Quantum-Mechanics Calculations Elucidate Skin-Sensitizing Pharmaceutical Compounds.

Skin sensitization is a critical end point in occupational toxicology that necessitates the use of fast, accurate, and affordable models to aid in establishing handling guidance for worker protection. While many in silico models have been developed, the scarcity of reliable data for active pharmaceutical ingredients (APIs) and their intermediates (together regarded as pharmaceutical compounds) brings into question the reliability of these tools, which are largely constructed using publicly available nonspecialty chemicals. Here, we present the quantum-mechanical (QM) Computer-Aided Discovery and REdesign (CADRE) model, which was developed with the bioactive and structurally complex chemical space in mind by relying on the fundamentals of chemical interactions in key events (versus structural attributes of training-set data). Validated in this study on 345 APIs and intermediates, CADRE achieved 95% accuracy, sensitivity, and specificity and a combined 79% accuracy in assigning potency categories compared to the mouse local lymph node assay data. We show how historical outcomes from CADRE testing in the pharmaceutical space, generated over the past 10 years on ca. 2500 chemicals, can be used to probe the relationships between sensitization mechanisms (or the underlying chemical classes) and the probability of eliciting a sensitization response in mice of a given potency. We believe this information to be of value to both practitioners, who can use it to quickly screen and triage their data sets, as well as to model developers to fine-tune their structure-based tools. Lastly, we leverage our experimentally validated subset of APIs and intermediates to show the importance of dermal permeability on the sensitization potential and potency. We demonstrate that common physicochemical properties used to assess permeation, such as the octanol-water partition coefficient and molecular weight, are poor proxies for the more accurate energy-pair distributions that can be computed from mixed QM and classical simulations using model representations of the stratum corneum.

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.

Harmonisation of read-across methodology for drug substance extractables and leachables (E&Ls).

Health-based exposure limits (HBELs) are derived for leachables from polymeric components that interact with the drug substance which exceed a safety concern threshold (SCT). However, given the nature of leachables, there is not always chemical-specific toxicology data. Read-across methodology specific to extractables and leachables (E&Ls) was developed based on survey data collected from 11 pharmaceutical companies and methodology used in other industries. One additional challenge for E&L read-across is most toxicology data is from the oral route of administration, whereas the parenteral route is very common for the leachable HBEL derivation. A conservative framework was developed to estimate oral bioavailability and the corresponding oral to parenteral extrapolation factor using physical chemical data. When this conservative framework was tested against 73 compounds with oral bioavailability data, it was found that the predicted bioavailability based on physico-chemical properties was conservatively greater than or equal to the experimental bioavailability 79% of the time. In conclusion, an E&L read-across methodology has been developed to provide a consistent, health protective framework for deriving HBELs when toxicology data is limited.

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.

An Evaluation of the Occupational Health Hazards of Peptide Couplers.

Peptide couplers (also known as amide bond-forming reagents or coupling reagents) are broadly used in organic chemical syntheses, especially in the pharmaceutical industry. Yet, occupational health hazards associated with this chemical class are largely unexplored, which is disconcerting given the intrinsic reactivity of these compounds. Several case studies involving occupational exposures reported adverse respiratory and dermal health effects, providing initial evidence of chemical sensitization. To address the paucity of toxicological data, a pharmaceutical cross-industry task force was formed to evaluate and assess the potential of these compounds to cause eye and dermal irritation as well as corrosivity and dermal sensitization. The goal of our work was to inform health and safety professionals as well as pharmaceutical and organic chemists of the occupational health hazards associated with this chemical class. To that end, 25 of the most commonly used peptide couplers and five hydrolysis products were selected for in vivo, in vitro, and in silico testing. Our findings confirmed that dermal sensitization is a concern for this chemical class with 21/25 peptide couplers testing positive for dermal sensitization and 15 of these being strong/extreme sensitizers. We also found that dermal corrosion and irritation (8/25) as well as eye irritation (9/25) were health hazards associated with peptide couplers and their hydrolysis products (4/5 were dermal irritants or corrosive and 4/5 were eye irritants). Resulting outcomes were synthesized to inform decision making in peptide coupler selection and enable data-driven hazard communication to workers. The latter includes harmonized hazard classifications, appropriate handling recommendations, and accurate safety data sheets, which support the industrial hygiene hierarchy of control strategies and risk assessment. Our study demonstrates the merits of an integrated, in vivo -in silico analysis, applied here to the skin sensitization endpoint using the Computer-Aided Discovery and REdesign (CADRE) and Derek Nexus programs. We show that experimental data can improve predictive models by filling existing data gaps while, concurrently, providing computational insights into key initiating events and elucidating the chemical structural features contributing to adverse health effects. This interactive, interdisciplinary approach is consistent with Green Chemistry principles that seek to improve the selection and design of less hazardous reagents in industrial processes and applications.

Framework for sensitization assessment of extractables and leachables in pharmaceuticals.

During the toxicological assessment of extractables and leachables in drug products, localized hazards such as irritation or sensitization may be identified. Typically, because of the low concentration at which leachables occur in pharmaceuticals, irritation is of minimal concern; therefore, this manuscript focuses on sensitization potential. The primary objective of performing a leachable sensitization assessment is protection against Type IV induction of sensitization, rather than prevention of an elicitation response, as it is not possible to account for the immunological state of every individual. Sensitizers have a wide range of potencies and those which induce sensitization upon exposure at a low concentration (i.e. strong, or extreme sensitizers) pose the highest risk to patients and should be the focus of the risk assessment. The Extractables and Leachables Safety Information Exchange (ELSIE) consortium has reviewed the status of dermal, respiratory, and systemic risk assessment in cosmetic and pharmaceutical industries, and proposes a framework to evaluate the safety of known or potential dermal sensitizers in pharmaceuticals. Due to the lack of specific regulatory guidance on this topic, the science-driven risk-based approach proposed by ELSIE encourages consistency in the toxicological assessment of extractables and leachables to maintain high product quality and ensure patient safety.

The utility of hERG channel inhibition data in the derivation of occupational exposure limits.

Inhibition of the human ether-à-go-go (hERG) channel may lead to QT prolongation and fatal arrhythmia. While pharmaceutical drug candidates that exhibit potent hERG channel inhibition often fail early in development, many drugs with both cardiac and non-cardiac indications proceed to market. In this study, the relationship between in vitro hERG channel inhibition and published occupational exposure limit (OEL) was evaluated. A total of 23 cardiac drugs and 44 drugs with non-cardiac indications with published hERG channel IC50 and published OELs were identified. There was an apparent relationship between hERG IC50 potency and the OEL for cardiac and non-cardiac drugs. Twenty cardiac and non-cardiac drugs were identified that had a potent hERG IC50 (≤25 µM) and a contrastingly large OEL value (≥100 µg/m3). OELs or hazard banding corresponding to ≤100 µg/m3 should be sufficiently protective of effects following occupational exposure to the majority of APIs with hERG IC50 values ≤ 100 µM. It is important to consider hERG IC50 values and possible cardiac effects when deriving OEL values for drugs, regardless of indication. These considerations may be particularly important early in the drug development process for establishing exposure control bands for drugs that do not yet have full clinical safety data.

Use of the bacterial reverse mutation assay to predict carcinogenicity of N-nitrosamines.

Under ICH M7, impurities are assessed using the bacterial reverse mutation assay (i.e., Ames test) when predicted positive using in silico methodologies followed by expert review. N-Nitrosamines (NAs) have been of recent concern as impurities in pharmaceuticals, mainly because of their potential to be highly potent mutagenic carcinogens in rodent bioassays. The purpose of this analysis was to determine the sensitivity of the Ames assay to predict the carcinogenic outcome with curated proprietary Vitic (n = 131) and Leadscope (n = 70) databases. NAs were selected if they had corresponding rodent carcinogenicity assays. Overall, the sensitivity/specificity of the Ames assay was 93-97% and 55-86%, respectively. The sensitivity of the Ames assay was not significantly impacted by plate incorporation (84-89%) versus preincubation (82-89%). Sensitivity was not significantly different between use of rat and hamster liver induced S9 (80-93% versus 77-96%). The sensitivity of the Ames is high when using DMSO as a solvent (87-88%). Based on the analysis of these databases, the Ames assay conducted under OECD 471 guidelines is highly sensitive for detecting the carcinogenic hazards of NAs.

Setting impurity limits for endogenous substances: Recommendations for a harmonized procedure and an example using fatty acids.

Endogenous substances, such as fatty, amino, and nucleic acids, are often purposefully used in parenterally pharmaceuticals, but may be present as impurities. Currently, no consensus guidance exists on setting impurity limits for these substances. Specific procedures are needed, as the amount and types of toxicity data available for endogenous substances are typically far less than those for other chemical impurities. Additionally, the parenteral route of administration of these substances is inherently non-physiological, resulting in potentially different or increased severity of toxicity. Risk Assessment Process Maps (RAPMAPs) are proposed as a model to facilitate the development of health-based exposure limits (HBELs) for endogenous substances. This yielded a framework that was applied to derive HBELs for several fatty acids commonly used in parenteral pharmaceuticals. This approach was used to derive HBELs with further vetting based on anticipated perturbations in physiological serum levels, impacts of dose-rate, and consideration of intermittent dosing. Parenteral HBELs of 100-500 mg/day were generated for several fatty acids, and a proposed class-based limit of 50 mg/day to be used in the absence of chemical-specific data. This default limit is consistent with the low toxicity of this chemical class and ICH Q3C value for Class 3 solvents.

Harmonized 3Rs-based non-mutagenic impurity qualification study designs developed using the results of an IQ consortium survey.

As per the ICH Q3A(R2) and Q3B(R2) regulatory guidelines, safety studies may be needed when an impurity in new drug substances or products is above the qualification threshold, and such qualification studies should be conducted in one nonclinical species for a duration of 14-90 days. However, the guidelines do not specify details about species selection, recommended study design, and the exact study duration that would support clinical use of a specific duration. This lack of guidance leads to ambiguity and sponsors have used various study designs to qualify impurities. In 2018, the European Medicines Agency provided a draft reflection paper encouraging the incorporation of 3Rs (Replacement, Reduction, and Refinement) principles for animal use into impurity qualification. As a response, the IQ DruSafe Impurity Working Group (WG) surveyed the IQ member companies to capture the current practices for impurity qualification, and evaluate study designs for a potential reduction in animal testing. This article summarizes the results and learnings from the survey. Additionally, the WG leveraged the survey learnings and provided harmonized study design considerations aimed towards achieving the study objectives, while supporting the 3Rs initiative in reducing the total number of animals used (up to 90%) for impurity qualification.

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.

A cross-industry collaboration to assess if acute oral toxicity (Q)SAR models are fit-for-purpose for GHS classification and labelling.

This study assesses whether currently available acute oral toxicity (AOT) in silico models, provided by the widely employed Leadscope software, are fit-for-purpose for categorization and labelling of chemicals. As part of this study, a large data set of proprietary and marketed compounds from multiple companies (pharmaceutical, plant protection products, and other chemical industries) was assembled to assess the models' performance. The absolute percentage of correct or more conservative predictions, based on a comparison of experimental and predicted GHS categories, was approximately 95%, after excluding a small percentage of inconclusive (indeterminate or out of domain) predictions. Since the frequency distribution across the experimental categories is skewed towards low toxicity chemicals, a balanced assessment was also performed. Across all compounds which could be assigned to a well-defined experimental category, the average percentage of correct or more conservative predictions was around 80%. These results indicate the potential for reliable and broad application of these models across different industrial sectors. This manuscript describes the evaluation of these models, highlights the importance of an expert review, and provides guidance on the use of AOT models to fulfill testing requirements, GHS classification/labelling, and transportation needs.

Deriving Compound-Specific Exposure Limits for Chemicals Used in Pharmaceutical Synthesis: Challenges in Expert Decision-Making Exemplified Through a Case Study-Based Workshop.

A workshop entitled "Deriving Compound-Specific Exposure Limits for Chemicals Used in Pharmaceutical Synthesis" was held at the 2018 Genetic Toxicology Association annual meeting. The objectives of the workshop were to provide an educational forum and use case studies and live multiple-choice polling to establish the degree of similarity/diversity in approach/opinion of the industry experts and other delegates present for some of the more challenging decision points that need to be considered when developing a compound-specific exposure limit (ie, acceptable intake or permissible or permitted daily exposure). Herein we summarize the relevant background and case study information for each decision point topic presented as well as highlight significant polling responses and discussion points. A common observation throughout was the requirement for expert judgment to be applied at each of the decision points presented which often results in different reasoning being applied by the risk assessor when deriving a compound-specific exposure limit. This supports the value of precompetitive cross-industry collaborations to develop compound-specific limits and harmonize the methodology applied, thus reducing the associated uncertainty inherent in the application of isolated expert judgment in this context. An overview of relevant precompetitive cross-industry collaborations working to achieve this goal is described.

Management of pharmaceutical ICH M7 (Q)SAR predictions - The impact of model updates.

Pharmaceutical applicants conduct (Q)SAR assessments on identified and theoretical impurities to predict their mutagenic potential. Two complementary models-one rule-based and one statistical-based-are used, followed by expert review. (Q)SAR models are continuously updated to improve predictions, with new versions typically released on a yearly basis. Numerous releases of (Q)SAR models will occur during the typical 6-7 years of drug development until new drug registration. Therefore, it is important to understand the impact of model updates on impurity mutagenicity predictions over time. Compounds representative of pharmaceutical impurities were analyzed with three rule- and three statistical-based models covering a 4-8 year period, with the individual time frame being dependent on when the individual models were initially made available. The largest changes in the combined outcome of two complementary models were from positive or equivocal to negative and from negative to equivocal. Importantly, the cumulative change of negative to positive predictions was small in all models (<5%) and was further reduced when complementary models were combined in a consensus fashion. We conclude that model updates of the type evaluated in this manuscript would not necessarily require re-running a (Q)SAR prediction unless there is a specific need. However, original (Q)SAR predictions should be evaluated when finalizing the commercial route of synthesis for marketing authorization.

Considerations when deriving compound-specific limits for extractables and leachables from pharmaceutical products: Four case studies.

Leachables from pharmaceutical container closure systems are a subset of impurities that present in drug products and may pose a risk to patients or compromise product quality. Extractable studies can identify potential leachables, and extractables and leachables (E&Ls) should be evaluated during development of the impurity control strategy. Currently, there is a lack of specific regulatory guidance on how to risk assess E&Ls; this may lead to inconsistency across the industry. This manuscript is a cross-industry Extractables and Leachables Safety Information Exchange (ELSIE) consortium collaboration and follow-up to Broschard et al. (2016), which aims to provide further clarity and detail on the conduct of E&L risk assessments. Where sufficient data are available, a health-based exposure limit termed Permitted Daily Exposure (PDE) may be calculated and to exemplify this, case studies of four common E&Ls are described herein, namely bisphenol-A, butylated hydroxytoluene, Irgafos® 168, and Irganox® 1010. Relevant discussion points are further explored, including the value of extractable data, how to perform route-to-route extrapolations and considerations around degradation products. By presenting PDEs for common E&L substances, the aim is to encourage consistency and harmony in approaches for deriving compound-specific limits.

Qualification of impurities based on metabolite data.

Regulatory Guidance documents ICH Q3A (R2) and ICH Q3B (R2) state that "impurities that are also significant metabolites present in animal and/or human studies are generally considered qualified". However, no guidance is provided regarding data requirements for qualification, nor is a definition of the term "significant metabolite" provided. An opportunity is provided to define those categories and potentially avoid separate toxicity studies to qualify impurities. This can reduce cost, animal use and time, and avoid delays in drug development progression. If the concentration or amount of a metabolite, in animals or human, is similar to that of the known, structurally identical impurity (arising from the administered test material), the qualification of the impurity on the grounds of it also being a metabolite is justified. We propose two complementary approaches to support conclusions to this effect: 1) demonstrate that the impurity is formed by metabolism in animals and/or man, based preferably on plasma exposures or, alternatively, amounts excreted in urine, and, where appropriate, 2) show that animal exposure to (or amount of) the impurity/metabolite is equal or greater in animals than in humans. An important factor of both assessments is the maximum theoretical concentration (or amount) (MTC or MTA) of the impurity/metabolite achievable from the administered dose and recommendations on the estimation of the MTC and MTA are presented.

Skin sensitization in silico protocol.

The assessment of skin sensitization has evolved over the past few years to include in vitro assessments of key events along the adverse outcome pathway and opportunistically capitalize on the strengths of in silico methods to support a weight of evidence assessment without conducting a test in animals. While in silico methods vary greatly in their purpose and format; there is a need to standardize the underlying principles on which such models are developed and to make transparent the implications for the uncertainty in the overall assessment. In this contribution, the relationship between skin sensitization relevant effects, mechanisms, and endpoints are built into a hazard assessment framework. Based on the relevance of the mechanisms and effects as well as the strengths and limitations of the experimental systems used to identify them, rules and principles are defined for deriving skin sensitization in silico assessments. Further, the assignments of reliability and confidence scores that reflect the overall strength of the assessment are discussed. This skin sensitization protocol supports the implementation and acceptance of in silico approaches for the prediction of skin sensitization.

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.

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.