Generation of TD50 values for carcinogenicity study data

Critical comparison of BMD and TD50 methods for the calculation of acceptable intakes for N-nitroso compounds

The tumorigenic dose 50 (TD50) is a widely used measure of carcinogenic potency which has historically been used to determine acceptable intake limits for carcinogenic compounds. Although broadly used, the TD50 model was not designed to account for important biological factors such as DNA repair and cell compensatory mechanisms, changes in absorption, etc., leading to the development of benchmark dose (BMD) approaches, which use more flexible dose-response models that are better able to account for these processes. Using a nitrosamine dataset as a case study, we compare the impact of moving to a BMD-based limit as opposed to a TD50-based limit. Although there are differences in individual potency estimates between the two approaches for some compounds, we show that the key metrics such as the 5th percentile of the respective potency distributions, used when calculating class-specific default acceptable intakes, are not greatly affected. Furthermore, potency estimates for nitrosamine compounds relevant to read-across do not vary by more than a factor of 3, which is little in the context of the inherent variability in a biological response, in an overall landscape wherein potencies can vary by four orders of magnitude. This suggests a move to BMD-based limits is achievable without significant disruption to existing limits while utilising a more robust methodology.

Highly sensitive and robust LC-MS/MS method for determination of up to 15 small molecule nitrosamine impurities in pharmaceutical drug substances

Nitrosamine impurities have been classified as probable human carcinogens for decades. These impurities were reported in water, food, tobacco, pesticides, and plastics but received attention in mid-2018 when N-nitrosodimethylamine (NDMA) was reported in valsartan drug products. Subsequently, it was revealed that several small molecule and complex nitrosamine impurities can form in any active pharmaceutical ingredient (API) or drug product in which secondary or tertiary amines are present (as API or as impurities) along with a nitrosating agent. Consequently, regulators have provided several guidelines for the risk assessment of nitrosamine formation during manufacturing, storage, or from contaminated supply chains. This has led to a demand for validated analytical methods that quantify N-nitrosamine impurities in pharmaceutical products. In this study, a highly sensitive and robust analytical method was developed and validated for quantitatively determining up to 15 small nitrosamines at low levels (0.01 ppm) in sartan drug substances. The study also suggests that this method can be extended not only to corresponding sartan drug products but could also be used as a generic screening method to test a variety of drug substances, and drug products with the minimum required optimization of method conditions.

Determining recommended acceptable intake limits for N-nitrosamine impurities in pharmaceuticals: Development and application of the Carcinogenic Potency Categorization Approach (CPCA)

N-Nitrosamine impurities, including nitrosamine drug substance-related impurities (NDSRIs), have challenged pharmaceutical industry and regulators alike and affected the global drug supply over the past 5 years. Nitrosamines are a class of known carcinogens, but NDSRIs have posed additional challenges as many lack empirical data to establish acceptable intake (AI) limits. Read-across analysis from surrogates has been used to identify AI limits in some cases; however, this approach is limited by the availability of robustly-tested surrogates matching the structural features of NDSRIs, which usually contain a diverse array of functional groups. Furthermore, the absence of a surrogate has resulted in conservative AI limits in some cases, posing practical challenges for impurity control. Therefore, a new framework for determining recommended AI limits was urgently needed. Here, the Carcinogenic Potency Categorization Approach (CPCA) and its supporting scientific rationale are presented. The CPCA is a rapidly-applied structure-activity relationship-based method that assigns a nitrosamine to 1 of 5 categories, each with a corresponding AI limit, reflecting predicted carcinogenic potency. The CPCA considers the number and distribution of α-hydrogens at the N-nitroso center and other activating and deactivating structural features of a nitrosamine that affect the α-hydroxylation metabolic activation pathway of carcinogenesis. The CPCA has been adopted internationally by several drug regulatory authorities as a simplified approach and a starting point to determine recommended AI limits for nitrosamines without the need for compound-specific empirical data.

New Theobromine Apoptotic Analogue with Anticancer Potential Targeting the EGFR Protein: Computational and In Vitro Studies

AIM

The aim of this study was to design and examine a novel epidermal growth factor receptor (EGFR) inhibitor with apoptotic properties by utilizing the essential structural characteristics of existing EGFR inhibitors as a foundation.

METHOD

The study began with the natural alkaloid theobromine and developed a new semisynthetic derivative (T-1-PMPA). Computational ADMET assessments were conducted first to evaluate its anticipated safety and general drug-likeness. Deep density functional theory (DFT) computations were initially performed to validate the three-dimensional (3D) structure and reactivity of T-1-PMPA. Molecular docking against the EGFR proteins was conducted to investigate T-1-PMPA's binding affinity and inhibitory potential. Additional molecular dynamics (MD) simulations over 200 ns along with MM-GPSA, PLIP, and principal component analysis of trajectories (PCAT) experiments were employed to verify the binding and inhibitory properties of T-1-PMPA. Afterward, T-1-PMPA was semisynthesized to validate the proposed design and in silico findings through several in vitro examinations.

RESULTS

DFT studies indicated T-1-PMPA's reactivity using electrostatic potential, global reactive indices, and total density of states. Molecular docking, MD simulations, MM-GPSA, PLIP, and ED suggested the binding and inhibitory properties of T-1-PMPA against the EGFR protein. The in silico ADMET predicted T-1-PMPA's safety and general drug-likeness. In vitro experiments demonstrated that T-1-PMPA effectively inhibited EGFRWT and EGFR790m, with IC50 values of 86 and 561 nM, respectively, compared to Erlotinib (31 and 456 nM). T-1-PMPA also showed significant suppression of the proliferation of HepG2 and MCF7 malignant cell lines, with IC50 values of 3.51 and 4.13 μM, respectively. The selectivity indices against the two cancer cell lines indicated the overall safety of T-1-PMPA. Flow cytometry confirmed the apoptotic effects of T-1-PMPA by increasing the total percentage of apoptosis to 42% compared to 31, and 3% in Erlotinib-treated and control cells, respectively. The qRT-PCR analysis further supported the apoptotic effects by revealing significant increases in the levels of Casp3 and Casp9. Additionally, T-1-PMPA controlled the levels of TNFα and IL2 by 74 and 50%, comparing Erlotinib's values (84 and 74%), respectively.

CONCLUSION

In conclusion, our study's findings suggest the potential of T-1-PMPA as a promising apoptotic anticancer lead compound targeting the EGFR.

The comprehensive prediction of carcinogenic potency and tumorigenic dose (TD50) for two problematic N-nitrosamines in food: NMAMPA and NMAMBA using toxicology in silico methods

The identification and toxicological assessment of potential carcinogens is of paramount importance for public health and safety. This study aimed to predict the carcinogenic potency and tumorigenic dose (TD50) for two problematic N-nitrosamines (N-NAs) commonly found in food: N-2-methylpropyl-N-1-methylacetonylnitrosamine (NMAMPA, CAS: 93755-83-0) and N-3-Methylbutyl-N-1-methylacetonylnitrosamine (NMAMBA, CAS: 71016-15-4). To achieve this goal, in silico toxicology methods were employed due to their practical applications and potential in risk assessment. The justification for conducting these studies was incoherent results published by the European Food Safety Authority (EFSA). For this purpose, we applied various in silico tools, including qualitative methods (ToxTree, ProTox II and CarcinoPred-EL) and quantitative methods (QSAR Toolbox and LAZAR) were applied to predict the carcinogenic potency. These tools leverage computational approaches to analyze chemical structures for finding toxicophores and generating predictions, making them efficient alternatives to traditional in vivo experiments. The results obtained indicated that N-NAs are carcinogenic compounds, and quantitative data was obtained in the form of estimated doses of TD50 for the compounds tested.

Application of the Key Characteristics Framework to Identify Potential Breast Carcinogens Using Publicly Available in Vivo, in Vitro, and in Silico Data

BACKGROUND

Chemicals that induce mammary tumors in rodents or activate estrogen or progesterone signaling are likely to increase breast cancer (BC) risk. Identifying chemicals with these activities can prompt steps to protect human health.

OBJECTIVES

We compiled data on rodent tumors, endocrine activity, and genotoxicity to assess the key characteristics (KCs) of rodent mammary carcinogens (MCs), and to identify other chemicals that exhibit these effects and may therefore increase BC risk.

METHODS

Using authoritative databases, including International Agency for Research on Cancer (IARC) Monographs and the US Environmental Protection's (EPA) ToxCast, we selected chemicals that induce mammary tumors in rodents, stimulate estradiol or progesterone synthesis, or activate the estrogen receptor (ER) in vitro. We classified these chemicals by their genotoxicity and strength of endocrine activity and calculated the overrepresentation (enrichment) of these KCs among MCs. Finally, we evaluated whether these KCs predict whether a chemical is likely to induce mammary tumors.

RESULTS

We identified 279 MCs and an additional 642 chemicals that stimulate estrogen or progesterone signaling. MCs were significantly enriched for steroidogenicity, ER agonism, and genotoxicity, supporting the use of these KCs to predict whether a chemical is likely to induce rodent mammary tumors and, by inference, increase BC risk. More MCs were steroidogens than ER agonists, and many increased both estradiol and progesterone. Enrichment among MCs was greater for strong endocrine activity vs. weak or inactive, with a significant trend.

DISCUSSION

We identified hundreds of compounds that have biological activities that could increase BC risk and demonstrated that these activities are enriched among MCs. We argue that many of these should not be considered low hazard without investigating their ability to affect the breast, and chemicals with the strongest evidence can be targeted for exposure reduction. We describe ways to strengthen hazard identification, including improved assessments for mammary effects, developing assays for more KCs, and more comprehensive chemical testing. https://doi.org/10.1289/EHP13233.

Update of the Cancer Potency Database (CPDB) to enable derivations of Thresholds of Toxicological Concern (TTC) for cancer potency

The purpose of this study was to update the existing Cancer Potency Database (CPDB) in order to support the development of a dataset of compounds, with associated points of departure (PoDs), to enable a review and update of currently applied values for the Threshold of Toxicological Concern (TTC) for cancer endpoints. This update of the current CPDB, last reviewed in 2012, includes the addition of new data (44 compounds and 158 studies leading to additional 359 dose-response curves). Strict inclusion criteria were established and applied to select compounds and studies with relevant cancer potency data. PoDs were calculated from dose-response modeling, including the benchmark dose (BMD) and the lower 90% confidence limits (BMDL) at a specified benchmark response (BMR) of 10%. The updated full CPDB database resulted in a total of 421 chemicals which had dose-response data that could be used to calculate PoDs. This candidate dataset for cancer TTC is provided in a transparent and adaptable format for further analysis of TTC to derive cancer potency thresholds.

Use of the TD50 99 % CI for single dose rodent carcinogenicity studies

The Tumorigenic dose 50 (TD50) is a widely used measure of carcinogenic potency representing the dosage at which 50 % of animals exposed to a compound will develop tumours. The popularity of the TD50 is in part due to the large amount of publicly available data. TD50s were calculated for a large number of compounds in the Carcinogenic Potency Database, which has since been extended in the freely available Lhasa Carcinogenicity database, containing TD50s from over 7500 studies and 1700 compounds. However, due to the age of these studies many are of low quality, often comprising only a single test dosage, therefore raising questions about the applicability of such TD50 sfor toxicological risk assessment. We investigate whether the lower 99 % confidence interval is sufficient to produce conservative TD50 estimates for these studies.

Anti-breast cancer potential of a new xanthine derivative: In silico, antiproliferative, selectivity, VEGFR-2 inhibition, apoptosis induction and migration inhibition studies

BACKGROUND

The overexpression of VEGFR-2 receptors in breast cancer provides a valuable approach to anticancer strategies. Targeting VEGFR-2, a new semisynthetic compound (T-1-MCPAB) has been designed.

METHODS

Computational methods (ADMET, toxicity, DFT, Molecular Docking, Molecular Dynamics Simulations, MM-GBSA, PLIP, and PCAT) were conducted. In addition to the semi-synthesis, in vitro studies (anti-VEGFR-2, anti-proliferative, flow cytometry, and wound scratch assay) were employed.

RESULTS

ADME and toxicity profiles of T-1-MCPAB studies indicated its overall drug-likeness showing results much better than Sorafenib. Then, T-1-MCPAB's exact 3D structure, stability, and reactivity were evoked by the DFT calculations. Molecular docking, molecular dynamics simulations, MM-GPSA, PLIP, and PCAT studies denoted the correct binding and inhibiting potential of T-1-MCPAB, towards VEGFR-2 protein. After the semisynthesis, T-1-MCPAB inhibited VEGFR-2 with an IC50 of 0.135 µM, which was comparable to sorafenib's IC50 of 0.0591 µM. T-1-MCPAB also showed a notable performance against MCF7 and T47D breast cancer cell lines with IC50 values of 30.95 µM and 63.64 µM, respectively, and had high selectivity index values of 3.7 and 1.8, respectively. Furthermore, T-1-MCPAB influenced early and late apoptosis and significantly decreased the potential of MCF7 cells to heal and migrate.

CONCLUSION

T-1-MCPAB is a promising VEGFR-2 inhibitor with potential for breast cancer treatment. Further chemical and biological studies are needed to explore its potential as a therapeutic agent.

Maximizing use of existing carcinogenicity data to support acceptable intake levels for mutagenic impurities in pharmaceuticals: Learnings from N-nitrosamine case studies

The unexpected finding of N-nitrosamine (NA) impurities in many pharmaceutical products raised significant challenges for industry and regulators. In addition to well-studied small molecular weight NAs, many of which are potent rodent carcinogens, novel NAs associated with active pharmaceutical ingredients have been found, many of which have limited or no safety data. A tiered approach to establishing Acceptable Intake (AI) limits for NA impurities has been established using chemical-specific data, read-across, or a class-specific TTC limit. There are ∼140 NAs with some rodent carcinogenicity data, but much of it is older and does not meet current guidelines for what constitutes a 'robust' bioassay. Nevertheless, these data are an important source of information to ensure the best science is used for assessing NA impurities and assuring consumer safety while minimizing impact that can lead to drug shortages. We present several strategies to maximize the use of imperfect data including using a lower confidence limit on a rodent TD50, and leveraging data from multiple NAs. Information on the chemical structure known to impact potency can also support development of an AI or potentially conclude that a particular NA does not fall in the cohort of concern for potent carcinogenicity.

Computational Prediction of Metabolic α-Carbon Hydroxylation Potential of N-Nitrosamines: Overcoming Data Limitations for Carcinogenicity Assessment

Recent withdrawal of several drugs from the market due to elevated levels of N-nitrosamine impurities underscores the need for computational approaches to assess the carcinogenicity risk of nitrosamines. However, current approaches are limited because robust animal carcinogenicity data are only available for a few simple nitrosamines, which do not represent the structural diversity of the many possible nitrosamine drug substance related impurities (NDSRIs). In this paper, we present a novel method that uses data on CYP-mediated metabolic hydroxylation of CH₂ groups in non-nitrosamine xenobiotics to identify structural features that may also help in predicting the likelihood of metabolic α-carbon hydroxylation in N-nitrosamines. Our approach offers a new avenue for tapping into potentially large experimental data sets on xenobiotic metabolism to improve risk assessment of nitrosamines. As α-carbon hydroxylation is the crucial rate-limiting step in nitrosamine metabolic activation, identifying and quantifying the influence of various structural features on this step can provide valuable insights into their carcinogenic potential. This is especially important considering the scarce information available on factors that affect NDSRI metabolic activation. We have identified hundreds of structural features and calculated their impact on hydroxylation, a significant advancement compared to the limited findings from the small nitrosamine carcinogenicity data set. While relying solely on α-carbon hydroxylation prediction is insufficient for forecasting carcinogenic potency, the identified features can help in the selection of relevant structural analogues in read across studies and assist experts who, after considering other factors such as the reactivity of the resulting electrophilic diazonium species, can establish the acceptable intake (AI) limits for nitrosamine impurities.

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.

Risk assessment of N-nitrosamines in food

EFSA was asked for a scientific opinion on the risks to public health related to the presence of N-nitrosamines (N-NAs) in food. The risk assessment was confined to those 10 carcinogenic N-NAs occurring in food (TCNAs), i.e. NDMA, NMEA, NDEA, NDPA, NDBA, NMA, NSAR, NMOR, NPIP and NPYR. N-NAs are genotoxic and induce liver tumours in rodents. The in vivo data available to derive potency factors are limited, and therefore, equal potency of TCNAs was assumed. The lower confidence limit of the benchmark dose at 10% (BMDL10) was 10 μg/kg body weight (bw) per day, derived from the incidence of rat liver tumours (benign and malignant) induced by NDEA and used in a margin of exposure (MOE) approach. Analytical results on the occurrence of N-NAs were extracted from the EFSA occurrence database (n = 2,817) and the literature (n = 4,003). Occurrence data were available for five food categories across TCNAs. Dietary exposure was assessed for two scenarios, excluding (scenario 1) and including (scenario 2) cooked unprocessed meat and fish. TCNAs exposure ranged from 0 to 208.9 ng/kg bw per day across surveys, age groups and scenarios. 'Meat and meat products' is the main food category contributing to TCNA exposure. MOEs ranged from 3,337 to 48 at the P95 exposure excluding some infant surveys with P95 exposure equal to zero. Two major uncertainties were (i) the high number of left censored data and (ii) the lack of data on important food categories. The CONTAM Panel concluded that the MOE for TCNAs at the P95 exposure is highly likely (98-100% certain) to be less than 10,000 for all age groups, which raises a health concern.

Quantum-Mechanical Approach to Predicting the Carcinogenic Potency of N-Nitroso Impurities in Pharmaceuticals

N-Nitroso contaminants in medicinal products are of concern due to their high carcinogenic potency; however, not all these compounds are created equal, and some are relatively benign chemicals. Understanding the structure-activity relationships (SARs) that drive hazards in one molecule versus another is key to both protecting human health and alleviating costly and sometimes inaccurate animal testing. Here, we report on an extension of the CADRE (computer-aided discovery and REdesign) platform, which is used broadly by the pharmaceutical and personal care industries to assess environmental and human health endpoints, to predict the carcinogenic potency of N-nitroso compounds. The model distinguishes compounds in three potency categories with 77% accuracy in external testing, which surpasses the reproducibility of rodent cancer bioassays and constraints imposed by limited (high-quality) data. The robustness of predictions for more complex pharmaceuticals is maximized by capturing key SARs using quantum mechanics, that is, by hinging the model on the underlying chemistry versus chemicals in the training set. To this end, the present approach can be leveraged in a quantitative hazard assessment and to offer qualitative guidance using electronic structure comparisons between well-studied analogues and unknown contaminants.

Enhancing global and local decision making for chemical safety assessments through increasing the availability of data

Toxicity safety assessments are a fundamental part of the lifecycle of products and aim to protect human health and the environment from harmful exposures to chemical substances. To make decisions regarding the suitability of testing strategies, the applicability of individual tests or concluding an assessment for an individual chemical requires data. This review outlines how different forms of data sharing, from enhancing publicly-available data to extracting knowledge from commercially-sensitive data, leads to increased quantity and quality of evidence being available for safety assessors to review. This can result in more confident decisions for different use cases in the context of chemical safety assessments. Although a number of challenges remain with progressing the evolution of toxicity safety assessments, data sharing should be considered as a key approach to accelerating the development and uptake of new best practices.

Strategies for Assessing Acceptable Intakes for Novel N-Nitrosamines Derived from Active Pharmaceutical Ingredients

The detection of N-nitrosamines, derived from solvents and reagents and, on occasion, the active pharmaceutical ingredient (API) at higher than acceptable levels in drug products, has led regulators to request a detailed review for their presence in all medicinal products. In the absence of rodent carcinogenicity data for novel N-nitrosamines derived from amine-containing APIs, a conservative class limit of 18 ng/day (based on the most carcinogenic N-nitrosamines) or the derivation of acceptable intakes (AIs) using structurally related surrogates with robust rodent carcinogenicity data is recommended. The guidance has implications for the pharmaceutical industry given the vast number of marketed amine-containing drugs. In this perspective, the rate-limiting step in N-nitrosamine carcinogenicity, involving cytochrome P450-mediated α-carbon hydroxylation to yield DNA-reactive diazonium or carbonium ion intermediates, is discussed with reference to the selection of read-across analogs to derive AIs. Risk-mitigation strategies for managing putative N-nitrosamines in the preclinical discovery setting are also presented.

What Makes a Potent Nitrosamine? Statistical Validation of Expert-Derived Structure-Activity Relationships

The discovery of carcinogenic nitrosamine impurities above the safe limits in pharmaceuticals has led to an urgent need to develop methods for extending structure-activity relationship (SAR) analyses from relatively limited datasets, while the level of confidence required in that SAR indicates that there is significant value in investigating the effect of individual substructural features in a statistically robust manner. This is a challenging exercise to perform on a small dataset, since in practice, compounds contain a mixture of different features, which may confound both expert SAR and statistical quantitative structure-activity relationship (QSAR) methods. Isolating the effects of a single structural feature is made difficult due to the confounding effects of other functionality as well as issues relating to determining statistical significance in cases of concurrent statistical tests of a large number of potential variables with a small dataset; a naïve QSAR model does not predict any features to be significant after correction for multiple testing. We propose a variation on Bayesian multiple linear regression to estimate the effects of each feature simultaneously yet independently, taking into account the combinations of features present in the dataset and reducing the impact of multiple testing, showing that some features have a statistically significant impact. This method can be used to provide statistically robust validation of expert SAR approaches to the differences in potency between different structural groupings of nitrosamines. Structural features that lead to the highest and lowest carcinogenic potency can be isolated using this method, and novel nitrosamine compounds can be assigned into potency categories with high accuracy.

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 Lhasa Limited Products for the In Silico Prediction of Drug Toxicity

Lhasa Limited have had a role in the in silico prediction of drug and other chemical toxicity for over 30 years. This role has always been multifaceted, both as a provider of predictive software such as Derek Nexus, and as an honest broker for the sharing of proprietary chemical and toxicity data. A changing regulatory environment and the drive for the Replacement, Reduction and Refinement (the 3Rs) of animal testing have led both to increased acceptance of in silico predictions and a desire for the sharing of data to reduce duplicate testing. The combination of these factors has led to Lhasa Limited providing a suite of products and coordinating numerous data-sharing consortia that do indeed facilitate a significant reduction in the testing burden that companies would otherwise be laboring under. Many of these products and consortia can be organized into workflows for specific regulatory use cases, and it is these that will be used to frame the narrative in this chapter.

Oxidation of Drugs during Drug Product Development: Problems and Solutions

Oxidation is the second most common degradation pathway for pharmaceuticals, after hydrolysis. However, in contrast to hydrolysis, oxidation is mechanistically more complex and produces a wider range of degradation products; oxidation is thus harder to control. The propensity of a drug towards oxidation is established during forced degradation studies. However, a more realistic insight into degradation in the solid state can be achieved with accelerated studies of mixtures of drugs and excipients, as the excipients are the most common sources of impurities that have the potential to initiate oxidation of a solid drug product. Based on the results of these studies, critical parameters can be identified and appropriate measures can be taken to avoid the problems that oxidation poses to the quality of a drug product. This article reviews the most common types of oxidation mechanisms, possible sources of reactive oxygen species, and how to minimize the oxidation of a solid drug product based on a well-planned accelerated study.

Developing Structure-Activity Relationships for N-Nitrosamine Activity

The detection of N-nitrosodimethylamine (NDMA) in several marketed drugs led regulatory agencies to require that N-nitrosamine risk assessments be performed on all marketed medical products [EMA/351053/2019 rev 1 (2019)]. Regulation of N-nitrosamine impurity levels in pharmaceutical drug substances and products is described in the ICH M7(R1) guideline where they are referred to as "cohort-of-concern" compounds as several are potent rodent carcinogens [Kroes et. al. 2004]. EMA, U.S. FDA and other regulatory agencies have set provisional acceptable daily intake limits for N-nitrosamines calculated from rodent carcinogenicity TD₅₀ values for experimentally measured N-nitrosamines or the measured TD₅₀ values of close analogs. The class-specific limit can be adjusted based upon a structure activity relationship analysis (SAR) and comparison with analogs having established carcinogenicity data [EMA/369136/2020, (2020)]. To investigate whether improvements in SARs can more accurately predict N-nitrosamine carcinogenic potency, an ad hoc workgroup of 23 companies and universities was established with the goals of addressing several scientific and regulatory issues including: reporting and review of N-nitrosamine mutagenicity and carcinogenicity reaction mechanisms, collection and review of available, public relevant experimental data, development of structure-activity relationships consistent with mechanisms for prediction of N-nitrosamine carcinogenic potency categories, and improved methods for calculating acceptable intake limits for N-nitrosamines based upon mechanistic analogs. Here we describe this collaboration and review our progress to date towards development of mechanistically based structure-activity relationships. We propose improving risk assessment of N-nitrosamines by first establishing the dominant reaction mechanism prior to retrieving an appropriate set of close analogs for use in read-across exercises.

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.

How sensitive does chemical characterization of medical devices need to be? Calibration of analytical evaluation thresholds with the carcinogenic potency database

Chemical characterization is a component of the safety evaluation of medical devices. An analytical evaluation threshold (AET) is recommended to calculate the required analytical sensitivity. There is a lack of consensus whether to use 1.5 or 120 μg/day in calculating the AET with the lower value often requiring sensitivities beyond analytical capabilities. The Carcinogenic Potency Database (CPDB) was reviewed to compare risks associated with using either value to calculate an AET. The TD50s for non-Cohort of Concern (non-COC) substances in the CPDB were used to extrapolate the doses to an excess cancer risk of 10^-5 and calculate the total doses. The number of non-COC substances that would exceed this risk using an AET calculated using 1.5 μg/day or 120 μg/day were then compared. From the 199 substances evaluated, only two posed an excess risk at an AET calculated with 1.5 μg/day and only seven more with 120 μg/day. Furthermore, over 95 percent of non-COC substances would not pose an excess cancer risk using an AET calculated with 120 μg/day. Based on our evaluation, an AET based on 120 μg/day is protective and practical for chemical characterization of short and long-term medical devices.

Utilisation of parametric methods to improve percentile-based estimates for the carcinogenic potency of nitrosamines

N-Nitrosamines have recently been the subject of intense regulatory scrutiny, including the setting of low exposure limits (18 ng/day) (European Medicines Agency (EMA), 2020). This paper evaluates different methodologies to determine statistically robust bounds on the carcinogenic potency of chemical classes, using historic TD₅₀ data (Peto et al., 1984; Thresher et al., 2019) and exemplified for N-nitrosamines. Initially, the distribution of TD₅₀ values (TD₅₀s) for N-nitrosamines of known potency was characterised. From this, it is possible to compare parametric and non-parametric methods to obtain percentiles of interest from the distribution of TD₅₀s, which are shown to be robust to uncertainty in the initial TD₅₀ estimates. These methods may then be applied to different chemical subclasses. The values obtained may be of use in refining acceptable intakes for N-nitrosamines and their subclasses.

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.

Identification, isolation, structural characterization, in silico toxicity prediction and in vitro cytotoxicity assay of simeprevir acidic and oxidative degradation products

Simeprevir is a new direct-acting antiviral drug used for the treatment of chronic hepatitis C. In this work, a simple, fast and economical chromatographic method was developed for the determination of simeprevir in the presence of its acidic and oxidative degradation products. The stress studies performed herein showed that simeprevir degraded under acidic and oxidative conditions but was stable under thermal and alkaline conditions. Chromatographic separation was achieved on a reversed-phase Eclipse XDB C18 column (4.6 × 150 mm, 5 μm). The mobile phase consisted of methanol-0.05 M ammonium acetate (pH 4) (90 : 10, v/v) and was used at a flow rate of 1 mL min-1. The column effluent was monitored at 237 nm. The calibration curve was linear over the concentration range of 0.1-20 μg mL-1. The relative standard deviations for the intra-day and inter-day precision were less than 2%, and good percentage recoveries that met the acceptance criteria of the International Conference on Harmonization (ICH) guidelines were obtained. The robustness was assessed using the Plackett-Burman design. The simeprevir degradation products were isolated by flash chromatography and confirmed by 1H NMR and LC-MS/MS techniques. The fully validated chromatographic method can be applied as a stability-indicating method for simeprevir and for routine analysis during quality control. Additionally, in silico toxicity prediction of the degradation products demonstrated a hepatotoxicity alert for DP 1, DP 2, DP 4 and DP 5 and a carcinogenicity alert for DP 3. In view of safety aspects, an in vitro cytotoxicity assay was carried out for simeprevir degradation products. They were found to be non-toxic in vitro at the tested concentrations.