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Background <p>Combination therapy is a central strategy to overcome drug resistance in hepatocellular carcinoma (HCC), yet systematic identification of synergistic combinations is constrained by the combinatorial search space.</p> Methods <p>We developed a data-driven workflow to integrate two orthogonal single-agent resources for 244 drugs, including cell viability profiles across 11 liver cancer cell lines and bioactivity signatures across 1,925 biochemical and cell-based assays.</p> Results <p>We calculated the correlation of the bioactivity profiles between each drug pair yielding 821 high-confidence combinations involving 190 unique drugs, from which eight anchors and a 125-drug library were selected by frequency-guided prioritization. Fixed-concentration screening of 992 anchor-library combinations in the Hep 3B2.1-7 cell line yielded 89 combinations with potency enhancement. Panobinostat showed the largest potency gains when combined with briciclib or thiocolchicine (69.5-fold and 81.1-fold lower IC₅₀, respectively). Matrix-based screening of these two representative combinations across 11 cell lines revealed pronounced concentration- and context-dependence. At optimal dose pairs, strong synergy was observed in Hep 3B2.1-7 (Zero Interaction Potency (ZIP) = 40.80 ± 4.22 with 58.53 ± 6.54% inhibition), whereas antagonism was observed in some cellular backgrounds.</p> Conclusion <p>This label-free integration strategy reduces the experimental burden for hypothesis-driven combination discovery and provides context-specific preclinical leads for follow-up evaluation in HCC.</p>

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In vitro bioactivity profile-driven discovery of drug synergies in hepatocellular carcinoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月28日

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  • Tuan Xu,
  • Miao Xu,
  • Deborah K. Ngan,
  • Tongan Zhao,
  • Catherine Z. Chen,
  • Wei Zheng,
  • Ruili Huang

摘要

Background

Combination therapy is a central strategy to overcome drug resistance in hepatocellular carcinoma (HCC), yet systematic identification of synergistic combinations is constrained by the combinatorial search space.

Methods

We developed a data-driven workflow to integrate two orthogonal single-agent resources for 244 drugs, including cell viability profiles across 11 liver cancer cell lines and bioactivity signatures across 1,925 biochemical and cell-based assays.

Results

We calculated the correlation of the bioactivity profiles between each drug pair yielding 821 high-confidence combinations involving 190 unique drugs, from which eight anchors and a 125-drug library were selected by frequency-guided prioritization. Fixed-concentration screening of 992 anchor-library combinations in the Hep 3B2.1-7 cell line yielded 89 combinations with potency enhancement. Panobinostat showed the largest potency gains when combined with briciclib or thiocolchicine (69.5-fold and 81.1-fold lower IC₅₀, respectively). Matrix-based screening of these two representative combinations across 11 cell lines revealed pronounced concentration- and context-dependence. At optimal dose pairs, strong synergy was observed in Hep 3B2.1-7 (Zero Interaction Potency (ZIP) = 40.80 ± 4.22 with 58.53 ± 6.54% inhibition), whereas antagonism was observed in some cellular backgrounds.

Conclusion

This label-free integration strategy reduces the experimental burden for hypothesis-driven combination discovery and provides context-specific preclinical leads for follow-up evaluation in HCC.