Advancing Prediction of Nitrosamine Carcinogenicity Potency

This week, Dr. Kevin Cross from Instem is presenting at the 2022 Genetic Toxicology Association meeting on recent progress in predicting N-nitrosamine carcinogenicity potency. The presentation outlines progress over the last year and provides an update on different collaborative working groups, including a recently announced EMA-MutAmind project funded by the European Medicine’s Agency and led by Fraunhofer ITEM that includes Instem as a partner.1

The presentation provides an overview of the different reaction mechanisms involved in N-nitrosamines mutagenicity and compares Nitrosamine Drug Substance-Related Impurities (NDSRIs) against historical information on tested nitrosamines. This includes an assessment of historical Ames negative studies that have historically tested positive for rodent carcinogenicity. Recent testing of these ‘false negatives’ are presented and discussed relative to experimental protocols used and structural classes where issues occur, allowing for more directed future assessments of nitrosamines.

The presentation is available to view:

<View presentation>

Please get in touch if we can help answer any questions on this important topic (Glenn Myatt,


  1. Instem Awarded EMA Research Grant.

Predicting toxicity using read-across

Read-across is an example of a flexible in silico toxicology methodology whereby a toxicity prediction for a chemical (often referred to as the target compound) is made using experimental data on one or more similar analogs (often referred to as source compound(s)). This is a lengthy process highly reliant on an expert review of the information to identify and justify the analogs selected and the prediction made.

Read-across is used in many current and emerging applications, including supporting the assessment of repeated dose toxicity of chemicals or N-nitrosamines carcinogenicity potency categories.

At the heart of any read-across is the need to identify suitable analogs. This is where comprehensive high-quality structure-searchable databases are critical, containing both public and proprietary experimental data. Global similarity is often used to search such databases by identifying and prioritizing chemical analogs based on their whole molecule similarity. However, in many instances it is important to retrieve analogs that have a local similarity around the structural class that is most likely associated with the toxicological endpoint being assessed. This may also consider the structural environment responsible for any activation or deactivation of the specific toxicity of interest. The use of structural alerts or QSAR models may support this assessment and justification, when run against both the target and source compounds(s). Actually, mechanistic similarity can be in general explored by running alert profilers, such as reaction domain or bioactivation profilers, across both the target and source chemicals to identify similarities and differences. In this way, reactivity of the source and target chemicals is compared in terms of structural alerts or reactive features. Indeed, justifying the selected analogs based on common biological properties or mechanisms, and toxicity profiles is key in a read-across framework. Further analysis of the physico-chemical properties is also prudent.

The read-across process can be assisted and simplified by the availability of both high-quality structure-searchable databases coupled with user-friendly read-across tools that are aligned with regulatory expectations, including appropriate documentation of the results. We are actively developing such methodologies and technologies to support further acceptance of read-across. If you are interested in collaborating in this area, please get in touch (Glenn Myatt;

In silico applications in the assessment of Extractables and Leachables

The Product Quality Research Institute (PQRI) recently published on their recommendations for safety thresholds and best practices for extractables and leachables (E&L) in Parenteral Drug products1. The publication describes, and addresses, issues related to the definition and procedures for defining safety thresholds. Within the document, areas of application for in silico methods are also defined. We see three main categories for the in silico approaches.

  1. Established procedures

Such methods have well defined and documented procedures and could be accepted for regulatory submissions. The in silico assessment of mutagenicity falls within this category and it is important to examine the factors/characteristics of this assessment type as we look forward to developing other areas. There is scientific consensus on the use and application of in silico approaches to assess mutagenicity based on the publication of a detailed guideline, ICH M72. Further, the availability of data3, and growth of SAR principles4 and application of the methods5 have allowed for advanced interpretation of results.

  1. Evolving procedures

The areas which are considered evolving have foundational elements laid out and the application structure is being defined. In these areas, models are available, SAR understanding is acceptable and various application methods are being considered6. Skin sensitization and irritation/corrosion models appear to fall into this category. In these areas there is a need for documented procedures and questions remain as how to interpret and utilize potency predictions in the context of safety thresholds. The ELSIE consortium is working on advancing this area and others.

  1. Emerging applications 

Within the publication by PQRI, special consideration is given to biological products where the potential for a reactive E&L compound to react with therapeutic proteins is mentioned. Here the proof of concept for the application of theoretically relevant models is needed. As such, efforts are ongoing with the in silico protocol initiative to examine the performance of relevant models against empirical data and examine the scope of potentially reactive chemicals within the E&L chemical space.

At the Extractables and Leachables 2022 conference, I presented on these topics with focus on emerging applications. Here we demonstrated the conservative nature of a consensus result based on a prediction of peptide reactivity and reaction domain profiling, while detailing a decision workflow for the application of in silico models to assess the reactivity of E&L molecules with biomolecules. Noted from the conference is the ongoing work in the application of in silico methods in the context of E&L evaluations; and additional details to be provided in the ISO-10993 guideline and various upcoming publications.

If you would like to get in touch, please do not hesitate to contact


  1. Product Quality Research Institute (PQRI). 2021. Safety Thresholds and Best Demonstrated Practices for Extractables and Leachables in Parenteral Drug Products (Intravenous, Subcutaneous, and Intramuscular).
  2. ICH, 2017. M7 (R1) Assessment and control of DNA reactive (mutagenic) impurities in pharmaceuticals to limit potential carcinogenic risk (No. EMA/CHMP/ICH/83812/2013), ICH Harmonised Guideline. European Medicines Agency.
  3. Landry, Curran et al. 2019. “Transitioning to Composite Bacterial Mutagenicity Models in ICH M7 (Q)SAR Analyses.” Regulatory Toxicology and Pharmacology 109: 104488.
  4. Ahlberg, Ernst et al. 2016. “Extending (Q)SARs to Incorporate Proprietary Knowledge for Regulatory Purposes: A  Case Study Using Aromatic Amine Mutagenicity.” Regulatory toxicology and pharmacology : RTP 77: 1–12.
  5. Amberg, Alexander et al. 2019. “Principles and Procedures for Handling Out-of-Domain and Indeterminate Results as Part of ICH M7 Recommended (Q)SAR Analyses.” Regulatory Toxicology and Pharmacology.
  6. Johnson, Candice et al. 2022. “Evaluating Confidence in Toxicity Assessments Based on Experimental Data and in Silico Predictions.” Computational Toxicology 21: 100204.

Neurotoxicity hazard assessment framework that integrates in silico approaches

This week we are pleased to welcome Dr. Kevin Crofton and Dr. Arianna Bassan as guest contributors to the blog.

Within the in silico toxicology project, a position paper on neurotoxicity is appearing in a special issue of the journal of Computational Toxicology1.

This paper discusses the need for the development of more informative new approach methodologies (NAM) to assess developmental (DNT) and adult (NT) neurotoxicity induced by xenobiotics. The use of NAMs (including in silico methodologies that predict toxicity from chemical structure such as QSARs and structural alerts) is ideally based on the understanding of the biological mechanism underpinning neurotoxicity (e.g., blocking of GABAA receptors by organochlorines leading to seizures and pyrethroid effects on voltage-gated sodium channels).

In our paper, we discuss the development of a decision assessment framework that builds on the emerging vision of mechanistically informed approaches. Such assessment framework requires the identification of relevant effects/mechanisms and endpoints together with their mutual relationships as it is meant to guide the integration of data from different sources (in vitro data, animal in vivo data, human data) for the assessment of neurotoxicity. The expedited advances in toxicology and the ever-increasing understanding of the biological pathways underlying NT and DNT will impose a continuous adaptation of this framework. However, in our work, we have tried to capture the current knowledge to provide a construct that integrates the use of in silico methods for DNT and NT assessments and that may inform future development of in silico approaches.

The paper also includes overviews on:

  • Known mechanisms underlying DNT/NT
  • Current state of the art in DNT/NT testing
  • In silico approaches for the assessment of neurotoxicity based on knowledge of chemical structure

This work should support the future development of protocols, namely standardized approaches, for the use of in silico methods for the assessments of NT and DNT based on chemical structures ensuring transparent, consistent, and defendable results. Because of the large numbers of chemicals used in all aspects of global commerce, such standardized procedures would be beneficial for compliance across related regulatory programs (e.g., FDA and EFSA, TSCA and REACH).

For more information on this collaboration or related projects, please contact Glenn Myatt (


  1. Crofton et al (2022) , Current status and future directions for a neurotoxicity hazard assessment framework that integrates in silico approaches, 22, 100223,

A protocol to support the weight-of-evidence for human carcinogenicity assessment of pharmaceuticals

This week we are pleased to welcome Dr. Arianna Bassan as a guest contributor to the blog.

The recently published draft addendum of the ICH S1B guideline1 introduces a weight-of-evidence (WoE) approach to assess human carcinogenic potential of small molecule pharmaceuticals and determine whether a 2-year rat carcinogenicity study would add value. Application of this integrated analysis “would reduce the use of animals in accordance with the 3Rs (reduce/refine/replace) principles, and shift resources to focus onto generating more scientific mechanism-based carcinogenicity assessments, while promoting safe and ethical development of new small molecule pharmaceuticals1. The WoE approach described in the S1B addendum is based on a comprehensive assessment of relevant factors including:

  • Data that inform carcinogenic potential based on drug target biology and the primary pharmacologic mechanism of the compound including carcinogenicity information available on the drug class
  • Results from secondary pharmacology screens, especially those that inform carcinogenic risk
  • Histopathology data from repeated dose toxicity studies completed with the test agent, with particular emphasis on the long-term rat study including exposure margin assessments
  • Evidence for hormonal perturbation
  • Genetic toxicology study data
  • Evidence of immune modulation

Instem has recently initiated a new collaborative working group (WG) to develop a protocol (i.e., standardized approach) that can support the carcinogenicity WoE integrated assessment in a transparent, consistent, and defendable manner. The group aims at developing a high-level protocol that integrates in silico methods while assembling information from the different WoE factors described in the S1B draft addendum. The WG activities are building upon the extensive work already completed as part of the in silico toxicology protocol project2 that may feed the S1B higher-level, fit-for-purpose protocol with previous outcome such as:

  1. the genetic toxicology in silico protocol3 that may support the corresponding S1B WoE factor;
  2. the principles to evaluate reliability and confidence of a toxicity assessment4 as the integration of data should be complemented by evaluation of reliability and confidence scores based on the quality of the information used to derive the assessment and the overall uncertainty of such an assessment;
  3. the framework based on the 10 key characteristics (KCs) of carcinogens5 that can sustain the organization of granular information to support each of the WoE areas and help provide a comprehensive assessment based upon what we currently know about tumorigenesis.

The very first objective of the S1B consortium WG is a high-level conceptual paper that describes how the S1B WoE assessment may take advantage of a decision support system making use of in silico toxicology protocols. This work will also include mapping the KCs onto the S1B WoE factors and describing each WoE factor in terms of the pieces and elements that would define the assessment protocol.

For more information on this collaboration or related projects, please contact Glenn Myatt (


1 Addendum to the ICH guideline S1B on testing for carcinogenicity of pharmaceuticals Draft version Endorsed on 10 May 2021, Currently under public consultation,

2 Myatt, G.J., Ahlberg, E., Akahori, Y., Allen, D., Amberg, A., Anger, L.T., Aptula, A., Auerbach, S., Beilke, L., Bellion, P., Benigni, R., Bercu, J., Booth, E.D., Bower, D., Brigo, A., Burden, N., Cammerer, Z., Cronin, M.T.D., Cross, K.P., Custer, L., Dettwiler, M., Dobo, K., Ford, K.A., Fortin, M.C., Gad-McDonald, S.E., Gellatly, N., Gervais, V., Glover, K.P., Glowienke, S., Van Gompel, J., Gutsell, S., Hardy, B., Harvey, J.S., Hillegass, J., Honma, M., Hsieh, J.-H., Hsu, C.-W., Hughes, K., Johnson, C., Jolly, R., Jones, D., Kemper, R., Kenyon, M.O., Kim, M.T., Kruhlak, N.L., Kulkarni, S.A., Kümmerer, K., Leavitt, P., Majer, B., Masten, S., Miller, S., Moser, J., Mumtaz, M., Muster, W., Neilson, L., Oprea, T.I., Patlewicz, G., Paulino, A., Lo Piparo, E., Powley, M., Quigley, D.P., Reddy, M.V., Richarz, A.-N., Ruiz, P., Schilter, B., Serafimova, R., Simpson, W., Stavitskaya, L., Stidl, R., Suarez-Rodriguez, D., Szabo, D.T., Teasdale, A., Trejo-Martin, A., Valentin, J.-P., Vuorinen, A., Wall, B.A., Watts, P., White, A.T., Wichard, J., Witt, K.L., Woolley, A., Woolley, D., Zwickl, C., Hasselgren, C., 2018. In silico toxicology protocols. Regul. Toxicol. Pharmacol. 96, 1–17.

3 Hasselgren, C., Ahlberg, E., Akahori, Y., Amberg, A., Anger, L.T., Atienzar, F., Auerbach, S., Beilke, L., Bellion, P., Benigni, R., Bercu, J., Booth, E.D., Bower, D., Brigo, A., Cammerer, Z., Cronin, M.T.D., Crooks, I., Cross, K.P., Custer, L., Dobo, K., Doktorova, T., Faulkner, D., Ford, K.A., Fortin, M.C., Frericks, M., Gad-McDonald, S.E., Gellatly, N., Gerets, H., Gervais, V., Glowienke, S., Van Gompel, J., Harvey, J.S., Hillegass, J., Honma, M., Hsieh, J.-H., Hsu, C.-W., Barton-Maclaren, T.S., Johnson, C., Jolly, R., Jones, D., Kemper, R., Kenyon, M.O., Kruhlak, N.L., Kulkarni, S.A., Kümmerer, K., Leavitt, P., Masten, S., Miller, S., Moudgal, C., Muster, W., Paulino, A., Lo Piparo, E., Powley, M., Quigley, D.P., Reddy, M.V., Richarz, A.-N., Schilter, B., Snyder, R.D., Stavitskaya, L., Stidl, R., Szabo, D.T., Teasdale, A., Tice, R.R., Trejo-Martin, A., Vuorinen, A., Wall, B.A., Watts, P., White, A.T., Wichard, J., Witt, K.L., Woolley, A., Woolley, D., Zwickl, C., Myatt, G.J., 2019. Genetic toxicology in silico protocol. Regul. Toxicol. Pharmacol. 107, 104403.

4 Johnson, C., Anger, L.T., Benigni, R., Bower, D., Bringezu, F., Crofton, K.M., Cronin, M.T.D., Cross, K.P., Dettwiler, M., Frericks, M., Melnikov, F., Miller, S., Roberts, D.W., Suarez-Rodrigez, D., Roncaglioni, A., Lo Piparo, E., Tice, R.R., Zwickl, C., Myatt, G.J., 2022. Evaluating confidence in toxicity assessments based on experimental data and in silico predictions. Computational Toxicology 21, 100204. 5 Tice, R.R., Bassan, A., Amberg, A., Anger, L.T., Beal, M.A., Bellion, P., Benigni, R., Birmingham, J., Brigo, A., Bringezu, F., Ceriani, L., Crooks, I., Cross, K., Elespuru, R., Faulkner, D., Fortin, M.C., Fowler, P., Frericks, M., Gerets, H.H.J., Jahnke, G.D., Jones, D.R., Kruhlak, N.L., Lo Piparo, E., Lopez-Belmonte, J., Luniwal, A., Luu, A., Madia, F., Manganelli, S., Manickam, B., Mestres, J., Mihalchik-Burhans, A.L., Neilson, L., Pandiri, A., Pavan, M., Rider, C.V., Rooney, J.P., Trejo-Martin, A., Watanabe-Sailor, K.H., White, A.T., Woolley, D., Myatt, G.J., 2021. In silico approaches in carcinogenicity hazard assessment: current status and future needs. Comput. Toxicol. 20, 100191.

In Silico Methods for Predicting Drug Toxicity, Second Edition

In Silico Methods for Predicting Drug Toxicity, edited by Emilio Benfenati1 addresses new in silico methodologies, their integrated use, and regulatory implications for the assessment of pharmaceuticals. The second edition contains both updated and new chapters, reflecting advancements in computational toxicology.  Each chapter covers specific areas such as the use of modeling a pharmaceutical in the body (physiologically based pharmacokinetic (PBPK) models), in silico models for specific endpoints, platforms for evaluating pharmaceuticals and scientific and societal challenges.

Our contribution to this effort describes the implementation of in silico toxicology protocols within Leadscope platforms. Here we present the fundamental principles governing the review of in silico predictions, assignment of reliability, relevance and confidence. Case studies were used to demonstrate the tools which facilitate a systematic review of integrated data streams in a reproducible and transparent manner.

We congratulate Emilio for this incredible compilation of resources. If you would like additional information please contact me at


In silico solutions at SOT 2022

At this time of year, we look forward to liaising with our collaborators and colleagues while we provide updates on ongoing work. This year, our scientists are excited to have both an in person and online presence at the Society of Toxicology (SOT) conference in San Diego, CA on March 27-31, 2022.1 Here are some presentations that you can expect from us and our collaborators.

Workshop sessions

Dr. Glenn Myatt will be presenting in a workshop session on the use of computational methods for addressing occupational safety: opportunities to support the 3Rs. The session is going to delve into industry specific use cases where computational tools are applied to an assessment of occupational concerns. His presentation is titled ‘Assessment of Whether Acute Toxicity In Silico Models Are Fit for Purpose for Classification and Labeling’. Dr. Myatt will detail how acute toxicity models are shown to be fit-for-purpose across various industries, and present a workflow based on acute toxicity model predictions, and use of existing in vitro data in expert review.

Poster sessions

  1. 1) C. Johnson, D. Bower, K. Cross, S. Miller, and G. Myatt. “The Use of Leadscope’s Skin Sensitization Alerts in the OECD 497 Integrated Testing Strategy (ITS) Defined Approach Workflow”
  2. Instem, Columbus, OH.

Here we describe the use of a combination of Leadscope’s skin sensitization alerts, Direct Peptide Reactivity Assay (DPRA) and Human Cell Line Activation Test (h-CLAT) assay results to assess the skin sensitization hazard or potency of substances.

2) Z. Mou1, R. Racz1, K. Cross2, S. Chakravarti3, and L. Stavitskaya1, “Quantitative Structure-Activity Relationship Model to Predict Cardiac Adverse Effects”

1US FDA, Silver Spring, MD; 2Instem, Columbus, OH; and 3MultiCASE, Inc., Beachwood, OH.

This poster describes the challenges in predicting cardiac toxicity using computational approaches and the expansion of training sets to be used for enhanced model development.

3) J. Hsieh1, S. Miller2, A. Sedykh3, K. P. Cross2, J. Erickson4, S. Nolte4, C. Schmitt4, G. Myatt2, and S. S. Auerbach4. “A Chemical Landscape Based on In Silico Data Availability Profile across Diverse In Vitro/In Vivo Assays to Support Read-Across Evaluations”           

1NIEHS/NTP, Research Triangle Park, NC; 2Instem, Columbus, OH; 3Sciome LLC, Durham, NC; and 4NIEHS/NTP, Durham, NC.

This work describes the use the applicability domain of in silico models to determine data-poor regions of chemical space for which read-across may be applied.

Exhibitor-hosted sessions

  1. 1) Target Safety Assessment (TSA) – Accelerating and Optimizing Your Journey to Regulatory Submission: Presented by Instem
  2. Wednesday 30th March, 12.00pm – 1.00pm (PST), Room 22

This presentation will provide insight on how our pioneering, technology-enabled service is transforming the TSA process, driving quality, pace, and insight in R&D.

2) Using Leadscope Computational Software to assess N-nitrosamine potency classes: Presented by Instem

Tuesday 29th March, 3.00pm – 4.00pm (PST), Room 23C

This session will outline how new in silico models and first-to-market software capabilities from Instem are supporting regulatory submissions, classification and labelling, and various new R&D activities. We will also include a review of recent updates to support the assessment of N-nitrosamine potency classes based on an extensive industry collaboration.

We look forward to seeing you at SOT 2022. If you have any questions, please feel free to reach out to me (Candice Johnson;



New book on mutagenic impurities

We were delighted to contribute to a new book on Mutagenic Impurities, edited by Andrew Teasdale – “Mutagenic Impurities: Strategies for Identification and Control”.1

The book incorporates a discussion on the ICH M7 guideline and covers the in silico assessment of mutagenicity, including the use of structure-activity relationship methodologies, to support the evaluation of impurities. The book also covers N-Nitrosamines, a critical mutagenic impurity issue facing the pharmaceutical industry today.

The book is organized into 4 sections:

Section 1 The Development of Regulatory Guidelines for Mutagenic/Genotoxic Impurities – Overall Process

Section 2 In Silico Assessment of Mutagenicity

Section 3 Toxicological Perspective on Mutagenic Impurities

Section 4 Quality Perspective on Genotoxic Impurities

We would like to congratulate Andrew on the completion of this impressive and valuable book on mutagenic impurities.



Predicting the 6-pack

The 6-pack provides information on health hazards from short-term exposure to a test substance. It is a battery of in vivo tests that evaluate 1) acute systemic toxicity by different routes of exposure (i.e., oral, inhalation and dermal); 2) skin and eye irritation/corrosion; 3) dermal sensitization. It is used in the assessment of many products and there is a great deal of focus on the development of non-animal alternatives to the 6-pack, including in silico models.

We have been working for a long time on the development of a comprehensive in silico assessment of the endpoints from the 6-pack. To date, we have published our approaches to predicting sensitization1 and acute toxicity2. These papers, and others, explain the methodologies and they outline protocols to perform such assessments and expert review3. We have also been collaborating within cross-industry groups to establish whether these methods are fit-for-purpose. For example, in a recent assessment of acute toxicity prediction results across different industrial sectors, we showed that 95% of acute toxicity in silico assessments (i.e., predictions of GHS category) were either correctly predicted or predicted in a more conservative category.

We are also in the process of generating further publications supporting the acceptance of these approaches including a description of our skin and eye irritation/corrosion models and fit-for-purpose assessments of other endpoints related to the 6-pack studies. There is also an opportunity to share knowledge derived from proprietary databases, without sharing any information on confidential chemicals or studies, based on the SAR fingerprinting methodology4.

If you are interested in collaborating on non-animal approaches, and more specifically in silico methods, for the replacement to the 6-pack, please get in touch (Glenn Myatt;


1 Johnson, C., et al., (2020) Skin sensitization in silico protocol. Regul. Toxicol. Pharmacol. 116, 104688.

2 Bercu, J., et al., (2021) 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, 104843.

3 Johnson, C., et al. (2021) “Evaluating confidence in toxicity assessments based on experimental data and in silico predictions.” Computational Toxicology: 100204.

4 Ahlberg, E., et al. (2016) Extending (Q)SARs to incorporate proprietary knowledge for regulatory purposes: A case study using aromatic amine mutagenicity. Regul. Toxicol. Pharmacol. 77, 1–12.

Improving the prediction of bioactivation in relation to drug-drug interactions using proprietary data

The bioactivation of drugs may result in the generation of reactive metabolites that irreversibly inactivate cytochrome P450 (requiring synthesis of new enzyme for recovery of activity). This process is referred to as mechanism-based inhibition (MBI) or time-dependent inhibition and is often involved in damaging drug-drug interactions (DDIs).

Within the FDA guidance “In Vitro Drug Interaction Studies – Cytochrome P450 (CYP) Enzyme- and Transporter-Mediated Drug Interactions guidance”, structural alerts associated with MBI may be used to support the selection of in vitro CYP enzyme inhibition studies to help understand DDIs.1 To support this in silico assessment, we compiled a series of structural alerts (based on over 700 unique substructure searches) associated with bioactivation leading to MBI of P450. These alerts have been incorporated within the Leadscope model applier to profile a substance and provide information on the underlying MBI pathways.

A recent poster at the American College of Toxicology 2021 meeting provided an overview of this approach, alongside a discussion of the preliminary qualification of these alerts using proprietary data.2

We are currently collaborating with a number of organizations to use additional proprietary data to qualify and refine the current alerts, as well as to identify additional alerts. This is based on a SAR-fingerprint approach3 whereby knowledge of structure-activity relationships is shared without disclosing any information on proprietary chemicals or study data. We are looking to publish the results and document the improved predictivity.

If you are interested in collaborating on this project, or would like a copy of the recent poster, please get in touch with me (Glenn Myatt;


  2. Bassan, A., Selvam, R., Bower, D., Cross, K.P., Stavitskaya, L., Yang, X., Volpe D.A., Amberg, A., Myatt, G.J., 2021. Development of a structure activity relationship profiler to predict mechanism based inhibition of a metabolite on CYP enzymes. Presented at the ACT Virtual 42nd Annual Meeting.
  3. Ahlberg, E., Amberg, A., Beilke, L.D., Bower, D., Cross, K.P., Custer, L., Ford, K.A., Gompel, J.V., Harvey, J., Honma, M., Jolly, R., Joossens, E., Kemper, R.A., Kenyon, M., Kruhlak, N., Kuhnke, L., Leavitt, P., Naven, R., Neilan, C., Quigley, D.P., Shuey, D., Spirkl, H.-P., Stavitskaya, L., Teasdale, A., White, A., Wichard, J., Zwickl, C., Myatt, G.J., 2016. Extending (Q)SARs to incorporate proprietary knowledge for regulatory purposes: A case study using aromatic amine mutagenicity. Regulatory Toxicology and Pharmacology 77, 1–12. doi:10.1016/j.yrtph.2016.02.003