Jae H. Chang

1.6k total citations
41 papers, 763 citations indexed

About

Jae H. Chang is a scholar working on Oncology, Molecular Biology and Pharmacology. According to data from OpenAlex, Jae H. Chang has authored 41 papers receiving a total of 763 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Oncology, 19 papers in Molecular Biology and 13 papers in Pharmacology. Recurrent topics in Jae H. Chang's work include Drug Transport and Resistance Mechanisms (12 papers), Pharmacogenetics and Drug Metabolism (11 papers) and Drug-Induced Hepatotoxicity and Protection (5 papers). Jae H. Chang is often cited by papers focused on Drug Transport and Resistance Mechanisms (12 papers), Pharmacogenetics and Drug Metabolism (11 papers) and Drug-Induced Hepatotoxicity and Protection (5 papers). Jae H. Chang collaborates with scholars based in United States, Taiwan and France. Jae H. Chang's co-authors include Jonathan Cheong, Leslie Z. Benet, Emile G. Plise, Mark O. J. Olson, Molly Lin, Ruina Li, David H. Hackos, W. Allan Walker, Y Li and Long Su and has published in prestigious journals such as Journal of Biological Chemistry, Journal of Neuroscience and Blood.

In The Last Decade

Jae H. Chang

38 papers receiving 734 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Jae H. Chang United States 16 328 202 161 105 71 41 763
Jean‐Michel Scherrmann France 16 419 1.3× 523 2.6× 123 0.8× 81 0.8× 66 0.9× 18 1.2k
Harish V. Pai India 14 445 1.4× 105 0.5× 160 1.0× 107 1.0× 23 0.3× 19 761
Irena Manov Israel 17 349 1.1× 113 0.6× 148 0.9× 127 1.2× 47 0.7× 31 829
Melanie A. Felmlee United States 17 430 1.3× 198 1.0× 78 0.5× 149 1.4× 29 0.4× 30 1.1k
Zheng Tian China 15 407 1.2× 78 0.4× 52 0.3× 77 0.7× 75 1.1× 54 700
Julius Halaschek-Wiener Canada 14 420 1.3× 155 0.8× 60 0.4× 151 1.4× 19 0.3× 15 832
Ryoichi Nagata Japan 15 239 0.7× 285 1.4× 346 2.1× 65 0.6× 49 0.7× 73 999
John P. Umland United States 14 509 1.6× 312 1.5× 161 1.0× 94 0.9× 48 0.7× 17 1.2k
David M. Truong United States 17 952 2.9× 389 1.9× 106 0.7× 89 0.8× 37 0.5× 25 1.4k
Ryutaro Adachi Japan 13 232 0.7× 128 0.6× 49 0.3× 88 0.8× 87 1.2× 26 583

Countries citing papers authored by Jae H. Chang

Since Specialization
Citations

This map shows the geographic impact of Jae H. Chang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jae H. Chang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jae H. Chang more than expected).

Fields of papers citing papers by Jae H. Chang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jae H. Chang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jae H. Chang. The network helps show where Jae H. Chang may publish in the future.

Co-authorship network of co-authors of Jae H. Chang

This figure shows the co-authorship network connecting the top 25 collaborators of Jae H. Chang. A scholar is included among the top collaborators of Jae H. Chang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jae H. Chang. Jae H. Chang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
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Ray, Arghya, Melissa R. Junttila, Ting Du, et al.. (2021). CD73 Inhibition Overcomes Immunosuppression and Triggers Autologous T-Cell Mediated Multiple Myeloma Cell Lysis in the Bone Marrow Milieu. Blood. 138(Supplement 1). 2675–2675. 2 indexed citations
3.
Ly, Justin Q., et al.. (2021). Investigating the Utility of Humanized Pregnane X Receptor-Constitutive Androstane Receptor-CYP3A4/7 Mouse Model to Assess CYP3A-Mediated Induction. Drug Metabolism and Disposition. 49(7). 540–547. 3 indexed citations
4.
Pathak, Shiva, Suman Acharya, Shobha Regmi, et al.. (2020). Particulate‐Based Single‐Dose Local Immunosuppressive Regimen for Inducing Tolerogenic Dendritic Cells in Xenogeneic Islet Transplantation. Advanced Healthcare Materials. 10(2). e2001157–e2001157. 11 indexed citations
5.
Jones, Robert S., et al.. (2020). Evaluation of a Competitive Equilibrium Dialysis Approach for Assessing the Impact of Protein Binding on Clearance Predictions. Journal of Pharmaceutical Sciences. 110(1). 536–542. 4 indexed citations
6.
Rothenberg, Michael E., Michael Tagen, Jae H. Chang, et al.. (2019). Safety, Tolerability, and Pharmacokinetics of GDC-0276, a Novel NaV1.7 Inhibitor, in a First-in-Human, Single- and Multiple-Dose Study in Healthy Volunteers. Clinical Drug Investigation. 39(9). 873–887. 30 indexed citations
7.
Su, Chien‐Wen, Long Su, Y Li, et al.. (2017). Helminth-induced alterations of the gut microbiota exacerbate bacterial colitis. Mucosal Immunology. 11(1). 144–157. 86 indexed citations
8.
Chang, Jae H., et al.. (2017). Rosuvastatin Pharmacokinetics in Asian and White Subjects Wild Type for Both OATP1B1 and BCRP Under Control and Inhibited Conditions. Journal of Pharmaceutical Sciences. 106(9). 2751–2757. 63 indexed citations
9.
Takahashi, Ryan, Sheerin Shahidi-Latham, Susan Wong, & Jae H. Chang. (2017). Applying Stable Isotope Labeled Amino Acids in Micropatterned Hepatocyte Coculture to Directly Determine the Degradation Rate Constant for CYP3A4. Drug Metabolism and Disposition. 45(6). 581–585. 13 indexed citations
10.
Wang, Xiaojing, Aleksandr Kolesnikov, Suzanne Tay, et al.. (2017). Discovery of 5-Azaindazole (GNE-955) as a Potent Pan-Pim Inhibitor with Optimized Bioavailability. Journal of Medicinal Chemistry. 60(10). 4458–4473. 19 indexed citations
11.
Takahashi, Ryan, Xiaojing Wang, Nathaniel L. Segraves, et al.. (2017). CYP1A1-Mediated Intramolecular Rearrangement of Aminoazepane in GDC-0339. Drug Metabolism and Disposition. 45(10). 1084–1092. 6 indexed citations
12.
Chang, Rudy, Jae H. Chang, Vitaly Vasilevko, et al.. (2017). Blood–Brain Barrier Penetrating Biologic TNF-α Inhibitor for Alzheimer’s Disease. Molecular Pharmaceutics. 14(7). 2340–2349. 74 indexed citations
13.
14.
Chang, Jae H., et al.. (2016). Rifampin-Mediated Induction of Tamoxifen Metabolism in a Humanized PXR-CAR-CYP3A4/3A7-CYP2D6 Mouse Model. Drug Metabolism and Disposition. 44(11). 1736–1741. 9 indexed citations
15.
Chang, Jae H., et al.. (2015). NF-κB Enhances Androgen Receptor Expression through 5′-UTR Binding in Gingival Cells. Journal of Dental Research. 94(10). 1439–1445. 6 indexed citations
16.
Hu, Huiyong, Xiaojing Wang, Jae H. Chang, et al.. (2015). Discovery of 3,5-substituted 6-azaindazoles as potent pan-Pim inhibitors. Bioorganic & Medicinal Chemistry Letters. 25(22). 5258–5264. 19 indexed citations
17.
Chang, Jae H., Justin Q. Ly, Emile G. Plise, et al.. (2014). Differential Effects of Rifampin and Ketoconazole on the Blood and Liver Concentration of Atorvastatin in Wild-Type and Cyp3a and Oatp1a/b Knockout Mice. Drug Metabolism and Disposition. 42(6). 1067–1073. 23 indexed citations
18.
19.
Chang, Jae H., et al.. (2009). The Role of pH in the Glucuronidation of Raloxifene, Mycophenolic Acid and Ezetimibe. Molecular Pharmaceutics. 6(4). 1216–1227. 20 indexed citations
20.
Chang, Jae H., et al.. (1988). cDNA and deduced primary structure of rat protein B23, a nucleolar protein containing highly conserved sequences.. Journal of Biological Chemistry. 263(26). 12824–12827. 43 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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