The distribution of Drucker-Prager Cap model parameters for pharmaceutical materials

Patients who are prescribed a pharmaceutical product for their ailment have only one way to assess the quality of the product that they are taking, and that is visually. The drug content, dissolution rate, and purity of the active ingredient are all invisible. These products are often expensive and often critical for the patient’s health. Patients rightfully demand dosages that are free from defects that might lead them to question the product’s quality. Pharmaceutical manufacturers take even extremely low levels of visual defects in their products seriously. Those defects can lead to time consuming and costly quality investigations and raise questions about the development and manufacturing capabilities from regulatory agencies.

Solid dosage forms, consisting of tablet and capsules, continue to be the most popular dosage forms for the pharmaceutical industry. These dosage forms are not only easily administered by patients but can be used to deliver precise amounts of drug controllably and reliably. Consistent production is an absolute necessity when millions of doses must be created with each batch to meet demand. Each dose must not only contain the correct amount of drug, the tablet or capsule must also be produced with consistent dissolution properties. The dosage forms must also be physically robust enough to endure subsequent processing and shipping to the customer. These quality demands require expensive and time-consuming formulation and process development. In addition, if any problems with the composition or process occur at scale, very expensive studies, or inefficient processes, such as hand inspection, could result.

Problems that have been observed in many products include tablets that require high compaction forces, cracking, lamination, or capping [1]. If tablets are not formed properly the tablets may be damaged before reaching the patient. Clearly, an understanding of the compaction process is desired for the efficient production of tablets. This would allow formulation and process to be designed to provide high quality tablets.

Tablet properties and the compaction process are evaluated using a variety of empirical methods [2,3]. These measures include compactibility, compressibility, and elastic recovery. These measures are frequently used to resolve compaction issues throughout the pharmaceutical industry. Parameters based on a model of compaction will be correlated with combinations of empirical measures, as they are based on the same underlining information. However, most empirical measures have been utilized because of their convenience of measurement rather than based on a fundamental framework of the relationships between solid deformation and stress states. Comparing material’s properties based on such a framework has the potential to allow better prediction of compaction behavior and an increased understanding of the mechanisms behind tableting problems [4].

A model of the compaction process that has come into increasing use with the pharmaceutical industry is the modified Drucker-Prager Cap (DPC) model [1,[5], [6], [7], [8], [9]]. It has long been used as a physical model capable of predicting compact density and stress distribution when used with finite element method (FEM) simulations [[7], [8], [9], [10], [11]]. The calibration experiments carry important information about the compaction properties of the material and summarize that information in the model parameters [4,9]. These model experiments allow many material’s properties to be determined in a consistent manner [5,12].

The research presented here is a continuation of the effort to understand compaction behavior and scale-up risk as a function of material parameters using the DPC model. This effort was first reported in LaMarche et al. 2014 [4] and was based on 14 materials at manufacturing scale. It was found that certain compaction parameters could be used to build a qualitative risk assessment for compaction issues observed at scale. The effort reported here expands that original dataset to 60 materials. This larger dataset allows any material’s properties to be examined relative to the properties of the set. This allows materials with atypical behavior to be identified and flagged as either higher or lower than normal risk. This can allow researchers to focus their efforts on materials with higher compaction risks, saving material, time, and other resources during drug development.