The non-receptor protein tyrosine phosphatase SHP2, encoded by PTPN11, has an important role in signal transduction downstream of growth factor receptor signalling and was the first reported oncogenic tyrosine phosphatase. Activating mutations of SHP2 have been associated with developmental pathologies such as Noonan syndrome and are found in multiple cancer types, including leukaemia, lung and breast cancer and neuroblastoma. SHP2 is ubiquitously expressed and regulates cell survival and proliferation primarily through activation of the RAS–ERK signalling pathway. It is also a key mediator of the programmed cell death 1 (PD-1) and B- and T-lymphocyte attenuator (BTLA) immune checkpoint pathways. Reduction of SHP2 activity suppresses tumour cell growth and is a potential target of cancer therapy. Here we report the discovery of a highly potent (IC50 = 0.071 μM), selective and orally bioavailable small-molecule SHP2 inhibitor, SHP099, that stabilizes SHP2 in an auto-inhibited conformation. SHP099 concurrently binds to the interface of the N-terminal SH2, C-terminal SH2, and protein tyrosine phosphatase domains, thus inhibiting SHP2 activity through an allosteric mechanism. SHP099 suppresses RAS–ERK signalling to inhibit the proliferation of receptor-tyrosine-kinase-driven human cancer cells in vitro and is efficacious in mouse tumour xenograft models. Together, these data demonstrate that pharmacological inhibition of SHP2 is a valid therapeutic approach for the treatment of cancers.

Representing and understanding the three-dimensional (3D) structural information of protein-ligand complexes is a critical step in the rational drug discovery process. Traditional analysis methods are proving inadequate and inefficient in dealing with the massive amount of structural information being generated from X-ray crystallography, NMR, and in silico approaches such as structure-based docking experiments. Here, we present SIFt (structural interaction fingerprint), a novel method for representing and analyzing 3D protein-ligand binding interactions. Key to this approach is the generation of an interaction fingerprint that translates 3D structural binding information from a protein-ligand complex into a one-dimensional binary string. Each fingerprint represents the "structural interaction profile" of the complex that can be used to organize, analyze, and visualize the rich amount of information encoded in ligand-receptor complexes and also to assist database mining. We have applied SIFt to tackle three common tasks in structure-based drug design. The first involved the analysis and organization of a typical set of results generated from a docking study. Using SIFt, docking poses with similar binding modes were identified, clustered, and subsequently compared with conventional scoring function information. A second application of SIFt was to analyze approximately 90 known X-ray crystal structures of protein kinase-inhibitor complexes obtained from the Protein Databank. Using SIFt, we were able to organize the structures and reveal striking similarities and diversity between their small molecule binding interactions. Finally, we have shown how SIFt can be used as an effective molecular filter during the virtual chemical library screening process to select molecules with desirable binding mode(s) and/or desirable interaction patterns with the protein target. In summary, SIFt shows promise to fully leverage the wealth of information being generated in rational drug design.

Since the advent of high-throughput screening (HTS), there has been an urgent need for methods that facilitate the interrogation of large-scale chemical biology data to build a mode of action (MoA) hypothesis. This can be done either prior to the HTS by subset design of compounds with known MoA or post HTS by data annotation and mining. To enable this process, we developed a tool that compares compounds solely on the basis of their bioactivity: the chemical biological descriptor "high-throughput screening fingerprint" (HTS-FP). In the current embodiment, data are aggregated from 195 biochemical and cell-based assays developed at Novartis and can be used to identify bioactivity relationships among the in-house collection comprising ~1.5 million compounds. We demonstrate the value of the HTS-FP for virtual screening and in particular scaffold hopping. HTS-FP outperforms state of the art methods in several aspects, retrieving bioactive compounds with remarkable chemical dissimilarity to a probe structure. We also apply HTS-FP for the design of screening subsets in HTS. Using retrospective data, we show that a biodiverse selection of plates performs significantly better than a chemically diverse selection of plates, both in terms of number of hits and diversity of chemotypes retrieved. This is also true in the case of hit expansion predictions using HTS-FP similarity. Sets of compounds clustered with HTS-FP are biologically meaningful, in the sense that these clusters enrich for genes and gene ontology (GO) terms, showing that compounds that are bioactively similar also tend to target proteins that operate together in the cell. HTS-FP are valuable not only because of their predictive power but mainly because they relate compounds solely on the basis of bioactivity, harnessing the accumulated knowledge of a high-throughput screening facility toward the understanding of how compounds interact with the proteome.

Bridging chemical and biological space is the key to drug discovery and development. Typically, cheminformatics methods operate under the assumption that similar chemicals have similar biological activity. Ideally then, one could predict a drug's biological function(s) given only its chemical structure by similarity searching in libraries of compounds with known activities. In practice, effectively choosing a similarity metric is case dependent. This work compares both 2D and 3D chemical descriptors as tools for predicting the biological targets of ligand probes, on the basis of their similarity to reference molecules in a 46,000 compound, biologically annotated chemical database. Overall, we found that the 2D methods employed here outperform the 3D (88% vs 67% success) in correct target prediction. However, the 3D descriptors proved superior in cases of probes with low structural similarity to other compounds in the database (singletons). Additionally, the 3D method (FEPOPS) shows promise for providing pharmacophoric alignment of the small molecules' chemical features consistent with those seen in experimental ligand/ receptor complexes. These results suggest that querying annotated chemical databases with a systematic combination of both 2D and 3D descriptors will prove more effective than employing single methods.

SHP2 is a nonreceptor protein tyrosine phosphatase (PTP) encoded by the PTPN11 gene involved in cell growth and differentiation via the MAPK signaling pathway. SHP2 also purportedly plays an important role in the programmed cell death pathway (PD-1/PD-L1). Because it is an oncoprotein associated with multiple cancer-related diseases, as well as a potential immunomodulator, controlling SHP2 activity is of significant therapeutic interest. Recently in our laboratories, a small molecule inhibitor of SHP2 was identified as an allosteric modulator that stabilizes the autoinhibited conformation of SHP2. A high throughput screen was performed to identify progressable chemical matter, and X-ray crystallography revealed the location of binding in a previously undisclosed allosteric binding pocket. Structure-based drug design was employed to optimize for SHP2 inhibition, and several new protein-ligand interactions were characterized. These studies culminated in the discovery of 6-(4-amino-4-methylpiperidin-1-yl)-3-(2,3-dichlorophenyl)pyrazin-2-amine (SHP099, 1), a potent, selective, orally bioavailable, and efficacious SHP2 inhibitor.