Journal of Advances in Bio- pharmaceutics and Pharmacovigilance (e-ISSN: 2583-8202)

Smart Vaccinovigilance for Smart Vaccines: Digital Surveillance, Real-World Evidence, and the Expanding Role of Pharm D Practitioners

Bharathi Bhogenahalli Venkatappa — 2026-04-01

The accelerated global immunization success through the introduction of next-generation vaccines, specifically, mRNA and viral vector-based systems, has revolutionized the current practice of immunization, allowing a faster design, mass production, and response to new infectious diseases. Nevertheless, the magnitude and rate of these vaccination efforts have illuminated significant shortcomings in the conventional vaccinovigilance systems, which depends heavily on passive adverse events reporting and they are limited by under-reporting, slow signal identification, and incomplete information. This review introduces the idea of a smart vaccinovigilance, a more sophisticated model that combines digital health tools with real-life evidence to provide the opportunity to monitor vaccine safety continuously and proactively. Electronic health records, mobile health applications, wearable devices, spontaneous reporting systems, and population registries, with the help of artificial intelligence and machine learning, enable early identification of safety, automatic causality, and dynamic benefit-risk analysis. Pharm D professionals, as a growing part of active surveillance, digital pharmacovigilance, patient education, and regulatory safety reporting, are also mentioned in the review. Intelligent vaccinovigilance enhances surveillance of vaccine safety, promotes confidence in the population, and evidence-based immunization policies.

Nanoformulation of Phytochemicals: A New Frontier in Targeted Drug Delivery and Therapeutic Efficacy

D. Vignesh — 2026-02-13

Phytochemicals are bioactive substances produced from plants that show promise as treatments for cancer, heart disease, diabetes, and neurological conditions. Poor solubility, fast metabolism, low bioavailability, and restricted tissue targeting, however, impede their clinical translation. Recent developments in nanotechnology have made Nanoformulations an inventive way to get around these restrictions. Advanced pharmacokinetic profiles, enhanced stability, controlled drug release, and site-specific targeting are provided by nanocarrier systems, which include polymeric nanoparticles, liposomes, solid lipid nanoparticles, niosomes, and dendrimers. Curcumin, resveratrol, and quercetin are notable phytochemicals that have been shown to exhibit higher therapeutic efficacy in cancer therapy, particularly hepatocellular carcinoma, with less systemic toxicity when encapsulated in nanoparticles. Neurological problems can now be treated because of the development of nanocarriers that can pass through biological barriers like the blood–brain barrier. Nano-phytochemicals have promise for treating chronic inflammatory diseases, promoting wound healing, and facilitating tissue regeneration, in addition to their oncology applications. Stimuli-responsive ("smart") nanocarriers, green synthesis techniques, and AI-driven formulation design are examples of emerging breakthroughs that improve therapeutic sustainability and precision. Nanoformulation of phytochemicals is a new frontier in targeted drug delivery that holds great potential for next-generation customized treatment, despite obstacles in large-scale manufacture, regulatory approval, and long-term safety monitoring.

Artificial Intelligence Approaches in Toxicology

Navya Ponugupati — 2026-02-25

Artificial Intelligence (AI) is transforming toxicology by improving the efficiency and accuracy of chemical and drug hazardous impact prediction, detection, and analysis. Large datasets from genomes, proteomics, and chemical structure databases are being used by AI-driven models to augment or replace traditional toxicological evaluations, which frequently depend on animal testing and in vitro techniques. Early toxicity, dose-response, and mechanism of action prediction is made possible by machine learning algorithms' ability to recognise intricate patterns in biological reactions. The sensitivity and specificity of toxicological predictions are being increased by modelling nonlinear interactions and high-dimensional data using deep learning approaches, such as neural networks. AI also makes it easier to use computational toxicology and high-throughput screening methods, which lessen the need for intensive laboratory testing and are consistent with the 3Rs (Replacement, Reduction, and Refinement) in animal research. Furthermore, natural language processing helps to glean important information from extensive toxicology studies and scientific literature. These developments help with drug research, environmental safety evaluations, regulatory decision-making, and personalised medicine. Notwithstanding notable advancements, issues with data quality, model transparency, and regulatory acceptability still exist. Continued use of AI in toxicology holds the potential to improve scientific ethics and efficiency, reduce exposure to hazardous compounds in humans and the environment, and speed up risk assessment procedures.