Thematic Structure and Evolution of a Pharmaceutical Sciences Research Corpus from Dr. Anroop Nair

Objective

Drug delivery and pharmaceutical-analysis research spans a wide range of sub-disciplines - nanocarrier formulation, transdermal and iontophoretic delivery, analytical method development, and computational drug discovery - that are rarely mapped together quantitatively. This study applies scientometric network analysis to a Scopus-indexed pharmaceutical sciences publication corpus to characterize its thematic structure, temporal evolution, and most productive contributors.

Design/Methodology/Approach

A total of 289 documents published between 2003 and 2026 were retrieved from Scopus. A keyword co-occurrence network was built from the Author Keywords and Index Keywords fields using a Python-based pipeline (networkx, scikit-learn, matplotlib) implementing Louvain community detection, modularity (Q) and mean-silhouette (S) coherence scoring, TF-IDF cluster labelling, and Kleinberg-style burst detection - conceptually equivalent to the co-word mapping produced by VOSviewer or CiteSpace.

Findings

The network comprised 300 keyword nodes and 18,228 edges (density = 0.406), resolving into six clusters (Q = 0.141, weighted mean S = 0.172). The largest cluster (82 member terms) centered on drug-delivery-system formulation and release; the second largest (64 terms) on computational and mechanistic drug chemistry, including molecular docking. Annual output rose from single digits before 2018 to a peak of 40 documents in 2021, and the corpus accumulated 10,617 citations overall (≈36.7 per document). Fifteen keyword bursts were detected; the strongest was a 2009 surge in hydrogen-ion-concentration/pH-related terms (strength = 15.0), tied to iontophoretic and ungual drug-transport studies published that year.

Practical implications

The cluster structure and burst timeline provide a data-grounded map of where formulation science, analytical chemistry, and computational drug discovery intersect in this corpus, useful for identifying collaboration opportunities and under-examined niches such as green analytical chemistry and AI-assisted docking.

Originality/value

The analysis relies on keyword co-occurrence only, since the underlying export lacked a References field and citation-based co-citation mapping was therefore not possible. The modularity and silhouette scores obtained (Q = 0.141, S = 0.172) indicate a relatively diffuse rather than sharply modular network - a limitation reported explicitly rather than overstated - which itself is informative about how densely interconnected this corpus's vocabulary is.