Computational discovery of precision therapeutics for hidradenitis suppurativa - PROJECT SUMMARY/ABSTRACT Hidradenitis suppurativa (HS) is a complex chronic systemic inflammatory disorder that causes inflamed nodules, abscesses, tunnels, and sinus tracts in the skin, and is associated with numerous systemic comorbidities such as metabolic syndrome, cardiovascular disease, and depression. Only three FDA-approved medications are available for HS, and all other immunomodulatory therapies are being used off-label. There remains a large gap in knowledge regarding the pathophysiology and immunology of HS, including identifying targetable molecular pathways beyond those that are already known. This suggests the need for precision medicine for HS that considers individual variability in genetic and other molecular measurements. Here, I propose an unbiased approach to the computational discovery of precision therapies for hidradenitis suppurativa. In Aim 1, using a combination of locally generated and publicly available large-scale multi-omic datasets of hidradenitis suppurativa and other inflammatory skin conditions, I will identify immunologic signatures and pathways involved in the pathophysiology of HS. In Aim 2, I plan to leverage an in silico computational drug repositioning approach to identify disease-specific and cell-type specific novel single agent and combination therapies from existing drugs based on reversal of gene expression signatures. Finally, in Aim 3, I will comprehensively validate the effects of these single and combination agents on skin inflammation using an ex vivo skin culture model derived from HS skin by measuring immune cell activation/proliferation and cytokine production and characterize drug-target relationships using structural and computational methods. I expect this work to identify novel single and combination treatments that can be repurposed for the treatment of HS. This generalized systematic approach will be broadly applicable to the future discovery of drug candidates for other chronic inflammatory skin diseases. My preliminary data obtained from integrative bioinformatic analysis of microarray and single-cell RNA sequencing data has identified novel targetable dysregulated immunologic and transcriptomic pathways in HS, as well as several early drug candidates. In this mentored career development plan, I aim to expand on my preliminary findings to fill significant knowledge gaps with respect to HS pathophysiology and therapeutic development, with the goal of advancing precision medicine for HS. Under the guidance of Dr. Marina Sirota, an expert in computational immunology and integrative bioinformatics, and a committee of multidisciplinary scientific leaders (Dr. Michael Rosenblum, Dr. Wilson Liao, Dr. Haley Naik, and Dr. James Fraser), I will integrate and leverage my expertise in computational and structural biology, immunology, and dermatology to further my long-term goal of becoming an independent physician-scientist. I plan to lead an NIH-funded multidisciplinary basic and translational research lab focused on engineering new therapies for inflammatory skin diseases and translating advances from the bench to the bedside in my clinical practice. This proposal will enhance my skills in statistical machine learning, integrative multi-omics, and translational immunology, allowing me to identify novel therapeutics to treat chronic inflammatory skin diseases. This five-year career development plan will also provide comprehensive training in statistical analysis, scientific communication, laboratory management and leadership, laying the groundwork for my future role as an independent researcher contributing to our understanding of cutaneous biology.
