Professor Daniel Markl
Strathclyde Institute of Pharmacy and Biomedical Sciences
Personal statement
Daniel Markl’s research focuses on developing predictive systems, in-silico design methods and self-driving labs for drug product development and manufacturing that accelerate the pace at which new medicines are developed and delivered. He obtained a BSc (2010) and MSc (2012) in telematics with a focus on neural networks and a PhD (2015) in chemical engineering from Graz University of Technology. Daniel secured an Erasmus Mundus in 2010, which allowed him to study modelling and control system design for one year during his MSc at Lund University. During his PhD he was employed by the Research Center Pharmaceutical Engineering (RCPE) GmbH, where he was in the group Process and Manufacturing Science and involved in several projects at the interface of pharmaceutical engineering, materials science and process modelling. He continued as Senior Scientist and Scientific Project Leader at RCPE after completing his PhD. In 2016 he joined Professor Zeitler’s group (Terahertz Applications Group) as a postdoctoral research associate at the University of Cambridge. Daniel worked for two years in the Terahertz Applications Group before becoming a Chancellor’s Fellow and Lecturer/Assistant Professor at the University of Strathclyde in the Strathclyde Institute of Pharmacy and Biomedical Sciences (SIPBS).
Daniel is Associate Director at CMAC (www.cmac.ac.uk), Training Director of the EPSRC Centre of Doctoral Training in Cyberphysical Systems for Medicines Development and Manufacturing and leads the MHRA-funded Centre of Excellence in Regulatory Science and Innovation (CERSI) for the digital transformation of medicines development and manufacturing.
Prize And Awards
- President of Pharmaceutical Solid State Research Cluster
- Recipient
- 2020
- Fellowship of Higher Education
- Recipient
- 2020
- AAPS Pharmaceutical Research Meritorious Manuscript Award
- Recipient
- 2019
- Best Poster Award
- Recipient
- 2016
- Erasmus Mundus
- Recipient
- 2010
Publications
- Revisiting punch sticking in tablet compression : what do we really know?
- Wale Ishwari, Robertson John, Singaraju Aditya Bharadwaj, Markl Daniel
- International Journal of Pharmaceutics Vol 705 (2026)
- https://doi.org/10.1016/j.ijpharm.2026.127459
- Efficient DEM modelling of direct shear test with a representative volume element : balancing fidelity and performance across boundary conditions, particle shape, and size distribution
- Almudahka Anas, Pantaleev Stefan, Salehian Mohammad, Armstrong John, Johnston Blair F, Markl Daniel
- Powder Technology Vol 486 (2026)
- https://doi.org/10.1016/j.powtec.2026.123171
- Predictive compaction modelling of ternary direct compression formulations
- Tait Theo, Salehian Mohammad, Aroniada Magdalini, Shier Andrew P, Elkes Richard, Robertson John, Markl Daniel
- International Journal of Pharmaceutics Vol 702 (2026)
- https://doi.org/10.1016/j.ijpharm.2026.127253
- Transient hydrate formation during disintegration of film-coated pharmaceutical tablets
- Ma Mingrui, Zeng Xiaorui, Powell Daniel, Nassar Marwa, Teckoe Jason, Markl Daniel, Zeitler J Axel
- International Journal of Pharmaceutics Vol 701 (2026)
- https://doi.org/10.1016/j.ijpharm.2026.127136
- Digital and AI-enabled models in pharmaceutical development and manufacturing : a regulatory-focused industry survey
- Cook Graham, Houson Ian, Mustoe Chantal, Maloney Andrew GP, Schlindwein Walkiria, Markl Daniel
- International Journal of Pharmaceutics Vol 701 (2026)
- https://doi.org/10.1016/j.ijpharm.2026.127177
- From simplicity to complexity : how excipients shape stability and disintegration of immediate-release tablets
- Ibrahim Isra, Mann James, Winge Fredrik, Davis Adrian, Hens Bart, Khadra Ibrahim, Virrankoski Milena, DeAngelis Austin, Markl Daniel
- International Journal of Pharmaceutics Vol 701 (2026)
- https://doi.org/10.1016/j.ijpharm.2026.127130
Research Interests
Our laboratory conducts multidisciplinary research at the intersection of materials, processes, products, and performance, with the goal of transforming how medicines are designed, developed, and manufactured. We seek to establish a fundamental, predictive understanding of how material properties and processing conditions jointly determine product structure, quality, and performance.
Central to our approach is the coupling of self-driving laboratories, advanced measurement techniques, and digital process and product design. By integrating automation, high-throughput experimentation, real-time analytics, and data-driven modeling, we aim to dramatically accelerate development timelines while improving robustness, efficiency, and knowledge generation across the pharmaceutical lifecycle.
Our research includes the development of predictive systems, in-silico design methods, and autonomous experimental platforms for drug product development and manufacturing. These tools enable rapid exploration of complex formulation and process spaces, support rational decision-making, and reduce reliance on trial-and-error experimentation. Ultimately, our work lays the foundation for adaptive, intelligent manufacturing systems that deliver higher-quality medicines more efficiently and reliably.
Professional Activities
- From Prediction to Quality Control: Leveraging ML-based Feeder Model's output as Soft Sensors
- Contributor
- 12/5/2026
- A Digital Particle Engineering Framework: Predicting Product Manufacturability and Performance Attributes From Drug Material Properties
- Contributor
- 12/5/2026
- Introducing VERA: A Cited AI Assistant for Digital CMC Regulatory Guidance
- Speaker
- 11/3/2026
- Digital CMC Center of Excellence in Regulatory Science & Innovation (CERSI)
- Speaker
- 2025
- Self-Driving Tableting DataFactory
- Speaker
- 2025
- Accelerated Drug Product Development using a Digital Formulator and a Self-Driving Tableting DataFactory
- Speaker
- 2025
Projects
- DPMC-CRAD-07575 - Software Development for CMAC
- Kumar, Anmol (Researcher) Houson, Ian (Principal Investigator) Markl, Daniel (Project Lead) Hamilton, Andrew (Administrator) Miller, Ross (Administrator) Wijesekara, Pathum (Researcher) Mustoe, Chantal (Researcher)
- Sandbox Backend Software Development for CERSI Model which is a pharmaceutical stability prediction toolkit developed at the University of Strathclyde.
- 17-Jan-2025 - 31-Jan-2026
- AI enabled CMC Datafactory
- Markl, Daniel (Principal Investigator) Florence, Alastair (Co-investigator)
- 01-Jan-2025 - 30-Jan-2026
- CERSI for the Digital Transformation of Medicines Development and Manufacturing
- Markl, Daniel (Principal Investigator) Florence, Alastair (Co-investigator) Johnston, Blair (Co-investigator)
- 01-Jan-2025 - 31-Jan-2026
- INtegrated Spectroscopy and Photonics for Increased productivity and Resource Efficiency in MEDicines manufacture: Sustainable Medicines Manufacturing Expression of Interest
- Nordon, Alison (Principal Investigator) Littlejohn, David (Co-investigator) Markl, Daniel (Co-investigator)
- 01-Jan-2025 - 30-Jan-2025
- DPN-MED: Digital Plug-and-Produce Network for Sustainable Medicines Development and Manufacturing
- Fitzpatrick, Stephen (Principal Investigator) Florence, Alastair (Principal Investigator) Florence, Alastair (Co-investigator) Johnston, Blair (Co-investigator) Markl, Daniel (Co-investigator)
- 01-Jan-2025 - 30-Jan-2025
- NF987
- Markl, Daniel (Principal Investigator) Macdonald, Janine (Co-investigator)
- 25-Jan-2024 - 19-Jan-2024
Contact
Professor
Daniel
Markl
Strathclyde Institute of Pharmacy and Biomedical Sciences
Email: daniel.markl@strath.ac.uk
Tel: 444 7115