Browsing by Author "Li, Lang"
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Item A comprehensive assessment of statin discontinuation among patients who concurrently initiate statins and CYP3A4-inhibitor drugs; a multistate transition model(Wiley, 2023) Donneyong, Macarius M.; Zhu, Yuxi; Zhang, Pengyue; Li, Yiting; Hunold, Katherine M.; Chiang, ChienWei; Unroe, Kathleen; Caterino, Jeffrey M.; Li, Lang; Medicine, School of MedicineAims: The aim of this study was to describe the 1-year direct and indirect transition probabilities to premature discontinuation of statin therapy after concurrently initiating statins and CYP3A4-inhibitor drugs. Methods: A retrospective new-user cohort study design was used to identify (N = 160 828) patients who concurrently initiated CYP3A4 inhibitors (diltiazem, ketoconazole, clarithromycin, others) and CYP3A4-metabolized statins (statin DDI exposed, n = 104 774) vs. other statins (unexposed to statin DDI, n = 56 054) from the MarketScan commercial claims database (2012-2017). The statin DDI exposed and unexposed groups were matched (2:1) through propensity score matching techniques. We applied a multistate transition model to compare the 1-year transition probabilities involving four distinct states (start, adverse drug events [ADEs], discontinuation of CYP3A4-inhibitor drugs, and discontinuation of statin therapy) between those exposed to statin DDIs vs. those unexposed. Statistically significant differences were assessed by comparing the 95% confidence intervals (CIs) of probabilities. Results: After concurrently starting stains and CYP3A, patients exposed to statin DDIs, vs. unexposed, were significantly less likely to discontinue statin therapy (71.4% [95% CI: 71.1, 71.6] vs. 73.3% [95% CI: 72.9, 73.6]) but more likely to experience an ADE (3.4% [95% CI: 3.3, 3.5] vs. 3.2% [95% CI: 3.1, 3.3]) and discontinue with CYP3A4-inhibitor therapy (21.0% [95% CI: 20.8, 21.3] vs. 19.5% [95% CI: 19.2, 19.8]). ADEs did not change these associations because those exposed to statin DDIs, vs. unexposed, were still less likely to discontinue statin therapy but more likely to discontinue CYP3A4-inhibitor therapy after experiencing an ADE. Conclusion: We did not observe any meaningful clinical differences in the probability of premature statin discontinuation between statin users exposed to statin DDIs and those unexposed.Item A multistate transition model for statin‐induced myopathy and statin discontinuation(Wiley, 2021) Zhu, Yuxi; Chiang, Chien-Wei; Wang, Lei; Brock, Guy; Milks, M. Wesley; Cao, Weidan; Zhang, Pengyue; Zeng, Donglin; Donneyong, Macarius; Li, Lang; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public HealthThe overarching goal of this study was to simultaneously model the dynamic relationships among statin exposure, statin discontinuation, and potentially statin-related myopathic outcomes. We extracted data from the Indiana Network of Patient Care for 134,815 patients who received statin therapy between January 4, 2004, and December 31, 2008. All individuals began statin treatment, some discontinued statin use, and some experienced myopathy and/or rhabdomyolysis while taking the drug or after discontinuation. We developed a militate model to characterize 12 transition probabilities among six different states defined by use or discontinuation of statin and its associated myopathy or rhabdomyolysis. We found that discontinuation of statin therapy was common and frequently early, with 44.4% of patients discontinuing therapy after 1 month, and discontinuation is a strong indicator for statin-induced myopathy (risk ratio, 10.8; p < 0.05). Women more likely than men (p < 0.05) and patients aged 65 years and older had a higher risk than those aged younger than 65 years to discontinue statin use or experience myopathy. In conclusion, we introduce an innovative multistate model that allows clear depiction of the relationship between statin discontinuation and statin-induced myopathy. For the first time, we have successfully demonstrated and quantified the relative risk of myopathy between patients who continued and discontinued statin therapy. Age and sex were two strong risk factors for both statin discontinuation and incident myopathy.Item Adherence and Tolerability of Alzheimer's Disease Medications: A Pragmatic Randomized Trial(Wiley, 2017-07) Campbell, Noll L.; Perkins, Anthony J.; Gao, Sujuan; Skaar, Todd C.; Li, Lang; Hendrie, Hugh C.; Fowler, Nicole; Callahan, Christopher M.; Boustani, Malaz A.; Department of Medicine, IU School of MedicineBACKGROUND/OBJECTIVES: Post-marketing comparative trials describe medication use patterns in diverse, real-world populations. Our objective was to determine if differences in rates of adherence and tolerability exist among new users to acetylcholinesterase inhibitors (AChEI's). DESIGN: Pragmatic randomized, open label comparative trial of AChEI's currently available in the United States. SETTING: Four memory care practices within four healthcare systems in the greater Indianapolis area. PARTICIPANTS: Eligibility criteria included older adults with a diagnosis of possible or probable Alzheimer's disease (AD) who were initiating treatment with an AChEI. Participants were required to have a caregiver to complete assessments, access to a telephone, and be able to understand English. Exclusion criteria consisted of a prior severe adverse event from AChEIs. INTERVENTION: Participants were randomized to one of three AChEIs in a 1:1:1 ratio and followed for 18 weeks. MEASUREMENTS: Caregiver-reported adherence, defined as taking or not taking study medication, and caregiver-reported adverse events, defined as the presence of an adverse event. RESULTS: 196 participants were included with 74.0% female, 30.6% African Americans, and 72.9% who completed at least twelfth grade. Discontinuation rates after 18 weeks were 38.8% for donepezil, 53.0% for galantamine, and 58.7% for rivastigmine (P = .063) in the intent to treat analysis. Adverse events and cost explained 73.1% and 25.4% of discontinuation. No participants discontinued donepezil due to cost. Adverse events were reported by 81.2% of all participants; no between-group differences in total adverse events were statistically significant. CONCLUSIONS: This pragmatic comparative trial showed high rates of adverse events and cost-related non-adherence with AChEIs. Interventions improving adherence and persistence to AChEIs may improve AD management. TRIAL REGISTRATION: Clinicaltrials.gov: NCT01362686 (https://clinicaltrials.gov/ct2/show/NCT01362686).Item Alt Event Finder: a tool for extracting alternative splicing events from RNA-seq data.(BMC, 2012) Zhou, Ao; Breese, Marcus R.; Hao, Yangyang; Edenberg, Howard J.; Li, Lang; Skaar, Todd C.; Liu, YunlongBACKGROUND: Alternative splicing increases proteome diversity by expressing multiple gene isoforms that often differ in function. Identifying alternative splicing events from RNA-seq experiments is important for understanding the diversity of transcripts and for investigating the regulation of splicing. RESULTS: We developed Alt Event Finder, a tool for identifying novel splicing events by using transcript annotation derived from genome-guided construction tools, such as Cufflinks and Scripture. With a proper combination of alignment and transcript reconstruction tools, Alt Event Finder is capable of identifying novel splicing events in the human genome. We further applied Alt Event Finder on a set of RNA-seq data from rat liver tissues, and identified dozens of novel cassette exon events whose splicing patterns changed after extensive alcohol exposure. CONCLUSIONS: Alt Event Finder is capable of identifying de novo splicing events from data-driven transcript annotation, and is a useful tool for studying splicing regulation.Item Analyzing Patterns of Literature-Based Phenotyping Definitions for Text Mining Applications(IEEE, 2018-06) Binkheder, Samar; Wu, Heng-Yi; Quinney, Sara; Li, Lang; BioHealth Informatics, School of Informatics and ComputingPhenotyping definitions are widely used in observational studies that utilize population data from Electronic Health Records (EHRs). Biomedical text mining supports biomedical knowledge discovery. Therefore, we believe that mining phenotyping definitions from the literature can support EHR-based clinical research. However, information about these definitions presented in the literature is inconsistent, diverse, and unknown, especially for text mining usage. Therefore, we aim to analyze patterns of phenotyping definitions as a first step toward developing a text mining application to improve phenotype definition. A set random of observational studies was used for this analysis. Term frequency-inverse document frequency (TF-IDF) and Term Frequency (TF) were used to rank the terms in the 3958 sentences. Finally, we present preliminary results analyzing phenotyping definitions patterns.Item Artificial intelligence framework identifies candidate targets for drug repurposing in Alzheimer’s disease(BMC, 2022-01-10) Fang, Jiansong; Zhang, Pengyue; Wang, Quan; Chiang, Chien‑Wei; Zhou, Yadi; Hou, Yuan; Xu, Jielin; Chen, Rui; Zhang, Bin; Lewis, Stephen J.; Leverenz, James B.; Pieper, Andrew A.; Li, Bingshan; Li, Lang; Cummings, Jeffrey; Cheng, Feixiong; Biostatistics and Health Data Science, School of MedicineBackground: Genome-wide association studies (GWAS) have identified numerous susceptibility loci for Alzheimer's disease (AD). However, utilizing GWAS and multi-omics data to identify high-confidence AD risk genes (ARGs) and druggable targets that can guide development of new therapeutics for patients suffering from AD has heretofore not been successful. Methods: To address this critical problem in the field, we have developed a network-based artificial intelligence framework that is capable of integrating multi-omics data along with human protein-protein interactome networks to accurately infer accurate drug targets impacted by GWAS-identified variants to identify new therapeutics. When applied to AD, this approach integrates GWAS findings, multi-omics data from brain samples of AD patients and AD transgenic animal models, drug-target networks, and the human protein-protein interactome, along with large-scale patient database validation and in vitro mechanistic observations in human microglia cells. Results: Through this approach, we identified 103 ARGs validated by various levels of pathobiological evidence in AD. Via network-based prediction and population-based validation, we then showed that three drugs (pioglitazone, febuxostat, and atenolol) are significantly associated with decreased risk of AD compared with matched control populations. Pioglitazone usage is significantly associated with decreased risk of AD (hazard ratio (HR) = 0.916, 95% confidence interval [CI] 0.861-0.974, P = 0.005) in a retrospective case-control validation. Pioglitazone is a peroxisome proliferator-activated receptor (PPAR) agonist used to treat type 2 diabetes, and propensity score matching cohort studies confirmed its association with reduced risk of AD in comparison to glipizide (HR = 0.921, 95% CI 0.862-0.984, P = 0.0159), an insulin secretagogue that is also used to treat type 2 diabetes. In vitro experiments showed that pioglitazone downregulated glycogen synthase kinase 3 beta (GSK3β) and cyclin-dependent kinase (CDK5) in human microglia cells, supporting a possible mechanism-of-action for its beneficial effect in AD. Conclusions: In summary, we present an integrated, network-based artificial intelligence methodology to rapidly translate GWAS findings and multi-omics data to genotype-informed therapeutic discovery in AD.Item Association between CYP2D6 genotype and tamoxifen-induced hot flashes in a prospective cohort(Springer, 2009-10) Henry, N. Lynn; Rae, James M.; Li, Lang; Azzouz, Faouzi; Skaar, Todd C.; Desta, Zereunesay; Sikora, Matthew J.; Philips, Santosh; Nguyen, Anne T.; Storniolo, Anna Maria; Hayes, Daniel F.; Flockhart, David A.; Stearns, VeredWomen with reduced CYP2D6 activity have low endoxifen concentrations and likely worse long term benefits from tamoxifen. We investigated the association between CYP2D6 genotype and tamoxifen-induced hot flashes in a prospective cohort. We collected hot flash frequency and severity data over 12 months from 297 women initiating tamoxifen. We performed CYP2D6 genotyping using the AmpliChip CYP450 test and correlated inherited genetic polymorphisms in CYP2D6 and tamoxifen-induced hot flashes. Intermediate metabolizers had greater mean hot flash scores after 4 months of tamoxifen therapy (44.3) compared to poor metabolizers (20.6, P = 0.038) or extensive metabolizers (26.9, P = 0.011). At 4 months, we observed a trend toward fewer severe hot flashes in poor metabolizers compared to intermediate plus extensive metabolizers (P = 0.062). CYP2D6 activity may be a modest predictive factor for tamoxifen-induced hot flashes. The presence or absence of hot flashes should not be used to determine tamoxifen's efficacy.Item Associations between genetic variants and the effect of letrozole and exemestane on bone mass and bone turnover(SpringerLink, 2015-11) Oesterreich, Steffi; Henry, N. Lynn; Kidwell, Kelley M.; Van Poznak, Catherine H.; Skaar, Todd C.; Dantzer, Jessica; Li, Lang; Hangartner, Thomas N.; Peacock, Munro; Nguyen, Anne T.; Rae, James M.; Desta, Zeruesenay; Philips, Santosh; Storniolo, Anna M.; Stearns, Vered; Hayes, Daniel F.; Flockhart, David A.; Medicine, School of MedicineAdjuvant therapy for hormone receptor (HR) positive postmenopausal breast cancer patients includes aromatase inhibitors (AI). While both the non-steroidal AI letrozole and the steroidal AI exemestane decrease serum estrogen concentrations, there is evidence that exemestane may be less detrimental to bone. We hypothesized that single nucleotide polymorphisms (SNP) predict effects of AIs on bone turnover. Early stage HR-positive breast cancer patients were enrolled in a randomized trial of exemestane versus letrozole. Effects of AI on bone mineral density (BMD) and bone turnover markers (BTM), and associations between SNPs in 24 candidate genes and changes in BMD or BTM were determined. Of the 503 enrolled patients, paired BMD data were available for 123 and 101 patients treated with letrozole and exemestane, respectively, and paired BTM data were available for 175 and 173 patients, respectively. The mean change in lumbar spine BMD was significantly greater for letrozole-treated (-3.2 %) compared to exemestane-treated patients (-1.0 %) (p = 0.0016). Urine N-telopeptide was significantly increased in patients treated with exemestane (p = 0.001) but not letrozole. Two SNPs (rs4870061 and rs9322335) in ESR1 and one SNP (rs10140457) in ESR2 were associated with decreased BMD in letrozole-treated patients. In the exemestane-treated patients, SNPs in ESR1 (Rs2813543) and CYP19A1 (Rs6493497) were associated with decreased bone density. Exemestane had a less negative impact on bone density compared to letrozole, and the effects of AI therapy on bone may be impacted by genetic variants in the ER pathway.Item Bi-EB: Empirical Bayesian Biclustering for Multi-Omics Data Integration Pattern Identification among Species(MDPI, 2022-10-30) Yazdanparast, Aida; Li, Lang; Zhang, Chi; Cheng, Lijun; BioHealth Informatics, School of Informatics and ComputingAlthough several biclustering algorithms have been studied, few are used for cross-pattern identification across species using multi-omics data mining. A fast empirical Bayesian biclustering (Bi-EB) algorithm is developed to detect the patterns shared from both integrated omics data and between species. The Bi-EB algorithm addresses the clinical critical translational question using the bioinformatics strategy, which addresses how modules of genotype variation associated with phenotype from cancer cell screening data can be identified and how these findings can be directly translated to a cancer patient subpopulation. Empirical Bayesian probabilistic interpretation and ratio strategy are proposed in Bi-EB for the first time to detect the pairwise regulation patterns among species and variations in multiple omics on a gene level, such as proteins and mRNA. An expectation-maximization (EM) optimal algorithm is used to extract the foreground co-current variations out of its background noise data by adjusting parameters with bicluster membership probability threshold Ac; and the bicluster average probability p. Three simulation experiments and two real biology mRNA and protein data analyses conducted on the well-known Cancer Genomics Atlas (TCGA) and The Cancer Cell Line Encyclopedia (CCLE) verify that the proposed Bi-EB algorithm can significantly improve the clustering recovery and relevance accuracy, outperforming the other seven biclustering methods-Cheng and Church (CC), xMOTIFs, BiMax, Plaid, Spectral, FABIA, and QUBIC-with a recovery score of 0.98 and a relevance score of 0.99. At the same time, the Bi-EB algorithm is used to determine shared the causality patterns of mRNA to the protein between patients and cancer cells in TCGA and CCLE breast cancer. The clinically well-known treatment target protein module estrogen receptor (ER), ER (p118), AR, BCL2, cyclin E1, and IGFBP2 are identified in accordance with their mRNA expression variations in the luminal-like subtype. Ten genes, including CCNB1, CDH1, KDR, RAB25, PRKCA, etc., found which can maintain the high accordance of mRNA-protein for both breast cancer patients and cell lines in basal-like subtypes for the first time. Bi-EB provides a useful biclustering analysis tool to discover the cross patterns hidden both in multiple data matrixes (omics) and species. The implementation of the Bi-EB method in the clinical setting will have a direct impact on administrating translational research based on the cancer cell screening guidance.Item A bioinformatics approach for precision medicine off-label drug drug selection among triple negative breast cancer patients(Oxford Academic, 2016-07) Cheng, Lijun; Schneider, Bryan P.; Li, Lang; Medical and Molecular Genetics, School of MedicineCancer has been extensively characterized on the basis of genomics. The integration of genetic information about cancers with data on how the cancers respond to target based therapy to help to optimum cancer treatment. OBJECTIVE: The increasing usage of sequencing technology in cancer research and clinical practice has enormously advanced our understanding of cancer mechanisms. The cancer precision medicine is becoming a reality. Although off-label drug usage is a common practice in treating cancer, it suffers from the lack of knowledge base for proper cancer drug selections. This eminent need has become even more apparent considering the upcoming genomics data. METHODS: In this paper, a personalized medicine knowledge base is constructed by integrating various cancer drugs, drug-target database, and knowledge sources for the proper cancer drugs and their target selections. Based on the knowledge base, a bioinformatics approach for cancer drugs selection in precision medicine is developed. It integrates personal molecular profile data, including copy number variation, mutation, and gene expression. RESULTS: By analyzing the 85 triple negative breast cancer (TNBC) patient data in the Cancer Genome Altar, we have shown that 71.7% of the TNBC patients have FDA approved drug targets, and 51.7% of the patients have more than one drug target. Sixty-five drug targets are identified as TNBC treatment targets and 85 candidate drugs are recommended. Many existing TNBC candidate targets, such as Poly (ADP-Ribose) Polymerase 1 (PARP1), Cell division protein kinase 6 (CDK6), epidermal growth factor receptor, etc., were identified. On the other hand, we found some additional targets that are not yet fully investigated in the TNBC, such as Gamma-Glutamyl Hydrolase (GGH), Thymidylate Synthetase (TYMS), Protein Tyrosine Kinase 6 (PTK6), Topoisomerase (DNA) I, Mitochondrial (TOP1MT), Smoothened, Frizzled Class Receptor (SMO), etc. Our additional analysis of target and drug selection strategy is also fully supported by the drug screening data on TNBC cell lines in the Cancer Cell Line Encyclopedia. CONCLUSIONS: The proposed bioinformatics approach lays a foundation for cancer precision medicine. It supplies much needed knowledge base for the off-label cancer drug usage in clinics.