Applications and challenges in designing VHH-based bispecific antibodies: leveraging machine learning solutions

Figures & data

Figure 1. Schematic representation of a conventional antibody vs a camelid-derived heavy chain-only antibody. (A) Conventional antibodies are composed of two heterodimeric chains, two identical heavy chain fragments consisting of the constant heavy domains CH1-CH3 and the antigen binding variable heavy domain (highlighted in pink) and two identical light chains composed of a constant light-chain domain and a variable light chain domain (highlighted in blue). (B) HCAb antibodies are composed of two identical heavy chain-only fragments that lack a CH1 domain and only have a single variable heavy domain (VHH). (C) Ribbon representation of the crystal structure of a VHH domain (PDB: 1I3V) showing the complementary-determining loops, CDR1 in green, CDR2 in magenta and CDR3 in orange. Framework regions are shown in gray. 3D structure created using Pymol.

Representation of human and llama antibodies using oval shapes for domains connected together by lines into a Y-shape. One of the llama oval shapes has a cartoon associated showing the structure of the domain in three dimensions.

Figure 2. Structural representation of framework 2 residues in human VH and camelid VHHs. Ribbon representation of the crystal structure of (A) human VH (PDB: 7OBF) and (B) camelid-derived VHH (PDB: 1JTT) highlighting the framework 2 positions for each domain. Position 37: Human Valine, Camelid Phenylalanine (red), Position 44: Human Glycine, Camelid Glutamic acid (blue), position 45: Human Leucine, Camelid Arginine (yellow) and position 47: Human Tryptophan, Camelid Glycine (cyan); Kabat numbering. The interaction position of the human variable light chain with the human variable heavy chain is represented by the dotted line. The complementary-determining loops, CDR1 in green, CDR2 in magenta and CDR3 in orange. Framework regions are shown in gray. 3D structure created using Pymol.

A cartoon representation of human and llama heavy chain domains with arrows and lines representing the structure of each domain. Spheres coloured in blue, red, yellow, and cyan represent amino acids in three-dimensional space.

Figure 3. Overview of VHH antibody discovery methods. VHH antibodies are typically sourced from either alpacas or llamas after immunization with an antigen of choice. An alternative source can be a naive library from llamas or alpacas, or a synthetic library design using a combination of sequence and structural information. Constructed libraries can be interrogated by phage display and combined with a machine learning approach to identify key attributes for affinity, developability and function.

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A four section picture with cartoon llama shapes. One llama has a syringe above it to represent an immunized llama library. One llama has nothing above it to represent a naive llama antibody library, and a computer screen icon has protein sequences next to it, representing a semi- or synthetic antibody library. To the right of the cartoon llama and computer is a cartoon representation of a phage in rectangular shapes with small, colored balls on curving lines to represent llama antibodies. To the right of this image is a computer icon representing machine learning and to the right of the computer icon is llama antibodies represented by using oval shapes for domains connected together by lines into a Y-shape. Below the antibodies are two straight lines representing a graph with waving lines in the center of the graph representing data.

Figure 4. Targeting tumor cells co-expressing two different receptors at the cell surface (left) not co-expressed on normal cells with a multispecific VHH, thereby minimizing antibody engagement against healthy cells.

A two section image showing how an antibody can bind to a tumor cell and T-cell at the same time. Gray shapes with two small lines projected outwards represent the cell membrane. Embedded in this membrane are colored rectangular boxes connected by thick lines. These structures represent the cell surface receptors responsible for tumor growth. On the left is an antibody represented by oval shapes connected by a wavy line, and the antibody is touching the t-cell and tumor cell at the same time. On the right, there is only one tumor cell with fewer receptors, and the antibody does not bind to the cell due to the low level of receptors.

Figure 5. Topological representation of examples of clinically relevant VHH-based therapeutic bispecifics.

A two section picture representing different types of antibody formats. On the left-hand side are five images one above the other. These images contain colored oval shapes representing llama or human antibody domains connected by wavy lines. On the right hand side of the image are three more colored oval shapes representing llama or human antibody domains connected by wavy lines. Below are two images that represent antibodies using oval shapes for domains connected together by lines into a Y-shape.

Table 1. Preclinical and clinical stage VHH-based bispecific molecules. All data correct as of 1st September 2023.

BsAb (Sponser)	Targets	Half life extension format	Indication	Status	Sequence (when available from REF^83)
Gefurulimab (ALXN-1720) Alexion/AZ	C5	HSA	Dermatomyositis Proteinuria Myasthenia gravis	Phase 3 NCT05556096	EVQLVESGGGLVKPGGSLRLSCAASGRPVSNYAAAWFRQAPGKEREFVSAINWQKTATYADSVKGRFTISRDNAKNSLYLQMNSLRAEDTA VYYCAAVFRVVAPKTQYDYDYWGQGTLVTVSSGGGGAGGGGAGGGGSEVQLVESGGGLVQPGGSLRLSCAASGRAHSDYAMAWFRQA PGQEREFVAGIGWSGGDTLYADSVRGRFTNSRDNSKNTLYLQMNSLRAEDTAVYYCAARQGQYIYSSMRSDSYDYWGQGTLVTVSS
KN046 Alphamab Oncology	PD-L1 × CTLA4	wt IgG1-Fc	Pancreatic cancer	Phase 3 NCT05149326NCT03733951	QVQLVESGGGLVQPGGSLRLSCAASGKMSSRRCMAWFRQAPGKERERVAKLLTTSGSTYLADSVKGRFTISRDNSKNTVYLQMNSLRAEDT AVYYCAADSFEDPTCTLVTSSGAFQYWGQGTLVTVSSGAPQVQLVESGGGLVQPGGSLRLSCAASGYIYSAYCMGWFRQAPGKGLEGV AAIYIGGGSTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCAADVIPTETCLGGSWSGPFGYWGQGTLVTVSSGSEPKSSDK THTCPPCPAPELLGGPSVFLFPPKPKDTLMISRTPEVTCVVVDVSHEDPEVKFNWYVDGVEVHNAKTKPREEQYNSTYRVVSVLTVLHQDWL NGKEYKCKVSNKALPAPIEKTISKAKGQPREPQVYTLPPSRDELTKNQVSLTCLVKGFYPSDIAVEWESNGQPENNYKTTPPVLDSDG SFFLYSKLTVDKSRWQQGNVFSCSVMHEALHNHYTQKSLSLSPGK
Solenokimab (M1095/ALX0761) Avillion	IL17-A x IL17F	HSA	Moderate-to-severe psoriasis	Phase 2 NCT03384745	DVQLVESGGGLVQPGGSLRLSCAASGRTFSSYVVGWFRQAPGKEREFIGAISGSGESIYYAVSEKGRFTISRDNSKNTLYLQMNSLRPEDTAVYYCTA DQEFGYLRFGRSEYWGQGTLVTVSSGGGGSGGGSEVQLVESGGGLVQPGNSLRLSCAASGFTFSSFGMSWVRQAPGKGLEWVSSISGSGSDTL YADSVKGRFTISRDNAKTTLYLQMNSLRPEDTAVYYCTIGGSLSRSSQGTLVTVSSGGGGSGGGSEVQLVESGGGLVQPGGSLRLSCAASGRTYD AMGWLRQAPGKEREFVAAISGSGDDTYYADSVKGRFTISRDNSKNTLYLQMNSLRPEDTAVYYCATRRGLYYVWDANDYENWGQGTLVTVSS
Enristomig (INBRX-105) Inhibrix	PD-L1 × 4-1BB	IgG1-Fc ΔGlu255, ΔLeu 256, ΔLeu257	Solid tumors (HNC)	Phase 2 NCT03809624	EVQLLESGGGEVQPGGSLRLSCAASGGIFAIKPISWYRQAPGKQREWVSTTTSSGATNYAESVKGRFTISRDNAKNTLYLQMSSLRAEDTAVYYCNV FEYWGQGTLVTVKPGGSGGSEVQLLESGGGEVQPGGSLRLSCAASGFSFSINAMGWYRQAPGKRREFVAAIESGRNTVYAESVKGRFTISRDNA KNTVYLQMSSLRAEDTAVYYCGLLKGNRVVSPSVAYWGQGTLVTVKPGGGGDKTHTCPPCPAPGGPSVFLFPPKPKDTLMISRTPEVTCVVVDV SHEDPEVKFNWYVDGVEVHNAKTKPREEQYNSTYRVVSVLTVLHQDWLNGKEYKCKVSNKALPAPIEKTISKAKGQPREPQVYTLPPSR DELTKNQVSLTCLVKGFYPSDIAVEWESNGQPENNYKTTPPVLDSDGSFFLYSKLTVDKSRWQQGNVFSCSVMHEALHNHYTQKSLSLSPGK
LAVA-1207 LAVA Therapeutics	Vδ2-TCR x PMSA	Human IgG1-Fc, modified hinge	Metastatic castration-resistant prostate cancer	Phase 2 NCT05369000
Tarperprumig (ALXN-1820) Alexion/AZ	Properdin	HSA	Sickle cell disease	Phase 2 NCT04631562	QVQLVESGGGLVKPGGSLRLSCAASGRPVSNYAAAWFRQAPGKEREFVSAINWQKTATYADSVKGRFTISRDNAKNSLYLQMNSLRAEDTAVYY CAAVFRVVAPKTQYDYDYWGQGTLVTVSSGGGGEGGGGEGGGGEVQLLESGGGLVQPGGSLRLSCAASGRISSIIHMAWFRQAPGKEREL VSEISRVGTTVYADSVKGRFTISRDNSKNTLYLQMNSLKPEDTAVYYCNALQYEKHGGADYWGQGTLVTVSS
SAR442970 Sanofi	TNFa x OX40L	HSA	Hidradenitis suppurativa	Phase 2 ACT16852
PM1032 Biotheus	CLDN18.2 × 4-1BB	Fc(LALA)	Solid tumours	Phase 2a NCT05839106
HPN536 Harpoon Therapeutics	CD3 × MSLN	HSA	Advanced cancers associated with mesothelin expression.	Phase 1/2a NCT03872206
HPN328 Harpoon Therapeutics	CD3 × DLL3	HSA	Metastatic small cell lung and other neuroendocrine cancers	Phase 1/2 NCT04471727
SAR444200	GPC3 × TCRab	none	Solid tumours	Phase 1/2 NCT05450562
SAR443765 Sanofi	TSLP x IL-13	HSA	Asthma	Phase 1b NCT05366764
BI836880 Boehringer Ingelheim	VEGF x Ang2	HSA	Solid tumors	Phase 1 NCT02674152 NCT02689505
HPN217 Harpoon Therapeutics	CD3 × BCMA	HSA	Hematological R/R MM	Phase 1 NCT04184050
Cancer Hospital Affiliate to Zhengzhou University and Henan Cancer Hospital, Henan, China	CD19 × CD20	CAR-T	Hematological R/R B-cell lymphoma	Phase 1 NCT03881761
IBI333 Innovent Biologics	VEGF-A x VEGF-C	wt IgG1-Fc	Neovascular age-related macular degeneration	Phase 1 CTR20222674
LAVA-1223 LAVA Therapeutics	EGFR x Vγ9 Vδ2	Human IgG1-Fc, modified hinge	EGFR+ solid tumours	Phase 1
PM1003 Biotheus	PD-L1 × 4-1BB	Fc(LALA)	Solid tumours	Phase 1 ChiCTR2100052887
PM1022 Biotheus	PD-L1 ×TIGIT	Fc(LALA)	Solid tumours	Phase 1 NCT05867771
RC1416 Regenecore	IL-4Ra x IL-5	IgG	Asthma	Phase 1 NCT06067490
GTB-3650 Second generation camelid GT Biopharma	CD16 × IL15 × CD33	N/A	Hematological Leukemia	IND-Enabling GMP manufacturing

Figure 6. Overview of machine learning tools for antibodies and VHHs. (A) Tools for structural prediction, encompassing the ImmuneBuilder suite, IgFold, and Nanonet. (B) Language model-based sequence optimization with AbLang, AntiBERTy, IgLM, Sapiens and NanoBERT, alongside Bayesian Optimization techniques like AntBO and LaMBO2. Additionally, humanization tools include Hu-Mab, PLAN and CUMAb.

A four section picture with curved arrows and lines curving into a barrel-like structure representing the three-dimensional structure of a llama heavy chain antibody domain. To the right of this image is a gray rectangular box with letters connected to the gray box by straight lines representing amino acids. To the right of this image is a three-dimensional graph represented by three gray surfaces at right-angles to each other. In the center of this graph is a blue mountain-like structure with yellow peaks. This image represents how Bayesian optimization works. To the right of this graph is a cartoon image of a llama, with an arrow pointing to a cartoon image of a male human figure. This image represents the humanization process.

Table 2. Summary of language models applicable to antibodies and VHHs.

Name	Domain	Data	Base Model Type	Reference	Notes
IgLM	Antibody	OAS (558 M subset)	GPT2 (transformer; decoder only)	Generative language modeling for antibody design	Conditional predictions on species
AbLang	Antibody	OAS (14 M heavy chains and 187K light chains)	BERT (transformer; encoder-only)	AbLang: an antibody language model for completing antibody sequences	Separate models for heavy and light chain
AntiBERTy	Antibody	OAS (558 M subset)	BERT (transformer; encoder-only)	Deciphering antibody affinity maturation with language models and weakly supervised learning
nanoBERT	VHH	INDI (10 M VHH chains)	BERT (transformer; encoder-only)	nanoBERT: a deep learning model for gene agnostic navigation of the nanobody mutational space
Sapiens (BioPhi)	Antibody	OAS	Deep neural network based on the transformer encoder architecture	BioPhi: A platform for antibody design, humanization, and humanness evaluation based on natural antibody repertoires and deep learning
ProtGPT2	Protein	UniRef50 2021_04	GPT2 (transformer; decoder only)	ProtGPT2 is a deep unsupervised language model for protein design
ProGen2	Protein	UniRef90 & BFD30	BERT (transformer; encoder-only)	ProGen2: Exploring the Boundaries of Protein Language Models
ESM-2	Protein	UniRef50 2021_04	RoBERTa (transformer; encoder-only)	Language models generalize beyond natural proteins
ProtTrans/Prot-T5	Protein	UniRef50 2021_04 (best)	T5 (transformer; encoder-decoder) (best)	ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning	A number of models are trained on several datasets