External validation of alert profilers for genotoxicity hazard within the OECD QSAR Toolbox
Genotoxic hazard identification is crucial for regulatory decision-making in many countries. Computational toxicity prediction serves as a useful first step in assessing pesticide impurities and metabolites. In addition to validated QSAR models, the OECD QSAR Toolbox offers mechanistic and endpoint-specific profiler information for chemical grouping, though their predictive performance varies by case. A study compared the genotoxicity-relevant profilers in the OECD QSAR Toolbox with experimental results from the CASE Ultra AMES mutagenicity and an in vivo MNT database to better understand their predictive accuracy and role in weight-of-evidence decisions for genotoxic endpoints.
Predicting Nitrosation of Individual Amines in Drug Molecules Using Statistical (Q)SAR Models
Nitrosamines (NAs) have been suspected of being carcinogenic for over 60 years and are particularly dangerous due to their potential to persist as drug impurities that are activated in vivo by cytochrome P450. Since 2018, several pharmaceutical recalls have occurred following the discovery of N-nitrosamine impurities in drug products. Secondary amines are of the greatest concern because they undergo nitrosation easily, while tertiary amines nitrosate 1000 times more slowly but can still pose a risk.
A Comprehensive Set of Structural Keys for N-Nitrosamine Fingerprinting and Determining Surrogate Relevance in Carcinogenic Potency Assessments
This study aims to identify structural features that enable efficient searching and quantitative comparison of nitrosamine surrogates. It focuses on finding surrogates that closely mimic the reactivity and structure of complex Nitrosamine Drug Substance Related Impurities (NDSRIs). Additionally, the study seeks to determine the structural features that influence CYP-450 mediated α-hydroxylation of nitrosamines, which is linked to their carcinogenic potency.
Modelling biodegradability based on OECD 301D data for the design of mineralising ionic liquids
Discover how new QSBR models help design eco-friendly ionic liquids (ILs) that fully mineralise post-application, preventing environmental pollution. Our study combines OECD 301D data and in-house experiments to create reliable biodegradability assessments, achieving promising accuracy and R² values. Further research will refine these models for better sustainability in ILs.
Applying Expert Knowledge for Nitrosamine Carcinogenicity Assessment
We are excited to introduce a groundbreaking Nitrosation Module for hazard characterization within QSAR Flex. This webinar covers a comprehensive review of the transformative year following the adoption of the Carcinogenic Potency Categorization Approach (CPCA) framework. This regulatory-endorsed method aims to establish a recommended Acceptable Intake limit for NDSRIs by identifying substructural features that influence potency categories. We also provide an overview of the general scope of CPCA, including categorical definitions based on potency scores. Key aspects of the expert review process are highlighted, with a focus on the preliminary hazard characterization of nitrosamine formation from active pharmaceutical ingredients (APIs) and route of synthesis. Additionally, we discuss the latest developments in the QSAR Flex nitrosamine suite, including an introduction to the new QSAR Flex Nitrosamine Formation Module.
‘Relevant’ versus ‘Similar’ Structural Analogs to Support Genotoxicity (Q)SAR Predictions
Structural analogs with experimental data are important for expert review of (Q)SAR model evaluations. Analogs help clarify uncertain (Q)SAR outcomes, making inconclusive or contradictory evaluations more definitive. Finding analogs is challenging when query compounds have multiple alerts of varied significance, a common scenario in statistical model evaluations. Conventional similarity metrics may yield structurally similar analogs but often lack relevance by failing to identify analogs with query compound’s alerts. Here we introduce a novel approach for finding analogs, employing a sophisticated fingerprinting technique that considers both specific alerts and general structural features.
Predicting Nitrosation of Secondary and Tertiary Amines Using Statistical (Q)SAR Models
Since the discovery of the carcinogenic nitrosamine NDMA in pharmaceuticals such as Valsartan in 2018, there has been a push to not only quantify the presence of N-nitrosamine (NA) impurities in existing APIs but to calculate what risk individual NA molecules pose. Ideally, reliable statistical (Q)SAR models can be created using machine learning algorithms trained on experimental nitrosation results to predict if a particular amine is likely to be nitrosated. However, published results can be limited with widely varying experimental conditions. Here, we limit our training set to amines evaluated with a reaction time of 3-4 hrs., at a pH of 3-4, and in the presence of a surplus of nitrite, which aligns with the WHO’s NAP test. This yielded a dataset of 153 secondary and tertiary amine-containing molecules, which were used to train 9 machine learning classifiers, ranging from Nearest Neighbors to Neural Net.
Exploring the CPCA Framework: Defining Acceptable Intake for NDSRIs
Discover the latest advancements in establishing acceptable intake limits for NDSRIs through the innovative Carcinogenic Potency Categorization Approach (CPCA) outlined by the EMA and FDA. Experts delve into the scope, background, and practical applications of CPCA using our QSAR Flex software, including case studies showcasing alternative AI limit derivation and the scientific rationale behind non-CPCA predictions. Featuring speakers Dr. Roustem Saiakhov from MultiCASE and Alejandra Trejo-Martin from Gilead Sciences, gain insights into the framework’s implementation and its impact on setting safety standards.
QSAR Models for Endocrine Disruption
MultiCASE’s Mounika Girireddy explores the cutting-edge world of QSAR models tailored for assessing endocrine disruption potential in various substances. The session provides a comprehensive overview of QSAR’s foundations and its application in predicting complex endpoints related to endocrine disruption. We delve into various refined in vitro assays and innovative layered modeling techniques that optimize predictions by combining in vivo and in vitro data.
Carcinogenicity Assessment of Nitrosamines: Current Status
Dr. Suman Chakravarti from MultiCASE unveils the latest updates to nitrosamine classification in alignment with the recent publications by EMA/CMDh. Dr. Chakravarti also shares insights into the upcoming modifications to MultiCASE’s QSAR Flex software, specifically designed to support these new guidelines.