Pre-Clinical SIG: AI in Medicinal Products

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.

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.

AIMS SIG - Open-Source Lunch Bites

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.

Connecting the False Discovery Rate to Shrunk Estimates

A 1 hour online event, that includes a presentation followed by Q&A. This talk will explore the “replication crisis” in science, focusing on how testing large numbers of hypotheses can lead to false positive findings. It introduces key statistical approaches—False Discovery Rate (FDR) and shrinkage methods—to address this issue, and explains their conceptual foundations and connections. The session will also highlight how these tools can be understood within an empirical-Bayesian framework, linking significance testing with effect size estimation.

PSI Career Young Virtual Event (Q2 2026)

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 Book Club: The AI Con – Joint with ASA Book Club

The Guardian described the authors of this book as refreshingly sarcastic! What is sold to us as AI, they announce, is just "a bill of goods": "A few major well-placed players are poised to accumulate significant wealth by extracting value from other people's creative work, personal data, or labour, and replacing quality services with artificial facsimiles."

PSI Book Club: Another Door Opens – Book Club Special Event

This is a Book Club Special Event in response to the changes in our industry and as a supportive move to create community and connection for those navigating redundancy and uncertainty. Read the book in advance of the book club session then join the zoom call to discuss ideas. There will be breakout groups to connect with others, exchange experiences of how the book has helped, and offer support.

PSI Book Club: Change: How organisations achieve hard-to-image results in uncertain and volatile times

Organizations have to adapt to the transforming landscape of our industry to ensure they continue to be successful in the future. Many of us are feeling the impact of organizational change. By reading John P Kotter’s book we can understand about organizational change and learn how to thrive, rather than just survive, through change. Change, by John P Kotter (and his team), is a summary of all that he has learned over his decades of research and leading change. His book describes why many current approaches to change are inadequate and explains why new solutions need to give people a voice and a role in a new, change-embracing organization. Develop your understanding of organisational change and become empowered to be part of your organisation’s change, by reading Change by John P Kotter and joining the Sept-Dec 2025 book club. You will be invited to join facilitated discussions of the concepts and ideas and apply knowledge from the book in-between sessions.

PSI Career Young Virtual Event (Q3 2026)

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 Career Young Virtual Event (Q4 2026)

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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