The HMS Laboratory of Systems Pharmacology

HMS 系统药理学实验室

• 批准号: 8769531
• 负责人: PETER Karl SORGER
• 金额: $ 234.61万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2019-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8769531
• 关键词: Address; Advisory Committees; Area; Asthma; Biochemistry; Biological Sciences; Biology; Boston; Cell Line; Cell model; Cells; Cellular biology; Chemicals; Clinical; Clinical Data; Combined Modality Therapy; Communities; Complex; Computational Biology; Custom; Data; Development; Discipline; Disease; Doctor of Philosophy; Dose; Drug Industry; Drug Kinetics; Drug Targeting; Drug effect disorder; Education; Education and Outreach; Educational workshop; Engineering; Environment; Faculty; Failure; Fellowship; Fibrosis; Frequencies; Funding; Genomics; Genotype; Goals; Government; Grant; Health; Hospitals; Housing; Individual; Industry; Inflammatory; Institution; International; Kinetics; Knowledge; Laboratories; Lead; Life; Link; Lung; Machine Learning; Measurement; Measures; Medical; Medicine; Methods; Mining; Modeling; Molecular; Molecular Biology; Mus; New Drug Approvals; Organism; Patients; Pharmaceutical Chemistry; Pharmaceutical Preparations; Pharmacodynamics; Pharmacologic Substance; Pharmacology; Pharmacology and Toxicology; Phosphotransferases; Physicians; Physiological; Physiology; Population; Postdoctoral Fellow; Price; Productivity; Proteins; Qualifying; Reaction Time; Records; Reproducibility; Research; Research Personnel; Research Project Grants; Resistance; Resolution; Role; Sampling; Science; Scientist; Signal Transduction; Societies; Staging; Structure; Students; System; Systems Biology; Therapeutic; Therapeutic Index; Time; Tissues; Translational Research; Translations; Underserved Population; Variant; Whole Organism; Work; advanced system; base; cancer type; cell type; cellular imaging; cost; design; drug development; drug discovery; drug distribution; extracellular; improved; innovation; insight; intravital imaging; kinase inhibitor; man; mathematical model; meetings; member; multi-scale modeling; novel; novel strategies; novel therapeutics; outreach; outreach program; pharmacodynamic model; pharmacokinetic model; programs; receptor; response; success; symposium; theories; tool; translational medicine; tumor xenograft; web site; young woman

PROJECT SUMMARY Systems and computational biologists, physician-scientists, pharmacologists, biochemists and cell biologists will collaborate in a new Laboratory of Systems Pharmacology (LSP) to apply a measure-model approach to understanding the mechanisms of action of therapeutic drugs in multiple disease areas. The LSP will pioneer the development of quantitative network-centric approaches to pharmacology and toxicology that include analysis of dose-time-response relationships at a single-cell level, modeling of cellular network dynamics and their perturbation by drugs, development of pharmacokinetic and pharmacodynamic models in mouse and ultimately in man and use of systematic approaches to identify and qualify new drug targets. Our approach combines mathematical modeling with empirical measurement (including work with clinical samples and data) and aims to create quantitative and predictive drug response models at different temporal and physical scales. We aim to reinvigorate pharmacology and toxicology as foundational disciplines of translational medicine, develop the conceptual underpinning for personalized and precision medicine and lower the cost of drug discovery by improving its predictability. Close interactions with industry and the FDA will help address the productivity gap in drug discovery and development. The LSP is innovative with respect to its goal of using problems in basic and translational pharmacology to link three disciplines (cell and molecular biology, computational biology and medicine) and its aim of advancing therapeutic science in the Boston area and beyond. Students and postdocs supervised by 18 faculty members, 2 independent postdoctoral fellows and two PhD-level staff from 7 institutions will work in immediate proximity in a new custom-designed laboratory. The lab will host a new graduate program in therapeutics and multi-factored outreach activities that will promote systems pharmacology internationally. These goals will be achieved through four inter-linked research programs (Aims 1-4), a core dedicated to efficient translation of LSP research (Aim 5), an education core and administrative/outreach activities (Aims 6-8). Aim 1 will focus on the determinants of dose-response at a single-cell level, including the role of cell-to-cell variability in fractional response and of timing and order-of-exposure in combination therapy. Aim 2 will take a network-level approach to understanding therapeutic, toxic and paradoxical drug responses by kinase inhibitors in three types of cancer. The mechanistic basis and consequences of poly-pharmacology will also be examined along with differential drug responsiveness by normal and diseased tissues. Aim 3 will address PK-PD by developing multi-scale models of drug actions at the level of cells, tissues and organisms and new methods for measuring drug distribution at the cellular and subcellular levels. Aim 4 will apply machine learning and causal reasoning to target discovery from clinical records in asthma, inflammatory disease and fibrosis and pursue a structure-guided approach to identifying regulators of "undruggable " targets.

项目摘要 系统和计算生物学家，物理学家，药理学家，生物化学家和细胞生物学家将在新的系统药理学实验室（LSP）中合作，应用测量模型方法来了解治疗药物在多个疾病领域的作用机制。LSP将率先开发以网络为中心的药理学和毒理学定量方法，包括在单细胞水平上分析剂量-时间-反应关系，建模细胞网络动力学及其药物干扰，开发小鼠和最终人体的药代动力学和药效学模型，以及使用系统方法来识别和鉴定新药靶点。我们的方法将数学建模与经验测量（包括临床样本和数据）相结合，旨在创建不同时间和物理尺度的定量和预测药物反应模型。我们的目标是重振药理学和毒理学作为转化医学的基础学科，发展个性化和精准医学的概念基础，并通过提高其可预测性来降低药物发现的成本。与行业和FDA的密切互动将有助于解决药物发现和开发的生产力差距。LSP是创新的，它的目标是利用基础和转化药理学中的问题将三个学科（细胞和分子生物学，计算生物学和医学）联系起来，并在波士顿地区及其他地区推进治疗科学。由18名教师，2名独立博士后研究员和来自7个机构的两名博士级工作人员监督的学生和博士后将在一个新的定制设计的实验室中工作。该实验室将举办一个新的研究生课程，在治疗和多因素的推广活动，将促进国际系统药理学。 这些目标将通过四个相互关联的研究方案（目标1-4）、一个致力于有效翻译LSP研究的核心（目标5）、一个教育核心和行政/外联活动（目标6-8）来实现。目标1将集中在单细胞水平的剂量反应的决定因素，包括细胞间变异性在部分反应中的作用，以及联合治疗中暴露的时间和顺序。目标2将采取网络水平的方法来理解激酶抑制剂在三种类型的癌症中的治疗，毒性和矛盾的药物反应。还将沿着检查多种药理学的机制基础和后果，以及正常和患病组织的不同药物反应性。目标3将通过在细胞、组织和生物体水平开发药物作用的多尺度模型以及在细胞和亚细胞水平测量药物分布的新方法来解决PK-PD。Aim 4将应用机器学习和因果推理从哮喘，炎症性疾病和纤维化的临床记录中发现目标，并采用结构指导的方法来识别“不可用”目标的调节剂。