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Jordan S, Ryu S, Burchett W, Davis C, Jones R, Zhang S, Zueva L, Chang G, Di L. Comparison of Tumor Binding Across Tumor Types and Cell Lines to Support Free Drug Considerations for Oncology Drug Discovery. J Pharm Sci 2024; 113:826-835. [PMID: 38042346 DOI: 10.1016/j.xphs.2023.11.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 11/24/2023] [Accepted: 11/24/2023] [Indexed: 12/04/2023]
Abstract
Tumor binding is an important parameter to derive unbound tumor concentration to explore pharmacokinetics (PK) and pharmacodynamics (PD) relationships for oncology disease targets. Tumor binding was evaluated using eleven matrices, including various commonly used ex vivo human and mouse xenograft and syngeneic tumors, tumor cell lines and liver as a surrogate tissue. The results showed that tumor binding is highly correlated among the different tumors and tumor cell lines except for the mouse melanoma (B16F10) tumor type. Liver fraction unbound (fu) has a good correlation with B16F10 tumor binding. Liver also demonstrates a two-fold equivalency, on average, with binding of other tumor types when a scaling factor is applied. Predictive models were developed for tumor binding, with correlations established with LogD (acids), predicted muscle fu (neutrals) and measured plasma protein binding (bases) to estimate tumor fu when experimental data are not available. Many approaches can be applied to obtain and estimate tumor binding values. One strategy proposed is to use a surrogate tumor tissue, such as mouse xenograft ovarian cancer (OVCAR3) tumor, as a surrogate for tumor binding (except for B16F10) to provide an early assessment of unbound tumor concentrations for development of PK/PD relationships.
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Affiliation(s)
- Samantha Jordan
- Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, Groton, CT, United States
| | - Sangwoo Ryu
- Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, Groton, CT, United States
| | - Woodrow Burchett
- Global Biometrics and Data Management, Pfizer Worldwide Research and Development, Groton, CT, United States
| | - Carl Davis
- Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, La Jolla, CA, United States
| | - Rhys Jones
- Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, La Jolla, CA, United States
| | - Sam Zhang
- Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, Groton, CT, United States
| | - Larisa Zueva
- Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, Groton, CT, United States
| | - George Chang
- Translational Modeling and Simulation, Pfizer Worldwide Research and Development, Groton, CT, United States
| | - Li Di
- Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, Groton, CT, United States.
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