Skip to content
Log in

Author

N. Jawahar

View on Semantic Scholar

2 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Review Jul 2026

Data Collection and Management for AI-Based Pharmaceutical Formulation Development: A Comprehensive Review.

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. · 0 citations
Review Aug 2026

Role of estrogen receptor α in the regulation of breast cancer progression: signaling pathways and therapeutic implications.

Estrogen receptor alpha (ERα) is central to breast-cancer initiation, progression, and endocrine resistance. This narrative review uses a transparent, non-systematic search of peer-reviewed English-language literature published from 2017 through 2025 to integrate ERα structure and isoforms, genomic and non-genomic signaling, post-translational regulation, tumor-microenvironment interactions, heterogeneity, and therapeutic development. The synthesis distinguishes mechanistic, preclinical, and clinical evidence and highlights areas of uncertainty. ESR1 ligand-binding-domain mutations are rare in untreated primary disease but are enriched after endocrine selection in metastatic disease; emerging oral SERDs, ER-directed PROTACs, and biomarker-matched pathway combinations show heterogeneous results across overall and molecularly defined populations. ER-low disease remains a clinically important category requiring confirmation of pathology, integration of tumor biology and disease setting, and individualized use of endocrine and chemotherapy-based strategies. Overall, therapeutic progress increasingly depends on matching ERα-directed interventions to dynamic biomarkers, resistance mechanisms, treatment history, and tolerability rather than assuming uniform benefit across ER-positive disease.

B. Samuel, Selvaraj Jubie, N. Jawahar et al. · 0 citations