Matthew Clark, Scientific Services, R&D Solutions, Elsevier, Philadelphia, USA & Thomas Steger-Hartmann, Investigational Toxicology, Bayer AG, Berlin, Germany
Abstract:
Attrition of drug candidates in clinical trials due to safety issues still contributes to a significant part of project closures besides other reasons such as the lack of efficacy, PK issues or strategic reasons. While failure of a candidate during preclinical development is a reflection of the primary task of the functions involved in this phase (i.e. toxicology, safety pharmacology and DMPK), failures during the later clinical phases often raise the question whether the preclinical safety studies are sufficiently predictive for the human outcome. Due to the fact that the First-in-Man study requires pivotal animal studies normally performed in two species, the focus of analysis of the debated predictivity centers around these animal studies. After the seminal study from Olson et al. (2000) numerous publications have shown that animal toxicity studies are predictive to a certain extent and that the predictivity varies among endpoints, some of them such as hematological, gastrointestinal, and cardiovascular events being better predicted than others (e.g. cutaneous adverse events). Most of these analyses compared the preclinical – clinical correlation for a rather limited set of compounds (<150) or for specific field of indications. The authors will present the results of this purely statistical approach based on data available for 3290 compounds in the commercial database Pharmapendium. The work provides answers to the implication of an observation in an animal for human risk and more specifically to the question whether concordance, i.e. the translatability of an observation from animal to human is dependent on the animal species. The statistical methods and procedures will be described in detail.
Registration:
Registration has now closed.
Scientific Meetings
How Well do Toxicology Studies Predict Clinical Safety Outcome? – A Translational Safety Big Data Analysis
Matthew Clark, Scientific Services, R&D Solutions, Elsevier, Philadelphia, USA & Thomas Steger-Hartmann, Investigational Toxicology, Bayer AG, Berlin, Germany
Abstract:
Attrition of drug candidates in clinical trials due to safety issues still contributes to a significant part of project closures besides other reasons such as the lack of efficacy, PK issues or strategic reasons. While failure of a candidate during preclinical development is a reflection of the primary task of the functions involved in this phase (i.e. toxicology, safety pharmacology and DMPK), failures during the later clinical phases often raise the question whether the preclinical safety studies are sufficiently predictive for the human outcome. Due to the fact that the First-in-Man study requires pivotal animal studies normally performed in two species, the focus of analysis of the debated predictivity centers around these animal studies. After the seminal study from Olson et al. (2000) numerous publications have shown that animal toxicity studies are predictive to a certain extent and that the predictivity varies among endpoints, some of them such as hematological, gastrointestinal, and cardiovascular events being better predicted than others (e.g. cutaneous adverse events). Most of these analyses compared the preclinical – clinical correlation for a rather limited set of compounds (<150) or for specific field of indications. The authors will present the results of this purely statistical approach based on data available for 3290 compounds in the commercial database Pharmapendium. The work provides answers to the implication of an observation in an animal for human risk and more specifically to the question whether concordance, i.e. the translatability of an observation from animal to human is dependent on the animal species. The statistical methods and procedures will be described in detail.
Registration:
Registration has now closed.
Training Courses
How Well do Toxicology Studies Predict Clinical Safety Outcome? – A Translational Safety Big Data Analysis
Matthew Clark, Scientific Services, R&D Solutions, Elsevier, Philadelphia, USA & Thomas Steger-Hartmann, Investigational Toxicology, Bayer AG, Berlin, Germany
Abstract:
Attrition of drug candidates in clinical trials due to safety issues still contributes to a significant part of project closures besides other reasons such as the lack of efficacy, PK issues or strategic reasons. While failure of a candidate during preclinical development is a reflection of the primary task of the functions involved in this phase (i.e. toxicology, safety pharmacology and DMPK), failures during the later clinical phases often raise the question whether the preclinical safety studies are sufficiently predictive for the human outcome. Due to the fact that the First-in-Man study requires pivotal animal studies normally performed in two species, the focus of analysis of the debated predictivity centers around these animal studies. After the seminal study from Olson et al. (2000) numerous publications have shown that animal toxicity studies are predictive to a certain extent and that the predictivity varies among endpoints, some of them such as hematological, gastrointestinal, and cardiovascular events being better predicted than others (e.g. cutaneous adverse events). Most of these analyses compared the preclinical – clinical correlation for a rather limited set of compounds (<150) or for specific field of indications. The authors will present the results of this purely statistical approach based on data available for 3290 compounds in the commercial database Pharmapendium. The work provides answers to the implication of an observation in an animal for human risk and more specifically to the question whether concordance, i.e. the translatability of an observation from animal to human is dependent on the animal species. The statistical methods and procedures will be described in detail.
Registration:
Registration has now closed.
Journal Club
How Well do Toxicology Studies Predict Clinical Safety Outcome? – A Translational Safety Big Data Analysis
Matthew Clark, Scientific Services, R&D Solutions, Elsevier, Philadelphia, USA & Thomas Steger-Hartmann, Investigational Toxicology, Bayer AG, Berlin, Germany
Abstract:
Attrition of drug candidates in clinical trials due to safety issues still contributes to a significant part of project closures besides other reasons such as the lack of efficacy, PK issues or strategic reasons. While failure of a candidate during preclinical development is a reflection of the primary task of the functions involved in this phase (i.e. toxicology, safety pharmacology and DMPK), failures during the later clinical phases often raise the question whether the preclinical safety studies are sufficiently predictive for the human outcome. Due to the fact that the First-in-Man study requires pivotal animal studies normally performed in two species, the focus of analysis of the debated predictivity centers around these animal studies. After the seminal study from Olson et al. (2000) numerous publications have shown that animal toxicity studies are predictive to a certain extent and that the predictivity varies among endpoints, some of them such as hematological, gastrointestinal, and cardiovascular events being better predicted than others (e.g. cutaneous adverse events). Most of these analyses compared the preclinical – clinical correlation for a rather limited set of compounds (<150) or for specific field of indications. The authors will present the results of this purely statistical approach based on data available for 3290 compounds in the commercial database Pharmapendium. The work provides answers to the implication of an observation in an animal for human risk and more specifically to the question whether concordance, i.e. the translatability of an observation from animal to human is dependent on the animal species. The statistical methods and procedures will be described in detail.
Registration:
Registration has now closed.
Webinars
How Well do Toxicology Studies Predict Clinical Safety Outcome? – A Translational Safety Big Data Analysis
Matthew Clark, Scientific Services, R&D Solutions, Elsevier, Philadelphia, USA & Thomas Steger-Hartmann, Investigational Toxicology, Bayer AG, Berlin, Germany
Abstract:
Attrition of drug candidates in clinical trials due to safety issues still contributes to a significant part of project closures besides other reasons such as the lack of efficacy, PK issues or strategic reasons. While failure of a candidate during preclinical development is a reflection of the primary task of the functions involved in this phase (i.e. toxicology, safety pharmacology and DMPK), failures during the later clinical phases often raise the question whether the preclinical safety studies are sufficiently predictive for the human outcome. Due to the fact that the First-in-Man study requires pivotal animal studies normally performed in two species, the focus of analysis of the debated predictivity centers around these animal studies. After the seminal study from Olson et al. (2000) numerous publications have shown that animal toxicity studies are predictive to a certain extent and that the predictivity varies among endpoints, some of them such as hematological, gastrointestinal, and cardiovascular events being better predicted than others (e.g. cutaneous adverse events). Most of these analyses compared the preclinical – clinical correlation for a rather limited set of compounds (<150) or for specific field of indications. The authors will present the results of this purely statistical approach based on data available for 3290 compounds in the commercial database Pharmapendium. The work provides answers to the implication of an observation in an animal for human risk and more specifically to the question whether concordance, i.e. the translatability of an observation from animal to human is dependent on the animal species. The statistical methods and procedures will be described in detail.
Registration:
Registration has now closed.
Careers Meetings
How Well do Toxicology Studies Predict Clinical Safety Outcome? – A Translational Safety Big Data Analysis
Matthew Clark, Scientific Services, R&D Solutions, Elsevier, Philadelphia, USA & Thomas Steger-Hartmann, Investigational Toxicology, Bayer AG, Berlin, Germany
Abstract:
Attrition of drug candidates in clinical trials due to safety issues still contributes to a significant part of project closures besides other reasons such as the lack of efficacy, PK issues or strategic reasons. While failure of a candidate during preclinical development is a reflection of the primary task of the functions involved in this phase (i.e. toxicology, safety pharmacology and DMPK), failures during the later clinical phases often raise the question whether the preclinical safety studies are sufficiently predictive for the human outcome. Due to the fact that the First-in-Man study requires pivotal animal studies normally performed in two species, the focus of analysis of the debated predictivity centers around these animal studies. After the seminal study from Olson et al. (2000) numerous publications have shown that animal toxicity studies are predictive to a certain extent and that the predictivity varies among endpoints, some of them such as hematological, gastrointestinal, and cardiovascular events being better predicted than others (e.g. cutaneous adverse events). Most of these analyses compared the preclinical – clinical correlation for a rather limited set of compounds (<150) or for specific field of indications. The authors will present the results of this purely statistical approach based on data available for 3290 compounds in the commercial database Pharmapendium. The work provides answers to the implication of an observation in an animal for human risk and more specifically to the question whether concordance, i.e. the translatability of an observation from animal to human is dependent on the animal species. The statistical methods and procedures will be described in detail.
Registration:
Registration has now closed.
Upcoming Events
Joint PSI/EFSPI Visualisation SIG 'Wonderful Wednesday' Webinars
Our monthly webinar explores examples of innovative data visualisations relevant to our day to day work. Each month a new dataset is provided from a clinical trial or other relevant example, and participants are invited to submit a graphic that communicates interesting and relevant characteristics of the data.
PSI Book Club - The Art of Explanation: How to Communicate with Clarity and Confidence
Develop your non-technical skills by reading The Art of Explanation by Ros Atkins and joining the Sept-Dec 2025 book club. You will be invited to join facilitated discussions of the concepts and ideas and apply skills from the book in-between sessions.
This course is aimed at biostatisticians with no or some pediatric drug development experience who are interested to further their understanding. We will give you an introduction to the pediatric drug development landscape. This will include identifying the key regulations and processes governing pediatric development, a discussion on the needs and challenges when conducting pediatric research and a focus on the ways to overcome these challenges from a statistical perspective.
This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
Pre-Clinical SIG Webinar: AI agents for drug discovery and development
AI agents are large language models equipped with tools that can autonomously tackle challenging tasks. This talk will explore how generative AI agents can enable biomedical discovery.
EFSPI/PSI Causal Inference SIG Webinar: Instrumental Variable Methods
The webinar is targeted at statisticians working in the pharmaceutical industry, and the objective is to 1) provide a basic understanding of IV methodology including how it relates to causal inference, and 2) present two inspirational pharma-relevant applications.
The Pre-Clinical Special Interest Group (SIG) Workshop 2025 will take place over two half-days on 7 - 8 October in Verona, Italy, bringing together experts from industry, academia, and regulatory institutions to discuss key challenges and innovations in pre-clinical research.
PSI Training Course: Introduction to Machine Learning
Four sessions will include ML foundation (including an introduction, data exploration for ML and dimensionality reduction and feature selection), Supervised learning (including support vector machines and model evaluation and interpretation), model optimization and unsupervised learning (including clustering) and advanced topics (including neural networks, deep learning and large language models).
The program will feature insightful sessions led by distinguished invited speakers, alongside a poster session showcasing the latest advancements in the field. Further details will be provided.
Date: 19 November 2025
This event is aimed at students with an interest in the field of Medical Statistics, for example within pharmaceuticals, healthcare and/or medical research.
This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
Associate Director Biostatistics in Early Development - Novartis
As an Associate Director Biostatistics Early Development, you will be a key member of our biostatistics group, you will play a crucial role in the design, analysis, and interpretation of clinical trials for early development programs.
Associate Director Biostatistics, Real World Data - Novartis
If you are passionate about biostatistics and real-world data, and are looking for an exciting opportunity to contribute to groundbreaking research, we encourage you to apply.
Are you passionate about making a difference in the world of healthcare? Novartis is seeking a dynamic and experienced professional to join our team in London at The Westworks.
Director of HTA Biostatistics & Medical Affairs - Novartis
As the Director of HTA Biostatistics & Medical Affairs, you will play a pivotal role in shaping the future of healthcare by providing strategic biostatistical leadership and expertise.
As a Senior Principal Biostatistician, you will be responsible and accountable for all statistical work, both scientific and operational, for one or more assigned clinical trials
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