Organogels as Topical Drug Delivery System

Bathula Swetha — 2026-05-04

A three-dimensional network of interwoven, self-assembling gelator fibers that immobilize an organic liquid phase constitutes organogels, which is a semi-solid structures. These systems have the appearance and rheological behaviour of solids, even though their composition is primarily liquid. Only in the last several decades has there been a notable increase in research interest in organogels, which has led to a better knowledge of their structure and function. However, many elements of organogel systems remain obscure, particularly the specific molecular mechanisms and circumstances that enable and govern gelation. Despite these knowledge gaps, the quick growth of research has resulted in the creation and use of numerous organogel systems in a variety of sectors. Their unique features, including thermodynamic stability, biocompatibility, and ease of synthesis, have made them particularly attractive as delivery matrices for bioactive chemicals. Because of their structural integrity and stability, organogels have been investigated for their potential in regulated and prolonged drug release as well as in applications relating to food, medicine, and cosmetics. The goal of this work is to illustrate the basic properties of organogels, with a particular emphasis on the many kinds of organogelators that create their unique networks. It also looks at how versatile organogels are as carriers in regulated delivery systems. By knowing their composition, formation mechanisms, and functional benefits, organogels can be better used in building effective and creative delivery platforms for a wide range of therapeutic and industrial uses.

Development and Validation of a Clinical Prediction Model for Insulin Requirement in Diabetes Mellitus Patients: The Insulin Requirement Probability Score (IRPS)

Mahmoud Younis — 2026-02-26

Background: Determining optimal pharmacological intervention in diabetes mellitus remains challenging, particularly in identifying patients requiring insulin therapy versus those manageable with oral agents. Current practice lacks validated quantitative tools integrating beta-cell function and glycemic parameters to predict insulin requirement.

Objective: To develop and validate a clinical prediction model estimating insulin requirement probability using readily available clinical and laboratory parameters.

Methods: This retrospective study included 300 adult patients with diabetes (85% Type 2, 15% Type 1). Using logistic regression, they developed the Insulin Requirement Probability Score (IRPS), incorporating serum C-peptide, glycated hemoglobin (HbA1c), diabetes duration, age, fasting blood glucose, and diabetes type. The dataset was split into training (n=240) and test (n=60) sets. Performance was assessed using the area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, and calibration statistics.

Results: The IRPS demonstrated excellent discrimination with an AUC-ROC of 0.91 (95% CI: 0.85-0.97). At the optimal threshold (0.50), the model achieved 86.7% accuracy, 89.3% sensitivity, and 84.4% specificity. C-peptide emerged as the strongest predictor (β = -2.0, p <0.001), followed by Type 1 diabetes classification (β = +3.5, p <0.001). The model explained 74% of variance (Nagelkerke R² = 0.74) with good calibration (Hosmer-Lemeshow p = 0.60). Risk stratification categorized patients into four groups, enabling individualized treatment recommendations.

Conclusion: The IRPS provides a validated, quantitative tool for predicting insulin requirement, integrating beta-cell function with clinical parameters to support evidence-based treatment decisions in diabetes management.

International Journal of Pharmaceutical Process Chemistry

Organogels as Topical Drug Delivery System

Development and Validation of a Clinical Prediction Model for Insulin Requirement in Diabetes Mellitus Patients: The Insulin Requirement Probability Score (IRPS)