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

Formulation and Physicochemical Evaluation of a Polyherbal Topical Gel Containing Withania Somnifera, Hibiscus Rosa-sinensis, and Aloe vera

Tue, 16 Jun 2026 00:00:00 +0000

A polyherbal topical gel containing extracts of Withania somnifera, Hibiscus rosa-sinensis, and Aloe
vera was developed and evaluated to investigate its physicochemical characteristics, diffusion
behavior, antimicrobial potential, and storage stability. The plant materials were shade-dried,
pulverized, and subjected to ethanolic extraction through the maceration technique. Qualitative
phytochemical investigation of the resulting extracts confirmed the presence of several bioactive
secondary metabolites, including alkaloids, flavonoids, tannins, phenolic compounds, glycosides,
and saponins. Preliminary chromatographic profiling was carried out using Thin Layer
Chromatography (TLC), which produced characteristic spots with distinct Rf values, indicating the
presence of identifiable phytochemical constituents. Three gel formulations (F1, F2, and F3) were
prepared using Carbopol 934 as the gelling agent and subsequently assessed for various
physicochemical attributes such as appearance, homogeneity, pH, viscosity, spreadability, and
consistency. Comparative evaluation of the prepared formulations identified F2 as the most suitable
formulation owing to its balanced physicochemical properties. The selected formulation was further
examined through an in vitro diffusion study employing a Franz diffusion cell, antimicrobial testing
against Pseudomonas aeruginosa, and accelerated stability evaluation. The optimized gel
demonstrated satisfactory diffusion characteristics over a 3-hour experimental period and produced
an inhibition zone of 17.25 mm against Pseudomonas aeruginosa, indicating notable antibacterial
activity. Furthermore, no significant changes in physical appearance or stability-related parameters
were observed during the study period. The overall results suggest that the developed polyherbal gel
possesses desirable formulation characteristics and promising preliminary in vitro performance.
Nevertheless, additional investigations, including ex vivo permeation studies, skin irritation
assessments, pharmacological evaluation, and clinical validation, are necessary to further establish
its safety profile and potential therapeutic applications.

A Comprehensive Review: Personalized Treatment Strategies in Stable Ischemic Heart Disease

Tue, 28 Apr 2026 00:00:00 +0000

Stable Ischemic Heart Disease (SIHD) is one of the leading cardiovascular disorders associated with high morbidity and mortality worldwide. Since early symptoms such as chest pain, fatigue, and shortness of breath are often non-specific and difficult to recognize, diagnosis becomes challenging, and many cases remain undetected until advanced stages or complications occur. Traditional diagnosis depends heavily on clinical evaluation, electrocardiography, stress testing, and imaging techniques. The development of improved treatment modalities, beyond conventional pharmacotherapy and revascularization strategies, remains an unmet medical need. Despite extensive research, there is still an urgent need to identify more disease-specific biomarkers that could further improve patient classification and therapeutic outcomes. A paradigm shift has been observed in SIHD research with advancements toward a better understanding of disease mechanisms, aiming for effective screening, early diagnosis, and personalized treatment strategies. This shift has been driven by innovations in genomics, proteomics, and advanced cardiovascular imaging technologies. Recent advances in molecular profiling and biomarker-driven strategies have significantly improved SIHD management. Non-invasive diagnostic techniques and digital health technologies have further enhanced early detection and risk stratification. This review aims to examine the evolving landscape of personalized therapies in SIHD, highlighting key biological targets, emerging therapeutic strategies, and future perspectives.

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

Ruhana Raffic, Maheshkumar V. P, Shobha Rani Rajeev Hiremath — Thu, 26 Feb 2026 00:00:00 +0000

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.

AI in Cancer Therapy - Smarter, Faster, Better Treatment: A Systemic Review

Sayama Javed Nadaf Sayamanadaf , Sanika Mote, Alfiya Mujawar, Suraj T Jadhav — Tue, 16 Jun 2026 00:00:00 +0000

AI has revolutionized cancer therapy, transforming it into a smarter, faster, and more effective
paradigm. Machine learning algorithms excel in image analysis, detecting tumors in mammograms,
CT scans, and MRIs with precision surpassing human radiologists often achieving 95% accuracy in
early-stage lung cancer detection. Deep learning models predict patient responses to chemotherapy
and immunotherapy by analyzing genomic data, histopathological images, and electronic health
records, enabling precision medicine that tailors therapies like CAR-T cells or targeted kinase
inhibitors to individual profiles. Key advancements include AI-driven drug discovery, where
generative adversarial networks (GANs) simulate molecular interactions to identify novel
compounds, slashing development timelines from years to months. Robotic surgery assisted by AI,
such as da Vinci systems with real-time anomaly detection, minimizes invasiveness and recurrence
rates. Predictive analytics forecast disease progression and side effects, supporting adaptive dosing
in radiotherapy via tools like convolutional neural networks (CNNs). Challenges persist, including
data biases, interpretability of "black-box" models, regulatory hurdles, and ethical concerns over
equity in access. Yet, hybrid AI-human workflows promise to mitigate these. Clinical trials, like those
using IBM Watson for leukemia prognostics, demonstrate up to 30% survival improvements. Looking
ahead, federated learning and quantum-enhanced AI could further accelerate breakthroughs. This
article synthesizes recent evidence, highlighting AI's potential to make cancer therapy not just
smarter and faster, but decisively better paving the way for curative strategies in an era of
exponential data growth.

Diagnostic Challenges in the Treatment of Sjogren Syndrome

Wed, 01 Apr 2026 00:00:00 +0000

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.