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ABSTRACT
The growing need for biologics to be administered subcutaneously and ocularly, coupled with certain indications requiring high doses, has resulted in an increase in drug substance (DS) and drug product (DP) protein concentrations. With this increase, more emphasis must be placed on identifying critical physico-chemical liabilities during drug development, including protein aggregation, precipitation, opalescence, particle formation, and high viscosity. Depending on the molecule, liabilities, and administration route, different formulation strategies can be used to overcome these challenges. However, due to the high material requirements, identifying optimal conditions can be slow, costly, and often prevent therapeutics from moving rapidly into the clinic/market. In order to accelerate and derisk development, new experimental and in-silico methods have emerged that can predict high concentration liabilities. Here, we review the challenges in developing high concentration formulations, the advances that have been made in establishing low mass and high-throughput predictive analytics, and advances in in-silico tools and algorithms aimed at identifying risks and understanding high concentration protein behavior.
Acknowledgments
The authors would like to thank Jasper Lin and Trevor Swartz for their careful review and comments.
Disclosure statement
No potential conflict of interest was reported by the authors.
Acronyms
| A3D | = | AGGRESCAN3D |
| AC-SINS | = | affinity-capture SINS |
| API | = | active pharmaceutical ingredient |
| AS | = | ammonium sulfate |
| AUC | = | analytical ultracentrifugation |
| AuNP | = | gold nanoparticles |
| BLI | = | biolayer interferometry |
| CDR | = | Complementarity-determining regions |
| CFD | = | computational fluid dynamics |
| CG | = | coarse grained |
| CIC | = | cross-interaction chromatography |
| CS-SINS | = | charge stabilized SINS |
| DI | = | developability index |
| DLS | = | dynamic light scattering |
| DS | = | drug substance |
| DP | = | drug product |
| EN | = | elastic network |
| Fv | = | Fragment variable |
| kD | = | diffusion interaction parameter |
| MALS | = | multi-angle light scattering |
| MAM | = | multi-attribute monitoring |
| MD | = | molecular dynamics |
| ML | = | machine learning |
| MP | = | mass photometry |
| MS | = | mass spectrometry |
| PEG | = | polyethylene glycol |
| PEG-SINS | = | PEG-stabilized SINS |
| PLL | = | poly-L-lysine |
| PPI | = | Protein-protein interactions |
| QSAR | = | quantitative structure-activity relationship |
| SAP | = | spatial aggregation propensity |
| SASA | = | solvent accessible surface area |
| SCM | = | spatial charge map |
| SEC | = | size exclusion chromatography |
| SI-BLI | = | self-interaction biolayer interferometry |
| SIC | = | self-interaction chromatography |
| SINS | = | self-interaction nanoparticle spectroscopy |
| SLS | = | static light scattering |
| SMAC | = | standup mono-layer adsorption chromatography |
| SVM | = | support vector machine |
| SvP | = | subvisible particle |
| TAP | = | therapeutic antibody profiler |
| TFF | = | tangential flow filtration |
| Tg’ | = | glass transition temperature |
| UFDF | = | ultrafiltration diafiltration |
| UHMR | = | ultra-high-mass-range |
Correction Statement
This article has been corrected with minor changes. These changes do not impact the academic content of the article.