Simulated Leaching (Migration) Study for a Model Container-Closure System Applicable to Parenteral and Ophthalmic Drug Products.

A simulating leaching (migration) study was performed on a model container-closure system relevant to parenteral and ophthalmic drug products. This container-closure system consisted of a linear low-density polyethylene bottle (primary container), a polypropylene cap and an elastomeric cap liner (closure), an adhesive label (labeling), and a foil overpouch (secondary container). The bottles were filled with simulating solvents (aqueous salt/acid mixture at pH 2.5, aqueous buffer at pH 9.5, and 1/1 v/v isopropanol/water), a label was affixed to the filled and capped bottles, the filled bottles were placed into the foil overpouch, and the filled and pouched units were stored either upright or inverted for up to 6 months at 40 °C. After storage, the leaching solutions were tested for leached substances using multiple complementary analytical techniques to address volatile, semi-volatile, and non-volatile organic and inorganic extractables as potential leachables.The leaching data generated supported several conclusions, including that (1) the extractables (leachables) profile revealed by a simulating leaching study can qualitatively be correlated with compositional information for materials of construction, (2) the chemical nature of both the extracting medium and the individual extractables (leachables) can markedly affect the resulting profile, and (3) while direct contact between a drug product and a system's material of construction may exacerbate the leaching of substances from that material by the drug product, direct contact is not a prerequisite for migration and leaching to occur.LAY ABSTRACT: The migration of container-related extractables from a model pharmaceutical container-closure system and into simulated drug product solutions was studied, focusing on circumstances relevant to parenteral and ophthalmic drug products. The model system was constructed specifically to address the migration of extractables from labels applied to the outside of the primary container. The study demonstrated that (1) the extractables that do migrate can be correlated to the composition of the materials used to construct the container-closure systems, (2) the extent of migration is affected by the chemical nature of the simulating solutions and the extractables themselves, and (3) even though labels may not be in direct contact with a contained solution, label-related extractables can accumulate as leachables in those solutions.

Extractables characterization for five materials of construction representative of packaging systems used for parenteral and ophthalmic drug products.

Polymeric and elastomeric materials are commonly encountered in medical devices and packaging systems used to manufacture, store, deliver, and/or administer drug products. Characterizing extractables from such materials is a necessary step in establishing their suitability for use in these applications. In this study, five individual materials representative of polymers and elastomers commonly used in packaging systems and devices were extracted under conditions and with solvents that are relevant to parenteral and ophthalmic drug products (PODPs). Extraction methods included elevated temperature sealed vessel extraction, sonication, refluxing, and Soxhlet extraction. Extraction solvents included a low-pH (pH = 2.5) salt mixture, a high-pH (pH = 9.5) phosphate buffer, a 1/1 isopropanol/water mixture, isopropanol, and hexane. The resulting extracts were chemically characterized via spectroscopic and chromatographic means to establish the metal/trace element and organic extractables profiles. Additionally, the test articles themselves were tested for volatile organic substances. The results of this testing established the extractables profiles of the test articles, which are reported herein. Trends in the extractables, and their estimated concentrations, as a function of the extraction and testing methodologies are considered in the context of the use of the test article in medical applications and with respect to establishing best demonstrated practices for extractables profiling of materials used in PODP-related packaging systems and devices. LAY ABSTRACT: Plastic and rubber materials are commonly encountered in medical devices and packaging/delivery systems for drug products. Characterizing the extractables from these materials is an important part of determining that they are suitable for use. In this study, five materials representative of plastics and rubbers used in packaging and medical devices were extracted by several means, and the extracts were analytically characterized to establish each material's profile of extracted organic compounds and trace element/metals. This information was utilized to make generalizations about the appropriateness of the test methods and the appropriate use of the test materials.

Validation using sensitivity and target transform factor analyses of neural network models for classifying bacteria from mass spectra.

Temperature constrained cascade correlation networks (TCCCNs) are computational neural networks that configure their own architecture, train rapidly, and give reproducible prediction results. TCCCN classification models were built using the Latin-partition method for five classes of pathogenic bacteria. Neural networks are problematic in that the relationships among the inputs (i.e., mass spectra) and the outputs (i.e., the bacterial identities) are not apparent. In this study, neural network models were constructed that successfully classified the targeted bacteria and the classification model was validated using sensitivity and target transformation factor analysis (TTFA). Without validation of the classification model, it is impossible to ascertain whether the bacteria are classified by peaks in the mass spectrum that have no causal relationships with the bacteria, but instead randomly correlate with the bacterial classes. Multiple single output network models did not offer any benefits when compared to single network models that had multiple outputs. A multiple output TCCCN model achieved classification accuracies of 96 +/- 2% and exhibited improved performance over multiple single output TCCCN models. Chemical ionization mass spectra were obtained from in situ thermal hydrolysis methylation of freeze-dried bacteria. Mass spectral peaks that pertain to the neural network classification model of the pathogenic bacterial classes were obtained by sensitivity analysis. A significant number of mass spectral peaks that had high sensitivity corresponded to known biomarkers, which is the first time that the significant peaks used by a neural network model to classify mass spectra have been divulged. Furthermore, TTFA furnishes a useful visual target as to which peaks in the mass spectrum correlate with the bacterial identities.