In the ever-evolving landscape of pharmaceutical manufacturing, the ability to precisely monitor and control the structural properties of drug products during production is paramount. Crystallinity and polymorphism — two fundamental solid-state characteristics — significantly affect drug efficacy, stability, and manufacturability. Traditionally, assessing these attributes has relied on offline analytical methods, which, while accurate, are cumbersome and fail to offer real-time insights necessary for agile and robust process control. However, a groundbreaking study published in the journal Frontiers of Chemical Science and Engineering sheds new light on how advanced soft sensor technologies can revolutionize the monitoring of critical quality attributes (CQAs) related to crystallinity and polymorphism during the pharmaceutical manufacturing process.
The research centers around the concept of soft sensors—mathematical or statistical models that leverage process data and predictive algorithms to estimate product quality parameters dynamically. This approach contrasts sharply with hard sensors that directly measure physical properties but are limited by the complexity and invasiveness of pharmaceutical processes. By incorporating soft sensors, the pharmaceutical industry could achieve real-time, non-invasive monitoring of changes in the solid-state of oral dosage forms, mitigating risks associated with polymorphic transformations and ensuring quality-by-design principles throughout the manufacturing lifecycle.
Focusing on drug products exhibiting moderate to high risk levels due to solid-state variability, researchers employed diverse modeling frameworks tailored to different manufacturing unit operations. These encompassed population balance models, which effectively characterize particle size distributions; semi-empirical models designed to correlate process variables with solid-state transformations; and statistical correlation methods for uncovering intricate relationships between process parameters and quality outcomes. Key unit operations studied include wet granulation, fluidized bed drying, milling, and tablet compression, all critical stages where polymorphism and crystallinity can evolve profoundly, impacting drug performance.
A notable innovation highlighted in the study is the application of a population balance model within the wet granulation process. This model predicted particle size distribution by accounting for nucleation, growth, aggregation, and breakage phenomena as a function of granulation parameters. Complementing this, a smoothing splines model elegantly mapped polymorphic transitions relative to the liquid-to-solid ratio, providing nuanced insights into phase conversion dynamics. Such integrative modeling facilitates a comprehensive understanding of how subtle variations in wet granulator settings can cascade into meaningful changes in product solid-state attributes.
Moving to the fluidized bed dryer, the researchers utilized the Midilli-Kucuk empirical model to predict crucial drying metrics, including product temperature and moisture content. These variables are pivotal in controlling crystallinity, as residual moisture and thermal history directly influence solid-state transformations. The ability to forecast these parameters dynamically empowers process engineers to implement timely adjustments, ensuring consistency in the final product’s polymorphic form and crystalline content.
In the milling phase, addressing particle size reduction and crystalline integrity simultaneously presents a formidable challenge. Here, a population balance model tracked particle size evolution, capturing the attrition kinetics consistent with mechanical stress. Alongside, an exponential decay model described crystallinity loss over operational time, highlighting the deleterious impact of extended milling on solid-state properties. This dual-model approach underscores the delicate balance required to achieve desired particle metrics without compromising molecular structure.
The final manufacturing unit analyzed was the tablet press, where compression pressure exerts profound effects on tablet strength and polymorphism. By deploying statistical correlation analyses, the study identified clear relationships between applied pressure, tablet tensile strength, and polymorphic alterations. These insights are instrumental in optimizing compression parameters to maintain product integrity while meeting mechanical performance criteria.
Beyond the technical modeling, the study employs sensitivity analysis anchored in the partial rank correlation coefficient (PRCC) method. This rigorous statistical technique quantifies the influence of individual process variables on CQAs, illuminating critical control points. For instance, in the wet granulation context, the liquid-to-solid ratio emerged as a dominant factor driving polymorphic transformation, while impeller speed and wet massing time significantly modulated particle size distribution. Such findings provide a rational basis for prioritizing process parameters during manufacturing design and control.
An important dimension of this work is its regulatory context. The authors acknowledge that while direct FDA guidance specific to soft sensors is currently absent, these technologies align well with broader regulatory frameworks emphasizing advanced manufacturing practices, quality-by-design (QbD), and process analytical technology (PAT). Soft sensors exemplify model-driven control strategies that enhance data integrity and risk management, key pillars in ensuring pharmaceutical quality and patient safety. Their integration into production environments promises not only to satisfy regulatory expectations but also to propel innovation in process control paradigms.
Looking ahead, the researchers foresee expanding the scope of soft sensor applications through multiscale data integration, combining molecular-scale insights with process-level variables. This holistic approach could unlock unprecedented predictive capabilities, particularly for continuous manufacturing platforms where real-time adaptability is critical. Additionally, the work underscores the necessity for collaborative efforts spanning pharmaceutical companies, academic researchers, and regulatory agencies. Focus areas include model validation protocols, lifecycle management of predictive tools, and workforce training, all essential to realize the full potential of digitalized manufacturing landscapes.
The implications of this study extend far beyond academic inquiry, heralding a new era in which pharmaceutical manufacturing is governed by intelligent systems capable of preemptively identifying and mitigating risks related to solid-state transformations. As drug complexity increases and patient safety demands intensify, such advances will be indispensable. This research not only paves the way for more robust, reproducible, and scalable manufacturing processes but also fosters regulatory confidence in deploying cutting-edge technologies that ensure therapeutic efficacy and quality standards.
In summary, integrating soft sensors into pharmaceutical manufacturing provides a powerful avenue to predict, monitor, and control crucial quality attributes associated with crystallinity and polymorphism. By applying diverse modeling approaches tailored to specific unit operations and supplemented by comprehensive sensitivity analyses, this study offers a blueprint for future process control strategies that are both scientifically rigorous and practically implementable. The alignment with regulatory goals further reinforces the strategic value of these technologies in driving the evolution of pharmaceutical production into the digital age.
The comprehensive nature of this research signifies a major step toward operationalizing advanced sensor-based models for real-time quality assurance in drug production. As the pharmaceutical sector embraces digital transformation, the adoption of soft sensors could become a cornerstone technology, ensuring that solid oral dosage forms meet exacting standards from raw material processing to final tablet compression. The prospect of continuous, data-driven control and optimization holds the promise of safer, more effective medicines delivered consistently to patients worldwide.
Subject of Research: Not applicable
Article Title: Soft sensors to predict critical quality attributes and monitor crystallinity and polymorphism change in solid oral dosage manufacturing: case studies
News Publication Date: 5-Dec-2025
Web References: http://dx.doi.org/10.1007/s11705-025-2622-6
Image Credits: HIGHER EDUCATON PRESS
Keywords
Chemistry
Tags: advancements in pharmaceutical technologyagile manufacturing processes in pharmacritical quality attributes in drug developmentmathematical models for drug quality assessmentmonitoring crystallinity and polymorphismnon-invasive monitoring techniquespredictive algorithms for drug qualityquality-by-design principles in pharmaceuticalsreal-time quality control in drug productionrisk mitigation in polymorphismsoft sensors in pharmaceutical manufacturingsolid-state characteristics of drugs
