Accelerated Medicines Development using a Digital Formulator and a Self-Driving Tableting DataFactory

2025-03-20

AI Summary

Traditional tablet development is time-consuming and resource-intensive, impeding efficient prediction and optimization of Critical Quality Attributes (CQAs). To address this, we propose an intelligent pharmaceutical platform integrating digital prescription design with a self-driving tablet manufacturing data factory, enabling a fully automated, closed-loop workflow—from raw material characterization to qualified tablet production. The platform introduces a hybrid mechanistic–data-driven modeling approach for the digital prescriber and incorporates Bayesian optimization alongside fully automated powder feeding, compaction, and real-time in-line performance testing. It reduces formulation development for a single tablet to under six hours using less than 5 g of active pharmaceutical ingredient (API), and completes small-batch production of up to 1,440 tablets within 24 hours. Validation across multiple APIs and drug loadings achieves 100% success rate. This framework significantly enhances development efficiency and resource utilization, establishing a scalable paradigm for accelerated formulation development.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Feature Construction/Reformulation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

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.

Problem

• Optimizes pharmaceutical tablet formulation using predictive modeling and automation
• Reduces material and time resources for tablet development and testing
• Integrates digital and physical systems for real-time quality control

Innovation

• Digital Formulator optimizes tablet formulations
• Self-Driving DataFactory automates tablet production
• Hybrid modeling combines data and mechanistic approaches