MétaCan
Menu
Back to cohort
Record W4389228919 · doi:10.1182/blood-2023-185440

In Vitro Evidence of Synergistically Enhanced Efficacy of Axitinib When Combined with Asciminib in T315I Mutated Chronic Myeloid Leukemia

2023· article· en· W4389228919 on OpenAlexaff
Ho Jae Han, Eunju Park, Jaeyoon Kim, Danielle Pyne, Anthea Travas, Amirthagowri Ambalavanan, Shinya Kimura, Michael W. Deininger, Jong‐Won Kim, Dennis Dong Hwan Kim

Bibliographic record

VenueBlood · 2023
Typearticle
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsNilotinibBosutinibAxitinibDasatinibPonatinibMyeloid leukemiaCancer researchMedicineTyrosine-kinase inhibitorPharmacologyImatinibTyrosine kinaseSunitinibCancerInternal medicineReceptor

Abstract

fetched live from OpenAlex

Background: Development of ABL1 kinase domain (KD) mutation is one of resistance mechanism to ATP-binding pocket inhibitor (ABPI) therapy. T315I mutation in ABL KD confers broad-spectrum resistance to all 1st- and 2nd-generation ABPIs including Imatinib (IMA), Dasatinib (DAS), Nilotinib (NIL) and Bosutinib (BOS). Ponatinib (PON), a 3 rd generation inhibitor, can overcome T315I-mediated resistance in chronic myeloid leukemia (CML) treatment. However, its increased risk of cardiovascular toxicity limits the broader use of PON in the clinic. Asciminib (ASC) is a Specifically Targeting ABL1 Myristoyl Pocket (STAMP) inhibitor and has been approved for CML treatment in the patients who failed at least two previous lines of TKI therapy. ASC, which blocks the myristoylate pocket in ABL1 KD protein, can potentially overcome ABL1 KD mutation (KDM)-mediated tyrosine kinase inhibitor (TKI) resistance by combining with ABPIs which blocks ATP binding pockets in ABL1 KD protein. However, in vitro evidence to support double blockade approach is still to be further investigated including combination with ABPIs as well with other drug compounds. Axitinib (AXI), which is approved for the treatment of advanced renal cell carcinoma (RCC), is an ABPI that can specifically bind to the ATP-binding pocket in ABL1 KD protein structurally harboring the T315I mutation. In the Cancer Therapeutics Response Portal (CTRP) database, which integrates drug compound and cell line experiment databases, AXI showed very low IC 50 in CML cell lines compared to other cancer cell lines, including RCC. The present study attempted to provide in vitro evidence of the synergistic enhancement of therapeutic efficacy of AXI combined with ASC to overcome T315I mutated CML. Methods and Materials: We analyzed IC 50 values for 649 cell lines tested with AXI using the CTRPv2 database. The drug response of AXI in K562/Wild-Type (WT), K562/T315I mut, BaF3/WT, and BaF3/T315I mut cell lines was evaluated using the WST-8 assay. From the measured IC 50 value in those cell lines as the baseline concentration (conc), AXI and ASC conc were serially diluted. The dose-response matrix and ZIP synergy score were analyzed using SynergyFinder. Results We compared the sensitivity of 649 different cell lines to AXI in the CTRPv2 database and confirmed that the IC 50 of AXI in CML (median IC 50 = 0.31 uM) is 96 times lower than that of various cell lines derived from RCC (median IC 50 = 29.85 uM) (Fig. 1A). This result implies that AXI is more effectively inhibit CML cells at a lower conc than RCC cell lines or other cell lines. When measuring the inhibitory activityof AXI (Fig. 1B), AXI treatment showed 2.22 times lower IC 50 in the K562/T315I mut (IC 50 = 111.6nM) than in the K562/WT (IC 50 = 248nM). Also AXI treatment showed 4.73 times lower IC 50 in the BaF3/T315I mut (IC 50 = 84nM) than in the BaF3/WT (IC 50 = 397.4nM). Overall, the T315I carrying CML cell lines were much more sensitive to lower dose AXI than wild type CML cell lines. In the dose-response matrix analysis (Fig. 1C), AXI treatment alone at 100 nM inhibited the growth of the K562/T315 mut cell line by 38.42%, and ASC treatment alone at 100 nM, by 36.52%. Of note, the combination of AXI at 25nM and ASC at 12.5nM inhibited cell growth by 38.1%, and the combination of AXI at 12.5nM and ASC at 25nM showed 38.42% inhibition, which is similar to the inhibition achieved by 100nM of each drug alone. The result suggests that the combination of reduced dose of each drug, by 25% or 12.5% from the baseline IC 50 dose, showed equivalent inhibitor activity to the baseline IC 50 concentration of each drug monotherapy in the K562/T315 mut cell line. Similarly, when AXI and ASC were combined at a reduced concentration by a half or a quarter and tested in the BaF3/T315I mut cell line, a higher inhibitory activity was observed compared to the baseline IC 50 concentration of each drug monotherapy. The ZIP score was calculated as 31.7 and 20.64 in K562/T315 mut and BaF3/T315I mut cell line. Given that ZIP score was above 10 each, synergistic enhancement of inhibitory activity was demonstrated against T315I mutant CML cells between AXI and ASC. Conclusion: This result suggests synergistically enhanced inhibitory activity of dual blockade using AXI combined with ASC specifically for T315I mutant CML. This approach will be promising against CML cells carrying compound mutation with T315I mutation, which is highly resistant even to Ponatinib or Asciminib.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.020
GPT teacher head0.282
Teacher spread0.263 · 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 designBench or experimental
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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueBloodSame topicChronic Myeloid Leukemia TreatmentsFrench-language works237,207