Huixiao Hong, Ph.D.
Division of Bioinformatics and Biostatistics
National Center for Toxicological Research, FDA
Ph.D. Computational Chemistry, Nanjing Univ, 1990
2007-present: Chemist, NCTR, FDA
2002-2007: Manager of Bioinformatics, Northrop Grumman Co. at NCTR/FDA
2000-2002: Scientist, Federal Data Co. at NCTR/FDA
1998-2000: Scientist, Sumitomo Chemical Co., Ltd., Japan
1995-1998: Visiting Scientist, NCI/NIH
Next-generation sequencing: technical robustness and variation of data analysis; Genome-wide association studies (GWAS): baseline practices for analyzing GWAS data, technical robustness of genotyping technology, sources of spurious associations, batch effects in GWAS; Proteomics: quality control and quality assessment of proteomics data, biomarkers for detecting hepatotoxicity, prediction protein function from protein sequences; Genomics: development of classifiers of diagnosis and prognosis based on gene expression profiles. Chemoinformatics: estrogenic activity database (www.fda.gov/ScienceResearch/BioinformaticsTools/EstrogenicActivityDatabaseEADB/default.htm) EADB and descriptors generator Mold2 (www.fda.gov/ScienceResearch/BioinformaticsTools/Mold2/default.htm).
Proposed Research Project for an FDA Commissioner's Fellow
Title: Developing Database and Atlases for Genetic Variants Causing Enzyme Function Changes of CYP2B6 and CYP2C19 for Informing Population Difference in Toxicity and Clinical Response to Drugs
Abstract: Cytochrome P-450 enzymes transform and metabolize many drugs and toxins. Among the many such enzymes, CYP2C19 and CYP2B6 are particularly known to have genetic variants that alter individual responses to drugs [3-5], and are sometimes reported in FDA drug labels. The mutation variants can alter both efficacy and toxicity of a drug, and some variants are known to cluster within ethnic groups. The genetic variants have been experimentally determined for many ethnic groups, but have not been systematically organized for efficient use in research and regulation generally and for comparison across minority and ethnic populations particularly. We will mine the rich accumulated literature with the first goal of determining CYP2B6 and CYP2C19 genetic variant frequencies across ethnic groups. The recent DNA sequencing of human genomes of many ethnic groups provides additional data for characterizing genetic variation in ethnic populations. As well, DNA sequence data will be analyzed to catalogue how specific mutations in CYP2B6 and CYP2C19 increase or decrease or completely knock out function. The genetic variants will be organized in a database and atlases according to prevalence across ethnicities and categories of alteration of CYP2B6 and CYP2C19 function change. The atlases could benefit health in several ways. Information will be readily available to inform improved drug labeling, so that patients and clinicians alike are apprised when ethnicity considerations could affect efficacy or safety of a selected treatment. Having the information integrated into the atlas could help inform clinical trial’s designs or regulatory reviews for drugs that are chemically similar to those contained in the database, or identify gaps where research is needed.
Expected outcomes and Regulatory Impact Statement: This research project is designed to curate and analyze genetic variant frequency data from the literature, to integrate all relevant types of data, including DNA sequencing data, to construct a database for storing and managing the data, and to generate frequency atlases of genetic variants that cause CYP2B6 and CYP2C19 enzyme function changes for guiding ethnicity-based clinical decisions.
The unique database that will be developed as a scientific resource from this project will provide a comprehensive data source for scientists studying the role of CYP2B6 and CYP2C19 genetic variants. The frequency atlases of CYP2B6 and CYP2C19 that will be derived from the database will be useful from a regulatory perspective in assessing potential differential risk/benefit in minority populations. Both the database and atlases can further be used to inform drug labels regarding sensitive populations and ultimately physicians in clinical applications. In addition, this information will help in understanding the impact of newly identified variants on enzyme activity and on drug response through phenotype-genotype association studies. They will also be useful to identify gaps where additional research is needed, especially if the data are made available to the scientific community.
The FDA Regulatory Science Priority Area for the project is Modernize Toxicology to Enhance Product Safety.
If selected, the applicant will (a) implement workflows for mining genetic data and analyzing next-generation sequencing data, (b) perform bioinformatics analysis in collaboration with our research group members and collaborators, and (c) develop new methods and approaches for management and visualization of genetic and related data.
The applicant should be a creative, self-motivated individual who has the ability and desire to pursue solutions to challenging and interdisciplinary problems in a fast-paced research environment. As evidence of qualifications, the applicant must meet the following requirements. 1) A Ph.D. degree in bioinformatics, genetics, biology or a relevant scientific discipline; 2) Proficiency with at least one programming language (C, C++, Python, Perl, Java, etc.) and experience in high-performance computing (HPC) environment; 3) Experience with software packages of MatLab, SAS, R, or other statistical analysis programs; 4) Experience with the analysis of next-generation sequencing data; 5) Excellent oral and written communication skills; Good judgment, clear sense of purpose, and accountability.
Selected Recent Publications
- J Shen, L Xu, H Fang, AM Richard, JD Bray, RS Judson, G Zhou, TJ Colatsky, JL Aungst, C Teng, SC Harris, W Ge, SY Dai, Z Su, AC Jacobs, W Harrouk, R Perkins, W Tong, Huixiao Hong. EADB: An Estrogenic Activity Database for Assessing Potential Endocrine Activity. Toxicological Sciences 2013, 135(2), 277–291.
- J Shen, W Zhang, H Fang, R Perkins, W Tong, Huixiao Hong. Homology modeling, molecular docking, and molecular dynamics simulations elucidated α-fetoprotein binding modes. BMC Bioinformatics 2013, 14(Suppl 14): S6.
- Huixiao Hong, Q Hong, J Liu, W Tong and L Shi. Estimating relative noise to signal in DNA microarray data. Int. J. Bioinformatics Research and Applications 2013, 9(5): 433-448.
- Huixiao Hong, W Zhang, J Shen, Z Su, B Ning, T Han, R Perkins, L Shi, W Tong. Critical role of bioinformatics in translating huge amounts of next-generation sequencing data into personalized medicine. Science China Life Sciences, 2013, 56(2): 110-118.
- Huixiao Hong, A Jawaid, J Wang, J Catalano, JC Fox, TB Hawkins. Combining genetic variations in CYP2C9 and VKORC1 with clinical factors for warfarin dosing determination improved clinical effectiveness. Pharmacogenomics, 2013, 14(5): 459-460.