Using Inverse Gas Chromatography (iGC SEA) to Identify Variations in the Dissolution Rate of Pharmaceutical Drugs

Active materials with batch-to-batch variations present a major issue to the pharmaceutical industry. On the whole, any batch-to-batch variation is undesirable. The range of techniques available for the characterization, analysis, and discrimination of products that are chemically identical can be restricted by the nature of the materials, and the sensitivity to slight physicochemical variations between them.

Inverse Gas Chromatography

Inverse gas chromatography (iGC SEA) is a physical characterization technique that makes it possible to view the differences between batches of the same product, which could not be seen using other techniques.

This is because iGC SEA serves as a highly sensitive surface tool, particularly when used at immeasurable dilution conditions, where only the interaction with the highest energy sites dictates the determination of the free energies of desorption or the dispersive surface energy.

Nycomed Pharma supplied three different batches (A, B and C) of a quick release painkiller for testing by iGC SEA. These batches were believed to be chemically identical, but the dissolution rates that were previously determined were noted to be significantly different (Table 1).

Table 1. Dissolution characteristics of the different batches

The dissolution rate of Batch A was found to be within the specification range; the dissolution rate of Batch B fell out of the error margin; and the dissolution rate of Batch C, produced through direct compression without wet processing, was beyond the specification range: A > B > C.

This study applies the iGC SEA technique to establish different surface properties of three chemically identical batches. This allows for the identification of variations in the results achieved for each batch, these variations can then be compared with the variations in dissolution rates between the batches.

Experimental Method

First, samples were examined as received. Glass columns with an internal diameter of 2 mm were treated with dimethyldichlorosilane to passivate the surface, filled with about 350 mg of sample, and then tapped until the powder has settled to a steady level. Next, samples were left to equilibrate under dry conditions at 0% relative humidity in the carrier gas flow at 303 K for a period of two hours before data recording.

The entire data was recorded and examined using the SMS-iGC SEA2000 and the SMS Standard Analysis Suite v1.12, respectively. Then, eluted peaks were determined using an FID at their maximum height from injections of 3% P/P0 vapor in helium, and dead volumes were determined using methane (20% P/P0).

The dispersive component of the solid surface energy was measured from the net retention volumes V_N, which were calculated for a range of alkane elutants such as heptanes, undecane, octane, decane, and nonane using the method of Schultz et. al.

This technique is based on a plot of RTln(V_N) against a(Y_L^D)^1/2 which creates a straight line with a slope equal to 2N_A(Y_S^D)^1/2, from which Y_S^D, the dispersive component of the surface energy, can be measured, with Y_L^D being the surface tension of the liquid elutant and ‘a’ the molecular area of the probe molecule (Figure 1).

The free energy of different polar probe molecules, including ethanol, ethyl acetate, 1,4-dioxane, and dichloromethane were measured by plotting the corresponding data in an analogous way (RTln(V_N) against a(Y_L^D)^1/2) and determining the distance of the matching point to the alkane straight line (Figure 1).

Figure 1. Dispersive surface energy plot (in red) and specific free energy of desorption for several polar probe molecules (in green) eluted through a column of batch C. 0% RH, 30 °C, 0.03P/P0

Samples A and C were examined twice consecutively on the same column, with a second conditioning of two hours between the two runs, to check for irreversible sorption effects and equilibrium. Sample B was examined once on two different columns to examine the heterogeneity within the sample.

Results and Discussion

Table 2 shows the entire set of results. All values shown are average values over the runs determined for each sample. The standard deviation values calculated for the results obtained for two runs performed consecutively on the same column, i.e., Batches A and C, are all within the characteristic error margin of < 3%.

Hence, it was inferred that a reversible physisorption mechanism is fully involved in the sorption, and that the two hour pre-treatment was adequate to dry the samples. The standard deviation values determined for the results found for two runs conducted on the same sample but on two different columns, i.e., Batch B, were also found to be within the standard error margin of < 4%. It was therefore inferred that Batch B is uniform with respect to surface characteristics, and, probably, so are Batches A and C.

Table 2. Dispersive component of the surface energy and specific free energy of desorption of different polar probe molecules for the three batches of painkiller investigated. 0% RH, 30oC, 0.03P/P0

The three Batches, A, B and C, do not exhibit any major variation in the dispersive surface energy (Figure 2). All three samples reveal a dispersive surface energy value of about 45 to 50 mJ.m^-2, which is normal for pharmaceutical ingredients.

Figure 2. Dispersive surface energy plot for the three different batches of painkiller

For sample A, no value is given for the specific free energy of desorption of ethanol. This is because the peak was so wide that no maximum could be detected. This may be attributed to a strong interaction of ethanol with Batch A. Thus, the specific free energy of desorption of ethanol was believed to be higher for sample A (> 16kJ.mol^-1), when compared to the other two batches.

For all three samples (lowest for dichloromethane, intermediate for 1,4-dioxane and ethyl acetate, and highest for ethanol), the overall trends of the specific free energies of desorption are identical, thus implying that the surface chemistry of the three batches is similar, as shown in Figure 3.

Figure 3. Specific free energy of desorption for the three batches (per batch)

With the interaction being strongest with ethanol, it appears that hydrophilic basic sites dominate the surface of all three samples. The interaction with 1,4-dioxane, which is considered basic and hydrophobic, and ethyl acetate, which is believed to be hydrophilic and weakly basic, is comparatively strong.

This indicates that acidic sites, both basic and hydrophilic, are well represented at the sample surface. Given that the interaction of the dichloromethane is lowest, basic and hydrophobic sites appear to be least well represented at the surface of the different batches.

Upon comparing the values of specific free energies achieved for the different batches (Figure 4), some major differences were found between them, particularly when the specific free energy of desorption of ethanol, ethyl acetate, and 1,4-dioxane are considered.

Figure 4. Specific free energy of desorption for the three batches (per probe)

Batch A displays higher values than that of Batch B, which itself exhibits higher values than that of Batch C. Therefore, the trend is A > B > C, as far as the specific free energies of desorption are concerned.

This is the same trend as the one seen for the dissolution rate. As a result, the variation in specific free energy of desorption can be corresponded with the dissolution rate.

The dissolution rate will be faster when the specific free energy of 1,4-dioxane, ethanol, or ethyl acetate is higher. Consequently, the specific free energy of ethyl acetate, ethanol, or 1,4-dioxane can be selected and utilized separately to predict the sample’s dissolution rate.

Conclusion

IGC SEA is a highly sensitive surface tool and is used for studying batch-to-batch variations. The three different batches of painkiller, provided by Nycomed Pharma, demonstrated considerable differences in their surface characteristics.

The surface chemistry differs from one sample to the other, and considerable variations in the values of the specific free energies of desorption of the polar probes have been demonstrated between three different batches.

Batch A displays the highest values of specific free energies for most polar probes analyzed; Batch B shows intermediate values; and Batch C exhibits the lowest values of specific free energies.

As a result, the variation in surface chemistry can be corresponded with the difference in dissolution performance. In addition, this study demonstrates that the wet processing pre-treatment has a remarkable effect on the surface properties of the particles, and specifically increases the number of available basic and acidic sites at the surface of the particles by re-orienting surface groups or removing a surface contaminant.

Thus, the prediction of the dissolution rate of batches of painkillers was made by measuring the specific free energy of the sample through the iGC SEA technique. The study also shows how different processing techniques can impact the dissolution properties of drugs.

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