Development of in silico models to predict viscosity and mouse clearance using a comprehensive analytical data set collected on 83 scaffold-consistent monoclonal antibodies

Abstract

Biologic drug discovery pipelines are designed to deliver protein therapeutics that have exquisite functional potency and selectivity while also manifesting biophysical characteristics suitable for manufacturing, storage, and convenient administration to patients. The ability to use computational methods to predict biophysical properties from protein sequence, potentially in combination with high throughput assays, could decrease timelines and increase the success rates for therapeutic developability engineering by eliminating lengthy and expensive cycles of recombinant protein production and testing. To support development of high-quality predictive models for antibody developability, we designed a sequence-diverse panel of 83 effector functionless IgG1 antibodies displaying a range of biophysical properties, produced and formulated each protein under standard platform conditions, and collected a comprehensive package of analytical data, including in vitro assays and in vivo mouse pharmacokinetics. We used this robust training data set to build machine learning classifier models that can predict complex protein behavior from these data and features derived from predicted and/or experimental structures. Our models predict with 87% accuracy whether viscosity at 150 mg/mL is above or below a threshold of 15 centipoise (cP) and with 75% accuracy whether the area under the plasma drug concentration-time curve (AUC_{0-672 h}) in normal mouse is above or below a threshold of 3.9 × 10⁶ h x ng/mL.

Keywords: Developability; high throughput; in vitro assays; mab; machine learning; pharmacokinetics; predictive model; therapeutic antibody; viscosity.

Figure 1.

Pairwise Spearman correlations for a selected set of in vitro assays. See table S3 for the complete set of pairwise correlations.

Figure 2.

Homology modeling pipeline accuracy measured across all 23 experimental crystal structures.

Figure 3.

Deamidation (a) and isomerization (b) rates for each indicated motif. Fractions indicate the # of sites with >/ = 2% modification after 4 weeks at 40°C over total number of CDR sites with coverage in the peptide mapping data.