Paul Seo

1.2k total citations
24 papers, 779 citations indexed

About

Paul Seo is a scholar working on Pharmaceutical Science, Molecular Biology and Spectroscopy. According to data from OpenAlex, Paul Seo has authored 24 papers receiving a total of 779 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Pharmaceutical Science, 10 papers in Molecular Biology and 6 papers in Spectroscopy. Recurrent topics in Paul Seo's work include Drug Solubulity and Delivery Systems (14 papers), Crystallization and Solubility Studies (6 papers) and Analytical Chemistry and Chromatography (5 papers). Paul Seo is often cited by papers focused on Drug Solubulity and Delivery Systems (14 papers), Crystallization and Solubility Studies (6 papers) and Analytical Chemistry and Chromatography (5 papers). Paul Seo collaborates with scholars based in United States, Sweden and United Kingdom. Paul Seo's co-authors include James E. Polli, Liang Zhao, Zeynep Şafak Teksin, Sandra Suarez‐Sharp, Million A. Tegenge, Hao Zhu, Rajanikanth Madabushi, John Duan, Xavier Pépin and Lawrence X. Yu and has published in prestigious journals such as Pharmaceutical Research, Journal of Pharmaceutical Sciences and Molecular Pharmaceutics.

In The Last Decade

Paul Seo

24 papers receiving 760 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Paul Seo United States 16 347 204 135 118 115 24 779
Patrick Marroum United States 17 295 0.9× 125 0.6× 99 0.7× 115 1.0× 82 0.7× 36 777
Sandra Suarez‐Sharp United States 11 337 1.0× 139 0.7× 62 0.5× 167 1.4× 57 0.5× 22 505
M. Sherry Ku United States 12 332 1.0× 174 0.9× 62 0.5× 169 1.4× 222 1.9× 14 974
Alan Parr United States 16 361 1.0× 98 0.5× 97 0.7× 64 0.5× 81 0.7× 41 776
Paulo Paixão Portugal 14 160 0.5× 143 0.7× 54 0.4× 57 0.5× 100 0.9× 34 608
Robert A. Carr United States 19 338 1.0× 193 0.9× 24 0.2× 156 1.3× 191 1.7× 41 986
Binfeng Xia United States 12 189 0.5× 96 0.5× 37 0.3× 50 0.4× 112 1.0× 19 470
Vikash K. Sinha Belgium 11 93 0.3× 178 0.9× 89 0.7× 29 0.2× 338 2.9× 12 878
Manuel Sánchez-Félix United States 14 209 0.6× 242 1.2× 19 0.1× 115 1.0× 85 0.7× 24 694
Henrike Potthast Germany 10 241 0.7× 51 0.3× 39 0.3× 97 0.8× 41 0.4× 20 421

Countries citing papers authored by Paul Seo

Since Specialization
Citations

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

Fields of papers citing papers by Paul Seo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Paul Seo

This figure shows the co-authorship network connecting the top 25 collaborators of Paul Seo. A scholar is included among the top collaborators of Paul Seo 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 Paul Seo. Paul Seo 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
1.
Mackie, Claire, Sumit Arora, Paul Seo, et al.. (2024). Physiologically Based Biopharmaceutics Modeling (PBBM): Best Practices for Drug Product Quality, Regulatory and Industry Perspectives: 2023 Workshop Summary Report. Molecular Pharmaceutics. 21(5). 2065–2080. 24 indexed citations
2.
Miao, Lei, Fang Wu, Xinning Yang, et al.. (2022). Application of Solubility and Dissolution Profile Comparison for Prediction of Gastric pH-Mediated Drug-Drug Interactions. The AAPS Journal. 24(1). 35–35. 3 indexed citations
3.
Anand, Om, et al.. (2022). The Use of Physiologically Based Pharmacokinetic Analyses—in Biopharmaceutics Applications -Regulatory and Industry Perspectives. Pharmaceutical Research. 39(8). 1681–1700. 31 indexed citations
4.
Seo, Paul, et al.. (2022). Characterization of Dissolution-Permeation System using Hollow Fiber Membrane Module and Utility to Predict in Vivo Drug Permeation Across BCS Classes. Journal of Pharmaceutical Sciences. 111(11). 3075–3087. 7 indexed citations
5.
Madabushi, Rajanikanth, Paul Seo, Liang Zhao, Million A. Tegenge, & Hao Zhu. (2022). Review: Role of Model-Informed Drug Development Approaches in the Lifecycle of Drug Development and Regulatory Decision-Making. Pharmaceutical Research. 39(8). 1669–1680. 102 indexed citations
6.
Seo, Paul, et al.. (2022). Dissolution-Hollow Fiber Membrane (D-HFM) System to Anticipate Biopharmaceutics Risk of Tablets and Capsules. Journal of Pharmaceutical Sciences. 112(3). 751–759. 3 indexed citations
7.
8.
Pépin, Xavier, Jennifer Dressman, Neil Parrott, et al.. (2020). In Vitro Biopredictive Methods: A Workshop Summary Report. Journal of Pharmaceutical Sciences. 110(2). 567–583. 17 indexed citations
9.
Li, Jia, et al.. (2020). Application of Physiologically‐Based Pharmacokinetic Modeling to Predict Gastric pH‐Dependent Drug–Drug Interactions for Weak Base Drugs. CPT Pharmacometrics & Systems Pharmacology. 9(8). 456–465. 32 indexed citations
10.
11.
Heimbach, Tycho, Sandra Suarez‐Sharp, Nico Holmstock, et al.. (2019). Dissolution and Translational Modeling Strategies Toward Establishing an In Vitro-In Vivo Link—a Workshop Summary Report. The AAPS Journal. 21(2). 29–29. 78 indexed citations
12.
Suarez‐Sharp, Sandra, Michael R. Cohen, Filippos Kesisoglou, et al.. (2018). Applications of Clinically Relevant Dissolution Testing: Workshop Summary Report. The AAPS Journal. 20(6). 93–93. 61 indexed citations
13.
Mehta, Mehul U., Ramana Uppoor, Dale P. Conner, et al.. (2017). Impact of the US FDA “Biopharmaceutics Classification System” (BCS) Guidance on Global Drug Development. Molecular Pharmaceutics. 14(12). 4334–4338. 25 indexed citations
14.
Suarez‐Sharp, Sandra, Angelica Dorantes, John Duan, et al.. (2016). Regulatory Perspectives on Strength-Dependent Dissolution Profiles and Biowaiver Approaches for Immediate Release (IR) Oral Tablets in New Drug Applications. The AAPS Journal. 18(3). 578–588. 10 indexed citations
15.
Suarez‐Sharp, Sandra, et al.. (2016). Regulatory Experience with In Vivo In Vitro Correlations (IVIVC) in New Drug Applications. The AAPS Journal. 18(6). 1379–1390. 67 indexed citations
16.
Duan, John, et al.. (2016). Scientific and Regulatory Considerations in Solid Oral Modified Release Drug Product Development. The AAPS Journal. 18(6). 1406–1417. 9 indexed citations
17.
Teksin, Zeynep Şafak, Paul Seo, & James E. Polli. (2010). Comparison of Drug Permeabilities and BCS Classification: Three Lipid-Component PAMPA System Method versus Caco-2 Monolayers. The AAPS Journal. 12(2). 238–241. 48 indexed citations
18.
Davit, Barbara M., Dale P. Conner, Sam Haidar, et al.. (2008). Highly Variable Drugs: Observations from Bioequivalence Data Submitted to the FDA for New Generic Drug Applications. The AAPS Journal. 10(1). 148–156. 94 indexed citations
19.
Seo, Paul, Zeynep Şafak Teksin, Joseph P. Y. Kao, & James E. Polli. (2006). Lipid composition effect on permeability across PAMPA. European Journal of Pharmaceutical Sciences. 29(3-4). 259–268. 42 indexed citations
20.
Seo, Paul, Vinod P. Shah, & James E. Polli. (2002). Novel Metrics to Compare Dissolution Profiles. Pharmaceutical Development and Technology. 7(2). 257–265. 6 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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