Lyophilizer Leak Rate Testing - An Industry Survey and Best Practice Recommendation.

The vacuum integrity of freeze dryers is critical for attaining adequate process control and maintaining confidence in sterility assurance which is key for the manufacture of sterile pharmaceutical products. Although discussions on the topic have been published, there is no industry standard established that is based on empirical data or that has a justifiable scientific rationale. This article provides a review of the scientific literature in the public domain and most importantly, a perspective from 14 Pharmaceutical companies on the leak rate specifications commonly used in industry. Using this information we recommend a best practice for the lyophilizer leak rate test which includes detailing necessary preparation activities following Steam-In-Place (SIP) sterilization, defining a period of stabilization to eliminate pressure and temperature fluctuations and details of the test conditions and the test period. We conclude that for routine manufacturing practice the operational leak rate should not exceed 20 µbar L/s and we provide additional guidance for large volume and older lyophilisation equipment.

Evaluation of the Robustness of A Novel NIR-based Technique to Measure the Residual Moisture In Freeze-dried Products.

(Bio)pharmaceutical products freeze-dried in vials must meet stringent quality specifications: among these, the residual moisture (RM) is crucial. The most common techniques adopted for measuring the RM are destructive, e.g. Karl Fisher titration, thus few samples from each batch are tested. Being a high intra-batch variability an intrinsic feature of batch freeze-drying, a high number of samples needs to be tested to get a representative measurement. Near-Infrared (NIR) spectroscopy was extensively applied in the past as a non-invasive method to quantify the RM. In this paper, an accurate Partial Least Square (PLS) model was developed and calibrated with a single product, focusing on a small but significative wavelength range of NIR spectra (model SR), characteristic of the water and not of the product. The salient feature of this approach is that the model SR appears to provide fairly accurate estimates with the same product but at a higher concentration, with other excipients and in presence of an amino acid at high concentration, without requiring any additional calibration with KF analysis, as in previous techniques; the irrelevance of the vial shape was also shown. This approach was compared to a simpler one, based on a single-variable linear regression, and to more complex one, using a wider wavelength range or calibrating the PLS model with several products. Model SR definitely ended up as the most accurate, and it appeared to have a great potential as a robust model, suitable also for products that were not involved in the calibration step.

Application of Near-Infrared Spectroscopy to statistical control in freeze-drying processes.

Batch freeze-drying of pharmaceutical products in vials may result in a high degree of intra-batch variability due to several reasons, e.g. non uniform heating rate in the drying chamber. Therefore, product quality in the final product has to be checked in a statistically significant number of samples, in particular in the stage of process development. Here, Fourier-Transform Near-Infrared Spectroscopy is proposed as a fast, non-destructive technique for an off-line Statistical Quality Control application. At first, results obtained in a batch where product features are satisfactory are used to identify a target quality threshold. Then, a statistical controller is developed in such a way that in a production run it is possible to quickly check if product quality exceeds the desired threshold or not. Two approaches based on multivariate analysis are presented: one employs the Hotelling T2 and Mahalanobis statistics to calculate control charts, the other is an application of Partial Least Squares for discriminant analysis (PLS-DA). Control charts and PLS-DA were trained with samples obtained in a run where sucrose solution was processed and validated in other runs where the final product was known to have the desired qualitative characteristics or not. Overall, out-of-specification samples can be predicted by control charts and PLS-DA with 99% and 98% accuracy respectively. PLS-DA was shown to be able to better identify samples correctly processed, while the control charts where more accurate to identify vials where something went wrong. Focusing on residual moisture of the final product, all samples where it was higher than the target value were always correctly identified.