A Perspective and a New Integrated Computational Strategy for Skin

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Perspective

A Perspective and a New Integrated Computational Strategy for Skin Sensitization Assessment Vinicius M Alves, Stephen Joseph Capuzzi, Rodolpho C Braga, Joyce Borba, Arthur Silva, Thomas Luechtefeld, Thomas Hartung, Carolina Horta Andrade, Eugene N. Muratov, and Alexander Tropsha ACS Sustainable Chem. Eng., Just Accepted Manuscript • DOI: 10.1021/ acssuschemeng.7b04220 • Publication Date (Web): 07 Feb 2018 Downloaded from http://pubs.acs.org on February 13, 2018

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ACS Sustainable Chemistry & Engineering

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A Perspective and a New Integrated Computational Strategy for

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Skin Sensitization Assessment

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Vinicius M. Alvesa,b, Stephen J. Capuzzia, Rodolpho C. Bragab, Joyce V. B. Borbab,

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Arthur C. Silvab, Thomas Luechtefeldc, Thomas Hartungc, Carolina Horta Andradeb,

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Eugene N. Muratova,d,*, and Alexander Tropshaa,*.

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a

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Hall, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA.

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b

Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, 301 Beard

Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás,

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Rua 240, Qd. 87, Setor Leste Universitario, Goiânia, GO, 74605-170, Brazil.

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c

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615 N. Wolfe Street, Baltimore, MD, 21205, USA

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d

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Odessa, 65000, Ukraine.

Johns Hopkins Bloomberg School of Public Health, Center for Alternatives to Animal Testing (CAAT),

Department of Chemical Technology, Odessa National Polytechnic University, 1 Shevchenko Ave,

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Corresponding Authors

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*Address for correspondence: 100K Beard Hall, UNC Eshelman School of Pharmacy, University of North Carolina,

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Chapel Hill, NC, 27599, USA; Telephone: (919) 966-2955; FAX: (919) 966-0204; E-mails: [email protected]

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and [email protected].

ACS Paragon Plus Environment

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ABSTRACT

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Traditionally, the skin sensitization potential of chemicals has been assessed using animal

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models. Due to growing ethical, political, and financial concerns, sustainable alternatives to

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animal testing need to be developed. As publicly available skin sensitization data continues to

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grow, computational approaches, such as alert-based systems, read-across, and QSAR models,

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are expected to reduce or replace animal testing for the prediction of human skin sensitization

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potential. Herein, we discuss current computational approaches to predicting skin sensitization

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and provide future perspectives of the field. As a proof-of-concept study, we have compiled the

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largest skin sensitization dataset in the public domain and benchmarked several methods for

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building skin sensitization models. We propose a new comprehensive approach, which integrates

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multiple QSAR models developed with in vitro, in chemico, animal, and human data, and a

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Naive Bayes model for predicting human skin sensitization. Both the datasets and the KNIME

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implementation of the model allowing skin sensitization prediction for molecules of interest have

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been made freely available.

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Keywords: Skin sensitization, QSAR, Naïve Bayes, alternative methods.


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INTRODUCTION

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Approximately 20% of the general human population suffer from allergic contact

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dermatitis (ACD), an immune-mediated inflammatory skin reaction caused by the contact with

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chemical allergens.1 The first phase of the ACD adverse outcome pathway (AOP) is an allergic

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response by the epidermis called skin sensitization.1 Since ACD has a significant impact on

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working ability and quality of life, chemicals with potential skin sensitization liabilities need to

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be identified and regulated. Routine chemical testing on humans, however, is not feasible due to

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ethical concerns, high cost, and low throughput.2 Consequently, animal testing is used commonly

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to evaluate chemically-induced skin sensitization.

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The murine local lymph node assay (LLNA)3 is the preferred animal testing model used

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by various regulatory agencies4,5. At the same time, opposition to animal testing has grown over

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the past several decades.6 Indeed, animal testing for cosmetic products was banned in the

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European Union (EU) in 2003,7 and the sale of cosmetic products tested on animals, except for

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complex toxicological properties, has been prohibited since 2009.8 In 2013, this ban was

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extended to all properties, along with the sale of cosmetics tested on animals outside of the EU.9

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It is worth noting that these regulations apply only to cosmetics; thus, the assessment of

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substances other than cosmetics for their skin sensitization potential still relies on animal testing.

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Along with ethical concerns, the validity of animal testing has also come under question

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in recent years. Several studies have shown that animal-based assay outcomes do not always

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equate with human response10,11 and that animal models are less reproducible than some

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alternative methods.12 Though these observations provide strong arguments against continued

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reliance on animal testing, the evaluation of a large number of chemicals by in vitro alternative

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methods may not be financially sustainable under the REACH law, which obligates


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manufacturers to provide detailed information on chemicals manufactured, marketed, or

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imported on a scale of more than one-ton per year in Europe.13

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Computational methods have begun to gain prominence as a practical solution for the

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evaluation of experimentally untested substances. Along with speed, low cost, and high-

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throughput, in silico methods can deliver accurate toxicity assessments that circumvent the need

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for animal testing.14,15 For chemical toxicity prediction, the most commonly used approaches are

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structural alerts, read-across, and Quantitative Structure-Activity Relationship (QSAR)

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modeling.16 These approaches leverage historical data generated for chemicals tested for skin

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sensitization potential across animal, non-animal, and human models. These approaches have

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been used for prioritizing compounds; however if carefully evaluated and rigorously validated,

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they may entirely replace animal testing.17

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This Perspective has three major objectives. First, we describe all skin sensitization data

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available in the open literature and report on our effort to compile and curate this data to form the

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largest skin sensitization dataset available in the public domain. Second, we discuss current

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computational approaches and models for predicting skin sensitization. Finally, we propose a

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new approach that integrates multiple computational models based on in vitro, animal (LLNA),

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and human data. We show that this new method affords models with the highest predictive

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power as compared to alternative computational or experimental approaches. We hope that the

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data, methods, and models summarized and discussed in this study will enable both scientists and

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regulators to move closer to fully adopting these intelligent computational approaches as

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scientifically sound and legitimate alternatives to animal testing.


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Skin sensitization data

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In this section, we provide a comprehensive overview of all data available on skin

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sensitization that can be used to develop computational models. These data encompass human,

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animal, and non-animal sources, making it the largest curated skin sensitization dataset reported

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to date (Table 1). The human data are composed by human repeat insult patch test and human

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maximization test18,19, animal data by local lymph node assay (LLNA)20–22, and non-animal data

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composed by Direct Peptide Reactivity Assay (DPRA), KeratinoSens, and the human Cell Line

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Activation Test (h-CLAT)23. We also discuss protocols for data collection, curation, and

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integration, as well as examine the important issues of assay concordance and chemical space

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coverage.

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(http://www.chemspider.com/) or SciFinder (https://scifinder.cas.org) databases using Chemical

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Abstracts Service (CAS) registry numbers and chemical names. Datasets were thoroughly

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curated for both chemical and biological data according to the workflows described by Fourches

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et

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(https://chembench.mml.unc.edu/).

al.24–26

Chemical

Curated

structures

datasets

were

are

retrieved

available

on

from

either

Chembench

ChemSpider

Web

Portal27

96 97

Table 1. Skin sensitization dataset compiled and curated in this study. Assay

Assay type

Human LLNA DPRA KeratinoSens h-CLAT

In vivo In vivo In chemico In vitro In vitro

No. of sensitizers 88 481 121 126 107

No. of non-sensitizers 50 519 73 64 53

Total 138 1000 194 190 160

References 18,19 20–22 23 23 23

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LLNA data

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Interagency Coordinating Committee on the Validation of Alternative Methods

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(ICCVAM)

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The LLNA database was compiled and made available by the National Toxicology

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Program Interagency Center for the Evaluation of Alternative Toxicological Methods on behalf

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of ICCVAM.20 The original database included 1,060 chemical records. Since a compound could

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be tested in multiple assays, several records were often found for the same compound. If the

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experimental properties associated with two duplicated structures were identical, then one

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compound was removed. However, if their experimental properties were significantly different,

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we removed both records from the dataset. After curation, 516 unique compounds (332

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sensitizers and 184 non-sensitizers) were retained.

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REACH

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REACH data are available for public access, and the dataset was downloaded from

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ECHA (European Chemical Agency) as described by Luechtefeld et al.21 This dataset initially

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comprised 10,588 records for 9,801 chemicals. The REACH dataset is composed of many types

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of assays and study categories. Two study categories were discarded from the dataset – the in

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vitro and “weight of evidence” categories. Data from different OECD (Organization for

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Economic Co-operation and Development) skin sensitization assays (OECD guidelines 406, 411,

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429 and 442B)28–31 were available; only the data corresponding to LLNA assays (429 and 442B)

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were selected, resulting in 1,275 LLNA records. After curation, 566 compounds (197 sensitizers

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and 369 non-sensitizers) were retained.


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Combined LLNA dataset

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We merged the curated data from ICCVAM and REACH and examined the content of

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this combined data. There were 58 pairs of duplicates between these two datasets, and the

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sensitization potential of five of these pairs was different. These discordant records were

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removed, and only one record for each concordant pair of duplicates was kept. The merged

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dataset had 1,000 unique compounds (481 sensitizers and 519 non-sensitizers) making it the

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largest curated LLNA dataset reported to date (cf. Table 1). The dataset is available from the

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Chembench Web Portal27 (https://chembench.mml.unc.edu/).

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Human data

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Human skin sensitization data were retrieved from the ICCVAM Test Method Evaluation

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Report18 and merged with the data collected by Strickland et al.19, in which the authors corrected

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the human result for six compounds present in the original ICCVAM report. The original human

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dataset from ICCVAM consisted of 302 chemical records and associated human data. Skin

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sensitization potency relies on human DSA05 data (dose per skin area that produces a positive

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response in 5% of the tested population). Compounds with reported DSA05 values were defined

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as sensitizers, and those with no DSA05 values were labeled as non-sensitizers.18 After curation

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of both chemical and biological data24–26, 135 unique substances remained. Twenty-six inorganic

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compounds or co-formulated mixtures were then removed from the modeling sets to avoid errors

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during descriptor generation. The final dataset contained 109 compounds. The data from

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Strickland et al.19 consisted of 96 compounds, 29 of which were absent in the ICCVAM dataset.

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In the end, 138 compounds (88 sensitizers and 50 non-sensitizers) were retained.

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Non-animal data

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Non-animal data compiled by Urbisch et al.23 were retrieved from the respective

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publication. In that study, the authors compiled non-animal data from several sources. Here, we

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considered one in chemico (DPRA, 194 compounds) and two in vitro (KeratinoSens, 190

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compounds, and h-CLAT, 160 compounds) sources. The DPRA assay was developed to address

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the molecular initiating event in the skin sensitization AOP through peptide reactivity

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measurements of test chemicals; therefore, it is regarded as an in chemico assay. This method

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quantifies the depletion of synthetic heptapeptides containing either lysine or cysteine.32,33 The

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KeratinoSens assay is associated with keratinocyte activation. The test method is a reporter gene

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assay, which uses an immortalized adherent cell-line derived from an expanded clone of HaCaT

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human keratinocytes transfected with a selectable plasmid. The plasmid contains the luciferase

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gene under transcriptional control of the SV40 promoter fused with the electrophile response

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element from the akr1c2 gene, which was identified as one of the genes upregulated by contact

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sensitizers in dendritic cells.34,35 The h-CLAT assay is associated with the activation of dendritic

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cells. This method measures the modulation of CD86 and CD54 by THP-1 cells using flow

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cytometry after 24 hours of exposure to a test substance.36,37

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Concordance analysis of LLNA and non-animal data vs. human data

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A concordance analysis of LLNA and non-animal data vs. human data was conducted to

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verify the relevance of the LLNA and non-animal data for human outcomes. Similar analysis

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comparing LLNA and human data was performed by our group recently.38 However, since the

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overlap of both datasets increased from 109 to 121 compounds and six annotations of the human

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data were corrected by Strickland et al.19, we elected to repeat this analysis. In addition,


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comparison of DPRA, KeratinoSens, and h-CLAT to LLNA23,39–41 with human23 data has been

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explored by others. Our present analysis differs insofar as we did not consider the compounds

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that were excluded from modeling, such as inorganics, mixtures, and organometallics. Moreover,

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we report the largest set of collated human skin sensitization data to date, as we have increased

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the number of overlapping compounds. In Figure 1, we present the distribution of the full skin

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sensitization data on LLNA, human, and non-animal assays.

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Figure 1. Distribution and overlap of skin sensitization data on LLNA (pink circle), human (green circle), and non-animal (blue circle) assays.

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It is commonly accepted that LLNA results correlate well with human skin sensitization

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potential; therefore, LLNA has been regarded as a reliable method to assess whether a chemical

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is expected to be a sensitizer or not.18,42 We repeated concordance analyses performed by our

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group recently38 with the new available data. Out of the 137 compounds with human data, 119

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were tested in LLNA; 109 – in DPRA; 111 – in KeratinoSens; and 105 – in h-CLAT. As one can

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see in Table 2, the accuracy of using LLNA results to predict human data is estimated to have a

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Correct Classification Rate [CCR, (sensitivity + specificity)/2] of 68%, sensitivity of 84%,

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positive predictive value (PPV) of 76%, specificity of 43%, and negative predictive value (NPV)


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of 65%. Compared with our previous analysis38, the 29 new compounds with human data and the

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correction of human sensitivity values for six compounds from our dataset reported earlier38

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increased the specificity of prediction from 43% to 52%. Despite this, however, LLNA results

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are still more sensitive than human outcomes. This oversensitivity may result in the rejection of

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safe cosmetic and agricultural compounds. When analyzing the non-animal assays, DPRA

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appears to be the most predictive human skin sensitization method with CCR as high as 79%,

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sensitivity and PPV above 82%, and specificity and NPV above 70%. The KeratinoSens assay

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alone presents low specificity (55%) and NPV (56%), while h-CLAT presents acceptable

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prediction metrics. The “2 out of 3” non-animal approach described by Natsch et al.41 was shown

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to be predictive, but this consensus approach is not as predictive as DPRA alone. Moreover, the

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use of LLNA in consensus with all non-animal assays to predict human skin sensitization

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presented lower CCR, PPV, specificity, and NPV than the DPRA assay and the “2 out of 3”

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approach, which advocates for the greater use of non-animal assays in place of LLNA for skin

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sensitization prediction.

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Table 2. Concordance between human vs. LLNA and non-animal outcomes for compounds with defined chemical structures. Assay LLNA (n=119) DPRA (n=109) KeratinoSens (n=111) h-CLAT (n=105) 2 out of 3 (non-animal) (n=113) Consensus (all assays) (n=100)

CCR 0.68 0.79 0.65 0.73 0.76 0.69

Sensitivity 0.84 0.82 0.75 0.85 0.85 0.86

PPV 0.76 0.86 0.74 0.81 0.82 0.75

Specificity 0.52 0.75 0.55 0.62 0.67 0.51

NPV 0.64 0.70 0.56 0.68 0.72 0.69

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Chemical space of current skin sensitization data

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In this section, we explore the chemical space of current skin sensitization data to verify

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if compounds tested on different assays share the same chemical space. The chemical space

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formed by LLNA, human, and non-animal skin sensitization data was analyzed by plotting the

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barycentric coordinates of all 1,033 unique structures, defined by the 2D DRAGON43

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descriptors. Barycentric coordinates correspond to the location of the points of a simplex (a

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triangle, tetrahedron, etc.) in the space, defined by the vertices44. In this case, a simplex is

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defined by all the DRAGON descriptors of a particular chemical substance. Barycentric

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coordinates were determined using the Methods of Data Analysis module in the HiT QSAR

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software45. As one can see from Figure 2, there is a large section of LLNA data (801

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compounds) that is not covered by human and non-animal data, meaning that certain structurally

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unique chemicals have been tested only in LLNA. There are 109 compounds with LLNA,

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human, and non-animal data, 79 compounds with both LLNA and non-animal data, and 44

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compounds with other assay combinations. Although results of the previous section reveal that

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LLNA is prone to false-positive human skin sensitization predictions, LLNA outcomes are the

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only data available for a large number of compounds. These LLNA data could still be utilized to

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build QSAR models that predict human skin sensitization. These models, which will have greater

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coverage of the chemical space than human models only, could thus be applied to a larger

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number of new compounds. These models could serve as an alternative to the murine LLNA

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assay.


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Figure 2. Chemical space of 1,033 investigated compounds in barycentric coordinates obtained from 2D DRAGON descriptors.

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Computational approaches to predict skin sensitization

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Computational approaches may represent a sustainable alternative to animal testing for

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reliable skin sensitization assessment. In this section, we provide an overview of existing in

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silico approach, highlighting their strengths and weakness in the field of skin sensitization

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prediction. We also provide a discussion of the attempts to model the various data sources.

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Structural alerts and read-across

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Structural alerts are molecular substructures that are associated with a particular adverse

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outcome (Figure 3).46 Structural alerts are widely accepted in chemical toxicology and regulatory

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decision support as a simple and transparent means to flag potential chemical hazards or to group

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compounds into categories for read-across.47 Otherwise known as “expert rules”, structural alerts


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are based on human expertise and are intended to reflect the chemical basis of the mechanism of

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action or, at least, the molecular initiating event in the case of more complex endpoints.48 Several

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tools such as the OCHEM ToxAlerts49 and Toxtree (http://toxtree.sourceforge.net/) web servers

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have a special module for skin sensitization.

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Read-across is a technique that extrapolates data based on structural similarity to

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previously tested compound(s) for compounds lacking experimental evaluation (Figure 3).50 This

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method has earned prominence due to its simplicity, transparency, and ease of interpretation.51

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From a regulatory perspective, read-across has attracted considerable attention, especially in the

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EU and has been promoted by significant legislation like REACH13 and the Cosmetic

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Regulation8. Based on structural similarity, read-across predictions and interpretation have been

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addressed by agencies like OECD (http://www.oecd.org/env/ehs/risk-assessment/hazard-

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assessment.htm) and ECHA that conceptualized the Read-Across Assessment Framework.52 This

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Framework aims to establish a consistent set of principles for estimating read-across

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justifications in the context of REACH regulations. The OECD QSAR Toolbox

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(https://www.qsartoolbox.org/) is an OECD-sponsored software application to predict

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(eco)toxicity based on chemical grouping and read-across that leaves the assessment of the

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prediction to the end user.53 This software also includes a skin sensitization module.

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Figure 3. Chemical toxicity prediction based on structural alerts and chemical read-across. The schema shown herein illustrates how the read-across approach is used to assign skin sensitization potential to phenyl salicylate, for which no experimental data is available. The presence of a known structural alert leads to annotation of this compound as a sensitizer. The read-across approach designates this compound as a possible non-sensitizer in human but sensitizer in LLNA. This example thus also illustrates discordance between LLNA and human data (see text for discussion).

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Despite the enthusiasm around structural alerts, there has been a growing concern that

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alerts disproportionally flag chemicals as toxic, which calls into question their reliability as

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toxicity markers.54 Recently, we have contrasted structural alerts-based predictions from

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ToxAlerts49 and QSAR Toolbox against QSAR models for skin sensitization.55 Although

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predictions made with QSAR Toolbox and ToxAlerts show higher sensitivity than our models

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when evaluating the same set of structures, our models featured a much higher PPV. These

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results indicate that the probability of correctly classifying sensitizers is much higher using

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QSAR models and that alert-based prediction has a bias towards false positives.

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A prior study has compared QSAR Toolbox and Toxtree to predict skin sensitization with

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LLNA and human data.56 The authors found that structural alerts could predict human data better

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than LLNA, concluding that in silico models for skin sensitization should be preferably


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developed using human data. Recently, we38 and others18,57 have shown that in some cases

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LLNA does not correlate well with human data and that QSAR models based on human data

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outperform the ability of LLNA to predict human response. Despite the similar conclusions made

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by both studies, it is worth noting that the simple use of alerts for chemical read-across often

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leads to erroneous assessments, since this approach tends to be oversensitive.55

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Read-across is an approach used to predict a property of interest (toxicity, activity, etc.)

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of a chemical using its structural analogs with known experimental values of this property. Read-

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across consists of the seven following steps: (i) decision context; (ii) data gap analysis; (iii)

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overarching similarity rationale; (iv) analogue identification; (v) analogue evaluation; (vi) data

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gap filling; and (vii) uncertainty assessment.58 However, to be useful for hazard/risk assessment,

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as well as to meet regulatory rules, only rigorously validated approaches should be used.

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Therefore, read-across should be used to aid expert decision-making only after analyzing all

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available information sources, e.g., predictions from externally validated QSAR models, in vitro

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and in vivo outcomes, etc.50,59,60

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Quantitative Structure-Activity Relationship (QSAR) modeling

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QSAR modeling is a computational approach that employs statistical or machine learning

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techniques to establish correlations between intrinsic chemical properties (chemical descriptors)

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and measured property (activity, toxicity, etc.). Developed models are used to forecast the

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respective target properties of novel or untested compounds, leading to substantial use both in

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toxicology and medicinal chemistry to evaluate chemical safety or design novel bioactive

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compounds, respectively.61,62 As we have demonstrated to in our recent papers,38,63,64 until 2015,

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most of the published QSAR models were not compliant with the best practices of model


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development and validation,61,65 and thus their reliability for assessing chemically-induced skin

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sensitization is not assured.

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Recently, Strickland et al.19 have developed models trained on 72 substances employing

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six physicochemical properties, in chemico and in vitro assay outcomes, as well as in silico read-

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across predictions of skin sensitization potential as descriptors to predict both LLNA and human

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response. These hybrid models predicted human skin sensitization potential better than LLNA or

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any of the alternative methods either alone or combined.19

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Despite progress in developing QSAR models for skin sensitization, this approach

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currently has two limitations: (i) LLNA results have limited concordance with human skin

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sensitization data and (ii) the majority of currently available models are binary, i.e., chemicals

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are classified as a sensitizer or non-sensitizer, which decreases their range of utility in skin

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sensitization risk assessment.66,67

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The TImes MEtabolism Simulator for predicting Skin Sensitization (TIMES-SS) is a

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semi-quantitative hybrid expert system combining metabolism and toxicity prediction.68,69 In this

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tool, models are generated by combining data from LLNA, GPMT (guinea pig maximization

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test), and a collection of data retrieved from the literature on substances with documented contact

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allergic properties in humans and animal experiments that have been evaluated by experts at the

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BgVV (German Federal Institute for Health Protection of Consumers and Veterinary Medicine).

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Models were externally validated using two external datasets: 96 compounds randomly selected

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from the literature70 and 40 compounds randomly selected and purchased for testing in LLNA.68

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Despite the high specificity (ca. 87.5%), the sensitivity of the model was poor (ca. 56%). The

320

TOPKAT (Toxicity Prediction Komputer-Assisted Technology) also provides semi-quantitative


16

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321

predictions, distinguishing weak/moderate and strong sensitizers. This tool is composed of

322

QSAR models built from GPMT for 315 chemicals71,72 that allow to assess skin sensitization.

323

Recently, several innovative in silico methods and tools providing quantitative and semi-

324

quantitative predictions of skin sensitization were reported. For instance, CADRE-SS73 is a

325

multivariate hierarchical model for skin sensitization. In this tool, skin permeability is evaluated

326

using Monte Carlo simulations, chemical reactive centers are determined with expert rules, and

327

protein reactivity is predicted by quantum-mechanical modeling. The authors reported

328

impressive results for predicting skin sensitization of an external set mostly composed by LLNA,

329

Buehler’s test, and GPMT data: sensitivity as high as 87%, specificity as high as 100%, and

330

balanced accuracy of 93%, which exceeded the reported74 concordance of 89% between LLNA

331

and GPMT.

332

Pred-Skin75 is a freely available web-based and mobile-enabled application for the

333

assessment of skin sensitization potential using externally validated QSAR models based on

334

animal (LLNA) and human data. The app represents a benchmark in the prediction of skin

335

sensitization, since it is the first tool to provide predictions from models based on human data.

336

Predictions for a single compound are produced within seconds. The following outputs are

337

provided: (i) binary predictions of human and murine skin sensitization potential; (ii) multiclass

338

predictions of murine skin sensitization potency; and (iii) probability maps76 illustrating the

339

predicted contribution of chemical fragments. This service is freely available at

340

http://labmol.com.br/predskin/ and in the iTunes App Store.

341

Zang et al.77 used in chemico and in vitro assays combined with physicochemical

342

properties to build multiclass models from both LLNA and human data. These models are

343

available to the public at https://ice.ntp.niehs.nih.gov/#!Workflows. Toropova and Toropov78


17


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344

developed the first continuous models for skin sensitization using descriptors calculated directly

345

from SMILES and molecular graphs optimized by the Monte Carlo method. These continuous

346

models are implemented on the CORAL software (http://www.insilico.eu/coral). These models,

347

however, suffer from several limitations. The models were developed from non-curated data,

348

which undermines model reliability, and the accuracy of models depends on how molecules are

349

rendered in SMILES format. Canipa et al.79 recently developed two-tiered, k-nearest neighbors

350

models based on in-house LLNA data comprising 659 compounds. Expert alert predictions from

351

Derek Nexus 5.0.2 (using Derek Knowledge Base 2015 2.0 and Derek EC3 Model - 1.0.6) were

352

used with molecular fingerprints as descriptors. The authors reported overall, and not very high,

353

accuracy (64 %) for the models only, which is not sufficient to guarantee the predictivity of the

354

models.

355 356

Using alternative methods to predict skin sensitization

357

Within the last two decades, the field of toxicology has begun moving toward a better

358

understanding of disease pathways at multiple biological levels.80,81 Skin sensitization is a

359

complex reaction, and it is now acknowledged that a single test will most likely not be able to

360

predict human response.82 As we discussed above, several in vitro tests have been proposed and

361

validated when used as integrated testing strategies (ITS).82 ITS that combine different chemical,

362

biological, and in silico methods have been recommended to replace conventional animal tests

363

due to the complexity of skin sensitization.83,84 For instance, an expert panel recently published a

364

study revealing that in chemico and in vitro assays correctly identified most of the compounds

365

requiring abiotic or biotic activation to cause skin sensitization, even though the assays not

366

always agree.85 In another study, the authors compared the results from a “2 out of 3” consensus


18

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367

ITS, using DPRA, KeratinoSens, and h-CLAT data to map key AOP steps of LLNA and human

368

data.23 This approach gave higher accuracy predictions for human data than LLNA data. Clouet

369

et al.86 employed a similar approach and concluded that assays should be performed in a certain

370

order so that assay disagreement is better managed and fewer assays are needed. As another

371

example, Jaworska et al.87 proposed a Bayesian network ITS that provides the LLNA distribution

372

over four potency classes using data from DPRA, KeratinoSens, and h-CLAT. This approach has

373

been implemented for public use at http://its.douglasconnect.com/. Otsubo et al.88 proposed a

374

binary method combining predictions of DPRA and h-CLAT and KeratinoSens and h-CLAT

375

independently, but both approaches were shown to be oversensitive.

376

Here, we present binary QSAR models for the three validated alternative methods

377

(DPRA, KeratinoSens, and h-CLAT). These models were developed and rigorously validated

378

according to the best practices of QSAR modeling65 using open-source chemical descriptors

379

based on ECFP4-like circular fingerprints with 2048 bits and an atom radius of 2 (Morgan2)

380

calculated in RDKit (http://www.rdkit.org), along with the support vector machines algorithm89

381

(radial basis function, C = 100, γ = 0.01) written in Python 2.7. We followed a 5-fold external

382

cross-validation procedure to estimate the predictive power of the models.61 The full set of

383

compounds with known experimental activities was divided into five subsets of similar size

384

(external folds) using the Kennard-Stone algorithm90. For each fold, we selected five models

385

with the highest correct classification rate (CCR, computed as the average of the model’s

386

sensitivity and specificity). The applicability domain (AD) was estimated as defined by Tropsha

387

and Golbraikh.91 Also, 20 rounds of Y-randomization92 were performed to assure the absence of

388

chance correlations. The statistical characteristics of binary QSAR models are summarized in


19


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389

Table 3. As one can see, all the models presented high accuracy as evaluated by CCR,

390

sensitivity, specificity, predictive positive value (PPV), and negative predictive value (NPV).

391 392

Table 3. Statistical characteristics of QSAR models for non-animal assays assessed by 5-fold external cross-validation. Assay Model DPRA KeratinoSens h-CLAT

CCR 0.86 0.80 0.81

Sensitivity 0.94 0.96 0.98

PPV 0.90 0.85 0.85

Specificity 0.77 0.63 0.63

NPV 0.86 0.88 0.94

393 394

Use of LLNA to predict human skin sensitization

395

Recently, we38 and others18,57 have shown that in some cases LLNA does not correlate

396

well with human potency, although it certainly contributes valuable information to the binary

397

categorization of sensitization. Despite progress towards non-animal testing for skin

398

sensitization, LLNA is still considered essential for the evaluation of compounds that lack human

399

skin sensitization data.42 Moreover, the amount of publicly available LLNA data is growing.21

400

Considering this, as well as the difficulties with routine human evaluation, we decided to update

401

the Pred-Skin web app75 with the new skin sensitization data reported in this paper (see LLNA

402

data section). The updated model was built using the same conditions described in previous

403

sections for LLNA data. The models showed CCR = 77%, sensitivity = 71%, PPV = 73%,

404

specificity = 84%, and NPV = 83%. Despite slight (4%) decrease of sensitivity compared to the

405

previous version of Pred-Skin75, other model characteristics increased, i.e., CCR (by 5%), PPV

406

(by 2%), specificity (by 14%), and NPV (by 10%), as a result of the overall improvement of data

407

quality.

408


20

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409


Integrating computational approaches for predicting skin sensitization

410

As we have advocated before38, further efforts are needed to identify additional high-

411

quality human data both by more extensive searches of substances in the literature and digital

412

sources with known effects on humans and by additional experimental testing. Rebuilding our

413

models with additional data for 29 compounds and correction in the sensitization outcome of six

414

compounds has increased the predictive power and reliability of the models by 5%. The

415

statistical characteristics of binary QSAR models for each skin sensitization assay (non-animal,

416

LLNA, and human) to predict human response are summarized in Table 4. Comparing the

417

models built for this project with those reported before in our web and mobile app Pred-Skin75,

418

we observed an increase of at least 10% for all major statistical characteristics except specificity:

419

10% in CCR (72% vs. 82%); 22% in sensitivity (72% vs. 94%); 12% in PPV (74% vs. 86%); and

420

17% in NPV (69% vs. 86%). These results reinforce the idea of using human data to build

421

predictive models for skin sensitization. Pred-Skin was the first publicly available tool to predict

422

skin sensitization based on human data.

423

Nevertheless, as non-animal data for skin sensitization continues to grow, we elected to

424

leverage this data to assist regulatory agencies and scientists with enhanced decision-making.

425

Available non-animal data based on DPRA, KeratinoSens, and h-CLAT allowed us to develop

426

predictive QSAR models for each of these datasets individually. However, as one can see from

427

Table 4, QSAR models developed only on LLNA and non-animal data, respectively, did not

428

have high accuracy in predicting human skin sensitization. Certainly, the predictivity of QSAR

429

models based on non-animal data will not have higher accuracy than the method itself to predict

430

human response, but this approach provides more powerful predictions than the mere use of

431

structural alerts alone.55 Following recent successes of using QSAR predictions as descriptors in


21


Page 22 of 46

432

hierarchical models93–98, we combined the outcomes of all QSAR models reported in this paper

433

into a Naive Bayes model to predict the human response. The developed Bayesian model was

434

found to be more predictive of the human response than the QSAR models based on human,

435

LLNA, and non-animal assays, respectively. The Bayesian model had a CCR = 89%, sensitivity

436

= 94%, PPV = 91%, specificity = 84%, and NPV = 89% (cf. Table 2 and Table 4).

437

Skin sensitization is an endpoint of interest for many industrial products, such as

438

cosmetics, drugs, pesticides, food additives, among others.18 This raises the question as to

439

whether QSAR models in general, and those reported in this study Bayesian in particular, could

440

predict not only cosmetics, but also compounds of other industrial classes. Certainly, models

441

developed on small organic molecules could not predict polymers, mixtures, inorganics, etc.

442

However, most of industrial chemicals, which are the object of immediate interest, are small

443

organic molecules. Moreover, we have demonstrated recently that QSAR models generated for

444

cosmetics, drugs, and pesticides can be used interchangeably, i.e., a model developed using

445

mainly drugs and drug-like molecules can be used to evaluate cosmetics and pesticides.99 This

446

finding greatly expands the applicability of QSAR models built for different classes of industrial

447

chemicals as reliable and sustainable alternatives to experimental testing for skin sensitization.

448

We analyzed if this would be true for the Bayesian model presented here. Thus, we

449

retrieved a diverse and unique set of 9,785 industrial chemicals labeled as cosmetics, drugs, or

450

pesticides curated earlier by us99 to estimate the applicability domain of the Bayesian model. The

451

ADs were calculated as Dcutoff = + Zs, where Z is a similarity threshold parameter defined

452

by a user (0.5 in this study), and and s are the average and standard deviation, respectively,

453

of all Euclidian distances in the multidimensional descriptor space between each compound and

454

its nearest neighbors for all compounds in the training set.91,100 In total, 8,854 compounds (90%)


22

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455

were inside the applicability domain of the Bayesian model. For individual classes, 83% (n =

456

3,862) drugs, 98% (n = 3,539) cosmetics, and 97% (n = 1453) pesticides were inside the AD of

457

our model. These results provide a strong argument in favor of using computational models as an

458

alternative to animal testing. The developed Bayesian model is available in a KNIME workflow

459

at https://doi.org/10.6084/m9.figshare.5758644.

460

Considering the high predictivity of the generated model and its wide applicability

461

domain to three major classes of industrial chemicals, we propose the following integrative

462

computational approach as a sustainable alternative to animal testing (Figure 4). New

463

compounds should be virtually screened by all reported QSAR models, and the corresponding

464

predictions will then serve as an input for the Bayesian model. We posit that this integrative

465

workflow could substitute animal testing and significantly reduce non-animal experimental

466

testing for skin sensitization. This will help to save resources, cost, and animal lives used for

467

chemical toxicity assessment.

468

All data used to generate all models described in this paper are available on Chembench

469

(https://chembench.mml.unc.edu/) allowing any interested party to reproduce our work.

470

Alternatively, users may predict compounds of their interest with QSAR models available at the

471

Pred-Skin portal (http://www.labmol.com.br/predskin/) and then employ the resulting predictions

472

as an input for the Bayesian model, which, as stated above, is available in as a KNIME

473

(https://www.knime.com/) workflow at https://doi.org/10.6084/m9.figshare.5758644.


23


474

Page 24 of 46

Table 4. Human data prediction using all available computational models. Model Human LLNA DPRA KeratinoSens h-CLAT Bayesian model

CCR 0.82 0.59 0.67 0.54 0.57 0.89

Sensitivity 0.94 0.65 0.84 0.84 0.92 0.94

PPV 0.86 0.71 0.75 0.66 0.68 0.91

Specificity 0.70 0.54 0.50 0.24 0.22 0.84

NPV 0.86 0.47 0.64 0.46 0.61 0.89

475

476 477

Figure 4. A novel integrative computational approach to predict skin sensitization.

478 479

FUTURE PERSPECTIVES

480

a) Promoting Alternative Approaches to Animal Testing

481

Although skin sensitization is one of the most well studied toxicity endpoints with an

482

already-established AOP, the determination of skin sensitization potential still involves animal

483

testing. Different countries require examination of different classes of chemicals, e.g., skin

484

sensitization potential of pesticides must be assessed in all countries, while policies for

485

cosmetics, drugs, household and workspace materials, and other industrial chemicals vary from

486

country to country. Currently, the preferred test for skin sensitization, i.e., the LLNA, and even

487

the reduced LLNA (rLLNA), requires more than 10 animals per substance tested. At the same


24

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488

time, the use of alternative methods for estimating skin sensitization potential is of high demand,

489

and we forecast that in near future LLNA will be replaced by these methods.

490

The first steps towards this achievement have been completed in recent years.101,102 For

491

instance, the International Cooperation on Alternative Test Methods (ICATM) is promoting the

492

standardization of defined approaches for skin sensitization testing to support their regulatory use

493

and international adoption.101 It was demonstrated that alternative methods currently adopted for

494

the estimation of skin sensitization, when used in isolation, are not sufficient to fulfill regulatory

495

requirements on the skin sensitization potential nor are the potencies of chemicals comparable to

496

those provided by the regulatory animal tests.103–105 Thus, these approaches will be modified,

497

improved, and, if needed, extended or corrected by other relevant methods to enhance their

498

regulatory consideration and adoption as an alternative to current animal testing. As another

499

example of such efforts, Cosmetic Europe’s Skin Tolerance Task Force is developing a data

500

integration approach for the skin sensitization safety assessment of cosmetic ingredients.82

501

In vitro – in vivo extrapolation is another promising direction for alternatives to animal

502

testing.106,107 The central goal of this approach is to develop fast and inexpensive in vitro tests

503

that will correlate with the results of slow and expensive in vivo animal studies.107 Toxcast and

504

Tox21 initiatives provide good starting points for the identification of in vitro tests capable of

505

replacing LLNA. The first steps have already been made,101 with special attention paid to in vitro

506

assays related to the skin sensitization AOP; however, statistical analyses of other tests may

507

reveal non-obvious in vitro – in vivo correlations which may result in new assay candidates.

508

The AOP for skin sensitization is already established, and alternative approaches should,

509

therefore, be mechanistically related to this pathway. At the same time, further studies of skin

510

sensitization may find new processes and/or mechanisms responsible for the adverse action of


25


Page 26 of 46

511

chemicals. We have recently demonstrated that skin permeability alone, despite being a key

512

element in skin sensitization AOP, does not necessarily correlate with skin sensitization.64 Thus,

513

some factors such as bioavailability, clearance, or blood-brain barrier permeability may play an

514

unexpected role. In addition, environmental factors as well as socioeconomic and demographic

515

variables may be shown to influence skin sensitization effects and its epidemiology.

516 517

b) Data Science of Skin Sensitization

518

Computational approaches for the prediction of skin sensitization potential have recently

519

garnered much attention.75,78,83,108 Despite obvious advantages and recent successes,

520

computational approaches have yet to receive acceptance for regulatory use. As obvious from

521

this paper, we strongly promote the substitution of current methods by computational tools, and

522

we will continue to improve our Bayesian model as well as advocate for its use in regulatory

523

purposes. However, before computational models can be considered as a reliable alternative,

524

several steps in the data science of skin sensitization testing should be executed.

525

In order to build predictive models, it is not enough just to utilize adequate chemical

526

descriptors and statistical techniques. The key to success in any modeling project is a clear

527

understanding of modeling endpoints and careful selection and curation of high quality data.

528

Ideally, all skin sensitization data generated by different regulatory agencies, research institutes,

529

industry, etc., should be compiled in an open, curated, and annotated database. In addition to

530

traditional data sources, the authors forecast an increased growth of data obtained by text mining

531

such sources as PDFs of scientific and non-scientific papers, as well as social media. These tools

532

already exist and are widely used.109,110 Certainly, the sources of the data and the reproducibility

533

of the results must be ensured.


26

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534

Compounds should be preferably classified according to their potency, in addition to

535

binary classification on sensitizers and non-sensitizers. This classification is mainly related to

536

sensitizers, as all non-sensitizers, by nature, will remain non-sensitizers. Compounds with

537

sensitization potential could be divided on several groups, e.g., weak, moderate, strong, and

538

extreme sensitizers. As a starting point, the authors recommend the six categories of human skin

539

sensitization proposed by Basketter et al.42 To increase the utility and reliability of the data, we

540

propose collecting all available information about the experiment, e.g., vehicle, concentrations,

541

pH, etc. As the next step, we suggest to employ a minimal information package about a new

542

compound that will be required for inclusion into the database.

543

The data should then be carefully curated from the perspective of both chemical structure

544

and biological activity. Duplicate analysis will help account for the variability of skin

545

sensitization potential associated with compounds tested several times in the same assay. After

546

curation, all information should be integrated and annotated in a single database with a defined

547

structure and ontology. During this process, unreliable data sources could be revealed and

548

excluded from further consideration.

549 550

c) Modeling of Skin Sensitization

551

Best practices for QSAR modeling are already well-established by the practitioners in the

552

field.65 All future skin sensitization models must comply both OECD principles111 and

553

aforementioned best practices65. Briefly, all the models should be built for defined endpoints

554

using curated data and unambiguous algorithms for descriptor generation. These models should

555

be externally validated as rigorously as possible, either by data excluded or unavailable during

556

the initial modeling or by n-fold external cross-validation procedures.112 Although current best


27


Page 28 of 46

557

practices61,65 state that statistical metrics of predictivity, robustness, goodness-of-fit, etc., should

558

be at least 60%, we hope that access to high quality data and vigilant curation will improve the

559

acceptance thresholds to above 75% in the future.

560

Most of the existing QSAR techniques were designed for use in computer-aided drug

561

discovery and are amenable mostly for small organic molecules. However, many cosmetics

562

represent a mixture of components with possible synergistic and/or antagonistic effects. We

563

forecast that there will be an emerging need to develop methods to predict the skin sensitization

564

effects of mixtures due to their increased relevance in the cosmetics industry. Likewise,

565

approaches to describe, model, and forecast the effects of polymers and their complexes with

566

small organic molecules will be in high demand. The attempts to model mixtures has already

567

been performed in the drug discovery field113–116, and similar approaches may be repurposed for

568

predictive toxicology.

569

Although the development of a single model for a single endpoint to successfully

570

characterize the skin sensitization potential of a new formulation is strongly desired, we expect

571

that only the combination of several models/endpoints could provide an adequate answer. As

572

discussed in this paper, we think that Bayesian models incorporating several endpoints will

573

outperform single endpoint models, irrespective to the degree of their complexity and dataset

574

size. Here is critical to establish simple in vitro tests, which, when used in combination, could

575

substitute animal testing. It will then become necessary to generate enough data from each test

576

and to unite them in a Bayesian network, which will provide the final prediction of skin

577

sensitization potential.

578


28

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579

d) Open Science

580

We want to emphasize that future skin sensitization studies should be oriented not only

581

towards their use as official regulatory mechanisms, but should also consider the interests of

582

manufacturers and consumers of cosmetics, drugs, pesticides, etc. Achieving this goal will

583

require transparency and constant communication among these groups. Open science principles

584

supported by our group will play an important role in the successful solution of this challenge.

585

Moreover, the resulting tests, models, and tools should be understandable and easily accessible to

586

a general audience through open-access publications and Web-portals.

587 588

FINAL REMARKS

589

Although the AOP for skin sensitization is well studied, prediction of chemically-induced

590

skin sensitization remains a challenge. While several in vitro and in chemico methods have been

591

proposed to address key points of the skin sensitization AOP, none of these alternative methods

592

have been approved to replace the animal-based LLNA, which remains the preferred test to

593

estimate skin sensitization potential. At first sight, computational approaches could serve as an

594

ideal alternative to animal testing; however, most of them are based on structural alerts or

595

chemical read-across systems. Despite their obvious attractiveness and transparency, these

596

approaches have very limited predictivity and are prone to high false positive rates. At the same

597

time, the amount of publicly available data on various skin sensitization assays continues to

598

emerge, which can facilitate the development of more predictive and statistically meaningful

599

computational models.

600

Although many computational studies for predicting skin sensitization have been recently

601

published, and several free and commercially available Web-applications and software exist,


29


Page 30 of 46

602

their common drawback is that they predict the outcome of LLNA, which fails to correlate well

603

with the human response. Here, we propose a new approach that integrates multiple QSAR

604

models based on in vitro, LLNA, and human data in a Bayesian model.

605

In developing this approach, we were inspired by enormous and continuous growth of

606

various forms of data, and we sought to leverage all available information on skin sensitization in

607

order to provide scientists, regulators, and other users with more accurate and informative

608

predictions. The currently available non-animal (DPRA, KeratinoSens, and h-CLAT), animal

609

(LLNA), and human data allowed us to develop predictive QSAR models for these endpoints. As

610

expected, none of these models had higher accuracy in predicting human response than the

611

experimental methods themselves. However, the strength of computational approaches is that

612

they could be used to predict the sensitization potential in a corresponding assay for any new

613

molecule, thereby eliminating the need for experimental testing. To further improve model

614

accuracy, we have combined the predictions of all developed QSAR models into a single

615

Bayesian model for predicting human response. Indeed, we found this model to have higher

616

predictive power (major statistical metrics are within the 84-94% range) than any existing

617

computational or experimental models.

618

Considering the high predictivity of the generated model, we propose this integrative

619

computational approach as a reliable and sustainable alternative to animal testing. New

620

compounds should be virtually screened by all reported QSAR models, and the corresponding

621

predictions will serve as an input for the developed Bayesian model. All QSAR models are

622

available online at the PredSkin (http://labmol.com.br/predskin/) web application; and all

623

datasets are available in the Chembench web portal (https://chembench.mml.unc.edu/). The


30

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624

Naive

Bayes

model

is

available

625

https://doi.org/10.6084/m9.figshare.5598406).

as

a

KNIME

workflow

(see


31


626

CONFLICT OF INTERESTS

627

The authors declare they have no actual or potential conflict of interests.

Page 32 of 46

628 629

ACKNOWLEDGEMENTS

630

This study was supported in part by NIH (grant 1U01CA207160) and CNPq (grant

631

400760/2014-2). JB and AS thank CAPES for PhD scholarships. SJC thanks the organizers of

632

the 22nd Annual Green Chemistry & Engineering Conference.

633 634

REFERENCES

635 636 637 638 639 640 641

(1)

Peiser, M.; Tralau, T.; Heidler, J.; Api, A. M.; Arts, J. H. E.; Basketter, D. A.; English, J.; Diepgen, T. L.; Fuhlbrigge, R. C.; Gaspari, A. A.; Johansen, J. D.; Karlberg, A. T.; Kimber, I.; Lepoittevin, J. P.; Liebsch, M.; Maibach, H. I.; Martin, S. F.; Merk, H. F.; Platzek, T.; Rustemeyer, T.; Schnuch, A.; Vandebriel, R. J.; White, I. R.; Luch, A. Allergic contact dermatitis: epidemiology, molecular mechanisms, in vitro methods and regulatory aspects. Cell. Mol. Life Sci. 2012, 69 (5), 763–781, DOI: 10.1007/s00018-0110846-8.

642 643

(2)

Basketter, D. A. The human repeated insult patch test in the 21st century: a commentary. Cutan. Ocul. Toxicol. 2009, 28 (2), 49–53, DOI: 10.1080/15569520902938032.

644 645 646

(3)

Basketter, D. A.; Evans, P.; Fielder, R. J.; Gerberick, G. F.; Dearman, R. J.; Kimber, I. Local lymph node assay - validation, conduct and use in practice. Food Chem. Toxicol. 2002, 40 (5), 593–598, DOI: 10.1016/S0278-6915(01)00130-2.

647 648 649 650

(4)

Cockshott, A.; Evans, P.; Ryan, C. A.; Gerberick, G. F.; Betts, C. J.; Dearman, R. J.; Kimber, I.; Basketter, D. A. The local lymph node assay in practice: a current regulatory perspective. Hum. Exp. Toxicol. 2006, 25 (7), 387–394, DOI: 10.1191/0960327106ht640oa.

651 652 653

(5)

EPA. Health Effects Test Guidelines: OPPTS 870.2600 Skin Sensitization https://www.regulations.gov/#!documentDetail;D=EPA-HQ-OPPT-2009-0156-0008 (accessed Jun 9, 2017).

654 655

(6)

Aziz, T.; Stein, J.; Yogeshwar, R. Animal testing: TV or not TV? Nature 2011, 470, 457– 459, DOI: 10.1038/470457a.

656

(7)

European Union. Directive 2003/15/EC; 2003; Vol. L 66/26, pp 26–35.

657 658

(8)

European Union. Regulation (EC) No 1223/2009. Off. J. Eur. Union 2009, No. L 342, 59– 209.


32

Page 33 of 46 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


659

(9)

European Union. Directive 2013/39/EU. Off. J. Eur. Union 2013, No. L 226, 1–17.

660 661

(10)

Hartung, T. Look back in anger - what clinical studies tell us about preclinical work. ALTEX 2013, 30 (3), 275–291, DOI: 10.1016/j.surg.2006.10.010.

662 663

(11)

Bailey, J.; Thew, M.; Balls, M. An analysis of the use of animal models in predicting human toxicology and drug safety. Altern. Lab. Anim. 2014, 42 (3), 181–199.

664 665

(12)

Hartung, T. Making big sense from big data in toxicology by read-across. ALTEX 2016, 33 (2), 83–93, DOI: 10.14573/altex.1603091.

666 667

(13)

European Union. Regulation (EC) No 1907/2006. Off. J. Eur. Union 2007, No. L 136, 3– 280.

668 669 670

(14)

Naven, R.; Louise-May, S. Computational toxicology: Its essential role in reducing drug attrition. Hum. Exp. Toxicol. 2015, 34 (12), 1304–1309, DOI: 10.1177/0960327115605440.

671 672 673 674

(15)

Baig, M. H.; Ahmad, K.; Roy, S.; Ashraf, J. M.; Adil, M.; Siddiqui, M. H.; Khan, S.; Kamal, M. A.; Provazník, I.; Choi, I. Computer Aided Drug Design: Success and Limitations. Curr. Pharm. Des. 2016, 22 (5), 572–581. DOI: 10.2174/1381612822666151125000550

675 676 677

(16)

Raies, A. B.; Bajic, V. B. In silico toxicology: computational methods for the prediction of chemical toxicity. Wiley Interdiscip. Rev. Comput. Mol. Sci. 2016, 6 (2), 147–172, DOI: 10.1002/wcms.1240.

678 679

(17)

Hartung, T.; Hoffmann, S. Food for thought ... on in silico methods in toxicology. ALTEX 2009, 26 (3), 155–166.

680 681 682 683

(18)

ICCVAM. Usefulness and limitations of the murine local lymph node assay for potency categorization of chemicals causing allergic contact dermatitis in humans http://ntp.niehs.nih.gov/pubhealth/evalatm/test-method-evaluations/immunotoxicity/llnapotency/tmer/index.html (accessed Feb 9, 2015).

684 685 686

(19)

Strickland, J.; Zang, Q.; Paris, M.; Lehmann, D. M.; Allen, D.; Choksi, N.; Matheson, J.; Jacobs, A.; Casey, W.; Kleinstreuer, N. Multivariate models for prediction of human skin sensitization hazard. J. Appl. Toxicol. 2017, 37 (3), 347–360, DOI: 10.1002/jat.3366.

687 688

(20)

ICCVAM. NICEATM Murine Local Lymph Node Assay (LLNA) Database http://ntp.niehs.nih.gov/go/40500 (accessed Aug 1, 2017).

689 690 691

(21)

Luechtefeld, T.; Maertens, A.; Russo, D. P.; Rovida, C.; Zhu, H.; Hartung, T. Analysis of publically available skin sensitization data from REACH registrations 2008-2014. ALTEX 2016, 33 (2), 135–148, DOI: 10.14573/altex.1510055.

692 693 694

(22)

Jaworska, J.; Dancik, Y.; Kern, P.; Gerberick, F.; Natsch, A. Bayesian integrated testing strategy to assess skin sensitization potency: from theory to practice. J. Appl. Toxicol. 2013, 33 (11), 1353–1364, DOI: 10.1002/jat.2869.

695 696 697

(23)

Urbisch, D.; Mehling, A.; Guth, K.; Ramirez, T.; Honarvar, N.; Kolle, S.; Landsiedel, R.; Jaworska, J.; Kern, P. S.; Gerberick, F.; Natsch, A.; Emter, R.; Ashikaga, T.; Miyazawa, M.; Sakaguchi, H. Assessing skin sensitization hazard in mice and men using non-animal


33


698 699

test methods. Regul. Toxicol. 10.1016/j.yrtph.2014.12.008.

Pharmacol.

2015,

Page 34 of 46

71

(2),

337–351,

DOI:

700 701 702

(24)

Fourches, D.; Muratov, E.; Tropsha, A. Trust, but verify: on the importance of chemical structure curation in cheminformatics and QSAR modeling research. J. Chem. Inf. Model. 2010, 50 (7), 1189–1204, DOI: 10.1021/ci100176x.

703 704 705

(25)

Fourches, D.; Muratov, E.; Tropsha, A. Trust, but Verify II: A Practical Guide to Chemogenomics Data Curation. J. Chem. Inf. Model. 2016, 56 (7), 1243–1252, DOI: 10.1021/acs.jcim.6b00129.

706 707

(26)

Fourches, D.; Muratov, E.; Tropsha, A. Curation of chemogenomics data. Nat. Chem. Biol. 2015, 11 (8), 535–535, DOI: 10.1038/nchembio.1881.

708 709 710

(27)

Capuzzi, S. J.; Kim, I. S.-J.; Lam, W. I.; Thornton, T. E.; Muratov, E. N.; Pozefsky, D.; Tropsha, A. Chembench: A Publicly Accessible, Integrated Cheminformatics Portal. J. Chem. Inf. Model. 2017, 57 (2), 105–108, DOI: 10.1021/acs.jcim.6b00462.

711 712

(28)

OECD. Test No. 406: Skin Sensitisation http://www.oecd-ilibrary.org/environment/testno-406-skin-sensitisation_9789264070660-en (accessed Mar 16, 2017).

713 714 715

(29)

OECD. Test No. 411: Subchronic Dermal Toxicity: 90-day Study http://www.oecdilibrary.org/environment/test-no-411-subchronic-dermal-toxicity-90-daystudy_9789264070769-en (accessed Jul 31, 2017).

716 717

(30)

OECD. Test No. 429: Skin Sensitisation http://www.oecd-ilibrary.org/environment/testno-429-skin-sensitisation_9789264071100-en (accessed Mar 16, 2017).

718 719

(31)

OECD. Test No. 442B: Skin Sensitization http://www.oecd-ilibrary.org/environment/testno-442b-skin-sensitization_9789264090996-en (accessed Mar 16, 2017).

720 721 722

(32)

EURL ECVAM. Recommendation on the Direct Peptide Reactivity Assay (DPRA) https://eurl-ecvam.jrc.ec.europa.eu/eurl-ecvam-recommendations/eurl-ecvamrecommendation-on-the-direct-peptide-reactivity-assay-dpra (accessed Jan 29, 2015).

723 724

(33)

OECD. TG 442C In chemico skin sensitisation: direct peptide reactivity assay (DPRA); 2015.

725 726 727 728

(34)

EURL ECVAM. Recommendation on the KeratinoSensTM assay for skin sensitisation testing https://eurl-ecvam.jrc.ec.europa.eu/eurl-ecvamrecommendations/recommendation-keratinosens-skin-sensitisation (accessed Jan 29, 2017).

729 730 731

(35)

OECD. Test No. 442D: In Vitro Skin Sensitisation: ARE-Nrf2 Luciferase test method; OECD Guidelines for the Testing of Chemicals, Section 4; DOI: 10.1787/9789264229822-en, 2015.

732 733 734 735

(36)

EURL ECVAM. Recommendation on the human Cell Line Activation Test (h-CLAT) for Skin Sensitisation testing https://eurl-ecvam.jrc.ec.europa.eu/eurl-ecvamrecommendations/eurl-ecvam-recommendation-on-the-human-cell-line-activation-test-hclat-for-skin-sensitisation-testing (accessed Jul 24, 2017).

736

(37)

OECD. Test No. 442E: In Vitro Skin Sensitisation http://www.oecd.org/publications/test-


34

Page 35 of 46 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


737

no-442e-in-vitro-skin-sensitisation-9789264264359-en.htm (accessed Jul 29, 2017).

738 739 740 741

(38)

Alves, V. M.; Capuzzi, S. J.; Muratov, E. N.; Braga, R. C.; Thornton, T. E.; Fourches, D.; Strickland, J.; Kleinstreuer, N.; Andrade, C. H.; Tropsha, A. QSAR models of human data can enrich or replace LLNA testing for human skin sensitization. Green Chem. 2016, 18 (24), 6501–6515, DOI: 10.1039/C6GC01836J.

742 743 744

(39)

Johansson, H.; Gradin, R. Skin Sensitization: Challenging the Conventional Thinking-A Case Against 2 Out of 3 as Integrated Testing Strategy. Toxicol. Sci. 2017, 159 (1), 3–5, DOI: 10.1093/toxsci/kfx115.

745 746 747 748

(40)

Urbisch, D.; Becker, M.; Honarvar, N.; Kolle, S. N.; Mehling, A.; Teubner, W.; Wareing, B.; Landsiedel, R. Assessment of Pre- and Pro-haptens Using Nonanimal Test Methods for Skin Sensitization. Chem. Res. Toxicol. 2016, 29 (5), 901–913, DOI: 10.1021/acs.chemrestox.6b00055.

749 750 751

(41)

Natsch, A.; Ryan, C. a; Foertsch, L.; Emter, R.; Jaworska, J.; Gerberick, F.; Kern, P. A dataset on 145 chemicals tested in alternative assays for skin sensitization undergoing prevalidation. J. Appl. Toxicol. 2013, 33 (February), 1337–1352, DOI: 10.1002/jat.2868.

752 753 754 755 756

(42)

Basketter, D. A.; Alépée, N.; Ashikaga, T.; Barroso, J.; Gilmour, N.; Goebel, C.; Hibatallah, J.; Hoffmann, S.; Kern, P.; Martinozzi-Teissier, S.; Maxwell, G.; Reisinger, K.; Sakaguchi, H.; Schepky, A.; Tailhardat, M.; Templier, M. Categorization of chemicals according to their relative human skin sensitizing potency. Dermat. contact, atopic, Occup. drug 2014, 25 (1), 11–21, DOI: 10.1097/DER.0000000000000003.

757 758

(43)

Kode. DRAGON 7.0 https://chm.kode-solutions.net/products_dragon.php (accessed May 7, 2017).

759 760 761

(44)

Vityuk, N.; Voskresenskaja, E.; Kuz’min, V. The Synergism of Methods Barycentric Coordinates and Trend-vector for Solution ―Structure-Property Tasks. Pattern Recognit. Image Anal. 1999, 3, 521–528.

762 763 764

(45)

Kuz’min, V. E.; Artemenko, A. G.; Muratov, E. N. Hierarchical QSAR technology based on the Simplex representation of molecular structure. J. Comput. Aided. Mol. Des. 2008, 22 (6–7), 403–421, DOI: 10.1007/s10822-008-9179-6.

765 766

(46)

Blagg, J. Structural Alerts for Toxicity. In Burger’s Medicinal Chemistry and Drug Discovery; DOI: 10.1002/0471266949.bmc128: Hoboken, NJ, USA, 2010; pp 301–334.

767 768 769 770

(47)

Enoch, S. J.; Roberts, D. W. Approaches for Grouping Chemicals into Categories. In Chemical Toxicity Prediction: Category Formation and Read-Across; Cronin, M., Madden, J., Enoch, S., Roberts, D., Eds.; DOI: 10.1039/9781849734400-00030, 2013; pp 30–43.

771 772 773

(48)

Allen, T. E. H.; Goodman, J. M.; Gutsell, S.; Russell, P. J. Defining molecular initiating events in the adverse outcome pathway framework for risk assessment. Chem. Res. Toxicol. 2014, 27 (12), 2100–2112, DOI: 10.1021/tx500345j.

774 775 776

(49)

Sushko, I.; Salmina, E.; Potemkin, V. a; Poda, G.; Tetko, I. V. ToxAlerts: a Web server of structural alerts for toxic chemicals and compounds with potential adverse reactions. J. Chem. Inf. Model. 2012, 52 (8), 2310–2316, DOI: 10.1021/ci300245q.


35


Page 36 of 46

777 778 779 780 781 782 783

(50)

Ball, N.; Cronin, M. T. D. M. T. D.; Shen, J.; Blackburn, K.; Booth, E. D. E. D.; Bouhifd, M.; Donley, E.; Egnash, L.; Hastings, C.; Juberg, D. R. D. R.; Kleensang, A.; Kleinstreuer, N.; Kroese, E. D. D.; Lee, A. C. A. C.; Luechtefeld, T.; Maertens, A.; Marty, S.; Naciff, J. M. J. M.; Palmer, J.; Pamies, D.; Penman, M.; Richarz, A.-N. A.-N.; Russo, D. P. D. P.; Stuard, S. B. S. B.; Patlewicz, G.; Van Ravenzwaay, B.; Wu, S.; Zhu, H.; Hartung, T. Toward Good Read-Across Practice (GRAP) guidance. ALTEX 2016, 33 (2), 149–166, DOI: 10.14573/altex.1601251.

784 785 786

(51)

Patlewicz, G.; Helman, G.; Pradeep, P.; Shah, I. Navigating through the minefield of readacross tools: A review of in silico tools for grouping. Comput. Toxicol. 2017, 3 (May), 1– 18, DOI: 10.1016/j.comtox.2017.05.003.

787 788

(52)

ECHA. Read-Across Assessment Framework (RAAF) https://echa.europa.eu/documents/10162/13628/raaf_en.pdf (accessed Aug 5, 2017).

789 790 791 792

(53)

OECD. Guidance document for using the OECD (Q)SAR application Toolbox to develop chemical categories according to the OECD guidance on grouping of chemicals http://www.oecd.org/officialdocuments/publicdisplaydocumentpdf/?doclanguage=en&cot e=env/jm/mono(2009)5 (accessed Jul 31, 2017).

793 794 795 796 797

(54)

Stepan, A. F.; Walker, D. P.; Bauman, J.; Price, D. A.; Baillie, T. A.; Kalgutkar, A. S.; Aleo, M. D. Structural alert/reactive metabolite concept as applied in medicinal chemistry to mitigate the risk of idiosyncratic drug toxicity: a perspective based on the critical examination of trends in the top 200 drugs marketed in the United States. Chem. Res. Toxicol. 2011, 24 (9), 1345–1410, DOI: 10.1021/tx200168d.

798 799 800 801

(55)

Alves, V.; Muratov, E.; Capuzzi, S.; Politi, R.; Low, Y.; Braga, R.; Zakharov, A. V.; Sedykh, A.; Mokshyna, E.; Farag, S.; Andrade, C.; Kuz’min, V.; Fourches, D.; Tropsha, A. Alarms about structural alerts. Green Chem. 2016, 18 (16), 4348–4360, DOI: 10.1039/C6GC01492E.

802 803 804 805

(56)

Urbisch, D.; Honarvar, N.; Kolle, S. N.; Mehling, A.; Ramirez, T.; Teubner, W.; Landsiedel, R. Peptide reactivity associated with skin sensitization: The QSAR Toolbox and TIMES compared to the DPRA. Toxicol. Vitr. 2016, 34, 194–203, DOI: 10.1016/j.tiv.2016.04.005.

806 807 808

(57)

Api, A. M.; Basketter, D.; Lalko, J. Correlation between experimental human and murine skin sensitization induction thresholds. Cutan. Ocul. Toxicol. 2014, 34 (0), 1–5, DOI: 10.3109/15569527.2014.979425.

809 810 811

(58)

ECETOC. TR 116 Category approaches, read-across, (Q)SAR http://www.ecetoc.org/publication/tr-116-category-approaches-read-across-qsar/ (accessed Aug 22, 2017).

812 813 814

(59)

Patlewicz, G.; Ball, N.; Becker, R. A.; Booth, E. D.; Cronin, M. T. D.; Kroese, D.; Steup, D.; van Ravenzwaay, B.; Hartung, T. Read-across approaches - misconceptions, promises and challenges ahead. ALTEX 2014, 31 (4), 387–396, DOI: 10.14573/altex.1410071.

815 816 817

(60)

Patlewicz, G.; Ball, N.; Boogaard, P. J.; Becker, R. A.; Hubesch, B. Building scientific confidence in the development and evaluation of read-across. Regul. Toxicol. Pharmacol. 2015, 72 (1), 117–133, DOI: 10.1016/j.yrtph.2015.03.015.


36

Page 37 of 46 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


818 819 820 821 822

(61)

Cherkasov, A.; Muratov, E. N.; Fourches, D.; Varnek, A.; Baskin, I. I.; Cronin, M.; Dearden, J.; Gramatica, P.; Martin, Y. C.; Todeschini, R.; Consonni, V.; Kuz’min, V. E.; Cramer, R.; Benigni, R.; Yang, C.; Rathman, J.; Terfloth, L.; Gasteiger, J.; Richard, A.; Tropsha, A. QSAR modeling: where have you been? Where are you going to? J. Med. Chem. 2014, 57 (12), 4977–5010, DOI: 10.1021/jm4004285.

823 824 825

(62)

Dearden, J. C. The History and Development of Quantitative Structure-Activity Relationships (QSARs). Int. J. Quant. Struct. Relationships 2016, 1 (1), 1–44, DOI: 10.4018/IJQSPR.2016010101.

826 827 828 829

(63)

Alves, V. M.; Muratov, E.; Fourches, D.; Strickland, J.; Kleinstreuer, N.; Andrade, C. H.; Tropsha, A. Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds. Toxicol. Appl. Pharmacol. 2015, 284 (2), 262–272, DOI: 10.1016/j.taap.2014.12.014.

830 831 832 833

(64)

Alves, V. M.; Muratov, E. N.; Fourches, D.; Strickland, J.; Kleinstreuer, N.; Andrade, C. H.; Tropsha, A. Predicting chemically-induced skin reactions. Part II: QSAR models of skin permeability and the relationships between skin permeability and skin sensitization. Toxicol. Appl. Pharmacol. 2015, 284 (2), 273–280, DOI: 10.1016/j.taap.2014.12.013.

834 835

(65)

Tropsha, A. Best practices for QSAR model development, validation, and exploitation. Mol. Inform. 2010, 29 (6–7), 476–488, DOI: 10.1002/minf.201000061.

836 837 838

(66)

Basketter, D. A.; White, I. R.; McFadden, J. P.; Kimber, I. Skin sensitization: Implications for integration of clinical data into hazard identification and risk assessment. Hum. Exp. Toxicol. 2015, 34 (12), 1222–1230, DOI: 10.1177/0960327115601760.

839 840 841

(67)

Kimber, I.; Gerberick, G. F.; Basketter, D. A. Quantitative risk assessment for skin sensitization: Success or failure? Regul. Toxicol. Pharmacol. 2017, 83, 104–108, DOI: 10.1016/j.yrtph.2016.11.020.

842 843 844 845 846 847

(68)

Patlewicz, G.; Dimitrov, S. D.; Low, L. K.; Kern, P. S.; Dimitrova, G. D.; Comber, M. I. H.; Aptula, A. O.; Phillips, R. D.; Niemelä, J.; Madsen, C.; Wedebye, E. B.; Roberts, D. W.; Bailey, P. T.; Mekenyan, O. G. TIMES-SS--a promising tool for the assessment of skin sensitization hazard. A characterization with respect to the OECD validation principles for (Q)SARs and an external evaluation for predictivity. Regul. Toxicol. Pharmacol. 2007, 48 (2), 225–239, DOI: 10.1016/j.yrtph.2007.03.003.

848 849 850 851 852

(69)

Roberts, D. W.; Patlewicz, G.; Dimitrov, S. D.; Low, L. K.; Aptula, A. O.; Kern, P. S.; Dimitrova, G. D.; Comber, M. I. H.; Phillips, R. D.; Niemelä, J.; Madsen, C.; Wedebye, E. B.; Bailey, P. T.; Mekenyan, O. G. TIMES-SS--a mechanistic evaluation of an external validation study using reaction chemistry principles. Chem. Res. Toxicol. 2007, 20 (9), 1321–1330, DOI: 10.1021/tx700169w.

853 854 855 856

(70)

Dimitrov, S. D.; Low, L. K.; Patlewicz, G. Y.; Kern, P. S.; Dimitrova, G. D.; Comber, M. H. I.; Phillips, R. D.; Niemela, J.; Bailey, P. T.; Mekenyan, O. G. Skin sensitization: modeling based on skin metabolism simulation and formation of protein conjugates. Int. J. Toxicol. 2005, 24 (4), 189–204, DOI: 10.1080/10915810591000631.

857 858

(71)

Enslein, K.; Gombar, V. K.; Blake, B. W.; Maibach, H. I.; Hostynek, J. J.; Sigman, C. C.; Bagheri, D. A quantitative structure-toxicity relationships model for the dermal


37


859 860

Page 38 of 46

sensitization guinea pig maximization assay. Food Chem. Toxicol. 1997, 35 (10–11), 1091–1098.

861 862 863

(72)

Rodford, R.; Patlewicz, G.; Walker, J. D.; Payne, M. P. Quantitative structure-activity relationships for predicting skin and respiratory sensitization. Environ. Toxicol. Chem. 2003, 22 (8), 1855–1861.

864 865 866

(73)

Kostal, J.; Voutchkova-Kostal, A. CADRE-SS, an in Silico Tool for Predicting Skin Sensitization Potential Based on Modeling of Molecular Interactions. Chem. Res. Toxicol. 2016, 29 (1), 58–64, DOI: 10.1021/acs.chemrestox.5b00392.

867 868

(74)

Anderson, S. E.; Siegel, P. D.; Meade, B. J. The LLNA: A Brief Review of Recent Advances and Limitations. J. Allergy 2011, 2011, 424203, DOI: 10.1155/2011/424203.

869 870 871 872

(75)

Braga, R. C.; Alves, V. M.; Muratov, E. N.; Strickland, J.; Kleinstreuer, N.; Tropsha, A.; Andrade, C. H. Pred-Skin: A Fast and Reliable Web Application to Assess Skin Sensitization Effect of Chemicals. J. Chem. Inf. Model. 2017, 57 (5), 1013–1017, DOI: 10.1021/acs.jcim.7b00194.

873 874 875

(76)

Riniker, S.; Landrum, G. a. Similarity maps - A visualization strategy for molecular fingerprints and machine-learning methods. J. Cheminform. 2013, 5 (9), 43, DOI: 10.1186/1758-2946-5-43.

876 877 878 879

(77)

Zang, Q.; Paris, M.; Lehmann, D. M.; Bell, S.; Kleinstreuer, N.; Allen, D.; Matheson, J.; Jacobs, A.; Casey, W.; Strickland, J. Prediction of skin sensitization potency using machine learning approaches. J. Appl. Toxicol. 2017, 37 (7), 792–805, DOI: 10.1002/jat.3424.

880 881 882

(78)

Toropova, A. P.; Toropov, A. A. Hybrid optimal descriptors as a tool to predict skin sensitization in accordance to OECD principles. Toxicol. Lett. 2017, 275, 57–66, DOI: 10.1016/j.toxlet.2017.03.023.

883 884 885 886 887 888

(79)

Canipa, S. J.; Chilton, M. L.; Hemingway, R.; Macmillan, D. S.; Myden, A.; Plante, J. P.; Tennant, R. E.; Vessey, J. D.; Steger-Hartmann, T.; Gould, J.; Hillegass, J.; Etter, S.; Smith, B. P. C.; White, A.; Sterchele, P.; De Smedt, A.; O’Brien, D.; Parakhia, R. A quantitative in silico model for predicting skin sensitization using a nearest neighbours approach within expert-derived structure-activity alert spaces. J. Appl. Toxicol. 2017, 37 (8), 985–995, DOI: 10.1002/jat.3448.

889 890 891 892 893 894

(80)

Langley, G.; Austin, C. P.; Balapure, A. K.; Birnbaum, L. S.; Bucher, J. R.; Fentem, J.; Fitzpatrick, S. C.; Fowle, J. R.; Kavlock, R. J.; Kitano, H.; Lidbury, B. A.; Muotri, A. R.; Peng, S.-Q.; Sakharov, D.; Seidle, T.; Trez, T.; Tonevitsky, A.; van de Stolpe, A.; Whelan, M.; Willett, C. Lessons from Toxicology: Developing a 21st-Century Paradigm for Medical Research. Environ. Health Perspect. 2015, 123 (11), 268–272, DOI: 10.1289/ehp.1510345.

895 896 897

(81)

Sturla, S. J.; Boobis, A. R.; FitzGerald, R. E.; Hoeng, J.; Kavlock, R. J.; Schirmer, K.; Whelan, M.; Wilks, M. F.; Peitsch, M. C. Systems Toxicology: From Basic Research to Risk Assessment. Chem. Res. Toxicol. 2014, 27 (3), 314–329, DOI: 10.1021/tx400410s.

898 899

(82)

Reisinger, K.; Hoffmann, S.; Alépée, N.; Ashikaga, T.; Barroso, J.; Elcombe, C.; Gellatly, N.; Galbiati, V.; Gibbs, S.; Groux, H.; Hibatallah, J.; Keller, D.; Kern, P.; Klaric, M.;


38

Page 39 of 46 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


900 901 902 903 904

Kolle, S.; Kuehnl, J.; Lambrechts, N.; Lindstedt, M.; Millet, M.; Martinozzi-Teissier, S.; Natsch, A.; Petersohn, D.; Pike, I.; Sakaguchi, H.; Schepky, A.; Tailhardat, M.; Templier, M.; van Vliet, E.; Maxwell, G. Systematic evaluation of non-animal test methods for skin sensitisation safety assessment. Toxicol. Vitr. 2015, 29 (1), 259–270, DOI: 10.1016/j.tiv.2014.10.018.

905 906 907 908

(83)

Strickland, J.; Zang, Q.; Kleinstreuer, N.; Paris, M.; Lehmann, D. M.; Choksi, N.; Matheson, J.; Jacobs, A.; Lowit, A.; Allen, D.; Casey, W. Integrated decision strategies for skin sensitization hazard. J. Appl. Toxicol. 2016, 36 (9), 1150–1162, DOI: 10.1002/jat.3281.

909 910 911 912

(84)

Patlewicz, G.; Kuseva, C.; Kesova, A.; Popova, I.; Zhechev, T.; Pavlov, T.; Roberts, D. W.; Mekenyan, O. Towards AOP application - Implementation of an integrated approach to testing and assessment (IATA) into a pipeline tool for skin sensitization. Regul. Toxicol. Pharmacol. 2014, 69 (3), 529–545, DOI: 10.1016/j.yrtph.2014.06.001.

913 914 915 916 917 918

(85)

Casati, S.; Aschberger, K.; Asturiol, D.; Basketter, D.; Dimitrov, S.; Dumont, C.; Karlberg, A.-T.; Lepoittevin, J.-P.; Patlewicz, G.; Roberts, D. W.; Worth, A. Ability of non-animal methods for skin sensitisation to detect pre- and pro-haptens: Report and recommendations of an EURL ECVAM expert meeting https://ec.europa.eu/jrc/en/publication/ability-non-animal-methods-skin-sensitisationdetect-pre-and-pro-haptens-report-and-recommendations (accessed Sep 16, 2017).

919 920 921

(86)

Clouet, E.; Kerdine-Römer, S.; Ferret, P.-J. Comparison and validation of an in vitro skin sensitization strategy using a data set of 33 chemical references. Toxicol. In Vitro 2017, 45 (Pt 3), 374–385, DOI: 10.1016/j.tiv.2017.05.014.

922 923 924 925

(87)

Jaworska, J. S.; Natsch, A.; Ryan, C.; Strickland, J.; Ashikaga, T.; Miyazawa, M. Bayesian integrated testing strategy (ITS) for skin sensitization potency assessment: a decision support system for quantitative weight of evidence and adaptive testing strategy. Arch. Toxicol. 2015, 89 (12), 2455–2483, DOI: 10.1007/s00204-015-1634-2.

926 927 928 929

(88)

Otsubo, Y.; Nishijo, T.; Miyazawa, M.; Saito, K.; Mizumachi, H.; Sakaguchi, H. Binary test battery with KeratinoSensTM and h-CLAT as part of a bottom-up approach for skin sensitization hazard prediction. Regul. Toxicol. Pharmacol. 2017, 88 (June), 118–124, DOI: 10.1016/j.yrtph.2017.06.002.

930 931

(89)

Cortes, C.; Vapnik, V. Support-vector networks. Mach. Learn. 1995, 20 (3), 273–297, DOI: 10.1007/BF00994018.

932 933

(90)

Kennard, R. W.; Stone, L. A. Computer Aided Design of Experiments. Technometrics 1969, 11 (1), 137–148, DOI: 10.2307/1266770.

934 935 936

(91)

Tropsha, A.; Golbraikh, A. Predictive QSAR modeling workflow, model applicability domains, and virtual screening. Curr. Pharm. Des. 2007, 13 (34), 3494–3504. DOI: 10.2174/138161207782794257

937 938 939

(92)

Kuz’min, V. E.; Muratov, E. N.; Artemenko, A. G.; Varlamova, E. V.; Gorb, L.; Wang, J.; Leszczynski, J. Consensus QSAR modeling of phosphor-containing chiral AChE inhibitors. QSAR Comb. Sci. 2009, 28 (6–7), 664–677, DOI: 10.1002/qsar.200860117.

940

(93)

Kuz’min, V. E.; Artemenko, A. G.; Lozitsky, V. P.; Muratov, E. N.; Fedtchouk, A. S.;


39


941 942 943 944 945 946

Page 40 of 46

Dyachenko, N. S.; Nosach, L. N.; Gridina, T. L.; Shitikova, L. I.; Mudrik, L. M.; Mescheriakov, A. K.; Chelombitko, V. A.; Zheltvay, A. I.; Vanden Eynde, J.-J. The analysis of structure-anticancer and antiviral activity relationships for macrocyclic pyridinophanes and their analogues on the basis of 4D QSAR models (simplex representation of molecular structure). Acta Biochim. Pol. 2002, 49 (1), 157–168, DOI: 12136936.

947 948 949 950 951

(94)

Artemenko, A. G.; Muratov, E. N.; Kuz’min, V. E.; Muratov, N. N.; Varlamova, E. V; Kuz’mina, A. V; Gorb, L. G.; Golius, A.; Hill, F. C.; Leszczynski, J.; Tropsha, A. QSAR analysis of the toxicity of nitroaromatics in Tetrahymena pyriformis: structural factors and possible modes of action. SAR QSAR Environ. Res. 2011, 22 (5–6), 575–601, DOI: 10.1080/1062936X.2011.569950.

952 953

(95)

Lagunin, A.; Zakharov, A.; Filimonov, D.; Poroikov, V. QSAR Modelling of Rat Acute Toxicity on the Basis of PASS Prediction. Mol. Inform. 2011, 30 (2–3), 241–250.

954 955 956

(96)

Zakharov, A. V; Peach, M. L.; Sitzmann, M.; Nicklaus, M. C. A new approach to radial basis function approximation and its application to QSAR. J. Chem. Inf. Model. 2014, 54 (3), 713–719, DOI: 10.1021/ci400704f.

957 958 959 960 961

(97)

Muratov, E. N.; Artemenko, A. G.; Varlamova, E. V; Polischuk, P. G.; Lozitsky, V. P.; Fedchuk, A. S.; Lozitska, R. L.; Gridina, T. L.; Koroleva, L. S.; Sil’nikov, V. N.; Galabov, A. S.; Makarov, V. A.; Riabova, O. B.; Wutzler, P.; Schmidtke, M.; Kuz’min, V. E. Per aspera ad astra: application of Simplex QSAR approach in antiviral research. Future Med. Chem. 2010, 2 (7), 1205–1226, DOI: 10.4155/fmc.10.194.

962 963 964 965

(98)

Janardhan, S.; Konova, V.; Lagunin, A.; Rao, V.; Sastry, N.; Poroikov, V. Recent Advances in the Development of Pharmaceutical Agents for Metabolic Disorders: A Computational Perspective. Curr. Med. Chem. 2017, 24 (E-pub ahead of print), DOI: 10.2174/0929867324666171002120647

966 967 968 969

(99)

Alves, V. M.; Muratov, E. N.; Zakharov, A.; Muratov, N. N.; Andrade, C. H.; Tropsha, A. Chemical toxicity prediction for major classes of industrial chemicals: Is it possible to develop universal models covering cosmetics, drugs, and pesticides? Food Chem. Toxicol. 2017, Just accep DOI: 10.1016/j.fct.2017.04.008.

970 971 972

(100) Golbraikh, A.; Shen, M.; Xiao, Z.; Xiao, Y.-D.; Lee, K.-H.; Tropsha, A. Rational selection of training and test sets for the development of validated QSAR models. J. Comput. Aided. Mol. Des. 2003, 17 (2–4), 241–253, DOI: 10.1023/A:1025386326946.

973 974 975 976 977 978

(101) Casati, S.; Aschberger, K.; Barroso, J.; Casey, W.; Delgado, I.; Kim, T. S.; Kleinstreuer, N.; Kojima, H.; Lee, J. K.; Lowit, A.; Park, H. K.; Régimbald-Krnel, M. J.; Strickland, J.; Whelan, M.; Yang, Y.; Zuang, V. Standardisation of defined approaches for skin sensitisation testing to support regulatory use and international adoption: position of the International Cooperation on Alternative Test Methods. Arch. Toxicol. 2017, No. 123456789, 1–7, DOI: 10.1007/s00204-017-2097-4.

979 980 981 982

(102) Klaric, M.; Alépée, N.; Allen, D.; Ashikaga, T.; Casey, W.; Clouet, E.; Cluzel, M.; Del Bufalo, A.; Gellatly, N.; Goebel, C.; Hoffmann, S.; Kern, P.; Kuehnl, J.; Mewes, K.; Miyazawa, M.; Petersohn, D.; Strickland, J.; Van Vliet, E.; Zang, D.; Kleinstreuer, N. Cosmetics Europe assessment of non-animal approaches for predicting skin sensitization.


40

Page 41 of 46 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

983


Toxicol. Lett. 2017, 280, S129, DOI: 10.1016/j.toxlet.2017.07.359.

984 985 986 987

(103) Sharma, N. S.; Jindal, R.; Mitra, B.; Lee, S.; Li, L.; Maguire, T. J.; Schloss, R.; Yarmush, M. L. Perspectives on non-animal alternatives for assessing sensitization potential in allergic contact dermatitis. Cell. Mol. Bioeng. 2011, 5 (1), 52–72, DOI: 10.1007/s12195011-0189-4.

988 989 990

(104) Roberts, D. W.; Patlewicz, G. Non-animal assessment of skin sensitization hazard: Is an integrated testing strategy needed, and if so what should be integrated? J. Appl. Toxicol. 2018, 38 (1), 41–50, DOI: 10.1002/jat.3479.

991 992 993 994

(105) Goebel, C.; Kosemund-Meynen, K.; Gargano, E. M.; Politano, V.; von Bölcshazy, G.; Zupko, K.; Jaiswal, N.; Zhang, J.; Martin, S.; Neumann, D.; Rothe, H. Non-animal skin sensitization safety assessments for cosmetic ingredients – What is possible today? Curr. Opin. Toxicol. 2017, 5, 46–54, DOI: 10.1016/j.cotox.2017.08.005.

995 996 997 998 999 1000

(106) Bell, S. M.; Chang, X.; Wambaugh, J. F.; Allen, D. G.; Bartels, M.; Brouwer, K. L. R.; Casey, W. M.; Choksi, N.; Ferguson, S. S.; Fraczkiewicz, G.; Jarabek, A. M.; Ke, A.; Lumen, A.; Lynn, S. G.; Paini, A.; Price, P. S.; Ring, C.; Simon, T. W.; Sipes, N. S.; Sprankle, C. S.; Strickland, J.; Troutman, J.; Wetmore, B. A.; Kleinstreuer, N. C. In vitro to in vivo extrapolation for high throughput prioritization and decision making. Toxicol. In Vitro 2017, 47, 213–227, DOI: 10.1016/j.tiv.2017.11.016.

1001 1002

(107) Emami, J. In vitro - in vivo correlation: from theory to applications. J. Pharm. Pharm. Sci. 2006, 9 (2), 169–189.

1003 1004 1005

(108) Sarath Kumar, K. L.; Tangadpalliwar, S. R.; Desai, A.; Singh, V. K.; Jere, A. Integrated Computational Solution for Predicting Skin Sensitization Potential of Molecules. PLoS One 2016, 11 (6), e0155419, DOI: 10.1371/journal.pone.0155419.

1006 1007 1008

(109) Rani, J.; Shah, A. B. R.; Ramachandran, S. pubmed.mineR: an R package with text-mining algorithms to analyse PubMed abstracts. J. Biosci. 2015, 40 (4), 671–682. DOI: 10.1007/s12038-015-9552-2

1009 1010 1011 1012

(110) Capuzzi, S. J.; Thornton, T. E.; Liu, K.; Baker, N.; Lam, W. I.; O’Banion, C. P.; Muratov, E. N.; Pozefsky, D.; Tropsha, A. Chemotext: A Publicly Available Web Server for Mining Drug–Target–Disease Relationships in PubMed. J. Chem. Inf. Model. 2018, Just accep DOI: 10.1021/acs.jcim.7b00589.

1013 1014 1015

(111) OECD. OECD principles for the validation, for regulatory purposes, of (Quantitative) Structure-Activity Relationship models http://www.oecd.org/chemicalsafety/riskassessment/37849783.pdf (accessed Jul 31, 2017).

1016 1017 1018 1019 1020 1021

(112) Ross, K. A.; Jensen, C. S.; Snodgrass, R.; Dyreson, C. E.; Jensen, C. S.; Snodgrass, R.; Skiadopoulos, S.; Sirangelo, C.; Larsgaard, M. L.; Grahne, G.; Kifer, D.; Jacobsen, H.-A.; Hinterberger, H.; Deutsch, A.; Nash, A.; Wada, K.; Aalst, W. M. P.; Dyreson, C.; Mitra, P.; Witten, I. H.; Liu, B.; Aggarwal, C. C.; Özsu, M. T.; Ogbuji, C.; Patel, C.; Weng, C.; Patel, C.; Weng, C.; Wright, A.; Shabo (Shvo), A.; et al. Cross-Validation. In Encyclopedia of Database Systems; DOI: 10.1007/978-0-387-39940-9_565: Boston, MA, 2009; pp 532–538.

1022 1023

(113) Muratov, E. N.; Varlamova, E. V.; Artemenko, A. G.; Polishchuk, P. G.; Kuz’min, V. E. Existing and Developing Approaches for QSAR Analysis of Mixtures. Mol. Inform. 2012,


41


1024

Page 42 of 46

31 (3–4), 202–221, DOI: 10.1002/minf.201100129.

1025 1026 1027 1028

(114) Oprisiu, I.; Varlamova, E.; Muratov, E.; Artemenko, A.; Marcou, G.; Polishchuk, P.; Kuz’Min, V.; Varnek, A. QSPR approach to predict nonadditive properties of mixtures. Application to bubble point temperatures of binary mixtures of liquids. Mol. Inform. 2012, 31 (6–7), 491–502, DOI: 10.1002/minf.201200006.

1029 1030 1031 1032

(115) Muratov, E. N.; Varlamova, E. V.; Artemenko, A. G.; Polishchuk, P. G.; Nikolaeva-Glomb, L.; Galabov, A. S.; Kuz’Min, V. E. QSAR analysis of poliovirus inhibition by dual combinations of antivirals. Struct. Chem. 2013, 24 (5), 1665–1679, DOI: 10.1007/s11224012-0195-8.

1033 1034 1035 1036

(116) Bulusu, K. C.; Guha, R.; Mason, D. J.; Lewis, R. P. I.; Muratov, E.; Kalantar Motamedi, Y.; Cokol, M.; Bender, A. Modelling of compound combination effects and applications to efficacy and toxicity: state-of-the-art, challenges and perspectives. Drug Discov. Today 2016, 21 (2), 225–238, DOI: 10.1016/j.drudis.2015.09.003.

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BIOGRAPHIES

Vinicius M. Alves

Vinicius M. Alves received his PhD in Pharmaceutical Sciences (2017) from the Federal University of Goias, Brazil. He is a cheminformatician, computational chemist, and data scientist with experience in the development and implementation of innovative cheminformatics and molecular modeling approaches. He received in 2014 the Chemical Information Scholarship for Scientific Excellence awarded by the American Chemical Society. He visited the University of Florida (USA) as a Research Intern (2011) and attended the University of North Carolina at Chapel Hill (USA) in 2012 and in 2015-2016 as a visiting scholar. Stephen J. Capuzzi is a Pharmaceutical Sciences PhD candidate in the Division of Chemical Biology and Medicinal Chemistry in the Eshelman School of Pharmacy at the University of North Carolina – Chapel Hill. His research interests include in silico drug design, computational drug repurposing, and machine learning. He was graduated summa cum laude and Phi Beta Kappa from St. Joseph’s University in 2012 with a BS in Chemical Biology.

Stephen J. Capuzzi

Rodolpho C. Braga

Rodolpho C. Braga is a Data Scientist and Drug Discovery Specialist at Altox Alternative Toxicology Inc. He worked 9 months on the Lead Optimization Latin America project (LOLA), sponsored by the Drugs for Neglected Diseases initiative (DNDi), as a cheminformatician and computational chemist. He was a Visiting Professor in Medicinal Chemistry at University of Turin (UniTO) (2015 – 2016). He received the CINF Scholarship for Scientific Excellence of the American Chemical Society (2014). He is a Research Scientist with experience conducting and overseeing a variety of successful computational chemistry, cheminformatics, project management, molecular modeling, and complex data analysis research projects. His academic qualifications including a Postdoctoral fellowship (2016), a Doctor of Philosophy (2015) and a Master of Science (2012) in Medicinal Chemistry from the Federal University of Goiás.


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Joyce V. B. Borba

Arthur C. Silva

Thomas Luechtefeld

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Joyce V. B. Borba is a PhD student at the Federal University of Goias, Brazil, where she also received her M.Sc. in Tropical Medicine and Public Health and her B.Sc. in Pharmacy. During her M.Sc. studies, she worked on the identification of potential antifungal candidates using molecular biology techniques. Currently, she is working on the identification and classification of Leishmania, Schistosoma and Plasmodium kinomes, as well as on the prioritization of protein kinases as drug targets and drug repurposing strategies. She has also developed of QSAR models for skin sensitization, skin irritation and eye irritation. Her main interests are in the fields of medicinal chemistry, cheminformatics, bioinformatics and molecular biology. Arthur C. Silva is a PhD student at the Federal University of Goias, Brazil, where he also he completed his M.Sc. in Pharmaceutical Sciences and his B.Sc. in Pharmacy. During his M.Sc. studies, he completed an internship at Faculty of Sciences of UdelaR (Universidad de la República, Montevideo, Uruguay) working with design and identification of potential anticancer agents. His current focus is on the application of cheminformatics and molecular modeling techniques for the design and identification of new therapies for neglected tropical diseases. He also applies cheminformatics methods to identify chemicals capable of causing skin sensitization and eye irritation. His main interests are in bioinformatics and medicinal chemistry specifically machine learning for applied drug discovery projects. Thomas Luechtefeld is a toxicology PhD candidate with a focus on computer science and machine learning. During his time as a PhD student, he indexed the European Chemical Agency database for REACH regulation, which resulted in 4 publications analyzing an unprecedented 800,000 chemical / toxicological studies. Following these publications, he created two small start-up companies focused on health informatics (insilica) and cheminformatics (toxtrack). These small companies have delivered multiple applications in active use in industry, academia and regulatory agencies. He continued to contribute to research publications and gave presentations of this work at the FDA, EPA, Society of Toxicology, American Society


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for Cellular and Computational Toxicology, National Center for Computational Toxicology, and in many industry settings. It is his goal to translate the same practices used in toxicology-focused bioinformatics to analysis of cancer data.

Thomas Hartung

Carolina Horta Andrade

Thomas Hartung, MD PhD, is the DoerenkampZbinden-Chair for Evidence-based Toxicology with a joint appointment for Molecular Microbiology and Immunology at Johns Hopkins Bloomberg School of Public Health, Baltimore. He holds a joint appointment as Professor for Pharmacology and Toxicology at University of Konstanz, Germany; he also is Director of Centers for Alternatives to Animal Testing (CAAT, http://caat.jhsph.edu) of both universities with the portal AltWeb (http://altweb.jhsph.edu). CAAT hosts the secretariat of the Evidence-based Toxicology Collaboration (http://www.ebtox.org), the Good ReadAcross Practice Collaboration, the Good Cell Culture Practice Collaboration, the Green Toxicology Collaboration and the Industry Refinement Working Group. As PI, he heads the Human Toxome project (http://humantoxome.com) funded as an NIH Transformative Research Grant. He is the former Head of the European Commission’s Center for the Validation of Alternative Methods (ECVAM), Ispra, Italy, and has authored more than 500 scientific publications. Adjunct Professor at Federal University of Goias. Graduated in Pharmacy (2004) and Doctor of Philosophy (2009) in Drugs and Medicines from University of Sao Paulo. Her research focuses on Computer-Aided Drug Design, with the aim to discover new drug candidates for Neglected Tropical Diseases and cancer. Her field of expertise also includes cheminformatics tool development and QSAR modeling for the prediction of toxicity properties of chemical compounds. She is a CNPq productivity research fellow since 2012. In 2014, she was awarded the “For Women in Science” award from L’Oréal-ABC-UNESCO, and, in 2015, she received the “International Rising Talents” from L’Oréal – UNESCO. In 2016, she was elected Affiliated member of the Brazilian Academy of Sciences. Currently, she is vice-director of the Medicinal Chemistry Division of the Brazilian Chemical Society.


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Eugene N. Muratov

Alexander Tropsha

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Eugene N. Muratov is a Research Assistant Professor and Associate Director of Laboratory for Molecular Modeling at the UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill. He received his MS in technology of organic substances from Odessa National Polytechnic University in 2000 and PhD in organic chemistry in 2004 from the A. V. Bogatsky Physical-Chemical Institute. From 2014-2017 he was an Invited Professor at the Federal University of Goias, Goiania, Brazil. His research interests are in the areas of cheminformatics (especially QSAR), computerassisted drug design, antiviral research, computational toxicology, and medicinal chemistry. He has coauthored more than 100 peer-reviewed publications, and edited two books. Alexander Tropsha, PhD. is K.H. Lee Distinguished Professor and Associate Dean for Pharmacoinformatics and Data Science at the UNC Eshelman School of Pharmacy, UNC-Chapel Hill. Prof. Tropsha obtained PhD in Chemical Enzymology in 1986 from Moscow State University, Russia and came to UNC-Chapel Hill in 1989 as a postdoctoral fellow. He joined the School of Pharmacy in 1991 as an Assistant Professor and became full professor in 2002. His research interests are in the areas of Computer-Assisted Drug Design, Computational Toxicology, Cheminformatics, (Nano)Materials Informatics, and Structural Bioinformatics. He has authored or co-authored more than 200 peer-reviewed research papers, reviews and book chapters, and co-edited two monographs. He is an Associate Editor of the ACS Journal of Chemical Information and Modeling. His research has been supported by multiple grants from the NIH, NSF, EPA, DOD, and private companies.

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A Perspective and a New Integrated Computational Strategy for Skin

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