The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Model-Based Population Pharmacokinetic Analysis of Nivolumab in Patients With Solid Tumors
Open accessAbstract
Nivolumab is a fully human monoclonal antibody that inhibits programmed death-1 activation. The clinical pharmacology profile of nivolumab was analyzed by a population pharmacokinetics model that assessed covariate effects on nivolumab concentrations in 1,895 patients who received 0.3-10.0 mg/kg nivolumab in 11 clinical trials. Nivolumab pharmacokinetics is linear with a time-varying clearance. A full covariate model was developed to assess covariate effects on pharmacokinetic parameters. Nivolumab clearance and volume of distribution increase with body weight. The final model included the effects of baseline performance status (PS), baseline body weight, and baseline estimated glomerular filtration rate (eGFR), sex, and race on clearance, and effects of baseline body weight and sex on volume of distribution in the central compartment. Sex, PS, baseline eGFR, age, race, baseline lactate dehydrogenase, mild hepatic impairment, tumor type, tumor burden, and programmed death ligand-1 expression had a significant but not clinically relevant (<20%) effect on nivolumab clearance.
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Estimating the Dimension of a Model
Schwarz, G. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344136 · Cited by 47,033
PD-1 and PD-L1 Immune Checkpoint Blockade to Treat Breast Cancer
Hartkopf, A. D., Taran, F.-A., Wallwiener, M., Walter, C. B., Krämer, B., Grischke, E.-M., & Brucker, S. Y. (2016) · Breast Care · doi:10.1159/000453569 · Cited by 30,845
Adverse Renal Effects of Immune Checkpoint Inhibitors: A Narrative Review
Wanchoo, R., Karam, S., Uppal, N. N., Barta, V. S., Deray, G., Devoe, C., Launay-Vacher, V., Jhaveri, K. D., & on behalf of Cancer and Kidney International Network Workgroup on Immune Checkpoint Inhibitors (2017) · American Journal of Nephrology · doi:10.1159/000455014 · Cited by 15,256
Safety, activity, and immune correlates of anti–PD-1 antibody in cancer
Topalian, S. L., Hodi, F. S., Brahmer, J. R., Gettinger, S. N., Smith, D. C., McDermott, D. F., Powderly, J. D., Carvajal, R. D., Sosman, J. A., Atkins, M. B., et al. (2012) · New England Journal of Medicine · doi:10.1056/nejmoa1200690 · Cited by 12,628
Self-citationHarrell, F. E. (2001) · Springer Series in Statistics · doi:10.1007/978-1-4757-3462-1 · Cited by 9,195
Safety and activity of anti-PD-L1 antibody in patients with advanced cancer
Brahmer, J. R., Tykodi, S. S., Chow, L. Q. M., Hwu, W.-J., Topalian, S. L., Hwu, P., Drake, C. G., Camacho, L. H., Kauh, J., Odunsi, K., et al. (2012) · New England Journal of Medicine · doi:10.1056/nejmoa1200694 · Cited by 7,945
Self-citationNivolumab in Previously Untreated Melanoma without BRAF Mutation
Robert, C., Long, G. V., Brady, B., Dutriaux, C., Maio, M., Mortier, L., Hassel, J. C., Rutkowski, P., McNeil, C., Kalinka-Warzocha, E., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1412082 · Cited by 5,519
Nivolumab plus Ipilimumab in Advanced Melanoma
Wolchok, J. D., Kluger, H., Callahan, M. K., Postow, M. A., Rizvi, N. A., Lesokhin, A. M., Segal, N. H., Ariyan, C. E., Gordon, R.-A., Reed, K., et al. (2013) · New England Journal of Medicine · doi:10.1056/nejmoa1302369 · Cited by 4,230
PD-1 Blockade with Nivolumab in Relapsed or Refractory Hodgkin's Lymphoma
Ansell, S. M., Lesokhin, A. M., Borrello, I., Halwani, A., Scott, E. C., Gutierrez, M., Schuster, S. J., Millenson, M. M., Cattry, D., Freeman, G. J., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1411087 · Cited by 3,560
Iwai, Y., Ishida, M., Tanaka, Y., Okazaki, T., Honjo, T., & Minato, N. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.192461099 · Cited by 3,203
Cited by
Armand, P., Engert, A., Younes, A., Fanale, M., Santoro, A., Zinzani, P. L., Timmerman, J. M., Collins, G. P., Ramchandren, R., Cohen, J. B., et al. (2018) · Journal of Clinical Oncology · doi:10.1200/jco.2017.76.0793 · Cited by 759
Clinical Pharmacokinetics and Pharmacodynamics of Immune Checkpoint Inhibitors
Centanni, M., Moes, D. J. A. R., Trocóniz, I. F., Ciccolini, J., & van Hasselt, J. G. C. (2019) · Clinical Pharmacokinetics · doi:10.1007/s40262-019-00748-2 · Cited by 355
Davis, K. L., Fox, E., Merchant, M. S., Reid, J. M., Kudgus, R. A., Liu, X., Minard, C. G., Voss, S., Berg, S. L., Weigel, B. J., et al. (2020) · The Lancet Oncology · doi:10.1016/s1470-2045(20)30023-1 · Cited by 337
Nazha, B., Mishra, M., Pentz, R., & Owonikoko, T. K. (2019) · American Society of Clinical Oncology Educational Book · doi:10.1200/edbk_100021 · Cited by 257
Clinical pharmacology considerations for the development of immune checkpoint inhibitors
Sheng, J., Srivastava, S., Sanghavi, K., Lu, Z., Schmidt, B. J., Bello, A., & Gupta, M. (2017) · The Journal of Clinical Pharmacology · doi:10.1002/jcph.990 · Cited by 124
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Metadata
- Authors
- Bajaj, G., Wang, X., Agrawal, S., Gupta, M., Roy, A., & Feng, Y.
- Venue
- CPT: Pharmacometrics & Systems Pharmacology
- Publisher
- Wiley
- Published
- 2016-12-26
- Volume
- 6
- Issue
- 1
- Pages
- 58-66
- ISSN
- 2163-8306 (ISSN-L)
- OpenAlex
- W2567068937
- Cited by
- 264
- References
- 33
- FWCI
- 13.60
- Type
- journal article
- Language
- en
- Open access
- gold
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- Bajaj, G., Wang, X., Agrawal, S., Gupta, M., Roy, A., & Feng, Y. (2017). Model-Based Population Pharmacokinetic Analysis of Nivolumab in Patients With Solid Tumors. CPT: Pharmacometrics & Systems Pharmacology. https://doi.org/10.1002/psp4.12143
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
