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: The cationic lipid stearylamine reduces the permeability of the cationic drugs verapamil and prochlorperazine to lipid bilayers: implications for drug delivery. The therapeutic activity of a wide variety of drugs is significantly improved when their longevity in the circulation is extended by encapsulation in liposomes. To improve the retention of cationic drugs in liposomes, we have investigated the effect of the cationic lipid stearylamine on the permeability of the calcium channel blocker verapamil and the antipsychotic drug prochlorperazine, both of which are also multidrug resistance modulators. Both drugs were efficiently incorporated into liposomes composed of DSPC/cholesterol that possessed a transmembrane pH gradient (inside acidic). However, the efflux of the loaded drugs was relatively rapid (i.e., 50% of the encapsulated verapamil was released after 4 h at 37 degrees C), despite the presence of a 3 unit pH gradient (pHi = 4.0, pHo = 7.5). Drug retention within the liposomes was improved by increasing the magnitude of the transmembrane pH gradient to approx. 5 units (pHi = 2.0, pHo = 7.5). Further improvements in drug retention were achieved by the addition of 10 mol% of the cationic lipid stearylamine in the DSPC/cholesterol liposomes. The combination of the 5 unit pH gradient and stearylamine resulted in increases of the retention of verapamil and prochlorperazine by approx. 20- and 5-fold, respectively. Calculation of the permeability coefficients for the charged (cationic) and neutral forms of the drugs indicated that the neutral forms of both drugs were approx. 10(4)-fold more permeable than were the cationic forms of the drugs. Further, the presence of stearylamine reduced the permeability coefficient for the cationic species of the drugs by approximately an order of magnitude, but had no effect on the neutral species of the drugs. The efflux curves observed for both verapamil and prochlorperazine could be mathematically modeled by assuming that the primary influence of stearylamine was on the development of a positive surface charge density on the inner monolayer of the liposome. Taken in sum, these results indicate that stearylamine is effective at decreasing the leakage of cationic drugs from liposomes, and may prove to be a valuable component of liposomal drug formulations.

sentence: The efflux curves observed for both verapamil and prochlorperazine could be mathematically modeled by assuming
[]