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<p>Bladder cancer is considered one of the most common cancers in the world and is significantly a danger to humanity due to the increase in the number of deaths. In recent years, many advances in bladder cancer treatment have been made, and many newer types of treatment are now being studied. Combination therapy is considered one of the most successful strategies for treating bladder cancer due to its high effectiveness in treatment and reducing toxicity. A wide range of studies were proposed using mathematical models to evaluate drug combinations in drug development. However, it is difficult to conclude the best models that must be used to aid in clinical trial simulations to know the effect of a drug combination. This paper aims to introduce a method that enhances the prediction accuracy of the most effective drug combinations to treat bladder cancer based on network analysis and genetic algorithms.&#xa0;We developed a computational framework that integrates network-based pathway analysis with a genetic algorithm to predict optimal drug combinations targeting the MAPK signaling pathway. The genetic algorithm (GA) integrates pathway topology and node effects to prioritize combinations. In vitro assays using T 24 cells were performed to confirm results of computational study.&#xa0;The results of our experiments prove the proposed method performs acceptably. The study showed suggested synergistic or additive effects of the combination of S6K1 inhibitors (PF 4708671), and ERK 1/2 inhibitor (FR 180204).&#xa0;Our method outperforms OCSANA in pathway coverage and scalability and may serve as a decision-support tool in preclinical cancer therapy design.</p>

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Optimizing drug combinations for human bladder carcinoma using genetic algorithm based on network analysis

  • 期刊论文

  • 发表日期：

    2025年9月22日

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  • Doaa Mohamed,
  • Marwa Hashem,
  • Nada Osama,
  • Fahd Kamal,
  • Ahmad Sharaf ElDin

摘要

Bladder cancer is considered one of the most common cancers in the world and is significantly a danger to humanity due to the increase in the number of deaths. In recent years, many advances in bladder cancer treatment have been made, and many newer types of treatment are now being studied. Combination therapy is considered one of the most successful strategies for treating bladder cancer due to its high effectiveness in treatment and reducing toxicity. A wide range of studies were proposed using mathematical models to evaluate drug combinations in drug development. However, it is difficult to conclude the best models that must be used to aid in clinical trial simulations to know the effect of a drug combination. This paper aims to introduce a method that enhances the prediction accuracy of the most effective drug combinations to treat bladder cancer based on network analysis and genetic algorithms. We developed a computational framework that integrates network-based pathway analysis with a genetic algorithm to predict optimal drug combinations targeting the MAPK signaling pathway. The genetic algorithm (GA) integrates pathway topology and node effects to prioritize combinations. In vitro assays using T 24 cells were performed to confirm results of computational study. The results of our experiments prove the proposed method performs acceptably. The study showed suggested synergistic or additive effects of the combination of S6K1 inhibitors (PF 4708671), and ERK 1/2 inhibitor (FR 180204). Our method outperforms OCSANA in pathway coverage and scalability and may serve as a decision-support tool in preclinical cancer therapy design.