In Vitro Exposure Systems and Dosimetry Assessment Tools for Aerosol Inhalation Toxicology

  • Free registration
  • 23-24 may 2019
  • Neuchatel, Switzerland
  • PMI
  • Agenda

This two day hands-on workshop hosted by Philip Morris International R&D at its research facility “The Cube” in Neuchatel will cover experimental and computational approaches for generation and delivery of aerosol mixtures containing particles and vapors, and their dosimetry calculations pertaining to in vitro aerosol exposure systems. Participants will learn experimental and computational methods used for the characterization of the exposure including microenvironment and physiology of cells exposed at the air-liquid interface. 

PBPK Modeling and quantitative in vitro-in vivo extrapolations

Physiologically based pharmacokinetic (PBPK) modelling plays a crucial role in next generation (non-animal) risk evaluations to inform the design of in vitro studies and to convert the data obtained with in vitro models into human dose−response or potency information. The goal of the course is to acquaint participants with setting up PBPK models based on non-animal input parameters and to subsequently use these models to extrapolate in vitro bioassay data to human dose-response or potency information.

The course consists of:

– Lectures that cover the basic principles of PBPK model-development and the application of PBPK modelling in different fields (e.g. safety evaluations of pharmaceuticals and industrial chemicals, development of alternatives to animal testing). 

– Hands-on experience focussing on how to derive adequate chemical-specific input parameters.

– Hands-on experience with developing PBPK models in Berkeley Madonna software.

– Hands-on experience with developing PBPK models with Simcyp’s Population-based Simulator.

– Use of the models to extrapolate in vitro bioassay results to human dose-response or potency information

Novel In Silico Models for Assessment of Cosmetics - Practical Applications

The course will introduce the use of models relevant for cosmetic ingredients, addressing properties like mutagenicity (Ames test and micronucleus), skin sensitizations, NOAEL, and others for human toxicity. The course will address hazard and exposure, such as skin permeation. New models, developed in the last year, will be also presented.

Questions addressed within the course will be:

How to evaluate the reliability of the models?

How to compare results from different models?

How to identify relevant chemicals for read across?

How to integrate the results from in silico models and read across?

In Vitro Lungs Model

This two-day hands-on training hosted by epithelix will cover practical use of respiratory in vitro 3D tissues and exposure devices to evaluate acute and repeated dose inhalation toxicity. To mimic systemic context, interconnection strategies of lung tissues will be presented.

Acute and chronic cardiotoxicity: CiPa (in silico & in vitro) assays

  • Fee-based
  • 14-15 november 2019
  • Leiden, Netherlands

The course is a two-day laboratory workshop. Participants will learn cell handling of human iPSC-derived cardiomyocytes. This training instructs the attendee in best practice procedure for assay preparation of cryopreserved hiPSC-derived cardiomyocytes in 48/96 or 348 well plates. We offer cell handling and preparation courses for several devices/ assays (including CiPA-like MEA assay).

Skin Sensitization

coming soon

contact us for more info

+32 (0)485 19 31 45

Past Events

Quantitative Human Cell & Effect Based In Vitro Bioanalysis for Assessing Endocrine Disrupting Compounds (EDCs)

  • Closed training
  • 14-15 March 2019
  • Parma, Italy
  • EFSA

I in-house training I

The training aims at reaching the following goal:

  • Gain knowledge on how in vitro methods (both testing and non-testing methods) are performed, 
  • Gain knowledge on the key information to consider when evaluating the results of the in vitro test methods
  • Gain knowledge on how to use and evaluate the date extracted from ToxCAST and TOXcast ER prediction model.