You will be shown a paragraph from a biomedical research article. Your task is to extract all drug combinations appearing in a specific sentence within in the paragraph. Each drug combination should be assigned to one of the following three categories:

- POS: Indicates a positive interaction between the drugs in the combination; for instance, drugs that have an additive or synergistic effect.
- NEG: Indicates a negative interaction between the drugs in the combination; for instance, combining these drugs might result in adverse side effects, or might reduce their efficacy.
- COMB: Indicates that the drugs are given in combination, but the type of interaction between the drugs cannot be determined based on the provided context.

Please format your answer as a json array. Each entry in the array should itself be an array formatted as [<drug_1>, <drug_2>, ..., <drug_n>, <category>] where <category> is one of the three categories listed above and <drug_1>, ..., <drug_n> are drug names copied verbatim from the input sentence. Different combinations may involve different numbers of drugs; in other words, the `n` in <drug_n> may vary.

As an example, the following output describes two extracted drug combinations; one between two drugs, and one between three drugs:

[["artesunate", "artemether", "COMB"], ["Nifedipine", "Labetalol", "Prazosin", "POS"]]

The paragraph and specific sentence are below. You should only extract relationships expressed in the specific sentence; the paragraph is provided for context.

In your output, return only the json array and no other text. If no drug combinations appear, return an empty array `[]`.

paragraph: Genome-wide CRISPR screen identifies regulators of MAPK and MTOR pathways mediating sorafenib resistance in acute myeloid leukemia. Drug resistance impedes the long-term effect of targeted therapies in acute myeloid leukemia (AML), necessitating the identification of mechanisms underlying resistance. Approximately 25% of AML patients carry FLT3 mutations and develop post-treatment insensitivity to FLT3 inhibitors, including sorafenib. Using a genome-wide CRISPR screen, we identified LZTR1, NF1, TSC1 or TSC2, negative regulators of the MAPK and MTOR pathways, as mediators of sorafenib resistance. Analyses of ex vivo drug sensitivity assays in FLT3-ITD AML patient samples revealed lower expression of LZTR1, NF1, and TSC2 correlated with sorafenib sensitivity. Importantly , MAPK and/or MTOR complex1 ( MTORC1 ) activity were upregulated in AML cells made resistant to several FLT3 inhibitors , including crenolanib , quizartinib , or sorafenib . These cells were sensitive to MEK inhibitors, and the combination of FLT3 and MEK inhibitors showed enhanced efficacy, suggesting its effectiveness in AML patients with FLT3 mutations and those with resistance to FLT3 inhibitors.

sentence: Importantly , MAPK and/or MTOR complex1 ( MTORC1 ) activity were upregulated in AML cells made resistant to several FLT3 inhibitors , including crenolanib , quizartinib , or sorafenib .
[]