Anna Kondic

1.2k total citations
18 papers, 734 citations indexed

About

Anna Kondic is a scholar working on Oncology, Immunology and Statistics and Probability. According to data from OpenAlex, Anna Kondic has authored 18 papers receiving a total of 734 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Oncology, 5 papers in Immunology and 4 papers in Statistics and Probability. Recurrent topics in Anna Kondic's work include Cancer Immunotherapy and Biomarkers (11 papers), CAR-T cell therapy research (7 papers) and Immunotherapy and Immune Responses (5 papers). Anna Kondic is often cited by papers focused on Cancer Immunotherapy and Biomarkers (11 papers), CAR-T cell therapy research (7 papers) and Immunotherapy and Immune Responses (5 papers). Anna Kondic collaborates with scholars based in United States, France and United Kingdom. Anna Kondic's co-authors include Rik de Greef, Julie A. Stone, Malidi Ahamadi, Tomoko Freshwater, Dinesh de Alwis, David C. Turner, Jeroen Elassaiss‐Schaap, Kapil Mayawala, Lokesh Jain and JA Stone and has published in prestigious journals such as Journal of Clinical Oncology, Clinical Cancer Research and European Journal of Cancer.

In The Last Decade

Anna Kondic

15 papers receiving 725 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Anna Kondic United States 8 543 213 160 93 84 18 734
Shruti Agrawal India 12 447 0.8× 137 0.6× 235 1.5× 44 0.5× 120 1.4× 33 726
Yada Kanjanapan Australia 12 583 1.1× 154 0.7× 273 1.7× 99 1.1× 100 1.2× 31 802
Marion Sassier France 14 669 1.2× 135 0.6× 250 1.6× 55 0.6× 97 1.2× 38 1.0k
Sophie Broutin France 16 311 0.6× 79 0.4× 96 0.6× 63 0.7× 213 2.5× 48 787
Paul Baverel United States 10 210 0.4× 90 0.4× 133 0.8× 67 0.7× 39 0.5× 28 442
Min Tao China 18 468 0.9× 118 0.6× 212 1.3× 40 0.4× 166 2.0× 57 822
Marta Batus United States 13 296 0.5× 74 0.3× 243 1.5× 51 0.5× 178 2.1× 60 569
Olga Ace Canada 6 573 1.1× 139 0.7× 156 1.0× 29 0.3× 147 1.8× 8 816
Francis Donaldson United Kingdom 8 222 0.4× 115 0.5× 174 1.1× 22 0.2× 142 1.7× 10 529
Ping-Min Chen United States 12 243 0.4× 439 2.1× 101 0.6× 28 0.3× 179 2.1× 18 854

Countries citing papers authored by Anna Kondic

Since Specialization
Citations

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

Fields of papers citing papers by Anna Kondic

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Anna Kondic

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

All Works

18 of 18 papers shown
1.
2.
Androulakis, Ioannis P., et al.. (2025). The dawn of a new era: can machine learning and large language models reshape QSP modeling?. Journal of Pharmacokinetics and Pharmacodynamics. 52(4). 36–36. 5 indexed citations
3.
Hu, Chuanpu, et al.. (2024). Visual predictive check of longitudinal models and dropout. Journal of Pharmacokinetics and Pharmacodynamics. 51(6). 859–875.
4.
Aggarwal, K.K., Chuanpu Hu, Anna Kondic, et al.. (2024). Novel endpoints based on tumor size ratio to support early clinical decision-making in oncology drug-development. Journal of Pharmacokinetics and Pharmacodynamics. 52(1). 9–9.
5.
Zhao, Yue, Amit Roy, & Anna Kondic. (2023). Time-Dependent Pharmacokinetics of Immune Checkpoint Inhibitors and their Implications and Considerations for Exposure–Response Analysis. Current Pharmacology Reports. 9(6). 397–403. 1 indexed citations
6.
Kondic, Anna, Dean Bottino, John M. Harrold, et al.. (2022). Navigating Between Right, Wrong, and Relevant: The Use of Mathematical Modeling in Preclinical Decision Making. Frontiers in Pharmacology. 13. 6 indexed citations
7.
Edwards, David A., et al.. (2020). Mathematical models for the effect of anti-vascular endothelial growth factor on visual acuity. Journal of Mathematical Biology. 81(6-7). 1397–1428. 5 indexed citations
8.
Stone, Julie A., Ellen Snyder, Leslie Lipka, et al.. (2019). Immunogenicity of pembrolizumab in patients with advanced tumors. Journal for ImmunoTherapy of Cancer. 7(1). 212–212. 44 indexed citations
9.
Turner, David C., Anna Kondic, Keaven M. Anderson, et al.. (2018). Pembrolizumab Exposure–Response Assessments Challenged by Association of Cancer Cachexia and Catabolic Clearance. Clinical Cancer Research. 24(23). 5841–5849. 156 indexed citations
10.
Freshwater, Tomoko, Anna Kondic, Malidi Ahamadi, et al.. (2017). Evaluation of dosing strategy for pembrolizumab for oncology indications. Journal for ImmunoTherapy of Cancer. 5(1). 43–43. 182 indexed citations
11.
Li, Hongshan, Jingyu Yu, Chao Liu, et al.. (2017). Time dependent pharmacokinetics of pembrolizumab in patients with solid tumor and its correlation with best overall response. Journal of Pharmacokinetics and Pharmacodynamics. 44(5). 403–414. 115 indexed citations
12.
Ahamadi, Malidi, Tomoko Freshwater, Marita Prohn, et al.. (2016). Model-Based Characterization of the Pharmacokinetics of Pembrolizumab: A Humanized Anti-PD-1 Monoclonal Antibody in Advanced Solid Tumors. CPT Pharmacometrics & Systems Pharmacology. 6(1). 49–57. 121 indexed citations
13.
14.
Greef, Rik de, Jeroen Elassaiss‐Schaap, Manash Chatterjee, et al.. (2016). Pembrolizumab: Role of Modeling and Simulation in Bringing a Novel Immunotherapy to Patients With Melanoma. CPT Pharmacometrics & Systems Pharmacology. 6(1). 5–7. 21 indexed citations
15.
Kang, Soonmo Peter, Manash Chatterjee, Malidi Ahamadi, et al.. (2015). 3344 Relationship between pembrolizumab exposure and efficacy/safety in 1016 patients (pts) with advanced or metastatic melanoma. European Journal of Cancer. 51. S682–S682. 2 indexed citations
16.
Soria, J.C., Eudald Felip, Leena Gandhi, et al.. (2015). 33LBA Efficacy and Safety of Pembrolizumab (Pembro; MK-3475) for Patients (Pts) With Previously Treated Advanced Non-Small Cell Lung Cancer (NSCLC) Enrolled in KEYNOTE-001. European Journal of Cancer. 51. S726–S727. 7 indexed citations
17.
Gangadhar, Tara C., Janice M. Mehnert, Amita Patnaik, et al.. (2015). Population pharmacokinetic (popPK) model of pembrolizumab (pembro; MK-3475) in patients (pts) treated in KEYNOTE-001 and KEYNOTE-002.. Journal of Clinical Oncology. 33(15_suppl). 3058–3058. 5 indexed citations
18.
Hallow, K. Melissa, Arthur Lo, Manoj C. Rodrigo, et al.. (2014). A model-based approach to investigating the pathophysiological mechanisms of hypertension and response to antihypertensive therapies: extending the Guyton model. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology. 306(9). R647–R662. 54 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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