Browsing by Author "Huang, Hui"

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    Computational Biomarker Discovery: From Systems Biology to Predictive and Personalized Medicine Applications
    (Office of the Vice Chancellor for Research, 2010-04-09) Chen, Jake Yue; Wu, Xiaogang; Zhang, Fan; Pandey, Ragini; Huang, Hui; Huan, Tianxiao
    With the advent of Genome-based Medicine, there is an escalating need for discovering how the modifications of biological molecules, either individually or as an ensemble, can be uniquely associated with human physiological states. This knowledge could lead to breakthroughs in the development of clinical tests known as "biomarker tests" to assess disease risks, early onset, prognosis, and treatment outcome predictions. Therefore, development of molecular biomarkers is a key agenda in the next 5-10 years to take full advantage of the human genome to improve human well-beings. However, the complexity of human biological systems and imperfect instrumentations of high-throughput biological instruments/results have created significant hurdles in biomarker development. Only recently did computational methods become an important player of the research topic, which has seen conventional molecular biomarkers development both extremely long and cost-ineffective. At Indiana Center for Systems Biology and Personalized Medicine, we are developing several computational systems biology strategies to address these challenges. We will show examples of how we approach the problem using a variety of computational techniques, including data mining, algorithm development to take into account of biological contexts, biological knowledge integration, and information visualization. Finally, we outline how research in this direction to derive more robust molecular biomarkers may lead to predictive and personalized medicine. Indiana Center for Systems Biology and Personalized Medicine (CSBPM) was founded in 2007 as an IUPUI signature center by Dr. Jake Chen and his colleagues in the Indiana University School of Informatics, School of Medicine, and School of Science. CSBPM is the only research center in the State of Indiana with the primary goal of pursuing predictive and personalized medicine. CSBPM currently consists of eleven faculty members from the School of Medicine, School of Science, School of Engineering, School of Informatics, and Indiana University Simon Cancer Center. The primary mission of the center is to foster the development and use of systems biology and computational modeling techniques to address challenges in future genome-based medicine. The ultimate goal of the center is to shorten the discovery-to-practice gap between integrative ―Omics‖ biology studies—including genomics, transcriptomics, proteomics, and metabolomics—and predictive and personalized medicine applications.
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    DMAP: a connectivity map database to enable identification of novel drug repositioning candidates
    (BioMed Central, 2015-09-25) Huang, Hui; Nguyen, Thanh; Ibrahim, Sara; Shantharam, Sandeep; Yue, Zongliang; Chen, Jake Yue; Department of Computer & Information Science, School of Science
    BACKGROUND: Drug repositioning is a cost-efficient and time-saving process to drug development compared to traditional techniques. A systematic method to drug repositioning is to identify candidate drug's gene expression profiles on target disease models and determine how similar these profiles are to approved drugs. Databases such as the CMAP have been developed recently to help with systematic drug repositioning. METHODS: To overcome the limitation of connectivity maps on data coverage, we constructed a comprehensive in silico drug-protein connectivity map called DMAP, which contains directed drug-to-protein effects and effect scores. The drug-to-protein effect scores are compiled from all database entries between the drug and protein have been previously observed and provide a confidence measure on the quality of such drug-to-protein effects. RESULTS: In DMAP, we have compiled the direct effects between 24,121 PubChem Compound ID (CID), which were mapped from 289,571 chemical entities recognized from public literature, and 5,196 reviewed Uniprot proteins. DMAP compiles a total of 438,004 chemical-to-protein effect relationships. Compared to CMAP, DMAP shows an increase of 221 folds in the number of chemicals and 1.92 fold in the number of ATC codes. Furthermore, by overlapping DMAP chemicals with the approved drugs with known indications from the TTD database and literature, we obtained 982 drugs and 622 diseases; meanwhile, we only obtained 394 drugs with known indication from CMAP. To validate the feasibility of applying new DMAP for systematic drug repositioning, we compared the performance of DMAP and the well-known CMAP database on two popular computational techniques: drug-drug-similarity-based method with leave-one-out validation and Kolmogorov-Smirnov scoring based method. In drug-drug-similarity-based method, the drug repositioning prediction using DMAP achieved an Area-Under-Curve (AUC) score of 0.82, compared with that using CMAP, AUC = 0.64. For Kolmogorov-Smirnov scoring based method, with DMAP, we were able to retrieve several drug indications which could not be retrieved using CMAP. DMAP data can be queried using the existing C2MAP server or downloaded freely at: http://bio.informatics.iupui.edu/cmaps CONCLUSIONS: Reliable measurements of how drug affect disease-related proteins are critical to ongoing drug development in the genome medicine era. We demonstrated that DMAP can help drug development professionals assess drug-to-protein relationship data and improve chances of success for systematic drug repositioning efforts.
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    Real-world comparative effectiveness of triplets containing bortezomib (B), carfilzomib (C), daratumumab (D), or ixazomib (I) in relapsed/refractory multiple myeloma (RRMM) in the US
    (Springer, 2021) Davies, Faith; Rifkin, Robert; Costello, Caitlin; Morgan, Gareth; Usmani, Saad; Abonour, Rafat; Palumbo, Antonio; Romanus, Dorothy; Hajek, Roman; Terpos, Evangelos; Cherepanov, Dasha; Stull, Dawn Marie; Huang, Hui; Leleu, Xavier; Berdeja, Jesus; Lee, Hans C.; Weisel, Katja; Thompson, Michael; Boccadoro, Mario; Zonder, Jeffrey; Cook, Gordon; Puig, Noemi; Vela-Ojeda, Jorge; Farrelly, Eileen; Raju, Aditya; Blazer, Marlo; Chari, Ajai; Medicine, School of Medicine
    Multiple available combinations of proteasome inhibitors, immunomodulators (IMIDs), and monoclonal antibodies are shifting the relapsed/refractory multiple myeloma (RRMM) treatment landscape. Lack of head-to-head trials of triplet regimens highlights the need for real-world (RW) evidence. We conducted an RW comparative effectiveness analysis of bortezomib (V), carfilzomib (K), ixazomib (I), and daratumumab (D) combined with either lenalidomide or pomalidomide plus dexamethasone (Rd or Pd) in RRMM. A retrospective cohort of patients initiating triplet regimens in line of therapy (LOT) ≥ 2 on/after 1/1/2014 was followed between 1/2007 and 3/2018 in Optum's deidentified US electronic health records database. Time to next treatment (TTNT) was estimated using Kaplan-Meier methods; regimens were compared using covariate-adjusted Cox proportional hazard models. Seven hundred forty-one patients (820 patient LOTs) with an Rd backbone (VRd, n = 349; KRd, n = 218; DRd, n = 99; IRd, n = 154) and 348 patients (392 patient LOTs) with a Pd backbone (VPd, n = 52; KPd, n = 146; DPd, n = 149; IPd, n = 45) in LOTs ≥2 were identified. More patients ≥75 years received IRd (39.6%), IPd (37.8%), and VRd (36.7%) than other triplets. More patients receiving VRd/VPd were in LOT2 vs other triplets. Unadjusted median TTNT in LOT ≥ 2: VRd, 13.9; KRd, 8.7; IRd, 11.4; DRd, not estimable (NE); and VPd, 12.0; KPd, 6.7; IPd, 9.5 months; DPd, NE. In covariate-adjusted analysis, only KRd vs DRd was associated with a significantly higher risk of next LOT initiation/death (HR 1.72; P = 0.0142); no Pd triplet was significantly different vs DPd in LOT ≥ 2. Our data highlight important efficacy/effectiveness gaps between results observed in phase 3 clinical trials and those realized in the RW.
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    SLDR: a computational technique to identify novel genetic regulatory relationships
    (Springer (Biomed Central Ltd.), 2014) Yue, Zongliang; Wan, Ping; Huang, Hui; Xie, Zhan; Chen, Jake Yue; Department of BioHealth Informatics, School of Informatics and Computing
    We developed a new computational technique called Step-Level Differential Response (SLDR) to identify genetic regulatory relationships. Our technique takes advantages of functional genomics data for the same species under different perturbation conditions, therefore complementary to current popular computational techniques. It can particularly identify "rare" activation/inhibition relationship events that can be difficult to find in experimental results. In SLDR, we model each candidate target gene as being controlled by N binary-state regulators that lead to ≤2N observable states ("step-levels") for the target. We applied SLDR to the study of the GEO microarray data set GSE25644, which consists of 158 different mutant S. cerevisiae gene expressional profiles. For each target gene t, we first clustered ordered samples into various clusters, each approximating an observable step-level of t to screen out the "de-centric" target. Then, we ordered each gene x as a candidate regulator and aligned t to x for the purpose of examining the step-level correlations between low expression set of x (Ro) and high expression set of x (Rh) from the regulator x to t, by finding max f(t, x): |Ro-Rh| over all candidate × in the genome for each t. We therefore obtained activation and inhibitions events from different combinations of Ro and Rh. Furthermore, we developed criteria for filtering out less-confident regulators, estimated the number of regulators for each target t, and evaluated identified top-ranking regulator-target relationship. Our results can be cross-validated with the Yeast Fitness database. SLDR is also computationally efficient with o(N²) complexity. In summary, we believe SLDR can be applied to the mining of functional genomics big data for future network biology and network medicine applications.
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    System biology modeling : the insights for computational drug discovery
    (2014) Huang, Hui; Chen, Jake; Wu, Huanmei; Al Hasan, Mohammad; Liu, Yunlong; Zhou, Yaoqi
    Traditional treatment strategy development for diseases involves the identification of target proteins related to disease states, and the interference of these proteins with drug molecules. Computational drug discovery and virtual screening from thousands of chemical compounds have accelerated this process. The thesis presents a comprehensive framework of computational drug discovery using system biology approaches. The thesis mainly consists of two parts: disease biomarker identification and disease treatment discoveries. The first part of the thesis focuses on the research in biomarker identification for human diseases in the post-genomic era with an emphasis in system biology approaches such as using the protein interaction networks. There are two major types of biomarkers: Diagnostic Biomarker is expected to detect a given type of disease in an individual with both high sensitivity and specificity; Predictive Biomarker serves to predict drug response before treatment is started. Both are essential before we even start seeking any treatment for the patients. In this part, we first studied how the coverage of the disease genes, the protein interaction quality, and gene ranking strategies can affect the identification of disease genes. Second, we addressed the challenge of constructing a central database to collect the system level data such as protein interaction, pathway, etc. Finally, we built case studies for biomarker identification for using dabetes as a case study. The second part of the thesis mainly addresses how to find treatments after disease identification. It specifically focuses on computational drug repositioning due to its low lost, few translational issues and other benefits. First, we described how to implement literature mining approaches to build the disease-protein-drug connectivity map and demonstrated its superior performances compared to other existing applications. Second, we presented a valuable drug-protein directionality database which filled the research gap of lacking alternatives for the experimental CMAP in computational drug discovery field. We also extended the correlation based ranking algorithms by including the underlying topology among proteins. Finally, we demonstrated how to study drug repositioning beyond genomic level and from one dimension to two dimensions with clinical side effect as prediction features.
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    TOWARDS A PATHWAY MODELING APPROACH TO ALZHEIMER’S DISEASE DRUG DISCOVERY
    (Office of the Vice Chancellor for Research, 2012-04-13) Ibrahim, Sara; Capouch, Don; Chandorkar, Sujay; Chen, Jake Yue; Saykin, Andrew J.; Wu, Xiaogang; Huang, Hui
    Network pharmacology has emerged as a new topic of study in recent years. Molecular connectivity maps between drugs and genes/proteins in specific disease contexts can be particularly valuable, since the functional approach with these maps helps researchers gain global perspectives on both the therapeutic and toxicological profiles of drugs. To assess drug pharmacological effects, we assume that “ideal” drugs for a patient can treat or prevent the disease by modulating gene expression profiles of this patient to the similar level with those in healthy people. Starting from this hypothesis, we build comprehensive disease-gene-drug connectivity relationships with drug-protein directionality (inhibit/activate) information based on a computational connectivity maps (CMaps) platform. In this work, we develop a novel approach based on integrative pathway modeling. Using Alzheimer’s disease (AD) as an example, we identify and rank AD-related drugs/compounds with their overall drug-protein “connectivity map” profile. First, we retrieve AD-associated proteins through the CMaps platform by using “Alzheimer’s disease” as a query term. Second, we retrieve AD-related pathways by using those AD-associated proteins as input and searching in the Human Pathway Database (HPD) and the PubMed. Third, we integrate the AD-related pathways into unified pathway models, from which we categorize the pharmaceutical effects of candidate drugs on all AD-associated proteins as either “therapeutic” or “toxic” (Figure 1). Finally, we transform the integrated pathways into network models and rank drugs based on the network topological features of drug targets, drug-affecting genes/proteins, and curated AD-associated proteins. We demonstrate that our approach can help identify AD drug candidates with significant therapeutic potentials with small toxic side effects. The case study correlates very well with the existing pharmacology of AD drugs and highlights the significance of the CMaps platform. Ongoing studies towards this direction also have the potential of changing future process of AD drug development. 1Indiana University School of Medicine.