Gordon J. Dear

2.6k total citations · 1 hit paper
55 papers, 2.0k citations indexed

About

Gordon J. Dear is a scholar working on Spectroscopy, Molecular Biology and Biomedical Engineering. According to data from OpenAlex, Gordon J. Dear has authored 55 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Spectroscopy, 22 papers in Molecular Biology and 13 papers in Biomedical Engineering. Recurrent topics in Gordon J. Dear's work include Analytical Chemistry and Chromatography (35 papers), Metabolomics and Mass Spectrometry Studies (18 papers) and Mass Spectrometry Techniques and Applications (17 papers). Gordon J. Dear is often cited by papers focused on Analytical Chemistry and Chromatography (35 papers), Metabolomics and Mass Spectrometry Studies (18 papers) and Mass Spectrometry Techniques and Applications (17 papers). Gordon J. Dear collaborates with scholars based in United Kingdom, United States and Netherlands. Gordon J. Dear's co-authors include Robert S. Plumb, John Ayrton, David N. Mallett, William J. Leavens, John N. Haselden, Chris L. Stumpf, José Castro‐Perez, Brian C. Sweatman, Stephen Pleasance and Claire Beaumont and has published in prestigious journals such as Analytical Chemistry, Nature Reviews Drug Discovery and Analytical Biochemistry.

In The Last Decade

Gordon J. Dear

55 papers receiving 1.9k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

The evolving role of investigative toxicology in the pharmaceutical industry

2023 132 citations Gordon J. Dear et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Gordon J. Dear United Kingdom	25	1.1k	842	313	258	239	55	2.0k
Lekha Sleno Canada	23	837 0.7×	945 1.1×	186 0.6×	137 0.5×	150 0.6×	94	2.2k
Kevin P. Bateman United States	32	1.1k 1.0×	1.4k 1.6×	733 2.3×	281 1.1×	202 0.8×	86	3.5k
Markus Godejohann Germany	26	548 0.5×	810 1.0×	184 0.6×	171 0.7×	334 1.4×	52	2.0k
Stephen Pleasance Canada	30	815 0.7×	703 0.8×	348 1.1×	154 0.6×	316 1.3×	46	2.2k
Rudolph C. Johnson United States	26	443 0.4×	361 0.4×	221 0.7×	83 0.3×	229 1.0×	89	1.9k
Giuseppe Paglia Italy	33	1.3k 1.1×	2.1k 2.5×	357 1.1×	46 0.2×	224 0.9×	88	3.7k
Terence S. M. Wan China	33	756 0.7×	528 0.6×	219 0.7×	130 0.5×	315 1.3×	129	3.1k
Anneli Kruve Estonia	30	1.5k 1.3×	1.0k 1.2×	610 1.9×	71 0.3×	663 2.8×	81	3.3k
Hubertus Irth Netherlands	38	1.5k 1.3×	1.9k 2.2×	849 2.7×	312 1.2×	476 2.0×	154	4.2k
C. J. W. Brooks United Kingdom	33	1.1k 0.9×	1.6k 1.9×	180 0.6×	450 1.7×	239 1.0×	157	3.7k

Countries citing papers authored by Gordon J. Dear

Since Specialization

Citations

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

Fields of papers citing papers by Gordon J. Dear

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gordon J. Dear. 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 Gordon J. Dear. The network helps show where Gordon J. Dear may publish in the future.

Co-authorship network of co-authors of Gordon J. Dear

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

All Works

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20 of 20 papers shown

Miao, Xiu‐Sheng, Gordon J. Dear, Claire Beaumont, et al.. (2024). Cyanide Trapping of Iminium Ion Reactive Metabolites: Implications for Clinical Hepatotoxicity. Chemical Research in Toxicology. 37(5). 698–710. 3 indexed citations

Li, Wenkui, Gordon J. Dear, Jason Boer, et al.. (2023). Metabolite Bioanalysis in Drug Development: Recommendations from the IQ Consortium Metabolite Bioanalysis Working Group. Clinical Pharmacology & Therapeutics. 115(5). 939–953. 1 indexed citations

Cahn, Anthony, J. Hamblin, Malcolm Begg, et al.. (2021). An Inhaled PI3Kδ Inhibitor Improves Recovery in Acutely Exacerbating COPD Patients: A Randomized Trial. International Journal of COPD. Volume 16. 1607–1619. 12 indexed citations

Bayfield, Oliver W., Martin C. R. Cockett, Gordon J. Dear, et al.. (2018). Using hyperpolarised NMR and DFT to rationalise the unexpected hydrogenation of quinazoline to 3,4-dihydroquinazoline. Chemical Communications. 54(73). 10375–10378. 8 indexed citations

Nedderman, Angus N. R., et al.. (2011). From definition to implementation: a cross-industry perspective of past, current and future MIST strategies. Xenobiotica. 41(8). 605–622. 31 indexed citations

Dear, Gordon J., et al.. (2008). Utilizing a −100°C microplate CCD Imager, yttrium silicate coated 384-microplates and ultra-performance liquid chromatography for improved profiling of radiolabeled drug metabolites in complex biological samples. Journal of Chromatography B. 868(1-2). 49–57. 10 indexed citations

Dear, Gordon J., et al.. (2008). Evaluation of preparative high performance liquid chromatography and cryoprobe-nuclear magnetic resonance spectroscopy for the early quantitative estimation of drug metabolites in human plasma. Journal of Chromatography B. 876(2). 182–190. 39 indexed citations

Dear, Gordon J., et al.. (2006). TopCount coupled to ultra-performance liquid chromatography for the profiling of radiolabeled drug metabolites in complex biological samples. Journal of Chromatography B. 844(1). 96–103. 29 indexed citations

Mallett, David N., et al.. (2006). The determination of nicotinic acid in plasma by mixed-mode liquid chromatography–tandem mass spectrometry following ion exchange solid phase extraction. Journal of Pharmaceutical and Biomedical Analysis. 41(2). 510–516. 18 indexed citations

10.

Dear, Gordon J., et al.. (2006). Ultra‐performance liquid chromatography coupled to linear ion trap mass spectrometry for the identification of drug metabolites in biological samples. Rapid Communications in Mass Spectrometry. 20(8). 1351–1360. 23 indexed citations

11.

Roberts, Andrew D., et al.. (2005). Characterization of the metabolites of alosetron in experimental animals and human. Xenobiotica. 35(2). 131–154. 5 indexed citations

12.

Plumb, Robert S., et al.. (2001). Determination of 4‐hydroxytamoxifen in mouse plasma in the pg/mL range by gradient capillary liquid chromatography/tandem mass spectrometry. Rapid Communications in Mass Spectrometry. 15(4). 297–303. 21 indexed citations

13.

Plumb, Robert S., Gordon J. Dear, David N. Mallett, et al.. (2001). Quantitative analysis of pharmaceuticals in biological fluids using high-performance liquid chromatography coupled to mass spectrometry: a review. Xenobiotica. 31(8-9). 599–617. 40 indexed citations

14.

Dear, Gordon J., et al.. (2000). 極性代謝産物を同定するための戦略としての直接結合イオン交換液体クロマトグラフィー‐質量分析及び液体クロマトグラフィー‐核磁気共鳴分光法の利用. Journal of Chromatography B. 748(1). 295–309. 7 indexed citations

15.

Dear, Gordon J., Robert S. Plumb, Brian C. Sweatman, et al.. (2000). Mass directed peak selection, an efficient method of drug metabolite identification using directly coupled liquid chromatography–mass spectrometry–nuclear magnetic resonance spectroscopy. Journal of Chromatography B Biomedical Sciences and Applications. 748(1). 281–293. 22 indexed citations

16.

Ayrton, John, R. A. Clare, Gordon J. Dear, David N. Mallett, & Robert S. Plumb. (1999). Ultra-high flow rate capillary liquid chromatography with mass spectrometric detection for the direct analysis of pharmaceuticals in plasma at sub-nanogram per millilitre concentrations. Rapid Communications in Mass Spectrometry. 13(16). 1657–1662. 34 indexed citations

17.

Dear, Gordon J., et al.. (1999). Tandem mass spectrometry linked fraction collection for the isolation of drug metabolites from biological matrices. Rapid Communications in Mass Spectrometry. 13(10). 886–894. 12 indexed citations

18.

Plumb, Robert S., et al.. (1999). The use of preparative high performance liquid chromatography with tandem mass spectrometric directed fraction collection for the isolation and characterisation of drug metabolites in urine by nuclear magnetic resonance spectroscopy and liquid chromatography/sequential mass spectrometry. Rapid Communications in Mass Spectrometry. 13(10). 845–854. 19 indexed citations

19.

Dear, Gordon J., et al.. (1995). Identification of urinary and biliary conjugated metabolites of the neuromuscular blocker 51w89 by liquid chromatography/mass spectrometry. Rapid Communications in Mass Spectrometry. 9(14). 1457–1464. 33 indexed citations

20.

Lawton, Linda A., Christine Edwards, Kenneth A. Beattie, et al.. (1995). Isolation and characterization of microcystins from laboratory cultures and environmental samples of Microcystis aeruginosa and from an associated animal toxicosis. Natural Toxins. 3(1). 50–57. 85 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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