PRED-SKIN

in silico AOP for skin sensitization

Fast, reliable and user-friendly tool and an alternative method for assessing skin sensitization potential of chemical substances

PRED-SKIN integrates multiple QSAR models trained on key events of the Adverse Outcome Pathway (AOP) for skin sensitization. The platform combines in chemico (DPRA), in vitro (KeratinoSens, h-CLAT, U-SENS), in vivo (LLNA), and human (HRIPT/HMT) endpoint models into a consensus prediction system trained on over 1,500 curated compounds.

The Integrated Testing Strategy (ITS) implements OECD 497 Defined Approaches, combining computational predictions with experimental data to provide GHS potency classification. The applicability domain assessment ensures predictions remain within the model's validated chemical space, supporting reliable and transparent results for cosmetic safety assessment and regulatory submissions.

AOP-BASED QSAR MODELS

Integrates multiple QSAR models covering key events of the Adverse Outcome Pathway for skin sensitization: protein binding (DPRA), keratinocyte activation (KeratinoSens, h-CLAT, U-SENS), dendritic cell activation (LLNA), and human adverse outcome (HRIPT/HMT data).

INTEGRATED TESTING STRATEGY

Implements OECD 497 Defined Approaches combining in silico predictions with experimental data. The ITS framework assigns scores to DPRA, U-SENS, and computational endpoints, providing GHS potency classification (1A, 1B, or Not Classified).

HIGH PREDICTIVE ACCURACY

Achieves 89% correct classification rate with 94% sensitivity and 84% specificity on external validation. Models are rigorously validated on independent test sets not used during training, ensuring reliable generalization to novel compounds.

AI KNOWLEDGE DISTILLATION

Leverages advanced AI through a Student-Teacher architecture where a lightweight Student model learns from an ensemble of Teacher models (ChemBERTa, Random Forest, SVM). Curriculum learning progressively optimizes training for superior predictive performance.

APPLICABILITY DOMAIN

Assesses prediction reliability by calculating the similarity between query molecules and the training set chemical space. Flags compounds outside the model's domain, providing confidence measures for each prediction.

OECD COMPLIANT

Designed according to OECD principles for QSAR validation. Provides defined endpoints (sensitizer/non-sensitizer, GHS potency), mechanistic basis (AOP key events), defined applicability domain, and documented validation metrics.

Pred-Skin Predictor

SELECT ANALYSIS MODE

In silico AOP

Prediction of skin sensitization potential based on in silico analysis as Adverse Outcome Pathway (AOP) Key Events.

Partial ITS analysis

Hybrid approach combining DPRA experimental data with in silico predictions.

Requires:DPRA

Full ITS analysis

Complete OECD 497 Integrated Testing Strategies for highest confidence predictions.

Requires:DPRAUSENS