Jul 2026· Journal of Translational Medicine· 0 citations
TL;DR
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 is developed.
Abstract
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.
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.
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.
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.
A one‐pot synthesis of aromatic aminopropyl lactams (ArAPLs) via hydrolysis of bicyclic amidines (DBN, DBU), followed by reductive amination with aromatic aldehydes supports the cytotoxic potential of ArAPLs.
M. Martins, Ruben Valente, Ruben Amaro et al.· ChemMedChem· 0 citations
Hepatocellular carcinoma (HCC) remains a leading cause of cancer mortality, with limited therapeutic options and poor clinical outcomes. Tumor heterogeneity is a defining feature of HCC and a major barrier to effective treatment. To address this, we established a biobank of patient-derived HCC organoids generated from diagnostic biopsies, capturing the diversity of clinical features across disease stages. Using a high-throughput phenotypic screening platform, we tested more than 1,600 compounds and identified multiple agents with strong antitumor activity, including candidates suitable for drug repurposing. To further improve therapeutic responses, we systematically evaluated rationally designed doublet and triplet combinations anchored on regorafenib. Several combinations showed enhanced efficacy across HCCOs while maintaining selectivity over non-tumoral cells. In vivo validation of a selected triplet confirmed improved tumor control and tolerability. Together, these findings establish patient-derived organoids as a scalable platform for functional drug discovery and support combination strategies improving response across heterogeneous HCC.
S. Nuciforo, L. Blukacz, Marie-Anne Heusler et al.· Cell Reports· 0 citations
Colorectal cancer (CRC) exhibits high genetic heterogeneity and frequent resistance to single-agent therapies, highlighting the need for rationally designed drug combinations. We apply a mechanistic modelling framework to prioritise synergistic combinations in 16 CRC cell lines. Simulations of 210 double perturbations among 21 targeted inhibitors guided the selection of 17 combinations for experimental validation. Of 704 tested conditions, 68% showed synergy, most involving PI3K/AKT/MTOR-related inhibitors. We report six novel compound combinations that inhibit CRC cell-line growth by targeting MTOR and ERK, or MTOR and BCL2. In silico analysis indicates that feedback suppression via S6K–FOXO3 mediates the MTORi–ERKi synergy, whereas apoptotic reinforcement explains the MTORi–BCL2i effect. These results demonstrate that model-guided prioritisation enables the efficient discovery of synergistic drug pairs, mechanistic insight into their action, and a scalable route to rational combination-therapy design in CRC.
Viviam Solangeli Bermúdez Paiva, H. Bwanika, M.S. Sigfúsdóttir et al.· npj Systems Biology and Appl...· 0 citations
ICBcDrug is a freely accessible and valuable resource for advancing ICB combination therapy that integrates 2311 reported or predicted compounds across 18 cancer types and accurately predicted both the efficacy and potential mechanisms of the known ICB enhancer entinostat.
Yu Lin, Wen Sun, Yun Xia et al.· Chinese Medical Journal· 0 citations
Precision oncology aims to match cancer patients with the most effective therapeutic drugs. Although cancer genomics has been widely applied, its low matching rate limits its clinical value. Image-based functional precision oncology, which directly characterizes the responses of patient-derived cells to drugs, is expected to achieve higher matching rates. However, acquiring only phenotypic information from images cannot distinguish specific target engagement from non-selective cytotoxicity, which usually leads to some false-positive results. In this study, using live-cell FRET microscopy, we can simultaneously capture drug-induced modulation of target-associated protein-protein interactions (PPIs) and cellular phenotypic information from living cells in a single step. Then we established a drug efficacy scoring method that integrates drug-induced modulation of target-associated PPIs into cellular phenotypic information (FRET-HCI). FRET-HCI enables discrimination of on-target drug effects from non-selective cytotoxicity, which not only reduces false positives inherent in image-based approaches but also provides a framework for ranking targeted drugs according to their on-target efficacy.
Lu Gao, Beini Sun, Chuan Peng et al.· European journal of medicina...· 0 citations
Breast cancer remains one of the leading causes of cancer-related mortality worldwide, highlighting the need for more selective and effective therapies. This study aimed to identify and validate novel anticancer peptides targeting Luminal A (MCF-7) and triple-negative (MDA-MB-231) breast cancer subtypes using an integrated in silico and in vitro approach. Overexpressed proteins in each cell line were identified through literature mining, followed by the construction of protein–protein interaction (PPI) networks using STRING. Conserved motifs within PPI components were identified via multiple sequence alignment using the MEME Suite. Immunogenicity and anticancer potential of the selected motifs were predicted using VaxiJen and AntiCP, respectively. Peptide–target interactions were assessed through molecular docking (PatchDock/FireDock) and refined using molecular dynamics simulations in GROMACS. Based on these analyses, two lead peptides per subtype were selected, synthesized, and experimentally evaluated for cytotoxicity and subcellular localization. Two lead peptides were identified for each breast cancer subtype. Notably, the peptides RVCGDRGFFF and WYLKMMWQW exhibited strong membrane-associated interactions and significantly reduced the viability of MDA-MB-231 cells by approximately 75% and 90%, respectively. The combined computational and experimental approach enabled the identification of peptides with selective cytotoxic effects and favorable predicted immunogenic profiles. These findings demonstrate that integrating computational screening with experimental validation is an effective strategy for accelerating the discovery of selective anticancer peptides. The identified candidates represent promising leads for the development of peptide-based therapeutics targeting specific breast cancer subtypes, with potential applications in oncology.
Isabella Fagundes Gurgel, Ana Carolini Almeida Marcarini, Carlos Marchiorio Lacerda et al.· International Journal of Pep...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
