Dengue is the most common global problem in recent times, particularly in tropical and subtropical areas, yet antivirals for therapy or prophylaxis are lacking. Millions of people are affected by this dengue virus, but no proper medication is available yet to cure this disease. One polyprotein that is encoded by the DENV genome is converted into structural and non-structural proteins that are necessary for viral pathogenesis and replication. Among these, the non-structural protein complex NS2B-NS3 is essential for viral polyprotein processing, replication, and host innate immune response control. It acts as a trypsin-like serine protease. The NS2B/NS3 protease is a key enzyme involved in viral replication and serves as a major target for drug development against the dengue virus. The NS3 protease has a conserved catalytic triad (His-Asp-Ser), whereas NS2B serves as an essential cofactor that stabilizes the active conformation of the enzyme and aids in substrate recognition. By disrupting interferon signalling pathways, the NS2B-NS3 protease not only aids in viral replication but also makes immune evasion easier. The compound that inhibits the action of this enzyme could be pioneering in the antiviral drug discovery process. This article provides a comprehensive overview of the detailed structural information of the viral protease (NS2B/NS3) enzyme with the mechanistic role of this enzyme, and highlights various inhibitors related to the NS2B/NS3 protease. A more thorough comprehension of this protease could facilitate the logical development of potent antiviral medications to prevent dengue infection.
INTRODUCTION
Traditional pharmacovigilance relies on slow clinical trials and post-marketing studies with limited coverage. This review synthesizes evidence on Real-World Data (RWD) integration with Artificial Intelligence (AI) for enhanced Adverse Drug Reaction (ADR) detection, evaluates generative AI like ChatGPT-4 and LLaMA-2 in Substance Use Disorder (SUD) scenarios, discusses current applications, and outlines future directions. The objective is to guide researchers, clinicians, and regulators in this evolving field.
METHODS
Literature was reviewed on RWD sources (EHRs, claims, registries, wearables), AI algorithms (supervised/ unsupervised learning, NLP, deep learning), and regulatory frameworks. Generative AI performance was assessed via clinician-blind evaluation of responses to Reddit-sourced SUD queries from r/stopdrinking, r/leaves, and r/OpiatesRecovery, with fact-checking against SAMHSA/FDA guidelines and consistency testing. Data included tables comparing RWD, algorithms, and AI models.
RESULTS
AI enables real-time ADR signals via RWD-AI in CCM, improving diagnostics, personalization, and drug discovery. ChatGPT-4 suggested unsafe opioid microdosing; LLaMA-2 referenced nonexistent resources and improper Xanax sharing, both showing severe inaccuracies in SUD contexts. Tables highlight RWD applications, algorithm uses, and AI limitations like bias and inconsistency.
DISCUSSION
RWD-AI transforms pharmacovigilance but faces bias, transparency, and validation challenges. FHIR/DLT enhance secure exchange; generative AIs require oversight. Implications include equitable safety monitoring via bias mitigation and regulatory compliance.
CONCLUSION
AI-RWD integration advances ADR detection and personalized safety, despite generative AI risks in SUD management. Future success demands validated LLMs, FHIR/blockchain infrastructure, and clinician collaboration for comprehensive, equitable pharmacovigilance.
Pritam Kayal, Priya Manna, Ramit Rahaman et al.· Current pharmaceutical desig...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
