Luechtefeld Thomas, Maertens Alexandra, Russo Daniel P, Rovida Costanza, Zhu Hao, Hartung Thomas
Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Environmental Health Sciences, Baltimore, MD, USA.
The Rutgers Center for Computational & Integrative Biology, Rutgers University at Camden, NJ, USA.
ALTEX. 2016;33(2):123-34. doi: 10.14573/altex.1510053. Epub 2016 Feb 11.
Public data from ECHA online dossiers on 9,801 substances encompassing 326,749 experimental key studies and additional information on classification and labeling were made computable. Eye irritation hazard, for which the rabbit Draize eye test still represents the reference method, was analyzed. Dossiers contained 9,782 Draize eye studies on 3,420 unique substances, indicating frequent retesting of substances. This allowed assessment of the test’s reproducibility based on all substances tested more than once. There was a 10% chance of a non-irritant evaluation after a prior severe-irritant result according to UN GHS classification criteria. The most reproducible outcomes were the results negative (94% reproducible) and severe eye irritant (73% reproducible). To evaluate whether other GHS categorizations predict eye irritation, we built a dataset of 5,629 substances (1,931 “irritant” and 3,698 “non-irritant”). The two best decision trees with up to three other GHS classifications resulted in balanced accuracies of 68% and 73%, i.e., in the rank order of the Draize rabbit eye test itself, but both use inhalation toxicity data (“May cause respiratory irritation”), which is not typically available. Next, a dataset of 929 substances with at least one Draize study was mapped to PubChem to compute chemical similarity using 2D conformational fingerprints and Tanimoto similarity. Using a minimum similarity of 0.7 and simple classification by the closest chemical neighbor resulted in balanced accuracy from 73% over 737 substances to 100% at a threshold of 0.975 over 41 substances. This represents a strong support of read-across and (Q)SAR approaches in this area.
来自欧洲化学品管理局（ECHA）在线卷宗的关于9801种物质的公共数据，涵盖326749项实验关键研究以及关于分类和标签的补充信息，已实现可计算化。对眼部刺激性危害进行了分析，兔眼Draize试验仍是该危害评估的参考方法。卷宗包含对3420种独特物质的9782项Draize兔眼试验研究，这表明物质经常进行重新测试。这使得能够基于所有经过多次测试的物质来评估该试验的可重复性。根据联合国全球化学品统一分类和标签制度（GHS）分类标准，在先前得出严重刺激性结果后，得出非刺激性评估结果的概率为10%。最具可重复性的结果是阴性结果（94%可重复）和严重眼部刺激性结果（73%可重复）。为了评估其他GHS分类是否能预测眼部刺激性，我们构建了一个包含5629种物质的数据集（1931种“刺激性”物质和3698种“非刺激性”物质）。两个最佳决策树结合多达三项其他GHS分类，得出的平衡准确率分别为68%和73%，即与Draize兔眼试验本身的排名顺序相当，但两者都使用了吸入毒性数据（“可能引起呼吸道刺激”），而该数据通常无法获取。接下来，将一个包含至少一项Draize试验研究的929种物质的数据集映射到PubChem，以使用二维构象指纹和Tanimoto相似度来计算化学相似度。使用0.7的最小相似度并通过最接近的化学邻域进行简单分类，得出的平衡准确率从737种物质时的73%到41种物质在阈值为0.975时的100%。这有力地支持了该领域的类推法和（定量）构效关系方法。
