Pre-Clinical SIG: AI in Medicinal Products

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

Topic: R Package Basics. 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, “R Package Basics,” will introduce the fundamentals of working with R packages—covering how to install, load, and manage them effectively to support data analysis and reproducible research. The session will provide a solid starting point, clarify common misconceptions, and offer valuable resources for continued learning.

PSI Mentoring 2026

Date: Ongoing 6 month cycle beginning late April/early May 2026 Are you a member of PSI looking to further your career or help develop others - why not sign up to the PSI Mentoring scheme? You can expand your network, improve your leadership skills and learn from more senior colleagues in the industry.

PSI Book Club Lunch and Learn: Communicating with Clarity and Confidence

If you have read Ros Atkins’ book The Art of Explanation or want to listen to the BBC’s ‘Communicator in Chief’, you are invited to join the PSI Book Club Lunch and Learn, to discuss the content and application with the author, Ros Atkins. Having written the book within the context of the news industry, Ros is keen to hear how we have applied the ideas as statisticians within drug development and clinical trials. There will be dedicated time during the webinar to ASK THE AUTHOR any questions – don’t miss out on this exclusive PSI Book Club event! Haven’t read the book yet? Pick up a copy today and join us. Explanation - identifying and communicating what we want to say - is described as an art, in the title of his book. However, the creativity comes from Ros’ discernment in identifying and describing a clear step-by-step process to follow and practice. Readers can learn Ros’ rules, developed and polished throughout his career as a journalist, to help communicate complex written or spoken information clearly.

PSI Training Course: Effective Leadership – the keys to growing your leadership capabilities

This course will consist of three online half-day workshops. The first will be aimed at building trust, the backbone of leadership and a key to becoming effective. This is key to building a solid foundation. The second will be on improving communication as a technical leader. This workshop will focus on communication strategies for different stakeholders and will involve tips on effective communication and how to develop the skills of active listening, coaching and what improv can teach us about good communication. The final workshop will bring these two components together to help leaders become more influential. This will also focus on how to use Steven Covey’s 7-Habits, in particular Habits 4, 5 and 6, which are called the habits of communication. The workshops will be interactive, allowing you to practice the concepts discussed. There will be plenty of time for questions and discussion. There will also be reflective time where you can think about what you are learning and how you might experiment with it.

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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