https://matjournals.net/pharmacy/index.php/IJPPC <p>IJPPC provide its readers with up-to-date information relevant to pharmaceutical Process Chemistry. The journal policy is to publish work deemed by peer reviewers to be a coherent and sound addition to scientific knowledge and to put less emphasis on interest levels, provided that the research constitutes a useful contribution to the field. The focus and scope of this Journal including Medicinal Chemistry, Chemical Development, Pharmaceutical Engineering, Pharmaceutical Industry, Pharmaceutical Drugs, Drug Design, pharmacokinetics, molecular modelling, Chemical Biology, Biological agents.</p> en-US International Journal of Pharmaceutical Process Chemistry https://matjournals.net/pharmacy/index.php/IJPPC/article/view/321 <p><strong><em>Background:</em></strong><em> 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.</em></p> <p><strong><em>Objective:</em></strong><em> To develop and validate a clinical prediction model estimating insulin requirement probability using readily available clinical and laboratory parameters.</em></p> <p><strong><em>Methods:</em></strong><em> 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.</em></p> <p><strong><em>Results:</em></strong><em> 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 &lt;0.001), followed by Type 1 diabetes classification (β = +3.5, p &lt;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.</em></p> <p><strong><em>Conclusion:</em></strong><em> 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.</em></p> Mahmoud Younis Copyright (c) 2026 International Journal of Pharmaceutical Process Chemistry 2026-02-26 2026-02-26 1 14