Meric Ovacik

688 total citations
17 papers, 407 citations indexed

About

Meric Ovacik is a scholar working on Molecular Biology, Radiology, Nuclear Medicine and Imaging and Immunology. According to data from OpenAlex, Meric Ovacik has authored 17 papers receiving a total of 407 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Molecular Biology, 8 papers in Radiology, Nuclear Medicine and Imaging and 6 papers in Immunology. Recurrent topics in Meric Ovacik's work include Monoclonal and Polyclonal Antibodies Research (8 papers), Biosimilars and Bioanalytical Methods (4 papers) and CAR-T cell therapy research (3 papers). Meric Ovacik is often cited by papers focused on Monoclonal and Polyclonal Antibodies Research (8 papers), Biosimilars and Bioanalytical Methods (4 papers) and CAR-T cell therapy research (3 papers). Meric Ovacik collaborates with scholars based in United States, Switzerland and Germany. Meric Ovacik's co-authors include Kedan Lin, Ioannis P. Androulakis, Marianthi Ierapetritou, Donald E. Mager, Kevin W. Gaido, Susan Y. Euling, Nicholas M. P. King, Michael Dillon, Teemu T. Junttila and Paul J. Carter and has published in prestigious journals such as Journal of Pharmacology and Experimental Therapeutics, BMC Bioinformatics and Clinical Pharmacology & Therapeutics.

In The Last Decade

Meric Ovacik

16 papers receiving 380 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Meric Ovacik United States 8 208 165 81 77 44 17 407
Sven Kronenberg Switzerland 12 162 0.8× 116 0.7× 139 1.7× 60 0.8× 33 0.8× 23 426
Barbara Mounho United States 10 210 1.0× 143 0.9× 221 2.7× 76 1.0× 73 1.7× 12 484
Sai Hu China 11 205 1.0× 59 0.4× 30 0.4× 63 0.8× 8 0.2× 35 332
Ally Perlina United States 10 261 1.3× 24 0.1× 47 0.6× 56 0.7× 21 0.5× 11 427
Lin Yu China 10 134 0.6× 38 0.2× 49 0.6× 77 1.0× 17 0.4× 22 292
Rachel H. Rose United Kingdom 10 231 1.1× 177 1.1× 109 1.3× 84 1.1× 2 0.0× 17 492
Ke Yao China 11 394 1.9× 37 0.2× 65 0.8× 85 1.1× 7 0.2× 12 632
David Brott United States 13 164 0.8× 17 0.1× 123 1.5× 48 0.6× 17 0.4× 28 468
Graham Healey United Kingdom 10 293 1.4× 308 1.9× 201 2.5× 250 3.2× 40 0.9× 28 871
Simone Borgoni Germany 9 232 1.1× 33 0.2× 110 1.4× 162 2.1× 8 0.2× 13 472

Countries citing papers authored by Meric Ovacik

Since Specialization
Citations

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

Fields of papers citing papers by Meric Ovacik

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Meric Ovacik

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

All Works

17 of 17 papers shown
1.
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.
2.
Li, Ran, Edward Dere, Mandy Kwong, et al.. (2024). A Bispecific Modeling Framework Enables the Prediction of Efficacy, Toxicity, and Optimal Molecular Design of Bispecific Antibodies Targeting MerTK. The AAPS Journal. 26(1). 11–11. 2 indexed citations
3.
Shah, Nirav N., Martin Lechmann, Hardik Mody, et al.. (2024). Industry Perspective on First‐in‐Human and Clinical Pharmacology Strategies to Support Clinical Development of T‐Cell Engaging Bispecific Antibodies for Cancer Therapy. Clinical Pharmacology & Therapeutics. 117(1). 34–55. 2 indexed citations
4.
Cai, Hao, Satoko Kakiuchi-Kiyota, Robert L. Hendricks, et al.. (2022). Nonclinical Pharmacokinetics, Pharmacodynamics, and Translational Model of RO7297089, A Novel Anti-BCMA/CD16A Bispecific Tetravalent Antibody for the Treatment of Multiple Myeloma. The AAPS Journal. 24(6). 100–100. 7 indexed citations
5.
6.
Ovacik, Meric, Christian Pohl, Sylvia Herter, et al.. (2021). A Novel Approach for Quantifying the Pharmacological Activity of T-Cell Engagers Utilizing In Vitro Time Course Experiments and Streamlined Data Analysis. The AAPS Journal. 24(1). 7–7. 4 indexed citations
7.
Ovacik, Meric & Kedan Lin. (2018). Tutorial on Monoclonal Antibody Pharmacokinetics and Its Considerations in Early Development. Clinical and Translational Science. 11(6). 540–552. 194 indexed citations
8.
Ait‐Oudhia, Sihem, Meric Ovacik, & Donald E. Mager. (2016). Systems pharmacology and enhanced pharmacodynamic models for understanding antibody-based drug action and toxicity. mAbs. 9(1). 15–28. 5 indexed citations
9.
Dillon, Michael, Yiyuan Yin, Jianhui Zhou, et al.. (2016). Efficient production of bispecific IgG of different isotypes and species of origin in single mammalian cells. mAbs. 9(2). 213–230. 58 indexed citations
10.
Ovacik, Meric, et al.. (2015). Logic-Based and Cellular Pharmacodynamic Modeling of Bortezomib Responses in U266 Human Myeloma Cells. Journal of Pharmacology and Experimental Therapeutics. 354(3). 448–458. 21 indexed citations
11.
Euling, Susan Y., L.D. White, Vickie S. Wilson, et al.. (2011). Use of genomic data in risk assessment case study: II. Evaluation of the dibutyl phthalate toxicogenomic data set. Toxicology and Applied Pharmacology. 271(3). 349–362. 40 indexed citations
12.
Ovacik, Meric & Ioannis P. Androulakis. (2010). Enzyme sequence similarity improves the reaction alignment method for cross-species pathway comparison. Toxicology and Applied Pharmacology. 271(3). 363–371. 7 indexed citations
13.
Ovacik, Meric, et al.. (2010). Pathway modeling of microarray data: A case study of pathway activity changes in the testis following in utero exposure to dibutyl phthalate (DBP). Toxicology and Applied Pharmacology. 271(3). 386–394. 15 indexed citations
14.
Iyer, Vidya, Meric Ovacik, Ioannis P. Androulakis, Charles M. Roth, & Marianthi Ierapetritou. (2010). Transcriptional and metabolic flux profiling of triadimefon effects on cultured hepatocytes. Toxicology and Applied Pharmacology. 248(3). 165–177. 15 indexed citations
15.
Ovacik, Meric, Siddharth Sukumaran, Richard R. Almon, et al.. (2010). Circadian signatures in rat liver: from gene expression to pathways. BMC Bioinformatics. 11(1). 540–540. 17 indexed citations
16.
Euling, Susan Y., L.D. White, Meric Ovacik, et al.. (2009). An approach to using genomic data in risk assessment: Dibutyl phthalate (DBP) case study. Reproductive Toxicology. 28(2). 119–119. 2 indexed citations
17.
Ovacik, Meric & Ioannis P. Androulakis. (2008). On the Potential for Integrating Gene Expression and Metabolic Flux Data. Current Bioinformatics. 3(3). 142–148. 13 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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