Predicting Moisture Uptake in Solid Dosage Packaging

Pharmaceutical Technology, Pharmaceutical Technology-01-02-2017, Volume 41, Issue 1
Pages: 44–49

Moisture uptake during the end-to-end manufacturing process and supply chain can affect product quality. Simulation tools based on mechanistic models help define storage and handling requirements for oral solid-dosage drugs.

In the pharmaceutical industry, considerable effort is made to develop products that are stable and have a long shelf life. It is well known that moisture uptake is the most common cause for a product failing to meet its specification (1), and during development particular attention is, therefore, paid to moisture. Uptake of moisture by oral solid-dosage forms, such as tablets or capsules, is well known to increase the mobility of chemical species, which causes an increase in the rate of degradation of the drug substance and an increase in the rate of production of undesired byproducts (2). Moisture can also have an effect on the physical attributes of the product, such as its drug release rate or appearance.

In cases where moisture causes an increased rate of degradation, it is possible to model the rate of reaction using an Arrhenius-like expression that also includes moisture (as shown, for example, by He et al. [3]). The parameters in this model can be determined using accelerated stability tests, as described by Waterman et al. (4, 5), and the amount of degradant can then be predicted provided that the temperature and the moisture content of the product or the relative humidity to which it is exposed are known. In this article, however, only moisture is considered, with the implicit assumption that this factor can be used to predict degradation.

The product may absorb moisture during handling and storage, and the fact that the product has adequate stability during its shelf life is demonstrated experimentally for a number of different packaging configurations at selected environmental conditions. Tablets in bottles can also absorb moisture during use by the patient, because bottles with a broken seal can have a much higher moisture permeability than sealed bottles and because the repeated opening and closing of a bottle to remove tablets may increase the rate of moisture transport into the bottle relative to a closed bottle. Figure 1 shows how the moisture content of the product can change from the point of manufacture to the point of administration by the patient. Two cases are shown in Figure 1: one where the moisture content starts at a low value and then gradually increases during packaging, handling, storage, and use; and one where it is packaged in a consumer bottle with a desiccant so that it starts at a higher value but decreases almost immediately after it is packaged. As Figure 1 shows, the moisture content of the product when it is administered by the patient can be controlled either by selecting the packaging components so as to include a desiccant in the container or by introducing a drying or conditioning step in the manufacturing process so that a desiccant is not required. To develop a supply chain that is cost effective and guaranteed to deliver a high-quality product to the patient, it is therefore of interest to consider moisture uptake during the entire lifetime of the product. 

Outlining the constraints on the supply chain from the point of view of stability is not straightforward because there are a number of unknowns. Fortunately, by examining the overall process and by introducing predictive models for each stage, it is easier to understand what parameters are important and to what extent they can be expected to affect the quality of the product. In this article, it is shown how simple experiments along with simple modeling tools can be used to help define requirements for storage and handling in the end-to-end manufacturing process and supply chain.

Using predictive tools to define the packing and handling process

As shown in Figure 1, the product may absorb moisture during packing, during storage in bulk and consumer containers, and during use by the patient. Moisture uptake by the product during these stages can be predicted using simple models.

 

Predictive model for water uptake due to exposure during packing

A simple and useful model for the moisture uptake of a product due to exposure during packing can be obtained by assuming that the rate of moisture uptake is proportional to the driving force for moisture uptake. The tablet moisture content, XT , can thus be modeled using an ordinary differential equation (Equation 1):

where τT is the time constant for moisture uptake, which is closely related to the mass transfer coefficient k, and 

 is the equilibrium moisture content. The solution to Equation 1 is given by Equation 2:

 

where 

 is the moisture content of the tablets before exposure. To determine the time constant for moisture uptake, a simple experiment may be performed that takes into account how the tablets are exposed. For example, in many cases tablets become exposed appreciably only when the packaging line has to be stopped during packing. If the tablets during this period have already been filled into bottles, the time constant for this mode of exposure may be characterized by measuring the weight increase of tablets in uncapped bottles in an environment with a known relative humidity. The experimental data can then be used to determine the time constant by fitting Equation 2 to the data, as shown in Figure 2. Packing into blisters can be handled in a similar fashion.

 

To model moisture uptake in a climate with a different relative humidity, it is reasonable to assume that the major effect is a change in the equilibrium moisture content, and that a change in the relative humidity has a much smaller effect on the time constant. The dependence of the equilibrium tablet moisture content on the relative humidity can be obtained by performing a separate experiment to measure the moisture sorption isotherm. The model in Equation 2 can then be employed to make predictions for any climate, as also indicated in Figure 2, and to construct a look-up table that shows the maximum exposure time that can be allowed for the moisture content not to exceed a critical value. Table I is an example of a look-up table for a case in which the initial moisture content of the tablet is 2% and the maximum allowed value is 4%.

 

Relative humidity(%)Max. exposure time (min)
451360
50870
55650
60520
65420
70360
75300

 

As discussed previously, the experiment to determine the time constant should take into account the mode of exposure, and it can then be used to model a similar situation. The rate of moisture uptake by the tablet is determined by the rate of transport inside the tablet as well as the rate of transport in the air surrounding the tablet. Ideally, one should determine these rates independently and then employ a model to predict the combined effect. In the experiment outlined previously, the rate of moisture transport in the tablet can be expected to be much slower than the rate of transport to the tablet via diffusion in air. The result obtained from this simple experiment may thus serve as a useful approximation in describing the rate of moisture uptake in other situations, such as in the models for the moisture uptake of tablets in bottles or in bulk packages that are described in the following section. It is important to consider that the temperature can be expected to have an effect on this time constant.

Predictive model for moisture uptake during storage

Models for moisture uptake by packaged tablets during storage have been described by Chen and Li (6), Vaczek (7), Possumato (8), and Waterman and MacDonald (9). These models rely mainly on the sorption isotherm of the product and the moisture permeability. In one model, the relative humidity of the headspace,

 , and the moisture content of the tablets and the desiccant, XT and XD, respectively, are described using three ordinary differential equations (Equations 3-5):

 

In Equations 3-5, MW is the molar mass of water, VH is the headspace volume, pSAT is the saturation pressure, R is the gas constant, T is the temperature, P is the moisture permeability of the container, mT is the mass of tablets, and mD is the mass of the desiccant. In addition, X*T and X*D represent the equilibrium moisture contents of the tablets and the desiccant, while τT and τD represent the time constants for moisture uptake by the tablet and the desiccant.

In the context of this model, it may be noted that the time constants for moisture uptake by the tablets and the desiccant may be determined as described previously. It is not always necessary to include this detail because moisture uptake by the tablets is almost always much faster than moisture transport into the container. In addition, it is usually also possible to neglect moisture in the headspace of the container because the amount of moisture in the headspace is usually negligible compared to the amount of moisture in the tablets.

Along with appropriate parameters and initial conditions, Equations 3-5 can be used to predict the moisture content of the tablets. One example of such prediction is given in Figure 3, which shows the moisture content of tablets packaged in a high-density polyethylene bottle with desiccant. The results show that the desiccant dries the tablets initially and that there is then a slow increase in the moisture content of the tablets due to the permeability of the bottle. In this context, it should be pointed out that the short-term predictions may not be entirely accurate because the model assumes quasi-steady moisture transport into the container; it is well known that there is an initial induction period during which this quasi-steady state is established.

 

Predictive model for bulk storage

Bulk storage differs from storage in consumer bottles mainly because the number of tablets per container is much larger in a bulk package, the type of packaging material differs, and the bulk package is larger than a consumer bottle. Because the time required to transport moisture a distance L by diffusion is proportional to L2, moisture equilibration inside a bulk container is slower than in a small bottle. For bulk storage, it is therefore not always appropriate to assume that the tablets have the same moisture content throughout the container.

Fortunately, it is possible to develop models to predict moisture transport in bulk packages as well. In such a model, the bulk package can be considered to be a bed of tablets. Moisture transport can then be modeled as a diffusive process with volumetrically distributed sources/sinks to represent moisture uptake or loss by the tablets. A desiccant can also be included. Since diffusion of moisture in the air between the tablets is modeled explicitly, it is, in this case, important that the rate constants for moisture uptake by the tablets and the desiccant include only the transport resistance in the tablet/desiccant. An example of such a prediction is shown in Figure 4, which shows a contour plot of the tablet moisture content in a cross-section of a bulk container some time after the tablets have been placed in the container along with a desiccant pouch.

 

Predictive model for in-use moisture uptake

It is also possible to employ predictive tools for moisture uptake during use. One such model is described by Simonutti et al. and Remmelgas et al. (10, 11). The main feature of this model is to account for moisture that enters the bottle each time it is opened and the subsequent moisture uptake of the tablets between openings. This model requires information about the amount of air that is exchanged with the environment every time the bottle is opened and about the permeability of a bottle with a breached seal (which may be much higher than a for a sealed bottle). Beyond these two pieces of information, however, this model does not require any experimental data that has not already been discussed.

A likely scenario for this process is sketched in Figure 5, which shows that the relative humidity in the headspace fluctuates significantly due to opening and closing the bottle, whereas the moisture content of the tablets increases slowly (albeit more quickly than for a closed bottle). In this context, it is of interest to note that the rate at which the tablets absorb moisture increases as tablets are removed from the bottle because fewer and fewer tablets are left to absorb the incoming moisture (which also increases due to the increased headspace).

Simulating moisture uptake during the entire supply chain

These models can be put together to simulate moisture uptake by the product from the point of manufacture to the point when it is administered by a patient. This approach to modeling moisture uptake in the end-to-end manufacturing and supply chain may be used to specify requirements on the manufacturing process, any conditioning steps, the packing process, and the packaging configuration. For example, there is frequently a specification on the end-point moisture content, and this specification can be used to back-calculate the requirements on the supply chain, as discussed in the following paragraphs.

The dark blue curve in Figure 6 shows a hypothetical example of a process for which the tablet moisture content is not within the specification limit after storage in a bottle. This situation will result in a substantial decrease in the shelf-life of the product unless a desiccant is included in the bottle, as indicated by the dashed red curve in Figure 6. It is straightforward to use prediction tools to select the amount of desiccant that will keep the moisture content below the specification limit. Such a predictive ability may be even more valuable for formulations in gelatin capsules because it is then necessary to also keep the moisture level above a certain lower limit to keep the capsule shells from becoming brittle.

The dark blue curve in Figure 6 represents tablets manufactured and packaged in one facility. However, tablets are frequently manufactured at one site and packaged at another, and not all facilities have the same ability to control environmental conditions during packaging. The dashed light blue curve in Figure 6 thus shows the moisture content of tablets that are packaged at a facility where exposure to a humid environment during the packing process increases the tablet moisture content beyond the specification limit. One solution to this problem is to simply not consider this site for packaging into consumer bottles. If tablets are, nevertheless, to be packaged at this facility, the tablets either have to be manufactured with a lower moisture content, as indicated by the solid light blue curve in Figure 6, or somehow conditioned so that their moisture content is lower before the packing process, as indicated by the green curve in Figure 6.

Although in the vast majority of cases, it is more practical and less expensive to manufacture tablets with a lower moisture content, it is not always possible. For example, tablets may not always be able to withstand the added handling that an extra drying step would imply. The green curve in Figure 6 thus represents a conditioning step in which the tablets are packaged into bulk aluminum bags with a desiccant. In this case, it is important that all tablets have an acceptable and approximately the same moisture content after conditioning. A predictive model can be valuable in determining and justifying the necessary conditioning time and the required amount of desiccant. Conditioning using a desiccant is not a method of choice, but that is precisely the point: by considering moisture uptake and degradation during the entire supply chain one can design a process that is cost effective and guaranteed to deliver a high-quality product.

Conclusion

It has been shown how predictive tools can be used to simulate the moisture uptake of oral solid-dosage forms from the point of manufacture to the point when it is administered by a patient. The model for moisture uptake can be coupled with a model for degradation in order to predict chemical degradation. By considering moisture uptake of the product in the end-to-end manufacturing and supply chain it is thus possible to select the most appropriate measures to ensure that the product meets its specification when it is administered by the patient.

References

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Article DetailsPharmaceutical Technology
Vol. 41, No. 1
Pages: 44–49

Citation:
When referring to this article, please cite it as J. Remmelgas, " Predicting Moisture Uptake in Solid Dosage Packaging," Pharmaceutical Technology 41 (1) 2017.

About the Author
Johan Remmelgas is associate principal scientist in Manufacturability, Pharmaceutical Technology & Development, AstraZeneca, Gothenburg, Sweden.