FutureLab: Chemistry in the Digital Age

Hosted by the CAMS-Data Analytics team and SciY, this week-long event features short sessions and panel discussions. Learn how data tools are being used in chemistry research and connect with others in the field.

Dr. Samantha Pearman-Kanza is a Principal Enterprise Fellow at the University of Southampton. She is the Principal Investigator for the Careers and Skills for Data-driven Research Network www.casdar.ac.uk, and the Pathfinder Lead on Process Recording for the Physical Sciences Data Infrastructure (PSDI) Initiative – www.psdi.ac.uk. Samantha sits on the Advisory Boards for the Future Labs Live Conference in Basel, London Labs Live in the UK, the Machines Learning Chemistry Project at the University of Nottingham, and the Knowledger Project at the University of North Florida, in addition to being a member of the UK electronic information Group (UKeiG) STRIX Committee. She is also a regular columnist for the Lab Horizons Magazine under the name CompSci Cat, discussing important issues around process recording and FAIR data. Samantha’s key research areas are ELNs, process recording, FAIR data, data stewardship and research data management, and semantic web technologies.

Webinar Abstract

A Whole New Lab: Transforming Research with ELNs

As scientific research communities strive to make data more FAIR—Findable, Accessible, Interoperable, and Reusable, there has been a growing movement to adopt digital solutions in the laboratories. One of the tools that has emerged as a key part of this are Electronic Lab Notebooks (ELNs). ELNs have evolved significantly since their early adoption in the late 1990s and early 2000s, when they were primarily used in industry to streamline documentation and improve compliance. Over time, their use has expanded into academia, driven by the need for better data management, collaboration, and reproducibility. Today, ELNs are seen not just as digital replacements for paper notebooks, but as central components of a modern, FAIR-aligned research infrastructure. However, their implementation is a complex sociotechnical challenge, requiring thoughtful planning, cultural change, and ongoing support and engagement. This talk will explore the complex landscape of ELN implementation, the barriers and considerations, and what it truly takes to make digital lab records work for researchers, institutions, and the wider scientific community.

Dr. Pascal Miéville has a background in nuclear magnetic resonance (NMR) spectroscopy, specializing in hyperpolarized NMR. After spending several years in the pharmaceutical industry, he took over the NMR platform at EPFL in Lausanne. He then became the leader and senior research and teaching associate of the Swiss Cat+ West Hub, an automated, data-driven laboratory for homogeneous catalysis discovery and optimization. He also teaches laboratory automation and structural analysis to chemistry students at EPFL and acts as a senior consultant for digital chemistry in industry. 

Webinar Abstract

Experiment coding and orchestration challenges in real self-driven organic chemistry

Workflow complexity is a key issue in lab automation. Coding experiments and organizing operations in a self-driven lab for organic synthesis is not simple because workflows are usually specific and can evolve dynamically according to chemical events, such as precipitation or changes in viscosity. In this webinar, we will present some strategies we are developing at the Swiss Cat+ West Hub at EPFL to address this complexity.

Yong Mei is a Senior Analytics Scientist with seven years of experience applying data science to advance pharmaceutical R&D. A specialist in manufacturing data analysis, He has worked on supporting aspects of continuous manufacturing and pioneering machine learning based visual inspection methods.

Webinar Abstract

Functional or cosmetic coatings are applied in the pharmaceutical industry to aid performance or appearance of tableted products and the application of machine vision for automated inspection has been gaining attention with recent advances in computing power and high-resolution sensors.
Traditional manual inspection is both time and resource intensive for large batches. Defects on tablet coating are not only an indication of product quality, but also of paramount importance to process understanding. Machine vision utilizing deep learning techniques can provide an automatic and fast tool to facilitate both inspection and diversion from the good product stream based on defined visual CQA’s (Critical Quality Attributes). The goal of the vision system currently under development is to become an alternative option for labor intensive visual AQL (Acceptable Quality Limit) testing for tablet elegance and, by examining a larger proportion of the batch, extract more information from the types of defects and their frequency of occurrence. Two approaches to machine vision of tablet inspection will be compared. The first is supervised training that requires labeling defects in training datasets/images and can generate defect specific information, which can show advantages in commercial manufacturing with large batch and relatively fixed formulas. The second is unsupervised training that uses only defect-free tablets, leading to an efficient method development. The advantages and considerations of each approach will be discussed along with a case study to demonstrate the results from both scenarios. Finally, the presentation will discuss the workflow, modeling approaches of the vision system, and its benefits for continuous manufacturing.

Andrew Anderson is a strategic pharma leader with extensive expertise in small molecule development and advanced analytical technologies. At GSK and Johnson & Johnson, he led the technology strategy, roadmaps, automation, and Process Analytical Technology (PAT) across R&D and commercial operations. Recognised for forging impactful government, academic, and cross-pharma partnerships, Andrew promotes innovation through cross-functional leadership and comprehensive tech implementation, delivering measurable improvements in efficiency, data integrity, and resource optimisation.

Webinar Abstract

Optimising tablet release with hybrid fusion testing.

Some companies are now adopting at-line or off-line non-destructive testing for tablets, usually using a single spectroscopic method. But what might be possible through combining different non-destructive testing techniques, and how could this influence the analytical testing of Oral Solid Dose products in laboratories?

Shaun Latham (MSc Biotechnology, MSc Data Science & AI) is Presales Data Scientist at ZONTAL. Shaun is no stranger to the digital challenges of a laboratory with previous experience in analytical science, acting to enable digital transformation at both Jazz and Pfizer, including in the areas of labroatory automation, Process Analytical Technologies (PAT) and ML/AI.
At ZONTAL, Shaun seeks to continue his work enabling digital transformation, now with the power of a best-in-class FAIR data platform which future-proofs your data and pipelines though highly-interoperable standardized, ontologically-aligned and vendor-agnostic data. 

Laura Pineiro Fernandez, Platform Specialist, Syngenta

Webinar Abstract

From Fragmentation to Integration: Single interface workflows with ZONTAL Operations.

 

Panellist

With nearly 30 years of visionary leadership, Wolfgang Colsman serves as the Chief Executive Officer of ZONTAL, a pioneering enterprise platform for information lifecycle management and digital archiving. Under his guidance, ZONTAL has emerged as a leader in scalable digital solutions, revolutionizing information management across industries.

Prior to founding ZONTAL, Wolfgang held key leadership roles at OSTHUS, where he served as the Chief Innovation Officer and Chief Technology Officer for over two decades. At OSTHUS, he spearheaded digitalization initiatives, driving innovation and shaping the company’s trajectory as a vendor-agnostic service provider.

In addition to his corporate leadership, Wolfgang has played a pivotal role in advancing industry-wide standards through his technical leadership at the Allotrope Foundation and the Pistoia Alliance Methods Hub project. His contributions have helped establish interoperable frameworks and collaborative ecosystems that accelerate innovation, improve data accessibility, and drive digital transformation across the life sciences and beyond. Wolfgang’s tenure in executive roles underscores his commitment to technological advancement and strategic growth, making him a respected figure in the digital landscape.

 

Webinar Abstact

 

Implementing Digital Analytical Methods: Foundation for Laboratory Automation, Frictionless Tech Transfer, and AI-Driven Innovation.

 

Digitizing analytical methods is more than a technical upgrade—it is a foundational step toward fully automated laboratories and AI-enabled decision-making in pharmaceutical and biotech R&D. By moving from manual transcription to standardized, machine-readable methods, organizations unlock new possibilities for greater data integrity, end-to-end workflow orchestration, advanced analytics, and predictive modeling. But achieving this requires more than technology adoption: it demands careful planning, cross-functional alignment, and a clear strategy for regulatory compliance and change management.

This panel will focus on the practical realities of implementation and the long-term opportunities it creates. Discussion topics will include:

• What should be included in a machine-readable digital analytical method (materials, equipment, lab instructions, parameters, calculations, references, etc.)?
• Strategies for integrating machine-readable methods with workflow orchestration, robotics, automated sample handling and instrument data systems.
• Preparing data pipelines to support AI/ML applications such as predictive method optimization, anomaly detection, and autonomous lab orchestration.
• How to pilot and scale digital analytical methods within existing IT and informatics ecosystems.  Where are the opportunities to “rethink” the current methods transfer process?
• Managing organizational change: educating staff, adapting SOPs, and building trust in digital systems.
• Navigating regulatory and data-integrity expectations as methods transition into digital, automated environments.

Attendees will leave with a roadmap for not only implementing digital analytical methods today but also leveraging it as a steppingstone toward the labs of the future—where automation and AI amplify productivity, compliance, and scientific discovery.

 

 

Panellist

 

Dr. Mark Sleeper is a Principal Scientist in GSK’s Digital Analytical Platforms Team (DAP-US) specializing in Data Standardization and Surfacing, Ux Development, and Dashboard Visualizations. He earned his Ph.D. in Chemistry from Duke University and went on to spend several years conducting pharmaceutical method development and validation. Mark currently works with the DAP US and UK teams to improve data acquisition, surfacing, and analysis on GSK’s global Open-Access HPLC fleet through software-based automation. He is also the Business System Owner for GSK’s implementation of ZONTAL’s Methods DB which is currently deployed to the Open-Access fleet.

 

 

 

 

 

 

Panellist

 

Dr. Gang Xue is Senior Director at Johnson & Johnson, where he currently leads the Global Data Integration & Modeling within the Therapeutic Development & Supply function. He earned a B.S. in Chemistry and a B.E. in Computer Science from Tsinghua University, followed by a Ph.D. in Analytical Chemistry from Iowa State University. Dr. Xue's team is dedicated to harnessing digital technology to drive CMC innovation, transforming the development and delivery of life-changing medicines. They focus on building comprehensive end-to-end data infrastructure to facilitate data-driven product and process development, utilizing structured data capture, semantic data aggregation, and advanced data analytics. Another key focus for Dr. Xue's team involves implementing a cross-modality PAT strategy, which supports both process design space exploration during development and advanced process control in manufacturing. As a founding member of the Allotrope Foundation, Dr. Xue has also contributed to a number of data standardization and ontology development initiatives such as Allotrope Foundation Ontology (AFO), Pistoia IDMP and the CMC Process Ontology projects. Before his current role, he garnered over 20 years of experience in Analytical Development, Lab Informatics, and Lab Automation, having worked as a Scientific Director at Amgen and as an Associate Research Fellow at Pfizer.

Panellist

Azzedine Dabo is a Senior Principal Scientist at GSK with focus in chromatography separation techniques including reverse phase HPLC, UPLC, Insilco modelling and machine learning retention time prediction in the pharmaceutical industry and presented his work at over 30 conferences globally.  Azzedine holds a BSc in Biological chemistry from Aston University (Birmingham, UK), M.Sc. in Analytical Science and Ph.D. in Analytical Chemistry from University of Warwick (Coventry, UK) and currenting studying and Global MBA at Warwick Business School (Coventry, UK). He also a member of Analytical Science Community Council of the Royal Society of Chemistry. 

 

 

 

Panellist

Vincent Antonucci has spent >30 yrs in pharmaceutical R&D in scientific, regulatory, and digital leadership roles at Merck & Co, Inc.  Vinny has been an active participant in pre-competitive collaborations that merge science, technology, and digital standards for ~15 yrs and is passionate about the impact these collaborations can have when industry works closely together on the right problems.  Currently, Vinny is co-chair of Allotrope Foundation, a not for profit organization whose mission is to accelerate scientific innovation through application of public semantic data standards to real world problems in the life sciences.  The goal is to consistently contextualize, structure, and represent data within and across data domains to robustly connect data and reduce time to insights.

 

 

 

 

 

 

Dr Jennifer Kingston, Director of Separation Sciences, Oncology Chemistry, AstraZeneca, Cambridge, UK. Jennifer Kingston is a separation scientist with >20 years’ experience delivering chromatographic solutions within drug discovery chemistry. She is currently director of separation science at AstraZeneca’s oncology R&D site in Cambridge, UK. Jenny has a keen interest in automating workflows. This encompasses robotics, automated data transfer and data processing and streamlining multi-step processes. She has recently designed and delivered an automated purification platform at AstraZeneca.

Webinar Abstract

Accelerating Achiral SFC Purification at AstraZeneca using Automated Processing and Machine Learning-Based Chromatographic Modelling

AstraZeneca synthesises thousands of novel compounds annually, using preparative column chromatography as the primary isolation method. To improve sustainability, the company has invested in supercritical fluid chromatography (SFC) for both chiral and achiral separations. SFC provides versatile separation capabilities but complicates method development and often requires extensive manual data processing. To address these challenges, AstraZeneca implemented an integrated workflow with automated data processing, streamlined sample tracking, and machine learning-driven chromatographic modelling to accelerate SFC purification in oncology research.

Automated software tools have increased workflow efficiency by reducing data processing time by over 70% and enabling routine achiral SFC purification. Machine learning models built from processed data predict retention behaviour and recommend optimal chromatographic conditions, minimising experimental screening. Targeted investments in SFC and predictive analytics have sped up medicinal chemistry workflows and improved sustainability. Between 2023 and 2024, sample throughput increased by 64% and achiral SFC use grew from 9% to 26%. This presentation highlights these technical advances, validation outcomes, and environmental metrics, offering practical guidance for sustainable and productive chromatographic processes.