A Kinetic Model for Assessing Potential Nitrosamine Carcinogenicity.

Understanding the potential carcinogenic potency of nitrosamines is necessary to setting acceptable intake limits. Nitrosamines and the components that can form them are commonly present in food, water, cosmetics, and tobacco. The recent observation of nitrosamines in pharmaceuticals highlighted the need for effective methods to determine acceptable intake limits. Herein, we describe two computational models that utilize properties based upon quantum mechanical calculations in conjunction with mechanistic insights and established data to determine the carcinogenic potency of a variety of common nitrosamines. These models can be applied to experimentally untested nitrosamines to aid in the establishment of acceptable intake limits.

Analysis of non-mutagenic substances in the context of drug impurity assessment - Few are potent toxicants.

ICH Q3A/B guidelines provide qualification thresholds for impurities or degradation products in new drug substances and products. However, the guidelines note that certain impurities/degradation products may warrant further safety evaluation for being unusually potent or toxic. The purpose of this study was to confirm that especially toxic non-mutagenic compounds are rare and to identify classes of compounds that could warrant lower qualification thresholds. A total of 2815 compounds were evaluated, of which 2213 were assessed as non-mutagenic. For the purpose of this analysis, compounds were considered potent when the point of departure was ≤0.2 mg/kg/day based on the qualification threshold (1 mg/day or 0.02 mg/kg/day for a 50 kg human) in a new drug substance, with an additional 10-fold margin. Only 54 of the entire set (2.4%) would be considered potent based on this conservative potency analysis, confirming that the existing ICH Q3A/B qualification thresholds are appropriate for the majority of impurities. If the Q3A/B threshold, without the additional 10-fold margin is used, 14 compounds (0.6%) are considered "highly potent". Very few non-mutagenic structural classes were identified, including organothiophosphates and derivatives, polychlorinated benzenes and polychlorinated polycyclic aliphatics, that correlate with potential high potency, consistent with prior publications.

Deriving acceptable limits for non-mutagenic impurities in medicinal products - Durational adjustments.

ICH Q3A/B guidelines are not intended for application during the clinical research phase of development and durationally adjusted qualification thresholds are not included. A central tenet of ICH Q3A is that lifetime exposure to 1 mg/day of an unqualified non-mutagenic impurity (NMI) is not a safety concern. An analysis of in vivo toxicology data from 4878 unique chemicals with established NO(A)ELs was conducted to determine whether durationally adjusted qualification limits can be supported. Although not recommended in ICH Q3A/B, a conservative approach was taken by using allometric scaling in the analysis. Following allometric scaling of the 5th percentile of the distribution of NO(A)ELs from available chronic toxicology studies, it was reconfirmed that there is a safety basis for the 1 mg/day qualification threshold in ICH Q3A. Additionally, allometric scaling of the 5th percentile of the distribution of NO(A)ELs from sub-acute and sub-chronic toxicology studies could support acceptable limits of 20 and 5 mg/day for an unqualified NMI for dosing durations of less than or greater than one month, respectively. This analysis supports durationally adjusted NMI qualification thresholds for pharmaceuticals that protect patient safety and contribute to 3Rs efforts for qualifying impurities using new approach methods.

N-Nitrosamine drug substance related impurities (NDSRIs) - A proposal for the addition of subcategories to carcinogenic potency categorization approach categories 1 and 2 for NDSRIs with a molecular weight > 200 Da.

The carcinogenicity potency categorization approach (CPCA) derived and harmonized by Health Authorities was a significant milestone, as it defined molecular properties that allowed for the rapid evaluation of the chemical structures of N-nitrosamine drug substance related impurities (NDSRIs) and the assignment of associated lifetime Acceptable Intake (AI) limits to inform on appropriate impurity control strategies in certain drug products. Nonetheless, it is important to continue to refine and improve on the CPCA based upon data-derived evidence. Herein, we focus on the default CPCA AI for NDSRIs, which is largely based on the small molecule N-nitrosamines (NAs). Considering the carcinogenic potency of NAs with a molecular weight >200 Da (NDSRIs molecular weight is typically 200-600 Da), we propose that in the absence of any compound specific data, the lowest lifetime Acceptable Intake for NAs, such as NDSRIs, should be 10x less (i.e., 150 ng/day) than the ICH M7 Threshold of Toxicological Concern of 1500 ng/day, (even for NDSRIs that are considered CPCA Category 1 and 2) which would conservatively result in a theoretical cancer risk of <1 in 100,000.

Assessing the utility of common arguments used in expert review of in silico predictions as part of ICH M7 assessments.

Expert review of two predictions, made by complementary (quantitative) structure-activity relationship models, to an overall conclusion is a key component of using in silico tools to assess the mutagenic potential of impurities as part of the ICH M7 guideline. In lieu of a specified protocol, numerous publications have presented best practise guides, often indicating the occurrence of common prediction scenarios and the evidence required to resolve them. A semi-automated expert review tool has been implemented in Lhasa Limited's Nexus platform following collation of these common arguments and assignment to the associated prediction scenarios made by Derek Nexus and Sarah Nexus. Using datasets primarily donated by pharmaceutical companies, an automated analysis of the frequency these prediction scenarios occur, and the likelihood of the associated arguments assigning the correct resolution, could then be conducted. This article highlights that a relatively small number of common arguments may be used to accurately resolve many prediction scenarios to a single conclusion. The use of a standardised method of argumentation and assessment of evidence for a given impurity is proposed to improve the efficiency and consistency of expert review as part of an ICH M7 submission.

Acceptable intakes (AIs) for 11 small molecule N-nitrosamines (NAs).

Low levels of N-nitrosamines (NAs) were detected in pharmaceuticals and, as a result, health authorities (HAs) have published acceptable intakes (AIs) in pharmaceuticals to limit potential carcinogenic risk. The rationales behind the AIs have not been provided to understand the process for selecting a TD50 or read-across analog. In this manuscript we evaluated the toxicity data for eleven common NAs in a comprehensive and transparent process consistent with ICH M7. This evaluation included substances which had datasets that were robust, limited but sufficient, and substances with insufficient experimental animal carcinogenicity data. In the case of robust or limited but sufficient carcinogenicity information, AIs were calculated based on published or derived TD50s from the most sensitive organ site. In the case of insufficient carcinogenicity information, available carcinogenicity data and structure activity relationships (SARs) were applied to categorical-based AIs of 1500 ng/day, 150 ng/day or 18 ng/day; however additional data (such as biological or additional computational modelling) could inform an alternative AI. This approach advances the methodology used to derive AIs for NAs.

Practical and Science-Based Strategy for Establishing Acceptable Intakes for Drug Product N-Nitrosamine Impurities.

The potential for N-nitrosamine impurities in pharmaceutical products presents a challenge for the quality management of medicinal products. N-Nitrosamines are considered cohort-of-concern compounds due to the potent carcinogenicity of many of the structurally simple chemicals within this structural class. In the past 2 years, a number of drug products containing certain active pharmaceutical ingredients have been withdrawn or recalled from the market due to the presence of carcinogenic low-molecular-weight N,N-dialkylnitrosamine impurities. Regulatory authorities have issued guidance to market authorization holders to review all commercial drug substances/products for the potential risk of N-nitrosamine impurities, and in cases where a significant risk of N-nitrosamine impurity is identified, analytical confirmatory testing is required. A key factor to consider prior to analytical testing is the estimation of the daily acceptable intake (AI) of the N-nitrosamine impurity. A significant proportion of N-nitrosamine drug product impurities are unique/complex structures for which the development of low-level analytical methods is challenging. Moreover, these unique/complex impurities may be less potent carcinogens compared to simple nitrosamines. In the present work, our objective was to derive AIs for a large number of complex N-nitrosamines without carcinogenicity data that were identified as potential low-level impurities. The impurities were first cataloged and grouped according to common structural features, with a total of 13 groups defined with distinct structural features. Subsequently, carcinogenicity data were reviewed for structurally related N-nitrosamines relevant to each of the 13 structural groups and group AIs were derived conservatively based on the most potent N-nitrosamine within each group. The 13 structural group AIs were used as the basis for assigning AIs to each of the structurally related complex N-nitrosamine impurities. The AIs of several N-nitrosamine groups were found to be considerably higher than those for the simple N,N-dialkylnitrosamines, which translates to commensurately higher analytical method detection limits.

Use of less-than-lifetime (LTL) durational limits for nitrosamines: Case study of N-Nitrosodiethylamine (NDEA).

The ICH M7(R1) guideline describes a framework to assess the carcinogenic risk of mutagenic and carcinogenic pharmaceutical impurities following less-than-lifetime (LTL) exposures. This LTL framework is important as many pharmaceuticals are not administered for a patient's lifetime and as clinical trials typically involve LTL exposures. While there has been regulatory caution about applying LTL concepts to cohort of concern (COC) impurities such as N-nitrosamines, ICH M7 does not preclude this and indeed literature data suggests that the LTL framework will be protective of patient safety for N-nitrosamines. The goal was to investigate if applying the LTL framework in ICH M7 would control exposure to an acceptable excess cancer risk in humans. Using N-nitrosodiethylamine as a case study, empirical data correlating exposure duration (as a percentage of lifespan) and cancer incidence in rodent bioassays indicate that the LTL acceptable intake (AI) as derived using the ICH M7 framework would not exceed a negligible additional risk of cancer. Therefore, controlling N-nitrosamines to an LTL AI based on the ICH M7 framework is thus demonstrated to be protective for potential carcinogenic risk to patients over the exposure durations typical of clinical trials and many prescribed medicines.

Calculating qualified non-mutagenic impurity levels: Harmonization of approaches.

The presence of impurities in drugs is unavoidable. As impurities offer no direct benefit to the patient, it is critical that impurities do not compromise patient safety. Current guidelines on the derivation of acceptable impurity levels leave aspects of calculations open for interpretation, resulting in inconsistencies across industry and regulators. To understand current impurity qualification practices from a safety standpoint, regulatory expectations and the safety risk that impurities pose, the IQ DruSafe Impurities Working Group (WG) conducted a pharmaceutical industry-wide survey. Survey results highlighted areas that could benefit from harmonization, including nonclinical species/sex selection and the application of adjustment factors (i.e., body surface area). Recommendations for alignment on these topics is included in this publication. Additionally, the WG collated repeat-dose toxicity information for 181 starting materials and intermediates, reflective of pharmaceutical impurities, to understand the toxicological risks they generally pose in relation to the drug substance (DS) and the assumptions surrounding the calculation of qualified impurity levels. An evaluation of this dataset and the survey were used to harmonize how to calculate a safe limit for an impurity based on toxicology testing of the impurity when present within the DS.

Extending (Q)SARs to incorporate proprietary knowledge for regulatory purposes: is aromatic N-oxide a structural alert for predicting DNA-reactive mutagenicity?

(Quantitative) structure-activity relationship or (Q)SAR predictions of DNA-reactive mutagenicity are important to support both the design of new chemicals and the assessment of impurities, degradants, metabolites, extractables and leachables, as well as existing chemicals. Aromatic N-oxides represent a class of compounds that are often considered alerting for mutagenicity yet the scientific rationale of this structural alert is not clear and has been questioned. Because aromatic N-oxide-containing compounds may be encountered as impurities, degradants and metabolites, it is important to accurately predict mutagenicity of this chemical class. This article analysed a series of publicly available aromatic N-oxide data in search of supporting information. The article also used a previously developed structure-activity relationship (SAR) fingerprint methodology where a series of aromatic N-oxide substructures was generated and matched against public and proprietary databases, including pharmaceutical data. An assessment of the number of mutagenic and non-mutagenic compounds matching each substructure across all sources was used to understand whether the general class or any specific subclasses appear to lead to mutagenicity. This analysis resulted in a downgrade of the general aromatic N-oxide alert. However, it was determined there were enough public and proprietary data to assign the quindioxin and related chemicals as well as benzo[c][1,2,5]oxadiazole 1-oxide subclasses as alerts. The overall results of this analysis were incorporated into Leadscope's expert-rule-based model to enhance its predictive accuracy.

Genetic toxicology in silico protocol.

In silico toxicology (IST) approaches to rapidly assess chemical hazard, and usage of such methods is increasing in all applications but especially for regulatory submissions, such as for assessing chemicals under REACH as well as the ICH M7 guideline for drug impurities. There are a number of obstacles to performing an IST assessment, including uncertainty in how such an assessment and associated expert review should be performed or what is fit for purpose, as well as a lack of confidence that the results will be accepted by colleagues, collaborators and regulatory authorities. To address this, a project to develop a series of IST protocols for different hazard endpoints has been initiated and this paper describes the genetic toxicity in silico (GIST) protocol. The protocol outlines a hazard assessment framework including key effects/mechanisms and their relationships to endpoints such as gene mutation and clastogenicity. IST models and data are reviewed that support the assessment of these effects/mechanisms along with defined approaches for combining the information and evaluating the confidence in the assessment. This protocol has been developed through a consortium of toxicologists, computational scientists, and regulatory scientists across several industries to support the implementation and acceptance of in silico approaches.

Principles and procedures for handling out-of-domain and indeterminate results as part of ICH M7 recommended (Q)SAR analyses.

The International Council for Harmonization (ICH) M7 guideline describes a hazard assessment process for impurities that have the potential to be present in a drug substance or drug product. In the absence of adequate experimental bacterial mutagenicity data, (Q)SAR analysis may be used as a test to predict impurities' DNA reactive (mutagenic) potential. However, in certain situations, (Q)SAR software is unable to generate a positive or negative prediction either because of conflicting information or because the impurity is outside the applicability domain of the model. Such results present challenges in generating an overall mutagenicity prediction and highlight the importance of performing a thorough expert review. The following paper reviews pharmaceutical and regulatory experiences handling such situations. The paper also presents an analysis of proprietary data to help understand the likelihood of misclassifying a mutagenic impurity as non-mutagenic based on different combinations of (Q)SAR results. This information may be taken into consideration when supporting the (Q)SAR results with an expert review, especially when out-of-domain results are generated during a (Q)SAR evaluation.

In silico toxicology protocols.

The present publication surveys several applications of in silico (i.e., computational) toxicology approaches across different industries and institutions. It highlights the need to develop standardized protocols when conducting toxicity-related predictions. This contribution articulates the information needed for protocols to support in silico predictions for major toxicological endpoints of concern (e.g., genetic toxicity, carcinogenicity, acute toxicity, reproductive toxicity, developmental toxicity) across several industries and regulatory bodies. Such novel in silico toxicology (IST) protocols, when fully developed and implemented, will ensure in silico toxicological assessments are performed and evaluated in a consistent, reproducible, and well-documented manner across industries and regulatory bodies to support wider uptake and acceptance of the approaches. The development of IST protocols is an initiative developed through a collaboration among an international consortium to reflect the state-of-the-art in in silico toxicology for hazard identification and characterization. A general outline for describing the development of such protocols is included and it is based on in silico predictions and/or available experimental data for a defined series of relevant toxicological effects or mechanisms. The publication presents a novel approach for determining the reliability of in silico predictions alongside experimental data. In addition, we discuss how to determine the level of confidence in the assessment based on the relevance and reliability of the information.

Management of veno-occlusive disease: the multidisciplinary approach to care.

Although it is considered a relatively rare disorder, veno-occlusive disease (VOD) is one of the main causes of overall, non-relapse mortality associated with haematopoietic stem cell transplantation (HSCT). This article, based on the consensus opinion of haemato-oncology nurses, haemato-oncologists and pharmacists from both adult and paediatric services at the VOD International Multi-Disciplinary Advisory Board at the European Society for Blood and Marrow Transplantation (EBMT) meeting, Istanbul, 2015, aims to explore the multidisciplinary approach to care for the management of VOD, with an emphasis on current challenges in this area. The careful monitoring of HSCT patients allows early detection of the symptoms associated with VOD and timely treatment, ultimately improving patient outcomes. As part of a multidisciplinary team, nurses have an essential role to play, from pretransplant assessment to medical management and overall care of the patient. Physicians and pharmacists have a responsibility to facilitate education and training so that nurses can work effectively within that team.

Resolution of contradiction between in silico predictions and Ames test results for four pharmaceutically relevant impurities.

The ICH M7 Guideline requires low level control of mutagenic impurities in pharmaceutical products to minimize cancer risk in patients (ICHM7, 2014). Bacterial mutagenicity (Ames) data is generally used to determine mutagenic and possible carcinogenic potential of compounds. Recently, a publication on experiences of using two in silico systems to identify potentially mutagenic impurities highlighted the importance of performing a critical review of published Ames data utilized as part of a mutagenicity assessment of impurities (Greene et al., 2015). Four compounds (2-amino-5-hydroxybenzoic acid, 2-amino-3-chlorobenzoic acid, methyl 2-amino-4-chlorobenzoate and 4-morpholinopyridine) reported mutagenic were identified in a two system in silico assessment and expert review of the structuresas non-mutagenic. Likely reasons for mutagenicity could not be identified and the purity of the compounds tested was proposed. In the current investigation, the purest available sample of the four compounds was tested in an OECD-compliant Ames test. The compounds were all found to be non-mutagenic. Possible reasons for the discrepancy between previously reported and current results are discussed. Additionally, important points to consider when conducting an expert review of available Ames data are provided particularly in cases where reported Ames results are discrepant with a two system in silico assessment.

Extending (Q)SARs to incorporate proprietary knowledge for regulatory purposes: A case study using aromatic amine mutagenicity.

Statistical-based and expert rule-based models built using public domain mutagenicity knowledge and data are routinely used for computational (Q)SAR assessments of pharmaceutical impurities in line with the approach recommended in the ICH M7 guideline. Knowledge from proprietary corporate mutagenicity databases could be used to increase the predictive performance for selected chemical classes as well as expand the applicability domain of these (Q)SAR models. This paper outlines a mechanism for sharing knowledge without the release of proprietary data. Primary aromatic amine mutagenicity was selected as a case study because this chemical class is often encountered in pharmaceutical impurity analysis and mutagenicity of aromatic amines is currently difficult to predict. As part of this analysis, a series of aromatic amine substructures were defined and the number of mutagenic and non-mutagenic examples for each chemical substructure calculated across a series of public and proprietary mutagenicity databases. This information was pooled across all sources to identify structural classes that activate or deactivate aromatic amine mutagenicity. This structure activity knowledge, in combination with newly released primary aromatic amine data, was incorporated into Leadscope's expert rule-based and statistical-based (Q)SAR models where increased predictive performance was demonstrated.

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.

Compensatory erythropoiesis has no impact on the outcome of the in vivo Pig-a mutation assay in rats following treatment with the haemolytic agent 2-butoxyethanol.

The Pig-a assay has rapidly gained international interest as a useful tool for assessing the mutagenic potential of compounds in vivo. Although a large number of compounds, including both mutagens and non-mutagens, have been tested in the rat Pig-a assay in haematopoietic cells, there is limited understanding of how perturbations in haematopoiesis affect assay performance. Of particular concern is the possibility that regenerative haematopoiesis alone, without exposure to a genotoxic agent, could result in elevated Pig-a mutant cell frequencies. To address this concern, Wistar-Han rats were dosed by oral gavage with a non-genotoxic haemolytic agent, 2-butoxyethanol (2-BE). Dose levels ranging from 0 to 450 mg/kg were tested using both single administration and 28-day treatment regimens. Haematology parameters were assessed at minimum within the first 24h of treatment and 8 days after the final administration. Pig-a mutant frequencies were assessed on Days 15 and ~30 for both treatment protocols and also on Days 43 and 57 for the 28-day protocol. Even at doses of 2-BE that induced marked intravascular lysis and strong compensatory erythropoiesis, the average Pig-a mutant phenotype red blood cell and reticulocyte frequencies were within the historical vehicle control distribution. 2-BE therefore showed no evidence of in vivo mutagenicity in these studies. The data suggest that perturbations in haematopoiesis alone do not lead to an observation of increased mutant frequency in the Pig-a assay.

A practical application of two in silico systems for identification of potentially mutagenic impurities.

The International Conference on Harmonization (ICH) M7 guidance for the assessment and control of DNA reactive impurities in pharmaceutical products includes the use of in silico prediction systems as part of the hazard identification and risk assessment strategy. This is the first internationally agreed guidance document to include the use of these types of approaches. The guideline requires the use of two complementary approaches, an expert rule-based method and a statistical algorithm. In addition, the guidance states that the output from these computer-based assessments can be reviewed using expert knowledge to provide additional support or resolve conflicting predictions. This approach is designed to maximize the sensitivity for correctly identifying DNA reactive compounds while providing a framework to reduce the number of compounds that need to be synthesized, purified and subsequently tested in an Ames assay. Using a data set of 801 chemicals and pharmaceutical intermediates, we have examined the relative predictive performances of some popular commercial in silico systems that are in common use across the pharmaceutical industry. The overall accuracy of each of these systems was fairly comparable ranging from 68% to 73%; however, the sensitivity of each system (i.e. how many Ames positive compounds are correctly identified) varied much more dramatically from 48% to 68%. We have explored how these systems can be combined under the ICH M7 guidance to enhance the detection of DNA reactive molecules. Finally, using four smaller sets of molecules, we have explored the value of expert knowledge in the review process, especially in cases where the two systems disagreed on their predictions, and the need for care when evaluating the predictions for large data sets.

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.