Insulin Growth Factor 1 Receptor (IGF-1R) Inhibitor, Linsitinib (OSI-906) in Combination with Bortezomib and Dexamethasone Demonstrates Favorable Safety Prolife and Clinical Activity in Patients with Relapsed/Refractory Multiple Myeloma
Bibliographic record
Abstract
Abstract Background: Preclinical studies have demonstrated anti-myeloma activity of IGF-1R inhibition against myeloma cell lines, primary patient samples and myeloma xenograft models. Further, it has previously been reported that IGF-1R inhibition enhances the in vitro activity of bortezomib (BTZ), suggesting that IGF-1R inhibitors may improve the clinical efficacy of proteosome inhibitors (PIs). Based upon these observations, we initiated a phase I trial combining linsitinib (a selective IGF-IR an IR inhibitor) with BTZ and dexamethasone (Dex) to clinically evaluate the tolerance and efficacy of this combination. Methods: This study investigates BTZ 1.3 mg/m2 and dex 20 mg given on days 1, 4, 8, and 11 together with escalating doses of oral linsitinib (75-150mg bid) given daily on a 21-day cycle. Continued daily linsitinib and BTZ dosing on days 1, 8, 15 and 22 of a 35-day cycle is allowed after 8 cycles for those benefitting and tolerating treatment. The primary objective is to determine the maximum tolerated dose (MTD) of the combination using a modified 3+3 design with 4 dose cohorts. The secondary objectives are to assess tolerability, pharmacokinetics (PKs), predictive biomarkers and clinical activity. Responses per dosing cohort are assessed by IMWG criteria (plus MRs as per the EBMT). Results: 18 patients (pts) have been enrolled to date into dose levels 75 mg (n=3), 100 mg (n=6), 125 mg (n=5) and 150 mg (n=4). All pts had refractory and/or progressive disease (PD); with a median number of 3 prior lines of treatment (range 1- 4). 67% of pts had received prior immunomodulatory drugs (IMiDs), 78% had prior PI exposure of which 3 were were confirmed refractory, and 50% had received both an IMiD and a PI. Two dose limiting toxicities (DLT) consisting of Gr 4 thrombocytopenia without bleeding (100 mg dose) and a grade 4 ALT (150 mg dose) were observed. All DLTs were reversible. The most common (>20%) adverse events (AEs) were anemia (41%), nausea (35%), fatigue (41%), creatinine increase (47%), thrombocytopenia (88%), cough (35%) and increase in ALT (24%). Most were grade 1-2. The most frequent Gr 3-4 AEs (>10%) regardless of causality were diarrhea (17.6%), back pain (11.8%), thrombocytopenia (47.1%), anemia (17.6%) and neutropenia (11.8%). Additional Gr 3 toxicities included renal insufficiency due to progressive disease (PD), pneumonia, altered mental state (not drug-related) and weight loss. Two deaths occurred; one due to PD, the other due to a cardiac event possibly related to BTZ. IGF-1R expression on CD138 positive cells was confirmed by flow cytometry in 8 pts. Analysis of substrate phosphorylation downstream of IGF-1R at baseline, are ongoing. PK analysis will also be reported. Of 18 pts assessable for response we observed 1 sCR, 1 VGPR, 7 PRs and 2 MR, for an overall response rate (ORR) of 50% and a clinical benefit rate of 61% (PR+MR). 5 pts achieved SD and 2 pts had PD. Of the 2 pts that had PD, neither expressed IGF-1R and of the 3 pts that were progressing on a PI-based regimen immediately prior to starting linistinib and BTZ, one achieved a PR, one an MR and one had SD. The median PFS is 7.3 months. Pts have received a median of 5 cycles of treatment (range 1-22). Dose escalation of linistinib at the 150 mg cohort is currently ongoing. Conclusions: The combination of BTZ and linsitinib is supported by preclinical rationale and has produced stable disease or better in 88% of advanced myeloma patients and appears to be safe and well tolerated. The optimal dose of linsitinib combined with BTZ however, has yet to be defined and data from the complete cohort of pts in addition to studies correlating response to tumor expression of IGF-1R and CD45 will be presented. Disclosures White: Novartis: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Janssen-Cilag: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Millennium: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria. Jakubowiak:SkylineDx: Membership on an entity's Board of Directors or advisory committees; Onyx: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Millennium: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Sanofi-Aventis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Karyopharm: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Sanofi-Aventis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Onyx: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Karyopharm: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; SkylineDx: Membership on an entity's Board of Directors or advisory committees; Millennium: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Bristol-Myers Squibb: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: institutional funding for support of clinical trial conduct, Speakers Bureau. Reece:Amgen: Honoraria; Bristol-Myers Squibb: Research Funding; Onyx: Consultancy; Otsuka: Research Funding; Millennium Takeda: Research Funding; Lundbeck: Honoraria; Merck: Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Novartis: Honoraria, Research Funding; Janssen-Cilag: Consultancy, Honoraria, Research Funding. Kukreti:Celgene: Honoraria; Amgen: Honoraria; Lundbeck: Honoraria; Roche: Honoraria; Janssen Ortho: Honoraria.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".