The Threshold of Toxicological Concern for prenatal developmental toxicity in rats and rabbits

Establishment of a threshold of toxicological concern for pharmaceutical intermediates based on historical repeat-dose data and its application in setting health based exposure limits

Availability of toxicological data for pharmaceutical intermediates (IMs) used in the manufacture of small molecules is often limited. Scarcity of data - in particular, repeat-dose toxicity (RDT) - renders the calculation of health-based exposure limits (HBELs) problematic. Establishment of HBELs, including occupational exposure limits (OELs) and permitted daily exposures (PDEs) facilitating worker and patient safety respectively, is however essential. Historic 28-day oral rodent toxicity data was analysed for 103 GSK isolated IMs. No-observed (adverse) effect levels (NO(A)ELs) and critical effects were extracted. The 5th percentile (p05) of the NO(A)EL distribution was 15 mg/kg/day. Substance specific HBELs were calculated, selecting the NO(A)EL as the Point of Departure (PoD); 99% of IMs (n = 102) were assigned an oral PDE ≥1000 μg/day and OEL ≥100 μg/m3. A default oral PDE of 1000 μg/day and OEL of 100 μg/m3 is thus proposed for IMs. Evaluation of an additional PoD - benchmark dose lower confidence limit (BMDL) - further supported the default HBELs. The default oral PDE can also serve as a threshold of toxicological concern (TTC) for IMs. Default limits can aid in setting HBELs for novel data-poor IMs, as well as supporting waiving of RDT in the future through read-across.

Semiquantitative sensitization safety assessment of extractable and leachables associated with parenteral pharmaceutical products

Extractable and leachables (E&Ls) associated with parenteral pharmaceutical products should be assessed for patient safety. One essential safety endpoint is local or systemic sensitization. However, there are no regulatory guidelines for quantitative sensitization safety assessment of E&Ls. A semiquantitative sensitization safety assessment workflow is developed to refine the sensitization safety assessment of E&Ls associated with parenteral pharmaceutical products. The workflow is composed of two sequential steps: local skin sensitization and systemic sensitization safety assessment. The local skin sensitization step has four tiers. The output from this step is the acceptable exposure level for local sensitization (AEL_ls) and this safety threshold can be used for local sensitization safety assessment. From the derived AEL_ls, the systemic sensitization safety assessment at step 2 proceeds in 2 tiers. The output from this workflow is the derivation of acceptable exposure level for systemic sensitization (AEL_ss). When the estimated human daily exposure (HDE) is compared with the AEL_ss, the margin of exposure is calculated to determine the sensitization safety of E&Ls following parenteral administration. The current work represents an initial effort to develop a scientifically robust process for sensitization safety assessment of E&Ls associated with parenteral pharmaceutical products.

Application of the 3Rs principles in the development of pharmaceutical generics

Although the 3Rs are broadly applied in nonclinical testing, a better appreciation of the 3Rs is needed in the field of differentiated or value-added pharmaceutical generics because the minor changes in formulation, dosage form, indication, and application route often do not require additional safety testing. The US FDA and the EU EMA have comprehensive regulations for such drugs based on quality, therapeutic equivalence, and safety guidelines. However, no scientific publications on how the concept of replacement and reduction from 3Rs principles can be applied in the safety assessment of differentiated generics were found in the public domain. In this review, we discuss the application of 3Rs in nonclinical testing requirements for differentiated generics. Practical examples are provided in the form of case studies from regulated markets. We highlight the need for utilization of existing data to establish equivalence (differentiated generic vs innovator) in efficacy and safety. The case studies indicate that data requirements from animal experiments have been reduced to a large extent in some major markets without compromising quality and safety. In this context, we also highlight the problem that on a global scale, a true reduction of animal experiments will only be achieved when all countries adopt similar practices.

Avoiding Regrettable Substitutions: Green Toxicology for Sustainable Chemistry

Green chemistry seeks to design less hazardous chemicals, but many of the efforts to replace chemicals have resulted in so-called "Regrettable Substitutions", when a chemical with an unknown or unforeseen hazard is used to replace a chemical identified as problematic. Here, we discuss the literature on regrettable substitution and focus on an oft-mentioned case, Bisphenol A, which was replaced with Bisphenol S-and the lessons that can be learned from this history. In particular, we focus on how Green Toxicology can offer a way to make better substitutions.

Profiling the ToxCast Library With a Pluripotent Human (H9) Stem Cell Line-Based Biomarker Assay for Developmental Toxicity

The Stemina devTOX quickPredict platform is a human pluripotent stem cell-based assay that predicts the developmental toxicity potential based on changes in cellular metabolism following chemical exposure [Palmer, J. A., Smith, A. M., Egnash, L. A., Conard, K. R., West, P. R., Burrier, R. E., Donley, E. L. R., and Kirchner, F. R. (2013). Establishment and assessment of a new human embryonic stem cell-based biomarker assay for developmental toxicity screening. Birth Defects Res. B Dev. Reprod. Toxicol. 98, 343-363]. Using this assay, we screened 1065 ToxCast phase I and II chemicals in single-concentration or concentration-response for the targeted biomarker (ratio of ornithine to cystine secreted or consumed from the media). The dataset from the Stemina (STM) assay is annotated in the ToxCast portfolio as STM. Major findings from the analysis of ToxCast_STM dataset include (1) 19% of 1065 chemicals yielded a prediction of developmental toxicity, (2) assay performance reached 79%-82% accuracy with high specificity (> 84%) but modest sensitivity (< 67%) when compared with in vivo animal models of human prenatal developmental toxicity, (3) sensitivity improved as more stringent weights of evidence requirements were applied to the animal studies, and (4) statistical analysis of the most potent chemical hits on specific biochemical targets in ToxCast revealed positive and negative associations with the STM response, providing insights into the mechanistic underpinnings of the targeted endpoint and its biological domain. The results of this study will be useful to improving our ability to predict in vivo developmental toxicants based on in vitro data and in silico models.

Integration of read-across and artificial neural network-based QSAR models for predicting systemic toxicity: A case study for valproic acid

We present a systematic, comprehensive and reproducible weight-of-evidence approach for predicting the no-observed-adverse-effect level (NOAEL) for systemic toxicity by using read-across and quantitative structure-activity relationship (QSAR) models to fill gaps in rat repeated-dose and developmental toxicity data. As a case study, we chose valproic acid, a developmental toxicant in humans and animals. High-quality in vivo oral rat repeated-dose and developmental toxicity data were available for five and nine analogues, respectively, and showed qualitative consistency, especially for developmental toxicity. Similarity between the target and analogues is readily defined computationally, and data uncertainties associated with the similarities in structural, physico-chemical and toxicological properties, including toxicophores, were low. Uncertainty associated with metabolic similarity is low-to-moderate, largely because the approach was limited to in silico prediction to enable systematic and objective data collection. Uncertainty associated with completeness of read-across was reduced by including in vitro and in silico metabolic data and expanding the experimental animal database. Taking the "worst-case" approach, the smallest NOAEL values among the analogs (i.e., 200 and 100 mg/kg/day for repeated-dose and developmental toxicity, respectively) were read-across to valproic acid. Our previous QSAR models predict repeated-dose NOAEL of 148 (males) and 228 (females) mg/kg/day, and developmental toxicity NOAEL of 390 mg/kg/day for valproic acid. Based on read-across and QSAR, the conservatively predicted NOAEL is 148 mg/kg/day for repeated-dose toxicity, and 100 mg/kg/day for developmental toxicity. Experimental values are 341 mg/kg/day and 100 mg/kg/day, respectively. The present approach appears promising for quantitative and qualitative in silico systemic toxicity prediction of untested chemicals.

Permitted Daily Exposure Values: Application Considerations in Toxicological Risk Assessments

Permitted daily exposure (PDE) values are used by some toxicologists to support the safety qualification of various types of impurities found in a drug substance (DS) or drug product (DP). Permitted daily exposure values are important tools for the toxicologist, but one must be aware of their limitations to ensure that they are used appropriately and effectively in the risk assessment process. First, a toxicologist must always perform a comprehensive analysis of all available animal and human safety data for an impurity, including identifying any data gaps that may exist. Second, if adequate data are available and there are no genotoxicity concerns, an appropriate well-designed repeat-dose toxicity study in animals should be chosen to calculate the PDE. It is important to note that PDE values qualify general systemic toxicity and not necessarily local toleration end points such as irritation and sensitization that are more concentration than dose dependent. In addition, a PDE value calculated from a general toxicity study in animals may not necessarily qualify for reproductive toxicology end points. Lastly, PDE values should never be thought of as analytical limits for or acceptable levels of an impurity in a DS or DP, as this ignores quality considerations. Using safety information from several chemicals as proxy impurities, this article serves as an educational primer to facilitate a better understanding of the development and use of PDE values in the risk assessment process.

Threshold of Toxicological Concern (TTC) for Botanical Extracts (Botanical-TTC) derived from a meta-analysis of repeated-dose toxicity studies

Threshold of Toxicological Concern (TTC) is a promising approach for evaluating the human health risk for systemic toxicity when there is a lack of toxicological information. The threshold for systemic toxicity is reportedly 1800, 540, and 90 μg/day for Cramer I-III chemical structures, according to Munro's structural decision tree, and 0.15 μg/day for genotoxic compounds. However, the concept of TTC has been developed for single substances; therefore, the applicability of TTC for mixtures remains unclear. To expand application of probability approach for mixtures, a validation study using the point of departures (PoDs) derived from mixtures is required. In the present study, we investigated novel TTC of botanical extracts (Botanical-TTC) for cosmetics from a meta-analysis based on the PoDs derived from repeated dose toxicity testing in botanical extracts. Accordingly, 213 PoDs were determined by repeated-dose toxicity studies and divided using a default uncertainty factor of 100 combined with the extrapolation factor of study duration to calculate the derived-no-effect-level (DNEL) and derived-minimal-effect-level (DMEL). The minimum DNEL/DMEL was 1.6-fold higher than the Cramer III TTC. In addition, because human health risk below the 1 st percentile value (663 μg/day) was considered as extremely limited, the exposure level can be proposed as Botanical-TTC.

Green Toxicology-Know Early About and Avoid Toxic Product Liabilities

Toxicology uniquely among the life sciences relies largely on methods which are more than 40-years old. Over the last 3 decades with more or less success some additions to and few replacements in this toolbox took place, mainly as alternatives to animal testing. The acceptance of such new approaches faces the needs of formal validation and the conservative attitude toward change in safety assessments. Only recently, there is growing awareness that the same alternative methods, especially in silico and in vitro tools can also much earlier and before validation inform decision-taking in the product life cycle. As similar thoughts developed in the context of Green Chemistry, the term of Green Toxicology was coined to describe this change in approach. Here, the current developments in the alternative field, especially computational and more organo-typic cell cultures are reviewed, as they lend themselves to front-loaded chemical safety assessments. The initiatives of the Center for Alternatives to Animal Testing Green Toxicology Collaboration are presented. They aim first of all for forming a community to promote this concept and then for a cultural change in companies with the necessary training of chemists, product stewards and later regulators.

Big-data and machine learning to revamp computational toxicology and its use in risk assessment

The creation of large toxicological databases and advances in machine-learning techniques have empowered computational approaches in toxicology. Work with these large databases based on regulatory data has allowed reproducibility assessment of animal models, which highlight weaknesses in traditional in vivo methods. This should lower the bars for the introduction of new approaches and represents a benchmark that is achievable for any alternative method validated against these methods. Quantitative Structure Activity Relationships (QSAR) models for skin sensitization, eye irritation, and other human health hazards based on these big databases, however, also have made apparent some of the challenges facing computational modeling, including validation challenges, model interpretation issues, and model selection issues. A first implementation of machine learning-based predictions termed REACHacross achieved unprecedented sensitivities of >80% with specificities >70% in predicting the six most common acute and topical hazards covering about two thirds of the chemical universe. While this is awaiting formal validation, it demonstrates the new quality introduced by big data and modern data-mining technologies. The rapid increase in the diversity and number of computational models, as well as the data they are based on, create challenges and opportunities for the use of computational methods.

Toxicological risk assessment and prioritization of drinking water relevant contaminants of emerging concern

Toxicological risk assessment of contaminants of emerging concern (CEC) in (sources of) drinking water is required to identify potential health risks and prioritize chemicals for abatement or monitoring. In such assessments, concentrations of chemicals in drinking water or sources are compared to either (i) health-based (statutory) drinking water guideline values, (ii) provisional guideline values based on recent toxicity data in absence of drinking water guidelines, or (iii) generic drinking water target values in absence of toxicity data. Here, we performed a toxicological risk assessment for 163 CEC that were selected as relevant for drinking water. This relevance was based on their presence in drinking water and/or groundwater and surface water sources in downstream parts of the Rhine and Meuse, in combination with concentration levels and physicochemical properties. Statutory and provisional drinking water guideline values could be derived from publically available toxicological information for 142 of the CEC. Based on measured concentrations it was concluded that the majority of substances do not occur in concentrations which individually pose an appreciable human health risk. A health concern could however not be excluded for vinylchloride, trichloroethene, bromodichloromethane, aniline, phenol, 2-chlorobenzenamine, mevinphos, 1,4-dioxane, and nitrolotriacetic acid. For part of the selected substances, toxicological risk assessment for drinking water could not be performed since either toxicity data (hazard) or drinking water concentrations (exposure) were lacking. In absence of toxicity data, the Threshold of Toxicological Concern (TTC) approach can be applied for screening level risk assessment. The toxicological information on the selected substances was used to evaluate whether drinking water target values based on existing TTC levels are sufficiently protective for drinking water relevant CEC. Generic drinking water target levels of 37 μg/L for Cramer class I substances and 4 μg/L for Cramer class III substances in drinking water were derived based on these CEC. These levels are in line with previously reported generic drinking water target levels based on original TTC values and are shown to be protective for health effects of the majority of contaminants of emerging concern evaluated in the present study. Since the human health impact of many chemicals appearing in the water cycle has been studied insufficiently, generic drinking water target levels are useful for early warning and prioritization of CEC with unknown toxicity in drinking water and its sources for future monitoring.

Machine Learning of Toxicological Big Data Enables Read-Across Structure Activity Relationships (RASAR) Outperforming Animal Test Reproducibility

Earlier we created a chemical hazard database via natural language processing of dossiers submitted to the European Chemical Agency with approximately 10 000 chemicals. We identified repeat OECD guideline tests to establish reproducibility of acute oral and dermal toxicity, eye and skin irritation, mutagenicity and skin sensitization. Based on 350-700+ chemicals each, the probability that an OECD guideline animal test would output the same result in a repeat test was 78%-96% (sensitivity 50%-87%). An expanded database with more than 866 000 chemical properties/hazards was used as training data and to model health hazards and chemical properties. The constructed models automate and extend the read-across method of chemical classification. The novel models called RASARs (read-across structure activity relationship) use binary fingerprints and Jaccard distance to define chemical similarity. A large chemical similarity adjacency matrix is constructed from this similarity metric and is used to derive feature vectors for supervised learning. We show results on 9 health hazards from 2 kinds of RASARs-"Simple" and "Data Fusion". The "Simple" RASAR seeks to duplicate the traditional read-across method, predicting hazard from chemical analogs with known hazard data. The "Data Fusion" RASAR extends this concept by creating large feature vectors from all available property data rather than only the modeled hazard. Simple RASAR models tested in cross-validation achieve 70%-80% balanced accuracies with constraints on tested compounds. Cross validation of data fusion RASARs show balanced accuracies in the 80%-95% range across 9 health hazards with no constraints on tested compounds.

Rethinking developmental toxicity testing: Evolution or revolution?

BACKGROUND

Current developmental toxicity testing adheres largely to protocols suggested in 1966 involving the administration of test compound to pregnant laboratory animals. After more than 50 years of embryo-fetal development testing, are we ready to consider a different approach to human developmental toxicity testing?

METHODS

A workshop was held under the auspices of the Developmental and Reproductive Toxicology Technical Committee of the ILSI Health and Environmental Sciences Institute to consider how we might design developmental toxicity testing if we started over with 21st century knowledge and techniques (revolution). We first consider what changes to the current protocols might be recommended to make them more predictive for human risk (evolution).

RESULTS

The evolutionary approach includes modifications of existing protocols and can include humanized models, disease models, more accurate assessment and testing of metabolites, and informed approaches to dose selection. The revolution could start with hypothesis-driven testing where we take what we know about a compound or close analog and answer specific questions using targeted experimental techniques rather than a one-protocol-fits-all approach. Central to the idea of hypothesis-driven testing is the concept that testing can be done at the level of mode of action. It might be feasible to identify a small number of key events at a molecular or cellular level that predict an adverse outcome and for which testing could be performed in vitro or in silico or, rarely, using limited in vivo models. Techniques for evaluating these key events exist today or are in development.

DISCUSSION

Opportunities exist for refining and then replacing current developmental toxicity testing protocols using techniques that have already been developed or are within reach.

3S - Systematic, systemic, and systems biology and toxicology

A biological system is more than the sum of its parts - it accomplishes many functions via synergy. Deconstructing the system down to the molecular mechanism level necessitates the complement of reconstructing functions on all levels, i.e., in our conceptualization of biology and its perturbations, our experimental models and computer modelling. Toxicology contains the somewhat arbitrary subclass "systemic toxicities"; however, there is no relevant toxic insult or general disease that is not systemic. At least inflammation and repair are involved that require coordinated signaling mechanisms across the organism. However, the more body components involved, the greater the challenge to reca-pitulate such toxicities using non-animal models. Here, the shortcomings of current systemic testing and the development of alternative approaches are summarized. We argue that we need a systematic approach to integrating existing knowledge as exemplified by systematic reviews and other evidence-based approaches. Such knowledge can guide us in modelling these systems using bioengineering and virtual computer models, i.e., via systems biology or systems toxicology approaches. Experimental multi-organ-on-chip and microphysiological systems (MPS) provide a more physiological view of the organism, facilitating more comprehensive coverage of systemic toxicities, i.e., the perturbation on organism level, without using substitute organisms (animals). The next challenge is to establish disease models, i.e., micropathophysiological systems (MPPS), to expand their utility to encompass biomedicine. Combining computational and experimental systems approaches and the chal-lenges of validating them are discussed. The suggested 3S approach promises to leverage 21st century technology and systematic thinking to achieve a paradigm change in studying systemic effects.