Objective
Our objectives are: 1) maximally leverage existing biological big data and state-of-the-art systems biology tools to better understand the convoluted genes and pathways behind T1D metabolic complications; 2) identify and prioritize existing FDA-approved drugs that can optimally counteract the genes and pathways associated with T1D metabolic complications; 3) experimentally test the efficacy of the top predicted drugs in providing greater glycemic and metabolic control in T1D rodent models.
Background Rationale
T1D accounts for 5-10% of all diabetic individuals worldwide, approximately 40 million cases, with an increasing incidence rate of 2-3% per year. Despite the clear increase of T1D patients in our population yearly, the primary treatment has remained the same since 1922, exogenous insulin. Although patients are expected to lead near to normal lives using insulin therapy alone, less than one third of T1D population achieve the target glycemic control, and fluctuations in glucose levels are common leading in the long term to a significant accumulation of time in hyper- and hypo-glycemic states resulting in secondary complications such as disease of the retina, kidney, and nerves, cardiovascular disease, cerebrovascular disease and peripheral vascular disease. In addition to suboptimal glucose control, T1D patients tend to have other metabolic complications including weight gain, insulin resistance, metabolic syndrome, and high lipid levels, which further contribute to poor glycemic control. To put this severity into further context, poor glucose control in T1D patients can lead to an eight to thirteen-year shorter lifespan than healthy individuals. Therefore, there is an urgent demand for adjunctive therapeutics to insulin for T1D.
Recent efforts to reposition T2D drugs for T1D adjunctive therapy have met with limited success. This is likely due to our lack of understanding of the complex mechanisms underlying poor glycemic and metabolic control in T1D. In recent years, big data-driven systems biology, which is a new discipline that aims to fully utilize the ample biological data generated by the scientific community over the past decades to comprehensively map out the perturbed genes and biological processes in individual tissues that lead to diseases, has become a powerful approach to address this critical knowledge gap. The same approach can be applied to better understand how individual drugs exert their treatment effects. When a drug is found to reverse the perturbations of genes and pathways in the same set of tissues involved in a disease, the drug is more likely to exhibit better efficacy and less side effects. In contrast to the traditional approaches that mainly rely on known drug targets and mode of actions, big data systems biology is based on data patterns and are unbiased to ensure more objective and comprehensive understanding of both diseases and drug activities, thereby serving as a more powerful approach to drive novel discoveries and selection of optimal repositioning of drugs for T1D metabolic complications. Our strong expertise in developing systems biology approaches and big data analytics and tools, and our extensive experience in systems biology of cardiometabolic diseases will ensure the success of the proposed studies.
Description of Project
A large proportion of T1D patients receiving insulin therapy experience poor metabolic control including hyper- or hypoglycemia, obesity, insulin resistance, and high lipid levels, ultimately leading to increased secondary complications such as disease of the retina, disease of the kidney, and cardiovascular risks. Currently, treatment strategies to provide more effective metabolic control are lacking. Drug repositioning, an approach rooted in the use of approved drugs for a different indication, is a powerful approach to leverage existing drugs for T1D treatment. Existing efforts in repositioning T2D drugs for T1D adjunctive therapy have yielded limited success, calling for alternative approaches to address this urgent unmet need for T1D patients. Building on the accumulating scientific knowledge that both metabolic control and drug activities are extremely complex processes, involving numerous tissues, genes, and molecular pathways, it is highly advantageous to utilize an approach that can take these complexities into consideration to allow for effective drugs to be identified. Here, we propose to use our extensive experience and expertise in big data approaches to gather and analyze a multitude of biological datasets from human and animal studies, each examining tens of thousands of genes in multiple tissues involved in metabolic control, to elucidate the tissue-specific processes contributing to T1D metabolic complications. We will then use these results to query a unique drug database we have built that contains the molecular footprints of FDA-approved drugs, that is, which genes are affected in which tissues when each drug is used. By matching the genes and tissues involved in T1D metabolic regulation with those affected by drugs, we can prioritize drugs that exhibit a better match as candidates for more effective metabolic control in T1D. Lastly, we will experimentally test at least three top candidate drugs predicted by our big data analysis for their efficacy in T1D mouse models. Our proposed studies represent the first effort to fully integrate the massive amount of existing datasets across human populations and animal models using an innovative computational approach. The findings will not only offer one of the most systematic understanding of the molecular processes in individual tissues involved in T1D metabolic regulation and drug actions, but will uncover drugs that are more likely to have translational value to confer better control of glucose levels, body weight, and high lipid levels to reduce long-term complications and improve overall health for T1D patients.
Anticipated Outcome
We expect to deliver a better and more comprehensive understanding of which genes in which tissues interact to affect blood glucose control in T1D, and how similar or different these genes and interactions are compared to glucose control in T2D. Based on this improved understanding, we will produce lists of prioritized drugs that have the strongest potential to be used as adjunctive therapy to better control glucose levels and other metabolic dysfunctions in T1D patients. By experimentally testing a few top predicted drugs, we also anticipate to demonstrate at least one drug that can improve glycemic control and prevent metabolic complications in T1D animal models.
Relevance to T1D
Our proposed studies directly addresses the unmet need in T1D patients to achieve desired glycemic control and reduce risk for long-term complications by predicting and validating drug candidates as adjunctive therapies to insulin treatment to help maintain normoglycemia and reduce glycemic variability. The proposed studies are also directly relevant to the JDRF RFA that calls for in silico approaches that take advantage of big omics datasets and innovative network-based systems biology methodologies to identify drugs likely to show efficacy for metabolic control in T1D.
Our proposed studies represent the first effort to fully integrate the massive amount of existing datasets across human populations and animal models using an innovative computational approach. The findings will not only offer one of the most systematic understanding of the molecular processes in individual tissues involved in T1D metabolic regulation and drug actions, but will uncover drugs that are more likely to have translational value to confer better control of glucose levels, body weight, and high lipid levels to reduce long-term complications and improve overall health for T1D patients.