Derek Nexus Research Articles

Structure-mutagenicity relationships on quinoline and indole analogues in the Ames test

BackgroundAlthough the in silico predictive ability of the Ames test results has recently made remarkable progress, there are still some chemical classes for which the predictive ability is not yet sufficient due to a lack of Ames test data. These classes include simple heterocyclic compounds. This study aimed to investigate the mutagenicity and structure-mutagenicity relationships for some heterocycles in the Ames test. In the present study, we selected 12 quinoline analogues containing one or two nitrogen atoms in the naphthalene ring and 12 indole analogues containing one to three nitrogen atoms in the indole ring, without any side moiety.ResultsThe Ames test was performed with five standard bacterial strains (TA100, TA1535, TA98, TA1537, and WP2uvrA) using the pre-incubation method with and without rat liver S9. Five quinoline and two indole analogues were mutagenic. Among the five quinoline analogues, four were mutagenic in the presence of S9 mix with TA100, whereas cinnoline was mutagenic in the absence of S9 mix with TA1537. Among the two indole analogues, indazole was mutagenic in the presence and absence of S9 mix with WP2uvrA and 4-azaindole was mutagenic in the absence of S9 mix with TA1537. The mechanisms underlying the induction of mutagenesis appear to differ between quinoline and indole analogues. In addition, we performed in silico analysis of the mutagenicity of all these analogues using DEREK Nexus 6.1.1 (Lhasa Limited) and GT_EXPERT from CASE Ultra 1.8.0.5 (MultiCASE Inc.) as knowledge-based models and GT1_BMUT from CASE Ultra 1.8.0.5 (MultiCASE Inc.) as a statistical-based model. The knowledge-based model showed low sensitivity for both the quinoline and indole analogues (DEREK Nexus and GT_EXPERT: 20% for quinolines and 0% for indoles). Conversely, the statistical model showed high sensitivity (100% for both quinolines and indoles) and low specificity (43% for quinolines and 10% for indoles).ConclusionBased on the Ames test results, we proposed structural alerts noting that quinoline analogues were mutagenic when they had nitrogens in any of the positions 2, 5, 7, or 8 in addition to 1, and indole analogues were mutagenic when they had nitrogens at positions 2 or 4 in addition to 1.

In Silico Phototoxicity Prediction of Drugs and Chemicals by using Derek Nexus and QSAR Toolbox.

Phototoxicity testing is crucial for evaluating the potential harmful effects of pharmaceuticals and chemicals on human skin when exposed to sunlight. Traditional in vivo models involving mice, rats, guinea pigs, as well as in vitro assays such as the 3T3 Neutral Red Uptake phototoxicity assay and methods based on the use of reconstructed human epidermis, have been established for phototoxicity testing. While these approaches are extremely valuable, they are costly in terms of both time and resources. Consequently, in silico approaches based on the use of predictive software tools can offer more rapid and cost-effective phototoxicity screening solutions. With this goal in mind, the current study evaluated two in silico tools - Derek Nexus 6.1.0/Derek Knowledge Base 2020 1.0 (Lhasa Limited, UK) and the QSAR Toolbox (v 4.5) developed by the Organisation for Economic Co-operation and Development (OECD) - for their capacity to predict the phototoxicity of several substances from diverse classes. Derek Nexus and the QSAR Toolbox were both found to be very useful for predicting the phototoxicity of drugs and other chemicals. Derek Nexus predicted phototoxicity of the compounds, with a sensitivity of 63%, specificity of 93%, Positive Predictive Values of 90% and Negative Predictive Value of 69%, overall accuracy of 77% and balanced accuracy of 78%. The QSAR Toolbox achieved sensitivity of 73%, specificity of 85%, Positive Predictive Value of 85% and Negative Predictive Value of 74%, overall accuracy of 79% and balanced accuracy of 79%. The results show that Derek Nexus and the QSAR Toolbox can be usefully incorporated in the workflow of phototoxicity testing for pharmaceuticals and chemicals.

Interpreting Neural Network Models for Toxicity Prediction by Extracting Learned Chemical Features.

Neural network models have become a popular machine-learning technique for the toxicity prediction of chemicals. However, due to their complex structure, it is difficult to understand predictions made by these models which limits confidence. Current techniques to tackle this problem such as SHAP or integrated gradients provide insights by attributing importance to the input features of individual compounds. While these methods have produced promising results in some cases, they do not shed light on how representations of compounds are transformed in hidden layers, which constitute how neural networks learn. We present a novel technique to interpret neural networks which identifies chemical substructures in training data found to be responsible for the activation of hidden neurons. For individual test compounds, the importance of hidden neurons is determined, and the associated substructures are leveraged to explain the model prediction. Using structural alerts for mutagenicity from the Derek Nexus expert system as ground truth, we demonstrate the validity of the approach and show that model explanations are competitive with and complementary to explanations obtained from an established feature attribution method.

New benzimidazole based Schiff bases as potent anti-alzheimer agents: Synthesis, bio-evaluation and molecular docking study

In search of potent anti-Alzheimer agent benzimidazole based Schiff base derivatives (1–18) were synthesized and evaluated as dual inhibitor for acetylcholinesterase and butyrylcholinesterase enzymes. All analogs among the series except analog 1 and 16 showed a variable degree of inhibitory activity with IC50 value ranging between 0.10 ± 0.01 to 12.40 ± 0.30 µM for acetylcholinesterase and 0.20 ± 0.01 to 11.10 ± 0.30 µM for butyrylcholinesterase. The most potent analog found among the series was analog 8 having IC50 value 0.10 ± 0.01 and 0.20 ± 0.01 µM for both acetylcholinesterase and butyrylcholinesterase inhibition respectively. The structures of all synthesized analogs were confirmed through NMR and HR-EIMS. Structure activity relationship (SAR) has been established for all newly synthesized derivatives. To understand the binding interaction of most active derivatives with enzyme active site, molecular docking study were performed. The toxicity and mutagenicity of compound 8 was predicted using in silico software, namely Derek Nexus® (version 6.3). Various toxicity endpoints, including chromosomal damage, skin sensitization, hepatotoxicity were predicted. The degradation profile of compound 8 was predicted in silico by Zeneth software (version 9.0.1) resulting in a probability of the formation of seven potential degradation products.

LC-HRMS and NMR studies for the characterization of degradation impurities of ubrogepant along with the in silico approaches for the prediction of degradation and toxicity

Ubrogepant is the first oral calcitonin gene-related peptide (CGRP) receptor antagonist which is used for the acute treatment of migraine in adults. The present study employs liquid chromatography-high resolution mass spectrometry (LC-HRMS) and nuclear magnetic resonance spectroscopy (NMR) techniques for the identification and characterization of degradation impurities of ubrogepant. The forced degradation study of ubrogepant was performed as per the International Council for Harmonisation (ICH) Q1A and Q1B guidelines. The in silico degradation profile of ubrogepant was predicted by Zeneth. It was observed that ubrogepant was labile to acidic hydrolysis, basic hydrolysis, and oxidative degradation conditions (H2O2), although it was stable in neutral hydrolysis and photolytic (UV light and visible light) conditions. Eight degradation impurities were formed, which were separated on reversed-phase HPLC with a gradient program on an InertSustain C8 column (4.6 × 250 mm, 5 µm) using 10 mM ammonium formate (pH unadjusted) and acetonitrile as the mobile phase. The structures of all the degradation impurities were characterized using the exact masses obtained from the HRMS/MS. Further, NMR studies were conducted on two major degradation impurities (UB-4 and UB-7). A plausible mechanism was proposed to support the structures of all the degradation impurities of UBR. In silico toxicity and mutagenicity assessment were done by DEREK Nexus, SARAH Nexus, and ProTox-II.

In vivo and in vitro metabolite profiling of nirmatrelvir using LC-Q-ToF-MS/MS along with the in silico approaches for prediction of metabolites and their toxicity.

Nirmatrelvir (NRV), a 3C-like protease or Mpro inhibitor of SARS-CoV-2, is used for the treatment of COVID-19 in adult and paediatric patients. The present study was accomplished to investigate the comprehensive metabolic fate of NRV using in vitro and in vivo models. The in vitro models used for the study were microsomes (human liver microsomes, rat liver microsomes, mouse liver microsomes) and S9 fractions (human liver S9 fractions and rat liver S9 fractions) with the appropriate cofactors, whereas Sprague-Dawley rats were used as the in vivo models. Nirmatrelvir was administered orally to Sprague-Dawley rats, which was followed by the collection of urine, faeces and blood at pre-determined time intervals. Protein precipitation was used as the sample preparation method for all the samples. The samples were then analysed by liquid chromatography-quadrupole time-of-flight tandem mass spectrometry (LC-Q-ToF-MS/MS) using an Acquity BEH C18 column with 0.1% formic acid and acetonitrile as the mobile phase. Four metabolites were found to be novel, which were formed via amide hydrolysis, oxidation and hydroxylation. Furthermore, an in silico analysis was performed using Meteor Nexus software to predict the probable metabolic changes of NRV. The toxicity and mutagenicity of NRV and its metabolites were also determined using DEREK Nexus and SARAH Nexus.

Utilizing integrated testing strategy (ITSv1) defined approach and read across to predict skin sensitization of cannabidiol

Cannabidiol (CBD) is increasingly being used as an ingredient in cosmetics, but to date no pre-clinical studies have been published to address the skin sensitization end point. This case study investigated its skin sensitization potential for use in a face cream application at 0.3 % using Next Generation Risk Assessment (NGRA) framework. Based on chemical structure and in-silico prediction using Derek Nexus, CBD was predicted to be weak sensitizer with a resorcinol alert moiety. In vitro testing was conducted confirming it to be sensitizer, but the New Approach Methodologies (NAM) data could not provide sufficient confidence to determine a point of departure (PoD). Integrated testing strategy (ITS)v1 Defined Approach (DA), adopted in OECD Guideline No. 497, was used for skin sensitization potency categorization. However, ITSv1 DA alone is not used for further refinement of the potency prediction based on EC3 (the estimated concentration that produces a stimulation index of 3 in murine local lymph node assay) values. Therefore, the application of read-across using Derek Nexus derived a PoD derived from the LLNA EC3 of 42 %. This led to a favorable NGRA conclusion and to support use of CBD at 0.3 % in face cream application.

Selective Stability Indicating Liquid Chromatographic Method Based on Quality by Design Framework and In Silico Toxicity Assessment for Infigratinib and Its Degradation Products.

Infigratinib, a protein kinase inhibitor employed in the therapeutic management of cholangiocarcinoma, was subjected to various stress conditions, including hydrolytic (acidic and alkaline), oxidative, photolytic, and thermal stress, in accordance with the rules established by the International Council for Harmonization. A cumulative count of five degradation products was observed. The application of the Quality by Design principle was utilized in the development of a rapid and specific separation method for Infigratinib and its degradation products. The methodology employed in this study was derived from an experimental design approach, which was utilized to examine the critical process parameters associated with chromatographic systems. The reversed-phase high-performance liquid chromatography technique, employing a C18 column and a mobile phase composed of a gradient mixture of 25 mM ammonium acetate buffer at pH 6.0 and acetonitrile, successfully facilitated the chromatographic separation. The methodology was expanded to include the utilization of UPLC-quadrupole tandem mass spectrometry in order to conduct a comprehensive analysis of the structural properties and characterize the degradation products. Overall, five degradation products were found in different stress conditions. The method was verified at certain working points, wherein a linearity range (5.0-200.0 µg/mL) was developed and other parameters such as accuracy, repeatability, selectivity, and system suitability were evaluated. Finally, the toxicity and mutagenicity of Infigratinib and its degradation products were predicted using in silico software, namely DEREK Nexus® (version 6.2.1) and SARAH Nexus® (version 3.2.1). Various toxicity endpoints, including chromosomal damage, were predicted. Additionally, two degradation products were also predicted to be mutagenic.

In silico and in vitro evaluation of a safety profile of a cosmetic ingredient: 4-methoxychalcone (4-MC)

Chalcone is an important scaffold within medicinal and cosmetic chemistry. The structure enables multiple modifications which may result in obtaining compounds with desirable bioactivity. One of the chalcone derivatives, 4-methoxychalcone is a known cosmetic ingredient indexed in Cosing database as an antioxidant, bleaching, and skin conditioning substance. We investigated its in silico and in vitro safety profile. In silico study using Derek Nexus showed its potential of skin sensitisation, equivocal nature of chromosome damage in vitro in mammals, but also no mutagenic properties. In vitro research proved its activity as melanogenesis inhibitor in B16F10 cell line at the doses 12.5–3.125 μM. Evaluations performed in various cell lines showed that the cytotoxic doses were 50–25 μM. Tests in Episkin™ proved its ability to penetrate across epidermis and enabled classification of 2% formulation in PEG as non-irritant. In micronucleus tests it showed no genotoxicity. Studies in Cunninghamella echinulata model proved that 4-methoxychalcone was metabolised to less lipophilic products. 4-methoxychalcone showed phototoxic potential, its EC50(+UV) = 3.57 μg/mL, PIF = 10.19 and MPE = 0.428 were comparable to chlorpromazine. Moreover, 4-methoxychalcone showed ecotoxic potential in Microtox® assay with EC50(5 min) = 0.0047 mg/L and EC50(15 min) = 0.0033 mg/L. Although active doses were lower than toxic ones, some potential safety risks were noticed. Especially, due to the phototoxicity potential of 4-methoxychalcone, its use as depigmenting agent should involve avoidance of sunlight and use of appropriate photoprotection.

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

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

Forced degradation study of baricitinib and structural characterization of its degradation impurities by high-resolution mass spectrometry and nuclear magnetic resonance spectroscopy.

Baricitinib (BARI), an inhibitor of Janus kinases 1 and 2 (JAK 1/2), is used for the treatment of rheumatoid arthritis and COVID-19. The present study focuses on establishing the forced degradation behavior of BARI under different degradation conditions (hydrolysis, oxidation, and photolysis) following International Council for Harmonization (ICH)guidelines of Q1A (R2)-Stability testing of new drug substances and products and Q1B-Photostability testing of new drug substances and products. This study helps in monitoring the quality and safety of BARI and its product development. Prior to conducting the study, the in silico degradation profile of BARI was predicted by Zeneth. Reversed-phase high-performance liquid chromatography employing a gradient program was used for the identification and separation of degradation impurities with an InertSustain C8 column (4.6 × 250 mm, 5μm). The mobile phases used were 10 mM ammonium formate (pH 2.89) and acetonitrile. High-resolution mass spectrometry (HRMS) was used for the structural elucidation of the degradation impurities. BARI was labile to hydrolytic (acidic, basic, and neutral) and photolytic degradation conditions which yielded 10 new degradation impurities and it was stable under oxidative (H2 O2 ) conditions. The separated degradation impurities were characterized by HRMS and the respective degradation pathways were proposed. The generated information helped to propose a mechanism for the formation of the degradation impurities. Additionally, one-dimensional and two-dimensional nuclear magnetic resonance spectroscopy were used for the characterization of two major degradation impurities. The forced degradation study of BARI was carried out in accordance with ICH Q1A and Q1B guidelines, which resulted in the formation of 10 new degradation impurities. In our analysis, three degradation impurities were matching with the Zeneth predictions. In silico tools, DEREK Nexus® and SARAH Nexus®, were used for predicting the toxicity and mutagenicity of BARI and its degradation impurities. Overall, this study sheds light on BARI's safety monitoring and storage circumstances.

Identification and characterization of forced degradation products of 5-hydroxymethyl-2-furaldehyde (5-HMF) by HPLC, LC-LTQ/Orbitrap and NMR studies

5-Hydroxymethyl-2-furaldehyde (5-HMF) is a kind of aldehyde compound with highly active furan ring, which is generated by dehydration of glucose, fructose, and other monosaccharides. It widely exists in drugs, foods, health products, cosmetics, and traditional Chinese medicine preparations with high sugar content. Due to the toxicity, the concentration of 5-HMF was always monitored to identify non-conformities and adulteration, as well as ensure the process efficiency, traceability and safety in foods or drugs in the pharmacopoeias of various countries. Herein, a comprehensive forced degradation study was performed to characterize the degradation products (DPs) of 5-HMF under hydrolytic (neutral, acidic, and alkaline) degradation, oxidative, thermal, humidity, and photolytic degradation conditions. A total of five degradants were identified, and two of them (DP-3 and DP-5) were novel DPs first reported in our study. Major DPs (i.e., DP-1 and DP-2) with relatively high peak areas were isolated using semi-preparative HPLC and characterized by LC-LTQ/Orbitrap and NMR. 5-HMF was only stable in alkaline hydrolysis condition. In addition, the degradation pathways and mechanism of these DPs were also explained using LC-LTQ/Orbitrap. In silico toxicity and metabolism behavior of the DPs were evaluated using Derek Nexus and Meteor Nexus software, respectively. The predicted toxicity data indicated that both the drug 5-HMF and its DPs bear the potential of hepatotoxicity, mutagenicity, chromosome damage, and skin sensitisation. Our research may be beneficial for the quality control and suitable storage conditions of 5-HMF.

An in silico workflow for assessing the sensitisation potential of extractables and leachables

As part of a wider toxicological risk assessment to ensure patient safety, extractables and leachables (E&Ls) which are observed above the relevant qualification threshold need to be assessed for their sensitisation potential. This study sought to investigate whether in silico toxicity models could be used to predict the sensitisation hazard and potency potential of E&Ls. An extensive dataset of relevant chemicals was collated by combining and standardising two lists of E&Ls previously published by ELSIE and the PQRI, resulting in a dataset of 790 unique materials. Sensitisation data was then located where possible, resulting in 290 chemicals being associated with dermal sensitisation hazard information, 106 chemicals with dermal sensitisation potency information, and 47 chemicals with respiratory sensitisation information. Existing expert knowledge, in the form of structural alerts within Derek Nexus, was able to accurately predict both the dermal and respiratory sensitisation potential of the E&Ls. 75 different statistical models were also built, using several algorithms and descriptors, and trained on the available dermal sensitisation data. A number of these models proved able to accurately predict the sensitisation potential of the E&Ls, which were found to occupy the same chemical space as the training sets. Finally, hybrid approaches combining expert knowledge and statistical models were investigated, including a tiered system where the skin sensitisation alerts in Derek Nexus provided a hazard prediction, followed by a potency prediction resulting from an alert-based k-nearest neighbours model. The inclusion of the Dermal Sensitisation Thresholds as default, worst-case scenario predictions in cases where similar chemicals were lacking ensured that a prediction was provided for every chemical. It is hoped that this novel workflow, which combines expert knowledge, a statistical model and existing toxicity thresholds, will aid toxicologists when assessing the sensitisation potential of E&Ls administered by any route of administration.

Toxicity Study and Binding Analysis of Newly Synthesized Antifungal N-(4-aryl/cyclohexyl)-2-(pyridine-4-yl carbonyl) hydrazinecarbothioamide Derivative with Bovine Serum Albumin

The presence of the p-aryl/cyclohexyl ring in the N-(4-aryl/cyclohexyl)-2-(pyridine-4-yl carbonyl) hydrazine carbothioamide derivative (2C) is reported to enhance the antifungal properties when compared to those of itraconazole. Serum albumins present in plasma bind and transport ligands, including pharmaceuticals. This study explored 2C interactions with BSA using spectroscopic methods such as fluorescence and UV-visible spectroscopy. In order to acquire a deeper comprehension of how BSA interacts with binding pockets, a molecular docking study was carried out. The fluorescence of BSA was quenched by 2C via a static quenching mechanism since a decrease in quenching constants was observed from 1.27 × 105 to 1.14 × 105. Thermodynamic parameters indicated hydrogen and van der Waals forces responsible for the BSA-2C complex formation with binding constants ranging between 2.91 × 105 and 1.29 × 105, which suggest a strong binding interaction. Site marker studies displayed that 2C binds to BSA's subdomains IIA and IIIA. Molecular docking studies were conducted to further comprehend the molecular mechanism of the BSA-2C interaction. The toxicity of 2C was predicted by Derek Nexus software. Human and mammalian carcinogenicity and skin sensitivity predictions were associated with a reasoning level of equivocal, inferring 2C to be a potential drug candidate.

Conducting bioinformatics analysis to predict sulforaphane-triggered adverse outcome pathways in healthy human cells

Sulforaphane (SFN) is a naturally occurring molecule present in plants from Brassica family. It becomes bioactive after hydrolytic reaction mediated by myrosinase or human gastrointestinal microbiota. Sulforaphane gained scientific popularity due to its antioxidant and anti-cancer properties. However, its toxicity profile and potential to cause adverse effects remain largely unidentified. Thus, this study aimed to generate SFN-triggered adverse outcome pathway (AOP) by looking at the relationship between SFN-chemical structure and its toxicity, as well as SFN-gene interactions. Quantitative structure-activity relationship (QSAR) analysis identified 2 toxophores (Derek Nexus software) that have the potential to cause chromosomal damage and skin sensitization in mammals or mutagenicity in bacteria. Data extracted from Comparative Toxicogenomics Database (CTD) linked SFN with previously proposed outcomes via gene interactions. The total of 11 and 146 genes connected SFN with chromosomal damage and skin diseases, respectively. However, network analysis (NetworkAnalyst tool) revealed that these genes function in wider networks containing 490 and 1986 nodes, respectively. The over-representation analysis (ExpressAnalyst tool) pointed out crucial biological pathways regulated by SFN-interfering genes. These pathways are uploaded to AOP-helpFinder tool which found the 2321 connections between 19 enriched pathways and SFN which were further considered as key events. Two major, interconnected AOPs were generated: first starting from disruption of biological pathways involved in cell cycle and cell proliferation leading to increased apoptosis, and the second one connecting activated immune system signaling pathways to inflammation and apoptosis. In both cases, chromosomal damage and/or skin diseases such as dermatitis or psoriasis appear as adverse outcomes.

Improvements to in silico skin sensitisation predictions through privacy-preserving data sharing

In silico models are often built solely on publicly available data which may mean that they are less predictive for proprietary chemical space. Data sharing initiatives can improve the performance of such models, but organisations are often unable to share their data due to the need to protect their business interests and maintain the confidentiality of the chemicals in their research and development programmes. In silico models like Derek Nexus, which use expert knowledge to develop structural alerts based on chemical toxicity, can use proprietary data to identify new areas of chemical space and/or refine existing alerts whilst still preserving the privacy of the confidential data. Five hundred and thirty seven proprietary chemicals with skin sensitisation data were shared which led to the implementation of 7 new alerts and 5 modified alerts, with a concomitant 19% increase in sensitivity and 3% increase in specificity of the model.

SkinSensPred as a Promising in Silico Tool for Integrated Testing Strategy on Skin Sensitization.

Skin sensitization is an important regulatory endpoint associated with allergic contact dermatitis. Recently, several adverse outcome pathway (AOP)-based alternative methods were developed to replace animal testing for evaluating skin sensitizers. The AOP-based assays were further integrated as a two-out-of-three method with good predictivity. However, the acquisition of experimental data is resource-intensive. In contrast, an integrated testing strategy (ITS) capable of maximizing the usage of laboratory data from AOP-based and in silico methods was developed as defined approaches (DAs) to both hazard and potency assessment. There are currently two in silico models, namely Derek Nexus and OECD QSAR Toolbox, evaluated in the OECD Testing Guideline No. 497. Since more advanced machine learning algorithms have been proposed for skin sensitization prediction, it is therefore desirable to evaluate their performance under the ITS framework. This study evaluated the performance of a new ITS DA (ITS-SkinSensPred) adopting a transfer learning-based SkinSensPred model. Results showed that the ITS-SkinSensPred has similar or slightly better performance compared to the other ITS models. SkinSensPred-based ITS is expected to be a promising method for assessing skin sensitization.

How to resolve inconclusive predictions from defined approaches for skin sensitisation in OECD Guideline No. 497

In June 2021 the Organisation for Economic Co-operation and Development published Guideline No. 497 on Defined Approaches for Skin Sensitisation (DASS GL). There are two DAs published, known as the 2o3 and the ITS. The 2o3 uses two concordant results from either the DPRA, KeratinoSens™, or the h-CLAT assays to predict hazard (sensitiser/non-sensitiser). The ITS applies a score to results from the DPRA, the h-CLAT and an in silico model to predict United Nations Globally Harmonized System (GHS) sub-categories (1A/1B/Not Classified). The ITS can use Derek Nexus as the in silico model (known as ITSv1) or use OECD QSAR Toolbox (known as ITSv2).As limitations of the individual in chemico/in vitro assays and in silico predictions are carried through to the DAs, inconclusive predictions are possible for chemicals with results in the borderline range, and chemicals with out of domain results. However, these inconclusive predictions can be resolved by applying a weight of evidence approach.Herein, four case studies are presented, each ‘inconclusive’ for skin sensitisation potential according to both DAs. A weight of evidence approach was applied to each using a robust scientific approach to provide a conclusive prediction, where possible, based on several additional, non-animal lines of evidence.

Preclinical toxicological assessment of levothyroxine and liothyronine Maillard impurities.

Following the introduction of new stability-indicating related substances methods, an unknown impurity was observed in levothyroxine (LeMI) and liothyronine (LiMI) tablets (ADVANZ PHARMA) in concentrations ≥1.0%, from 6months of storage onwards. The impurity was identified as a Maillard condensation product between lactose and LeMI/LiMI in the LeMI and LiMI tablets, respectively. To establish the toxicity profile of LeMI and LiMI in humans and to define appropriate shelf-life specification limits, a comprehensive nonclinical toxicological assessment was performed, including in silico (Leadscope and Derek Nexus analyses), in vitro (Ames test), and in vivo tests (7-day dose range finding and 90-day dose repeat studies in rats). In silico analyses indicated that potential LeMI and LiMI structures should not be considered bacterial mutagens or in vitro/in vivo clastogens, and that at the low oral exposure levels expected, the impurities are unlikely to cause harm. In vitro testing showed that neither LeMI nor LiMI were cytotoxic or mutagenic at up to 5000μg/plate, both in the presence and absence of metabolic activation. The 2 in vivo studies further confirmed that no systemic toxicity or other notable negative effects were evident at up to 200μg/kg/day for LeMI and 45μg/kg/day for LiMI, the highest doses tested. These doses represent 120-122 times the maximum daily exposures of LeMI and LiMI, based on body surface area (μg/m2). Based on these results, a proposal has been formulated to increase the limits of Maillard condensation products to ≤8.0% for LeMI and≤6.0% for LiMI at shelf life.

Updating the Dermal Sensitisation Thresholds using an expanded dataset and an in silico expert system

The Dermal Sensitisation Thresholds (DST) are Thresholds of Toxicological Concern, which can be used to justify exposure-based waiving when conducting a skin sensitisation risk assessment. This study aimed to update the published DST values by expanding the size of the Local Lymph Node Assay dataset upon which they are based, whilst assigning chemical reactivity using an in silico expert system (Derek Nexus). The potency values within the expanded dataset fitted a similar gamma distribution to that observed for the original dataset. Derek Nexus was used to classify the sensitisation activity of the 1152 chemicals in the expanded dataset and to predict which chemicals belonged to a High Potency Category (HPC). This two-step classification led to three updated thresholds: a non-reactive DST of 710 μg/cm2 (based on 79 sensitisers), a reactive (non-HPC) DST of 73 μg/cm2 (based on 331 sensitisers) and an HPC DST of 1.0 μg/cm2 (based on 146 sensitisers). Despite the dataset containing twice as many sensitisers, these values are similar to the previously published thresholds, highlighting their robustness and increasing confidence in their use. By classifying reactivity in silico the updated DSTs can be applied within a skin sensitisation risk assessment in a reproducible, scalable and accessible manner.