Wolfgang Muster

Computational toxicology in drug development

Computational tools for predicting toxicity have been envisaged for their potential to considerably impact the attrition rate of compounds in drug discovery and development. In silico techniques like knowledge-based expert systems (quantitative) structure activity relationship tools and modeling approaches may therefore help to significantly reduce drug development costs by succeeding in predicting adverse drug reactions in preclinical studies. It has been shown that commercial as well as proprietary systems can be successfully applied in the pharmaceutical industry. As the prediction has been exhaustively optimized for early safety-relevant endpoints like genotoxicity, future activities will now be directed to prevent the occurrence of undesired toxicity in patients by making these tools more relevant to human disease.

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.

Comparative Evaluation of in Silico Systems for Ames Test Mutagenicity Prediction: Scope and Limitations

The predictive power of four commonly used in silico tools for mutagenicity prediction (DEREK, Toxtree, MC4PC, and Leadscope MA) was evaluated in a comparative manner using a large, high-quality data set, comprising both public and proprietary data (F. Hoffmann-La Roche) from 9,681 compounds tested in the Ames assay. Satisfactory performance statistics were observed on public data (accuracy, 66.4-75.4%; sensitivity, 65.2-85.2%; specificity, 53.1-82.9%), whereas a significant deterioration of sensitivity was observed in the Roche data (accuracy, 73.1-85.5%; sensitivity, 17.4-43.4%; specificity, 77.5-93.9%). As a general tendency, expert systems showed higher sensitivity and lower specificity when compared to QSAR-based tools, which displayed the opposite behavior. Possible reasons for the performance differences between the public and Roche data, relating to the experimentally inactive to active compound ratio and the different coverage of chemical space, are thoroughly discussed. Examples of peculiar chemical classes enriched in false negative or false positive predictions are given, and the results of the combined use of the prediction systems are described.

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.

Mouse Lymphoma thymidine kinase locus gene mutation assay : International Workshop on Genotoxicity Test Procedures Workgroup Report

The Mouse Lymphoma Assay (MLA) Workgroup addressed and reached consensus on a number of issues. Discussion focused on five areas: (1) acceptable assay versions; (2) cytotoxicity measure; (3) 24‐hr treatment; (4) microwell colony counting and sizing; and (5) data acceptability/statistical analysis. Although the International Conference on Harmonisation (ICH) indicated a preference for the microwell over the soft agar method, all of the workgroup members agreed that both versions of the MLA are equally acceptable. The workgroup agreed that it is desirable for both assay versions to use the same measure of cytotoxicity to define the acceptable and required concentration range. Currently, laboratories using the microwell version use the relative survival (RS) determined by cloning immediately after the treatment. Laboratories using the soft agar method do not obtain an RS but use the relative total growth (RTG), a combination of the relative suspension growth (RSG) during the expression period and the relative cloning efficiency determined at the time of mutant selection. The workgroup agreed to investigate the RSG, the RS, and the RTG and to develop further guidance. In the interim, the workgroup reached consensus that the RTG be used as the standard measure of cytotoxicity. The ICH recommended a 24‐hr treatment in the absence of S9 when negative results are obtained with short (3–4 hr) treatments. The workgroup agreed to retain this requirement but acknowledged that more data are needed prior to making final recommendations concerning the need for and the specific protocol for the 24‐hr treatment. Environ. Mol. Mutagen. 35:185–190, 2000 Published 2000 Wiley‐Liss, Inc.

Establishing best practise in the application of expert review of mutagenicity under ICH M7.

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.

The synthesis and biological evaluation of A-ring substituted steroidal p-quinones

Abstract The preparation of A-ring steroidal 1,4-quinones involves m -CPBA/(BzO) 2 O/ hv oxidation of estrone (or estradiol 17-acetate), acid rearrangement of the obtained quinol, and oxidation. A detailed NMR analysis of 1,4-quinones and their derivatives, as well as the results of preliminary antibacterial and cytotoxicity tests is presented.

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.

Specific Correction of Alternative Survival Motor Neuron 2 Splicing by Small Molecules: Discovery of a Potential Novel Medicine To Treat Spinal Muscular Atrophy

Spinal muscular atrophy (SMA) is the leading genetic cause of infant and toddler mortality, and there is currently no approved therapy available. SMA is caused by mutation or deletion of the survival motor neuron 1 (SMN1) gene. These mutations or deletions result in low levels of functional SMN protein. SMN2, a paralogous gene to SMN1, undergoes alternative splicing and exclusion of exon 7, producing an unstable, truncated SMNΔ7 protein. Herein, we report the identification of a pyridopyrimidinone series of small molecules that modify the alternative splicing of SMN2, increasing the production of full-length SMN2 mRNA. Upon oral administration of our small molecules, the levels of full-length SMN protein were restored in two mouse models of SMA. In-depth lead optimization in the pyridopyrimidinone series culminated in the selection of compound 3 (RG7800), the first small molecule SMN2 splicing modifier to enter human clinical trials.

Phototoxicity and photogenotoxicity of nine pyridone derivatives.

Nine structurally related pyridone derivatives were assayed for photogenotoxicity and phototoxicity in the Ames test, the chromosomal aberration test in V79 cells and the neutral red uptake (NRU) test in 3T3 cells. All nine compounds absorb light to a comparable degree at wavelengths between 380 and 430 nm. Seven of the nine compounds were found to produce high quantities of singlet oxygen (1O(2)) upon irradiation in the presence of oxygen. These seven compounds were highly phototoxic in the NRU test, three were clearly and two were marginally photomutagenic in the Ames test, five were assessed as clearly and two as equivocally photoclastogenic in the chromosomal aberration test. Two compounds showed substantially lower 1O(2) yields. The pyridone ring of these two compounds is attached to a non-aromatic ring, while for the seven other compounds the chromophore system including the pyridone ring consists of two or three aromatic rings. One of the two compounds with low 1O(2) yields was distinctly less phototoxic and did not induce photogenotoxic effects. The other, structurally an indolo derivative and not the common thieno derivative, was, however, similarly phototoxic as the seven compounds with high 1O(2) quantum yield and was also clearly photogenotoxic indicating that different action pathways, not involving singlet oxygen, have to be considered at least for this compound.