Enhancing the discovery and development of immunotherapies for cancer using quantitative and systems pharmacology: Interleukin-12 as a case study.
Enhancing the discovery and development of immunotherapies for cancer using quantitative and systems pharmacology: Interleukin-12 as a case study.
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DOI：
10.1186/s40425-015-0069-x
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发表时间：
2015
影响因子：
10.9
通讯作者：
Klinke DJ 2nd
中科院分区：
文献类型：
--
作者：
Klinke DJ 2nd
最近免疫检查点调节剂的临床成功释放了一波与癌症免疫治疗相关的热情。然而，这种热情被与癌症免疫疗法相关的持续的翻译障碍所抑制，这反映了更广泛的制药行业。具体来说，与药物发现和开发相关的挑战源于对潜在药物靶向的人类生物学机制的不完全理解以及临床失败的财务影响。在扩大癌症免疫疗法提供的临床益处方面取得持续进展需要可靠地确定新的作用机制。沿着这些路线，定量和系统药理学（QSP）已被提出作为一种手段，以活跃药物发现和开发过程。在这篇综述中，我讨论了QSP在癌症免疫治疗中应用的两个中心主题。第一个主题侧重于以网络为中心的生物学观点，与当代药物发现和开发中普遍存在的“一基因、一受体、一机制”范式形成对比。湿实验室能力的进步使这一主题得以实现，以增加广度和分辨率来分析生物系统。第二个主题侧重于将机械建模和模拟与定量湿实验室研究相结合。从最近的QSP例子中，整合表型信号、细胞和组织水平行为的大规模机制模型有可能降低与癌症免疫治疗相关的许多翻译障碍。这些包括优先考虑免疫疗法，开发机制生物标志物，对患者群体进行分层，反映保护性宿主免疫反应的潜在强度和动态，并利用机制模型作为对话的载体，促进我们对潜在生物学的理解的明确分享。然而，创建这样的模型需要一种模块化的方法，假设生物网络在健康和疾病方面保持相似。由于肿瘤的发生与这些生物网络的重新连接有关，我还描述了一种方法，将机械建模与定量湿实验室实验相结合，以确定恶性细胞改变这些网络的方式，以白细胞介素-12为例。总的来说，QSP代表了一种新的整体方法，可能对如何进行转化科学具有深远的影响。
Recent clinical successes of immune checkpoint modulators have unleashed a wave of enthusiasm associated with cancer immunotherapy. However, this enthusiasm is dampened by persistent translational hurdles associated with cancer immunotherapy that mirror the broader pharmaceutical industry. Specifically, the challenges associated with drug discovery and development stem from an incomplete understanding of the biological mechanisms in humans that are targeted by a potential drug and the financial implications of clinical failures. Sustaining progress in expanding the clinical benefit provided by cancer immunotherapy requires reliably identifying new mechanisms of action. Along these lines, quantitative and systems pharmacology (QSP) has been proposed as a means to invigorate the drug discovery and development process. In this review, I discuss two central themes of QSP as applied in the context of cancer immunotherapy. The first theme focuses on a network-centric view of biology as a contrast to a “one-gene, one-receptor, one-mechanism” paradigm prevalent in contemporary drug discovery and development. This theme has been enabled by the advances in wet-lab capabilities to assay biological systems at increasing breadth and resolution. The second theme focuses on integrating mechanistic modeling and simulation with quantitative wet-lab studies. Drawing from recent QSP examples, large-scale mechanistic models that integrate phenotypic signaling-, cellular-, and tissue-level behaviors have the potential to lower many of the translational hurdles associated with cancer immunotherapy. These include prioritizing immunotherapies, developing mechanistic biomarkers that stratify patient populations and that reflect the underlying strength and dynamics of a protective host immune response, and facilitate explicit sharing of our understanding of the underlying biology using mechanistic models as vehicles for dialogue. However, creating such models require a modular approach that assumes that the biological networks remain similar in health and disease. As oncogenesis is associated with re-wiring of these biological networks, I also describe an approach that combines mechanistic modeling with quantitative wet-lab experiments to identify ways in which malignant cells alter these networks, using Interleukin-12 as an example. Collectively, QSP represents a new holistic approach that may have profound implications for how translational science is performed.
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DOI：
10.1084/jem.20021769
发表时间：
2003-02-17
期刊：
The Journal of experimental medicine
影响因子：
--
作者：
Fieschi C;Dupuis S;Catherinot E;Feinberg J;Bustamante J;Breiman A;Altare F;Baretto R;Le Deist F;Kayal S;Koch H;Richter D;Brezina M;Aksu G;Wood P;Al-Jumaah S;Raspall M;Da Silva Duarte AJ;Tuerlinckx D;Virelizier JL;Fischer A;Enright A;Bernhöft J;Cleary AM;Vermylen C;Rodriguez-Gallego C;Davies G;Blütters-Sawatzki R;Siegrist CA;Ehlayel MS;Novelli V;Haas WH;Levy J;Freihorst J;Al-Hajjar S;Nadal D;De Moraes Vasconcelos D;Jeppsson O;Kutukculer N;Frecerova K;Caragol I;Lammas D;Kumararatne DS;Abel L;Casanova JL
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DOI：
10.1084/jem.20130678
发表时间：
2013-12-16
期刊：
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影响因子：
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作者：
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影响因子：
6.4
作者：
Billerbeck, Eva;Labitt, Rachael N.;Ploss, Alexander
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Ploss, Alexander
DOI：
10.1084/jem.20091279
发表时间：
2010-01-18
期刊：
The Journal of experimental medicine
影响因子：
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作者：
Budhu S;Loike JD;Pandolfi A;Han S;Catalano G;Constantinescu A;Clynes R;Silverstein SC
通讯作者：
Silverstein SC
影响因子：
4.4
作者：
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