Kar Supratik, Leszczynski Jerzy
Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS 39217, USA.
Toxics. 2019 Mar 19;7(1):15. doi: 10.3390/toxics7010015.
Industrial advances have led to generation of multi-component chemicals, materials and pharmaceuticals which are directly or indirectly affecting the environment. Although toxicity data are available for individual chemicals, generally there is no toxicity data of chemical mixtures. Most importantly, the nature of toxicity of these studied mixtures is completely different to the single components, which makes the toxicity evaluation of mixtures more critical and challenging. Interactions of individual chemicals in a mixture can result in multifaceted and considerable deviations in the apparent properties of its ingredients. It results in synergistic or antagonistic effects as opposed to the ideal case of additive behavior i.e., concentration addition (CA) and independent action (IA). The CA and IA are leading models for the assessment of joint activity supported by pharmacology literature. Animal models for toxicity testing are time- and money-consuming as well as unethical. Thus, computational approaches are already proven efficient alternatives for assessing the toxicity of chemicals by regulatory authorities followed by industries. In silico methods are capable of predicting toxicity, prioritizing chemicals, identifying risk and assessing, followed by managing, the risk. In many cases, the mechanism behind the toxicity from species to species can be understood by in silico methods. Until today most of the computational approaches have been employed for single chemical's toxicity. Thus, only a handful of works in the literature and methods are available for a mixture's toxicity prediction employing computational or in silico approaches. Therefore, the present review explains the importance of evaluation of a mixture's toxicity, the role of computational approaches to assess the toxicity, followed by types of in silico methods. Additionally, successful application of in silico tools in a mixture's toxicity predictions is explained in detail. Finally, future avenues towards the role and application of computational approaches in a mixture's toxicity are discussed.
工业进步导致了多组分化学品、材料和药品的产生，这些物质正在直接或间接地影响环境。虽然有个别化学品的毒性数据，但一般来说，尚无化学混合物的毒性数据。最重要的是，这些研究混合物的毒性性质与单一成分完全不同，这使得混合物的毒性评估更加关键且具有挑战性。混合物中各化学品之间的相互作用会导致其成分的表观性质出现多方面且显著的偏差。这会产生协同或拮抗作用，与理想的加和行为情况（即浓度加和（CA）和独立作用（IA））相反。CA和IA是药理学文献支持的联合活性评估的主要模型。用于毒性测试的动物模型既耗费时间和金钱，又不符合伦理道德。因此，计算方法已被监管机构和行业证明是评估化学品毒性的有效替代方法。计算机模拟方法能够预测毒性、对化学品进行优先级排序、识别风险并进行风险评估及管理。在许多情况下，计算机模拟方法能够理解不同物种之间毒性背后的机制。到目前为止，大多数计算方法都用于单一化学品的毒性研究。因此，在文献中只有少数关于使用计算或计算机模拟方法预测混合物毒性的研究和方法。所以，本综述解释了评估混合物毒性的重要性、计算方法在评估毒性中的作用以及计算机模拟方法的类型。此外，还详细解释了计算机模拟工具在混合物毒性预测中的成功应用。最后，讨论了计算方法在混合物毒性方面的作用和应用的未来发展方向。
