Antibodies represent the current gold-standard in terms of affinity reagents. Their exact binding locations are initially unknown until a process called epitope mapping is employed. Mapping surface epitopes on molecules typically involves techniques such as x-ray crystallography and oligopeptide scanning, which are time-consuming, expensive, and success is not guaranteed. Herein, we explore different MOE tools (site finder, accessibility, pharmacophore query) for in silico Epitope Discovery. We compare this process with an in vitro approach based on artificial antibodies (also called nanoMIPs^TM) which are produced by self-assembling monomers in the presence of the target molecule. The in vitro Epitope Discovery process allows us to identify surface epitopes exposed on the target proteins and these identified epitopes can in turn be used to improve on the generation of higher affinity nanoMIPs^TM as alternatives to antibodies. Our initial data on Galectin-1 show excellent alignment between in-silico and in-vitro Epitope Discovery, and this suggests that the two techniques can complement each other and be used to improve on both the quality of the nanoMIPs produced and the confidence of the predictions by MOE.

Next-generation multi-specific antibody therapeutics have emerged as successful treatment options for complex, multifactorial illnesses. As with mono-specific antibodies, optimization of pharmacokinetic properties is also of great importance for multi-specific biologics. By profiling the surface patch landscape of the multi-specific cross-over dual variable Ig-like (CODV-Ig) format, we were able to correlate in silico derived molecular properties with in vivo clearance, establishing the principles of pharmacokinetic predictions for multi-specific antibodies.

In any drug discovery effort, the identification of hits for further optimisation is of crucial importance. In this talk we report a highly efficient strategy that combines state-of-the-art high throughput peptide synthesis with bioinformatics analysis to identify nanomolar cyclic peptide (CP) ligands of the human glucose-dependent insulinotropic peptide receptor hGIP-R. Following structure-activity relationship analysis (SAR) together with molecular modelling allows deduction of a thorough and holistic binding model, which suggests further chemical optimisation, including efficient half-life extension. As a result, we show first de novo GIP-competitive incretin receptor family-selective CPs. This workflow should be generally applicable to any selection target, improving and accelerating hit identification, validation, characterisation, and prioritisation for therapeutic development.

In this work, we present a method for modeling antibodies and performing pH-dependent conformational sampling, which can enhance property calculations. Structure-based charge descriptors are evaluated for their predictive performance on recently published antibody pI, viscosity, and clearance data. From this, we devised four rules for therapeutic antibody profiling which address developability issues arising from hydrophobicity and charged-based solution behavior, PK, and the ability to enrich for those that are approved by the U.S. Food and Drug Administration. Differences in strategy for optimizing the solution behavior of human IgG1 antibodies versus the IgG2 and IgG4 isotypes and the impact of pH alterations in formulation are discussed.

Simona Saporiti,¹ Chiara Parravicini,¹ Carlo Pergola,² Uliano Guerrini,¹ Mara Rossi,³ Fabio Centola,³ and Ivano Eberini⁴

¹Dipartimento di Scienze Farmacologiche e Biomolecolari, Università degli Studi di Milano, Milano, Italy;
²Analytical Development Biotech and ³Global Analytical Pharmaceutical Science and Innovation, Merck Serono S.p.A., Rome, Italy;
⁴Dipartimento di Scienze Farmacologiche e Biomolecolari & DSRC, Università degli Studi di Milano, Milano, Italy

Currently, monoclonal antibodies (mAbs) are the most used biopharmaceuticals for human therapy. One of the key aspects in their development is the control of effector functions mediated by the interaction between fragment crystallizable (Fc) and Fcg receptors, which is a secondary mechanism of action of biotherapeutics. N-glycosylation at the Fc portion can regulate these mechanisms, and much experimental evidence suggests that modifications of glycosidic chains can affect antibody binding to FcgRIIIa, consequently impacting the immune response. In this work, we try to elucidate via in silico procedures the structural role exhibited by glycans, particularly fucose, in mAb conformational freedom that can potentially affect the receptor recognition. By using adalimumab, a marketed IgG1, as a general template, after rebuilding its three-dimensional (3D) structure through homology modeling approaches, we carried out molecular dynamics simulations of three differently glycosylated species: aglycosylated, afucosylated, and fucosylated antibody. First, the use of AMBER10:EHT forcefield provided by MOE was validated, being a good parametrization method for the glycans used in our study. Then, trajectory analysis showed different dynamical behaviors and pointed out that sugars can influence the overall 3D structure of the antibody. As a result, we propose a putative structural mechanism by which the presence of fucose introduces conformational constraints in the whole antibody and not only in the Fc domain, preventing a conformation suitable for the interaction with the receptor. As secondary evidence, we observed a high flexibility of the antibodies that is translated into an asymmetric behavior of Fab portions shown by all the simulated biopolymers, making the dynamical asymmetry a new, to our knowledge, molecular aspect that may be further investigated. In conclusion, these findings can help understand the contribution of sugars on the structural architecture of mAbs, paving the way to novel strategies of pharmaceutical development.

Targeted Protein Degradation (TPD) has become an interesting novel modality in modern medicinal chemistry, whereby proteins of interest (POI) can be non-covalently linked to an E3 ubiquitin ligase to enable ubiquitination and subsequent degradation by the proteasome. Degrading molecules, including those developed by Amphista, typically comprise a binder for the POI connected to a binder of a component of the ubiquitinating complex via a linker to induce colocalization of the two proteins.

Given the larger size of these bifunctional degraders, they often have additional challenges when compared to traditional small molecule modulators of targets for the delivery of orally bioavailable agents, though Amphista degraders have typically been designed to give good oral profiles. As the first computational chemistry hire at Amphista, I have had an interesting opportunity to start with a blank canvas with regards to defining tools and approaches for further optimizing the profiles of Amphista hits and leads.

Here, there will be a mix of philosophy with regard to building the group, vision as to what we’re trying to build, as well as some of the decisions already made and infrastructure put in place. Much of this will be relevant to all comp chem groups, but with a focus on the delivery of bifunctional protein degrader molecules.

I will discuss work in my group on developing alchemical free energy techniques for application in guiding pharmaceutical lead optimization. A key focus will be on lessons learned. In addition to discussing relative and absolute binding free energy techniques, I will also discuss newer work in the group on a generalized approach to relative binding free energy calculations, called “separated topologies". I'll also briefly highlight some of the work going on in the Open Force Field Initiative, and show results from benchmarks with OpenFF force fields.