Megan Gibbs

2.1k total citations
46 papers, 1.3k citations indexed

About

Megan Gibbs is a scholar working on Oncology, Immunology and Pharmacology. According to data from OpenAlex, Megan Gibbs has authored 46 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Oncology, 12 papers in Immunology and 10 papers in Pharmacology. Recurrent topics in Megan Gibbs's work include Pharmacogenetics and Drug Metabolism (10 papers), Statistical Methods in Clinical Trials (8 papers) and Drug Transport and Resistance Mechanisms (7 papers). Megan Gibbs is often cited by papers focused on Pharmacogenetics and Drug Metabolism (10 papers), Statistical Methods in Clinical Trials (8 papers) and Drug Transport and Resistance Mechanisms (7 papers). Megan Gibbs collaborates with scholars based in United States, United Kingdom and Poland. Megan Gibbs's co-authors include Kent L. Kunze, Kenneth E. Thummel, Danny D. Shen, John P. Gibbs, Aaron H. Burstein, David J. Clark, Hélène M. Faessel, Natilie Hosea, David H. Salinger and Christopher J. Endres and has published in prestigious journals such as New England Journal of Medicine, SHILAP Revista de lepidopterología and Blood.

In The Last Decade

Megan Gibbs

43 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Megan Gibbs United States 21 378 313 300 202 165 46 1.3k
Miroslav Dostálek United States 22 367 1.0× 542 1.7× 226 0.8× 153 0.8× 105 0.6× 45 1.6k
Christophe Schmitt Switzerland 28 341 0.9× 597 1.9× 377 1.3× 168 0.8× 125 0.8× 73 3.8k
Yuji Kumagai Japan 25 254 0.7× 493 1.6× 412 1.4× 111 0.5× 149 0.9× 127 2.0k
Vijay Upreti United States 20 328 0.9× 403 1.3× 491 1.6× 171 0.8× 44 0.3× 55 1.7k
David D. Christ United States 25 472 1.2× 537 1.7× 335 1.1× 109 0.5× 67 0.4× 49 1.9k
Troels K. Bergmann Denmark 21 333 0.9× 457 1.5× 719 2.4× 76 0.4× 134 0.8× 44 2.1k
Karin Jorga Switzerland 25 325 0.9× 382 1.2× 388 1.3× 83 0.4× 37 0.2× 48 2.0k
Craig D. Fisher United States 24 320 0.8× 568 1.8× 426 1.4× 58 0.3× 165 1.0× 31 2.0k
Adam S. Darwich United Kingdom 18 218 0.6× 217 0.7× 222 0.7× 47 0.2× 161 1.0× 41 1.2k
Ann M. Moyer United States 24 680 1.8× 541 1.7× 311 1.0× 219 1.1× 84 0.5× 109 2.1k

Countries citing papers authored by Megan Gibbs

Since Specialization
Citations

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

Fields of papers citing papers by Megan Gibbs

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Megan Gibbs. 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 Megan Gibbs. The network helps show where Megan Gibbs may publish in the future.

Co-authorship network of co-authors of Megan Gibbs

This figure shows the co-authorship network connecting the top 25 collaborators of Megan Gibbs. A scholar is included among the top collaborators of Megan Gibbs 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 Megan Gibbs. Megan Gibbs 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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Pichardo‐Almarza, César, et al.. (2024). Coupling quantitative systems pharmacology modelling to machine learning and artificial intelligence for drug development: its pAIns and gAIns. SHILAP Revista de lepidopterología. 4. 1380685–1380685. 6 indexed citations
3.
Gibbs, Megan, et al.. (2024). Augmented intelligence in precision medicine: Transforming clinical decision‐making with AI/ML and/or quantitative systems pharmacology models. Clinical and Translational Science. 17(12). e70112–e70112. 4 indexed citations
4.
He, Zhijian, Xuyang Song, Cecil Chen, et al.. (2023). Population Pharmacokinetics and Exposure–Response Analysis of Tremelimumab 300 mg Single Dose Combined with Durvalumab 1500 mg Q4W (STRIDE) in Patients with Unresectable Hepatocellular Carcinoma. The Journal of Clinical Pharmacology. 63(11). 1221–1231. 4 indexed citations
5.
Peck, Richard, et al.. (2023). Synthetic Model Combination: A new machine‐learning method for pharmacometric model ensembling. CPT Pharmacometrics & Systems Pharmacology. 12(7). 953–962. 11 indexed citations
6.
He, Zhijian, Vincent Duval, António Gonçalves, et al.. (2023). Population Pharmacokinetics and Exposure–Response Analysis for the CTLA‐4 Inhibitor Tremelimumab in Metastatic NSCLC Patients in the Phase III POSEIDON Study. Clinical Pharmacology & Therapeutics. 114(6). 1375–1386. 2 indexed citations
7.
Song, Xuyang, Robin Kate Kelley, Michelle Green, et al.. (2023). Modeling of Proliferating CD4 and CD8 T‐Cell Changes to Tremelimumab Exposure in Patients with Unresectable Hepatocellular Carcinoma. Clinical Pharmacology & Therapeutics. 114(4). 874–882. 2 indexed citations
8.
Song, Xuyang, Robin Kate Kelley, Anis A. Khan, et al.. (2022). Exposure-Response Analyses of Tremelimumab Monotherapy or in Combination with Durvalumab in Patients with Unresectable Hepatocellular Carcinoma. Clinical Cancer Research. 29(4). 754–763. 21 indexed citations
9.
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Salinger, David H., Christopher J. Endres, Roland Martinꝉ, & Megan Gibbs. (2014). A semi‐mechanistic model to characterize the pharmacokinetics and pharmacodynamics of brodalumab in healthy volunteers and subjects with psoriasis in a first‐in‐human single ascending dose study. Clinical Pharmacology in Drug Development. 3(4). 276–283. 21 indexed citations
11.
Davda, Jasmine, et al.. (2014). A model-based meta-analysis of monoclonal antibody pharmacokinetics to guide optimal first-in-human study design. mAbs. 6(4). 1094–1102. 36 indexed citations
12.
Dong, Jennifer, David H. Salinger, Christopher J. Endres, et al.. (2011). Quantitative Prediction of Human Pharmacokinetics for Monoclonal Antibodies. Clinical Pharmacokinetics. 50(2). 131–142. 119 indexed citations
13.
Mandema, Jaap W., et al.. (2011). A Dose–Response Meta-Analysis for Quantifying Relative Efficacy of Biologics in Rheumatoid Arthritis. Clinical Pharmacology & Therapeutics. 90(6). 828–835. 61 indexed citations
14.
Gibbs, Megan, et al.. (2009). Evaluation of structural models to describe the effect of placebo upon the time course of major depressive disorder. Journal of Pharmacokinetics and Pharmacodynamics. 36(1). 63–80. 17 indexed citations
15.
Gibbs, Megan, et al.. (2007). Displacement of Serotonin and Dopamine Transporters by Venlafaxine Extended Release Capsule at Steady State. Journal of Clinical Psychopharmacology. 27(1). 71–75. 24 indexed citations
16.
McCarter, Roger, et al.. (2007). Plasma Glucose and the Action of Calorie Restriction on Aging. The Journals of Gerontology Series A. 62(10). 1059–1070. 33 indexed citations
17.
Faessel, Hélène M., et al.. (2006). Single‐Dose Pharmacokinetics of Varenicline, a Selective Nicotinic Receptor Partial Agonist, in Healthy Smokers and Nonsmokers. The Journal of Clinical Pharmacology. 46(9). 991–998. 86 indexed citations
18.
19.
Gibbs, Megan & Natilie Hosea. (2003). Factors Affecting the Clinical Development of Cytochrome P450 3A Substrates. Clinical Pharmacokinetics. 42(11). 969–984. 49 indexed citations
20.
Moltke, Lisa L. von, Karthik Venkatakrishnan, Brian Granda, et al.. (2002). Microsomal Protein Concentration Modifies the Apparent Inhibitory Potency of CYP3A Inhibitors. Drug Metabolism and Disposition. 30(12). 1441–1445. 61 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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