Computational Toxicology

Introduction to Computational Toxicology & Read Across Session 1
Sep
15

Introduction to Computational Toxicology & Read Across Session 1

Session 1: Introduction to Computational Toxicology: Principles, Applications, and Regulatory Considerations

Tuesday, September 15, 7:00 ET / 13:00 CET

Explore the fundamental principles of computational toxicology, its regulatory applications, and the role of in silico methods in modern safety assessments.

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Introduction to Computational Toxicology & Read Across Session 2
Sep
22

Introduction to Computational Toxicology & Read Across Session 2

Session 2: Selecting the Right Computational Approach: When and How to Apply In Silico Methods

Tuesday, September 22, 7:00 ET / 13:00 CET

Develop a practical framework for selecting the most appropriate computational approach based on the assessment objective and available data.

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Introduction to Computational Toxicology & Read Across Session 3
Sep
29

Introduction to Computational Toxicology & Read Across Session 3

Session 3: Finding and Evaluating Relevant Toxicity Data

Tuesday, September 29, 7:00 ET / 13:00 CET

Discover strategies for identifying, evaluating, and organizing relevant toxicity data from public resources to support safety assessments.

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Introduction to Computational Toxicology & Read Across Session 4
Oct
6

Introduction to Computational Toxicology & Read Across Session 4

Session 4: Using Cheminformatics and Pharmacokinetics to Predict Chemical Behavior

Tuesday, October 6, 7:00 ET / 13:00 CET

Apply cheminformatics and pharmacokinetic modeling tools to predict chemical behavior and support exposure and hazard assessments.

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Introduction to Computational Toxicology & Read Across Session 5
Oct
20

Introduction to Computational Toxicology & Read Across Session 5

Session 5: Predicting Toxicological Hazards Using QSAR Models

Tuesday, October 20, 7:00 ET / 13:00 CET

Examine how QSAR models predict toxicological hazards, select appropriate models, and interpret predictions within their applicability domains.

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Introduction to Computational Toxicology & Read Across Session 6
Oct
27

Introduction to Computational Toxicology & Read Across Session 6

Session 6: Estimating Quantitative Toxicity Values from Computational Models

Tuesday, October 27, 7:00 ET / 13:00 CET

Explore computational approaches for estimating quantitative toxicity values using points of departure (PoDs), benchmark dose modeling, and IVIVE.

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Introduction to Computational Toxicology & Read Across Session 7
Nov
3

Introduction to Computational Toxicology & Read Across Session 7

Session 7: Read Across Decision Making: Determining When Read Across Is Scientifically Justified

Tuesday, November 3, 7:00 ET / 13:00 CET

Evaluate when Read Across is scientifically justified and develop a regulatory-relevant strategy for addressing toxicological data gaps.

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Introduction to Computational Toxicology & Read Across Session 8
Nov
10

Introduction to Computational Toxicology & Read Across Session 8

Session 8: Conducting a Scientifically Defensible Read Across Assessment

Tuesday, November 10, 7:00 ET / 13:00 CET

Build a scientifically defensible Read Across assessment by identifying suitable analogs, evaluating similarity, and documenting uncertainty.

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Introduction to Computational Toxicology & Read Across Session 9
Nov
17

Introduction to Computational Toxicology & Read Across Session 9

Session 9: Evaluating Confidence in Computational Predictions: Applicability, Uncertainty, and WoE

Tuesday, November 17, 7:00 ET / 13:00 CET

Assess the confidence of computational predictions by evaluating applicability, uncertainty, and weight of evidence.

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Introduction to Computational Toxicology & Read Across Session 10
Nov
24

Introduction to Computational Toxicology & Read Across Session 10

Session 10: Documenting and Integrating Computational Evidence into Toxicological Risk Assessment

Tuesday, November 24, 7:00 ET / 13:00 CET

Integrate computational evidence into toxicological risk assessments using documentation practices that support transparent, scientifically sound decision making.

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