摘要

基於配體的藥物設計 (ligand-based drug design, LBDD) 與PARP 抑制劑在三陰性乳癌 (triple-negative breast cancer, TNBC) 治療中展現出顯著潛力。然而，現有文獻缺乏針對LBDD 與PARP 抑制劑在TNBC 治療應用的可視化分析。本研究以文獻計量法分析2013 年至2023 年間該領域的研究趨勢。從Web of Science Core Collection 中搜集並篩選共1,265 篇文獻，經由Bibliometrix、VOSviewer 及Scimago Graphics 進行視覺化分析，發現使用 LBDD 作為乳癌藥物研究方法的文獻數量穩定成長。關鍵詞分析顯示「定量構效關係 (QSAR)」技術在乳癌藥物設計中具有核心地位，促進了Olaparib 等 PARP 抑制劑的研發，為三陰性乳癌 (TNBC) 治療提供新策略。臺灣的研究正積極地融合機器學習技術，提升藥物設計模型的預測效能，將有望加速新藥研發並優化臨床治療方案。PARP 抑制劑在TNBC 治療文獻中引用數據年均成長率為18.47%，2018 至2019年臨床試驗的療效促使引用量激增。關鍵詞共現分析顯示五大聚類 (cluster)，涵蓋化療聯合療法、合成致死及細胞信號通路等。臨床試驗證實，Olaparib 對 BRCA 突變患者展現顯著療效，被納入 NCCN 指引中。PARP 抑制劑對HRD（同源重組缺陷）或甲基化BRCA 患者的療效將是未來研究方向。

 

ABSTRACT

Ligand-based drug design (LBDD) and PARP inhibitors demonstrate significant potential in treating triple-negative breast cancer (TNBC), though studies on their applications remain visually under-explored. This study applies bibliometric methods to analyze trends from 2013 to 2023, based on 1,265 publications from Web of Science and visualized through Bibliometrix, VOSviewer, and Scimago Graphics. Findings reveal continuous growth in LBDD research, with QSAR as a core method, facilitating the development of PARP inhibitors like Olaparib and offering new therapeutic strategies for triple-negative breast cancer (TNBC). Additionally, a trend in Taiwan toward integrating machine learning for predictive accuracy holds promise for accelerating drug discovery and optimizing clinical treatment protocols. PARP inhibitor studies in TNBC show an annual growth rate of 18.47%, notably rising in citations following clinical trials from 2018–2019. Keyword analysis identifies five main clusters in TNBC research—combination therapies, synthetic lethality, signaling pathways, and others-highlighting the diversity of therapeutic strategies. Clinical evidence suggests that Carboplatin and Olaparib are effective for BRCAmutated TNBC patients, while Olaparib may also benefit HRD or BRCA-methylated patients, suggesting directions for future exploration.

 

 

Submitted for publication: 2025.01.24; Accepted for publication: 2025.07.03