Prediction of genotoxic potential of cosmetic ingredients by an in silico battery system consisting of a combination of an expert rule-based system and a statistics-based system

Kaneko, Maki; Hirota, Morihiko; Kouzuki, Hirokazu; Mori, Masaaki

doi:10.2131/jts.40.77

Cited by 21 publications

(11 citation statements)

References 44 publications

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“…Herein, the principles for the validation of (Q)SAR models (OECD, 2007b ) have been developed as well as the OECD QSAR toolbox, which is supporting grouping and read-across approaches 5 . Regulatory agencies increasingly accept in silico results on genotoxicity (Cassano et al, 2014 ; Aiba nee Kaneko et al, 2015 ; Jolly et al, 2015 ).”In the absence of toxicological data, grouping of substances and read-across approaches are encouraged in the REACH legislation to predict complex endpoints, as resulting from repeated dose toxicity testing. A data analysis performed by ECHA shows that data gaps exist for one-third (32.9%) of endpoints of the so-called phase-in substances (substances with high production volumes of 100–1000 tons per year).…”

Section: Discussionmentioning

confidence: 99%

Innovative Strategies to Develop Chemical Categories Using a Combination of Structural and Toxicological Properties

et al. 2016

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Interest is increasing in the development of non-animal methods for toxicological evaluations. These methods are however, particularly challenging for complex toxicological endpoints such as repeated dose toxicity. European Legislation, e.g., the European Union's Cosmetic Directive and REACH, demands the use of alternative methods. Frameworks, such as the Read-across Assessment Framework or the Adverse Outcome Pathway Knowledge Base, support the development of these methods. The aim of the project presented in this publication was to develop substance categories for a read-across with complex endpoints of toxicity based on existing databases. The basic conceptual approach was to combine structural similarity with shared mechanisms of action. Substances with similar chemical structure and toxicological profile form candidate categories suitable for read-across. We combined two databases on repeated dose toxicity, RepDose database, and ELINCS database to form a common database for the identification of categories. The resulting database contained physicochemical, structural, and toxicological data, which were refined and curated for cluster analyses. We applied the Predictive Clustering Tree (PCT) approach for clustering chemicals based on structural and on toxicological information to detect groups of chemicals with similar toxic profiles and pathways/mechanisms of toxicity. As many of the experimental toxicity values were not available, this data was imputed by predicting them with a multi-label classification method, prior to clustering. The clustering results were evaluated by assessing chemical and toxicological similarities with the aim of identifying clusters with a concordance between structural information and toxicity profiles/mechanisms. From these chosen clusters, seven were selected for a quantitative read-across, based on a small ratio of NOAEL of the members with the highest and the lowest NOAEL in the cluster (< 5). We discuss the limitations of the approach. Based on this analysis we propose improvements for a follow-up approach, such as incorporation of metabolic information and more detailed mechanistic information. The software enables the user to allocate a substance in a cluster and to use this information for a possible read- across. The clustering tool is provided as a free web service, accessible at http://mlc-reach.informatik.uni-mainz.de.

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Section: Discussionmentioning

confidence: 99%

Innovative Strategies to Develop Chemical Categories Using a Combination of Structural and Toxicological Properties

et al. 2016

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“…Stability studies conducted on drug product development batches may provide information on the polymorphic conversion of several API isomers and consequently affect the selection of the API vendor as well. Decision tree #4 of ICH Q6A guidelines was explained to perform polymorphic test in API finished analysis [17][18][19][20][21].…”

Section: Particle Size Distribution (Psd)mentioning

confidence: 99%

Impact of API (Active Pharmaceutical Ingredient) Source Selection on Generic Drug Products

Ur¹,

Nair²,

Sankar³

et al. 2015

Pharmaceut Reg Affairs

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The pharmaceutical industry is emerging as a significant industrial sector with tremendous potential for providing innovative drugs to treat life-threatening diseases as well as for providing economical generic alternatives of supreme quality. Hence this sector is not only responsible to provide the much desired boost to the health of the society, especially of the developing countries, but also it is a competitive yet profitable sector from a business perspective. Currently, the primary focus of the pharmaceutical industry is to raise the bar for the quality, safety and efficacy of the drug products that are made available in the global market place. Product quality, price of raw materials [API (active pharmaceutical ingredient) and excipients] and market return competition are vital factors that determine the longevity or existence and profitability of a company in the crowded pharmaceutical market.. Hence these critical factors receive special consideration from drug product manufacturers. Active Pharmaceutical Ingredient (API) is the primary constituent of a pharmaceutical drug product that governs the final cost of the drug product as well as the commercial profit earned by the company. Most of the major generic drug manufacturing companies have their own API manufacturing facility and hence may not prefer to screen independent API suppliers as part of their generic drug development plan to procure additional API. Contradictorily, the generic drug manufacturers who do not synthesize the API themselves are dependent on external and independent API manufacturers for procurement of the API. Such generic drug manufacturing companies have to select suitable API suppliers by adapting a risk aversive approach. This article presents an informed and comprehensive discussion on the primary and alternate API supplier selection processes for generic drug products manufacturing firms. This API supplier selection process can be categorized into several stages which include preliminary assessment, documents review, samples analysis, onsite or offsite audit, results evaluation and final approval or rejection. This API selection process includes the anticipated product specific risk assessment with relation to API characteristics, specifications, analytical results, document review observations and inspection results. A generic drug product manufacturing company can choose an alternative API supplier or change the existing API supplier either during the development phase or after development of the drug product. Generic drug product manufacturing companies should rework on development activities if any API supplier change happens during the development phase. API supplier change or addition of an alternative API supplier has to be followed as per SUPAC guidance for US market and VARIATION filing procedures for European market.

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“…To predict mutagenicity, quantitative structure-activity relationship (QSAR) has been developed for pharmaceutical impurities (Valerio and Cross, 2012;Sutter et al, 2013) and cosmetic integrates (Aiba née Kaneko et al, 2015). The understanding for the mechanism is important for mutagenicity prediction and mechanism-based QSARs for mutagenicity were reviewed by Benigni and Bossa (2011).…”

Section: Introductionmentioning

confidence: 99%

A reaction mechanism-based prediction of mutagenicity: α-halo carbonyl compounds adduct with DNA by S<sub>N</sub>2 reaction

Haranosono¹,

Ueoka²,

Kito³

et al. 2018

J. Toxicol. Sci.

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Most of the α-halo carbonyl (AHC) compounds tend to be predicted as mutagenic by structure-activity relationship based on structural category only, because they have an alkyl halide structure as a structural alert of mutagenicity. However, some AHC compounds are not mutagenic. We hypothesized that AHC reacts with DNA by S2 reaction, and the reactivity relates to mutagenicity. As an index of S2 reactivity, we focused on molecular orbitals (MOs), as the direction and position of two molecules in collision are important in the S2 reaction. The MOs suitable for S2 reaction (SN2MOs) were selected by chemical-visual inspection based on the shape of the MO. We used the level gap and the energy gap between SN2MO and the lowest unoccupied molecular orbital as the descriptors of S2 reactivity. As the results, S2 reactivity related to mutagenicity and we were able to predict mutagenicity of 20 AHC compounds with 95.0% concordance. It was suggested that S2 reaction is a reaction mechanism of AHC compounds and DNA in the mutagenic process. The method allows for discrimination among structurally similar compounds by combination with quantitative structure-activity relationships. The combination approach is expected to be useful for the mutagenic assessment of pharmaceutical impurities.

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Prediction of genotoxic potential of cosmetic ingredients by an in silico battery system consisting of a combination of an expert rule-based system and a statistics-based system

Cited by 21 publications

References 44 publications

Innovative Strategies to Develop Chemical Categories Using a Combination of Structural and Toxicological Properties

Innovative Strategies to Develop Chemical Categories Using a Combination of Structural and Toxicological Properties

Impact of API (Active Pharmaceutical Ingredient) Source Selection on Generic Drug Products

A reaction mechanism-based prediction of mutagenicity: α-halo carbonyl compounds adduct with DNA by S<sub>N</sub>2 reaction

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