Prioritizing Combinational Drug Screening: A Ranking System for In Vitro Drug Combinations in Neurofibromatosis Type 1
Zhou, K.; Shehzad, S.; Ahmed, Z.; Zhao, O.; Sapriza, C.; Zamora, M.; Santamaria, U.; Zamora, P.
Show abstract
Neurofibromatosis type 1 (NF1) is a genetic disorder characterized by benign tumors, including plexiform neurofibromas, which can be difficult to treat. Currently, only two FDA-approved therapies exist: selumetinib, approved for pediatric patients with inoperable tumors, and mirdametinib, approved for patients aged two and older with symptomatic peripheral neuropathy where surgical resection is not possible. These limited options highlight the urgent need for novel therapeutic strategies, including combination therapies and therapies applicable to adult populations. In this study, we introduce the Composite Matrix Reduction Score (CMRS), a novel algorithm designed to evaluate the in vitro efficacy of drug combinations for NF1-related plexiform neurofibromas. Using a high-throughput 6x6 combinatorial matrix, we screened three cell lines: ipnNF95.11c (NF1+/-, non-tumor reference), and two NF1-/- tumor lines: ipNF05.5mc and ipNF95.6. Cell viability responses to drug combinations were normalized to vehicle controls, and combination effects were compared to single-agent responses. Tumor-to-non-tumor response ratios were aggregated to generate a composite ranking for each drug pair. Our results show that certain drug combinations outperformed single agents in reducing tumor cell viability, consistent with findings in other cancers. A focused analysis on selumetinib combinations supported the CMRS algorithm and identified potential synergistic partners that may surpass a single-agent therapy, highlighting candidates for continued investigation. CMRS provides a scalable, standardized framework for prioritizing drug combinations in NF1 and potentially other cancers. By integrating multi-cell line analysis, this approach enhances the identification of promising therapeutic candidates and mechanisms of action for further preclinical development.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- A distinct four-value blood signature of pyrexia under combination therapy of malignant melanoma with BRAF/MEK-inhibitors evidenced by an algorithm-defined pyrexia score 94%
- Comprehensive Live-cell Imaging Analysis of Cryptotanshinone and Synergistic Drug-Screening Effects in Various Human and Canine Cancer Cell Lines 93%
- Structure-based drug repositioning explains ibrutinib as VEGFR2 inhibitor 93%
Similar papers in this journal
Similar papers in this journal
- Multiscale Analysis And Validation Of Effective Drug Combinations Targeting Driver Kras Mutations In Non-Small Cell Lung Cancer 96%
- DReAmocracy: A Method to Capitalize on Prior Drug Discovery Efforts to Highlight Candidate Drugs for Repurposing 93%
- Protein profiling of WERI RB1 and etoposide resistant WERI ETOR reveals new insights into topoisomerase inhibitor resistance in retinoblastoma 93%
Similar papers in this journal
- Innovative, rapid, high throughput method for drug repurposing in a pandemic - a case study of SARS-CoV-2 and COVID-19 93%
- Development of a Novel Bruton's Tyrosine Kinase Inhibitor that exerts Anti-Cancer Activities Potentiates Response of Chemotherapeutic Agents In Multiple Myeloma Stem Cell-Like Cells 92%
- Evaluation of the current therapeutic approaches for COVID-19: a meta-analysis 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.