American Journal of PharmTech Research

ABSTRACT: Artificial Intelligence (AI) is emerging as a transformative technology in pharmaceutical regulatory affairs, enhancing efficiency, accuracy, and regulatory decision-making throughout the product lifecycle. Recent studies have demonstrated the application of AI and Machine Learning (ML) in regulatory intelligence, dossier preparation, document management, pharmacovigilance, signal detection, and compliance monitoring. Regulatory agencies worldwide are increasingly exploring AI-driven approaches to streamline regulatory submissions and improve review processes while maintaining safety, efficacy, and quality standards. This review aims to evaluate the role of AI in transforming regulatory affairs and to compare the adoption and implementation of AI-enabled regulatory practices by the Central Drugs Standard Control Organization (CDSCO), India, and Health Canada. The study examines current applications of AI in regulatory operations, including automated document review, adverse event monitoring, data analytics, and decision-support systems. Furthermore, it assesses the opportunities, challenges, and regulatory considerations associated with AI integration, such as data governance, transparency, validation, ethical concerns, and workforce readiness. The study concludes that AI has the potential to reshape the future of regulatory science by improving operational efficiency and regulatory effectiveness. Strategic adoption of AI technologies by regulatory authorities can contribute to faster access to safe and effective medicines while maintaining high regulatory standards4.

Oral medicated jellies are a promising alternative dosage forms for patients who experience difficulty in swallowing the conventional dosage forms like tablets and capsules. The present study aimed to formulate and evaluate oral medicated jellies containing Diclofenac sodium, a non-steroidal anti-inflammatory drug, and Dicyclomine HCl, an antispasmodic agent, using different concentrations and combinations of gelatin and xanthan gum. Solid dispersion of with beta- cyclodextrin was prepared by the kneading method. Nine formulations of oral jellies (F1–F9) were prepared by the heating and congealing method using gelatin, xanthan gum, and their combinations. The formulations were evaluated for physical appearance, compatibility using FTIR, weight variation, pH, syneresis, drug content, and in-vitro drug release. The dissolution studies were performed using USP apparatus II in phosphate buffer pH 6.8 for 120 min. Dissolution efficiency (%) and mean dissolution time (MDT) were determined. The results of FTIR studies indicated compatibility between the drugs and excipients. The formulations exhibited acceptable physicochemical properties. Formulation F4 showed the highest drug release of 91.11 ± 0.22% and 94.81 ± 0.21% at 30 min for Diclofenac sodium and Dicyclomine HCl, respectively. The Dissolution efficiency of formulation F4 was 57.31% and 68.75% at 30 min, while the mean dissolution time was 16.56 min and 15.08 min, respectively. Formulation F4 was selected as the best formulation due to high dissolution efficiency and rapid drug release, demonstrating the potential of oral medicated jelly as a patient-friendly dosage form.

Dapsone is a synthetic sulfone with antimicrobial and anti-inflammatory activity, but conventional topical formulations may provide limited skin penetration and short local retention. This study aimed to develop, optimize, and characterize a dapsone-loaded solid lipid nanoparticle (DDP-SLN) polymeric hydrogel for controlled dermal delivery. DDP-SLNs were prepared by hot homogenization followed by probe ultrasonication using glyceryl monostearate (GMS), soya lecithin, and Tween 80. A three-factor, three-level Box–Behnken design using Design-Expert® Version 13 was employed to optimize the formulation based on particle size, entrapment efficiency, and 12-h cumulative drug release. The optimized DDP-SLN dispersion was incorporated into Carbopol 934 hydrogel and evaluated for physicochemical, rheological, drug-release, and stability characteristics. The optimized DDP-SLN showed a particle size of 140.38 nm, PDI of 0.287, zeta potential of −35.0 mV, and entrapment efficiency of 87.93%. The hydrogel exhibited homogeneous texture, pH of 6.23 ± 0.08, viscosity of 8486.67 ± 70.95 cP, and spreadability of 18.42 g·cm/s. Cumulative drug release reached 87.90% over 12 h and followed the Higuchi model (R² = 0.9910), while the Korsmeyer–Peppas exponent (n = 0.612) indicated anomalous transport. The formulation remained stable under the evaluated storage conditions, demonstrating its potential as a topical delivery system for dapsone. The system was designed to enhance drug localization while providing sustained release and hydrogel performance. These findings demonstrate the suitability of combining SLN technology with a hydrogel to improve topical dapsone delivery and maintain formulation quality.