MultiCASE Event Schedule

Upcoming Events

MultiCASE presents latest research at multiple scientific conferences each year. We also host educational webinars exploring areas of significant challenge or exciting research within the QSAR field.
Please browse this page to uncover what we have in store for the remaining and upcoming year.

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December 2021: Nitrosamine Research

Event Description: Nitrosamines pose serious cancer risk in environment, food and as impurities in drugs. However, not all of them have the same carcinogenic potency. Nitrosamines pose varying degrees of cancer risk in environment, food and as impurities in drugs. However, it is a difficult task to determine potency of untested nitrosamines. Lack of adequate in vivo and in vitro carcinogenicity assay data is certainly a major challenge, but at the same time, carcinogenicity risk ofNitrosamines is considered to be related to their mutagenicity, metabolic profile, and physicochemical properties. Therefore, computational approaches are becoming a potential technique to relate structural features with carcinogenic potency.
In this session we will discuss research efforts in MultiCASE to develop databases, novel algorithms, and computational tools to handle this problem.
We have developed an analog based algorithm using structural environment of the nitrosamine functionality, i.e., local similarity. A special molecular fingerprint was developed which enables retrieval of relevant analogs, and gives indication of the carcinogenic potency of the query compound when combined with physicochemical properties and experimental carcinogenic potency of the analogs. We will discuss the merits and limitations of this approach and possible factors for consideration during expert review.

November 2021: The Role of Expert Review

Event Description: For 25 years, MultiCASE has developed cheminformatic tools for evaluating toxicity and metabolism potential for chemicals.  Throughout that time, the MultiCASE ICH M7 Suite has become a robust collection of models and tools available to perform ICH M7 compliant risk assessments.  In accordance with the ICH M7 guideline, both expert rule-based and statistical (Q)SAR methodologies are required to assess the DNA reactivity potential of a query chemical. Each (Q)SAR methodology produces an individual outcome related to the mutagenic potential of the impurity/query chemical. In some cases the individual model outcomes conflict with each other demanding further expert review to determine the overall classification. The MultiCASE Konsolidator tool provides a unique solution to settle conflicting calls from individual models used for ICH M7.  This session will explore how the Konsolidator tool augments the expert review process to reach a better and informed decision for ICH M7 class assignment and regulatory submission.

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Upcoming Conferences

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Tox Expo Booth 1315

Tox Expo Dates

3/28/2022 - 3/30/2022 | 9:00 AM - 4:30 PM PST

Scientific Poster

Abstract Title: A New Structural Similarity Method to Identify Surrogate Compounds for Assessing the Carcinogenicity of Nitrosamine Impurities
Authors: Suman Chakravarti (1), Govindaraj Kumaran (2), Roustem Saiakhov (1), Naomi L. Kruhlak (2)
1 MultiCASE Inc., Beachwood, OH
2 FDA Center for Drug Evaluation and Research (CDER), Silver Spring, MD
Session Title: Safety Assessment: Pharmaceutical—Drug Development II
Abstract Number/Poster Board number: 4798/P863
Session Date and Time: 3/30/2022 2:30:00 PM to 3/30/2022 4:15:00 PM
Abstract: N-nitroso compounds, which include N-nitrosamines (NAs), can be formed during the manufacture and storage of human drugs and can pose a safety risk since many are mutagens and potent rodent carcinogens. Ideally, empirical mutagenicity and carcinogenicity assay data are used to establish an acceptable intake (AI) for an NA in a drug product; however, many NAs have recently been identified in regulated pharmaceutical products and have not been robustly tested in standard mutagenicity and/or carcinogenicity assays. In these cases, AIs may be based on data from structurally-related, surrogate NAs, which are selected based on factors such as their local NA environment, potential for metabolic activation, and physicochemical properties. Identifying the most similar and relevant surrogate NAs can be subjective and potentially lead to differences in calculated AIs due to the selection of different surrogates. Therefore, the current study sought to develop a computational local similarity method to objectively calculate the most relevant surrogates to a data-poor NA. Circular fingerprints up to 5 bonds from the nitroso group were built using factors known to modulate carcinogenic potential, including the degree of substitution at the alpha-carbon position, the presence of the nitroso group in a ring, and the proximity of bulky or electron donating/withdrawing substituents. Separate smaller fingerprint segments were generated for every bond depth and concatenated to produce the final 990-element fingerprint. The fingerprint was then used to calculate a local similarity index for a data-poor NA relative to the 139 experimentally tested N-nitroso compounds in the database, rank-ordered by relevance. The method was tested using 6 recently reported data-poor NA impurities identified in regulated pharmaceuticals. The results showed that when compared to the top ranked surrogates identified through visual inspection by a human expert, the method correctly proposed the same surrogates within the top 9 hits, which could be further refined based on expert knowledge and the use of calculated physicochemical parameters. The results support the conclusion that this new method for calculating local NA similarity can provide a semi-automated approach to more objectively identify the most relevant surrogates for setting an AI for an untested NA impurity than visual inspection alone.


Computational Approaches for Metabolism Prediction + Assessing Carcinogenic Potency of Untested Nitrosamines
Date and Time: 3/30/2022 9:00 AM to 10:00 AM
Presenters: Dylan Parker, Roustem Saiakhov
MultiCASE Inc., Beachwood, OH

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