Journal of Advances in Pharmacy Practices (e-ISSN: 2582-4465)

Diagnostic Challenges in the Treatment of Sjogren Syndrome

2026-04-01T10:40:15+00:00

Sjogren's syndrome (SS), an autoimmune disease that mostly affects the exocrine glands, results in dryness of the mucosal surfaces, especially the oral and ocular ones. The clinical appearance may range from simple symptoms such as mucosal dryness, arthralgias, and moderate purpura to significant systemic manifestations; it is often linked to cancer, especially non-Hodgkin lymphoma. Histologically, SS is characterized by tissue damage due to lymphocyte infiltration. While the exact pathogenetic pathways are unknown, cellular B hyperactivity with auto-antibody synthesis is a significant factor. The primary immunological markers are La/SSB (the most specific), anti-Ro/SSA, and anti-nuclear antibodies (the most commonly identified). Recognizing cryoglobulinemia, hypergammaglobulinemia, hypocomplementemia, and rheumatoid factor positive as prognostic indicators is also crucial since it may assist determine who should receive more intensive therapy. In fact, the goal of this study is to concentrate on the practical elements of managing SS patients, with an emphasis on diagnosis and treatment. When it comes to diagnosis, it is crucial to stress that while a number of classification criteria have been established over time, they are not diagnostic criteria. Instead, the clinician makes the diagnosis, perhaps with the help of instrumental investigations such as magnetic resonance imaging of parotids, high-frequency ultrasound (which is helpful as an assisting tool in labial biopsy), and ultrasound. In order to determine where to apply earlier and more aggressive therapies, treatments (from symptomatic ones to new biological therapies) should instead be tailored to the severity and organ commitment of the disease, monitoring serologic changes, and stratifying patients for the risk of developing NHL.

Integrating Predictive Modeling in Pharmacy Practice: A Narrative Review of AI Applications and Implementation Challenges

2026-02-26T08:28:56+00:00

Background The integration of Artificial Intelligence (AI)–driven predictive modeling into pharmacy practice is expanding, offering opportunities to improve patient outcomes and optimize medication management. Advanced AI approaches, including machine learning (ML), deep learning (DL), and natural language processing (NLP), enable risk stratification, data-driven clinical decision-making, and interpretation of complex or unstructured clinical data.

Objectives This narrative review examines current and emerging applications of AI-based predictive modeling in pharmacy practice, highlighting their role in clinical decision support, medication safety, adherence prediction, personalized pharmacotherapy, polypharmacy management, operational efficiency, and public health planning, while identifying key challenges and future priorities.

Methods A literature search was conducted using PubMed, Scopus, and Google Scholar with keywords related to artificial intelligence, predictive modeling, and pharmacy practice. Peer-reviewed English-language articles were included, and reference lists were screened for additional relevant studies.

Results AI-driven predictive models support diverse pharmacy applications. ML and DL facilitate prediction of adverse drug events, optimization of pharmacotherapy, identification of non-adherence risk, and detection of high-risk polypharmacy, particularly in older adults. NLP strengthens pharmacovigilance and medication review by enabling analysis of unstructured clinical text. Operational applications include inventory forecasting and supply chain optimization, while population-level models support public health planning.