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
PSI Introduction to Industry Training (ITIT) Course - 2026/2027
An introductory course giving an overview of the pharmaceutical industry and the drug development process as a whole, aimed at those with 1-3 years' experience. It comprises of six 2-day sessions covering a range of topics including Research and Development, Toxicology, Data Management and the Role of a CRO, Clinical Trials, Reimbursement, and Marketing.
This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
This webinar brings together three bitesize complementary sessions to help PSI contributors create conference presentations and posters that communicate clearly and inclusively. Participants will explore how to refine their message, prepare materials effectively, and adopt practical habits that support confident, accessible delivery. A focused, supportive session designed to elevate every contribution.
Our monthly webinar series allows attendees to gain practical knowledge and skills in open-source coding and tools, with a focus on applications in the pharmaceutical industry. This month’s session, “Graphics Basics,” will introduce the fundamentals of producing graphics using the ggplot2 package.
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.
Join our Health Technology Assessment (HTA) European Special Interest Group (ESIG) for a webinar on the strategic role of statisticians in the Joint Clinical Assessment (JCA). The introduction of the JCA marks a new era for evidence generation and market access in Europe. As HTA requirements become more harmonized and methodologically demanding, the role of statisticians has evolved far beyond data analysis. Today, statistical expertise is central to shaping clinical development strategies, designing robust comparative evidence, and ensuring that submissions withstand the scrutiny of EU-level assessors. In this webinar, we explore how statisticians contribute strategically to successful JCA outcomes.
Statisticians in the Age of AI: On Route to Strategic Partnership
A 90-minute webinar featuring two case studies from Bayer and Roche demonstrating how statisticians successfully integrated into AI programs, followed by interactive discussion on strategies for elevating statistical expertise in the AI era.
Enhancing Clinical Study Reporting with the Estimand Framework
Join us for an insightful webinar where we explore practical strategies for applying the estimand framework in clinical study reporting. Drawing on real-world experiences and case studies, we will share recommendations to help you:
• Understand the role of estimands in improving transparency and interpretation of trial results.
• Navigate common challenges in implementing the framework during reporting.
• Apply best practices to enhance regulatory submissions, webposting in public registries (clinicaltrials.gov/CTIS), and scientific publications.
Whether you are involved in clinical trial design, data analysis, or regulatory submissions, this session will provide actionable guidance to realize the full potential of the estimand framework.
This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
GSK - Statistics Director - Vaccines and Infectious Disease
We are seeking an experienced and visionary Statistics Director to join our Team and lead strategic statistical innovation across GSK’s Vaccines and Infectious Disease portfolio.
As a Senior Biostatistician I at ICON, you will play a pivotal role in designing and analyzing clinical trials, interpreting complex medical data, and contributing to the advancement of innovative treatments and therapies.
As a Statistical Scientist at ICON, you will play a pivotal role in designing and analyzing clinical trials, interpreting complex medical data, and contributing to the advancement of innovative treatments and therapies.
We have an exciting opportunity for an Associate Director, Biostatistics to join a passionate team within Advanced Quantitative Sciences – Full Development.
: We have an exciting opportunity for an Associate Director (AD), Statistical Programming, to join a passionate team within Advanced Quantitative Sciences- Development.
Novartis - Senior Principal Statistical Programmer
We have an exciting opportunity for a Senior Principal Statistical Programmer, to join a passionate team within Advanced Quantitative Sciences – Development.
Pierre Fabre - Clinical Development Safety Statistics Expert M/F
We are seeking a highly skilled and proactive Clinical Development Safety Statistics Expert to join our Biometry Department and the Biometry Leadership Team based in Toulouse (31, Oncopole) or Boulogne (92).
Pierre Fabre - Lead Statistician – Real World Evidence -CDI- M/F
Pierre Fabre Laboratories are hiring a highly skilled and experienced Lead Statistician – Real World Evidence (RWE) to join the Biometry Department, part of the Data Science & Biometry Department, based in Toulouse (Oncopôle) or Boulogne.
Pierre Fabre - Lead Statistician- Clinical Trials M/F
We are seeking a highly skilled and experienced Lead Statistician in Clinical Trials to join our Biometry Department based in Toulouse (31, Oncopole) or Boulogne (92).
We are looking for Senior Statistical Programmers in the UK to join Veramed, where you'll deliver high-impact programming solutions in an FSP-style capacity, while advancing your career in a supportive, growth-driven environment.