Abstract: In recent years, there is an increasing interest in the pursuit of targeted covalent drugs as well as designing chemical biology and structural biology probes using covalent target modification. Covalent inhibitors often exhibit slow off-rates, increased binding potency and biochemical efficiency, enhanced selectivity, reduced propensity for target-based drug resistance and prolonged pharmacodynamic effects, whereas covalent chemical biology probes enable target fishing via selective modification of specific residues and enable confidence in rationale studies via target engagement or increasing base-line activation in assay development while covalent structural biology probes enable thermal stabilization of target proteins for crystallography studies. However, covalent inhibitors present unique pharmacokinetic and safety challenges when developed as therapeutic agents. Central to the rational design of covalent inhibitors with improved benefit-risk balance is elevating our understanding of the reaction between electrophilic inhibitors and biological nucleophiles based on physical organic chemistry principles.

This talk will describe ab initio calculations that were used to calculate theoretical reactivities of diverse electrophilic warheads to different biological nucleophiles such as cysteine and serine nucleophiles and comparative studies with simple, electrophilicity descriptor calculations based on ligands alone and the need for higher level theoretical methods. High level ab initio calculations were used to characterize reaction energy surfaces for a broad set of irreversible and slowly reversible warheads targeting nucleophiles in aqueous solutions and in protein binding sites. Results indicate that transition states and reaction barriers are impacted by various factors, such as frontier orbitals, solvation, steric effects and H-bonding activation. In addition, examples of structure-based design of fine-tuning reactivity of covalent war-heads by introducing specific functionalities will also be discussed with illustrative examples that lead to clinical compounds for the treatment of chronic diseases.

Breast Cancer Resistance Protein (BCRP) is a member of the ATP-binding cassette family of transporters collectively known for ATP-dependent transport of a wide range of endo- and xenobiotics. Highly expressed in humans at the gut, bile canaliculi and the blood-brain barrier among other barrier tissues, BCRP has been shown to impact absorption and excretion of a variety of chemotherapeutic drugs while its overexpression in tumors has been associated with increased multidrug resistance.¹

BCRP is a half-transporter that is believed to form a homodimer and assume similar topology to that of P-glycoprotein (P-gp), another ABC efflux transporter known to significantly affect pharmacokinetic profiles of many drugs. The two transporters have an overlapping but distinct substrate specificity and while much is known about P-gp’s substrate preference², that of BCRP’s is much less understood.

In this study we present results of our analysis of a large dataset of compounds determined to be either substrates or nonsubstrates of P-gp and BCRP in in vitro monolayer efflux assays. We compare their physicochemical property profiles with respect to preference for the two transporters and examine in vitro-in vivo correlation between BCRP /P-gp monolayer efflux and passive permeability and in vivo-measured brain-to-plasma ratios and K_p,uu values.

DNA gyrase is a important target for antibacterials, as evidenced by the broad clinical use of the quinolone class of antibiotics. Target-based resistance to these drugs has driven recent efforts to identify novel methods for inhibiting this enzyme complex. This talk will outline fragment-screening strategies that were employed to find novel chemical leads that were progressed by medicinal chemistry into inhibitors with in vivo activity. The use of X-ray crystallography and structure-based design techniques were critical to the advancement of the project and will be emphasized.

Alexey Teplyakov, Galina Obmolova, Thomas J. Malia, Jinquan Luo, Salman,Muzammil, Raymond Sweet, Juan Carlos Almagro† and Gary L. Gilliland. Janssen Research & Development LLC, 1400 McKean Road, Spring House, PA 19477, USA

The crystal structures of a set of 16 germline variants comprised of four different kappa light chains paired with four different heavy chains have been determined to support antibody therapeutic development. All four heavy chains of the Fabs have the same CDR H3 that was reported in an earlier Fab structure. The structure analyses include comparisons of the overall structures, canonical structures of the CDRs and the VH:VL packing interactions. The CDR conformations for the most part are tightly clustered, especially for the ones with shorter lengths. The longer CDRs with tandem glycines or serines have more conformational diversity than the others. CDR H3, despite having the same amino acid sequence, exhibits the largest conformational diversity. About half of the structures have CDR H3 conformations similar to that of the parent; the others diverge significantly. One conclusion is that the CDR H3 conformations are influenced by both their amino acid sequence and their structural environment determined by the heavy and light chain pairing. The stem regions of 14 of the variant pairs are in the ‘kinked’ conformation and only two are in the extended conformation. The packing of the VH and VL domains is consistent with our knowledge of antibody structure, and the tilt angles between these domains cover a range of 11 degrees. Two out of 16 structures showed particularly large variations in the tilt angles when compared with the other pairings. The structures and their analyses provide a rich foundation for future antibody modeling and engineering efforts.

Nikolay Plotnikov and Sandeep Kumar
nikolay.plotnikov@pfizer.com

Chemical degradation during manufacturing and storage is one of the main challenges in development of protein-based biologics. Deamidation of asparagine (Asn) and isomerization of aspartic acid (Asp) represent major chemical degradation pathways of monoclonal antibodies (mAb). These adverse chemical reactions can decrease potency of mAbs (1-3) or increase their immunogenicity. Presently, chemical liabilities of Asp-X and Asn-X motifs in a drug candidate are quantified experimentally, predominantly, at the formulation stage. Quantitative reliable modeling would facilitate earlier assessment of chemical degradation risk, at the discovery stage, when it is still possible to modify protein sequence. Empirical statistical models have provided some qualitative insight on degradation risks (4). However, quantitative accuracy of this knowledge-based approach is limited by quality and transferability of models’ training set, since they are based on establishing correlations of the existing experimental data with a set of empirically chosen sequence- and structure-based descriptors. In this contribution, we propose a parameterization-free (ab initio) approach to quantify degradation risks. This approach relies on computing relevant free energy barriers for chemical reactions involved in degradation with physics-based models. Thus, it can be used as a standalone tool (with no experimental data available) or in combination with empirical models to screen potential degradation sites in biologics. We demonstrate application of these tools to examine effects of the protein environment in characterized degradation sites of commercially available biologics and to evaluate existing mechanistic proposals for chemical reactions involved in degradation.

References

Computer modeling of protein-protein docking is an important tool for a variety of applications. This work presents a novel algorithm for performing docking calculations implemented using the MOE software package. Protein structures are represented by a coarse-grained bead model which accurately reproduces the Van der Waals, electrostatic, and solvation energies of the corresponding all-atom model at the same resolution. An exhaustive search of translational coordinates using a FFT approach is followed by a series of partial minimization and filtering steps to progressively reduce the number of solutions. Results are presented validating the performance of the method on the protein docking benchmark 5.0, as well as using automatically-generated constraints in the case of antibody receptors and simulated site constraints in the general case to reflect realistic applications. The output poses generated by the program are shown to produce good high-quality structures when further refined using all-atom minimization.

PhD, Global Head of Drug Structure, Prediction and Design Discovery Technologies,
EMD Serono Research and Development Institute

A critical step in the development of biologic drugs is the development of a process to express, formulate and stably store the antibody under relevant conditions for supplying clinical trials and, eventually, market. In typical antibody discovery work streams, early discovery research teams focus intensively on affinity and potency of candidate hit molecules, and select any sequence that performs well according to these criteria. These sequences may not be optimal for downstream analytics and manufacture, and can potentially contain sequence based liabilities such as deamidation motifs, cryptic glycosylation sites, oxidation sites, and others. A process for rapidly optimizing an antibody sequence to remove these liabilities while maintaining potency will be presented. The process relies on computational sequence analysis, molecular modeling and high throughput cloning, expression and testing of multiple sequence variants using automation. Data to support the improved stability of these optimized variants will be presented in addition to an unexpected improvement in potency.

Therapeutic antibodies generated from immunization or in vitro display approaches quite often have undesirable manufacturability properties and thus are not well-suited for further development. These manufacturability liabilities could arise due to chemical modification of antibody amino acids, antibody aggregation, viscosity or thermal instability, etc. Proactive antibody engineering and in slico manufacturability assessment, based on sequence, structure analysis combined with past experiences, allows remediating these liabilities early on and ensures that only functionally active and manufacturable molecules progress to the development phase. This talk will focus on sequence and structure based analysis of antibodies to identify their potential manufacturability liabilities, to remediate these liabilities via rational protein engineering, and to rank antibody candidates for their aggregation & viscosity liabilities.

Antibodies (Abs) are a crucial component of the immune system and are often used as diagnostic and therapeutic agents. The need for high-affinity and high-specificity antibodies in research and medicine is driving the development of computational tools for accelerating antibody design and discovery. We report a diverse set of antibody binding data with accompanying structures that can be used to evaluate methods for modeling antibody interactions. Our Antibody-Bind (AB-Bind) database includes 1101 mutants with experimentally determined changes in binding free energies (ΔΔG) across 32 complexes. The database, AB-Bind, was used to benchmark computational scoring potentials for their ability to predict observed changes in binding free energies. Although there was a clear signal in tests discriminating mutations that improved/reduced binding, the prediction performance of all methods was modest, indicating a continued need to improve computational approaches for binding affinity predictions.

Challenges for developing antibody-based therapies include protein variants often differ in their biophysical and biochemical properties it is essential to characterize protein stability along with pharmacokinetic and pharmacodynamic properties. Following the identification of a series of potent antibodies a substantial part of the optimization process focuses on comparing and improving the developability properties of lead molecules. We have developed a series of computational methods that augment experimental methodologies and are part of a strategy used to understand and optimize the structure of clinical candidates increasing protein homogeneity and providing a strategy for development. Using a web-based platform the computational analysis of biologic candidate molecules provides scientists comprehensive visualization of potential liabilities using annotations on both antibody sequence and 3D molecular structure. Finally, an automated pipeline was created to map the calculated properties on to the 3D molecular structure for interactive viewing of the 3D structure directly in the web browser.