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: First-line therapies in metastatic colorectal cancer: integrating clinical benefit with the costs of drugs. In light of the relevant expenses of pharmacological interventions, it might be interesting to make a balance between the cost of the new drugs administered and the added value represented by the improvement in progression free survival (PFS) in first-line for metastatic colorectal cancer CRC (mCRC). ### methods Phase III randomized controlled trials (RCTs) that compared at least two first-line chemotherapy regimens for mCRC patients were evaluated. Differences in PFS between the different arms were compared with the pharmacological costs (at the pharmacy of our hospital). The European Society for Medical Oncology Magnitude of Clinical Benefit Scale (ESMO-MCBS) was applied to the above RCTs. ### results Overall 28 phase III RCTs, including 19,958 patients, were analyzed. The FOLFOX resulted the least expensive (56 € per month of PFS gained) while the addition of irinotecan to FOLFOX (FOLFOXIRI) increased only marginally the costs (90 € per month of PFS gained). Treatments including the monoclonal antibodies showed a cost per month of PFS gained of 2823 € ( FOLFIRI with cetuximab in KRAS wild-type patients and liver-only metastases ) , of € 15,822 ( FOLFOX with panitumumab in KRAS wild type ) , and of 13,383 € ( FOLFOX with bevacizumab ) . According to the ESMO-MCBS, the treatments including an EGFR-inhibitor (cetuximab or panitumumab) were associated with a score of 4, while the inclusion of bevacizumab reached a score of 3. ### conclusions Our data demonstrate a huge difference in cost per month of PFS gained in modern first-line treatments in mCRC.

sentence: Treatments including the monoclonal antibodies showed a cost per month of PFS gained of 2823 € ( FOLFIRI with cetuximab in KRAS wild-type patients and liver-only metastases ) , of € 15,822 ( FOLFOX with panitumumab in KRAS wild type ) , and of 13,383 € ( FOLFOX with bevacizumab ) .
[]