Computational drug discovery for castration-resistant prostate cancers through in vitro drug response modeling.
Computational drug discovery for castration-resistant prostate cancers through in vitro drug response modeling.
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DOI：
10.1073/pnas.2218522120
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发表时间：
2023-04-25
影响因子：
11.1
通讯作者：
Huang, R. Stephanie
中科院分区：
文献类型：
--
作者：
Zhang, Weijie;Lee, Adam M.;Jena, Sampreeti;Huang, Yingbo;Ho, Yeung;Tietz, Kiel T.;Miller, Conor R.;Su, Mei-Chi;Mentzer, Joshua;Ling, Alexander L.;Li, Yingming;Dehm, Scott M.;Huang, R. Stephanie
关键词：
这项研究开发了一个计算框架，可以快速提出对激素治疗耐药的CRPC患者具有潜在临床影响的药物。此外，我们鉴定了一种药物COL-3，其在对恩杂鲁胺或阿比特龙显示耐药性的临床CRPC肿瘤中显示出高疗效。我们通过一对同基因CRPC细胞系模型验证了COL-3的优先功效，并进一步证明了我们的管道在有效发现具有临床影响的药物方面的实用性。前列腺癌（PC）是最常见的恶性肿瘤，也是美国男性癌症死亡的主要原因。许多PC病例转移并对全身激素治疗产生耐药性，这一阶段称为去势抵抗性前列腺癌（CRPC）。因此，迫切需要开发有效的CRPC治疗策略。传统的药物发现管道需要大量的时间和资金投入，这突出了对新方法的需求，以评估现有药物的重新定位潜力。在这里，我们提出了一个计算框架来预测临床CRPC肿瘤对各种现有化合物的药物敏感性，并确定具有高临床影响潜力的治疗方案。我们将该方法应用于CRPC患者队列，并提名药物以对抗对激素治疗的耐药性，包括阿比特龙和恩杂鲁胺。通过提名目前正在进行CRPC临床试验的多种药物证明了该方法的实用性。此外，该方法鉴别了四环素衍生物COL-3，我们验证了其在Enzalutamide耐药CRPC与Enzalutamide敏感CRPC的同基因细胞系模型中的更高疗效。在Enzalutamide耐药的CRPC细胞中，COL-3表现出更高的抑制细胞生长和迁移的活性，并诱导G1期细胞周期阻滞和凋亡。总的来说，这些发现证明了计算框架在CRPC临床试验中对正在测试的药物进行独立验证以及在enzalutamide耐药CRPC模型中提名具有增强生物活性的药物的实用性。这种方法相对于传统药物开发方法的效率表明，加速CRPC药物开发的潜力很大。
This research develops a computational framework to quickly propose drugs with potential clinical impact for CRPC patients that are resistant to hormonal therapies. Furthermore, we identified a drug—COL-3—which showed high efficacy in clinical CRPC tumors displaying resistance to enzalutamide or abiraterone. We validated COL-3’s preferential efficacy through a pair of isogenic CRPC cell line models and further demonstrated the utility of our pipeline for efficient discovery of drugs with clinical impact. Prostate cancer (PC) is the most frequently diagnosed malignancy and a leading cause of cancer deaths in US men. Many PC cases metastasize and develop resistance to systemic hormonal therapy, a stage known as castration-resistant prostate cancer (CRPC). Therefore, there is an urgent need to develop effective therapeutic strategies for CRPC. Traditional drug discovery pipelines require significant time and capital input, which highlights a need for novel methods to evaluate the repositioning potential of existing drugs. Here, we present a computational framework to predict drug sensitivities of clinical CRPC tumors to various existing compounds and identify treatment options with high potential for clinical impact. We applied this method to a CRPC patient cohort and nominated drugs to combat resistance to hormonal therapies including abiraterone and enzalutamide. The utility of this method was demonstrated by nomination of multiple drugs that are currently undergoing clinical trials for CRPC. Additionally, this method identified the tetracycline derivative COL-3, for which we validated higher efficacy in an isogenic cell line model of enzalutamide-resistant vs. enzalutamide-sensitive CRPC. In enzalutamide-resistant CRPC cells, COL-3 displayed higher activity for inhibiting cell growth and migration, and for inducing G1-phase cell cycle arrest and apoptosis. Collectively, these findings demonstrate the utility of a computational framework for independent validation of drugs being tested in CRPC clinical trials, and for nominating drugs with enhanced biological activity in models of enzalutamide-resistant CRPC. The efficiency of this method relative to traditional drug development approaches indicates a high potential for accelerating drug development for CRPC.
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影响因子：
7.3
作者：
Gao J;Aksoy BA;Dogrusoz U;Dresdner G;Gross B;Sumer SO;Sun Y;Jacobsen A;Sinha R;Larsson E;Cerami E;Sander C;Schultz N
通讯作者：
Schultz N
影响因子：
64.5
作者：
Basu A;Bodycombe NE;Cheah JH;Price EV;Liu K;Schaefer GI;Ebright RY;Stewart ML;Ito D;Wang S;Bracha AL;Liefeld T;Wawer M;Gilbert JC;Wilson AJ;Stransky N;Kryukov GV;Dancik V;Barretina J;Garraway LA;Hon CS;Munoz B;Bittker JA;Stockwell BR;Khabele D;Stern AM;Clemons PA;Shamji AF;Schreiber SL
通讯作者：
Schreiber SL
DOI：
10.3390/medsci10010015
发表时间：
2022-02-18
期刊：
Medical sciences (Basel, Switzerland)
影响因子：
--
作者：
Bahmad HF;Demus T;Moubarak MM;Daher D;Alvarez Moreno JC;Polit F;Lopez O;Merhe A;Abou-Kheir W;Nieder AM;Poppiti R;Omarzai Y
通讯作者：
Omarzai Y
影响因子：
45.3
作者：
Cianfrocca, M;Cooley, TP;Dezube, BJ
通讯作者：
Dezube, BJ
影响因子：
3.4
作者：
Chu, Quincy S C;Forouzesh, Bahram;Rowinsky, Eric K
通讯作者：
Rowinsky, Eric K