As you may be aware, the US Food and Drug Administration (FDA), the European Medicines Agencies (EMA), and Health Canada have recently released a major update on N-nitrosamine impurities in human medicinal products supporting the categorical prediction of N-nitrosamine potency. The new approach is referred to as the Carcinogenic Potency Categorization Approach (CPCA) and is suitable for creating submissions with recommended acceptable intake (AI) based on the potency category derived from the N-nitrosamine’s structure.
A new version of the Leadscope Model Applier™ solution has now been released to calculate the potency category and associated AI limit based on the regulatory-accepted CPCA decision tree. The tool encodes the procedures and chemistry described in “Recommended Acceptable Intake Limits for Nitrosamine Drug Substance-Related Impurities (NDSRIs) – Guidance for Industry”1 such that an analysis is performed in a consistent manner. The implementation of the CPCA decision removes any manual effort in deriving AI limits from the N-nitrosamine’s structure.
Please contact info@instem.com if you would like to discuss this in more detail.
When using experimental data and/or In Silico predictions, it is important to assess the quality of the information as part of an overall assessment of confidence. When assessing the reliability of experimental data, it is usual to consider the reproducibility of the test performed by comparing the study design and execution against published and accepted test guidelines and the extent to which this information is documented. The Klimisch score is a simple and widely used method to quantify the reliability of experimental data based on a score of 1-4, as summarized below1:
Score
Description
Summary
1
Reliable without restriction
• Well documented and accepted study or data from the literature • Performed according to valid and/or accepted test guidelines (e.g., OECD) • Preferably performed according to good laboratory practices (GLP)
2
Reliable with restriction
• Well documented and sufficient • Primarily not performed according to GLP • Partially complies with test guideline
3
Not reliable
• Interferences between the measuring system and test substance • Test system not relevant to exposure • Method not acceptable for the endpoint • Not sufficiently documented for an expert review
4
Not assignable
• Lack of experimental details • Referenced from short abstract or secondary literature
In Silico results are being increasing used for hazard and risk assessment alongside experimental data and hence it is important to assess the reliability of both types of results within a harmonized grading scheme. Such a score, called the Reliability Score, was generated as part of the in silico toxicology protocols project.2 This score was created by a team of over 80 experts in the field and reflects the current state of the art. It considers that there is an increase in reliability when multiple and complementary models are used that predict the same results. In addition, a further increase in reliability can be achieved by performing an expert review of the totality of information, including any output from the in silico models (e.g., results for chemicals analogs, important QSAR descriptors, proposed mechanism(s) of action). Where such a review increases the reliability, the Reliability Score is adjusted accordingly. A read-across assessment, which includes a similar type of expert review, is considered in this same category. The Reliability Score is a five-point grading that extends the Klimisch score to include In Silico model results as well the aforementioned factors that increase the reliability. The following table summarizes the Reliability Score alongside the Klimisch score:
Reliability Score
Klimish Score
Description
RS1
1
Data reliable without restriction
RS2
2
Data with restriction
RS3
–
Expert review of in silico result (s) and/or Klimisch 3 or 4 data
RS4
–
Multiple concurring prediction results
RS5
–
Single acceptable in silico result
RS5
3
Data not reliable
RS5
4
Data not assignable
Reliability is an important component of any confidence assessment for a toxicological hazard outcome, alongside an evaluation of the relevance and completeness of the information. A framework for assessing such a confidence measure is described in a number of recent publications.2,3
Please do not hesitate to get in touch if you wish to discuss such approaches with a scientist at Instem (info@instem.com).
References
Klimisch, H.-J., Andreae, M., Tillmann, U., 1997. A systematic approach for evaluating the quality of experimental toxicological and ecotoxicological data. Regul. Toxicol. Pharmacol. 25, 1–5. http://dx.doi.org/10.1006/rtph.1996.1076
Johnson et al., Evaluating confidence in toxicity assessments based on experimental data and insilico predictions, Comput. Toxicol. 21 (2022) 100204. https://doi.org/10.1016/j.comtox.2021.100204
In Silico methods are used to predict a toxicological outcome directly from the chemical structure. In general, the state-of-the-art is to use multiple methodologies in combination with an expert review of prediction results.1 These approaches are used either as a direct replacement for an in vitro or in vivo assay or a part of a weight of evidence (WoE) assessment in combination with other results. They are also used to support a variety of use cases including regulatory submissions, screening and chemicals prioritization. In silico toxicology has a number of compelling and often unique advantages including as they (1) support the reduction, replacement, and refinement of animal studies (3Rs), (2) often identify the structural basis of a prediction which can support the design of new chemicals with reduced toxicity as well as supporting an expert review, (3) may propose a mechanism of action associated with any predicted toxicity to support experimental planning and analysis of results, (4) do not require any test material, (5) are inexpensive to run, and (6) can generate results quickly, often in seconds. Of course, these benefits can only be realized once such methods have been validated. The following post identifies some considerations for assessing whether in silico models are fit-for-purpose.
Central to any validation is the prediction of an external test set containing a list of chemical structures with experimental data for the toxicological outcome being assessed. Such a set needs to include a sufficiently large number of diverse chemicals representative of real-world applications. External sets with small numbers of chemicals that only cover relatively small numbers of chemical classes have limited utility. Therefore, it is important to compare the external test set to the training or reference sets used in the development of the in silico models being assessed. Any overlapping chemicals between the two sets should be identified and any duplicates discarded from the external test set. It can also be helpful to compare the two sets based on chemical classes to support any interpretation of the validation results regarding the accuracy and coverage of the model (as well as the subset of model classes tested by the external validation set). When comparing multiple models with different training or reference sets, it is possible to introduce bias into the assessment if different subsets of the external test set are used to assess the comparative performance of the different models. Since most computational models use training data from the public domain, proprietary data is often a necessary source for external test sets.2
Having created an external test set, the next step is to run the in silico model(s) over this set and compare the predictions to the experimental data. One or more performance statistics are then calculated to provide an overall assessment. The selection of these statistics is dependent on the type of the data being predicted such as whether the data is binary (e.g., a chemical is mutagenic or non-mutagenic), whether the data is ordinal (e.g., GHS categories representing ranges of values), or continuous (e.g., a NOAEL value). Relevant statistics also depend on the context of use (e.g., a complete replacement of an in vivo methods or prioritization of chemicals) where the consequences of an incorrect predict should be considered. This includes false positive and false negative predictions for binary endpoints, and more and less conservative predictions for ordinal or continuous endpoints. Many factors may need to be taken into consideration. For example, as part of a recent assessment of acute toxicity models that are being considered as a direct replacement of the acute in vivo test for classification and labelling purposes, it was considered that more conservative predictions were desirable since this would be protective of health; however, such conservative predictions may add to transportation or other costs and so the overall accuracy of the predictions was also considered.2
It is also important to consider how the model predictions will be interpreted and any associated expert review will be performed, either as a standalone method or as part of the WoE. When using different models, the performance of each model may be assessed individually alongside the performance of different combinations of models. Different methodologies for deriving a consensus prediction from multiple models (and experimental data in the case of WoE approaches) will produce different overall results that may be optimized. In addition, some in silico methodologies may contribute towards enhancing the predictive performance whereas others may better support an expert review, yet both contribute to the overall ultimate assessment. Here, it may be helpful to consider the performance of the models, the performance of different approaches for deriving a consensus and the performance before and after an expert review. It may also be helpful to consider at the performance of different subsets of the external test set identifying those predictions with high vs. low confidence or predictions across different chemical classes, in order to further refine any recommendations. Finally, it is important to consider the variability in the underlying experimental data that will ultimately limit the performance of any in silico solution.
Please do not hesitate to get in touch with one of our subject matter experts if you wish to discuss these approaches (info@instem.com).
Bercu et al., A cross-industry collaboration to assess if acute oral toxicity (Q)SAR models are fit-for-purpose for GHS classification and labelling, Regul. Toxicol. Pharmacol. 120 (2021) 104843. https://doi.org/10.1016/j.yrtph.2020.104843
Next month, join Instem at EUROTOX 2023 in Ljubljana1. For the first time ever, the Slovenian Society of Toxicology will have the honour of hosting a EUROTOX Congress, an event for toxicologists not only working within Europe, but also from all over the world. EUROTOX 2023 will take place from 10 to 13 September with an aim of: multidisciplinary science leading to safer and sustainable life. Contributing to this aim, on Tuesday, 12 September, 12:00 (CEST), Room: Urška 2, Instem’s Frances Hall, PhD & Brenda Finney, PhD will be leading the session:
From Safety Testing to Success: ICH S1B Weight of Evidence and the Power of Data Sharing
In the session we will discuss how data sharing and data-based dossiers are inspiring innovation and propelling progress. In line with the 3Rs principles and the universal emphasis on in silico approaches, we will discuss two examples of how Instem is harnessing data for inspiring acceleration and driving transformative breakthroughs within pharma R&D.
Centrus®, Instem’s pioneering translational science solution suite for pharmaceutical R&D facilitates data sharing, warehousing and review with access to cutting-edge technologies, which drives critical insights from early discovery to post marketing of drugs.
Advance™, ICH S1B: New Approaches in the Carcinogenicity Assessment of Pharmaceuticals. A review of the recent guideline enabling the removal of the two-year rat carcinogenicity study by the execution of a Weight of Evidence document. With real-life case studies and updates from a collaborative international working group.
During the congress, please visit Instem at Booth 49. We look forward to connecting with you!
Please feel free to contact us if you’d like to discuss or would like any of the material from this conference. Brenda Finney, PhD, Vice President of In Silico & Translational Science Solutions Brenda.Finney@instem.com, Frances Hall, PhD, Senior Director for In Silico & Translational Science Solutions Frances.Hall@instem.com).
On Friday 4th August 2023, the United States Food and Drug Administration (FDA) provided some important updates on N-nitrosamine impurities in human medicinal products – “Recommended Acceptable Intake Limits for Nitrosamine Drug Substance-Related Impurities (NDSRIs) – Guidance for Industry.”1 The release also includes updated information on “Recommended Acceptable Intake Limits for Nitrosamine Drug Substance-Related Impurities (NDSRIs).”2
These documents are aligned with the recent releases from the European Medicines Agency3,4 and Health Canada5 which outline different approaches for deriving Acceptable Intake (AI) limits for substances without carcinogenicity data, including a carcinogenic potency categorization approach (CPCA). The CPCA is a decision tree in which the N-nitrosamine impurity may be assigned to one of five potency categories based on its chemical environment. The CPCA is based on structure-activity relationship (SAR) concepts, as it identifies structural features that directly increase or decrease the favorability of metabolic activation of nitrosamines (or that increase the clearance of the nitrosamine by other biological pathways). This information is used to assign an AI based on the potency category resulting from the nitrosamine structure.
Instem is currently engaged in providing functionality within the Leadscope Model Applier™ to support potency category calculations, providing an easy-to-use solution for rapidly generating the CPCA potency categories and their associated AI limits based on the latest guidance from authorities. The validated tool will automatically and consistently calculate the potency category and associated AI limits in seconds avoiding the need to manually process the detailed and complex chemistry rules.
If you would like to discuss these updates and solutions to consistently apply this updated information to N-nitrosamine impurities, please contact us (Kevin Cross, kevin.cross@instem.com).
In silico toxicology refers to computational methods that predicts toxicological endpoints or mechanisms. It offers a cost-effective, rapid, and animal-free approach to support toxicological hazard assessments. There are increasing numbers of applications where it has been shown that in silico toxicology can be very useful as well as standardized procedures for performing in silico assessments.1,2 However, as part of the development of a chemical product, when is the best time to perform such assessments?
In early drug discovery, large numbers of compounds are routinely assessed and in silico toxicology approaches can support the prioritization of the most promising candidates as well as decisions on how to progress those compounds. An in silico toxicology profile may help identify which follow-on test(s) to perform and in what order. It may also help to support the experimental design of any tests as well as what to focus on as part of an analysis of the results. For example, a 5-day in vivo study may be run as part of the lead identification and optimization process in which the major organ systems are examined. In silico screens may draw the attention to potential toxicological concerns and help a proper analysis of the experimental signals.3,4
In silico methods are also increasingly being used as part of a weight of evidence (WoE) assessments for major toxicological endpoints. For example, the recently updated ICH S1B guideline5 for pharmaceutical carcinogenicity assessment describes the use of 6 WoE factors – (1) target biology, (2) secondary pharmacology, (3) histopathology chronic studies, (4) hormonal effects, (5) genotoxicity, (6) immune modulation. An assessment of these factors can be performed throughout the R&D process to support product stewardship.6
An assessment of the target biology may be performed before any leads have been identified in order to detect possible carcinogenicity risks from biological pathways related to the target and artificial intelligence supports the analysis of pathways and mechanisms from available databases
In lead identification and optimization, in silico methods predictive of WoE factors 2-6, alongside any non-GLP in vitro and 5-day in vivo results can form the basis of a WoE assessment
In the pre-clinical phase when many of the GLP test results supportive of WoE factors 2, 4, 5, and 6 are generated, in silico methods may help to fill any data gaps and explain false positive signals in the data
In parallel to clinical trials, the results from a 6-month repeated dose study will support WoE factor 3, and used in combination with information generated on the other WoE factors supporting a carcinogenicity assessment document (CAD) to be submitted to international regulatory authorities and in silico analysis may also support a mechanistic understanding of available signals helping the interpretation of the findings and their human relevance
There are situations where the performance of in silico methods has been shown to be fit-for-purpose for specific applications and may be used as a direct replacement. For example, for classification and labelling, in silico methods may provide predictions for relevant endpoints for data-poor substances; in the case of acute oral toxicity, it has been shown that using multiple in silico methodologies that predict this endpoint, in combination with an expert review, is sufficiently predictive. A recent paper showed that for a dataset of approximately 2,000 proprietary chemicals with acute toxicity test results, 95% of chemicals were either predicted in the correct Globally Harmonized System of Classification and Labelling of Chemicals (GHS) category or in a more conservative (i.e., it is protective of health).7
In some situations, there is not a sufficient quantity of the test material available to perform an in vitro or in vivo test. For example, when assessing the potential genotoxicity of impurities or extractables & leachables, there is generally not enough material to perform an Ames test or other relevant tests. Time and cost limitations may also prevent the performance of experimental tests. An in silico assessment offers a sufficiently predictive, rapid, and cost-effective alternative approach which can be generated from the chemical structure alone.2,8,9,10
The following figure summarizes the applications above for pharmaceuticals and illustrates the product cycle phases where in silico toxicology is applicable.
Please get in touch if you would like to discuss any of these topics (Glenn Myatt; glenn.myatt@instem.com).
Myatt et al., Implementation of in silico toxicology protocols within a visual and interactive hazard assessment platform, Comput. Toxicol. 21 (2022) 100201. https://doi.org/10.1016/j.comtox.2021.100201
Bassan et al., In silico approaches in organ toxicity hazard assessment: current status and future needs in predicting liver toxicity, Comput. Toxicol. 20 (2021) 100187. https://doi.org/10.1016/j.comtox.2021.100187
Bassan et al., In silico approaches in organ toxicity hazard assessment: current status and future needs for predicting heart, kidney and lung toxicities, Comput. Toxicol. 20 (2021) 100188. https://doi.org/10.1016/j.comtox.2021.100188
Bercu et al., A cross-industry collaboration to assess if acute oral toxicity (Q)SAR models are fit-for-purpose for GHS classification and labelling, Regul. Toxicol. Pharmacol. 120 (2021) 104843. https://doi.org/10.1016/j.yrtph.2020.104843
Amberg et al., Principles and procedures for implementation of ICH M7 recommended (Q)SAR analyses, Regul. Toxicol. Pharmacol. 77 (2016) 13–24. https://doi.org/10.1016/j.yrtph.2016.02.004
Amberg et al., Principles and procedures for handling out-of-domain and indeterminate results as part of ICH M7 recommended (Q)SAR analyses, Regul. Toxicol. Pharmacol. 102 (2019) 53–64. https://doi.org/10.1016/j.yrtph.2018.12.007
The World Congress on Alternatives and Animal Use in the Life Sciences is where scientists from academia, industry, government, and the non-profit sector meet to advance the 3Rs—to reduce, refine, and replace the use of animals. This year the conference – WC12 – is being held at Niagara Falls in Canada.1
The conference is organized into 6 parallel tracks covering (1) regulatory acceptance and global harmonization; (2) next-gen education; (3) ethics, welfare, policies, and regulation; (4) human-centred biomedical research; (5) refinement and impact on science; and (6) 21st century predictive toxicology.
Instem is pleased to be co-chairing with the NC3Rs2 and presenting in session S403 around the replacement of the acute toxicity test. We are looking forward to the discussion around how in silico solutions can support this goal.
Please get in touch if you would like to discuss this topic in more detail (Glenn Myatt; glenn.myatt@instem.com).
On Friday 7th July 2023, the European Medicines Agency (EMA) released a major update on N-nitrosamine impurities in human medicinal products1,2 including a new approach on determining limits for N-nitrosamines. According to EMA, this approach is expected to contribute the management of products with nitrosamines while ensuring availability of the pharmaceutical drug supply.
The Q&A document1 outlines four approaches to support deriving Acceptable Intake (AI) limits for substances without carcinogenicity data: (1) a carcinogenic potency categorization approach (CPCA), (2) an enhanced Ames test, (3) the use of a surrogate based on SAR and read-across considerations, and (4) a negative result from an in vivo mutagenicity test.
The CPCA is outlined as a decision tree in which the N-nitrosamine impurity may be assigned to five potency categories based on its chemical environment. The CPCA is based on structure-activity relationship (SAR) concepts and it identifies structural features that directly increase or decrease the favorability of metabolic activation of nitrosamines (or that increase the clearance of the nitrosamine by other biological pathways). These structural rules consider the number of hydrogens on the α-carbon and the presence of additional deactivating or activating features. This information is used to assign an AI based on the potency category resulting from the nitrosamine structure. Five potency categories are defined with corresponding limits: potency category 5 (AI=1500 ng/day); potency category 4 (AI = 1500 ng/day); potency category 3 (AI = 400 ng/day); potency category 2 (AI = 100 ng/day); potency category 1 (AI = 18 ng/day).
The release also includes an updated list of acceptable intakes for established N-nitrosamines2 and a description of an enhanced Ames test for nitrosamine hazard identification.
Instem is currently engaged in providing functionality within the Leadscope Model Applier to support potency category calculations as well as read-across functionality to support the use of a surrogate based on SAR for derivation of AI limits.
If you would like to discuss these updates and solutions to consistently apply this updated information to N-nitrosamine impurities, please contact us (Kevin Cross, kevin.cross@instem.com).
Given the movement towards the use of alternatives to animal studies, there is a lot of new interest in in silico toxicological methods. While the ins and outs of in silico toxicology may be familiar to our typical readership, I would like to take a step back and provide foundational content to readers who may be new to in silico methods, or those who would like a refresher. Seasoned users of in silico models may also want to read on, as the acute rat oral model is discussed; and unlike the single (Quantitative) structure activity relationship (QSAR) approach for predicting mutagenicity for example, this model uses categorical QSARs to derive a consensus call to predict GHS categories.
What is it?
At the very basis of in silico toxicology is the chemical structure. This chemical structure is one key used to unlock the chemical-biological interactions that may lead to toxicity. The chemical structure is characterized by features, which define the structure. In silico toxicology uses an established link between structural features and toxicity to make a prediction. This link may be derived through a statistical association between structural features and biological activity, or through knowledge of sub-structures encoded as expert alerts (they may encode different non-reactive mechanisms). For example, the statistical acute oral toxicity model uses statistical correlations between GHS categories and structural features (together with physical and chemical descriptors) derived from a training set of over 22,000 structures. Figure 1 shows the result of a test article which is assessed using the acute rat oral statistical model. The red highlighting indicates structural features which are associated with toxicity at the GHS category queried whereas, the blue shading indicates a negative association. The model predicts that the test article is assigned to GHS category IV as this is the most severe queried category which returned a positive outcome. This positive outcome is based on the predicted value (calculate using the presence and absence of chemical features in the test compounds as well as whole molecule descriptors).
Figure 1: Assignment of GHS categories using a categorical approach.
The acute oral expert alerts use a reference set of over 40, 000 structures to support the prediction of GHS categories. In the same test article, two alerts were identified, Figure 2. These alerts map to over 500 structures with experimentally derived exact or higher GHS categories with a precision greater than 0.9. The alerts assign that test article to GHS category of V. A consensus approach is then used to derive the overall GHS category from the statistical model and the alert assignment as GHS category IV.
Figure 2: Sub-structures highlighted in red indicate those features which are identified as alerting fragments for acute oral toxicity at the GHS categories specified.
A consensus approach is then used to derive the overall GHS category from the statistical model and the alert assignment as GHS category IV.
What it is not?
In silico methods are not testing methods, and therefore, no animals are used to derive the GHS categories. In silico methods are also not black boxes! It is important to understand the structural and ideally mechanistic basis of the prediction to gain confidence in the result. The process by which we seek to interrogate the quality of the prediction is known as an expert review. There are detailed processes for performing an expert review, and these have been discussed in various publications1,2 and prior blogs3,4. Very briefly, an assessment to training/reference examples and structural analogs are important aspects. The structural similarity between the test article and an analog is considered to determine whether they are expected to exert similar toxicities. Additionally, any mechanistic information that supports the prediction would be useful. In the case of the test article assessed above, the availability of structurally similar analogs which the model predicts similarly to the test article and information included with the prediction such as the pharmacological action, histopathological findings, clinical chemistry, and in vivo findings (e.g. somnolence) for chemicals matching the alert could be used to support the reliability of the prediction.
Figure 3: Structural analogs and the model predictions for these analogs
Performance
Bercu et al.1 assessed the fitness for purpose of the acute oral toxicity (Q)SAR models for GHS classification and labelling. In this study, the performance of the acute oral toxicity models was assessed against over 2000 proprietary and marketed chemicals. The authors found that 95% of these chemicals were predicted to a correct or more conservative category when benchmarked against experimentally derived GHS categories. The authors also detail an acute oral toxicity specific expert review and workflow to incorporate in silico models to support GHS assignment. The robustness of these models support their use in the replacement of animal studies and as contributors to the 3Rs initiative.
Bercu J, Masuda-Herrera MJ, Trejo-Martin A, Hasselgren C, Lord J, Graham J, Schmitz M, Milchak L, Owens C, Lal SH, Robinson RM, Whalley S, Bellion P, Vuorinen A, Gromek K, Hawkins WA, van de Gevel I, Vriens K, Kemper R, Naven R, Ferrer P, Myatt GJ. A cross-industry collaboration to assess if acute oral toxicity (Q)SAR models are fit-for-purpose for GHS classification and labelling. Regul Toxicol Pharmacol. 2021 Mar;120:104843. doi: 10.1016/j.yrtph.2020.104843. Epub 2020 Dec 17. Erratum in: Regul Toxicol Pharmacol. 2022 Jun;131:105165. PMID: 33340644; PMCID: PMC8005249.
Myatt GJ, Ahlberg E, Akahori Y, Allen D, Amberg A, Anger LT, Aptula A, Auerbach S, Beilke L, Bellion P, Benigni R, Bercu J, Booth ED, Bower D, Brigo A, Burden N, Cammerer Z, Cronin MTD, Cross KP, Custer L, Dettwiler M, Dobo K, Ford KA, Fortin MC, Gad-McDonald SE, Gellatly N, Gervais V, Glover KP, Glowienke S, Van Gompel J, Gutsell S, Hardy B, Harvey JS, Hillegass J, Honma M, Hsieh JH, Hsu CW, Hughes K, Johnson C, Jolly R, Jones D, Kemper R, Kenyon MO, Kim MT, Kruhlak NL, Kulkarni SA, Kümmerer K, Leavitt P, Majer B, Masten S, Miller S, Moser J, Mumtaz M, Muster W, Neilson L, Oprea TI, Patlewicz G, Paulino A, Lo Piparo E, Powley M, Quigley DP, Reddy MV, Richarz AN, Ruiz P, Schilter B, Serafimova R, Simpson W, Stavitskaya L, Stidl R, Suarez-Rodriguez D, Szabo DT, Teasdale A, Trejo-Martin A, Valentin JP, Vuorinen A, Wall BA, Watts P, White AT, Wichard J, Witt KL, Woolley A, Woolley D, Zwickl C, Hasselgren C. In silico toxicology protocols. Regul Toxicol Pharmacol. 2018 Jul;96:1-17. doi: 10.1016/j.yrtph.2018.04.014. Epub 2018 Apr 17. PMID: 29678766; PMCID: PMC6026539.
We recently attended the 20th International Workshop on (Q)SAR in Environmental and Health Sciences (QSAR 2023) held in Copenhagen. It was a great meeting with many interesting topics, including read-across approaches, new methods, and applications of (Q)SARs.
We were happy to provide a course on regulatory uses of in silico modelling, covering different applications across many industries along with case studies. The course focused on a number of current applications such as the ICH M7 pharmaceutical impurities guideline (including an N-nitrosamines read-across example), the recently revised ICH S1B(R1) (carcinogenicity weight of evidence) guideline, and how in silico toxicology protocols can support current and future applications.
We also presented in a read-across session on predicting N-nitrosamines carcinogenicity potency in support of regulating acceptable intake levels of drugs.
In addition, we participated in the session “Regulatory use of acute oral toxicity predictions” organized by ECHA where we presented “A cross-industry collaboration to assess if acute oral toxicity (Q)SAR models are fit-for-purpose for GHS classification and labelling”. Following the presentation there was a panel discussion discussing important topics related to the use and acceptance of acute (Q)SAR models.
Please get in touch if you would like to review any of the material we presented at the conference (Glenn Myatt; glenn.myatt@instem.com).
