ABSTRACT
Chromatographic Performance from Protein Structure Data: A Case Study. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 256 8.6.1 Steric Mass Action Formalism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257 8.6.2 Chromatographic Transport Models . . . . . . . . . . . . . . . . . . . . . . . . . . 258 8.6.3 Protein Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259 8.6.4 QSPR Model Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 260 8.6.5 The Multiscale Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 260 8.6.6 Summary of Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 262
8.7 QSPR as a Bioprocess Development Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263 8.8 Advances in QSPR Modeling Techniques and Future Directions . . . . 265
8.8.1 Physically Interpretable Descriptors . . . . . . . . . . . . . . . . . . . . . . . . . . 265
SHUKLA: “dk3347_c008” — 2006/5/24 — 16:24 — page 246 — #2
. . . . . . . . . 267 8.9 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 268 Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 268 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269
The a priori prediction of chromatographic behavior directly from protein structure data has been a long-standing goal in the separations field. The availability of predictive models can decrease the uncertainty associated with most chromatographic development work, reducing the time needed to bring biological drug products to the market. Furthermore, such investigations will also enable us to gain insights into the factors influencing the affinity and selectivity of biomolecules in different chromatographic systems. This information can in turn be employed to design more efficient processes, and perhaps even enable the development of tailored chromatographic resin materials with unique selectivities for specific separation applications.
