Pre-Clinical SIG: AI in Medicinal Products

PSI Introduction to Industry Training (ITIT) Course - 2025/2026

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.

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 Webinar: Atomic Habits - The Science of Getting Your Act Together

The book club’s usual focus is to read and discuss professional development books. In this short format event you can more easily develop you career without the commitment of reading the whole book - simply listen to the 1-hour long podcast before joining the interactive session on 21 May.

PSI Webinar: Methods and tools integrating clinical trial evidence with historical or real-world data, Bayesian borrowing, and causal inference

This webinar is organised by the RWD SIG and the Historical Data SIG. We will review recent methods, applications, and tools of integrating subject-level-data from clinical trial with external data using Bayesian methods and/or causal inference methods.

PSI Career Young Virtual Event (Q2 2025)

This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.

PSI Webinar: Applying the Estimand Framework to Clinical Pharmacology Trials with a Case Study in Bioequivalance

This will be a 45 minute webinar which will explain the topic presented in the published paper, ‘Applying the Estimand Framework to Clinical Pharmacology Trials with a Case Study in Bioequivalance’. There will be 15 minutes for a panel Q&A with some of the authors following the presentation.

PSI Webinar: Methodology and first results of the iRISE (improving Reproducibility In SciencE) consortium

This 1-hour webinar will be an opportunity to hear about the methodology and first results of the iRISE consortium. iRISE is working towards a better understanding of reproducibility and the interventions that work to improve it. At the end of the presentation there will also be the opportunity to ask questions.

One-day PSI/PHUSE Event: Change Management for Moving to R/Open-Source

This one-day event focuses on the comprehensive management of transitioning to R/Open-Source, addressing the challenges and providing actionable insights. Attendees will participate in sessions covering essential topics such as training best practices, creating strategic plans, making the case to senior management, and managing both statistical and programming aspects of the transition.

PSI Training Course: Pediatric Extrapolation

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.

PSI Career Young Virtual Event (Q3 2025)

This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.

PSI/EFSPI Vaccines Statistics Workshop

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.

PSI Career Young Virtual Event (Q4 2025)

This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.

Speaker	Biography	Abstract
Dr. Liam Childs	Dr. Liam Childs received his Doctorate in Natural Sciences (Dr. rer. nat.) from the Max-Planck Institute for Molecular Plant Physiology, with a research focus on machine learning and bioinformatics. He has over two decades of experience in artificial intelligence, data science, and bioinformatics and is currently the AI Working Group Lead at the Paul-Ehrlich-Institut in Germany, where he is responsible for developing and implementing the institute's AI strategy. Throughout his career, Liam has held multiple academic and industry positions, including postdoctoral fellowships in bioinformatics at the German Cancer Research Center (DKFZ) and Humboldt University, Berlin, as well as a role as a Senior Software Developer at Gotthardt Healthgroup.	Artificial Intelligence (AI) is increasingly integrated into the development and regulation of medicinal products, offering new opportunities in drug discovery, clinical trials, and manufacturing. AI-driven approaches, particularly machine learning and bioinformatics, facilitate target identification, optimize patient selection in clinical trials, and enhance process control in pharmaceutical manufacturing. In the context of biomedicine, AI plays a critical role in predicting drug-target interactions, modeling peptide-MHC binding for immunotherapy, and improving the precision of CRISPR-Cas9 genome editing. Despite its potential, AI applications in medicinal products present challenges, including data quality, algorithm validation, and regulatory compliance. The necessity for robust performance metrics and risk assessment frameworks is paramount to ensure the reliability and safety of AI-driven methodologies.

Speaker	Biography	Abstract
Dr. Liam Childs	Dr. Liam Childs received his Doctorate in Natural Sciences (Dr. rer. nat.) from the Max-Planck Institute for Molecular Plant Physiology, with a research focus on machine learning and bioinformatics. He has over two decades of experience in artificial intelligence, data science, and bioinformatics and is currently the AI Working Group Lead at the Paul-Ehrlich-Institut in Germany, where he is responsible for developing and implementing the institute's AI strategy. Throughout his career, Liam has held multiple academic and industry positions, including postdoctoral fellowships in bioinformatics at the German Cancer Research Center (DKFZ) and Humboldt University, Berlin, as well as a role as a Senior Software Developer at Gotthardt Healthgroup.	Artificial Intelligence (AI) is increasingly integrated into the development and regulation of medicinal products, offering new opportunities in drug discovery, clinical trials, and manufacturing. AI-driven approaches, particularly machine learning and bioinformatics, facilitate target identification, optimize patient selection in clinical trials, and enhance process control in pharmaceutical manufacturing. In the context of biomedicine, AI plays a critical role in predicting drug-target interactions, modeling peptide-MHC binding for immunotherapy, and improving the precision of CRISPR-Cas9 genome editing. Despite its potential, AI applications in medicinal products present challenges, including data quality, algorithm validation, and regulatory compliance. The necessity for robust performance metrics and risk assessment frameworks is paramount to ensure the reliability and safety of AI-driven methodologies.

Speaker	Biography	Abstract
Dr. Liam Childs	Dr. Liam Childs received his Doctorate in Natural Sciences (Dr. rer. nat.) from the Max-Planck Institute for Molecular Plant Physiology, with a research focus on machine learning and bioinformatics. He has over two decades of experience in artificial intelligence, data science, and bioinformatics and is currently the AI Working Group Lead at the Paul-Ehrlich-Institut in Germany, where he is responsible for developing and implementing the institute's AI strategy. Throughout his career, Liam has held multiple academic and industry positions, including postdoctoral fellowships in bioinformatics at the German Cancer Research Center (DKFZ) and Humboldt University, Berlin, as well as a role as a Senior Software Developer at Gotthardt Healthgroup.	Artificial Intelligence (AI) is increasingly integrated into the development and regulation of medicinal products, offering new opportunities in drug discovery, clinical trials, and manufacturing. AI-driven approaches, particularly machine learning and bioinformatics, facilitate target identification, optimize patient selection in clinical trials, and enhance process control in pharmaceutical manufacturing. In the context of biomedicine, AI plays a critical role in predicting drug-target interactions, modeling peptide-MHC binding for immunotherapy, and improving the precision of CRISPR-Cas9 genome editing. Despite its potential, AI applications in medicinal products present challenges, including data quality, algorithm validation, and regulatory compliance. The necessity for robust performance metrics and risk assessment frameworks is paramount to ensure the reliability and safety of AI-driven methodologies.

Speaker	Biography	Abstract
Dr. Liam Childs	Dr. Liam Childs received his Doctorate in Natural Sciences (Dr. rer. nat.) from the Max-Planck Institute for Molecular Plant Physiology, with a research focus on machine learning and bioinformatics. He has over two decades of experience in artificial intelligence, data science, and bioinformatics and is currently the AI Working Group Lead at the Paul-Ehrlich-Institut in Germany, where he is responsible for developing and implementing the institute's AI strategy. Throughout his career, Liam has held multiple academic and industry positions, including postdoctoral fellowships in bioinformatics at the German Cancer Research Center (DKFZ) and Humboldt University, Berlin, as well as a role as a Senior Software Developer at Gotthardt Healthgroup.	Artificial Intelligence (AI) is increasingly integrated into the development and regulation of medicinal products, offering new opportunities in drug discovery, clinical trials, and manufacturing. AI-driven approaches, particularly machine learning and bioinformatics, facilitate target identification, optimize patient selection in clinical trials, and enhance process control in pharmaceutical manufacturing. In the context of biomedicine, AI plays a critical role in predicting drug-target interactions, modeling peptide-MHC binding for immunotherapy, and improving the precision of CRISPR-Cas9 genome editing. Despite its potential, AI applications in medicinal products present challenges, including data quality, algorithm validation, and regulatory compliance. The necessity for robust performance metrics and risk assessment frameworks is paramount to ensure the reliability and safety of AI-driven methodologies.

Speaker	Biography	Abstract
Dr. Liam Childs	Dr. Liam Childs received his Doctorate in Natural Sciences (Dr. rer. nat.) from the Max-Planck Institute for Molecular Plant Physiology, with a research focus on machine learning and bioinformatics. He has over two decades of experience in artificial intelligence, data science, and bioinformatics and is currently the AI Working Group Lead at the Paul-Ehrlich-Institut in Germany, where he is responsible for developing and implementing the institute's AI strategy. Throughout his career, Liam has held multiple academic and industry positions, including postdoctoral fellowships in bioinformatics at the German Cancer Research Center (DKFZ) and Humboldt University, Berlin, as well as a role as a Senior Software Developer at Gotthardt Healthgroup.	Artificial Intelligence (AI) is increasingly integrated into the development and regulation of medicinal products, offering new opportunities in drug discovery, clinical trials, and manufacturing. AI-driven approaches, particularly machine learning and bioinformatics, facilitate target identification, optimize patient selection in clinical trials, and enhance process control in pharmaceutical manufacturing. In the context of biomedicine, AI plays a critical role in predicting drug-target interactions, modeling peptide-MHC binding for immunotherapy, and improving the precision of CRISPR-Cas9 genome editing. Despite its potential, AI applications in medicinal products present challenges, including data quality, algorithm validation, and regulatory compliance. The necessity for robust performance metrics and risk assessment frameworks is paramount to ensure the reliability and safety of AI-driven methodologies.

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