Using a hybrid read-across method to evaluate chemical toxicity based on chemical structure and biological data

Abstract

Read-across has become a primary approach to fill data gaps for chemical safety assessments. Chemical similarity based on structure, reactivity, and physic-chemical property information is a traditional approach applied for read-across toxicity studies. However, toxicity mechanisms are usually complicated in a biological system, so only using chemical similarity to perform the read-across for new compounds was not satisfactory for most toxicity endpoints, especially when the chemically similar compounds show dissimilar toxicities. This study aims to develop an enhanced read-across method for chemical toxicity predictions. To this end, we used two large toxicity datasets for read-across purposes. One consists of 3979 compounds with Ames mutagenicity data, and the other contains 7332 compounds with rat acute oral toxicity data. First, biological data for all compounds in these two datasets were obtained by querying thousands of PubChem bioassays. The PubChem bioassays with at least five compounds from either of these two datasets showing active responses were selected to generate comprehensive bioprofiles. The read-across studies were performed by using chemical similarity search only and also by using a hybrid similarity search based on both chemical descriptors and bioprofiles. Compared to traditional read-across based on chemical similarity, the hybrid read-across approach showed improved accuracy of predictions for both Ames mutagenicity and acute oral toxicity. Furthermore, we could illustrate potential toxicity mechanisms by analyzing the bioprofiles used for this hybrid read-across study. The results of this study indicate that the new hybrid read-across approach could be an applicable computational tool for chemical toxicity predictions. In this way, the bottleneck of traditional read-across studies can be overcome by introducing public biological data into the traditional process. The incorporation of bioprofiles generated from the additional biological data for compounds can partially solve the "activity cliff" issue and reveal their potential toxicity mechanisms. This study leads to a promising direction to utilize data-driven approaches for computational toxicology studies in the big data era.

Keywords: Big data; Biosimilarity; Computational toxicology; Hybrid approach; Read-across; Toxicity mechanisms.

Fig. 1.

The hybrid read-across workflow

Fig. 2.

The comparison of biosimilarity results of the compounds with their chemical nearest neighbors for (A) Ames mutagenicity; (B) Rat acute oral toxicity. A biosimilarity threshold (0.80) was set to evaluate whether a target compound and its chemical nearest neighbor are biologically similar or not. The blue columns represent the numbers of compounds which are also biosimilarity to their chemcial nearest neighbors (S_bio>0.80); the red columns represent the numbers of compounds which are biodissimilar to their chemical nearest neighbors (S_bio<0.80).

Fig. 3.

The distribution of read-across predictions for compounds in Ames mutagenicity. The green crosses are correct predictions and the red round dots are incorrect predictions. The read-across predictions were divided into four areas by using two threshold values (Chemical similarity = 0.90 and Biosimilarity = 0.80): The area A includes compound pairs with high chemical similarity and high biosimilarity; the area B includes compound pairs with high chemical similarity and low biosimilarity; the area C includes compound pairs with low chemical similarity and low biosimilarity; and the area D includes compound pairs with low chemical similarity and high biosimilarity.

Fig. 4.

The correlation between experimental and predicted acute toxicity values for compounds in acute oral toxicity dataset (Values shown as −log₁₀ LD50). The red dots represent compound pairs with high chemical similarity and high biosimilarity; the black dots represent pairs in other cases (i.e. either chemically disimiar or biodisimiar). The dots between two dashed lines represent accurate predictions (absolute errors less than 0. 50).