DrugMap: A quantitative pan-cancer analysis of cysteine ligandability.

Cysteine-focused chemical proteomic platforms have accelerated the clinical development of covalent inhibitors for a wide range of targets in cancer. However, how different oncogenic contexts influence cysteine targeting remains unknown. To address this question, we have developed "DrugMap," an atlas of cysteine ligandability compiled across 416 cancer cell lines. We unexpectedly find that cysteine ligandability varies across cancer cell lines, and we attribute this to differences in cellular redox states, protein conformational changes, and genetic mutations. Leveraging these findings, we identify actionable cysteines in NF-κB1 and SOX10 and develop corresponding covalent ligands that block the activity of these transcription factors. We demonstrate that the NF-κB1 probe blocks DNA binding, whereas the SOX10 ligand increases SOX10-SOX10 interactions and disrupts melanoma transcriptional signaling. Our findings reveal heterogeneity in cysteine ligandability across cancers, pinpoint cell-intrinsic features driving cysteine targeting, and illustrate the use of covalent probes to disrupt oncogenic transcription-factor activity.

DrugMap: A quantitative pan-cancer analysis of cysteine ligandability.

Cysteine-focused chemical proteomic platforms have accelerated the clinical development of covalent inhibitors of a wide-range of targets in cancer. However, how different oncogenic contexts influence cysteine targeting remains unknown. To address this question, we have developed DrugMap , an atlas of cysteine ligandability compiled across 416 cancer cell lines. We unexpectedly find that cysteine ligandability varies across cancer cell lines, and we attribute this to differences in cellular redox states, protein conformational changes, and genetic mutations. Leveraging these findings, we identify actionable cysteines in NFκB1 and SOX10 and develop corresponding covalent ligands that block the activity of these transcription factors. We demonstrate that the NFκB1 probe blocks DNA binding, whereas the SOX10 ligand increases SOX10-SOX10 interactions and disrupts melanoma transcriptional signaling. Our findings reveal heterogeneity in cysteine ligandability across cancers, pinpoint cell-intrinsic features driving cysteine targeting, and illustrate the use of covalent probes to disrupt oncogenic transcription factor activity.

Precision Oncology Comes of Age: Designing Best-in-Class Small Molecules by Integrating Two Decades of Advances in Chemistry, Target Biology, and Data Science.

Small-molecule drugs have enabled the practice of precision oncology for genetically defined patient populations since the first approval of imatinib in 2001. Scientific and technology advances over this 20-year period have driven the evolution of cancer biology, medicinal chemistry, and data science. Collectively, these advances provide tools to more consistently design best-in-class small-molecule drugs against known, previously undruggable, and novel cancer targets. The integration of these tools and their customization in the hands of skilled drug hunters will be necessary to enable the discovery of transformational therapies for patients across a wider spectrum of cancers. SIGNIFICANCE: Target-centric small-molecule drug discovery necessitates the consideration of multiple approaches to identify chemical matter that can be optimized into drug candidates. To do this successfully and consistently, drug hunters require a comprehensive toolbox to avoid following the "law of instrument" or Maslow's hammer concept where only one tool is applied regardless of the requirements of the task. Combining our ever-increasing understanding of cancer and cancer targets with the technological advances in drug discovery described below will accelerate the next generation of small-molecule drugs in oncology.

STX-478, a Mutant-Selective, Allosteric PI3Kα Inhibitor Spares Metabolic Dysfunction and Improves Therapeutic Response in PI3Kα-Mutant Xenografts.

Phosphoinositide 3-kinase α (PIK3CA) is one of the most mutated genes across cancers, especially breast, gynecologic, and head and neck squamous cell carcinoma tumors. Mutations occur throughout the gene, but hotspot mutations in the helical and kinase domains predominate. The therapeutic benefit of isoform-selective PI3Kα inhibition was established with alpelisib, which displays equipotent activity against the wild-type and mutant enzyme. Inhibition of wild-type PI3Kα is associated with severe hyperglycemia and rash, which limits alpelisib use and suggests that selectively targeting mutant PI3Kα could reduce toxicity and improve efficacy. Here we describe STX-478, an allosteric PI3Kα inhibitor that selectively targets prevalent PI3Kα helical- and kinase-domain mutant tumors. STX-478 demonstrated robust efficacy in human tumor xenografts without causing the metabolic dysfunction observed with alpelisib. Combining STX-478 with fulvestrant and/or cyclin-dependent kinase 4/6 inhibitors was well tolerated and provided robust and durable tumor regression in ER+HER2- xenograft tumor models. SIGNIFICANCE: These preclinical data demonstrate that the mutant-selective, allosteric PI3Kα inhibitor STX-478 provides robust efficacy while avoiding the metabolic dysfunction associated with the nonselective inhibitor alpelisib. Our results support the ongoing clinical evaluation of STX-478 in PI3Kα-mutated cancers, which is expected to expand the therapeutic window and mitigate counterregulatory insulin release. See related commentary by Kearney and Vasan, p. 2313. This article is featured in Selected Articles from This Issue, p. 2293.

Discovery of BLU-945, a Reversible, Potent, and Wild-Type-Sparing Next-Generation EGFR Mutant Inhibitor for Treatment-Resistant Non-Small-Cell Lung Cancer.

While epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) have changed the treatment landscape for EGFR mutant (L858R and ex19del)-driven non-small-cell lung cancer (NSCLC), most patients will eventually develop resistance to TKIs. In the case of first- and second-generation TKIs, up to 60% of patients will develop an EGFR T790M mutation, while third-generation irreversible TKIs, like osimertinib, lead to C797S as the primary on-target resistance mutation. The development of reversible inhibitors of these resistance mutants is often hampered by poor selectivity against wild-type EGFR, resulting in potentially dose-limiting toxicities and a sub-optimal profile for use in combinations. BLU-945 (compound 30) is a potent, reversible, wild-type-sparing inhibitor of EGFR+/T790M and EGFR+/T790M/C797S resistance mutants that maintains activity against the sensitizing mutations, especially L858R. Pre-clinical efficacy and safety studies supported progression of BLU-945 into clinical studies, and it is currently in phase 1/2 clinical trials for treatment-resistant EGFR-driven NSCLC.

Acquired On-Target Clinical Resistance Validates FGFR4 as a Driver of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality worldwide with no clinically confirmed oncogenic driver. Although preclinical studies implicate the FGF19 receptor FGFR4 in hepatocarcinogenesis, the dependence of human cancer on FGFR4 has not been demonstrated. Fisogatinib (BLU-554) is a potent and selective inhibitor of FGFR4 and demonstrates clinical benefit and tumor regression in patients with HCC with aberrant FGF19 expression. Mutations were identified in the gatekeeper and hinge-1 residues in the kinase domain of FGFR4 upon disease progression in 2 patients treated with fisogatinib, which were confirmed to mediate resistance in vitro and in vivo. A gatekeeper-agnostic, pan-FGFR inhibitor decreased HCC xenograft growth in the presence of these mutations, demonstrating continued FGF19-FGFR4 pathway dependence. These results validate FGFR4 as an oncogenic driver and warrant further therapeutic targeting of this kinase in the clinic. SIGNIFICANCE: Our study is the first to demonstrate on-target FGFR4 kinase domain mutations as a mechanism of acquired clinical resistance to targeted therapy. This further establishes FGF19-FGFR4 pathway activation as an oncogenic driver. These findings support further investigation of fisogatinib in HCC and inform the profile of potential next-generation inhibitors.See related commentary by Subbiah and Pal, p. 1646.This article is highlighted in the In This Issue feature, p. 1631.

Targeting kinases with precision.

Cancer genomics and mechanistic studies have revealed that heterogeneous mutations within a single kinase can result in a variety of activation mechanisms. The challenge has been to match these insights with tailored drug discovery strategies to yield potent, highly selective drugs. With optimized drugs in hand, physicians could apply the principles of personalized medicine with an increasing number of options to treat patients with improved precision according to their tumor's molecular genotype.

A precision therapy against cancers driven by KIT/PDGFRA mutations.

Targeting oncogenic kinase drivers with small-molecule inhibitors can have marked therapeutic benefit, especially when administered to an appropriate genomically defined patient population. Cancer genomics and mechanistic studies have revealed that heterogeneous mutations within a single kinase can result in various mechanisms of kinase activation. Therapeutic benefit to patients can best be optimized through an in-depth understanding of the disease-driving mutations combined with the ability to match these insights to tailored highly selective drugs. This rationale is presented for BLU-285, a clinical stage inhibitor of oncogenic KIT and PDGFRA alterations, including activation loop mutants that are ineffectively treated by current therapies. BLU-285, designed to preferentially interact with the active conformation of KIT and PDGFRA, potently inhibits activation loop mutants KIT D816V and PDGFRA D842V with subnanomolar potency and also inhibits other well-characterized disease-driving KIT mutants both in vitro and in vivo in preclinical models. Early clinical evaluation of BLU-285 in a phase 1 study has demonstrated marked activity in patients with diseases associated with KIT (aggressive systemic mastocytosis and gastrointestinal stromal tumor) and PDGFRA (gastrointestinal stromal tumor) activation loop mutations.

First Selective Small Molecule Inhibitor of FGFR4 for the Treatment of Hepatocellular Carcinomas with an Activated FGFR4 Signaling Pathway.

UNLABELLED: Aberrant signaling through the fibroblast growth factor 19 (FGF19)/fibroblast growth factor receptor 4 (FGFR 4) signaling complex has been shown to cause hepatocellular carcinoma (HCC) in mice and has been implicated to play a similar role in humans. We have developed BLU9931, a potent and irreversible small-molecule inhibitor of FGFR4, as a targeted therapy to treat patients with HCC whose tumors have an activated FGFR4 signaling pathway. BLU9931 is exquisitely selective for FGFR4 versus other FGFR family members and all other kinases. BLU9931 shows remarkable antitumor activity in mice bearing an HCC tumor xenograft that overexpresses FGF19 due to amplification as well as a liver tumor xenograft that overexpresses FGF19 mRNA but lacks FGF19 amplification. Approximately one third of patients with HCC whose tumors express FGF19 together with FGFR4 and its coreceptor klotho ß (KLB) could potentially respond to treatment with an FGFR4 inhibitor. These findings are the first demonstration of a therapeutic strategy that targets a subset of patients with HCC. SIGNIFICANCE: This article documents the discovery of BLU9931, a novel irreversible kinase inhibitor that specifically targets FGFR4 while sparing all other FGFR paralogs and demonstrates exquisite kinome selectivity. BLU9931 is efficacious in tumors with an intact FGFR4 signaling pathway that includes FGF19, FGFR4, and KLB. BLU9931 is the first FGFR4-selective molecule for the treatment of patients with HCC with aberrant FGFR4 signaling.

Large scale meta-analysis of fragment-based screening campaigns: privileged fragments and complementary technologies.

A first step in fragment-based drug discovery (FBDD) often entails a fragment-based screen (FBS) to identify fragment "hits." However, the integration of conflicting results from orthogonal screens remains a challenge. Here we present a meta-analysis of 35 fragment-based campaigns at Novartis, which employed a generic 1400-fragment library against diverse target families using various biophysical and biochemical techniques. By statistically interrogating the multidimensional FBS data, we sought to investigate three questions: (1) What makes a fragment amenable for FBS? (2) How do hits from different fragment screening technologies and target classes compare with each other? (3) What is the best way to pair FBS assay technologies? In doing so, we identified substructures that were privileged for specific target classes, as well as fragments that were privileged for authentic activity against many targets. We also revealed some of the discrepancies between technologies. Finally, we uncovered a simple rule of thumb in screening strategy: when choosing two technologies for a campaign, pairing a biochemical and biophysical screen tends to yield the greatest coverage of authentic hits.

Inside the mind of a medicinal chemist: the role of human bias in compound prioritization during drug discovery.

Medicinal chemists' "intuition" is critical for success in modern drug discovery. Early in the discovery process, chemists select a subset of compounds for further research, often from many viable candidates. These decisions determine the success of a discovery campaign, and ultimately what kind of drugs are developed and marketed to the public. Surprisingly little is known about the cognitive aspects of chemists' decision-making when they prioritize compounds. We investigate 1) how and to what extent chemists simplify the problem of identifying promising compounds, 2) whether chemists agree with each other about the criteria used for such decisions, and 3) how accurately chemists report the criteria they use for these decisions. Chemists were surveyed and asked to select chemical fragments that they would be willing to develop into a lead compound from a set of ~4,000 available fragments. Based on each chemist's selections, computational classifiers were built to model each chemist's selection strategy. Results suggest that chemists greatly simplified the problem, typically using only 1-2 of many possible parameters when making their selections. Although chemists tended to use the same parameters to select compounds, differing value preferences for these parameters led to an overall lack of consensus in compound selections. Moreover, what little agreement there was among the chemists was largely in what fragments were undesirable. Furthermore, chemists were often unaware of the parameters (such as compound size) which were statistically significant in their selections, and overestimated the number of parameters they employed. A critical evaluation of the problem space faced by medicinal chemists and cognitive models of categorization were especially useful in understanding the low consensus between chemists.

Identification of 2-oxatriazines as highly potent pan-PI3K/mTOR dual inhibitors.

We recently described several highly potent, triazine (1) and triazolopyrimidine (2) scaffold-based, dual PI3K/mTOR-inhibitors (e.g., 1, PKI-587) that were efficacious in both in vitro and in vivo models. In order to further optimize these compounds we devised a novel series, the 2-oxatriazines, which also exhibited excellent potency and good metabolic stability. Some 2-oxatriazines showed promising in vivo biomarker suppression and induced apoptosis in the MDA-MB-361 breast cancer xenograft model.

Biased retrieval of chemical series in receptor-based virtual screening.

Using the kinases in the DUD dataset and an in-house HTS dataset from PI3K-γ, receptor-based virtual screening experiments were performed using Glide SP docking. While significant enrichments were observed for eight of the nine targets in the set, more detailed analyses highlighted that much of the early enrichment (10-80%) is the result of retrieval of a single cluster of active compounds. This biased retrieval was not necessarily due to early enrichment of the cluster containing the co-crystallized ligand. Virtual screening validation studies could thus benefit from including cluster-based analyses to assess enrichment of diverse chemotypes.

Chemical space sampling in virtual screening by different crystal structures.

Retrospective virtual screening experiments were carried out to investigate the effects of combining hit lists from different crystal structures of the same target using consensus scoring. An in-house High Throughput Screening (HTS) dataset from PI3K-γ was used and docked against five diverse PI3K-γ crystal structures. The results show that consensus scoring prioritizes compounds that score moderately against individual crystal structures and is thus complementary to individual crystal structure screening leading to an increase in the diversity of hits. Enrichment factors (EFs) of the consensus score for two or three structures are often as high as or higher than the EF of the individual structures used in the consensus score. Combining four or five structures in the consensus score generally yields lower enrichments. Compounds in the top 500 of the consensus score that are also found in the top 500 of an individual X-ray structure used in the consensus score calculations yield the largest number of hits with the lowest number of false positives.

A lead discovery strategy driven by a comprehensive analysis of proteases in the peptide substrate space.

We present here a comprehensive analysis of proteases in the peptide substrate space and demonstrate its applicability for lead discovery. Aligned octapeptide substrates of 498 proteases taken from the MEROPS peptidase database were used for the in silico analysis. A multiple-category naïve Bayes model, trained on the two-dimensional chemical features of the substrates, was able to classify the substrates of 365 (73%) proteases and elucidate statistically significant chemical features for each of their specific substrate positions. The positional awareness of the method allows us to identify the most similar substrate positions between proteases. Our analysis reveals that proteases from different families, based on the traditional classification (aspartic, cysteine, serine, and metallo), could have substrates that differ at the cleavage site (P1-P1') but are similar away from it. Caspase-3 (cysteine protease) and granzyme B (serine protease) are previously known examples of cross-family neighbors identified by this method. To assess whether peptide substrate similarity between unrelated proteases could reliably translate into the discovery of low molecular weight synthetic inhibitors, a lead discovery strategy was tested on two other cross-family neighbors--namely cathepsin L2 and matrix metallo proteinase 9, and calpain 1 and pepsin A. For both these pairs, a naïve Bayes classifier model trained on inhibitors of one protease could successfully enrich those of its neighbor from a different family and vice versa, indicating that this approach could be prospectively applied to lead discovery for a novel protease target with no known synthetic inhibitors.

A protein relational database and protein family knowledge bases to facilitate structure-based design analyses.

The Protein Data Bank is the most comprehensive source of experimental macromolecular structures. It can, however, be difficult at times to locate relevant structures with the Protein Data Bank search interface. This is particularly true when searching for complexes containing specific interactions between protein and ligand atoms. Moreover, searching within a family of proteins can be tedious. For example, one cannot search for some conserved residue as residue numbers vary across structures. We describe herein three databases, Protein Relational Database, Kinase Knowledge Base, and Matrix Metalloproteinase Knowledge Base, containing protein structures from the Protein Data Bank. In Protein Relational Database, atom-atom distances between protein and ligand have been precalculated allowing for millisecond retrieval based on atom identity and distance constraints. Ring centroids, centroid-centroid and centroid-atom distances and angles have also been included permitting queries for pi-stacking interactions and other structural motifs involving rings. Other geometric features can be searched through the inclusion of residue pair and triplet distances. In Kinase Knowledge Base and Matrix Metalloproteinase Knowledge Base, the catalytic domains have been aligned into common residue numbering schemes. Thus, by searching across Protein Relational Database and Kinase Knowledge Base, one can easily retrieve structures wherein, for example, a ligand of interest is making contact with the gatekeeper residue.

5-ureidobenzofuranone indoles as potent and efficacious inhibitors of PI3 kinase-alpha and mTOR for the treatment of breast cancer.

A series of 5-ureidobenzofuran-3-one indoles as potent inhibitors of PI3Kalpha and mTOR has been developed. The best potency in cells was obtained when the urea group was extended to a 4-[2-(dimethylamino)ethyl]methylamino amidophenyl group. A 7-fluoro group on the indole ring also enhanced cellular potency. Compound 18i, incorporating the optimal functional groups, showed high potency in cellular lines and was further studied in vivo. It was able to inhibit the biomarker phosphorylation for 8h when dosed at 25 mg/kg iv. In the MDA-MB-361 breast cancer model, it shrank the tumor size remarkably when dosed at 25 mg/kg iv on days 1, 5, and 9.

Chemical space sampling by different scoring functions and crystal structures.

Virtual screening has become a popular tool to identify novel leads in the early phases of drug discovery. A variety of docking and scoring methods used in virtual screening have been the subject of active research in an effort to gauge limitations and articulate best practices. However, how to best utilize different scoring functions and various crystal structures, when available, is not yet well understood. In this work we use multiple crystal structures of PI3 K-gamma in both prospective and retrospective virtual screening experiments. Both Glide SP scoring and Prime MM-GBSA rescoring are utilized in the prospective and retrospective virtual screens, and consensus scoring is investigated in the retrospective virtual screening experiments. The results show that each of the different crystal structures that was used, samples a different chemical space, i.e. different chemotypes are prioritized by each structure. In addition, the different (re)scoring functions prioritize different chemotypes as well. Somewhat surprisingly, the Prime MM-GBSA scoring function generally gives lower enrichments than Glide SP. Finally we investigate the impact of different ligand preparation protocols on virtual screening enrichment factors. In summary, different crystal structures and different scoring functions are complementary to each other and allow for a wider variety of chemotypes to be considered for experimental follow-up.

4-Substituted-7-azaindoles bearing a ureidobenzofuranone moiety as potent and selective, ATP-competitive inhibitors of the mammalian target of rapamycin (mTOR).

A series of 5-ureidobenzofuranones was discovered as potent and selective inhibitors of mTOR with good cellular activity. Molecular modeling studies revealed several hydrogen bond interactions of the ureido group with the enzyme at the ATP-binding site. Furthermore, modeling showed that the ureido group is best situated at C-5 of the benzofuranone. Syntheses of 4-ureido and 5-ureidobenzofuranones are presented.

Discovery and optimization of 2-(4-substituted-pyrrolo[2,3-b]pyridin-3-yl)methylene-4-hydroxybenzofuran-3(2H)-ones as potent and selective ATP-competitive inhibitors of the mammalian target of rapamycin (mTOR).

We discovered 2-(4-substituted-pyrrolo[2,3-b]pyridin-3-yl)methylene-4-hydroxybenzofuran-3(2H)-ones as potent and selective ATP-competitive inhibitors of the mammalian target of rapamycin (mTOR). Since phenolic OH groups pose metabolic liability, one of the two hydroxyl groups was selectively removed. The SAR data showed the structural features necessary for subnanomolar inhibitory activity against mTOR kinase as well as selectivity over PI3Kalpha. An X-ray co-crystal structure of one inhibitor with the mTOR-related PI3Kgamma revealed the key hydrogen bonding interactions.