Saroja Ramanujan

3.4k total citations
46 papers, 2.4k citations indexed

About

Saroja Ramanujan is a scholar working on Oncology, Radiology, Nuclear Medicine and Imaging and Molecular Biology. According to data from OpenAlex, Saroja Ramanujan has authored 46 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Oncology, 13 papers in Radiology, Nuclear Medicine and Imaging and 11 papers in Molecular Biology. Recurrent topics in Saroja Ramanujan's work include Monoclonal and Polyclonal Antibodies Research (13 papers), Computational Drug Discovery Methods (10 papers) and CAR-T cell therapy research (6 papers). Saroja Ramanujan is often cited by papers focused on Monoclonal and Polyclonal Antibodies Research (13 papers), Computational Drug Discovery Methods (10 papers) and CAR-T cell therapy research (6 papers). Saroja Ramanujan collaborates with scholars based in United States, United Kingdom and France. Saroja Ramanujan's co-authors include Rakesh K. Jain, Alain Pluen, Yves Boucher, Trevor D. McKee, Edward B. Brown, Kapil Gadkar, Takeshi Gohongi, R. B. Campbell, Yotaro Izumi and Emmanuelle di Tomaso and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Immunity and PLoS ONE.

In The Last Decade

Saroja Ramanujan

43 papers receiving 2.3k citations

Peers

Saroja Ramanujan
Eric L. Kaijzel Netherlands
J.B.M. Boezeman Netherlands
Vikas Kundra United States
Jin Zhu China
Ute Reuning Germany
Jennifer E. Koblinski United States
James O. Park United States
Zu‐Xi Yu United States
Eric L. Kaijzel Netherlands
Saroja Ramanujan
Citations per year, relative to Saroja Ramanujan Saroja Ramanujan (= 1×) peers Eric L. Kaijzel

Countries citing papers authored by Saroja Ramanujan

Since Specialization
Citations

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

Fields of papers citing papers by Saroja Ramanujan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Saroja Ramanujan

This figure shows the co-authorship network connecting the top 25 collaborators of Saroja Ramanujan. A scholar is included among the top collaborators of Saroja Ramanujan 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 Saroja Ramanujan. Saroja Ramanujan 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.
Joslyn, Louis R., Mohammad Jafarnejad, Rajbharan Yadav, et al.. (2025). A quantitative systems pharmacology model to inform clinical translation of dynamic PKPD relationships of engineered IL-15. European Journal of Pharmaceutical Sciences. 214. 107303–107303.
2.
Hosseini, Iraj, Jennifer A. Getz, Jérémie Decalf, et al.. (2024). A Minimal PBPK/PD Model with Expansion-Enhanced Target-Mediated Drug Disposition to Support a First-in-Human Clinical Study Design for a FLT3L-Fc Molecule. Pharmaceutics. 16(5). 660–660.
3.
Joslyn, Louis R., Weize Huang, Dale Miles, Iraj Hosseini, & Saroja Ramanujan. (2024). Digital twins elucidate critical role of Tscm in clinical persistence of TCR-engineered cell therapy. npj Systems Biology and Applications. 10(1). 11–11. 11 indexed citations
4.
Joshi, Amita, Saroja Ramanujan, & Jin Y. Jin. (2023). The convergence of pharmacometrics and quantitative systems pharmacology in pharmaceutical research and development. European Journal of Pharmaceutical Sciences. 182. 106380–106380. 8 indexed citations
5.
Li, Chi‐Chung, Kapil Gadkar, Genevive Hernandez, et al.. (2023). Systems‐based digital twins to help characterize clinical dose–response and propose predictive biomarkers in a Phase I study of bispecific antibody, mosunetuzumab, in NHL. Clinical and Translational Science. 16(7). 1134–1148. 44 indexed citations
6.
Lü, Dan, Rajbharan Yadav, Patrick G. Holder, et al.. (2023). Complex PK-PD of an engineered IL-15/IL-15Rα–Fc fusion protein in cynomolgus monkeys: QSP modeling of lymphocyte dynamics. European Journal of Pharmaceutical Sciences. 186. 106450–106450. 9 indexed citations
7.
Deng, Rong, Xiaoju Zhou, Dongwei Li, et al.. (2019). Preclinical and translational pharmacokinetics of a novel THIOMAB™ antibody-antibiotic conjugate againstStaphylococcus aureus. mAbs. 11(6). 1162–1174. 28 indexed citations
8.
Hosseini, Iraj, et al.. (2018). gPKPDSim: a SimBiology®-based GUI application for PKPD modeling in drug development. Journal of Pharmacokinetics and Pharmacodynamics. 45(2). 259–275. 19 indexed citations
9.
Gadkar, Kapil, Daniel C. Kirouac, Neil Parrott, & Saroja Ramanujan. (2016). Quantitative systems pharmacology: a promising approach for translational pharmacology. Drug Discovery Today Technologies. 21-22. 57–65. 29 indexed citations
10.
Gadkar, Kapil, Daniela Bumbaca Yadav, Jessica A. Couch, et al.. (2016). Mathematical PKPD and safety model of bispecific TfR/BACE1 antibodies for the optimization of antibody uptake in brain. European Journal of Pharmaceutics and Biopharmaceutics. 101. 53–61. 40 indexed citations
11.
Sukumaran, Siddharth, Kapil Gadkar, Crystal Zhang, et al.. (2014). Mechanism-Based Pharmacokinetic/Pharmacodynamic Model for THIOMAB™ Drug Conjugates. Pharmaceutical Research. 32(6). 1884–1893. 32 indexed citations
12.
Centola, Michael, G. Cavet, Yijing Shen, et al.. (2013). Development of a Multi-Biomarker Disease Activity Test for Rheumatoid Arthritis. PLoS ONE. 8(4). e60635–e60635. 130 indexed citations
13.
Curtis, Jeffrey R., Annette H M van der Helm–van Mil, Rachel Knevel, et al.. (2012). Validation of a novel multibiomarker test to assess rheumatoid arthritis disease activity. Arthritis Care & Research. 64(12). 1794–1803. 138 indexed citations
14.
Shoda, Lisl K.M., Huub T. C. Kreuwel, Kapil Gadkar, et al.. (2010). The Type 1 Diabetes PhysioLab® Platform: a validated physiologically based mathematical model of pathogenesis in the non-obese diabetic mouse. Clinical & Experimental Immunology. 161(2). 250–267. 43 indexed citations
15.
Gadkar, Kapil, Lisl K.M. Shoda, Huub T. C. Kreuwel, et al.. (2007). Dosing and Timing Effects of Anti‐CD40L Therapy. Annals of the New York Academy of Sciences. 1103(1). 63–68. 9 indexed citations
16.
Zheng, Yanan, Huub T. C. Kreuwel, Daniel L. Young, et al.. (2007). The Virtual NOD Mouse. Annals of the New York Academy of Sciences. 1103(1). 45–62. 7 indexed citations
17.
Young, Daniel L., Saroja Ramanujan, Huub T. C. Kreuwel, et al.. (2006). Mechanisms Mediating Anti‐CD3 Antibody Efficacy. Annals of the New York Academy of Sciences. 1079(1). 369–373. 4 indexed citations
18.
Ramanujan, Saroja, Alain Pluen, Trevor D. McKee, et al.. (2002). Diffusion and Convection in Collagen Gels: Implications for Transport in the Tumor Interstitium. Biophysical Journal. 83(3). 1650–1660. 422 indexed citations
19.
Koike, Chieko, Trevor D. McKee, Alain Pluen, et al.. (2002). Solid stress facilitates spheroid formation: potential involvement of hyaluronan. British Journal of Cancer. 86(6). 947–953. 65 indexed citations
20.
Pluen, Alain, Saroja Ramanujan, Yves Boucher, et al.. (2000). Relaxin increases the transport of large molecules in high collagen content tumors. Research Explorer (The University of Manchester). 41. 64. 4 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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