The systematic monitoring of post-marketing drug safety is paramount to ensuring patient
welfare and population-level health, requiring the timely detection and prevention of adverse
drug events. Traditional pharmacovigilance schemes, which largely rely on spontaneous
reporting, have weaknesses, including underreporting, delays in the detection of signals, and a
lack of cohesion in data sources. As heterogeneous health data grows exponentially,
conventional methods are becoming less effective at providing complete, real-time ADR
monitoring.
This study examined the potential of improved computational methods, particularly artificial
intelligence (AI) and deep learning, to enhance pharmacovigilance practices. Neural network
architectures, including Bidirectional Encoder Representations with Transformers (BERT), Long
Short-term Memory (LSTM), and Convolutional Neural Networks (CNNs), are capable of
detecting meaningful patterns in unstructured and complex data. Natural language processing
methods have been identified as capable of interpreting free-text clinical narratives, PROs, and
biomedical literature relevant to drug safety monitoring.
A wide range of data environments has been studied, including electronic health records, global
safety-reporting schemes, the scientific literature, and patient communities on the Internet. The
commonly used performance evaluation metrics were also reviewed in this study to assess the
model's strength and reliability. The feasibility of such technologies in practice can be seen in the
field of operation of these robots, such as automated ADR detection using the FDA Adverse
Event Reporting System (FAERS) and early signal detection via social media mining.
These results indicate the potential for substantial improvements in current pharmacovigilance
systems through the use of AI-based models that can identify latent relationships and deliver
safety alerts in near-real time. Nevertheless, there are still problematic areas, including data
heterogeneity, lack of standardization, algorithmic bias, low interpretability, and ethical and
regulatory issues. This work highlights the need to ensure the integration of AI in
pharmacovigilance by collaborating with other disciplines and revising regulatory guidelines.
Overall, this research offers practical recommendations for the development of data-driven
surveillance of drug safety in recent healthcare systems.
C. R. Darwin, Meruva Sathish Kumar, S. Marakatham et al.· Current Drug Safety· 0 citations
G-protein Coupled Receptors (GPCRs) are the largest family of classical membrane receptors and the most important class of pharmacological targets, playing roles in many physiological and pathological processes. Targeting a GPCR is a challenging approach because traditional drugs and similar therapeutics have a number of significant drawbacks, including low aqueous solubility, low bioavailability, rapid metabolic degradation, low tissue specificity, and off-target effects. This review discusses different nanocarrier-based drug delivery strategies to improve the therapeutic efficacy, targeting efficiency, and translational potential of drugs acting on GPCRs.
A systematic review of peer-reviewed published articles to explore the latest developments in GPCR biology, GPCR signalling pathways, and drug delivery using nanocarriers was conducted. Major nanoplatforms, such as liposomes, polymeric nanoparticles, dendrimers, and hybrid nanosystems, were studied based on their design principles, targeting strategies, and therapeutic applications. Preclinical, mechanistic, and early translational studies that were relevant were included.
Nanocarrier-based systems have multiple advantages in GPCR-targeted therapy, such as enhanced drug stability, increased bioavailability, controlled release, and reduced systemic toxicity. Functionalization of nanocarriers may improve delivery in a receptor-specific manner and facilitate transport across biological barriers, such as the blood-brain barrier. These systems also support multiple functions, including co-delivery of therapeutics, nucleic acids, and diagnostic components. Promising applications have been identified in oncology, neurological disorders, cardiovascular diseases, and inflammatory conditions. However, there are still challenges in translation, particularly with regard to immunogenicity, long-term safety, manufacturing scalability, and regulatory approval.
The combined use of GPCR pharmacology with state-of-the-art nanocarrier engineering is a promising approach to improve receptor selectivity and therapeutic precision. Novel instruments, such as artificial intelligence, molecular modelling, and systems pharmacology, may further aid the optimisation of ligand selection, carrier design, and personalised therapeutic development.
Nanocarrier-mediated drug delivery is a sensible and highly promising strategy to address the limitations of conventional GPCR therapeutics. In addition, developments in targeted nanomedicine may accelerate the development of accurate and individualised GPCR-targeted therapeutic treatments.
Pranay Wal, Jyotsana Dwivedi, K. Khairunnisa et al.· Current Drug Targets· 0 citations
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