A library approach for the de novo high-throughput isolation of humanized VHH domains with favorable developability properties following camelid immunization

Figures & data

Figure 1. Overview of the library construction process.

(a) The VHH CDR3 diversity was amplified from cDNA derived from PBMCs of an immunized llama and grafted onto humanized and sequence optimized sdAb backbones with artificially diversified CDR1 and CDR2 regions. (b) Library design for the humanized backbone libraries. The framework (FR) regions were derived from human IGHV3-23 × 1. Two humanized libraries were generated with different hallmark (HM) signatures, referred to as FERF and VGLW libraries. Residues used in combinatorial diversification of CDR1 and CDR2 are given in cyan and bold. Amino acids observed with frequencies of more than 4% in NGS data sets of WT llama repertoires, which were eliminated from the final design due to in silico developability and diversity aspects are indicated in gray. Figure generated using www.biorender.com and PyMOL software version 2.3.0.

Figure 2. Amino acid distribution of CDR1 and CDR2 of non-immunized llamas compared with artificially designed diversities and observed compositions in humanized sdAb libraries.

Corresponding CDR residues of human germline IGHV3-23 × 1 given below. Amino acid distributions were observed by NGS.

Figure 3. Yeast surface display enables the isolation of (rh) NKp46-targeting sdAbs from both humanized libraries.

(a) FACS-based enrichment of the FERF, VGLW, and the WT libraries by applying a two-dimensional sorting strategy for simultaneous detection of antigen binding and full-length sdAb display. Two consecutive rounds of selection (Round 1 and Round 2) were conducted for each library. (b) Similarity of CDR3 sequences as analyzed by UMAP dimensionality reduction. Each dot represents the CDR3 of an individual VHH sequence. Dots are colored based on the library origin (FERF shown in blue, VGLW in green, and WT given in gray).

Table 1. CDR1–3 and hallmark (HM) sequences of immunized llama WT or humanized VHH libraries (FERF or VGLW) that were selected for production and experimental profiling (shown in Table 3). Residues that are different from the most potent sequences within each CDR3 sequence cluster are shown in orange; susceptible chemical liability and post-translational modification motifs (DG, NG, M, unpaired C, and NXS/T) are indicated as red boxes.

Table 2. In silico developability assessment of VHHs obtained from different library approaches. sdAbs were analyzed for their sequence identity compared to the most similar human germline (MOST SIMILAR GERMLINE) either based on the entire variable chain region (SEQ-ID) or the framework region only (SEQ-ID FR), as well as for their total number of specific chemical liabilities and PTMs, i.e., non-canonical cysteines, methionine oxidations, asparagine deamidations or aspartate isomerizations, and N-glycosylations, in structurally exposed CDR residues as derived from automatically generated models. As calculated physicochemical developability descriptors (IN SILICO PHYSCHEM), structure-based pI values (pIfv 3D), the AggScores of the entire variable regions, and the AggScores of CDR regions only (CDR AggScore), as well as the positive patch energy of the CDRs (CDR positive patch energy), are shown. The complementing color coding indicates scores within one standard deviation from a benchmark mean (dataset of 77 biotherapeutics approved for human application) as green, scores above one standard deviation as yellow, and scores above two standard deviations as red. For the AggScores, this classification was slightly adjusted based on correlation analyses to internal experimental HIC data.

Table 3. Analytical and early developability data of one-armed constructs organized by cluster and affinity, including SEC purity, HIC retention time, mean Tonset, and dissociation constant measured via BLI.

ID	SEC Purity [%]	HIC tR [min]	Mean Tonset [°C]	KD [nM]
WT.cluster1.1	96.3	5.8	58.8	1.5
FERF.cluster1.2	100	4.9	57.6	4.1
WT.cluster1.3	98.8	5.5	58.1	6.9
FERF.cluster1.4	98.8	5.5	58.4	7.7
FERF.cluster1.5	100	5.4	58.1	12.0
WT.cluster1.6	92.2	5.6	57.8	12.9
FERF.cluster1.7	99.0	5.3	57.2	19.8
WT.cluster1.8	97.9	5.0	57.9	45.0
VGLW.cluster1.9	96.7	6.6	57.4	non binding
FERF.cluster2.10	100	4.9	57.5	2.9
FERF.cluster2.11	94.5	4.8	57.7	4.4
FERF.cluster2.12	96.6	5.6	58.7	6.4
WT.cluster2.13	97.8	5.3	58.2	7.4
VGLW.cluster2.14	98.9	7.3	57.5	7.9
FERF.cluster2.15	99.2	4.8	58.0	12.0
FERF.cluster3.16	100	4.9	58.0	0.7
FERF.cluster3.17	100	4.8	58.1	2.2
FERF.cluster3.18	98.8	5.2	58.2	2.8
FERF.cluster4.21	98.4	4.9	57.5	5.0
FERF.cluster4.22	100	5.3	57.9	6.1
VGLW.cluster4.23	97.0	6.1	57.8	non binding
WT.cluster5.25	100	4.6	57.6	0.5
FERF.cluster5.26	96.2	4.9	57.4	3.6
FERF.cluster5.27	98.4	4.5	57.1	9.3
FERF.cluster6.28	96.9	5.3	58.6	3.4
FERF.cluster6.29	99.0	5.5	57.5	5.0
WT.cluster7.31	9.7	4.6	59.5	20.4
WT.cluster8.33	100	5.3	58.5	0.5
WT.cluster9.35	95.7	5.0	52.5	0.1
WT.cluster10.36	99.4	5.1	58.0	0.7
WT.cluster11.37	95.6	5.4	58.5	1.7
WT.cluster12.38	97.1	5.1	56.6	1.9
WT.cluster13.39	99.1	5.0	57.5	2.4
WT.cluster14.40	99.3	5.1	58.0	3.1
FERF.cluster15.41	95.6	5.2	58.4	2.2
VGLW.cluster16.42	96.8	7.2	58.8	722.8
WT.cluster17.43	97.8	5.3	51.6	non binding
WT.cluster18.44	100	5.5	57.9	3.8
VGLW.cluster20.46	84.2	5.5	57.6	non binding
VGLW.cluster21.47	92.9	6.6	53.0	non binding
WT.cluster22.48	100	5.1	56.3	non binding
WT.cluster27.50	100	4.6	56.0	non binding

Figure 4. Binding capacities of humanized sdAbs and WT VHHs reformatted as one-armed SEEDbodies as determined by BLI.

(a) Binding kinetics of reformatted sdAbs belonging to different CDR3 sequence clusters. sdAbs originating from humanized FERF library design are represented as dots, clones derived from humanized VGLW approach are shown as triangles, and the VHHs obtained from WT library are given as squares. (b)–(d) Affinity determination by BLI is exemplarily shown for individual clones derived from the different library approaches (WT, FERF, and VGLW). sdAb-derived SEEDbodies were loaded onto sensor tips. After sensor rinsing, antigen binding was conducted at different concentrations for 300 s, followed by a dissociation step in KB buffer for 300 s.

Table 4. Production yields and analytical purity of nominated sequences and their ability to function as NKCE with humanized cetuximab (ctx) on the GA chain, as indicated by the EC₅₀ for killing of target cells. Evaluation of AC-SINC is included for the one-armed constructs.

one armed (hu)VHH-SEEDbodies				VHH x Ctx SEEDbodies
ID	Yield o.a. [mg/L]	SEC Purity [%]	AC-SINS	SEC Purity [%]	EC50 [nM]
FERF.cluster1.4	193.2	100	0.1	93.0	no killing
FERF.cluster1.7	147.9	100	−0.2	95.7	no killing
WT.cluster1.3	122.3	100	−0.4	96.1	no killing
FERF.cluster2.15	183.2	100	0.0	97.6	0.05
WT.cluster2.13	146.8	100	−0.5	97.9	4.07
VGLW.cluster2.14	98.4	100	−0.3	97.1	no killing
FERF.cluster2.10	131.3	100	−0.5	96.0	0.06
FERF.cluster2.11	133.4	100	−0.4	94.6	0.05
WT.cluster5.25	193.0	100	−0.4	88.8	0.03
FERF.cluster5.26	221.1	97.6	−0.5	98.0	4.65

Figure 5. NK cell engagers (NKCEs) harboring de novo humanized sdAbs from the FERF design trigger significant NK cell mediated killing of EGFR overexpressing tumor cells.

(a) Schematic depiction of NK cell redirection by exploiting an Fc-silenced SEEDbody harboring a de novo humanized sdAb targeting NKp46 on the NK cell and a Fab arm derived from cetuximab (ctx) for binding to EGFR on A431 cells. (b)–(d) Fluorescence-based NK cell killing assay of A431 target cells using freshly isolated NK cells from PBMCs of human healthy donors at an E:T ratio of 5:1. Respective Fc-silenced SEEDbodies were tested at increasing concentrations. Tumor cell lysis was normalized to Cetuximab at 50 nM. Killing capacities shown for NKCEs harboring different sdAbs derived from the FERF (blue) and VGLW (green) de novo library designs and obtained from the WT library (gray). An EGFR-targeting one-armed SEEDbody with an effector-negative Fc region was exploited as negative control. Mean values ± SEM of eight independent experiments with duplicates are shown.