In Silico toxicology

Question 1

Definition of In Silico Toxicology

Answer 1

Computational approaches to predict safety and toxicology of compounds, reducing reliance on animal testing.

Question 2

Advantages of In Silico Toxicology

Answer 2

Ethical alternative to animal testing, faster, cost-effective, scalable, early identification of toxicology concerns.

Question 3

Challenges of In Silico Toxicology

Answer 3

Data quality, regulatory acceptance, AI generalization issues.

Question 4

Thalidomide Tragedy

Answer 4

A sedative marketed for pregnant women, led to severe birth defects due to lack of teratogenic testing.

Question 5

TGN1412 Clinical Trial Failure

Answer 5

Monoclonal antibody caused immune reaction in humans due to species-specific immune function differences.

Question 6

Drug-Induced Liver Injury (DILI)

Answer 6

AI models integrate multi-omics data to improve DILI risk prediction.

Question 7

Mechanisms of Drug Toxicity

Answer 7

Includes on-target toxicity, off-target toxicity, immune hypersensitivity, bioactivation, and drug-drug interactions.

Question 8

Structural Alerts in Toxicity Prediction

Answer 8

Identifies toxicophores like anilines, nitroaromatic groups, quinones.

Question 9

Ligand-Based Approaches

Answer 9

Uses known active compounds to predict similar toxic behavior.

Question 10

Structure-Based Approaches

Answer 10

Predicts toxicity based on molecular docking and binding affinity.

Question 11

QSAR Modeling

Answer 11

Uses molecular descriptors (e.g., dipole moment, molecular weight) to predict toxicity via ML models.

Question 12

AI-Based Toxicity Prediction

Answer 12

Deep Learning (CNNs, RNNs, GNNs) detects hidden toxicity patterns in chemical structures.

Question 13

hERG Inhibition & Cardiotoxicity

Answer 13

hERG potassium channel block leads to QT prolongation and arrhythmia risk.

Question 14

AI for Drug-Induced Liver Injury (DILI)

Answer 14

DeepDILI model integrates transcriptomics and metabolomics with clinical reports.

Question 15

Ames Mutagenicity Prediction

Answer 15

AI models reduce false positives in Ames bacterial mutagenicity assays.

Question 16

Carcinogenicity Prediction

Answer 16

Hybrid Deep Learning models predict carcinogens with improved accuracy.

Question 17

LD50 Prediction

Answer 17

XGBoost, RF, and GNNs outperform traditional lethal dose prediction models.

Question 18

Skin Sensitization Prediction

Answer 18

GCNN models achieve high accuracy in predicting allergenic potential of chemicals.

Question 19

FDA Predictive Toxicology Roadmap

Answer 19

Focus on AI-powered toxicity prediction, microphysiological systems, multi-omics integration.

Question 20

AI in Toxicology

Answer 20

Faster, scalable, predicts rare toxicities, integrates diverse datasets (genomics, proteomics).

Question 21

Challenges in AI Toxicology

Answer 21

Data bias, black-box models, regulatory hurdles.

Question 22

Regulatory AI Initiatives

Answer 22

FDA & EMA working on explainable AI (SHAP, LIME) for approval of AI toxicity predictions.

Question 23

Data Standardization (FAIR Principles)

Answer 23

Ensures toxicity datasets are Findable, Accessible, Interoperable, and Reusable.

Question 24

Hybrid AI & Experimental Models

Answer 24

Combining AI predictions with organs-on-a-chip for more human-relevant toxicity assessment.

Question 25

Future of In Silico Toxicology

Answer 25

Multi-omics integration, mechanistic AI models, improved regulatory adoption.