Microflow Imaging Analyses Reflect Mechanisms of Aggregate Formation: Comparing Protein Particle Data Sets Using the Kullback-Leibler Divergence.

Subvisible particles in therapeutic protein formulations are an increasing manufacturing and regulatory concern because of their potential to cause adverse immune responses. Flow imaging microscopy is used extensively to detect subvisible particles and investigate product deviations, typically by comparing imaging data using histograms of particle descriptors. Such an approach discards much information and requires effort to interpret differences, which is problematic when comparing many data sets. We propose to compare imaging data using the Kullback-Leibler divergence, an information theoretic measure of the difference of distributions (Kullback S, Leibler RA. 1951. Ann Math Stat. 22:79-86). We use the divergence to generate scatter plots representing the similarity between data sets and to classify new data into previously determined categories. Our approach is multidimensional, automated, and less biased than traditional techniques. We demonstrate the method with FlowCAM® imagery of protein aggregates acquired from monoclonal antibody samples subjected to different stresses. The method succeeds in classifying aggregated samples by stress condition and, once trained, is able to identify the stress that caused aggregate formation in new samples. In addition to potentially detecting subtle incipient manufacturing faults, the method may have applications to verification of product uniformity after manufacturing changes, identification of counterfeit products, and development of closely matching bio-similar products.

High throughput prediction of the long-term stability of pharmaceutical macromolecules from short-term multi-instrument spectroscopic data.

Changes in the measurements of a macromolecular biopharmaceutical's physical form are often used to predict changes in the drug's long-term stability. These can in turn be used as important markers of changes to a drug's efficacy and safety. Such stability estimates traditionally require human judgment and are frequently tentative. We introduce methods for developing mathematical models that predict a drug's long-term storage stability profile from measurements of short-term physical form and behavior. We measured the long-term (2 year) chemical and colloidal stability of Granulocyte Colony Stimulating Factor (GCSF) in 16 different liquid formulations. Shortly after formulations were placed on stability, we also employed various spectroscopic techniques to characterize the short-term thermal unfolding response of GCSF in the 16 formulations. The short-term data were processed using several data reduction methods, including reduction to spectra at low temperature, to melt curves, and to transition temperatures. Least squares fitting was used to predict the long-term stability measurements from the reduced short-term spectroscopic measurements. On the basis of the cross-validation and a permutation test, many of the long-term stability predictions have less than 1% probability of occurring by chance.

An improved methodology for multidimensional high-throughput preformulation characterization of protein conformational stability.

The empirical phase diagram (EPD) technique is a vector-based multidimensional analysis method for summarizing large data sets from a variety of biophysical techniques. It can be used to provide comprehensive preformulation characterization of a macromolecule's higher-order structural integrity and conformational stability. In its most common mode, it represents a type of stimulus-response diagram using environmental variables such as temperature, pH, and ionic strength as the stimulus, with alterations in macromolecular structure being the response. Until now, EPD analysis has not been available in a high-throughput mode because of the large number of experimental techniques and environmental stressor/stabilizer variables typically employed. A new instrument has been developed that combines circular dichroism, ultraviolet absorbance, fluorescence spectroscopy, and light scattering in a single unit with a six-position, temperature-controlled cuvette turret. Using this multifunctional instrument and a new software system, we have generated EPDs for four model proteins. Results confirm the reproducibility of the apparent phase boundaries and protein behavior within the boundaries. This new approach permits two EPDs to be generated per day using only 0.5 mg of protein per EPD. Thus, the new methodology generates reproducible EPDs in high-throughput mode and represents the next step in making such determinations more routine.

Multidimensional methods for the formulation of biopharmaceuticals and vaccines.

Determining and preserving the higher order structural integrity and conformational stability of proteins, plasmid DNA, and macromolecular complexes such as viruses, virus-like particles, and adjuvanted antigens are often a significant barrier to the successful stabilization and formulation of biopharmaceutical drugs and vaccines. These properties typically must be investigated with multiple lower resolution experimental methods because each technique monitors only a narrow aspect of the overall conformational state of a macromolecular system. This review describes the use of empirical phase diagrams (EPDs) to combine large amounts of data from multiple high-throughput instruments and construct a map of a target macromolecule's physical state as a function of temperature, solvent conditions, and other stress variables. We present a tutorial on the mathematical methodology, an overview of some of the experimental methods typically used, and examples of some of the previous major formulation applications. We also explore novel applications of EPDs including potential new mathematical approaches as well as possible new biopharmaceutical applications such as analytical comparability, chemical stability, and protein dynamics.