Integrated Computational Solution for Predicting Skin Sensitization Potential of Molecules

<div><p>Introduction</p><p>Skin sensitization forms a major toxicological endpoint for dermatology and cosmetic products. Recent ban on animal testing for cosmetics demands for alternative methods. We developed an integrated computational solution (SkinSense) that offers a robust solution and addresses the limitations of existing computational tools i.e. high false positive rate and/or limited coverage.</p><p>Results</p><p>The key components of our solution include: QSAR models selected from a combinatorial set, similarity information and literature-derived sub-structure patterns of known skin protein reactive groups. Its prediction performance on a challenge set of molecules showed accuracy = 75.32%, CCR = 74.36%, sensitivity = 70.00% and specificity = 78.72%, which is better than several existing tools including VEGA (accuracy = 45.00% and CCR = 54.17% with ‘High’ reliability scoring), DEREK (accuracy = 72.73% and CCR = 71.44%) and TOPKAT (accuracy = 60.00% and CCR = 61.67%). Although, TIMES-SS showed higher predictive power (accuracy = 90.00% and CCR = 92.86%), the coverage was very low (only 10 out of 77 molecules were predicted reliably).</p><p>Conclusions</p><p>Owing to improved prediction performance and coverage, our solution can serve as a useful expert system towards Integrated Approaches to Testing and Assessment for skin sensitization. It would be invaluable to cosmetic/ dermatology industry for pre-screening their molecules, and reducing time, cost and animal testing.</p></div

Comparative performance of our prediction workflows with VEGA v1.08.

<p>Panel A: Molecules of challenge set-1 processed by our prediction workflows (= 74) and VEGA v1.08 (= 69) used for computation; Panel B: 69 molecules of challenge set-1 processed by our prediction workflows as well as VEGA v1.08 were used for computation; Panel C: Molecules of challenge set-2 processed by our prediction workflows (= 77) and VEGA v1.08 (= 68) used for computation; Panel D: 68 molecules of challenge set-2 processed by our prediction workflows as well as VEGA v1.08 were used for computation. VEGA v1.08: orange bars; PW-1: blue bars; PW-2: green bars. CCR: Correct Classification Rate.</p

Percent prediction accuracy of short-listed variants of models.

<p>Color-coded scale from green to red indicates decreasing prediction accuracy. RTS and Challenge-1 sets are expanded to show the prediction accuracy for each category of sensitizers and non-sensitizers. Internal: Internal test set; RTS: Representative test set; Challenge-1: Challenge set-1; Both: Internal & RTS; X: Extreme; St: Strong; S: Sensitizer with unknown potency; M: Moderate; W: Weak; N: Non-sensitizer.</p

Datasets used for building QSAR models.

<p>Datasets used for building QSAR models.</p

Performance of prediction workflows with machine learning methods and knowledge-based optimization.

<p>Performance of prediction workflows with machine learning methods and knowledge-based optimization.</p

Weights used for components of prediction workflows in knowledge-based optimization.

<p>Weights used for components of prediction workflows in knowledge-based optimization.</p

Steps followed for building QSAR models.

<p>QSAR: Quantitative Structure-Activity Relationship; GPMT: Guinea Pig Maximization Test; HSDB: Hazardous Substance DataBase; LLNA: Local Lymph Node Assay; REACH: Registration, Evaluation and Authorization of Chemicals; MLP: Multi-Layer Perceptron; RF: Random Forest; SL: Simple Logistic; SMO: Sequential Minimal Optimization; Numbers in curly brackets represent the count of respective entities (i.e. molecules, descriptors and fingerprints).</p

Comparative performance of our prediction workflows with VEGA v1.08.

<p>Panel A: Molecules of challenge set-1 processed by our prediction workflows (= 74) and VEGA v1.08 (= 69) used for computation; Panel B: 69 molecules of challenge set-1 processed by our prediction workflows as well as VEGA v1.08 were used for computation; Panel C: Molecules of challenge set-2 processed by our prediction workflows (= 77) and VEGA v1.08 (= 68) used for computation; Panel D: 68 molecules of challenge set-2 processed by our prediction workflows as well as VEGA v1.08 were used for computation. VEGA v1.08: orange bars; PW-1: blue bars; PW-2: green bars. CCR: Correct Classification Rate.</p

Leave-one out analysis to assess the contributions of QSAR models, similarity information and sub-structure pattern to the prediction performance of prediction workflows.

<p>Leave-one out analysis to assess the contributions of QSAR models, similarity information and sub-structure pattern to the prediction performance of prediction workflows.</p

Integration of QSAR models, similarity information and sub-structure pattern into prediction workflows (PWs).

<p>Blue and red colors depict components that differ in the two Prediction Workflows, PW-1 and PW-2. Components in black and grey are those that are common in both PW-1 and PW-2. QSAR: Quantitative Structure-Activity Relationship; MLP: Multi-Layer Perceptron; SMO: Sequential Minimal Optimization; E_o: Energy-optimized dataset; RTS: Representative test set; Challenge-1: Challenge set-1; Challenge-2: Challenge set-2; m₂, m₃, m₄, s_similarity and s_substr are predictions from QSAR. models-2, 3 and 4, similarity information and sub-structure pattern, and w_m2, w_m3, w_m4, w_similarity and w_substr are their corresponding weights.</p