Exploration of Computational Approaches to Predict the Toxicity of Chemical Mixtures
- toxicity data vary with different combinations of the same chemicals in a mixture;
- form of exposure;
- identification of each chemicals in a specific mixture is also difficult due to the presence of very small quantities; and
- complex interactions among chemicals.
2. Why Exploration of Toxicity of Chemical Mixtures is Important?
3. Hypothesis for a Mixture’s Toxicity Exploration and Data for Computational Modeling
- If chemicals in a mixture showed same mechanism of action for a specific response and act on same site of action, then there are chances of dilution of the response. This method is known as concentration addition (CA).
- If chemicals in mixtures act on different sites of action with dissimilar modes of action (MOA), this may disclose statistically independent responses without interaction. This method is known as independent action (IA).
- If chemicals are interactive in nature, then they may show synergistic or antagonistic effects.
3.1. Determination of Dosage Response Curves for All Chemicals in a Mixture
3.2. Determining the Effect of the Chemical Mixture
3.3. Modeling with Identified Hypothesis
3.3.1. Concentration Addition (CA)
3.3.2. Independent Action (IA)
3.3.3. Synergistic and Antagonistic Actions
3.3.4. Generalized Concentration Addition (GCA) Models
4. Importance of Computational Approaches to Determine the Toxicity of Chemical Mixtures
- To stop the unethical use of animal cruelty in the name of animal modeling. The application and acceptance of in silico approaches can decrease the use of animals in toxicity testing.
- Using in silico models from existing chemical mixtures, one can assess/predict the toxicity of untested and/or new different combinations of chemical mixtures for a specific species or systems if they fall under the applicability domain (AD).
- Regulatory agencies like United States Environmental Protection Agency (US EPA), European Union regulations like the Registration, Evaluation, Authorization and Restriction of Chemicals (REACH), and Health Canada consider and depend on in silico methods for toxicity and risk assessment followed by decision making.
- In silico methods are reliable tools to analyse the quantity of risk followed by methods to manage it.
- Without any doubt, in silico tools are cost- and time-effective compared to in vivo and in vitro methods.
- A reliable source of methods to fill gaps in mixture toxicity data as the majority of mixtures have no toxicity data at all.
5. Types of Computational Approach for a Mixture’s Toxicity Prediction
- Methods are easy to implement and interpret.
- Help to determine how compounds should be transformed to decrease their toxicity.
- Capable of categorizing the structure of likely metabolites.
- The presence or absence of SAs does not offer understanding of the biological pathways of toxicity.
- If all SAs are not identified properly, the method can increase false negatives.
6. Successful Application of Computational Modeling for Predicting a Mixture’s Toxicity
- Daphnia EC20 QSAR:
- Mesocosm NOEC QSAR:
7. Future Avenues of Chemical Mixture Toxicity Research
- Mixture assessment should use low doses, for example up to the no-observed-adverse-effect level (NOAEL);
- There is no ultimate or universal method, and one needs to develop new or modified approaches from case to case to address the complex issue of mixture, noting that old-style animal-based toxicology practices are insufficient for such a multifaceted issue;
- Collaborative efforts between experimentalist and computational communities are must to address majority of issues and challenges related to mixture toxicity;
- A variant of the Hausdorff measure, called Hausdorff-like similarity (Hs), can be useful in modeling a complex system like mixtures . To quantify the similarity degree between two systems, it is not suitable to account only for mutual or dissimilar features, but all the features of the systems have to be measured in the assessment. Hausdorff formula are capable of equally weighing both the existence of common/comparable elements. To measure the diversity relationship between the two sets X and Y, the Hausdorff formula can be defined as follows:
Conflicts of Interest
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|Dimension||Description||Representative Example of Descriptors or Computational Method||Reference|
|0D||Chemical formula derived descriptors||Constitutional indices (Molecular Weight (MW), sum of properties etc.), molecular property descriptors, count descriptors (count of bond, atom, non-hydrogen atom etc.)|||
|1D||Descriptors are derived using the representation of various sub-structural molecular fragments||Fingerprints, count of fragments, H-Bond acceptor/donor, Crippen AlogP98, PSA, SMARTS etc.|||
|2D||Descriptors are obtained from the graph theoretical representation of molecules including various structural and/or physicochemical property indices||Topological descriptors, eigenvalue-based descriptors, connectivity indices, descriptors containing topological and electronic information.|||
|3D||These independent variables encode various spatial as well as geometrical information of compounds and are derived using 3D representation of structure. Such parameters basically portray static representation of a ligand.||WHIM descriptors, MoRSE descriptors, Jurs parameters, GETAWAY descriptors, quantum-chemical descriptors, atomic coordinates, size, steric, surface and volume descriptors. Techniques e.g., Comparative Molecular Field Analysis (CoMFA), Comparative molecular similarity index analysis (CoMSIA) etc.||[25,26]|
|4D||Depict multiple representation of the ligand molecule using various configurations, orientation, and protonation state representation.||Volsurf, GRID, Raptor etc. derived descriptors.|||
|5D||Descriptors consider the induced fit parameters and aim to establish a ligand-based virtual or pseudo receptor model.||Flexible-protein docking.|||
|6D||These are derived using the representation of various solvation circumstances along with the information obtained from 5D-descriptors.||Quasar.|||
|7D||Such analysis comprises real receptor or target-based receptor model data.||−|||
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Kar, S.; Leszczynski, J. Exploration of Computational Approaches to Predict the Toxicity of Chemical Mixtures. Toxics 2019, 7, 15. https://doi.org/10.3390/toxics7010015
Kar S, Leszczynski J. Exploration of Computational Approaches to Predict the Toxicity of Chemical Mixtures. Toxics. 2019; 7(1):15. https://doi.org/10.3390/toxics7010015Chicago/Turabian Style
Kar, Supratik, and Jerzy Leszczynski. 2019. "Exploration of Computational Approaches to Predict the Toxicity of Chemical Mixtures" Toxics 7, no. 1: 15. https://doi.org/10.3390/toxics7010015