Anti-EFNA4 Calicheamicin Conjugates Effectively Target Triple-Negative Breast and Ovarian Tumor-Initiating Cells to Result in Sustained Tumor Regressions.

PURPOSE: Triple-negative breast cancer (TNBC) and ovarian cancer each comprise heterogeneous tumors, for which current therapies have little clinical benefit. Novel therapies that target and eradicate tumor-initiating cells (TIC) are needed to significantly improve survival. EXPERIMENTAL DESIGN: A panel of well-annotated patient-derived xenografts (PDX) was established, and surface markers that enriched for TIC in specific tumor subtypes were empirically determined. The TICs were queried for overexpressed antigens, one of which was selected to be the target of an antibody-drug conjugate (ADC). The efficacy of the ADC was evaluated in 15 PDX models to generate hypotheses for patient stratification. RESULTS: We herein identified E-cadherin (CD324) as a surface antigen able to reproducibly enrich for TIC in well-annotated, low-passage TNBC and ovarian cancer PDXs. Gene expression analysis of TIC led to the identification of Ephrin-A4 (EFNA4) as a prospective therapeutic target. An ADC comprising a humanized anti-EFNA4 monoclonal antibody conjugated to the DNA-damaging agent calicheamicin achieved sustained tumor regressions in both TNBC and ovarian cancer PDX in vivo. Non-claudin low TNBC tumors exhibited higher expression and more robust responses than other breast cancer subtypes, suggesting a specific translational application for tumor subclassification. CONCLUSIONS: These findings demonstrate the potential of PF-06647263 (anti-EFNA4-ADC) as a first-in-class compound designed to eradicate TIC. The use of well-annotated PDX for drug discovery enabled the identification of a novel TIC target, pharmacologic evaluation of the compound, and translational studies to inform clinical development.

Structure-based druggability assessment of the mammalian structural proteome with inclusion of light protein flexibility.

Advances reported over the last few years and the increasing availability of protein crystal structure data have greatly improved structure-based druggability approaches. However, in practice, nearly all druggability estimation methods are applied to protein crystal structures as rigid proteins, with protein flexibility often not directly addressed. The inclusion of protein flexibility is important in correctly identifying the druggability of pockets that would be missed by methods based solely on the rigid crystal structure. These include cryptic pockets and flexible pockets often found at protein-protein interaction interfaces. Here, we apply an approach that uses protein modeling in concert with druggability estimation to account for light protein backbone movement and protein side-chain flexibility in protein binding sites. We assess the advantages and limitations of this approach on widely-used protein druggability sets. Applying the approach to all mammalian protein crystal structures in the PDB results in identification of 69 proteins with potential druggable cryptic pockets.

Applying physics-based scoring to calculate free energies of binding for single amino acid mutations in protein-protein complexes.

Predicting changes in protein binding affinity due to single amino acid mutations helps us better understand the driving forces underlying protein-protein interactions and design improved biotherapeutics. Here, we use the MM-GBSA approach with the OPLS2005 force field and the VSGB2.0 solvent model to calculate differences in binding free energy between wild type and mutant proteins. Crucially, we made no changes to the scoring model as part of this work on protein-protein binding affinity--the energy model has been developed for structure prediction and has previously been validated only for calculating the energetics of small molecule binding. Here, we compare predictions to experimental data for a set of 418 single residue mutations in 21 targets and find that the MM-GBSA model, on average, performs well at scoring these single protein residue mutations. Correlation between the predicted and experimental change in binding affinity is statistically significant and the model performs well at picking "hotspots," or mutations that change binding affinity by more than 1 kcal/mol. The promising performance of this physics-based method with no tuned parameters for predicting binding energies suggests that it can be transferred to other protein engineering problems.

Computational approaches for fragment-based and de novo design.

Fragment-based and de novo design strategies have been used in drug discovery for years. The methodologies for these strategies are typically discussed separately, yet the applications of these techniques overlap substantially. We present a review of various fragment-based discovery and de novo design protocols with an emphasis on successful applications in real-world drug discovery projects. Furthermore, we illustrate the strengths and weaknesses of the various approaches and discuss how one method can be used to complement another. We also discuss how the incorporation of experimental data as constraints in computational models can produce novel compounds that occupy unique areas in intellectual property (IP) space yet are biased toward the desired chemical property space. Finally, we present recent research results suggesting that computational tools applied to fragment-based discovery and de novo design can have a greater impact on the discovery process when coupled with the right experiments.

Energetic analysis of fragment docking and application to structure-based pharmacophore hypothesis generation.

We have developed a method that uses energetic analysis of structure-based fragment docking to elucidate key features for molecular recognition. This hybrid ligand- and structure-based methodology uses an atomic breakdown of the energy terms from the Glide XP scoring function to locate key pharmacophoric features from the docked fragments. First, we show that Glide accurately docks fragments, producing a root mean squared deviation (RMSD) of <1.0 A for the top scoring pose to the native crystal structure. We then describe fragment-specific docking settings developed to generate poses that explore every pocket of a binding site while maintaining the docking accuracy of the top scoring pose. Next, we describe how the energy terms from the Glide XP scoring function are mapped onto pharmacophore sites from the docked fragments in order to rank their importance for binding. Using this energetic analysis we show that the most energetically favorable pharmacophore sites are consistent with features from known tight binding compounds. Finally, we describe a method to use the energetically selected sites from fragment docking to develop a pharmacophore hypothesis that can be used in virtual database screening to retrieve diverse compounds. We find that this method produces viable hypotheses that are consistent with known active compounds. In addition to retrieving diverse compounds that are not biased by the co-crystallized ligand, the method is able to recover known active compounds from a database screen, with an average enrichment of 8.1 in the top 1% of the database.