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Systems Pharmacology of Therapeutic and Adverse Responses to ImmuneCheckpoint and Small Molecule Drugs

免疫检查点和小分子药物治疗和不良反应的系统药理学

基本信息

批准号：
9886211
负责人：
PETER Karl SORGER
金额：
$ 214.92万
依托单位：
HARVARD MEDICAL SCHOOL
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-03-08 至 2023-02-28
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9886211
关键词：
Achievement Adverse effects Animal Model Archives Area BRAF gene Back Biological Assay Biopsy CD8-Positive T-Lymphocytes CD8B1 gene Cancer Biology Cancer Center Cancer Patient Cell Communication Cell Line Cell Lineage Cell model Cells Clinical Clinical Trials Code Computer Models Computer software Computing Methodologies Data Data Analyses Data Science Differential Equation Disease Disease Progression Drug Combinations Drug Targeting Drug resistance Ecosystem Education and Outreach Ensure Equilibrium Fostering Funding Generations Genetic Transcription Genotype Glioblastoma Goals Health Image Immune Immune checkpoint inhibitor Immunotherapy Individual Laboratory Study Lead Logic MEKs Machine Learning Malignant - descriptor Malignant Neoplasms Malignant neoplasm of brain Measurement Measures Mediating Medical Metabolic Methods Modeling Mus Mutation Newsletter Non-Malignant Pathway interactions Patients Pharmaceutical Preparations Pharmacology Phenotype Physicians Pilot Projects Population Postdoctoral Fellow Pre-Clinical Model Proteomics Regulatory T-Lymphocyte Resistance Role Sampling Scientist Series Signal Pathway Signal Transduction Skin Software Tools Specimen Students System Systems Biology Teacher Professional Development Technology Testing Therapeutic Therapeutic Effect Therapeutic Studies Therapeutic antibodies Time Tissue Sample Tissues Toxic effect Training Training and Education Translating acquired drug resistance base bench to bedside cancer therapy cancer type career cell type clinical research site computing resources data acquisition deep learning effector T cell imaging modality immune checkpoint improved inhibiting antibody inhibitor/antagonist innovation insight machine learning method meetings melanoma metabolomics mouse model multi-scale modeling multidimensional data multiple omics mutant non-genetic novel novel drug combination novel strategies novel therapeutics outreach pre-clinical precision oncology resistance mechanism response response biomarker small molecule spatiotemporal supervised learning targeted treatment therapy resistant transcriptomics treatment response triple-negative invasive breast carcinoma tumor tumor microenvironment tumor-immune system interactions web site

项目摘要

SUMMARY- OVERALL COMPONENT We will establish a Center for Cancer Systems Pharmacology (CSP Center) that constructs and applies network-level computational models to understand mechanisms of drug response, resistance and toxicity for targeted small molecule drugs and immune checkpoint inhibitors (ICIs). We hypothesize that improved understanding of fundamental cell signaling pathways and interactions between cancer and immune cells will result in greater efficacy while minimizing toxicity. Intrinsic and acquired drug resistance pose the primary challenges to broader application of all cancer therapies. By systematically dissecting how resistance to targeted therapies and ICIs arises, we aim to understand and overcome resistance mechanisms using new drugs or drug combinations, while simultaneously predicting and balancing potential toxicities. These goals will be accomplished by translating findings from the bedside to the bench and then back to the bedside focusing on melanoma, a type of cancer in which both ICIs and targeted drugs are effective, and triple negative breast cancer (TNBC) and brain cancers (GBM) for which ICIs are not approved but where sporadic responses have been observed. We will develop, validate and apply innovative pharmacological concepts and instantiate these in practical form using computational models. Such models will explicitly consider the impact of mutations, phenotypic variability, cell-to-cell interaction and the composition of the tumor microenvironment in mechanisms of action of sequential or simultaneous combinations of targeted drugs and ICIs. Hypothesis generation will focus on deep phenotyping of patient-derived specimens followed by hypothesis testing in pre- clinical settings using complementary multi-omic and computational methods. We will also create and distribute new measurement and software methods to promote systems pharmacology in other areas of cancer biology. Aim 1 will establish an Administrative Core to oversee and coordinate all center activities. Aim 2 will establish a Systems Pharmacology Core to coordinate experimental and computational resources for proteomic, transcriptomic, metabolomic and imaging assays across all three Projects. Aim 3 will establish an Outreach core that promotes training via a website and seminars and ensures curation and distribution of Center data according to FAIR standards. Aim 4 (Project 1) will develop multi-scale computational models of adaptive drug resistance in melanoma that capture and ultimately explain the wide diversity of changes in cell states associated with resistance to RAF/MEK inhibitors. Aim 5 (Project 2) will measure and model the tumor microenvironment before and during treatment, and at the time of drug resistance using a range of innovative, highly-multiplexed assays for malignant and non-malignant cells. Aim 6 (Project 3) will measure and model cell type-specific metabolic, signaling, and transcriptional mechanisms that contribute to the efficacy of ICI combinations, in order to develop improved therapeutic strategies for patients unresponsive to monotherapy.

摘要-整体组件