Integrative 3D-QSAR and In Vitro Studies on Medicinal Plants Identify Bergenia ligulata
Abstract
Abstract Background: Bladder cancer remains a major global health challenge and there is an urgent need for new an effective therapeutic agent with reduced toxicity. Natural products from medicinal plants are an invaluable source of bioactive scaffolds; combining computational screening with targeted in vitro testing can accelerate active principle identification. Methods: We screened 477 phytochemicals from four Ayurvedic plants Bergenia ligulata (Pashanbheda), Aerva lanata (Badra), Boerhavia diffusa (Punarnava), Tribulus terrestris (Gokshura) and using a tiered in silico pipeline (SwissADME, ProTox‑II and Molib biofilm prediction) to select 160 drug‑like candidates. From these, 40 top candidates (10 per plant) were advanced to 3D‑QSAR modelling (EasyQSAR) and partial least squares (PLS) regression combined with 5‑fold cross‑validation. The predicted leads were subsequently validated experimentally: aqueous and hydroalcoholic extracts of the four plants were assayed for cytotoxicity against the human bladder carcinoma cell line HT-1376 originating from a grade IV transitional cell carcinoma and grade III bladder carcinoma, respectively. Results: The PLS QSAR model for Bergenia ligulate demonstrated strong predictive performance (r2=0.8578; q2=0.868), and identified several flavonoids and alkaloids (including eryodictiol‑7‑O‑β‑D‑glucopyranoside, isorhamnetin, quercetin, aervine) as top candidates. In vitro study, Bergenia ligulata extracts produced the most potent, time‑ and…
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Evidence Hub reads this as a preprint, done in cell cultures (in vitro) and computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
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