Agonist antibody discovery: Experimental, computational, and rational engineering approaches

Abstract

Agonist antibodies that activate cellular signaling have emerged as promising therapeutics for treating myriad pathologies. Unfortunately, the discovery of rare antibodies with the desired agonist functions is a major bottleneck during drug development. Nevertheless, there has been important recent progress in discovering and optimizing agonist antibodies against a variety of therapeutic targets that are activated by diverse signaling mechanisms. Herein, we review emerging high-throughput experimental and computational methods for agonist antibody discovery as well as rational molecular engineering methods for optimizing their agonist activity.

Keywords: Activation; Agonist; Antibody; Biologic; Discovery; Engineering; Function-based screening; High throughput; mAb; signaling.

Figure 1.

Overview of approaches for discovering and optimizing agonist antibodies. Affinity-based selections involve first identifying antigen-specific antibodies and subsequently screening them for agonist activity. Activity-based selections directly screen for activity during the antibody discovery process. Computational design approaches involve either predicting lead antibodies with agonist activity or antibody mutants with increased agonist activity. Rational molecular engineering methods involve using different antibody formats, valences, and Fc engineered variants to increase agonist activity.

Figure 2.

Antibodies targeted against specific OX40 receptor domains induce potent agonism. (a) Graphic illustration of differential agonist activity and ligand-blocking properties as a function of the target cysteine-rich domain (CRD). (b) Antibodies against CRD2 and CRD4 strongly enhance CD8+ T effector cell activation in a B8R peptide immunization model. (c) Antibodies targeting CRD2 and CRD4 reduce cancer progression rate in vivo in BALB/c mice. Adapted from .

Figure 3.

Discovery of anti-idiotypic antibodies with agonist activity against the prolactin (PRL) receptor. (a) Graphic illustration of anti-idiotypic antibody discovery. The antibodies are first generated against the natural ligand PRL, and then serve as an antigen source for a second immunization. The resulting antibodies display agonist activity against the PRL receptor via ligand mimicry (b). The agonist mAb B-7 competes with the natural ligand (PRL) for binding. (c) The monoclonal antibody (mAb) B7 activates cellular proliferation in ligand-responsive cells. Adapted from .

Figure 4.

Autocrine-based functional screening for agonist nanobodies against the G-protein-coupled receptor (GPCR) apelin receptor. (a) Antibody libraries were generated by first immunizing a camel with apelin receptor (APJ) nanodiscs. The resulting immune repertoire was cloned into a phage display vector and the library was pre-enriched against APJ-binding clones using phage display. Next, the sublibrary was cloned into a lentiviral transfer plasmid for mammalian cell display via GPI-anchoring. (b) Cytograms for the parental GPI-anchored library and after the third round of activity-based sorting. A high ratio of product/substrate indicates agonist activity in the APJ B-arrestin reporter cells. (c) After three rounds of sorting, reporter cells bearing nanobody genes were sorted as single cells per well and evaluated for agonist activity. (d) The top-performing agonist nanobody (JN300) demonstrated agonist activity in the soluble format, as shown for PathHunter B-arrestin assay. Adapted from .

Figure 5.

Autocrine-based functional screening for agonist antibodies against the erythropoietin (EPO) receptor (EpoR). (a) A lentiviral antibody library is transduced into TF-1 cells in which cell proliferation is dependent on EpoR activation. Rare antibodies and combinations thereof that induce EpoR signal activation result in colony formation. Activity can theoretically result from single antibodies, antibody combinations, bispecific antibodies, or combinations thereof. (b) In vitro analysis reveals that the most-active molecular species is a bispecific antibody V-1/V-2, whereas monoclonal antibodies V1 and V2 do not show agonist activity either alone or in combination. (c) The mechanism of action appears to be that the bispecific antibody V-1/V-2 forces EpoR dimers into a conformation similar to that induced by the natural ligand (EPO). Adapted from .

Figure 6.

Conversion of an antagonistic nanobody into an agonist nanobody by rational design. (a) Insertion of a tyrosine into the tip of CDR3 of the wild-type nanobody results in new contacts that also occur between the natural ligand and receptor. In vitro analysis reveals that this insertion converts the wild-type antagonist into a full agonist, as the agonist displays poor receptor inhibition (b) and strong receptor activation (c). Adapted from [55].

Figure 7.

Tetravalent biepitopic targeting of OX40 improves FcγR-independent receptor agonism. (a) OX40:Fab1:Fab2 ternary complex [adapted from Protein Data Bank (PDB): 60GX] illustrates Fab1 and Fab2 binding to unique, nonoverlapping epitopes on the OX40 receptor. (b) The tetravalent, biepitopic antibody is proposed to engage two OX40 molecules at unique epitopes and promote daisy-chain-like, higher order receptor clustering. (c) In vitro activation of CD4+ T cells without FcγR crosslinking or CD28 co-stimulation demonstrates superior agonism by tetravalent, biepitopic antibodies. (d) In vivo activation of CD4+ effector memory T cells in a KLH-immunization in human OX40 knock-in mice reveals superior agonism by tetravalent, biepitopic molecules independent of FcγR-mediated clustering. Adapted from .