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: Synergistic anti-tumor efficacy of doxorubicin and flavopiridol in an in vivo hepatocellular carcinoma model. A previous study showed that flavopiridol increased doxorubicin sensitivity in hypoxic hepatocellular carcinoma (HCC) cells by increasing apoptosis through suppressing hypoxia-inducible N-myc downstream-regulated gene-1 (NDRG1) expression. However, this has not been investigated in an in vivo HCC model. Therefore, we aimed to elucidate whether the combination of doxorubicin and flavopiridol has a synergistic anti-tumor effect in an in vivo HCC model. ### methods An HCC mouse model was established by implanting C3H/He mouse with MH134 cells. Then , doxorubicin with or without flavopiridol was injected . The anti-tumor efficacy was assessed by evaluating tumor volumes, and the underlying mechanism was investigated by quantifying apoptotic cells, the Ki-67 proliferation index, and microvessel densities (MVDs). Immunohistochemistry of NDRG1 was performed to determine the underlying mechanism. ### results Tumor growth was significantly suppressed in the doxorubicin + flavopiridol combination group compared to the other three groups. The percentage of apoptotic cells was significantly higher, and Ki-67-positive proliferating cells were significantly lower in the combination group compared to the other groups; however, MVDs were not significantly different across the groups. Increased apoptosis by flavopiridol occurred by suppressing hypoxia-inducible NDRG1 expression. ### conclusions These results show that a combination of doxorubicin and flavopiridol has a synergistic anti-tumor effect in an in vivo HCC model. This synergistic effect of combination therapy was attributed to increased apoptosis and decreased proliferation of tumor cells rather than decreased angiogenesis. These findings suggest that flavopiridol might be an effective adjuvant therapy to doxorubicin-resistant HCC cells by inducing apoptosis through suppression of NDRG1 expression.

sentence: Then , doxorubicin with or without flavopiridol was injected .
[["doxorubicin", "flavopiridol", "POS"]]