Kirill Peskov

946 total citations
54 papers, 697 citations indexed

About

Kirill Peskov is a scholar working on Oncology, Molecular Biology and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Kirill Peskov has authored 54 papers receiving a total of 697 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Oncology, 15 papers in Molecular Biology and 11 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Kirill Peskov's work include Cancer Immunotherapy and Biomarkers (10 papers), Lung Cancer Treatments and Mutations (7 papers) and Monoclonal and Polyclonal Antibodies Research (5 papers). Kirill Peskov is often cited by papers focused on Cancer Immunotherapy and Biomarkers (10 papers), Lung Cancer Treatments and Mutations (7 papers) and Monoclonal and Polyclonal Antibodies Research (5 papers). Kirill Peskov collaborates with scholars based in Russia, United States and United Kingdom. Kirill Peskov's co-authors include Gabriel Helmlinger, Yuri Kosinsky, Veronika Voronova, Lulu Chu, Oleg Demin, Victor Sokolov, Ivan Azarov, Kirill Zhudenkov, David W. Boulton and Phyllis Chan and has published in prestigious journals such as Journal of Clinical Oncology, SHILAP Revista de lepidopterología and PLoS ONE.

In The Last Decade

Kirill Peskov

51 papers receiving 684 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kirill Peskov Russia 16 283 218 109 86 73 54 697
Matthew M. Riggs United States 11 179 0.6× 100 0.5× 38 0.3× 66 0.8× 92 1.3× 24 524
Veronika Voronova United States 10 98 0.3× 153 0.7× 75 0.7× 44 0.5× 22 0.3× 18 329
Nicolas Frey Switzerland 19 237 0.8× 89 0.4× 103 0.9× 151 1.8× 230 3.2× 48 932
Teemu D. Laajala Finland 17 457 1.6× 119 0.5× 178 1.6× 116 1.3× 130 1.8× 44 987
Mark Penney United Kingdom 15 507 1.8× 75 0.3× 207 1.9× 77 0.9× 17 0.2× 18 1.1k
Nenad Sarapa United States 22 527 1.9× 358 1.6× 58 0.5× 35 0.4× 21 0.3× 48 1.4k
Monika Lamba Saini India 12 269 1.0× 261 1.2× 40 0.4× 55 0.6× 21 0.3× 32 668
Suyu Wang China 15 320 1.1× 97 0.4× 82 0.8× 69 0.8× 41 0.6× 42 809
Alok Jaiswal Finland 13 344 1.2× 108 0.5× 80 0.7× 47 0.5× 44 0.6× 30 622

Countries citing papers authored by Kirill Peskov

Since Specialization
Citations

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

Fields of papers citing papers by Kirill Peskov

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kirill Peskov

This figure shows the co-authorship network connecting the top 25 collaborators of Kirill Peskov. A scholar is included among the top collaborators of Kirill Peskov 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 Kirill Peskov. Kirill Peskov 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.
Peskov, Kirill, et al.. (2025). Systematic review and quantitative meta-analysis of age-dependent human T-lymphocyte homeostasis. Frontiers in Immunology. 16. 1475871–1475871. 1 indexed citations
2.
Zhudenkov, Kirill, et al.. (2025). A Systematic Comparative Analysis of Tumor Size Models Based on Erlotinib Clinical Data in Advanced NSCLC. CPT Pharmacometrics & Systems Pharmacology. 14(12). 1970–1981.
3.
Helmlinger, Gabriel, et al.. (2024). Mathematical modeling in autoimmune diseases: from theory to clinical application. Frontiers in Immunology. 15. 1371620–1371620. 2 indexed citations
4.
Peskov, Kirill, et al.. (2024). Optimization of Romiplostim Biosimilar Efficacy Trial Using In Silico Clinical Trial Approach for Patients With Immune Thrombocytopenia. Clinical Pharmacology in Drug Development. 14(2). 116–126. 1 indexed citations
5.
Peskov, Kirill, et al.. (2024). An integrative mechanistic model of thymocyte dynamics. Frontiers in Immunology. 15. 1321309–1321309. 1 indexed citations
6.
Шульгин, Б. В., et al.. (2024). Optimization of the MACE endpoint composition to increase power in studies of lipid-lowering therapies—a model-based meta-analysis. Frontiers in Cardiovascular Medicine. 10. 1242845–1242845.
7.
Peskov, Kirill, et al.. (2024). Population Pharmacokinetic and Pharmacodynamic Modeling of Romiplostim Biosimilar GP40141 and Reference Product in Healthy Volunteers to Evaluate Biosimilarity. Clinical Pharmacology in Drug Development. 13(4). 419–431. 1 indexed citations
8.
Peskov, Kirill, et al.. (2023). Model-Based Assessment of the Reference Values of CAVI in Healthy Russian Population and Benchmarking With CAVI0. American Journal of Hypertension. 37(1). 77–84. 3 indexed citations
9.
Peskov, Kirill, et al.. (2023). A tutorial for model‐based evaluation and translation of cardiovascular safety in preclinical trials. CPT Pharmacometrics & Systems Pharmacology. 13(1). 5–22. 1 indexed citations
10.
Peskov, Kirill, et al.. (2022). Comparison of the novel START vascular stiffness index with the CAVI index, assessment of their values and correlations with clinical parameters. SHILAP Revista de lepidopterología. 28(1). 5272–5272. 4 indexed citations
11.
Voronova, Veronika, Kirill Peskov, Yuri Kosinsky, et al.. (2021). Evaluation of Combination Strategies for the A2AR Inhibitor AZD4635 Across Tumor Microenvironment Conditions via a Systems Pharmacology Model. Frontiers in Immunology. 12. 617316–617316. 13 indexed citations
12.
Goltsov, Alexey, Maciej J. Swat, Kirill Peskov, & Yuri Kosinsky. (2020). Cycle Network Model of Prostaglandin H Synthase-1. Pharmaceuticals. 13(10). 265–265. 5 indexed citations
13.
14.
Voronova, Veronika, Victor Sokolov, Sara Straniero, et al.. (2020). A Physiology-Based Model of Bile Acid Distribution and Metabolism Under Healthy and Pathologic Conditions in Human Beings. Cellular and Molecular Gastroenterology and Hepatology. 10(1). 149–170. 33 indexed citations
15.
16.
Peskov, Kirill, Ivan Azarov, Lulu Chu, et al.. (2019). Quantitative Mechanistic Modeling in Support of Pharmacological Therapeutics Development in Immuno-Oncology. Frontiers in Immunology. 10. 924–924. 32 indexed citations
17.
Sokolov, Victor, Gabriel Helmlinger, Catarina Nilsson, et al.. (2019). Comparative quantitative systems pharmacology modeling of anti-PCSK9 therapeutic modalities in hypercholesterolemia. Journal of Lipid Research. 60(9). 1610–1621. 17 indexed citations
18.
Helmlinger, Gabriel, Yuri Kosinsky, Lulu Chu, et al.. (2018). Abstract 2098: Quantitative modeling as a systematic approach for drug combination evaluation in immuno-oncology (IO). Cancer Research. 78(13_Supplement). 2098–2098. 2 indexed citations
19.
Kosinsky, Yuri, Simon J. Dovedi, Kirill Peskov, et al.. (2018). Radiation and PD-(L)1 treatment combinations: immune response and dose optimization via a predictive systems model. Journal for ImmunoTherapy of Cancer. 6(1). 17–17. 83 indexed citations
20.
Shashkova, Tatiana, Anna Popenko, Alexander Tyakht, et al.. (2016). Agent Based Modeling of Human Gut Microbiome Interactions and Perturbations. PLoS Computational Biology. 11(2). 1–26. 12 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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