The adoption of Continuous Manufacturing (CM) for Oral Solid Dosages (OSD) is often challenged by the limited sensitivity of traditional Process Analytical Technology (PAT), such as Near-infrared (NIR) and Raman spectroscopy, to provide sufficient accuracy for process monitoring and control of low-dose or fixed-dose formulations. This manuscript explores solutions by highlighting advanced control strategies and alternative manufacturing technologies. These strategies include enhanced spectroscopic methods (e.g., Spatially resolved-NIRS, Light-induced fluorescence) to provide improved accuracy/precision, the use of process data and process models (Residence Time Distribution, Multivariate Statistical Process Control) as soft sensors, hybrid PAT and process models and more traditional at-line/off-line monitoring using NIR, Raman or high-sensitivity liquid chromatography with stratified sampling and bracketing. Alternatively, several technologies inherently ensure high content uniformity, such as semi-Continuous Manufacturing (sCM) with accurate mini-batch dispensing and Dry Coating Technology. For Twin-Screw Hot Melt Extrusion (HME) molecular-level mixing delivers more uniform blends, but current low-dose applications still require pre-blending of the drug substance with suitable excipients. When fed with a uniform powder blend, twin screw wet granulation also ensures compliant content uniformity without the need for PAT monitoring. In conclusion, a successful CM of low dose products may be possible when strategically combining advanced spectral and data approaches, modelling, and innovative platforms to build robust and validated process controls. This has been demonstrated across multiple peer reviewed studies and is now gradually being incorporated into control strategies for the commercial manufacture of pharmaceutical products.
Pseudomonas aeruginosa is an antibiotic-resistant pathogen and leading cause of hospital-acquired infections. Its ability to form biofilms enables persistent colonization by protecting bacterial cells from antibiotics and host immune action. The Pel exopolysaccharide is a key component of the biofilm, contributing to intercellular adhesion and structural integrity. Although Pel has been implicated in P. aeruginosa pathogenesis, its immunogenic potential remains underexplored. In this study, a panel of synthetic Pel-derived oligosaccharides was prepared with a thiol-functionalized linker to enable site-selective conjugation to the carrier protein CRM197. The resulting glycoconjugates were administered to mice and elicited robust antibody responses. Among these, one hexasaccharide conjugate induced antibodies that bound strongly to both the natural Pel polysaccharide and biofilm forming P. aeruginosa cells. These findings support the feasibility of using well-defined synthetic Pel-structures in glycoconjugate vaccine development and provide a molecular framework for targeting biofilm-associated antigens in P. aeruginosa.
Deep learning models that infer clinically relevant biomarker status from tissue images are being explored as rapid and low-cost alternatives to molecular testing. Here we show, through statistical analysis across multiple cancer types, datasets and modelling approaches, that the datasets used to train these models contain strong dependencies between biomarkers and clinicopathological features, which prevent models from isolating the effect of a single biomarker and lead them to learn confounded signals. Consequently, their prediction accuracy varies substantially with the status of codependent biomarkers and clinicopathological variables, and for several biomarkers, the gain over what a pathologist can already infer from routine histopathological features, such as grade, remains modest. These findings indicate that current approaches are not yet suitable as substitutes for molecular testing but can support triage or complementary decision-making with caution. Unconfounded biomarker prediction will require models that learn causal rather than correlational relationships between biomarkers and tissue morphology.
Niraparib was approved in the EU in 2017 as maintenance treatment for platinum-sensitive, recurrent ovarian cancer, and in 2020 as first-line maintenance after response to platinum-based chemotherapy. Results from a prospective, noninterventional, single-arm, postauthorization safety study characterizing the risk of developing myelodysplastic syndrome (MDS)/acute myeloid leukemia (AML) and other second primary malignancies (SPMs) in patients treated with niraparib in routine clinical practice are reported. Adult patients with epithelial ovarian cancer from Germany, Italy, the Netherlands, and Spain who received niraparib maintenance were enrolled. Patients were followed from niraparib initiation (index date) to the earliest of study completion at 5 years’ follow-up, study discontinuation, death, or final database lock (July 11, 2024). Incidence of MDS/AML, and other SPMs were reported, and treatment-emergent adverse events were summarized. Analyses were stratified by niraparib maintenance treatment line. Overall, 745 patients (181 first-line maintenance; 564 recurrent) were enrolled and included in this analysis (median age, 65 years; stage III/IV at diagnosis, 89.5
Detection of N-nitrosamines (NA) in pharmaceuticals became a point of interest due to the mutagenic and carcinogenic potential of some compounds of this class, and their identification as nitrosated forms of marketed drugs, otherwise known as NA Drug Substance-Related Impurities (NDSRIs). The Ames test is used to assess the mutagenic potential of drug impurities, including NAs. Concerns over the sensitivity of the Ames test, as recommended in Organization for Economic Co-operation and Development Test Guideline 471, in predicting the rodent carcinogenic potential of NAs has prompted optimization of several test parameters used for detecting the mutagenicity of NAs (e.g. methods used for metabolic activation and the selection of tester strains). In order to discuss optimal Ames test conditions for the evaluation of NAs, including NDSRIs, the Office of New Drugs in the US Food and Drug Administration's Center for Drug Evaluation and Research and the Health and Environmental Sciences Institute's Genetic Toxicology Technical Committee co-organized and co-sponsored a workshop entitled "Nitrosamines: Ames Data Review and Method Development Workshop". The workshop featured five sessions addressing charge questions pertinent to the Ames test conditions and performance through the presentation of data and panel discussions. This report outlines the key takeaway points from the workshop.
