Quantitative Systems Pharmacology Approaches for Immuno-Oncology: Adding Virtual Patients to the Development Paradigm.
Quantitative Systems Pharmacology Approaches for Immuno-Oncology: Adding Virtual Patients to the Development Paradigm.
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DOI：
10.1002/cpt.1987
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发表时间：
2021-03
影响因子：
6.7
通讯作者：
Kierzek AM
中科院分区：
文献类型：
--
作者：
Chelliah V;Lazarou G;Bhatnagar S;Gibbs JP;Nijsen M;Ray A;Stoll B;Thompson RA;Gulati A;Soukharev S;Yamada A;Weddell J;Sayama H;Oishi M;Wittemer-Rump S;Patel C;Niederalt C;Burghaus R;Scheerans C;Lippert J;Kabilan S;Kareva I;Belousova N;Rolfe A;Zutshi A;Chenel M;Venezia F;Fouliard S;Oberwittler H;Scholer-Dahirel A;Lelievre H;Bottino D;Collins SC;Nguyen HQ;Wang H;Yoneyama T;Zhu AZX;van der Graaf PH;Kierzek AM
肿瘤学中的药物开发通常利用分子生物学工具通过细胞反应的重新编程来获得治疗效果。免疫肿瘤学（IO）的目标是引导患者自身的免疫系统对抗癌症。针对 PD1/PD-L1 和 CTLA4 受体的抗体在目标患者群体中取得了巨大成功后，进一步开发的重点已转向联合疗法。然而，目前利用大量可能的组合靶点和给药方案的药物开发方法已被证明具有挑战性，并且可以说是低效的。特别是，测试不同组合的空前数量的临床试验可能不再适合现有患者群体。 IO 的进一步发展需要在候选疗法的选择和验证方面做出重大改变，以降低开发损耗率并限制临床试验的数量。定量系统药理学（QSP）建议通过机械建模和模拟来应对这一挑战。通过定量动态模型描述化合物的药代动力学、靶点结合和作用机制以及有关潜在肿瘤和免疫系统生物学的现有知识，旨在预测新组合的临床结果。在这里，我们回顾了当前的 QSP 方法、定量临床药理学家可用的描述肿瘤与免疫系统之间相互作用的数学模型的遗产，以及 IO QSP 平台模型的最新发展。我们认为，QSP 和虚拟患者可以作为新工具整合到现有 IO 药物开发方法中，以提高寻找新型联合疗法的效率和有效性。
Drug development in oncology commonly exploits the tools of molecular biology to gain therapeutic benefit through reprograming of cellular responses. In immuno‐oncology (IO) the aim is to direct the patient’s own immune system to fight cancer. After remarkable successes of antibodies targeting PD1/PD‐L1 and CTLA4 receptors in targeted patient populations, the focus of further development has shifted toward combination therapies. However, the current drug‐development approach of exploiting a vast number of possible combination targets and dosing regimens has proven to be challenging and is arguably inefficient. In particular, the unprecedented number of clinical trials testing different combinations may no longer be sustainable by the population of available patients. Further development in IO requires a step change in selection and validation of candidate therapies to decrease development attrition rate and limit the number of clinical trials. Quantitative systems pharmacology (QSP) proposes to tackle this challenge through mechanistic modeling and simulation. Compounds’ pharmacokinetics, target binding, and mechanisms of action as well as existing knowledge on the underlying tumor and immune system biology are described by quantitative, dynamic models aiming to predict clinical results for novel combinations. Here, we review the current QSP approaches, the legacy of mathematical models available to quantitative clinical pharmacologists describing interaction between tumor and immune system, and the recent development of IO QSP platform models. We argue that QSP and virtual patients can be integrated as a new tool in existing IO drug development approaches to increase the efficiency and effectiveness of the search for novel combination therapies.
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影响因子：
5.6
作者：
Ermakov S;Forster P;Pagidala J;Miladinov M;Wang A;Baillie R;Bartlett D;Reed M;Leil TA
通讯作者：
Leil TA
影响因子：
3.5
作者：
Jain, Harsh;Jackson, Trachette
通讯作者：
Jackson, Trachette
影响因子：
3.5
作者：
Bunimovich-Mendrazitsky, Svetlana;Shochat, Eliezer;Stone, Lewi
通讯作者：
Stone, Lewi
DOI：
10.1002/psp4.12071
发表时间：
2016-05
期刊：
CPT: pharmacometrics & systems pharmacology
影响因子：
--
作者：
Gadkar K;Kirouac DC;Mager DE;van der Graaf PH;Ramanujan S
通讯作者：
Ramanujan S
影响因子：
11.2
作者：
Benzekry, Sebastien;Lamont, Clare;Hahnfeldt, Philip
通讯作者：
Hahnfeldt, Philip