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

Figures & data

Table 1. Data collected on the antibody panel.

Attribute	Method	[mAb]	Conditions
titer	Octet ProA binding	1 mg/mL	CHO conditioned medium
purified yield	A280	1 mg/mL	mAbSelect SuRe + SP HP
purity	SEC % MP	1 & 70 mg/mL
	nrMCE % MP	1 & 70 mg/mL
Tm	DSF	1 & 70 mg/mL
Tagg	DSF	1 & 70 mg/mL
viscosity	cone-and-plate	70 & 150 mg/L
kD	DLS	2, 6, 10, & 20 mg/mL
zeta potential	DLS	20 mg/mL
molecular weight	nr rpLC-MS	1 mg/mL
aggregation	SEC % HMW	1 & 70 mg/mL	t = 0
		1 & 70 mg/mL	t = 2 wk @ 5, 25, 40°C
degradation	SEC % LMW	1 & 70 mg/mL	t = 4 wk @ 5, 25, 40°C
		1 & 70 mg/mL	t = 6 mo @ 5°C
chemical liabilities	rpLC-MS/MS	70 mg/mL	192 klux-hr visible light
		70 mg/mL	t = 4 wk @ 40°C
colloidal stability	PEG precipitation	1 mg/mL	0–40% w/v PEG
	(NH4)2SO4 precipitation	1 mg/mL	0.5–1.8 M (NH4)2SO4
self-interaction	AC-SINS	0.1 mg/mL	10 mM NaOAc, 150 mM NaCl, pH 5.2
cross interaction	BVP ELISA	33 nM
	membrane prep ELISA	33 nM
hydrophobic interaction	SEPAX chromatography	1 mg/mL
	HIC chromatography	1 mg/mL
charged interaction	heparin chromatography	0.4 mg/mL	5–400 mM NaCl gradient
	PEI ELISA	33 nM
	poly-D-Lys ELISA	33 nM
FcRn binding	FcRn chromatography	0.4 mg/mL	pH 5.5 - pH 8.0 gradient
	FcRn Alphascreen	0.0005–1 mg/mL
clearance	WT mouse cassette dosing		Female BALB/c mice
AUC			2 mg/kg per mAb
Vss			5 mAbs per cassette
3D Fab structure	x-ray crystallography		see Supporting Information

Figure 1. Pairwise Spearman correlations for a selected set of in vitro assays. See table S3 for the complete set of pairwise correlations.

A visual cross-correlation matrix for 20 different assays in which the strength of the correlation is proportional to the size of a circle in the matrix and the direction of the correlation is represented by color on a blue-white-red scale with dark blue = perfect positive correlation, dark red = perfect negative correlation, and white = no correlation. The matrix diagonal (self-correlation) is shown as large blue circles; most other squares show small circles, with a pocket of larger blue circles in assays related to charge (heparin chromatography, zeta potential, poly-D-Lys score, PEI score).

Figure 2. Homology modeling pipeline accuracy measured across all 23 experimental crystal structures.

Nine panels with six colorful curves each that rise from left to right to demonstrate the accuracy of structure prediction for different antibody regions (6 CDRs, the full Fv, VH, and VL). The brown DeepAb curve is generally furthest left in each panel, indicating higher accuracy across all antibody regions.

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.

Two greyscale bar charts showing number of sites with either deamidation or isomerization by CDR sequence motif. Three to four tall bars are clustered to the left in both plots with the rest of the positions at baseline, indicating a small number of sequence motifs account for most of the chemical modifications observed.

Table 2. Spearman rank correlations of the top features in each category that achieve correlations with measured viscosity of magnitude $\ge 0.4$. Structure-based feature names are as defined by the MOE software package. All p-values are $<0.001$.

Feature	Category	Spearman’s Correlation
DLS Interaction Parameter kD (mL/g)	HT attribute	−0.64
AC-SINS λmax (nm)	HT attribute	0.43
AC-SINS Δλmax	HT attribute	0.43
pro_pI_seq	Structure	−0.51
pro_zeta	Structure	−0.51
pro_cdr_net_charge	Structure	−0.51
pro_mobility	Structure	−0.51
pro_app_charge	Structure	−0.50
pro_net_charge	Structure	−0.50
pro_Fv_net_charge	Structure	−0.49
pro_pI_3D	Structure	−0.48
pro_dipole_moment	Structure	−0.47
pro_patch_cdr_neg_n	Structure	0.45

Table 3. Median test metrics of viscosity binary classifiers. One hundred random stratified train-test splits were performed for various feature combinations. Median test metrics of logistic regression models trained with an elastic net regularization penalty applied are reported. HT = high throughput experimental features (AC-SINS, DLS, and PEG precipitation). Structure = 112 structure-based features derived from structural models. AUC = area under the receiver-operator curve. MCC = Matthews correlation coefficient. PLR = positive likelihood ratio (true positive rate/false positive rate). The confusion matrix for the best model (HT + Structure) is as follows: true positives = 40. True negatives = 23. False positives = 5. False negatives = 3. Here, ‘positive’ refers to a molecule with low viscosity (<15 cP @ 150 mg/ml).

Feature Set	Balanced Accuracy	AUC	Precision	Recall	MCC	PLR
HT	0.85	0.92	0.86	0.92	0.72	4.5
Structure	0.75	0.87	0.77	0.85	0.52	2.3
HT + Structure	0.87	0.95	0.87	0.92	0.74	4.5

Table 4. Spearman rank correlations of the top features in each category that achieve correlations with measured PK AUCt of magnitude $\ge 0.4$. Structure-based feature names are as defined by the MOE software package. All p values are $<0.003$.

Feature	Category	Spearman’s Correlation
Heparin Chromatography % Buffer B at Elution Peak	Experimental	−0.48
Heparin Chromatography Conductivity at Elution Peak (mS/cm)	Experimental	−0.45
DSF Tagg Onset, 1 mg/mL (degC)	Experimental	−0.41
pro_cdr_net_charge	Structure	−0.45
pro_pI_seq	Structure	−0.44
pro_pI_3D	Structure	−0.40
pro_Fv_net_charge	Structure	−0.4

Table 5. Median test metrics of PK clearance binary classifiers. One hundred random stratified train-test splits were performed for various feature combinations. Median test metrics of logistic regression models trained with an elastic net regularization penalty applied are reported. Experimental = (heparin chromatography, DLS, zeta potential, ammonium sulfate precipitation, PEG precipitation, BVP, membrane Prep assay, poly-D-Lysine assay, PEI assay, HIC, DSF, SEPAX, and AC-SINS). Structure = 112 structure-based features derived from structural models. AUC = area under the receiver-operator curve. MCC = Matthews correlation coefficient. PLR = positive likelihood ratio (true positive rate/false positive rate). The confusion matrix for the best model (structure) is as follows: true positives = 3. True negatives = 49. False positives = 1. False negatives = 2. Here, ‘positive’ refers to a molecule with high clearance ($\ge$ 3.9 × 10⁶ h x ng/mL).

Feature Set	Balanced Accuracy	AUC	Precision	Recall	MCC	PLR
Experimental	0.75	0.90	0.5	1.0	0.47	7.5
Structure	0.75	0.97	0.67	0.5	0.68	15
Experimental + Structure	0.72	0.93	0.5	0.5	0.43	7.5

Temel DB, Landsman P, Brader ML. Orthogonal methods for characterizing the unfolding of therapeutic monoclonal antibodies: differential scanning calorimetry, isothermal chemical denaturation, and intrinsic fluorescence with concomitant static light scattering. Methods Enzymol. 2016;567:359–89. doi:10.1016/bs.mie.2015.08.029. PMID: 26794361.

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