Science Pool

Understanding the Applicability and Limitations of In Silico and In Vitro Safety Models Towards the Design and Selection of the Safest Drug Candidates

The second webinar in this series on New Approaches in Toxicology was presented by Takafumi Takai PhD from Takeda.

About the Webinar

A variety of in silico and in vitro safety assays are widely used in lead optimization in order to capture the safety-related liability as early as possible. Understanding the applicability and limitation of each assay based on the chemical space/chemotype is important for the effective usage. Several case studies of the applicability analysis including in silico phototoxicity prediction and low-dose in vivo toxicity prediction by cytotoxicity assay will be presented.

About the Speaker

Takafumi Takai PhD Senior Scientist | Takeda Takafumi has more than 15 years of industry experience in drug discovery. He currently manages safety assessments mainly during compound optimization at Takeda California by using his expertise in medicinal and computational chemistry. Prior to joining the drug safety department, he led drug discovery projects as a medicinal chemist at Takeda in Japan. He holds a Ph.D. from Okayama University, Japan as well as a Master’s Degree from Tohoku University, Japan.

Watch the webinar to learn more!

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Drug Discovery and Development:  Challenges and Opportunities in Toxicology

The first webinar in this series on new approaches in toxicology was presented by Professor Ruth Roberts from ApconiX.

About the Webinar

Ruth will present lessons learned from >20 years’ experience in the pharmaceutical industry focusing on attrition, prediction and the challenges posed by non-clinical to clinical concordance. Several case studies that focus on science-driven design will be shared for discussion. Chemical liabilities should be eliminated early in discovery while there are still options in chemistry. Equally important is a thorough understanding of potential target-related risks and their likelihood of occurrence. The triple challenge of attrition, concordance and the 3Rs should drive us to take a longer term, strategic approach to drug discovery and safety testing. New technologies such as micro physiological systems (MPS), artificial intelligence and big data all offer opportunities to do things differently. But are we ready for change?  These issues and potential solutions will be presented for discussion.

About the Speaker

Professor Ruth Roberts PhD, ATS, FBTS, ERT, FRSB, FR CPath Director and Co-founder | ApconiX Ruth is chair of drug discovery at Birmingham University, UK and Co-founder of ApconiX, an integrated toxicology and ion channel company that brings together a team of world-renowned nonclinical safety experts with over 300 years of drug discovery and development experience.  Before that Ruth was Global Head of Regulatory Safety at AstraZeneca and Director of Toxicology for Aventis in Paris, France. With >150 publications in peer reviewed journals, she is interested in developing and implementing new models to increase success rates in drug discovery and development. Ruth is former president of EUROTOX, former past president of the Academy of Toxicological Sciences and was recently elected to vice-chair of the Board of Trustees of the Health and Environmental Sciences Institute (HESI), based in Washington, DC. She was the recipient of the SOT Achievement award in 2002, the EUROTOX Bo Holmstedt Award in 2009 and the SOT 2018 Founders award, given in recognition of outstanding leadership in fostering the role of toxicological sciences in safety decision making.

Watch the webinar to learn more!

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Learn more about our ligand binding assays and how they can support biological drug testing in a regulated environment, including:

• Regulated bioanalysis
• Immunogenicity
• Immunotoxicity
• Biomarkers (safety and efficacy)
• Clinical Pathology
• Sample Management
• Additional activities

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This article forms a book chapter in Artificial Intelligence in Drug Design.

In this review article, we discuss the use of machine learning approaches in large omics studies

The chapter includes:

• an overview of the typical analysis of an omics dataset using machine learning
• demonstration of how to build a model predicting drug-induced liver injury (DILI) using transcriptomics data
• best practices and pitfalls to consider during initial data exploration and model training through to validation and analysis of the final model

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Learn more about our chronic exposure nephrotoxicity assay including:

• Background information
• Assay details and protocol summary
• Data generated in the chronic exposure nephrotoxicity assay

Learn more about our apoptosis and necrosis assay including:

• Background information
• Assay details and protocol summary
• Data generated in the apoptosis and necrosis assay

Learn more about our 3D combined hypertrophy and cardiotoxicity assay including:

• Background information
• Assay details and protocol summary
• Data generated in the3D combined hypertrophy and cardiotoxicity assay

Learn more about the GreenScreen HC genotoxicity assay including:

• Background information
• Assay details and protocol summary
• Data generated in the GreenScreen HC genotoxicity assay

Learn more about our high context toxicology (HCS) cytotoxicity screening panel including:

• Background information
• Assay details and protocol summary
• Data generated in the HCS cytotoxicity screening panel

Learn more about our in vitro micronucleus (MNT) test using flow cytometry and TK6 cells including:

• Background information
• Assay details and protocol summary
• Data generated in the in vitro micronucleus (MNT) test using flow cytometry and TK6 cells