Ultra scale-down approach to correct dispersive and retentive effects in small-scale columns when predicting larger scale elution profiles.

Ultra scale-down approaches represent valuable methods for chromatography development work in the biopharmaceutical sector, but for them to be of value, scale-down mimics must predict large-scale process performance accurately. For example, one application of a scale-down model involves using it to predict large-scale elution profiles correctly with respect to the size of a product peak and its position in a chromatogram relative to contaminants. Predicting large-scale profiles from data generated by small laboratory columns is complicated, however, by differences in dispersion and retention volumes between the two scales of operation. Correcting for these effects would improve the accuracy of the scale-down models when predicting outputs such as eluate volumes at larger scale and thus enable the efficient design and operation of subsequent steps. This paper describes a novel ultra scale-down approach which uses empirical correlations derived from conductivity changes during operation of laboratory and pilot columns to correct chromatographic profiles for the differences in dispersion and retention. The methodology was tested by using 1 mL column data to predict elution profiles of a chimeric monoclonal antibody obtained from Protein A chromatography columns at 3 mL laboratory- and 18.3 L pilot-scale. The predictions were then verified experimentally. Results showed that the empirical corrections enabled accurate estimations of the characteristics of larger-scale elution profiles. These data then provide the justification to adjust small-scale conditions to achieve an eluate volume and product concentration which is consistent with that obtained at large-scale and which can then be used for subsequent ultra scale-down operations.

A decision-support model for evaluating changes in biopharmaceutical manufacturing processes.

A simulation is described that evaluates the impacts of altering bio-manufacturing processes. Modifications designed to improve production levels, times and costs were assessed, including increasing feed volumes/titres, replacing initial downstream stages with packed or expanded bed affinity steps and removing ion exchange steps. Options were evaluated for manufactured product mass, COG, batch times and development costs and timescales. Metrics were combined using multi-attribute-decision-making techniques generating a single assessment metric for each option. The utility of this approach was illustrated by application to an FDA-approved process manufacturing rattlesnake anti-venom (Protherics U.K.). Currently, ovine serum containing anti-venom IgG is purified by precipitation/centrifugation, prior to antibody proteolysis by papain. An ion exchanger removes F(C), before affinity chromatography yields the final anti-venom. An expanded bed affinity column operating with an 80% higher IgG titre, 66% higher feed volume and without the ion exchanger delivered the best multi-attribute-decision-making value, potentially providing the most desirable alternative.

Breast cancer treatment in clinical practice compared to best evidence and practice guidelines.

There is sparse evidence on community practice patterns in treating women with breast cancer. This study compared care of women with breast cancer with evidence from meta-analyses and US National Comprehensive Cancer Network (NCCN) clinical guidelines. Records of 4395 women with breast cancer were abstracted from practices of 19 surgeon oncologists in six specialty practices in the Philadelphia region during 1995-1999. Patients were followed through December 2001. Low-frequency data were obtained on all patients. All other data were from a random sample of 464 women, minimum of 50 patients per practice. Actual care provided was compared to NCCN guidelines and results of meta-analyses. Fewer than half the women received treatments reflecting meta-analysis results or NCCN guidelines, by disease stage/TNM status. Adherence to either standard varied from 0% for LCIS to 87% for stages IIA or IIB node positive. There are multiple interactive reasons for low adherence to guidelines or meta-analyses results, including insufficient health system supports to clinicians, inadequate organisation and delivery systems and ineffective continuing medical education. The paucity of written information from patient records on physician/patient interactions limits the understanding of treatment decisions.