is unknown remains a scientific challenge. Similarly, defining when an in silico model prediction can be considered to be reliable is also difficult. Reaction mechanisms and the functional group building blocks of chemistry are pretty much constant, and so when data-gaps appear, it tends to be for compounds that have been regularly used but rarely tested.

New and emerging technologies for genetic toxicity testing.

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 established an Emerging Technologies and New Strategies Workgroup to review the current State of the Art in genetic toxicology testing. The aim of the workgroup was to identify promising technologies that will improve genotoxicity testing and assessment of in vivo hazard and risk, and that have the potential to help meet the objectives of the IVGT. As part of this initiative, HESI convened a workshop in Washington, DC in May 2008 to discuss mature, maturing, and emerging technologies in genetic toxicology. This article collates the abstracts of the New and Emerging Technologies Workshop together with some additional technologies subsequently considered by the workgroup. Each abstract (available in the online version of the article) includes a section addressed specifically to the strengths, weaknesses, opportunities, and threats associated with the respective technology. Importantly, an overview of the technologies and an indication of how their use might be aligned with the objectives of IVGT are presented. In particular, consideration was given with regard to follow-up testing of positive results in the standard IVGT tests (i.e., Salmonella Ames test, chromosome aberration assay, and mouse lymphoma assay) to add weight of evidence and/or provide mechanism of action for improved genetic toxicity risk assessments in humans.

The computational prediction of genotoxicity.

IMPORTANCE OF THE FIELD: The computational prediction of genotoxicity is important to the early identification of those chemical entities that have the potential to cause carcinogenicity in humans. AREAS COVERED IN THIS REVIEW: The review discusses key scientific developments in the prediction of Ames mutagenicity and in vitro chromosome damage over the past 4 - 5 years. The performance and limitations of computational approaches are discussed in relation to published and internal validation exercises. Their application to the modern drug discovery paradigm is also discussed. WHAT THE READER WILL GAIN: Key highlights of a review of the recent scientific literature for the prediction of Ames mutagenicity and chromosome damage and an appreciation of the factors that limit the predictive performance of in silico systems. TAKE HOME MESSAGE: Current in silico systems perform well in the mutagenicity prediction of the publicly-derived data on which they are based, but their performance outside the applicability domain is considerably lower. We conclude that it is the lack of mechanistic structure-activity relationships and limited access to high quality proprietary data which are holding back computational genotoxicity from reaching higher predictive levels.

Developing structure-activity relationships for the prediction of hepatotoxicity.

Drug-induced liver injury is a major issue of concern and has led to the withdrawal of a significant number of marketed drugs. An understanding of structure-activity relationships (SARs) of chemicals can make a significant contribution to the identification of potential toxic effects early in the drug development process and aid in avoiding such problems. This process can be supported by the use of existing toxicity data and mechanistic understanding of the biological processes for related compounds. In the published literature, this information is often spread across diverse sources and can be varied and unstructured in quality and content. The current work has explored whether it is feasible to collect and use such data for the development of new SARs for the hepatotoxicity endpoint and expand upon the limited information currently available in this area. Reviews of hepatotoxicity data were used to build a structure-searchable database, which was analyzed to identify chemical classes associated with an adverse effect on the liver. Searches of the published literature were then undertaken to identify additional supporting evidence, and the resulting information was incorporated into the database. This collated information was evaluated and used to determine the scope of the SARs for each class identified. Data for over 1266 chemicals were collected, and SARs for 38 classes were developed. The SARs have been implemented as structural alerts using Derek for Windows (DfW), a knowledge-based expert system, to allow clearly supported and transparent predictions. An evaluation exercise performed using a customized DfW version 10 knowledge base demonstrated an overall concordance of 56% and specificity and sensitivity values of 73% and 46%, respectively. The approach taken demonstrates that SARs for complex endpoints can be derived from the published data for use in the in silico toxicity assessment of new compounds.

Early toxicity screening strategies.

Despite a steady increase in the total amount spent on pharmaceutical R&D over the past decade, the number of new drug approvals has declined in recent years. Toxicity continues to account for more than 30% of compound attrition during the drug development process and remains one of the major causes for drugs to be withdrawn after approval. Since R&D costs increase exponentially along the drug development timeline, late stage failures are heavily contributing to an unsustainable business model for the pharmaceutical industry. Improved early identification of toxicities associated with new drug entities will allow resources to be focused only on those compounds most likely to succeed.

Cyclin-dependent kinase 4 inhibitors as a treatment for cancer. Part 1: identification and optimisation of substituted 4,6-bis anilino pyrimidines.

Using a high-throughput screening campaign, we identified the 4,6-bis anilino pyrimidines as inhibitors of the cyclin-dependent kinase, CDK4. Herein we describe the further chemical modification and use of X-ray crystallography to develop potent and selective in vitro inhibitors of CDK4.