Titolo della tesi: Study of innovative technologies for the evaluation of the characteristics and quality of new generation pharmaceutical products
The quality control (QC) of pharmaceutical products represents one of the most critical aspects of the entire drug life cycle, from development through production to clinical application. It forms the foundation for ensuring that every medicinal product intended for patients is not only effective but also safe, stable, and consistent across different production batches. In an era marked by the rapid development of increasingly complex therapeutic modalities—including plasma-derived products, recombinant proteins, monoclonal antibodies, and nucleic acid–based vaccines—the challenges associated with pharmaceutical quality control have become significantly more demanding. Unlike small-molecule drugs, whose chemical and physicochemical properties can generally be assessed through relatively straightforward analytical techniques, biological and biotechnological products exhibit pronounced structural and functional heterogeneity. This complexity requires adopting multiparametric analytical approaches with high sensitivity and specificity, capable of detecting even subtle variations. The primary objective of QC is to ensure that each pharmaceutical product complies with stringent regulatory standards, providing consistent therapeutic performance while safeguarding patient health. This is achieved through a series of tests and validation processes that examine critical parameters such as identity, purity, potency, and stability—for both active pharmaceutical ingredients (APIs) and final formulations. The systematic identification and control of risks such as contamination, impurities, or manufacturing deviations prevent the release of non-compliant or counterfeit drugs that could compromise patient safety or cause adverse health effects.
QC therefore plays a central role not only in protecting public health but also in ensuring regulatory compliance and maintaining industry accountability. Global regulatory authorities—such as the U.S. Food and Drug Administration (FDA), the European Medicines Agency (EMA), and the World Health Organization (WHO)—impose rigorous protocols to guarantee that drugs consistently meet predefined quality parameters. This is particularly relevant for complex biologics and generics, where even slight variations in composition or structure can significantly affect efficacy or safety. Traditional QC strategies mainly focus on verifying product composition, biological activity, and shelf life. However, the heterogeneity of biological products calls for increasingly advanced testing methods aligned with international standards such as the European Pharmacopoeia, which mandate the identification of aggregates, degradation products, and structural anomalies. Classical analytical techniques are often inadequate to detect subtle molecular or structural variations arising from differences in raw materials, manufacturing processes, or storage conditions. Such variations can have significant clinical consequences, particularly for plasma-derived medicines such as human serum albumin (HSA) or innovative vaccine formulations developed during the COVID-19 pandemic. These challenges highlight the need for multiparametric analytical platforms capable of comprehensively evaluating the structural, functional, and rheological properties of pharmaceutical formulations. Given the increasing complexity of biological products, the pharmaceutical industry is increasingly relying on advanced analytical techniques—including high-performance liquid chromatography (HPLC), mass spectrometry, and microbiological testing—to detect trace impurities or degradation products and ensure product quality throughout shelf life.
Beyond regulatory compliance, these methods provide mechanistic insights into complex biological systems, support process optimization, improve batch-to-batch reproducibility, and promote the development of real-time, non-destructive monitoring techniques. Consequently, rigorous QC emerges as both a regulatory requirement and a scientific necessity—not only for batch release and post-marketing surveillance but also for fostering innovation, process optimization, and the reliability of safe and globally accessible therapies.
Building on this framework, the present research project was designed to develop and optimize a multi-target analytical approach for evaluating the quality and physicochemical properties of biologically derived pharmaceutical products, integrating complementary techniques capable of detecting pronounced and subtle compositional, structural, and functional variations critical for product quality. This strategy integrates thermogravimetry (TGA), rheology, and MicroNIR spectroscopy with advanced chemometric tools, enabling a comprehensive and multidimensional characterization of complex biological formulations and overcoming the limitations of conventional QC methods. The study examined two representative classes of products—COVID-19 vaccines and human serum albumin (HSA) solutions—and extended to the blood matrix, aiming to provide a unified multiparametric framework for evaluating advanced biological therapeutics. These products, characterized by structural complexity, sensitivity to formulation and production variables, and significant clinical implications, represent ideal candidates for an integrated analytical assessment.
All samples were analyzed by MicroNIR spectroscopy, enabling the non-destructive capture of their molecular signatures. For COVID-19 vaccines - including inactivated virus vaccines (AstraZeneca, Janssen), protein subunit vaccines (Nuvaxovid), and mRNA-based vaccines (Comirnaty, Moderna) - spectra were pretreated and analyzed using Principal Component Analysis (PCA) to reveal inherent patterns and optimize separation among formulations. The insights gained from PCA guided the development of Partial Least Squares Discriminant Analysis (PLS-DA) and Partial Least Squares (PLS) regression models, which were validated against relevant performance metrics. The PLS-DA models enabled reliable differentiation of vaccines according to their active ingredient and manufacturer, highlighting compositional differences associated with formulation and production processes. In parallel, the PLS regression model was developed for the quantification of the active component in mRNA-based vaccines (0.05, 0.1 and 0.2 mg/mL), demonstrating high predictive accuracy and linearity. These results confirm the potential of the spectroscopic–chemometric approach as a rapid, non-destructive, and robust analytical tool for both qualitative and quantitative quality assessment of vaccine formulations.
MicroNIR spectroscopy was also applied to HSA solutions, enabling the development of PLS-DA and PLS models capable of distinguishing products from two different manufacturers based not only on the production process but also on the origin of the plasma (national or international, obtained via plasmapheresis or whole blood). Furthermore, a quantitative PLS model was established to accurately determine the concentration of plasma protein (25%, 20% e 5%) in each solution, providing an integrated view of both qualitative and quantitative aspects of albumin products.
Rheological profiling complemented spectroscopic analysis for both vaccines and HSA solutions. Measurements included rotational viscosity at high (η200) and low (η1) shear rates, as well as viscoelastic parameters (G′ and G″), providing information on flow behavior, elasticity, and potential effects on micro- and macro-circulatory dynamics. For HSA solutions, rheology was studied at concentrations of 25%, 20%, and 5%, comparing products from different manufacturers and plasma origins. These findings are particularly relevant from a clinical perspective, given albumin’s role in plasma volume regulation and microcirculatory dynamics, as well as the influence of concentration, manufacturing processes, and post-translational modifications on its functional performance.
Finally, thermogravimetric analysis (TGA) coupled with chemometrics was applied to both vaccines and HSA solutions. TGA allowed the identification and quantification of thermally induced decomposition processes, revealing differences in stability and composition. The synergistic integration of TGA, rheology, and spectroscopy within a chemometric framework allowed the development of a robust analytical platform capable of identifying key quality parameters and ensuring reproducible pharmaceutical performance consistent with manufacturing specifications.
Overall, this multi-analytical and chemometrically driven approach highlights the power of combining complementary techniques to assess quality, detect subtle differences, and develop predictive models, with direct applications to both vaccine evaluation and the standardization of albumin-based therapeutics.
Furthermore, during the research period at Claude Bernard University in Lyon, this multi-analytical approach was extended to the human blood matrix, to investigate potential differences between healthy individuals and patients affected by sickle cell disease (SCD). Building on preliminary studies, whole-blood samples were analyzed using MicroNIR spectroscopy, TGA, and rheology, and the results were compared with those from previous studies by the same research group that employed ectacytometry and viscosimetry to characterize rheological alterations in SCD. The combined analyses enabled the identification of subtle variations in protein content, red blood cell deformability, and plasma composition, which were then correlated with macroscopic rheological behavior. This integration provided a deeper understanding of how molecular and structural changes translate into altered blood flow properties, highlighting the versatility of a multi-analytical, chemometrics-driven strategy for investigating complex biological matrices.
In conclusion, the proposed approach demonstrates the potential of combining complementary techniques to assess the quality of pharmaceutical products systematically, detect significant differences, and develop predictive models. Its applications range from vaccine evaluation and albumin standardization to the study of pathological blood samples. By unifying spectroscopic, rheological, and thermal analyses, this work establishes a solid and robust platform for the characterization of complex biological medicines, supporting both scientific understanding and regulatory quality assurance, and providing innovative tools to improve batch reproducibility, manufacturing efficiency, and diagnostic applications.