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.

Public Health Relevance

The computational biologists, physician-scientists and pharmacologists in the Laboratory of Systems Pharmacology will apply a combined measure-model approach to understanding the mechanisms of action of therapeutic drugs in multiple disease areas. They will develop and apply new tools to (i) investigate the factors that determine the therapeutic index of drugs at a single cell level and in tissues (ii) apply knowledge of cellular networks to develop a rational approach to combination therapy (iii) identify and qualify new drug targets for significant unmet medical needs. Success with our approach will advance personalized medicine and reduce the frequency of late-stage failure in drug discovery, thereby reducing the cost of new medicines.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Specialized Center (P50)
Project #
1P50GM107618-01A1
Application #
8874812
Study Section
Special Emphasis Panel (ZGM1)
Project Start
Project End
Budget Start
2014-09-01
Budget End
2015-05-31
Support Year
1
Fiscal Year
2014
Total Cost
Indirect Cost
Name
Harvard Medical School
Department
Type
DUNS #
City
Boston
State
MA
Country
United States
Zip Code
02115
Coy, Shannon; Rashid, Rumana; Lin, Jia-Ren et al. (2018) Multiplexed immunofluorescence reveals potential PD-1/PD-L1 pathway vulnerabilities in craniopharyngioma. Neuro Oncol 20:1101-1112
Cranford, Jonathan P; O'Hara, Thomas J; Villongco, Christopher T et al. (2018) Efficient Computational Modeling of Human Ventricular Activation and Its Electrocardiographic Representation: A Sensitivity Study. Cardiovasc Eng Technol 9:447-467
Iglesias, Nahid; Currie, Mark A; Jih, Gloria et al. (2018) Automethylation-induced conformational switch in Clr4 (Suv39h) maintains epigenetic stability. Nature 560:504-508
Musri, Melina M; Coll-Bonfill, NĂºria; Maron, Bradley A et al. (2018) MicroRNA Dysregulation in Pulmonary Arteries from Chronic Obstructive Pulmonary Disease. Relationships with Vascular Remodeling. Am J Respir Cell Mol Biol 59:490-499
Wang, Rui-Sheng; Loscalzo, Joseph (2018) Network-Based Disease Module Discovery by a Novel Seed Connector Algorithm with Pathobiological Implications. J Mol Biol 430:2939-2950
Weinstein, Zohar B; Kuru, Nurdan; Kiriakov, Szilvia et al. (2018) Modeling the impact of drug interactions on therapeutic selectivity. Nat Commun 9:3452
Leopold, Jane A; Loscalzo, Joseph (2018) Emerging Role of Precision Medicine in Cardiovascular Disease. Circ Res 122:1302-1315
Spady, Emma S; Wyche, Thomas P; Rollins, Nathanael J et al. (2018) Mammalian Cells Engineered To Produce New Steroids. Chembiochem 19:1827-1833
Cheng, Feixiong; Desai, Rishi J; Handy, Diane E et al. (2018) Network-based approach to prediction and population-based validation of in silico drug repurposing. Nat Commun 9:2691
Vinegoni, Claudio; Feruglio, Paolo Fumene; Gryczynski, Ignacy et al. (2018) Fluorescence anisotropy imaging in drug discovery. Adv Drug Deliv Rev :

Showing the most recent 10 out of 77 publications