International Journal of Pharmaceutical Process Chemistry

An Extensive Analysis of Artificial Intelligence in Drug Development and Discovery: A Comprehensive Review

Vinay Gaikwad — 2026-06-10

The development of new pharmaceuticals typically requires significant time and financial investment, often exceeding a decade and costing billions of dollars. Researchers must evaluate millions of compounds to discover a single effective drug. Artificial Intelligence (AI) is transforming this landscape by serving as a powerful digital assistant that accelerates processes, lowers costs, and enhances accuracy. AI employs advanced computer algorithms to analyse extensive datasets that would take humans a lifetime to process. It aids in multiple aspects, including identifying appropriate biological targets for diseases, designing novel chemical compounds from the ground up, and predicting the safety or toxicity of potential drugs before human testing. Additionally, it can uncover new applications for existing medications. Despite its potential, AI faces certain challenges, high-quality data is essential for effective learning, and it can be challenging for researchers to comprehend the rationale behind AI's conclusions. Nevertheless, by merging the rapid capabilities of technology with the expertise of human researchers, AI is facilitating the development of life-saving treatments more efficiently, bringing us closer to an era of personalised medicine for all.

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.

Preparation and Characterization of Drug Nanocrystals to Enhance Solubility of Poorly Water-Soluble Drugs

Md. Ali — 2026-06-10

This work investigates the preparation and characterization of drug nanocrystals as a strategy to enhance the solubility and dissolution behavior of poorly water-soluble drugs, particularly those classified under Biopharmaceutics Classification System (BCS) Class II and IV. Poor aqueous solubility remains a critical challenge in pharmaceutical development, often leading to limited bioavailability and suboptimal therapeutic performance. In this study, drug nanocrystals were prepared using both top-down and bottom-up approaches, specifically high-pressure homogenization and antisolvent precipitation. Stabilization was achieved using suitable surfactants and polymers to prevent aggregation and ensure nanoscale stability. Comprehensive physicochemical characterization was performed using dynamic light scattering (DLS) for particle size and polydispersity index, zeta potential analysis for stability assessment, scanning electron microscopy (SEM) for morphology, differential scanning calorimetry (DSC) for thermal behavior, and X-ray diffraction (XRD) for crystallinity evaluation. In addition, in vitro dissolution studies were conducted to assess drug release performance. The results showed a significant reduction in particle size to the range of 100–300 nm with a narrow size distribution and sufficient zeta potential, indicating good colloidal stability. Morphological analysis confirmed the formation of uniform and nanosized particles, while thermal and XRD studies indicated retention of crystalline structure with slight amorphization. Notably, nanocrystal formulations exhibited a 2–3 fold enhancement in dissolution rate and improved saturation solubility compared to the coarse drug. Overall, this study demonstrates that nanocrystal technology is a scalable, carrier-free, and effective approach for improving the solubility, dissolution, and potential bioavailability of poorly water-soluble drugs.

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.