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| Main Authors: | , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.17411 |
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| _version_ | 1866908277452832768 |
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| author | Abbas, Faisal Salehian, Mohammad Hou, Peter Moores, Jonathan Goldie, Jonathan Tsioutsios, Alexandros Portela, Victor Boulay, Quentin Thiolliere, Roland Stark, Ashley Schwartz, Jean-Jacques Guerin, Jerome Maloney, Andrew G. P. Moldovan, Alexandru A. Reynolds, Gavin K. Mantanus, Jérôme Clark, Catriona Chapman, Paul Florence, Alastair Markl, Daniel |
| author_facet | Abbas, Faisal Salehian, Mohammad Hou, Peter Moores, Jonathan Goldie, Jonathan Tsioutsios, Alexandros Portela, Victor Boulay, Quentin Thiolliere, Roland Stark, Ashley Schwartz, Jean-Jacques Guerin, Jerome Maloney, Andrew G. P. Moldovan, Alexandru A. Reynolds, Gavin K. Mantanus, Jérôme Clark, Catriona Chapman, Paul Florence, Alastair Markl, Daniel |
| contents | Pharmaceutical tablet formulation and process development, traditionally a complex and multi-dimensional decision-making process, necessitates extensive experimentation and resources, often resulting in suboptimal solutions. This study presents an integrated platform for tablet formulation and manufacturing, built around a Digital Formulator and a Self-Driving Tableting DataFactory. By combining predictive modelling, optimisation algorithms, and automation, this system offers a material-to-product approach to predict and optimise critical quality attributes for different formulations, linking raw material attributes to key blend and tablet properties, such as flowability, porosity, and tensile strength. The platform leverages the Digital Formulator, an in-silico optimisation framework that employs a hybrid system of models - melding data-driven and mechanistic models - to identify optimal formulation settings for manufacturability. Optimised formulations then proceed through the self-driving Tableting DataFactory, which includes automated powder dosing, tablet compression and performance testing, followed by iterative refinement of process parameters through Bayesian optimisation methods. This approach accelerates the timeline from material characterisation to development of an in-specification tablet within 6 hours, utilising less than 5 grams of API, and manufacturing small batch sizes of up to 1,440 tablets with augmented and mixed reality enabled real-time quality control within 24 hours. Validation across multiple APIs and drug loadings underscores the platform's capacity to reliably meet target quality attributes, positioning it as a transformative solution for accelerated and resource-efficient pharmaceutical development. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_17411 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Accelerated Medicines Development using a Digital Formulator and a Self-Driving Tableting DataFactory Abbas, Faisal Salehian, Mohammad Hou, Peter Moores, Jonathan Goldie, Jonathan Tsioutsios, Alexandros Portela, Victor Boulay, Quentin Thiolliere, Roland Stark, Ashley Schwartz, Jean-Jacques Guerin, Jerome Maloney, Andrew G. P. Moldovan, Alexandru A. Reynolds, Gavin K. Mantanus, Jérôme Clark, Catriona Chapman, Paul Florence, Alastair Markl, Daniel Computational Engineering, Finance, and Science Pharmaceutical tablet formulation and process development, traditionally a complex and multi-dimensional decision-making process, necessitates extensive experimentation and resources, often resulting in suboptimal solutions. This study presents an integrated platform for tablet formulation and manufacturing, built around a Digital Formulator and a Self-Driving Tableting DataFactory. By combining predictive modelling, optimisation algorithms, and automation, this system offers a material-to-product approach to predict and optimise critical quality attributes for different formulations, linking raw material attributes to key blend and tablet properties, such as flowability, porosity, and tensile strength. The platform leverages the Digital Formulator, an in-silico optimisation framework that employs a hybrid system of models - melding data-driven and mechanistic models - to identify optimal formulation settings for manufacturability. Optimised formulations then proceed through the self-driving Tableting DataFactory, which includes automated powder dosing, tablet compression and performance testing, followed by iterative refinement of process parameters through Bayesian optimisation methods. This approach accelerates the timeline from material characterisation to development of an in-specification tablet within 6 hours, utilising less than 5 grams of API, and manufacturing small batch sizes of up to 1,440 tablets with augmented and mixed reality enabled real-time quality control within 24 hours. Validation across multiple APIs and drug loadings underscores the platform's capacity to reliably meet target quality attributes, positioning it as a transformative solution for accelerated and resource-efficient pharmaceutical development. |
| title | Accelerated Medicines Development using a Digital Formulator and a Self-Driving Tableting DataFactory |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2503.17411 |