Timothy Schulz-Utermoehl

542 total citations
18 papers, 399 citations indexed

About

Timothy Schulz-Utermoehl is a scholar working on Oncology, Pharmacology and Computational Theory and Mathematics. According to data from OpenAlex, Timothy Schulz-Utermoehl has authored 18 papers receiving a total of 399 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Oncology, 13 papers in Pharmacology and 3 papers in Computational Theory and Mathematics. Recurrent topics in Timothy Schulz-Utermoehl's work include Pharmacogenetics and Drug Metabolism (13 papers), Drug Transport and Resistance Mechanisms (11 papers) and Drug-Induced Hepatotoxicity and Protection (5 papers). Timothy Schulz-Utermoehl is often cited by papers focused on Pharmacogenetics and Drug Metabolism (13 papers), Drug Transport and Resistance Mechanisms (11 papers) and Drug-Induced Hepatotoxicity and Protection (5 papers). Timothy Schulz-Utermoehl collaborates with scholars based in United Kingdom, Denmark and Italy. Timothy Schulz-Utermoehl's co-authors include Sunil Sarda, Ian D. Wilson, Alan R. Boobis, Kathryn Pickup, Robert J. Edwards, Yoshio Morikawa, Stephen H. Day, Andrew J. Bennett, Randall R. Miller and Maria Beconi and has published in prestigious journals such as Analytical Biochemistry, Brain Research and Drug Metabolism and Disposition.

In The Last Decade

Timothy Schulz-Utermoehl

18 papers receiving 385 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Timothy Schulz-Utermoehl United Kingdom 14 249 163 116 37 36 18 399
Daniel R. Mudra United States 11 270 1.1× 200 1.2× 217 1.9× 55 1.5× 31 0.9× 18 647
Philip Worboys United Kingdom 13 228 0.9× 168 1.0× 143 1.2× 84 2.3× 33 0.9× 14 558
Helen Hammer Germany 11 162 0.7× 147 0.9× 154 1.3× 18 0.5× 45 1.3× 30 469
Fengmei Hua United States 8 175 0.7× 167 1.0× 54 0.5× 62 1.7× 17 0.5× 10 353
Claire Denizot France 13 110 0.4× 309 1.9× 151 1.3× 51 1.4× 19 0.5× 27 563
Dorothy T. Steimel United States 9 400 1.6× 171 1.0× 274 2.4× 38 1.0× 42 1.2× 9 662
Carlo Sensenhauser United States 11 140 0.6× 73 0.4× 126 1.1× 16 0.4× 90 2.5× 12 352
Abu Jafar Md. Sadeque United States 12 263 1.1× 137 0.8× 163 1.4× 63 1.7× 63 1.8× 21 535
Sidney D. Nelson United States 10 268 1.1× 88 0.5× 118 1.0× 45 1.2× 20 0.6× 12 423
Koichi SUGENO Japan 9 256 1.0× 105 0.6× 169 1.5× 41 1.1× 33 0.9× 33 523

Countries citing papers authored by Timothy Schulz-Utermoehl

Since Specialization
Citations

This map shows the geographic impact of Timothy Schulz-Utermoehl'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 Timothy Schulz-Utermoehl with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Timothy Schulz-Utermoehl more than expected).

Fields of papers citing papers by Timothy Schulz-Utermoehl

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Timothy Schulz-Utermoehl. 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 Timothy Schulz-Utermoehl. The network helps show where Timothy Schulz-Utermoehl may publish in the future.

Co-authorship network of co-authors of Timothy Schulz-Utermoehl

This figure shows the co-authorship network connecting the top 25 collaborators of Timothy Schulz-Utermoehl. A scholar is included among the top collaborators of Timothy Schulz-Utermoehl 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 Timothy Schulz-Utermoehl. Timothy Schulz-Utermoehl is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

18 of 18 papers shown
1.
Shepherd, Emma L., et al.. (2021). Application of HepG2/C3A liver spheroids as a model system for genotoxicity studies. Toxicology Letters. 345. 34–45. 15 indexed citations
2.
Martin, Scott, Sunil Sarda, Timothy Schulz-Utermoehl, et al.. (2013). In vitro exploration of potential mechanisms of toxicity of the human hepatotoxic drug fenclozic acid. Archives of Toxicology. 87(8). 1569–1579. 12 indexed citations
3.
Foster, John R., Matt Jacobsen, J. Gerry Kenna, et al.. (2012). Differential Effect of Troglitazone on the Human Bile Acid Transporters, MRP2 and BSEP, in the PXB Hepatic Chimeric Mouse. Toxicologic Pathology. 40(8). 1106–1116. 26 indexed citations
4.
Samuelsson, Kristin, Kathryn Pickup, Sunil Sarda, et al.. (2012). Pharmacokinetics and metabolism of midazolam in chimeric mice with humanised livers. Xenobiotica. 42(11). 1128–1137. 18 indexed citations
5.
Sarda, Sunil, et al.. (2012). In vitromodels of xenobiotic metabolism in trout for use in environmental bioaccumulation studies. Xenobiotica. 43(5). 421–431. 19 indexed citations
6.
Schulz-Utermoehl, Timothy, Sunil Sarda, John R. Foster, et al.. (2011). Evaluation of the pharmacokinetics, biotransformation and hepatic transporter effects of troglitazone in mice with humanized livers. Xenobiotica. 42(6). 503–517. 26 indexed citations
7.
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Schulz-Utermoehl, Timothy, Michael L. Spear, Sunil Sarda, et al.. (2010). In Vitro Hepatic Metabolism of Cediranib, a Potent Vascular Endothelial Growth Factor Tyrosine Kinase Inhibitor: Interspecies Comparison and Human Enzymology. Drug Metabolism and Disposition. 38(10). 1688–1697. 14 indexed citations
10.
Lenz, Eva M., et al.. (2010). Characterisation and identification of the human N+-glucuronide metabolite of cediranib. Journal of Pharmaceutical and Biomedical Analysis. 53(3). 526–536. 9 indexed citations
11.
Meneses‐Lorente, Georgina, et al.. (2006). A quantitative high-throughput trapping assay as a measurement of potential for bioactivation. Analytical Biochemistry. 351(2). 266–272. 33 indexed citations
12.
Day, Stephen H., et al.. (2005). A semi-automated method for measuring the potential for protein covalent binding in drug discovery. Journal of Pharmacological and Toxicological Methods. 52(2). 278–285. 50 indexed citations
13.
Schulz, Thomas, Renate Thiel, Diether Neubert, et al.. (2001). Assessment of P450 induction in the marmoset monkey using targeted anti-peptide antibodies. Biochimica et Biophysica Acta (BBA) - Protein Structure and Molecular Enzymology. 1546(1). 143–155. 17 indexed citations
14.
Schulz-Utermoehl, Timothy, Robert J. Edwards, & Alan R. Boobis. (2000). Affinity and Potency of Proinhibitory Antipeptide Antibodies Against CYP2D6 Is Enhanced using Cyclic Peptides as Immunogens. Drug Metabolism and Disposition. 28(5). 544–551. 16 indexed citations
15.
Schulz-Utermoehl, Timothy, et al.. (2000). Selective and Potent Inhibition of Human CYP2C19 Activity by a Conformationally Targeted Antipeptide Antibody. Drug Metabolism and Disposition. 28(7). 715–717. 8 indexed citations
16.
Schulz-Utermoehl, Timothy, et al.. (2000). Structure-Function Analysis of Human CYP3A4 Using a Specific Proinhibitory Antipeptide Antibody. Drug Metabolism and Disposition. 28(7). 718–725. 3 indexed citations
17.
Schulz-Utermoehl, Timothy, Andrew J. Bennett, S. W. Ellis, et al.. (1999). Polymorphic debrisoquine 4-hydroxylase activity in the rat is due to differences in CYP2D2 expression. Pharmacogenetics. 9(3). 357–366. 45 indexed citations
18.
Edwards, Robert J., et al.. (1999). Expression and localisation of CYP2D enzymes in rat basal ganglia. Brain Research. 822(1-2). 175–191. 20 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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