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Integrated efficacy and safety exposure response (ER) analysis of tivozanib (TIVO) for the treatment of renal cell cancer (RCC).

2025· article· en· W4407700909 on OpenAlexaff
Bradley A. McGregor, Toni K. Choueiri, Laurence Albigès, Katy Beckermann, Philippe Barthélémy, Roberto Iacovelli, Sheik Emambux, Javier Molina‐Cerrillo, Benjamin Garmezy, Pedro C. Barata, Rana R. McKay, Alex Chehrazi‐Raffle, Hans J. Hammers, Daniel Yick Chin Heng, Bo Jin, Monette Cotreau, Edgar Braendle, Claudia Lebedinsky, Robert J. Motzer

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineCancerOncologyCancer researchInternal medicine

Abstract

fetched live from OpenAlex

461 Background: TIVO is an oral vascular endothelial growth factor receptor (VEGFR) tyrosine kinase inhibitor (TKI) approved in the US for treatment of patients with RCC following ≥2 prior systemic therapies. The approved TIVO monotherapy starting dose is 1.34 mg once daily on days (D) 1-21 Q28D, with allowable dose modifications to manage adverse events. In the randomized TiNivo-2 trial, the addition of NIVO 480 mg to TIVO 0.89 mg D1-21 Q28D (lower dose of TIVO was studied given assumed risk of hypertension [HTN]) did not improve outcomes compared with TIVO 1.34 mg D1-21 Q28D. There was a trend toward worse progression-free survival (PFS) in the combination arm. Methods: Using a predeveloped population pharmacokinetic (PK) model, existing ER models based on Tivo-1 and Tivo-3 studies were augmented to characterize the relationship between TIVO at clinically relevant exposures and central reviewer–based PFS, tumor size (TS) reduction, and safety endpoints. TiNivo-2 trial results were integrated to update the PK and ER models for PFS (Cox proportional hazard), TS (sum of longest diameters longitudinal model), and HTN (logistic regression) and to simulate the ER-based risk/benefit profile of TIVO. Results: The visual predictive check of the PK model on TiNivo-2 PK data confirmed that the dose-proportional TIVO PK is unaffected by concurrent NIVO. The PFS range of 5.6-9.7 months and TS reduction models, with a range of −7.02% to −23.8%, showed a significant relationship with TIVO exposure (Table). Concurrent NIVO did not add discernible benefit to TIVO at the dose of 0.89 mg. An ER modeling analysis between maximum concentration and HTN showed that the predicted HTN incidence was similar between TIVO 1.34 mg and TIVO 0.89 mg (41.3% vs 38.8% for any-grade HTN; 23.8% vs 21.5% for grade ≥3 HTN). An effect term for NIVO in the ER model for HTN was nonsignificant. Conclusions: The efficacy ER models predicted that TIVO 1.34 mg would provide greater antitumor activity than the 0.89-mg dose, while the predicted HTN incidence (any grade and grade ≥3) was comparable at the 0.89- and 1.34-mg doses. The TIVO monotherapy dose selection of 1.34 mg is important, based on the ER analysis and its safety profile. The results from the TiNivo-2 data set further confirmed that re-challenge with immunotherapy does not add benefit and optimal dosing of TKI provides the highest clinical benefit. Clinical trial information: NCT04987203 . Efficacy endpoint TIVO average concentration, ng/mL n Observed value,combined studies (range) PFS 13.9-38.4 192 5.6 months PFS 38.4-47.9 191 7.3 months PFS 47.9-62.0 191 9.1 months PFS 62.0-177 191 9.7 months CFB TS 13.9-38.4 176 −7.02% (−16% to 1.93%) CFB TS 38.4-47.9 173 −11.7% (−21.3% to −2.05%) CFB TS 47.9-62.0 185 −17.3% (−26.4% to −8.27%) CFB TS 62.0-177 183 −23.8% (−33.5% to −14.1%) CFB, change from baseline.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.004
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.103
GPT teacher head0.407
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
Has abstractyes

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