A Phase I MMRC Clinical Trial Testing the Combination of Bortezomib and Tipifarnib in Relapsed/Refractory Multiple Myeloma
Bibliographic record
Abstract
Abstract Background: Preclinical work from our group and others has demonstrated that the combination of a farnesyl transferase inhibitor (FTI) and the proteasome inhibitor bortezomib results in enhanced plasma cell apoptosis and is associated with AKT activation (David, Blood 2005). More recently, further preclinical data suggests that the mechanism responsible for this profound synergy is due to inhibition of HDAC6 with a resultant inhibition of both the proteasome and aggresome pathway (David ASH 2007). Based upon these observations, with the MMRC we initiated a phase I trial combining the FTI tipifarnib with bortezomib to clinically evaluate the efficacy of this combination. Methods: Patients with relapsed or refractory myeloma were treated with bortezomib at 1.0 mg/m2 given on days 1,4,8, and 11 in conjunction with escalating doses of tipifarnib (100–400mg/BID) given on days 2–15 every 21 days. Dose escalation was accomplished using an adaptive phase I design (Escalation With Overdose Control (EWOC)). Eligibility criteria included a serum creatinine of <2.5, normal liver function, ANC>500, and platelets >25. If dose escalation is able to proceed to 400mg of tipifarnib with 1.0 mg/m2, the tipifarnib dose escalation will restart with bortezomib given at 1.3 mg/m2. Results: Sixteen patients have been enrolled to date into respective tipifarnib dose levels 100 mg(n=6),200mg (n=5) and 300mg (n=5). Median age for the enrolled patients is 59 (range 43–76) and median time from myeloma diagnosis was 4.7 years. 15/16 patients had received prior high dose therapy. The average number of prior therapies was 4.5, and of the16 patients, 8 were refractory to prior bortezomib (relapsed on therapy or within 6 months) 4 were bortezomib naïve, and 4 were previously exposed to bortezomib but not known to be refractory. Among these patients with advanced myeloma and refractory disease, stabilization of disease or better was seen among 7/16 patients with 2 of the 7 achieving an MR. Of note, among the patients achieving clinical benefit, 1 patient had a stable M-protein, but experienced an 80% reduction in circulating plasma cells while on therapy, and another has had a 75% reduction in the free light chain assay. The most common drug related side effects were was Gr2 diarrhea (23.5%). Hematologic toxicities were difficult to ascertain as patients had advanced myeloma and many were entered onto study with platelet counts between 25 and 50. Additional grade 3 toxicities included renal insufficiency (related to progression), pneumonia and altered mental status which were all considered unrelated to study drug, but were associated with progression of disease. There were no Grade 3 –5 drug related toxicities. There were no cardiac events or DVT, and 1 patient experienced grade 2 peripheral neuropathy who did not have pre existing PN at baseline. Conclusions: The combination of bortezomib and tipifarnib is supported by preclinical rationale and has produced stable disease or better among a group of patients with refractory and advanced myeloma. To date the optimal dose of both tipifarnib and bortezomib have yet to be defined, and additional patients will be enrolled to define the MTD for tipifarnib with 1.0mg/m2 of bortezomib, followed by escalation of tipifarnib with 1.3mg/m2 of bortezomib. Correlative studies evaluating the effect of the combination on HDAC6 and plasma cell apoptosis will be presented.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".