From World War I to 1968, the United States produced chemical weapons and warfare agents as a deterrent against use of similar weapons by other countries. Sulfur mustard (HD, Figure 1), a vesicant and alkylating chemical, is one such agent [1,2] Although never used during warfare by the United States, these chemicals are still abundant and are now deteriorating with age. In 1985, the U.S. Congress mandated that the Department of Defense be responsible for establishing a Chemical and Biological Defense (CBD) program, U.S. Code Title 50, Sections 1521 through 153, and provide for chemical weapons disposal and destruction. In accordance with this congressional mandate the Center for Disease Control and Prevention’s (CDC), Environmental Public Health Readiness Branch (EPHRB), Chemical Weapons Elimination section has been tasked with overseeing the Army’s destruction of chemical weapons to ensure that the general population, worker population and environment are protected.

The Pueblo Chemical Agent Pilot Plant (PCAPP) plans to utilize neutralization processes to destroy the HD stored at the Pueblo Chemical Army Depot (PCAD). This process creates several breakdown products during and after the neutralization. The majority of these breakdown products do not have sufficient toxicity data to properly assess the human health impacts related to their exposure or to select appropriate personnel protective equipment (PPE) which will ensure the safety of the personnel. Thus, various stakeholders, viz., state, local, territorial, and tribal public health departments have stated concerns that only are the toxicity databases weak even indicators of overt mammalian toxicity like the lethal dose values that kill fifty percent of test population (LD₅₀) are not available for such chemicals. The LD₅₀ value is the lethal dose of a substance that will kill 50% of the test animals/organisms within 24 hours of exposure to a chemical [3,4]. LD₅₀ values have been used to express the relative hazards associated with the acute toxicity of chemicals and are often used for the initial evaluation of toxicity. Computer-assisted Structure Activity Relationship (SAR) and Quantitative Structure Activity Relationship (QSAR) models, are being increasingly used to fill the data gaps in chemical (pharmaceutical, agrochemical, food additives and other industrial) toxicity databases. These in silico tools thus provide a means of assessing toxicity of chemicals that lack appropriate experimental test data [5,6,7,8,9,10,11,12,13].

Most in silico prediction systems used to estimate qualitative and quantitative toxicity are either SAR or QSAR models [14,15,16,17,18]. An SAR model, and/or an expert system, establishes qualitative association between a chemical’s substructures and its potential toxicity [19,20]. The confidence in the predictions of a new chemical is based on whether such identified structural alerts are present in the structure of a chemical of interest. Because expert systems are based on qualitative SARs, they usually do not make a quantitative prediction of toxic effect; predictions are expressed in a binary fashion i.e., toxic, indeterminate, or non-toxic. A QSAR model, on the other hand, is a mathematical relationship between the chemical’s quantitative molecular descriptors and its toxicological, biological, and physicochemical activities [9,13,21,22,23]. Molecular descriptors derived from atomic or molecular properties that encode physicochemical, topological, and surface properties of molecules are typically used as the backbone of a predictive QSAR model [21,24,25]. These descriptors are then correlated with a toxicological response of interest through a suitable statistical approach such as linear multiple regression, nearest neighbor, clustering, random forest, discriminant analysis, recursive partitioning, artificial neural networks, etc. [26,27,28,29,30,31,32,33,34,35]. Unlike an expert system, a QSAR model provides discrete quantitative predicted values given just the values of the molecular descriptors finally selected in the QSAR model. Choosing the appropriate SAR or QSAR model(s) is a challenge for federal agencies because several such models are available that predict a wide array of endpoints including the LD₅₀ values. Additionally, acute oral mammalian toxicity (LD₅₀) is one of the more complex toxicological endpoints to predict using QSAR methods. Presence of a gamut of biological and molecular events that lead to variable biological mechanisms and a lack of accurate and true understanding of the mechanism of toxicity contribute to this complexity [8,36].

A total of 22 chemicals including the parent chemical HD, and its potential breakdown products, experimentally tested or untested, were evaluated using TOPKAT [37], ADMET Predictor [38], and T.E.S.T. [39] (Table 1). Experimental rat oral LD₅₀ data for this study was available from two sources, ChemIDplus and Registry of Toxic Effects of Chemical Substances databases.

Sulfur mustard neutralization breakdown products are a diverse group of chemicals that either have no or minimal test data available regarding their toxicological effects [1]. Some members of this group and their breakdown products even lack LD₅₀ values. Hence a 2-step in silico approach was adopted wherein a QSAR analysis was performed. In the first step we estimated LD₅₀ values using four different QSAR LD₅₀ models. Each one of the models is internally and externally validated, robust, and has good applicability domains. The LD₅₀ predicted values were compared with the available experimental values when available, and were found to be within acceptable ranges (10 fold) used in chemical risk assessment. Analyses were performed to evaluate the use of these chemicals in the training set used for the model development, if they were part of the external validation set, or they were not used in the model development. Through this process we could evaluate which of the chemicals in this assessment were used to develop each of the models, thus we can identify the applicability domain of each model.

In the second step, using the above models, we determined the LD₅₀ values for breakdown products that lack experimental data (Table 3). The key application of QSAR is to fill the data gaps for chemical toxicity. We achieved this through considerations of the strengths and weaknesses of each model, knowing that none of them is a perfect model. If the strength and advantage of a model is clearly known, the output of such a model can be used easily. TOPKAT model is well known and used by regulatory community, particularly federal agencies. The applicability domain of the model for this particular data set of chemicals was poor, i.e., seven of the twenty two chemical were outside the optimum prediction space (OPS). On the contrary, ADMET predictor and T.E.S.T. had high chemical applicability domain for this data set. These models use both internal and external validated compared with TOPKAT that uses only one validation method. These models not only showed a high chemical applicability domain but also broad activity ranges in this applicability domain for this data set. However, the T.E.S.T model had superior performance compared to ADMET predictor.

Taking an average of estimates when using multiple models could compensate the limitations of the individual models that use different descriptors and statistical methods to model different aspects of the toxicological affects. Thus, the use of a consensus model is always more beneficial than an individual.

When using multiple models with varying modeling techniques (molecular descriptors, statistical methods and validation), it is a bigger challenge to judge the model output if their performances are comparable but slightly different [30,41,42,43,44]. In such cases, the conventional wisdom is to use an arithmetic averaging scheme which was also used in our study (Table 4), or use the most conservative values estimated by the models. For example, as shown in Table 3, the estimated LD₅₀ values of bis-[2-(2-hydroxyethylthioethyl)] ether (TOH) and thiodiglycol sulfone are less toxic by the T.E.S.T. model (much higher LD₅₀ values) compared to the other models, in such case, the T.ES.T model estimate could be excluded using a more safe approach.

With an increase in the number of QSAR models available, commercially or open source, that are developed using a variety of approaches, the final interpretation and application will depend on the user confidence and the transparency of the model [44,45,46,47] The LD₅₀ values, experimental or QSAR, are used to determine the relative toxicity of a series of chemicals in which the LD₅₀ value of a given chemical is compared with the LD₅₀ values of other chemicals [4]. Using acceptable toxicity scales, the chemicals are assigned to various groups. One of the most common scales used is the Gosselin, Smith and Hodge scale. Super toxic (<5 mg/kg), extremely toxic (5–50 mg/kg), very toxic (50–500 mg/kg), moderately toxic (500–5,000 mg/kg), slightly toxic (5,000–15,000 mg/kg) and practically non-toxic (>15,000 mg/kg) [48].

Using this scale, the QSAR estimates enabled the ranking of these chemicals on the basis of potential toxicities, in a rapid manner. The breakdown products of sulfur mustard (HD) hydrolysis reaction, thiodiglycol (TDG), 1,4-dithiane, 1,4-oxathiane and 2-hydroxyethyl vinyl sulfide (Scheme 1) were estimated to have moderate LD₅₀ values (>500 mg/kg), when compared with HD, the parent compound.

The dissolution of HD in presence of water leads to the formation of the intermediate sulfonium ion which in turn reacts with another molecule of HD to form 1,2-bis-(2-chloroethylthio) ethane (Q) and 1,2-dichloroethane (Scheme 2). The estimated LD₅₀ values of 1, 2-bis-(2-chloroethylthio) ethane is <500 mg/kg which is very toxic, but less than HD.

HD and its hydrolysis product TDG are oxidized to give the sulfoxide and sulfone analogs of HD and TDG (Scheme 3). The sulfoxide and sulfone analogs of HD are less toxic than HD; the estimated LD₅₀ values were between 339.90 and 299.79 mg/kg, respectively. TDG and its sulfoxide and sulfone analogs are slightly and moderately toxic, respectively, when compared to the extremely toxic parent compound, HD.

The estimated LD₅₀ values of HD, O-mustard (T) and sesquimustard (Q) and their hydroxylated analogs TDG, TOH and QOH show that these analogs are much less toxic (Figure 8). However, when compared with their vinyl analogs, TDG, TOH and QOH are shown to have a higher potential toxicity, but less than HD, T and Q (Figure 5).

The QSAR estimated LD₅₀ values for the breakdown products ranged from 299.79 mg/kg to 5,764.45 mg/kg. These data indicate that five breakdown products fall under very toxic class (50–500 mg/kg) while seven fall under moderately toxic (LD₅₀ values 0.5–5 g/kg) class. None of the chemicals fall under the extremely toxic or super toxic categories (Table 4). Thus, the experimentally untested breakdown products are potentially less toxic than those chemicals that were experimentally tested (range from 17 mg/kg to 30,600 mg/kg). The experimentally tested chemicals including the parent chemical fall under the extremely toxic class (5–50 mg/kg) and a breakdown product falls under the very toxic (50–500 mg/kg) class. Six of the breakdown products fall under moderately toxic (LD₅₀ values 500–5,000 mg/kg), while one each falls under slightly toxic (5,000–15,000 mg/kg) and practically non toxic (>15,000 mg/kg) class. None fall under super toxic class (less than 5 mg/Kg). Thus, overall the potential toxicity of QSAR estimated breakdown products is potentially lower than the parent compound. The QSAR LD₅₀ models estimates not only are within a factor of 10 of the experimental available data [except for bis(2-chloroethyl) sulfide], but also have shown an overall degree of conservatism, except for the HD, which was predicted as very toxic by TOPKAT, ADMET 2D and 3D models, compared to extremely toxic experimental value. This information will be useful to stakeholders involved in HD neutralization processes to assess risk associated with these breakdown products.

Mammalian toxicity, particularly LD₅₀ values are much more challenging because the mechanism of acute lethal toxicity is complex, and is not fully understood because of complex interactions between the organism and pharmacokinetic and pharmaco-dynamics of the chemicals. However, a growing number of in silico tools and QSAR models continue to be developed because of demand for such estimates due to resource limitations and timely needs, apart from ethical reasons [49]. Gaining knowledge from these kinds of activities should help in vitro to in vivo extrapolations, especially since large in vitro data generating efforts are ongoing at the National Academy of Science and the EPA [50,51,52]. Through these efforts, meaningful structural features can be identified that will lead to the development of robust predictive models, and specific selection criteria for use of such models. A thoughtful application and prudent use of such models can help estimate the toxicity of chemicals that lack experimental data, and prioritize chemicals for screening and subsequent toxicity testing while saving cost and time, thus minimizing experimental animal testing while optimizing overall use of resources. Recent laws are pushing the acceptance of these methods and their use by the regulatory and public health communities in the mitigation of potential hazardous exposures that could compromise the quality of human health and environment [53,54,55]. In silico tools can play a pivotal role for assessing the health risk of environmental and pharmaceutical chemicals, especially when the only characteristic known is the structure of a chemical.

The chemical and biological defense program of the Department of Defense uses multiple chemical reactions such as dissolution, hydrolysis, oxidation, and neutralization (see discussion section) to dispose, decontaminate and destroy chemicals weapons such as sulfur mustard (HD) that have been stored for a long time with varies type of stabilizers.

For the present work, we used the following three QSAR modeling packages to estimate the LD₅₀ values of HD [bis-(2-chloroethyl) sulfide (Figure 1)] and its breakdown products: TOPKAT^® (Toxicity Prediction by Komputer Assisted Technology, Version 6.2, Accelrys, Inc.) and ADMET Predictor 5.0 (Simulation Plus Inc.) and Toxicity Estimation Software Tool (T.E.S.T. US EPA) [37,38,39].

This study has integrated commercial and open source robust QSAR LD₅₀ models to develop an approach for evaluation of the toxicity of chemicals that lack experimental toxicity data. This project was undertaken to determine the potential toxicity of sulfur mustard neutralization breakdown products toxicity using QSAR analysis since there is no experimental toxicity data available for such breakdown products. The QSAR estimates obtained show that the breakdown products will have less potential toxicity than some of the better known breakdown products of sulfur mustard. This evaluation can provide stakeholders with the potential toxicity values to make risk based decisions related to these breakdown products.