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<p>Advances in drug discovery and clinical research have shifted the bottleneck in medicines development to chemistry, manufacturing, and controls activities, a critically step for regulatory approval. This includes formulation and process development of a new drug product, which traditionally requires extensive resources, often leading to suboptimal outcomes. These development processes must adapt to follow the advances in drug discovery and clinical research and ultimately shorten timelines while ensuring product quality and safety. In this work, we present an integrated platform for tablet formulation and process development that couples a digital formulator, an in-silico optimisation tool using a predictive material-to-tablet model, with a self-driving tableting data factory, which applies Bayesian optimisation within an automated, fully integrated per-tablet manufacturing to testing workflow. The results demonstrate a reduction in the time from material characterisation to in-specification tablets to 6 h and a reduction in API material use by 65% compared to current state-of-the-art methods.</p>

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Accelerated drug development using a digital formulator and a self-driving tableting data factory

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月1日

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  • Faisal Abbas,
  • Mohammad Salehian,
  • Peter Hou,
  • Jonathan Moores,
  • Jonathan Goldie,
  • Alexandros Tsioutsios,
  • Theo Tait,
  • Victor Portela,
  • Quentin Boulay,
  • Roland Thiolliere,
  • Ashley Stark,
  • Jean-Jacques Schwartz,
  • Jerome Guerin,
  • Andrew G. P. Maloney,
  • Alexandru A. Moldovan,
  • Gavin K. Reynolds,
  • Jérôme Mantanus,
  • Catriona Clark,
  • Paul Chapman,
  • Alastair Florence,
  • Daniel Markl

摘要

Advances in drug discovery and clinical research have shifted the bottleneck in medicines development to chemistry, manufacturing, and controls activities, a critically step for regulatory approval. This includes formulation and process development of a new drug product, which traditionally requires extensive resources, often leading to suboptimal outcomes. These development processes must adapt to follow the advances in drug discovery and clinical research and ultimately shorten timelines while ensuring product quality and safety. In this work, we present an integrated platform for tablet formulation and process development that couples a digital formulator, an in-silico optimisation tool using a predictive material-to-tablet model, with a self-driving tableting data factory, which applies Bayesian optimisation within an automated, fully integrated per-tablet manufacturing to testing workflow. The results demonstrate a reduction in the time from material characterisation to in-specification tablets to 6 h and a reduction in API material use by 65% compared to current state-of-the-art methods.