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.

The approval of imatinib to treat chronic myelogenous leukemia in 2001 marked the beginning of the small-molecule precision oncology era. In subsequent years, more than 90 small-molecule targeted therapies have been approved to treat various cancers (1). Developing targeted therapies requires a hypothesis-driven mechanistic framework that contrasts the decades-old empirical approaches used to develop cytotoxic chemotherapies. Patient selection is based on molecular markers and directs therapies to the patients most likely to benefit, and pharmacodynamic biomarkers provide insight into target modulation, allowing a link to be drawn between the mechanism of action and efficacy. Establishing a pharmacokinetic/pharmacodynamic/efficacy relationship in appropriate preclinical models builds confidence in target-mediated efficacy and provides thresholds for pharmacodynamic modulation that can be used as criteria for compound optimization. Furthermore, applying correction factors for plasma protein binding across species allows one to predict drug concentrations required for target engagement in humans that can be used for human dose projection and then tested using pharmacodynamic assays during dose escalation in phase I trials (2). Following this framework provides a mechanistic link between the drug and target allowing the therapeutic hypothesis to be tested and provides confidence to advance a drug to later-stage clinical development to confirm efficacy (reviewed in refs. 3, 4).

In spite of the progress made in developing targeted therapies, only ∼7% of patients derive benefit (5). Developing effective therapies for broader patient populations, targeting this white space of medical need, will require adherence to the principles learned regarding target selection, the importance of potency and selectivity, and addressing resistance. New insights gained in cancer biology have informed novel target identification, and the evolution of medicinal chemistry and data science has expanded the drug hunter's toolbox to support the development of potent, selective drugs (Fig. 1). Although immuno-oncology has emerged as a critical field area in cancer treatment and drug discovery, there are still many lessons to be learned around target selection and translation to the clinic and therefore will not be covered here. This review will provide an overview of the major learnings with select examples and highlight recent advances in technologies used in small-molecule drug discovery that will be needed to deliver precision medicines to a wider population of patients with cancer.

Figure 1.

Scientific and technical advances in biology, chemistry, and data science over the past two decades have driven the development of novel first-in-class drugs and the evolution of best-in-class drugs in oncology. ctDNA, circulating tumor DNA; DepMap, Cancer Dependency Map; DL, deep learning; FEP, free energy perturbation; GEMM, genetically engineered mouse model; LTS MD, long time-scale molecular dynamics; PDB, Protein Data Bank; PROTAC, proteolysis-targeting chimeras. Herceptin is manufactured by Genentech, Gleevec by Novartis, Iressa by AstraZeneca, Zelboraf by Genentech, Xalkori by Pfizer, Imbruvica by Pharmacyclics/AbbVie and Janssen, Zykadia by Novartis, Tagrisso by AstraZeneca, Vitrakvi by Bayer, Lorbrena by Pfizer, and Lumakras by Amgen.

Figure 1.

Scientific and technical advances in biology, chemistry, and data science over the past two decades have driven the development of novel first-in-class drugs and the evolution of best-in-class drugs in oncology. ctDNA, circulating tumor DNA; DepMap, Cancer Dependency Map; DL, deep learning; FEP, free energy perturbation; GEMM, genetically engineered mouse model; LTS MD, long time-scale molecular dynamics; PDB, Protein Data Bank; PROTAC, proteolysis-targeting chimeras. Herceptin is manufactured by Genentech, Gleevec by Novartis, Iressa by AstraZeneca, Zelboraf by Genentech, Xalkori by Pfizer, Imbruvica by Pharmacyclics/AbbVie and Janssen, Zykadia by Novartis, Tagrisso by AstraZeneca, Vitrakvi by Bayer, Lorbrena by Pfizer, and Lumakras by Amgen.

Close modal

Cancer Biology

Since the discovery of the first human oncogenes and tumor suppressor genes in the 1980s, the amount of available information on the genetic drivers of cancer has exploded. Completion of the Human Genome Project in 2003 delivered the first nearly complete sequence of the human genome. This provided scientists with a normal reference that allowed the comparison of DNA sequences in cancers to the normal DNA sequence, dramatically improving our ability to identify the recurrent genetic alterations that contribute to tumor initiation and progression. Improvements in DNA sequencing technologies, including the initial introduction of massively parallel sequencing by Roche 454 and Illumina in 2004 to 2006, followed shortly by technologies for DNA sequence enrichment that allowed focused sequencing of specific regions of interest, made genome-scale and whole-exome sequencing (WES) of normal and cancer samples feasible (6).

Armed with these next-generation sequencing technologies (NGS), large-scale efforts were initiated to profile the molecular alterations present in patient tumors and cancer cell lines. The Cancer Genome Atlas (TCGA) project, a joint effort between the National Cancer Institute and the National Human Genome Research Institute, was launched in 2006 with “the aim of obtaining a comprehensive understanding of the genomic alterations that underlie all major cancers.” Similar efforts were initiated in the United Kingdom as the Cancer Genome Project (7). Shortly thereafter, the International Cancer Genome Consortium (ICGC) was launched to coordinate large-scale cancer profiling projects being conducted in numerous countries around the world (8). These efforts were accompanied by the development of novel computational and statistical approaches to distinguish functional genetic variants and candidate driver genes from the numerous passenger mutations that accumulate in cancer cells (9, 10).

These profiling projects have expanded beyond cataloging genomic changes to characterize transcriptomic, proteomic, and epigenomic alterations as well. To date, these efforts have profiled tens of thousands of patient samples across more than 30 different tumor types. This has led to the systematic identification and characterization of diverse types of genetic alterations including substitutions, indels, fusions, copy-number alterations, complex structural variations, and somatic driver mutations in noncoding regions, as well as recurrent changes in mRNA splicing, chromatin architecture, and cancer-associated proteoforms (9, 11–13).

In addition to reconfirming the prevalence of mutations in known oncogenes and tumor suppressor genes, such as KRAS, TP53, PTEN, PIK3CA, and EGFR, these sequencing efforts identified many new candidate driver genes and provided insights into novel oncologic processes. Positive selection for genetic alterations in transcriptional regulators, chromatin modifiers, metabolic pathway genes, and components of the spliceosome suggests many potential therapeutic targets beyond the oncogenic kinases and their regulators that were targeted by the first generation of precision oncology drugs (14, 15).

Numerous Web-based portals and visualization tools have been developed that allow the broader scientific community to access and analyze the vast amounts of “omics” data and associated clinical and biological data that have been generated though these large-scale profiling efforts (Table 1).

Table 1.

Databases and visualization tools for molecular characterization of human tumors and tumor cell lines

DatabaseLinkTypes of data/analyses
cBioPortal https://www.cbioportal.org/ Mutations, putative CNVs; mRNA expression, protein/phosphoprotein level; survival analyses (173, 174
COSMIC https://cancer.sanger.ac.uk/cosmic Curated somatic mutations across tumors and cell lines 
ICGC Data Portal https://dcc.icgc.org/ Somatic mutations, somatic CNVs, somatic structural variants, germline mutations, DNA methylation, gene/protein expression, miRNA expression, exon junction; epidemiologic and clinical data 
UCSC Genome Browser https://genome.ucsc.edu/ Mutations, CNVs, mRNA and miRNA expression, splice variants, DNA methylation, protein expression, clinical data 
Genomic Data Commons https://gdc.cancer.gov/ Mutations, CNVs, mRNA and miRNA expression, structural variants, splice variants, DNA methylation, protein expression 
FireBrowse http://firebrowse.org/ Interface for analyzing TCGA data 
OncoKB https://www.oncokb.org/ Mutations, CNVs, fusions (175
DepMap https://depmap.org/portal/ Genetic loss-of-function screening, pharmacologic dependencies, CCLE omics characterizations 
canSAR.ai https://cansar.ai/ Integrates biology, chemistry, pharmacology, structural biology, cellular networks and clinical annotations, and applies machine learning approaches to develop predictions useful in drug discovery (117
DatabaseLinkTypes of data/analyses
cBioPortal https://www.cbioportal.org/ Mutations, putative CNVs; mRNA expression, protein/phosphoprotein level; survival analyses (173, 174
COSMIC https://cancer.sanger.ac.uk/cosmic Curated somatic mutations across tumors and cell lines 
ICGC Data Portal https://dcc.icgc.org/ Somatic mutations, somatic CNVs, somatic structural variants, germline mutations, DNA methylation, gene/protein expression, miRNA expression, exon junction; epidemiologic and clinical data 
UCSC Genome Browser https://genome.ucsc.edu/ Mutations, CNVs, mRNA and miRNA expression, splice variants, DNA methylation, protein expression, clinical data 
Genomic Data Commons https://gdc.cancer.gov/ Mutations, CNVs, mRNA and miRNA expression, structural variants, splice variants, DNA methylation, protein expression 
FireBrowse http://firebrowse.org/ Interface for analyzing TCGA data 
OncoKB https://www.oncokb.org/ Mutations, CNVs, fusions (175
DepMap https://depmap.org/portal/ Genetic loss-of-function screening, pharmacologic dependencies, CCLE omics characterizations 
canSAR.ai https://cansar.ai/ Integrates biology, chemistry, pharmacology, structural biology, cellular networks and clinical annotations, and applies machine learning approaches to develop predictions useful in drug discovery (117

Abbreviations: CCLE, Cancer Cell Line Encyclopedia; CNV, copy-number variation; DepMap, Cancer Dependency Map.

Large-scale sequencing efforts have revealed hundreds of potential driver genes, each with numerous different coding variants. Elucidation of the functional role of these genes in cancer and the phenotypic consequences of specific genetic alterations requires experimental manipulation in relevant model systems. In the past few decades, several new types of cancer models have been developed that recapitulate key features of tumor heterogeneity and the microenvironment, including but not limited to genetically engineered mouse models, patient-derived xenograft models, and patient-derived organoid models. However, cancer cell lines remain the workhorse model system for studying cancer biology and characterizing the effects of genetic and pharmacologic perturbations due to their scalability and ease of manipulation.

The Cancer Cell Line Encyclopedia (CCLE) project began in 2008 as a collaboration between the Broad Institute and the Novartis Institutes for BioMedical Research to comprehensively characterize the molecular features of a large panel of human cell lines. This collaboration was later joined by the MD Anderson Cancer Center and Harvard Medical School. Since its initiation, this effort has profiled over 1,000 cell lines from more than 30 cancer lineages at the genomic, transcriptomic, proteomic, and metabolic levels. All, or a significant subset of the lines, have been profiled for whole-genome sequencing, WES, mRNA expression, RNA splicing, microRNA expression, DNA methylation, histone modifications, reverse-phase protein array, and metabolites (16, 17). In 2018, the CCLE project became part of the Cancer Dependency Map (DepMap) program (discussed below); CCLE profiling data are available through the DepMap portal. The Wellcome Sanger Institute's Catalogue of Somatic Mutations in Cancer (COSMIC) database is another useful resource with large-scale genomic data on tumors and tumor cell lines (Table 1).

Coupled with this extensive panel of well-characterized cell line models, the advent and adoption of a variety of functional genomics tools provided new approaches to characterize the biological role of the many cancer-related genes identified through these large profiling efforts. In the early 2000s, RNAi became a valuable and widely used tool to silence gene expression, thus allowing scientists to assess the function of any gene of interest in nearly any cell type. The use of siRNA and short hairpin RNA (shRNA) technologies to characterize the phenotypic consequences of knockdown of individual genes in cancer cell lines became the standard approach for evaluating the functions of candidate oncogenes and tumor suppressor genes. Genome-wide barcode shRNA screens coupled to NGS became widely used for the identification of new therapeutic targets in cancer (18–20). However, it soon became understood that the use of RNAi approaches resulted in the silencing of numerous unintended (off-target) transcripts due to seed region sequence complementarity, leading to many false positives when assessing cancer dependencies in pooled screens and on an individual gene basis (21). To overcome this limitation, researchers have made improvements in the promoter and microRNA context for shRNA expression and incorporated increased numbers of shRNA sequences per gene in large-scale screens (22–24).

In many instances, the use of “dirty” validation tools such as early RNAi modalities and nonselective tool compounds led to significant resources being deployed against the wrong targets. One example of an incorrectly validated target is the Maternal Embryonic Leucine Zipper Kinase (MELK), in which inhibition by RNAi and promiscuous kinase inhibitors showed strong effects on the viability of triple-negative breast and other cancer cell lines (25–28). On the basis of numerous publications on this dependency, several companies developed MELK inhibitors. However, later characterization using CRISPR–Cas9 knockout and selective MELK inhibitors clearly showed that MELK activity is not required for cell proliferation, survival, or stress tolerance (29, 30). The precise role of MELK in cancer is still being explored. This highlights the importance of using selective inhibitors for target validation.

As the use of RNAi screening was exploding, scientists were characterizing the prokaryotic CRISPR–Cas system and developing tools for CRISPR–Cas9-mediated genome editing in mammalian cells (31–34). Although initially used to drive gene knockout, it was soon determined that this system could be exploited to knock in specific genetic alterations by providing a DNA sequence that the cell can use as a repair template to drive homology-directed repair, thus providing a means to determine the functional consequence of individual coding sequence variants in oncogenes and tumor suppressor genes. There are still instances where CRISPR-mediated editing has unintended “off-target” effects. For instance, CRISPR targeting of amplified genes can lead to viability effects resulting from excessive DNA damage rather than loss of gene function (35, 36). Nevertheless, CRISPR technology has proven to have fewer overall off-target effects than RNAi and has revolutionized how cancer biologists approach target identification and validation.

To explore the role of every gene in nearly every cancer type, scientists at the Broad Institute (Project Achilles) and the Wellcome Sanger Institute (Project Score) initiated genome-wide CRISPR-Cas9 (and, previously, RNAi) loss-of-function screens in hundreds of molecularly characterized cell lines to systematically identify genotype-specific selective dependencies. The two institutes entered a strategic collaboration to accelerate these efforts, known as the DepMap, which has produced an integrated genome-wide screening dataset spanning more than 900 cell lines (37–39). This project also includes a large-scale drug-sensitivity profiling project, PRISM, that utilizes a molecular barcoding method to pool cell lines in order to rapidly profile the viability effects of thousands of compounds across hundreds of cell lines (40). As of the summer of 2018, the CCLE also became part of the Broad DepMap Project. The DepMap portal (Table 1; Fig. 2), which is publicly accessible, integrates datasets from all of these screening efforts and the CCLE. DepMap has become a fundamental resource used by academia and industry alike for further hypothesis-driven target validation and targeted drug discovery efforts.

Figure 2.

Databases and visualization tools for molecular characterization of human tumors and tumor cell lines. DepMap visualization for the PIK3CA gene indicating that cell lines with functional/activating PIK3CA mutations are dependent on PIK3CA for proliferation (16, 176). RNA-seq, RNA sequencing; WGS, whole-genome sequencing; WT, wild-type.

Figure 2.

Databases and visualization tools for molecular characterization of human tumors and tumor cell lines. DepMap visualization for the PIK3CA gene indicating that cell lines with functional/activating PIK3CA mutations are dependent on PIK3CA for proliferation (16, 176). RNA-seq, RNA sequencing; WGS, whole-genome sequencing; WT, wild-type.

Close modal

Pairing tumor genetic data with functional genomics data from DepMap and other large-scale screens has contributed to the development of several novel therapies that are nearing or have entered clinical trials. The foundation for this approach has its origins in the identification of mutant BRAF as a validated oncogene. Activating BRAF mutations were first identified through sequencing tumor cell lines followed by functional validation in vitro and confirmation in a range of primary human tumor samples (41). This discovery invigorated efforts to develop selective BRAF inhibitors, which have transformed the treatment landscape in metastatic melanoma, and also provided useful tools to gain insights into the mechanisms of activation of various BRAF mutations (42). Today, there are three BRAF inhibitors approved to treat a range of tumors with BRAFV600 mutations, including vemurafenib, dabrafenib, and encorafenib.

The elucidation of synthetic lethal relationships has also been a major advancement coming out of the pairing of sequencing and functional genomics efforts and has led to many novel therapeutic targets. The vulnerability of MTAP-deleted cancer cells to inhibition of PRMT5 and other enzymes that support its function was first discovered through Project Achilles and Project DRIVE shRNA screening data (43, 44). MTAP is a key enzyme in the methionine salvage pathway, and the MTAP gene is frequently deleted in cancers due to its proximity to the CDKN2A tumor suppressor gene. These initial findings paved the way for deeper exploration of susceptibilities conferred by MTAP deletion (45, 46), and there are now several clinical trials underway in patients with MTAP-deleted cancer for agents targeting PRMT5 and its supporting enzymes, including the MTA-cooperative PRMT5 inhibitors AMG-193, TNG462, and MRTX1719, and the MAT2A inhibitors IDE397 and AG-270 (47).

Importantly, synthetic lethality has opened the door to selective targeting of certain tumor suppressor gene mutations. PARP inhibitors provided the first clinical validation of this approach when the discovery of the synthetic dependency on PARP1 in BRCA1/2-deficient cancers led to the development of a number of PARP inhibitors, namely, olaparib, niraparib, rucaparib, and talazoparib. These medicines have revolutionized the treatment of BRCA-mutant ovarian, breast, prostate, and pancreatic cancers (reviewed in ref. 48). Components of the BAF (SWI/SNF) chromatin-remodeling complexes, such as SMARCA4, ARID1A, SMARCB1, and others, represent another class of tumor suppressor genes that are frequently lost or mutated in cancer. Together, deficiencies in BAF complex subunits occur in over 20% of human cancers. Synthetic lethal interactions between various components of the complex were identified through functional genomics screening efforts (reviewed in ref. 49). Several agents including SMARCA2 degraders and BRD9 degraders are currently in clinical trials in patients with SMARCA4 mutations and SMARCB1 loss, respectively. There are also efforts underway to target mutant tumor suppressors directly. For example, PC14586, a small-molecule structural corrector that restores wild-type function to the Y220C mutant p53 protein, is currently in clinical trials in patients carrying the TP53Y220C mutation.

More recently, scientists have expanded functional genomics screens to use dual-gene CRISPR systems to explore the compensatory effects of paralog genes that underly selective digenic dependencies (50). Due to their sequence and structural homology, small-molecule drugs are often active across paralogs, and thus these digenic dependencies present new therapeutic opportunities.

Although the underlying basis for the selective dependency on many genes can be linked to specific mutations, such as increased dependency on KRAS in KRAS-mutant cell lines or the synthetic lethal dependency on SMARCA4 in SMARCA2-deficient cell lines, as well as the examples discussed above, there are still numerous selective dependencies identified through these large-scale screens that have not been clearly linked to a specific molecular marker or profile. The relationship between genetic dependencies and cancer genomes is nonlinear, with the interplay between multiple genetic alterations often determining the degree of dependency on any one gene. Moreover, additional nongenetic factors such as alterations in the epigenome, transcriptome, proteome, and microenvironment of cancer cells may also contribute to selective dependency. As these genes represent potential targets for the development of novel therapies, it will be crucial to elucidate the unique cellular features (biomarkers) that predict dependence to better identify patients likely to benefit from new therapies. To this end, deep learning methods are being developed in an effort to predict gene dependencies or drug sensitivities from complex genomic and transcriptomic profiles (51, 52). It remains to be determined whether these approaches will lead to the identification of predictive biomarkers for novel druggable targets.

The large number of patient tumors and cell lines that have been extensively profiled has led to the identification of both lineage-specific genetic alterations and driver genes as well as alterations that are shared across many cancer types. These pan-cancer analyses have revealed that cancers of different tissues can share the same drivers and be biologically more similar to each other than to other tumors of the same tissue of origin. The similarities in driver gene dependencies across indications have also been borne out in large-scale functional genomics screens, ultimately changing the way we think about developing new therapies to include both indication-centric treatments and treatments that are appropriate for genetically defined subsets of patients across multiple indications.

The ability to both identify and experimentally manipulate the macromolecules that are altered in cancers using relevant model systems has led to the discovery of numerous potential therapeutic targets. New drugs against several of the oncogenic proteins identified through these efforts are now in clinical trials or have been recently approved. Still, other novel agents have not been as efficacious in patients, as predicted by preclinical models. This may be due to the presence of multiple driver mutations in a single tumor, niche-derived resistance factors, intratumoral heterogeneity, or other factors, all of which point to the need for drug combination strategies. For instance, patients with colorectal cancer harboring KRASG12C mutations derive less benefit from KRASG12C inhibitors compared with patients with non–small cell lung cancer (NSCLC) who carry this mutation, likely due to high levels of EGFR activity in colorectal cancer (53). Clinical trials exploring the efficacy of KRASG12C inhibitors in combination with EGFR antibodies are currently underway, with early data indicating improved response rates with the combination (54–56).

Selective biological dependency is only one factor when prioritizing targets for drug discovery efforts. Perhaps equally important is the assessment of druggability. Many classes of oncogenic proteins remain challenging to drug using traditional approaches, but are beginning to be tackled through improvements in medicinal chemistry strategies and data science tools.

Advances in Medicinal Chemistry

Medicinal chemistry has undergone a revolution over the past two decades. Multiple advances have come together to create a powerful toolbox that, when applied in concert, promises to help address targets hereto considered undruggable.

Property-Based Drug Design

One significant advance is the use of physicochemical properties to design small-molecule compounds most likely to have good drug-like properties. This approach has been termed property-based drug design. It traces its origin to the proposal of the “rule of 5” (Ro5) in 1997, which states that compounds with hydrogen bond (H-bond) donors ≤5, H-bond acceptors ≤10, molecular weight (MW) ≤500, and logP (a measure of lipophilicity) ≤5 are more likely to have good oral absorption than those that fail these rules (57). This concept was extended to allow for the prediction of other important drug-like properties such as central nervous system penetration (58), solubility (59), and safety liabilities (60). All the physicochemical properties for these relationships can be calculated in silico based on the chemical structure at the design stage before synthesis, thereby increasing drug discovery efficiency and speed and reducing attrition.

Macrocycles

Over the last few decades, desirable drug targets have expanded to include those with shallow and extended binding pockets for which obtaining good binding activity is challenging. This has led to the exploration of the possibilities offered by compounds “beyond the rule of 5” (BRo5), with MW >500 and a higher number of H-bond donors and acceptors, capable of binding more tightly to these difficult pockets while retaining cell permeability and oral absorption (61). One of the main strategies to accomplish this has been through macrocyclization. Macrocycles are present in multiple orally bioavail­able BRo5 natural products, such as rapamycin (ref. 62; see “Novel Drug Modalities: Molecular Glues and Degraders” section below), and there has been an effort to apply learnings from these compounds as strategies for de novo–designed macrocycles (63). One finding has been that the preorganization offered by the ring structure can result in the energetic accessibility of conformations that allow intramolecular H-bond formation between H-bond donors and acceptors. These intramolecular H-bonds conceal some of the molecule's polarity, thereby increasing membrane permeability. This strategy has seen a resurgence, in part due to a 2008 review (63), and has been extended to achieve good permeability and absorption with nonmacrocyclic BRo5 compounds. Macrocyclization has multiple benefits beyond improving permeability, including increased binding affinity, selectivity, and metabolic stability, due to favoring the bioactive conformation and disfavoring of other conformations that antitargets and metabolic enzymes can recognize. Hence, it is also utilized for these purposes in the Ro5 chemical space, as is the case in the ALK inhibitor lorlatinib (ref. 64; see “Potency Matters: ALK Inhibitors” section below) and in the recently approved JAK2/FLT3 inhibitor pacritinib (65).

Allostery

Allosteric ligands that modulate the activity of a protein by binding to a site distinct from the active or orthosteric site have seen a resurgence over the last decade. Some of the first allosteric modulators were developed against G protein–coupled receptors. In recent years, there has been a renewed interest in them due to the many challenges in drug discovery that they can help address (66). Because they do not bind to the highly conserved orthosteric site, they offer the opportunity for selectivity against closely related proteins or for mutants against wild-type proteins. Furthermore, allosteric modulators provide the opportunity to tune the activity of the orthosteric ligand, allowing for partial inhibition or activation, or altered downstream signaling, and can help address resistance due to mutations in the orthosteric site. For example, the BCR–ABL inhibitor asciminib binds to the allosteric myristate site and maintains activity against orthosteric inhibitor–resistant ATP-site mutations (ref. 67; see “Targeting through Orthosteric and Allosteric Mechanisms: BCR–ABL” section below). Significantly, allosteric modulators can help tackle challenging biological targets such as those with poor orthosteric binding sites, including some protein–protein interactions, and targets with high-affinity endogenous ligands that would be difficult to displace with inhibitors. An example is the discovery of SHP099, which inhibits SHP2 phosphatase by interacting with an allosteric site (68) and is often credited for reinvigorating this target class, as selective orthosteric inhibition of phosphatases with drug-like molecules is challenging. The MEK inhibitor trametinib binds in an allosteric site adjacent to the ATP-binding site and further illustrates the concept (69). The disadvantages of pursuing an allosteric approach include the difficulty in identifying these sites in targets of interest, particularly since some may be cryptic—that is, only present in specific protein conformations. Furthermore, some allosteric sites may not affect the desired function once identified. The advantages outlined above, progress in understanding allosteric modulation mechanisms, the increased availability of structural biology information, and computational approaches to model protein dynamics have significantly bolstered this area. KRASG12C inhibitors that bind to a cryptic allosteric site adjacent to the nucleotide-binding pocket epitomize this class of compounds and are further discussed below (ref. 70; see “Covalent Binders” and “Covalent Targeting to Reveal Cryptic Drug-Binding Pockets: KRASG12C” sections).

Fragment-Based Drug Discovery

Fragment-based drug discovery (FBDD), the identification of relatively low-MW compounds that bind efficiently to their biological targets, and can be evolved into higher MW compounds with greater affinity and drug-like properties, has become a prevalent approach in drug discovery (71). FBDD was first practically demonstrated in 1996 by Abbott scientists in the discovery of FKBP ligands (72) and has since been applied to a multitude of targets. The physicochemical properties of the compounds screened in FBDD have been defined as the “rule of 3,” which includes the use of fragments with MW ≤300 (73). Because these fragments are small and relatively simple in structure, it is possible to cover, with a relatively small library of a few thousand compounds, a similar breadth of chemical space as with a traditional, significantly larger collection of higher-MW compounds. Fragment screens are usually conducted using biophysical techniques to detect hits with relatively weak binding affinity, which are then evaluated using ligand efficiency calculations. This metric normalizes their binding affinity to their size (74). The initial fragment hits are then elaborated to potent molecules either by merging two fragments or fragment growth through the attachment of additional functionality. The process is typically guided by X-ray crystallography or other structural biology techniques. An alternative process involves screening by high-throughput X-ray crystallography and has the benefit of providing structural information to guide hit optimization directly from the screen (75). This approach has been successfully optimized for academic and industrial applications at the XChem facility at Diamond Light Source (76). Due to its efficient coverage of chemical space, FBDD enables hit finding against challenging targets and the identification of allosteric sites and ligands, such as in the discovery of the BCR–ABL inhibitor asciminib (ref. 67; see “Targeting through Orthosteric and Allosteric Mechanisms: BCR–ABL” section below). In addition to asciminib, several oncology-approved drugs have originated from fragments. They include the first fragment-based approved drug, the BRAF inhibitor vemurafenib (77), the BCL-2 inhibitor venetoclax, which originated with a fragment identified using the original nuclear magnetic resonance (NMR) screening approach (78), and the FGFR inhibitor erdafitinib (79). In addition, capivasertib, an AKT inhibitor in phase III clinical trials, originated independently from a fragment similar to vemurafenib's original fragment (80, 81).

Degraders

Identifying degraders, compounds that cause the selective degradation of a protein of interest (POI), has emerged as a promising strategy for drugging targets for which developing functional inhibitors is difficult or insufficient (82). Molecular glues are small molecules capable of stabilizing the interactions between two proteins through the formation of a ternary complex to alter their function (83). Glue degraders, a subclass of molecular glues, have emerged as a promising type of monovalent degraders (84). They are compounds that bind to an E3 ubiquitin ligase and induce or enhance its interaction with a POI, leading to ubiquitination and proteasomal degradation of the POI. Typically, they do not bind independently to the POI. The prototypical glues are the immunomodulatory imide drugs (IMiD), such as thalidomide, which were discovered serendipitously. They bind the cereblon E3 ligase and induce degradation of the IKAROS family zinc finger proteins, among others. Unfortunately, the de novo identification of glue degraders for a specific POI has proven challenging.

Proteolysis-targeting chimeras (PROTAC), a more modular type of degrader, were first described in 2001 (85). PROTACs are bifunctional molecules that contain a binder to an E3 ubiquitin ligase, a binder to the POI, and a linker that joins the two (86). The PROTAC thus forms a ternary complex with the E3 ligase and POI, bringing the two proteins into proximity and allowing for ubiquitination and degradation of the POI. One major advantage of PROTACs is that the binder to the POI can be “silent” or devoid of functional activity, as PROTACs can exploit favorable silent allosteric sites. Other advantages, which they share with glue degraders, include their catalytic nature, which can lower the requirement for strong affinity for the POI and for high in vivo exposure, their possible extended duration of action, and the opportunity for novel pharmacology arising from degradation as opposed to specific functional inhibition. PROTAC disadvantages include the current need for empirical optimization despite their modularity and typically BRo5 characteristics. Despite this, the application of BRo5 approaches has resulted in 15 oral PROTACs, spanning nine different targets, advancing to clinical trials (ref. 86; see “Molecular Glues and Degraders” section).

Covalent Binders

Another area of significant progress has been the rational design of covalent small-molecule drugs (87, 88), resulting in more than 40 approved covalent drugs to date. Covalent compounds bind to their biological targets in a two-step process. First, they bind reversibly through specific noncovalent interactions that place the ligand's reactive functionality close to the target's reactive amino acid, enabling subsequent covalent bond formation between the ligand and target. The initial specific noncovalent binding requirement is critical to achieving selectivity for the desired target. Although covalent drugs have been in use for a long time, it has only recently been demonstrated that one can, by design, add a reactive electrophilic group to an existing noncovalent ligand. This approach was first applied to the tyrosine kinase EGFR (ref. 89; see “Targeting Resistance Mutations: Four Generations of EGFR Inhibitors” section) and subsequently to multiple other targets. Notably, the increased binding affinity obtained with covalent compounds has been utilized to address targets that have proven challenging using noncovalent ligands, as with KRASG12C inhibitors (90). The KRASG12C inhibitor example used a different approach than that used in EGFR, which is based on FBDD and involves first identifying a covalent fragment hit and then evolving it to a higher-MW compound with enhanced noncovalent interactions with the target (70). This work also demonstrates the utility of this approach for identifying novel allosteric or cryptic pockets (see “Covalent Targeting to Reveal Cryptic Drug-Binding Pockets: KRASG12C” section below). Most of the designed covalent drugs, including those against EGFR and KRASG12C, target cysteine residues, as these can have high reactivity. Still, efforts are ongoing to expand them to other potentially reactive amino acids such as lysine and tyrosine. The potential advantages of covalent ligands include the ability to obtain good potency even in relatively shallow binding pockets, enhanced selectivity in cases in which the covalently bound amino acid residue is unique to the desired target, extended duration of action after the inhibitor has been cleared from the body, and reduction in off-target toxicity by rapid and extended target engagement. One common criticism of covalent compounds is the possibility of off-target or idiosyncratic toxicity arising from the covalent modification of undesired proteins.

Developments in the area of chemical proteomics and particularly in competitive activity-based protein profiling (ABPP) have greatly enabled covalent drug discovery (91). Identifying covalent fragment hits, as in the KRASG12C case, promises to provide good starting points for covalent drugs against challenging targets. Although initially focused on investigations of enzyme families, ABPP was first utilized in 2016 to identify covalent fragment ligands for cysteine residues across the proteome, leveraging advances in higher throughput mass spectroscopy (MS; ref. 92). This approach moved covalent fragment discovery from a target-focused activity to one that could be done broadly across the proteome to identify allosteric and cryptic pockets for challenging targets (93). The approach relies on incubating live cells (or cell lysate) with a compound, followed by treatment of each sample with a cysteine-reactive probe that covalently binds to accessible cysteines not previously modified by the compound. Following proteolysis, the probe is used to pull down the peptides containing probe-modified cysteines, and the samples are injected into the MS. The hits are identified by a loss of signal in any of the peptide-probe MS peaks in the compound-treated samples relative to the control. MS-enabled competitive ABPP finds ligandable hotspots across the proteome and identifies covalent fragment hits against some of these sites that can be elaborated to covalent drugs. The technique can also determine the selectivity of covalent compounds against cysteines across the proteome to help reduce their off-target or idiosyncratic toxicity risk. Notably, this technique allows for screening targets in their native cellular context. Furthermore, the method can identify covalent E3 ligands that could be transformed into covalent PROTACs or molecular glues.

Chemical Libraries and Probes

Enhancements in compound screening collections have also been a critical enabling tool. This extends to better curated high-throughput sequencing collections, target-focused collections, and well-designed noncovalent and covalent fragment libraries (94). DNA-encoded libraries (DEL), first described conceptually in 1992 (95), have become a valuable method of identifying binding hits for challenging targets, and there are now at least three compounds in clinical development targeting sEH, RIP1, and ATX that originated from DEL hits, although these are all being developed for nononcology indications (96, 97). They are extensive combinatorial small-molecule libraries in which each compound is attached to a DNA oligomer that encodes the identity of the small-molecule. They are screened by affinity capture with the target protein, and the hits are decoded by PCR amplification and sequencing of the attached DNA tag. Because of their very large size, DELs can be helpful when smaller libraries have not afforded hits. The binders they identify can be screened for functional activity or used as the POI binder in a PROTAC approach. Covalent DELs can also identify hits to enable covalent programs, demonstrating their versatility.

Chemical probes are well characterized and selective small-molecule modulators of a specific protein that are useful for exploring the biological function or role of its target, and for validating or invalidating the target for drug discovery. They are complementary to genetic approaches. Unfortunately, the broad use of low-quality chemical probes, particularly with low selectivity, has led to erroneous conclusions in the literature. The Chemical Probes Portal (https://www.chemicalprobes.org/) was established as an expert-curated resource for high-quality chemical probes for usage in biomedical and drug discovery efforts (98, 99) and exemplifies the benefits of the close integration of chemistry with biology.

Advances in Data Science

Many of the advances in medicinal chemistry have been accelerated by advances in structure-enabled drug discovery approaches. The significant increases in computational power, including the development of graphical processing units (GPU), have enabled the implementation of more sophisticated algorithms and the execution of larger-scale experiments.

The Protein Data Bank, established in 1971, currently contains >200,000 entries covering X-ray crystallography structures and also structures determined by other techniques such as NMR, cryo-electron microscopy (cryoEM), and other diffraction methods (100). Since then, the receptor-based drug design field quickly emerged as a tool in drug discovery and evolved through increases in computational power (101, 102) and available structural information. The first publication on a small-molecule docking algorithm appeared in 1982 (103). With the sequencing of the human genome, there was renewed interest in assigning function to every protein in the human genome to understand human disease and enable drug discovery. To support those efforts, in the early 2000s, the Structural Genomics Consortium was started as a public–private partnership to solve crystal structures of novel proteins (104, 105).

Computational tools are now routinely used to find novel chemical matter for targets of interest via virtual screening or scaffold hopping, and for the structure-guided optimization of existing small-molecule binders to targets of interest to further enhance potency and/or selectivity over undesirable off-targets.

Recently, the enumeration of ever-larger chemical libraries (106, 107) has emerged, using available building blocks and precedented chemical reactions—Enamine REAL and the WuXi AppTec virtual library. These collections now cover billions of structures, which can be reliably synthesized within short time frames of 2 to 3 weeks at a reasonable cost. Although ligand-based approaches can be utilized to analyze these large collections comprehensively, screening these collections using receptor-based virtual screening requires too much computing time. In order to address those constraints, a combination of docking and docking-based machine learning approaches is being explored to prioritize compounds for testing (108, 109).

Deep learning has also enabled the development of generative chemistry engines to derive novel chemical structures based on existing chemical space, like, for example, those embedded in ChEMBL (110) or ZINC (111). Generally, either SMILES representations or graphical representations are used to describe the molecules, and different architectures can be used to generate novel molecules (112–115). In order to generate compounds within a specific chemical space of biological interest, the generic model is fine-tuned using compounds with the desired property of interest (115, 116). A more focused resource for cancer-centric drug discovery is canSAR (https://cansar.ai; Table 1), which integrates medicinal chemistry information with structural biology data and multiomic data (117).

In addition to advances in docking algorithms and integration of deep learning approaches, predictions of free energies of binding of small molecules to protein targets have become significantly more accurate in the last decade. Methods like free energy perturbation (FEP) and thermodynamic integration (TI) have benefited from improvements in molecular force fields, development of enhanced sampling methods, and utilization of GPUs instead of central processing units to make these methods important new tools in drug discovery (118, 119).

With the advances in force fields and hardware, molecular dynamics simulations and enhanced sampling methods have also become important tools for understanding protein dynamics–function relationships and small-molecule binding to protein targets (120–123). The increased simulation times that are now feasible have enabled reproduction of compound binding processes to cryptic binding sites (124–126) and the prediction of unexplored sites.

There has been a long-standing interest in the protein folding problem, or the question of how the amino acid sequence of a protein determines its 3D structure (127). Protein structure prediction methods have been an area of considerable interest, and since 1994, the biannual Critical Assessment of Techniques for Protein Structure Prediction (CASP) event has been held. CASP allows different groups to test their methods objectively on unpublished structures (128). With the incorporation of novel deep learning approaches, the DeepMind team developed AlphaFold (129) and placed first in CASP13 (130). CASP14 then saw multiple groups iterate on AlphaFold's advances, resulting in the development of RosettaFold (131), improvements to MULTICOM (132), and further development by DeepMind and the release of AlphaFold2 (129). Since the release of AlphaFold2, predicted structures for the complete human proteome and other species have become readily available. The utility of these models has been assessed in virtual screening (133) and FEP (134), and also to better define construct boundaries for crystallography and help resolve cryoEM and X-ray structures (135). However, there are many targets and domains of proteins that are poorly characterized experimentally, and/or have a significant amount of disorder to them, in which these approaches can provide little insight. In addition, proteins are highly dynamic, and the characterization of multiple dynamic states is poorly captured (135).

Targeting through Orthosteric and Allosteric Mechanisms: BCR–ABL

ABL1 is a receptor tyrosine kinase and proto-oncogene in chronic myelogenous leukemia (CML). Translocation between the ABL1 gene and the breakpoint cluster region (BCR) gene generates the BCR–ABL oncogene and is pathognomonic for CML (Philadelphia chromosome). Targeting BCR–ABL in CML with imatinib represents the foundational example of small-molecule precision oncology: In the first randomized phase III study, imatinib treatment led to improvements in complete cytogenic responses (76.2%) compared with the interferon-alpha plus cytarabine combination (14.5%; ref. 136). Imatinib is an ATP-competitive (i.e., orthosteric) inhibitor of ABL1 kinase and provided critical proof of concept for targeting a kinase, demonstrating that a small-molecule drug can compete with millimolar concentrations of ATP in the cell, and that sufficient selectivity could be achieved such that only a narrow spectrum of kinases are inhibited, enabling a wide therapeutic index.

Targeting BCR–ABL in CML also provided a benchmark for follow-on inhibitors that demonstrated additional key concepts in small-molecule precision oncology, such as the importance of potency, selectivity, and the utility of drugs with orthogonal mechanisms of action that can overcome resistance. Clear evidence of the impact of improved potency and selectivity was demonstrated through head-to-head studies of nilotinib, another ATP-competitive inhibitor of BCR–ABL, versus imatinib. Depending on the assay used, nilotinib is ∼10- to 30-fold more potent than imatinib on BCR–ABL but roughly equipotent on cKIT and PDGFRβ (137). In a phase III study, nilotinib treatment led to a higher complete molecular response rate (26% at 300 mg twice/day and 21% at 400 mg twice/day) compared with imatinib (10% at 400 mg daily) and fewer patients progressing to accelerated or blast phase and fewer CML-related deaths (138).

Although additional orthosteric BCR–ABL inhibitors have been developed in CML, the acquisition of resistance mutations is a universal challenge (reviewed in ref. 139). A novel approach to overcome this issue was discovered by a team at Novartis that demonstrated that BCR–ABL could be inhibited through an allosteric mechanism by optimizing a small molecule to occupy the myristoyl pocket, locking the kinase in an inactive conformation (140). This concept led to the development of asciminib (ABL-001), which specifically targets the ABL myristoyl pocket, and preclinical studies demonstrated its ability to inhibit the most common and broadly resistant mutant to orthosteric inhibitors, BCR–ABLT315I. Although preclinical studies suggested that asciminib-resistance mutations in BCR–ABL could arise, these mutants were effectively inhibited by imatinib, and the combination of asciminib + imatinib completely prevented the emergence of resistance mutations in a mouse tumor xenograft model of CML (141). Asciminib received accelerated approval to treat patients with CML who have failed two or more prior BCR–ABL inhibitors or in patients who have a BCR–ABLT315I mutation. Asciminib is also being tested in combination with imatinib (e.g., NCT03578367) and nilotinib (e.g., NCT03874858) in human clinical trials to test the hypothesis that combining orthogonal mechanisms of action will further delay or prevent the emergence of resistance.

Overall, the journey of targeting BCR–ABL with small mole­cules and incrementally improving patient outcomes spans more than 20 years and exemplifies part of the evolution of small-molecule discovery (139). Prior to the introduction of imatinib, patients were treated with interferon and chemotherapy and mostly progressed rapidly from the chronic phase and died in blast crisis, while today CML is a chronic disease that most patients can live with for decades, with about 5% to 10% achieving treatment-free remission after cessation of tyrosine kinase inhibitor (TKI) therapy (142).

Potency Matters: ALK Inhibitors

ALK is a receptor tyrosine kinase, and fusions between the ALK gene and nucleophosmin (NPM) or echinoderm microtubule-associated protein-like 4 (EML4) occur in approximately 60% of anaplastic large cell lymphomas and 2% to 7% of NSCLC. These fusions lead to enhanced dimerization and activation of the ALK kinase domain and drive oncogenic transformation. The discovery of ALK translocations in NSCLC led to the evaluation of the multitargeted ALK kinase inhibitor, crizotinib, and validated EML4–ALK as a therapeutic target. In a phase I study in patients with ALK+ NSCLC, crizotinib treatment resulted in a 47% response rate (RR), and in a subsequent randomized phase III trial, crizotinib treatment resulted in a 74% RR and median progression-free survival (PFS) of 10.9 months, which compared favorably with chemotherapy (45% and 7 months; ref. 143). Crizotinib is an orthosteric kinase inhibitor that was initially developed as a MET inhibitor and in fact inhibits several other kinases at similar concentrations as ALK and MET, leaving open the possibility to create second-generation inhibitors with greater potency against ALK. For example, the cellular potency of alectinib is approximately 4-fold more potent than crizotinib (∼10 nmol/L vs. ∼40 nmol/L IC50; ref. 144), and in a head-to-head phase III trial, alectinib provided a higher RR and improved median PFS versus crizotinib (92% vs. 79% and 34.1 vs. 10.2 months; refs. 145, 146). With the therapeutic benefit of ALK inhibitors clearly established, the macrocycle small-molecule lorlatinib (see “Advances in Medicinal Chemistry” section) was developed with best-in-class potency against ALK (∼2 nmol/L cellular IC50; ref. 144) and in a phase III trial demonstrated improved efficacy over crizotinib with an RR of 76% versus 58% (147) and 3-year PFS of 64% versus 19% (148). The improvement in clinical efficacy demonstrated by lorlatinib compared with crizotinib clearly demonstrates the potential for a more potent and target-optimized small-molecule inhibitor to displace an earlier-generation inhibitor in the first-line setting and represents a major advance for patients with NSCLC whose tumors express ALK translocations (Fig. 3). Although the case for increased potency can be made with ALK inhibitors and other targeted therapies, sufficient selectivity versus off-targets, especially for kinase inhibitors, must be maintained in order to realize improvements in therapeutic benefit.

Figure 3.

The evolution of ALK inhibitors to treat ALK+ NSCLC. Kaplan–Meier plot illustrating the improvement in PFS of crizotonib vs. chemotherapy (143) on the left and lorlatinib vs. crizotinib (147) on the right. Reprinted with permission from NEJM. CI, confidence interval; SBDD, structure-based drug design.

Figure 3.

The evolution of ALK inhibitors to treat ALK+ NSCLC. Kaplan–Meier plot illustrating the improvement in PFS of crizotonib vs. chemotherapy (143) on the left and lorlatinib vs. crizotinib (147) on the right. Reprinted with permission from NEJM. CI, confidence interval; SBDD, structure-based drug design.

Close modal

Targeting Resistance Mutations: Four Generations of EGFR Inhibitors

EGFR is a receptor tyrosine kinase that plays a critical role in epithelial cell proliferation and homeostasis. Activating mutations in EGFR occur in approximately 20% of NSCLC tumors, the vast majority of which consist of exon 19 deletions (EGFRExon19del) or the L858R mutation (EGFRL858R). Erlotinib and gefitinib are ATP-competitive EGFR inhibitors that were first approved to treat NSCLC based on the hypothesis that EGFR overexpression drives tumors in this indication. However, it was soon recognized that only patients with EGFR-mutant tumors realize clinical benefit (149, 150). Although the clinical benefit in the EGFR-mutant patient population was confirmed in randomized clinical studies (151, 152), the majority of patients still relapsed in less than 1 year. As with other small-molecule targeted therapies, tumors developed resistance and the most frequent mechanism was through the acquisition of the T790M gatekeeper mutation in the EGFR ATP-binding pocket (153, 154). This mutation causes resistance due to increasing the affinity for ATP (lowers ATP Km), making it more difficult for erlotinib or gefitinib to bind. Although second-generation EGFR inhibitors were developed, differentiated from first-generation inhibitors through their covalent mode of binding, these compounds failed to effectively inhibit the T790M mutant.

The third-generation EGFR covalent inhibitor osimertinib was specifically designed to inhibit EGFRL858R;T790M and EGFRExon19del;T790M. In a phase II clinical trial in patients who relapsed on a prior EGFR inhibitor with the T790M mutation, osimertinib treatment resulted in a 70% RR, providing clinical proof of concept (155). Osimertinib was subsequently tested head-to-head against gefitinib or erlotinib in first-line NSCLC patients with EGFR mutations and demonstrated a comparable RR to the first-generation inhibitors (80% vs. 76%) but significant improvements in median PFS (18.9 vs. 10.2 months) and overall survival (38.6 vs. 31.8 months; refs. 156, 157).

First-line osimertinib treatment represents a significant improvement in therapeutic benefit to patients; however, acquired resistance to osimertinib still occurred through a range of mechanisms but most notably not through the acquisition of T790M (reviewed in ref. 158). Some of the on-target mechanisms of resistance represent opportunities for the development of next-generation EGFR inhibitors. For example, the EGFR C797S mutation causes resistance to osimertinib through mutation of the cysteine to which the drug covalently binds, opening the opportunity for next-generation noncovalent EGFR inhibitors. Fourth-generation EGFR inhibitors such as BLU-945 selectively inhibit EGFRL858R;T790M by binding in the orthosteric pocket through a noncovalent mechanism and therefore have the potential to treat osimertinib-resistant tumors with a C797S mutation (159). BLU-945 (NCT04862780) and several other so-called fourth-generation EGFR inhibitors are currently undergoing preclinical and clinical development. The development of drugs that inhibit different resistance mutations provides the potential to move combinations of EGFR inhibitors into first-line treatment and potentially prevent the emergence of on-target resistance mechanisms, similar to the strategy for ABL inhibitors as described above.

Covalent Targeting to Reveal Cryptic Drug-Binding Pockets: KRASG12C

KRAS is a small GTPase that transmits growth factor receptor signals from the cell membrane to intracellular signal transduction cascades, including the RAF–MEK–ERK mitogen-activated protein kinase pathway and the PI3K pathway. KRAS is the most frequently mutated oncogene, occurring in approximately 16% of all human cancers, and has been the focus of drug discovery efforts for more than 25 years. However, due to its high affinity for GTP and the lack of an alternative pocket, it remained an undruggable target until the focus turned to the creation of KRASG12C mutant–specific inhibitors through covalent binding to the mutant cysteine (70). The identification of cysteine-reactive fragments that selectively bind the mutant cysteine revealed a shallow cryptic pocket adjacent to the nucleotide-binding pocket. This seminal work launched multiple drug discovery efforts, culminating in the development and approval of sotorasib, the first inhibitor of KRASG12C in patients with NSCLC. Approval was based on a single-arm, phase II study in second-line KRASG12C-mutant NSCLC patients previously treated with standard therapies in which sotorasib demonstrated a 37% RR, a median PFS of 6.8 months, and median overall survival of 12.5 months (55). A second KRASG12C inhibitor, adagrasib, which has a similar mechanism of action, was also recently approved in second-line KRASG12C-mutant NSCLC patients and demonstrated comparable efficacy: a 42.9% RR, a median PFS of 6.5 months, and a median overall survival of 12.6 months (160).

The successful development of inhibitors against KRASG12C spurred the discovery of drugs that selectively target KRASG12D noncovalently (161), KRASG12R covalently (162), and wild-type KRAS noncovalently (163) using inhibitors that bind the same cryptic pocket. Leveraging cysteine-reactive small molecules to identify cryptic pockets is an approach that could be applied to other difficult-to-drug targets and encompasses the rapidly growing field of covalent chemical proteomics (92). The ability to screen cysteine-reactive fragment probes or more drug-like small molecules in live cells has the potential to identify cryptic pockets on thousands of proteins that were previously considered undruggable (see “Advances in Medicinal Chemistry” section).

Novel Drug Modalities: Molecular Glues and Degraders

Small molecules that promote the formation of a ternary complex between two proteins to alter the function or induce the degradation of a target protein create the potential for novel mechanisms of action against target proteins that are otherwise difficult to drug. A thorough review of the field is beyond the scope of this article, but the reader can refer to recent review articles (83, 86). Instead, we will provide a brief history of small-molecule glues and degraders with examples of oncology drugs or drug candidates in this class that have been approved or are currently undergoing clinical trials.

The natural product rapamycin was one of the first molecular glues to be discovered, inducing a ternary complex between mTOR and FKBP12, resulting in the allosteric inhibition of mTOR activity (reviewed in ref. 62). Rapamycin analogues with improved drug-like properties such as temsirolimus and everolimus have been approved to treat renal cell carcinoma, with the latter also approved to treat HR+/HER2 breast cancer and pancreatic neuroendocrine tumors. Recently, a bisteric inhibitor of mTORC1 has been described, which is composed of an FKBP12 binding moiety linked to an orthosteric mTOR kinase inhibitor (164), and a clinical candidate, RMC-5552, is undergoing a phase I clinical trial in solid tumors (NCT04774952). Bifunctional inhibitors of KRASG12C have also been described, which create a ternary complex with FKBP12 or cyclophilin A (165), and a phase I trial has recently started with RMC-6236, a so-called tricomplex inhibitor that generates a ternary complex with KRAS and cyclophilin A (NCT05379985).

The discovery of the mechanism of action of thalidomide, a drug used to treat multiple myeloma, involves the formation of a ternary complex between members of the IKAROS family of transcription factors (IKZF1 and IKZF3) and the E3 ubiquitin ligase cereblon (166), leading to the degradation of IKZF1/3. This discovery accelerated the emergence of new classes of drugs that function as monovalent degraders through cere­blon engagement. For example, monovalent degraders of GSPT1 such as CC-90009 (NCT02848001) and MRT-2359 (NCT05546268) or IKZF2 such as DKY709 (NCT03891953) are currently undergoing clinical trials, and major efforts are ongoing to identify new chemical matter and new E3 ligases that can function together as molecular glue degraders (reviewed in ref. 167). This mechanism has the advantage of not requiring a drug-binding pocket in the POI, but rather relies on the generation of a drug-binding pocket at the interface of the two proteins. Thalidomide derivatives have also been used as cereblon engagers in heterobifunctional degraders (PROTAC), in which they are linked to a small molecule that binds to the POI. Such heterobifunctional degraders can also be generated using small molecules that engage other E3 ubiquitin ligases such as VHL, IAP, or MDM2. There are numerous heterobifunctional degraders in clinical deve­lopment targeting a wide range of proteins including BCL-xL, BRD9, BTK, EGFRL858R, BRAFV600E, ER, AR, TRK, and IRAK4 (reviewed in ref. 86).

INTEGRATION OF THE TECHNOLOGICAL TOOLBOX

These technology advances over the past 20 years have dramatically expanded the drug hunter's toolbox to address an equally expanding universe of validated, driver oncology targets. Maximally leveraging these technologies requires an integrated and synergistic approach, not simply deploying them in a linear, one-at-a-time manner. For example, advanced chemical proteomics tools can uncover cryptic pockets in proteins (93), but these pockets, while ligandable by reactive probes, may not be large enough or open long enough to make them druggable. Motion-based computational drug discovery may separately reveal transient pockets in such proteins (125). Combining these technologies allows one to understand when and where these reactive cryptic pockets become available for small-molecule ligands, and once identified, how best to advance the structure–activity relationship, aided by structural biology and computational techniques, to provide additional, noncovalent points of interaction to increase the potency and selectivity of the emerging drug-like leads. Additionally, machine learning tools that augment the drug hunter's experience and skill to accelerate specific aspects of drug design have now begun to make their way into the toolbox (112–116). When machine learning is integrated with chemical proteomics, for example, the identification of cryptic pockets and the features of ligands that can bind in these pockets may become predictable with sufficient starting datasets. Deployment of a single tool (“hammer”) against a challenging drug target may yield suboptimal candidate design, if at all, as not all drug design challenges are a “nail” (Maslow's hammer concept). As these technologies become more commonplace in the industry, we expect their integrated use (“toolbox”) to become more and more critical to targeting a wide range of oncology targets to arrive at high-quality compound designs (Fig. 4).

Figure 4.

Integration of biology, chemistry, and data science is required to support the identification of novel targets and develop optimized, high-quality drug candidates. SBDD, structure-based drug design.

Figure 4.

Integration of biology, chemistry, and data science is required to support the identification of novel targets and develop optimized, high-quality drug candidates. SBDD, structure-based drug design.

Close modal

With these combined tools in hand, the oncology drug hunter now faces the challenge of choosing the right targets with profiles that balance the technical and commercial risks of oncology drug discovery. Although precision targeted therapies can be highly effective in small patient populations, there remains a wide area of therapeutic “white spaces” where exquisitely selective candidates with enhanced drug profiles against high-impact targets may deliver transformational patient outcomes (Fig. 5), which can be divided into three categories.

  1. 1.

    Clinically validated targets with current therapies that have suboptimal properties and leave significant room for additional patient benefit, such as from improved selectivity over anti–targets of interest or improved pharmacokinetic properties that expand the treatable sites or target coverage in patients. In NSCLC, the ALK inhibitor progression from suboptimal EML4–ALK targeting (crizotinib) to more optimized compounds has yielded remarkable improvement in patient outcomes. KRASG12C, a previously “undruggable” target, has now been clinically validated by commercially available medicines, but newer, differentiated, and potentially “best-in-class” compounds are now being tested in the clinic. New classes of mutant-selective PI3Kα inhibitors such as RLY-2608, LOXO-783, and STX-478 have now entered clinical trials (NCT05216432, NCT05307705, and NCT05768139, respectively). These inhibitors have the potential to prevent the on-target toxicities associated with PI3Kα inhibitors, such as alpelisib, that are caused by the inhibition of wild-type PI3Ka in normal tissues (168).

  2. 2.

    Classically “undruggable” targets that do not have a natural receptor pocket, which are well validated, typically with robust clinical cancer genetics and a preclinical functional genetic data package supporting a precision medicine hypothesis. These include transcription factors—a large class of well-validated tumor vulnerabilities, such as MYC, which is one of the most frequently aberrantly expressed, amplified, or translocated transcription factors across a range of tumors. Examples include lineage-dependent transcription factors (with ER and AR as prime “druggable” examples) or emerging synthetic lethal targets.

  3. 3.

    Novel targets that have not been previously identified or validated, which nevertheless emerge from newer computational biology analysis of large datasets. These targets represent a new frontier of oncology drug discovery and, once validated using cell-based and animal model systems, could significantly expand the number of patients who can benefit from precision oncology. Exciting examples of these that have recently emerged include the GEMINI targets that integrate both cancer somatic and germline population-level genetics to identify targets that are synthetically lethal with highly frequent loss-of-heterozygosity (LOH) events in tumors (169). In this article, the authors identified an essential gene, the DNA primase PRIM1, which is located on a chromosomal locus with high-frequency LOH events in a variety of tumors and which contains polymorphisms common in human populations. Selective targeting of one of the polymorphisms using allele-selective CRISPR techniques led to specific cell killing of patient-derived cells containing the targeted polymorphism, whereas patient-derived cells containing the nontargeted polymorphism were unaffected. These experiments offer proof of concept for the potential to use silent polymorphisms to convert common essential targets to highly selective precision medicine targets.

Figure 5.

The majority of targeted therapies serve patient populations of <10,000. Circles represent precision medicines against the indicated target, and colors represent tumor type as shown in the legend. Source: Boston Consulting Group analysis of Decision Resources Group epidemiology, ClinicalTrials.gov, FDA labels, and company websites. Data were gathered for approved precision oncology assets labeled according to their biological target and overall response rate (ORR) vs. prevalence of the relevant metastatic cancer. In instances in which there were several assets approved with the same biological target, ORR was based on the drug with the strongest response. AML, acute myelogenous leukemia; BCC, basal cell carcinoma; CLL, chronic lymphocytic leukemia; CML, chronic myelogenous leukemia; CRC, colorectal cancer; FL, follicular lymphoma; GIST, gastrointestinal stromal tumor; HCC, hepatocellular carcinoma; MCL, mantle cell lymphoma; MM, multiple myeloma; MZL, marginal zone lymphoma; NSCLC, non–small cell lung cancer; RCC, renal cell carcinoma; STS, soft tissue sarcoma; TGCT, tenosynovial giant cell tumor; WM, Waldenstrom macroglobulinemia.

Figure 5.

The majority of targeted therapies serve patient populations of <10,000. Circles represent precision medicines against the indicated target, and colors represent tumor type as shown in the legend. Source: Boston Consulting Group analysis of Decision Resources Group epidemiology, ClinicalTrials.gov, FDA labels, and company websites. Data were gathered for approved precision oncology assets labeled according to their biological target and overall response rate (ORR) vs. prevalence of the relevant metastatic cancer. In instances in which there were several assets approved with the same biological target, ORR was based on the drug with the strongest response. AML, acute myelogenous leukemia; BCC, basal cell carcinoma; CLL, chronic lymphocytic leukemia; CML, chronic myelogenous leukemia; CRC, colorectal cancer; FL, follicular lymphoma; GIST, gastrointestinal stromal tumor; HCC, hepatocellular carcinoma; MCL, mantle cell lymphoma; MM, multiple myeloma; MZL, marginal zone lymphoma; NSCLC, non–small cell lung cancer; RCC, renal cell carcinoma; STS, soft tissue sarcoma; TGCT, tenosynovial giant cell tumor; WM, Waldenstrom macroglobulinemia.

Close modal

The final challenge before the drug hunter embarks on a campaign against a target from one of these three categories is prioritization. Unlike therapeutic areas in which there is a dearth of validated targets and robust model systems, such as neurologic diseases like Alzheimer's, the oncology field has benefited from the immense expansion of validated target space over the past 20 years. For clinically validated targets, one prioritization approach is to systematically and comprehensively map the approved therapeutic and target landscape for precision oncology according to the patient population size and the RR or PFS these existing drugs have achieved in patients, as shown in Fig. 5. The upper right quadrant of such an analysis contains highly effective therapies for large populations of patients with cancer. The lower left quadrant are therapies with minimal patient impact (overall response rate <40%) in small patient populations. Low response rates for drugs against some of these targets may reflect tumor biology—such a target is simply not enough of a dependency in the given tumor to lead to meaningful patient responses regardless of how optimally the drugs inhibit the protein. Alternatively, these targets may represent rich opportunities for an integrated toolbox of drug discovery to create more selective, more potent drugs with improved drug metabolism and pharmacokinetic properties, overcoming the limitations of existing therapies. Similar systematic approaches can be applied to an investigational drug landscape, or even a preclinical target landscape, mapping preclinical data, such as functional genetic dependency scores (universally or selectively in certain cell types), to prioritize targets for drug discovery. The various technological approaches and classification of targets described above should also be considered in the context of more detailed metrics that characterize overall druggability such as target class, protein structure, chemical tractability, and precedent as previously described (170).

Prior to 2000, cancer treatment had mostly relied on crude methods such as chemotherapy, radiation, and surgery to combat this aggressive disease with moderate success (171). The advent of the genomic era enabled the scientific community to begin using genetic targets such as BCR–ABL for the design of precision drugs. Thus, precision oncology was born in 2001 with the transformational success of imatinib in CML (172). In the 20 years following imatinib, we have used available chemistry and data science tools and emerging biology insights to drive small-molecule designs for genetically defined targets. Of the 160 approved oncology drugs between 2001 and 2021, 68% were small molecules, thus illustrating its relative impact. However, due to existing limitations in our knowledge of target biology and the absence of more recent chemistry and data science tools, most new compounds could only address targets representing rare patient populations. In addition, many compounds were characterized by narrow therapeutic windows due to toxicities and other undesired properties that could not be engineered out of the molecules. Cumulatively, targeted therapies are estimated to benefit approximately 7% of patients today (5). Despite these limitations, some exceptional molecules have been developed, such as the ALK inhibitor lorlatinib and the EGFR inhibitor osimertinib, both for segments of patients with NSCLC (147, 156, 157). The disparity between the number of new drugs and the paucity of patient benefit can be addressed only with a consistent improvement of molecule designs that allow access to more common cancer targets and offer cleaner drug profiles for wider therapeutic windows. In addition, beyond the science of drug discovery, patient access to genetic testing and approved medicines must improve to maximize benefit in the future.

As is illustrated by the 20-year journey of targeting BCR–ABL with imatinib and several generations of subsequent TKIs, patient outcomes can be incrementally improved (139), transforming CML into a chronic disease with about 5% to 10% of patients achieving treatment-free remission after cessation of TKI therapy (142). With an expanded toolbox of advanced technologies, this time window may be substantially reduced for new cancer targets.

As we have summarized above, the last 20 years have brought an extensive evolution of our biological knowledge base and investigational biology tools accompanied by the introduction of novel technologies in chemistry and data science that now allow a more sophisticated and integrated approach to small-molecule drug discovery. In particular, the access to the full toolbox paired with the scientific skill set of trained drug hunters enables the integration of these tools and their application to the right targets. With an increased ambition to tailor the selectivity of the compound for the target in the diseased cell but not the wild-type cell, and carefully defined drug properties such as improved pharmacokinetic and pharmacodynamic profiles, next-generation small molecules may deliver the desired transformational clinical effects more consistently than ever before. The ambition should now go beyond improvements over existing drugs that have suboptimally addressed known targets and extend to previously undruggable targets and novel targets with complex biology.

Although the outlook for more precise targeted therapies has certainly improved, there will remain biologically relevant cancer targets that cannot be directly drugged even with novel tools such as chemical proteomics. Such targets may be transcription factors with cryptic pockets to address some mutations of p53 or the MYC oncogene, among others. The druggable universe the drug hunter can access has grown but is not indefinite.

In totality, with this integrated approach, we expect more frequent creation of transformational medicines for underserved patient populations, driven by better drug profiles offering wider therapeutic windows, better tolerability, longer duration of treatment, more efficacy, and a broader ability to combine new small molecules with other drug classes for optimized therapy and ultimate opportunities to cure disease.

D.D. Stuart reports other support from Scorpion Therapeutics outside the submitted work. A. Guzman-Perez reports other support from Scorpion Therapeutics outside the submitted work. N. Brooijmans reports other support from Scorpion Therapeutics outside the submitted work. E.L. Jackson reports other support from Scorpion Therapeutics during the conduct of the study. G.V. Kryukov reports other support from Scorpion Therapeutics and KSQ Therapeutics outside the submitted work. A.A. Friedman reports other support from Scorpion Therapeutics outside the submitted work. A. Hoos reports other support from Scorpion Therapeutics during the conduct of the study, as well as other support from Scorpion Therapeutics outside the submitted work. The authors are all employees of Scorpion Therapeutics and receive a salary and hold stock options.

The authors thank Boston Consulting Group for the analysis presented in Fig. 5, Allison Bruce for graphic design, and Sejal Patel for useful discussion.

1.
Zhong
L
,
Li
Y
,
Xiong
L
,
Wang
W
,
Wu
M
,
Yuan
T
, et al
.
Small mole­cules in targeted cancer therapy: advances, challenges, and future perspectives
.
Signal Transduct Target Ther
2021
;
6
:
201
.
2.
Smith
DA
,
Di
L
,
Kerns
EH
.
The effect of plasma protein binding on in vivo efficacy: misconceptions in drug discovery
.
Nat Rev Drug Discov
2010
;
9
:
929
39
.
3.
Yap
TA
,
Sandhu
SK
,
Workman
P
,
de Bono
JS
.
Envisioning the future of early anticancer drug development
.
Nat Rev Cancer
2010
;
10
:
514
23
.
4.
Morgan
P
,
Van Der Graaf
PH
,
Arrowsmith
J
,
Feltner
DE
,
Drummond
KS
,
Wegner
CD
, et al
.
Can the flow of medicines be improved? Fundamental pharmacokinetic and pharmacological principles toward improving phase II survival
.
Drug Discov Today
2012
;
17
:
419
24
.
5.
Haslam
A
,
Kim
MS
,
Prasad
V
.
Updated estimates of eligibility for and response to genome-targeted oncology drugs among US cancer patients, 2006–2020
.
Ann Oncol
2021
;
32
:
926
32
.
6.
Wheeler
DA
,
Wang
L
.
From human genome to cancer genome: the first decade
.
Genome Res
2013
;
23
:
1054
62
.
7.
Dickson
D
.
Wellcome funds cancer database
.
Nature
1999
;
401
:
729
.
8.
Hayden
EC
.
International genome project launched
.
Nature
2008
;
451
:
378
9
.
9.
Bailey
MH
,
Tokheim
C
,
Porta-Pardo
E
,
Sengupta
S
,
Bertrand
D
,
Weerasinghe
A
, et al
.
Comprehensive characterization of cancer driver genes and mutations
.
Cell
2018
;
174
:
1034
5
.
10.
Gonzalez-Perez
A
,
Mustonen
V
,
Reva
B
,
Ritchie
GR
,
Creixell
P
,
Karchin
R
, et al
.
Computational approaches to identify functional genetic variants in cancer genomes
.
Nat Methods
2013
;
10
:
723
9
.
11.
Elliott
K
,
Larsson
E
.
Non-coding driver mutations in human cancer
.
Nat Rev Cancer
2021
;
21
:
500
9
.
12.
Fonseca-Montano
MA
,
Blancas
S
,
Herrera-Montalvo
LA
,
Hidalgo-Miranda
A
.
Cancer genomics
.
Arch Med Res
2022
;
53
:
723
31
.
13.
Zack
TI
,
Schumacher
SE
,
Carter
SL
,
Cherniack
AD
,
Saksena
G
,
Tabak
B
, et al
.
Pan-cancer patterns of somatic copy number alteration
.
Nat Genet
2013
;
45
:
1134
40
.
14.
Kandoth
C
,
McLellan
MD
,
Vandin
F
,
Ye
K
,
Niu
B
,
Lu
C
, et al
.
Mutational landscape and significance across 12 major cancer types
.
Nature
2013
;
502
:
333
9
.
15.
Seiler
M
,
Peng
S
,
Agrawal
AA
,
Palacino
J
,
Teng
T
,
Zhu
P
, et al
.
Somatic mutational landscape of splicing factor genes and their functional consequences across 33 cancer types
.
Cell Rep
2018
;
23
:
282
96
.
16.
Ghandi
M
,
Huang
FW
,
Jane-Valbuena
J
,
Kryukov
GV
,
Lo
CC
,
McDonald
ER
3rd
, et al
.
Next-generation characterization of the cancer cell line encyclopedia
.
Nature
2019
;
569
:
503
8
.
17.
Li
H
,
Ning
S
,
Ghandi
M
,
Kryukov
GV
,
Gopal
S
,
Deik
A
, et al
.
The landscape of cancer cell line metabolism
.
Nat Med
2019
;
25
:
850
60
.
18.
Luo
J
,
Emanuele
MJ
,
Li
D
,
Creighton
CJ
,
Schlabach
MR
,
Westbrook
TF
, et al
.
A genome-wide RNAi screen identifies multiple synthetic lethal interactions with the Ras oncogene
.
Cell
2009
;
137
:
835
48
.
19.
Ngo
VN
,
Davis
RE
,
Lamy
L
,
Yu
X
,
Zhao
H
,
Lenz
G
, et al
.
A loss-of-function RNA interference screen for molecular targets in cancer
.
Nature
2006
;
441
:
106
10
.
20.
Schlabach
MR
,
Luo
J
,
Solimini
NL
,
Hu
G
,
Xu
Q
,
Li
MZ
, et al
.
Cancer proliferation gene discovery through functional genomics
.
Science
2008
;
319
:
620
4
.
21.
Jackson
AL
,
Burchard
J
,
Schelter
J
,
Chau
BN
,
Cleary
M
,
Lim
L
, et al
.
Widespread siRNA “off-target” transcript silencing mediated by seed region sequence complementarity
.
RNA
2006
;
12
:
1179
87
.
22.
Fellmann
C
,
Hoffmann
T
,
Sridhar
V
,
Hopfgartner
B
,
Muhar
M
,
Roth
M
, et al
.
An optimized microRNA backbone for effective single-copy RNAi
.
Cell Rep
2013
;
5
:
1704
13
.
23.
Kampmann
M
,
Bassik
MC
,
Weissman
JS
.
Integrated platform for genome-wide screening and construction of high-density genetic interaction maps in mammalian cells
.
Proc Natl Acad Sci U S A
2013
;
110
:
E2317
26
.
24.
Kampmann
M
,
Horlbeck
MA
,
Chen
Y
,
Tsai
JC
,
Bassik
MC
,
Gilbert
LA
, et al
.
Next-generation libraries for robust RNA interference-based genome-wide screens
.
Proc Natl Acad Sci U S A
2015
;
112
:
E3384
91
.
25.
Beke
L
,
Kig
C
,
Linders
JT
,
Boens
S
,
Boeckx
A
,
van Heerde
E
, et al
.
MELK-T1, a small-molecule inhibitor of protein kinase MELK, decreases DNA-damage tolerance in proliferating cancer cells
.
Biosci Rep
2015
;
35
:
e00267
.
26.
Gray
D
,
Jubb
AM
,
Hogue
D
,
Dowd
P
,
Kljavin
N
,
Yi
S
, et al
.
Maternal embryonic leucine zipper kinase/murine protein serine-threonine kinase 38 is a promising therapeutic target for multiple cancers
.
Cancer Res
2005
;
65
:
9751
61
.
27.
Lin
ML
,
Park
JH
,
Nishidate
T
,
Nakamura
Y
,
Katagiri
T
.
Involvement of maternal embryonic leucine zipper kinase (MELK) in mammary carcinogenesis through interaction with Bcl-G, a pro-apoptotic member of the Bcl-2 family
.
Breast Cancer Res
2007
;
9
:
R17
.
28.
Wang
Y
,
Begley
M
,
Li
Q
,
Huang
HT
,
Lako
A
,
Eck
MJ
, et al
.
Mitotic MELK-eIF4B signaling controls protein synthesis and tumor cell survival
.
Proc Natl Acad Sci U S A
2016
;
113
:
9810
5
.
29.
Giuliano
CJ
,
Lin
A
,
Smith
JC
,
Palladino
AC
,
Sheltzer
JM
.
MELK expression correlates with tumor mitotic activity but is not required for cancer growth
.
Elife
2018
;
7
:
e32838
.
30.
Huang
HT
,
Seo
HS
,
Zhang
T
,
Wang
Y
,
Jiang
B
,
Li
Q
, et al
.
MELK is not necessary for the proliferation of basal-like breast cancer cells
.
Elife
2017
;
6
:
e26693
.
31.
Gasiunas
G
,
Barrangou
R
,
Horvath
P
,
Siksnys
V
.
Cas9-crRNA ribonucleoprotein complex mediates specific DNA cleavage for adaptive immunity in bacteria
.
Proc Natl Acad Sci U S A
2012
;
109
:
E2579
86
.
32.
Jinek
M
,
Chylinski
K
,
Fonfara
I
,
Hauer
M
,
Doudna
JA
,
Charpentier
E
.
A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity
.
Science
2012
;
337
:
816
21
.
33.
Mali
P
,
Yang
L
,
Esvelt
KM
,
Aach
J
,
Guell
M
,
DiCarlo
JE
, et al
.
RNA-guided human genome engineering via Cas9
.
Science
2013
;
339
:
823
6
.
34.
Ran
FA
,
Hsu
PD
,
Wright
J
,
Agarwala
V
,
Scott
DA
,
Zhang
F
.
Genome engineering using the CRISPR-Cas9 system
.
Nat Protoc
2013
;
8
:
2281
308
.
35.
Aguirre
AJ
,
Meyers
RM
,
Weir
BA
,
Vazquez
F
,
Zhang
CZ
,
Ben-David
U
, et al
.
Genomic copy number dictates a gene-independent cell response to CRISPR/Cas9 targeting
.
Cancer Discov
2016
;
6
:
914
29
.
36.
Munoz
DM
,
Cassiani
PJ
,
Li
L
,
Billy
E
,
Korn
JM
,
Jones
MD
, et al
.
CRISPR screens provide a comprehensive assessment of cancer vulnerabilities but generate false-positive hits for highly amplified genomic regions
.
Cancer Discov
2016
;
6
:
900
13
.
37.
Behan
FM
,
Iorio
F
,
Picco
G
,
Goncalves
E
,
Beaver
CM
,
Migliardi
G
, et al
.
Prioritization of cancer therapeutic targets using CRISPR-Cas9 screens
.
Nature
2019
;
568
:
511
6
.
38.
Dempster
JM
,
Pacini
C
,
Pantel
S
,
Behan
FM
,
Green
T
,
Krill-Burger
J
, et al
.
Agreement between two large pan-cancer CRISPR-Cas9 gene dependency data sets
.
Nat Commun
2019
;
10
:
5817
.
39.
Meyers
RM
,
Bryan
JG
,
McFarland
JM
,
Weir
BA
,
Sizemore
AE
,
Xu
H
, et al
.
Computational correction of copy number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells
.
Nat Genet
2017
;
49
:
1779
84
.
40.
Yu
C
,
Mannan
AM
,
Yvone
GM
,
Ross
KN
,
Zhang
YL
,
Marton
MA
, et al
.
High-throughput identification of genotype-specific cancer vulnerabilities in mixtures of barcoded tumor cell lines
.
Nat Biotechnol
2016
;
34
:
419
23
.
41.
Davies
H
,
Bignell
GR
,
Cox
C
,
Stephens
P
,
Edkins
S
,
Clegg
S
, et al
.
Mutations of the BRAF gene in human cancer
.
Nature
2002
;
417
:
949
54
.
42.
Yao
Z
,
Torres
NM
,
Tao
A
,
Gao
Y
,
Luo
L
,
Li
Q
, et al
.
BRAF mutants evade ERK-dependent feedback by different mechanisms that determine their sensitivity to pharmacologic inhibition
.
Cancer Cell
2015
;
28
:
370
83
.
43.
Kryukov
GV
,
Wilson
FH
,
Ruth
JR
,
Paulk
J
,
Tsherniak
A
,
Marlow
SE
, et al
.
MTAP deletion confers enhanced dependency on the PRMT5 arginine methyltransferase in cancer cells
.
Science
2016
;
351
:
1214
8
.
44.
Mavrakis
KJ
,
McDonald
ER
3rd
,
Schlabach
MR
,
Billy
E
,
Hoffman
GR
,
deWeck
A
, et al
.
Disordered methionine metabolism in MTAP/CDKN2A-deleted cancers leads to dependence on PRMT5
.
Science
2016
;
351
:
1208
13
.
45.
Kalev
P
,
Hyer
ML
,
Gross
S
,
Konteatis
Z
,
Chen
CC
,
Fletcher
M
, et al
.
MAT2A inhibition blocks the growth of MTAP-deleted cancer cells by reducing PRMT5-dependent mRNA splicing and inducing DNA damage
.
Cancer Cell
2021
;
39
:
209
24
.
46.
Marjon
K
,
Cameron
MJ
,
Quang
P
,
Clasquin
MF
,
Mandley
E
,
Kunii
K
, et al
.
MTAP deletions in cancer create vulnerability to targeting of the MAT2A/PRMT5/RIOK1 axis
.
Cell Rep
2016
;
15
:
574
87
.
47.
Konteatis
Z
,
Travins
J
,
Gross
S
,
Marjon
K
,
Barnett
A
,
Mandley
E
, et al
.
Discovery of AG-270, a first-in-class oral MAT2A inhibitor for the treatment of tumors with homozygous MTAP deletion
.
J Med Chem
2021
;
64
:
4430
49
.
48.
Groelly
FJ
,
Fawkes
M
,
Dagg
RA
,
Blackford
AN
,
Tarsounas
M
.
Targeting DNA damage response pathways in cancer
.
Nat Rev Cancer
2023
;
23
:
78
94
.
49.
Wanior
M
,
Kramer
A
,
Knapp
S
,
Joerger
AC
.
Exploiting vulnerabilities of SWI/SNF chromatin remodelling complexes for cancer therapy
.
Oncogene
2021
;
40
:
3637
54
.
50.
Ito
T
,
Young
MJ
,
Li
R
,
Jain
S
,
Wernitznig
A
,
Krill-Burger
JM
, et al
.
Paralog knockout profiling identifies DUSP4 and DUSP6 as a digenic dependence in MAPK pathway-driven cancers
.
Nat Genet
2021
;
53
:
1664
72
.
51.
Chiu
YC
,
Zheng
S
,
Wang
LJ
,
Iskra
BS
,
Rao
MK
,
Houghton
PJ
, et al
.
Predicting and characterizing a cancer dependency map of tumors with deep learning
.
Sci Adv
2021
;
7
:
eabh1275
.
52.
Itzhacky
N
,
Sharan
R
.
Prediction of cancer dependencies from expression data using deep learning
.
Mol Omics
2021
;
17
:
66
71
.
53.
Amodio
V
,
Yaeger
R
,
Arcella
P
,
Cancelliere
C
,
Lamba
S
,
Lorenzato
A
, et al
.
EGFR blockade reverts resistance to KRAS(G12C) inhibition in colorectal cancer
.
Cancer Discov
2020
;
10
:
1129
39
.
54.
Fakih
MG
,
Kopetz
S
,
Kuboki
Y
,
Kim
TW
,
Munster
PN
,
Krauss
JC
, et al
.
Sotorasib for previously treated colorectal cancers with KRAS(G12C) mutation (CodeBreaK100): a prespecified analysis of a single-arm, phase 2 trial
.
Lancet Oncol
2022
;
23
:
115
24
.
55.
Skoulidis
F
,
Li
BT
,
Dy
GK
,
Price
TJ
,
Falchook
GS
,
Wolf
J
, et al
.
Sotorasib for lung cancers with KRAS p.G12C mutation
.
N Engl J Med
2021
;
384
:
2371
81
.
56.
Yaeger
R
,
Weiss
J
,
Pelster
MS
,
Spira
AI
,
Barve
M
,
Ou
SI
, et al
.
Adagrasib with or without cetuximab in colorectal cancer with mutated KRAS G12C
.
N Engl J Med
2023
;
388
:
44
54
.
57.
Lipinski
C
,
Lombardo
F
,
Dominy
B
,
Feeney
P
.
In vitro models for selection of development candidates experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings
.
Adv Drug Deliv Rev
1997
;
23
:
3
25
.
58.
Wager
TT
,
Chandrasekaran
RY
,
Hou
X
,
Troutman
MD
,
Verhoest
PR
,
Villalobos
A
, et al
.
Defining desirable central nervous system drug space through the alignment of molecular properties, in vitro ADME, and safety attributes
.
ACS Chem Neurosci
2010
;
1
:
420
34
.
59.
Lovering
F
,
Bikker
J
,
Humblet
C
.
Escape from flatland: increasing saturation as an approach to improving clinical success
.
J Med Chem
2009
;
52
:
6752
6
.
60.
Edwards
MP
,
Price
DA
.
Role of physicochemical properties and ligand lipophilicity efficiency in addressing drug safety risks
.
Annu Rep Med Chem
2010
;
45
:
380
91
.
61.
Doak
BC
,
Kihlberg
J
.
Drug discovery beyond the rule of 5: opportunities and challenges
.
Expert Opin Drug Discov
2017
;
12
:
115
9
.
62.
Sabatini
DM
.
Twenty-five years of mTOR: uncovering the link from nutrients to growth
.
Proc Natl Acad Sci U S A
2017
;
114
:
11818
25
.
63.
Driggers
EM
,
Hale
SP
,
Lee
J
,
Terrett
NK
.
The exploration of macrocycles for drug discovery—an underexploited structural class
.
Nat Rev Drug Discov
2008
;
7
:
608
24
.
64.
Johnson
TW
,
Richardson
PF
,
Bailey
S
,
Brooun
A
,
Burke
BJ
,
Collins
MR
, et al
.
Discovery of (10R)-7-amino-12-fluoro-2,10,16-trimethyl-15-oxo-10,15,16,17-tetrahydro-2H-8,4-(metheno)pyrazolo[4,3-h][2,5,11]-benzoxadiazacyclotetradecine-3-carbonitrile (PF-06463922), a macrocyclic inhibitor of anaplastic lymphoma kinase (ALK) and c-ros oncogene 1 (ROS1) with preclinical brain exposure and broad-spectrum potency against ALK-resistant mutations
.
J Med Chem
2014
;
57
:
4720
44
.
65.
William
AD
,
Lee
AC
,
Blanchard
S
,
Poulsen
A
,
Teo
EL
,
Nagaraj
H
, et al
.
Discovery of the macrocycle 11-(2-pyrrolidin-1-yl-ethoxy)-14,19-dioxa-5,7,26-triaza-tetracyclo[19.3.1.1(2,6).1(8,12)]heptacosa-1(25),2(26),3,5,8,10,12(27),16,21,23-decaene (SB1518), a potent Janus kinase 2/fms-like tyrosine kinase-3 (JAK2/FLT3) inhibitor for the treatment of myelofibrosis and lymphoma
.
J Med Chem
2011
;
54
:
4638
58
.
66.
Lu
S
,
He
X
,
Ni
D
,
Zhang
J
.
Allosteric modulator discovery: from serendipity to structure-based design
.
J Med Chem
2019
;
62
:
6405
21
.
67.
Schoepfer
J
,
Jahnke
W
,
Berellini
G
,
Buonamici
S
,
Cotesta
S
,
Cowan-Jacob
SW
, et al
.
Discovery of asciminib (ABL001), an allosteric inhibitor of the tyrosine kinase activity of BCR-ABL1
.
J Med Chem
2018
;
61
:
8120
35
.
68.
Chen
YN
,
LaMarche
MJ
,
Chan
HM
,
Fekkes
P
,
Garcia-Fortanet
J
,
Acker
MG
, et al
.
Allosteric inhibition of SHP2 phosphatase inhibits cancers driven by receptor tyrosine kinases
.
Nature
2016
;
535
:
148
52
.
69.
Gilmartin
AG
,
Bleam
MR
,
Groy
A
,
Moss
KG
,
Minthorn
EA
,
Kulkarni
SG
, et al
.
GSK1120212 (JTP-74057) is an inhibitor of MEK activity and activation with favorable pharmacokinetic properties for sustained in vivo pathway inhibition
.
Clin Cancer Res
2011
;
17
:
989
1000
.
70.
Ostrem
JM
,
Peters
U
,
Sos
ML
,
Wells
JA
,
Shokat
KM
.
K-Ras(G12C) inhibitors allosterically control GTP affinity and effector interactions
.
Nature
2013
;
503
:
548
51
.
71.
Erlanson
DA
,
Fesik
SW
,
Hubbard
RE
,
Jahnke
W
,
Jhoti
H
.
Twenty years on: the impact of fragments on drug discovery
.
Nat Rev Drug Discov
2016
;
15
:
605
19
.
72.
Shuker
SB
,
Hajduk
PJ
,
Meadows
RP
,
Fesik
SW
.
Discovering high-affinity ligands for proteins: SAR by NMR
.
Science
1996
;
274
:
1531
4
.
73.
Congreve
M
,
Carr
R
,
Murray
C
,
Jhoti
H
.
A ‘rule of three’ for fragment-based lead discovery?
Drug Discov Today
2003
;
8
:
876
7
.
74.
Hopkins
AL
,
Groom
CR
,
Alex
A
.
Ligand efficiency: a useful metric for lead selection
.
Drug Discov Today
2004
;
9
:
430
1
.
75.
Hartshorn
MJ
,
Murray
CW
,
Cleasby
A
,
Frederickson
M
,
Tickle
IJ
,
Jhoti
H
.
Fragment-based lead discovery using X-ray crystallography
.
J Med Chem
2005
;
48
:
403
13
.
76.
Collins
PM
,
Douangamath
A
,
Talon
R
,
Dias
A
,
Brandao-Neto
J
,
Krojer
T
, et al
.
Achieving a good crystal system for crystallographic X-ray fragment screening
.
Methods Enzymol
2018
;
610
:
251
64
.
77.
Bollag
G
,
Tsai
J
,
Zhang
J
,
Zhang
C
,
Ibrahim
P
,
Nolop
K
, et al
.
Vemurafenib: the first drug approved for BRAF-mutant cancer
.
Nat Rev Drug Discov
2012
;
11
:
873
86
.
78.
Oltersdorf
T
,
Elmore
SW
,
Shoemaker
AR
,
Armstrong
RC
,
Augeri
DJ
,
Belli
BA
, et al
.
An inhibitor of Bcl-2 family proteins induces regression of solid tumours
.
Nature
2005
;
435
:
677
81
.
79.
Murray
CW
,
Newell
DR
,
Angibaud
P
.
A successful collaboration between academia, biotech and pharma led to discovery of erdafitinib, a selective FGFR inhibitor recently approved by the FDA
.
Med Chem Comm
2019
;
10
:
1509
11
.
80.
Addie
M
,
Ballard
P
,
Buttar
D
,
Crafter
C
,
Currie
G
,
Davies
BR
, et al
.
Discovery of 4-amino-N-[(1S)-1-(4-chlorophenyl)-3-hydroxypropyl]-1-(7H-pyrrolo[2,3-d]pyrimidin-4-yl)piperidine-4-carboxamide (AZD5363), an orally bioavailable, potent inhibitor of Akt kinases
.
J Med Chem
2013
;
56
:
2059
73
.
81.
Caldwell
JJ
,
Davies
TG
,
Donald
A
,
McHardy
T
,
Rowlands
MG
,
Aherne
GW
, et al
.
Identification of 4-(4-aminopiperidin-1-yl)-7H-pyrrolo[2,3-d]pyrimidines as selective inhibitors of protein kinase B through fragment elaboration
.
J Med Chem
2008
;
51
:
2147
57
.
82.
Lai
AC
,
Crews
CM
.
Induced protein degradation: an emerging drug discovery paradigm
.
Nat Rev Drug Discov
2017
;
16
:
101
14
.
83.
Schreiber
SL
.
The rise of molecular glues
.
Cell
2021
;
184
:
3
9
.
84.
Dong
G
,
Ding
Y
,
He
S
,
Sheng
C
.
Molecular glues for targeted protein degradation: from serendipity to rational discovery
.
J Med Chem
2021
;
64
:
10606
20
.
85.
Sakamoto
KM
,
Kim
KB
,
Kumagai
A
,
Mercurio
F
,
Crews
CM
,
Deshaies
RJ
.
Protacs: chimeric molecules that target proteins to the Skp1-Cullin-F box complex for ubiquitination and degradation
.
Proc Natl Acad Sci U S A
2001
;
98
:
8554
9
.
86.
Bekes
M
,
Langley
DR
,
Crews
CM
.
PROTAC targeted protein degraders: the past is prologue
.
Nat Rev Drug Discov
2022
;
21
:
181
200
.
87.
Singh
J
,
Petter
RC
,
Baillie
TA
,
Whitty
A
.
The resurgence of covalent drugs
.
Nat Rev Drug Discov
2011
;
10
:
307
17
.
88.
Boike
L
,
Henning
NJ
,
Nomura
DK
.
Advances in covalent drug discovery
.
Nat Rev Drug Discov
2022
;
21
:
881
98
.
89.
Singh
J
,
Dobrusin
EM
,
Fry
DW
,
Haske
T
,
Whitty
A
,
McNamara
DJ
.
Structure-based design of a potent, selective, and irreversible inhibitor of the catalytic domain of the erbB receptor subfamily of protein tyrosine kinases
.
J Med Chem
1997
;
40
:
1130
5
.
90.
Lanman
BA
,
Allen
JR
,
Allen
JG
,
Amegadzie
AK
,
Ashton
KS
,
Booker
SK
, et al
.
Discovery of a covalent inhibitor of KRAS(G12C) (AMG 510) for the treatment of solid tumors
.
J Med Chem
2020
;
63
:
52
65
.
91.
Spradlin
JN
,
Zhang
E
,
Nomura
DK
.
Reimagining druggability using chemoproteomic platforms
.
Acc Chem Res
2021
;
54
:
1801
13
.
92.
Backus
KM
,
Correia
BE
,
Lum
KM
,
Forli
S
,
Horning
BD
,
Gonzalez-Paez
GE
, et al
.
Proteome-wide covalent ligand discovery in native biological systems
.
Nature
2016
;
534
:
570
4
.
93.
Lu
W
,
Kostic
M
,
Zhang
T
,
Che
J
,
Patricelli
MP
,
Jones
LH
, et al
.
Fragment-based covalent ligand discovery
.
RSC Chem Biol
2021
;
2
:
354
67
.
94.
Keeley
A
,
Petri
L
,
Abranyi-Balogh
P
,
Keseru
GM
.
Covalent fragment libraries in drug discovery
.
Drug Discov Today
2020
;
25
:
983
96
.
95.
Brenner
S
,
Lerner
RA
.
Encoded combinatorial chemistry
.
Proc Natl Acad Sci U S A
1992
;
89
:
5381
3
.
96.
Neri
D
,
Lerner
RA
.
DNA-encoded chemical libraries: a selection system based on endowing organic compounds with amplifiable information
.
Annu Rev Biochem
2018
;
87
:
479
502
.
97.
Satz
AL
,
Brunschweiger
A
,
Flanagan
ME
,
Gloger
A
,
Hansen
NJV
,
Kuai
L
, et al
.
DNA-encoded chemical libraries
.
Nat Rev Methods Primers
2022
;
2
:
3
.
98.
Arrowsmith
CH
,
Audia
JE
,
Austin
C
,
Baell
J
,
Bennett
J
,
Blagg
J
, et al
.
The promise and peril of chemical probes
.
Nat Chem Biol
2015
;
11
:
536
41
.
99.
Antolin
AA
,
Sanfelice
D
,
Crisp
A
,
Villasclaras Fernandez
E
,
Mica
IL
,
Chen
Y
, et al
.
The Chemical Probes Portal: an expert review-based public resource to empower chemical probe assessment, selection and use
.
Nucleic Acids Res
2023
;
51
:
D1492
D502
.
100.
A celebration of structural biology
.
Nat Methods
2021
;
18
:
427
.
101.
Warren
GL
,
Andrews
CW
,
Capelli
AM
,
Clarke
B
,
LaLonde
J
,
Lambert
MH
, et al
.
A critical assessment of docking programs and scoring functions
.
J Med Chem
2006
;
49
:
5912
31
.
102.
Perola
E
,
Walters
WP
,
Charifson
PS
.
A detailed comparison of current docking and scoring methods on systems of pharmaceutical relevance
.
Proteins
2004
;
56
:
235
49
.
103.
Kuntz
ID
,
Blaney
JM
,
Oatley
SJ
,
Langridge
R
,
Ferrin
TE
.
A geometric approach to macromolecule-ligand interactions
.
J Mol Biol
1982
;
161
:
269
.
104.
Edwards
AM
,
Isserlin
R
,
Bader
GD
,
Frye
SV
,
Willson
TM
,
Yu
FH
.
Too many roads not taken
.
Nature
2011
;
470
:
163
5
.
105.
Williamson
AR
.
Creating a structural genomics consortium
.
Nat Struct Biol
2000
;
7
Suppl 11
:
953
.
106.
Walters
WP
.
Virtual chemical libraries
.
J Med Chem
2019
;
62
:
1116
24
.
107.
Warr
WA
,
Nicklaus
MC
,
Nicolaou
CA
,
Rarey
M
.
Exploration of ultralarge compound collections for drug discovery
.
J Chem Inf Model
2022
;
62
:
2021
34
.
108.
Lyu
J
,
Wang
S
,
Balius
TE
,
Singh
I
,
Levit
A
,
Moroz
YS
, et al
.
Ultra-large library docking for discovering new chemotypes
.
Nature
2019
;
566
:
224
9
.
109.
Yang
Y
,
Yao
K
,
Repasky
MP
,
Leswing
K
,
Abel
R
,
Shoichet
BK
, et al
.
Efficient exploration of chemical space with docking and deep learning
.
J Chem Theory Comput
2021
;
17
:
7106
19
.
110.
Gaulton
A
,
Hersey
A
,
Nowotka
M
,
Bento
AP
,
Chambers
J
,
Mendez
D
, et al
.
The ChEMBL database in 2017
.
Nucleic Acids Res
2017
;
45
:
D945
D54
.
111.
Irwin
JJ
,
Tang
KG
,
Young
J
,
Dandarchuluun
C
,
Wong
BR
,
Khurelbaatar
M
, et al
.
ZINC20 a free ultralarge-scale chemical database for ligand discovery
.
J Chem Inf Model
2020
;
60
:
6065
73
.
112.
Bilodeau
C
,
Jin
W
,
Jaakkola
T
,
Barzilay
R
,
Jensen
KF
.
Generative models for molecular discovery: recent advances and challenges
.
Wiley Interdiscip Rev Comput Mol Sci
2022
;
12
:
e1608
.
113.
Gómez-Bombarelli
R
,
Wei
JN
,
Duvenaud
D
,
Hernández-Lobato
JM
,
Sánchez-Lengeling
B
,
Sheberla
D
, et al
.
Automatic chemical design using a data-driven continuous representation of molecules
.
ACS Cen Sci
2018
;
4
:
268
76
.
114.
Walters
WP
,
Barzilay
R
.
Applications of deep learning in molecule generation and molecular property prediction
.
Acc Chem Res
2021
;
54
:
263
70
.
115.
Zhavoronkov
A
,
Ivanenkov
YA
,
Aliper
A
,
Veselov
MS
,
Aladinskiy
VA
,
Aladinskaya
AV
, et al
.
Deep learning enables rapid identification of potent DDR1 kinase inhibitors
.
Nat Biotechnol
2019
;
37
:
1038
40
.
116.
Merk
D
,
Friedrich
L
,
Grisoni
F
,
Schneider
G
.
De dovo design of bioactive small molecules by artificial intelligence
.
Mol Inform
2018
;
37
:
1700153
.
117.
Mitsopoulos
C
,
Di Micco
P
,
Fernandez
EV
,
Dolciami
D
,
Holt
E
,
Mica
IL
, et al
.
canSAR: update to the cancer translational research and drug discovery knowledgebase
.
Nucleic Acids Res
2020
;
49
:
D1074
D82
.
118.
Wang
L
,
Wu
Y
,
Deng
Y
,
Kim
B
,
Pierce
L
,
Krilov
G
, et al
.
Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field
.
J Am Chem Soc
2015
;
137
:
2695
703
.
119.
Wan
S
,
Tresadern
G
,
Pérez-Benito
L
,
Vlijmen
H
,
Coveney
PV
.
Accuracy and precision of alchemical relative free energy predictions with and without replica-exchange
.
Adv Theory Simul
2020
;
3
:
1900195
.
120.
Klepeis
JL
,
Lindorff-Larsen
K
,
Dror
RO
,
Shaw
DE
.
Long-timescale molecular dynamics simulations of protein structure and function
.
Curr Opin Struct Biol
2009
;
19
:
120
7
.
121.
Vivo
MD
,
Masetti
M
,
Bottegoni
G
,
Cavalli
A
.
Role of molecular dynamics and related methods in drug discovery
.
J Med Chem
2016
;
59
:
4035
61
.
122.
Decherchi
S
,
Cavalli
A
.
Thermodynamics and kinetics of drug-target binding by molecular simulation
.
Chem Rev
2020
;
120
:
12788
833
.
123.
Kuzmanic
A
,
Bowman
GR
,
Juarez-Jimenez
J
,
Michel
J
,
Gervasio
FL
.
Investigating cryptic binding sites by molecular dynamics simulations
.
Acc Chem Res
2020
;
53
:
654
61
.
124.
Buch
I
,
Giorgino
T
,
Fabritiis
GD
.
Complete reconstruction of an enzyme-inhibitor binding process by molecular dynamics simulations
.
Proc Natl Acad Sci U S A
2011
;
108
:
10184
9
.
125.
Shan
Y
,
Mysore
VP
,
Leffler
AE
,
Kim
ET
,
Sagawa
S
,
Shaw
DE
.
How does a small molecule bind at a cryptic binding site?
PLoS Comput Biol
2022
;
18
:
e1009817
.
126.
Shan
Y
,
Kim
ET
,
Eastwood
MP
,
Dror
RO
,
Seeliger
MA
,
Shaw
DE
.
How does a drug molecule find its target binding site?
J Am Chem Soc
2011
;
133
:
9181
3
.
127.
Dill
KA
,
MacCallum
JL
.
The protein-folding problem, 50 years on
.
Science
2012
;
338
:
1042
6
.
128.
Moult
J
,
Pedersen
JT
,
Judson
R
,
Fidelis
K
.
A large-scale experiment to assess protein structure prediction methods
.
Proteins
1995
;
23
:
ii
iv
.
129.
Senior
AW
,
Evans
R
,
Jumper
J
,
Kirkpatrick
J
,
Sifre
L
,
Green
T
, et al
.
Improved protein structure prediction using potentials from deep learning
.
Nature
2020
;
577
:
706
10
.
130.
AlQuraishi
M
.
AlphaFold at CASP13
.
Bioinformatics
2019
;
35
:
4862
5
.
131.
Baek
M
,
DiMaio
F
,
Anishchenko
I
,
Dauparas
J
,
Ovchinnikov
S
,
Lee
GR
, et al
.
Accurate prediction of protein structures and interactions using a three-track neural network
.
Science
2021
;
373
:
871
6
.
132.
Liu
J
,
Wu
T
,
Guo
Z
,
Hou
J
,
Cheng
J
.
Improving protein tertiary structure prediction by deep learning and distance prediction in CASP14
.
Proteins
2022
;
90
:
58
72
.
133.
Scardino
V
,
Filippo
JID
,
Cavasotto
CN
.
How good are AlphaFold models for docking-based virtual screening?
iScience
2023
;
26
:
105920
.
134.
Beuming
T
,
Martín
H
,
Díaz-Rovira
AM
,
Díaz
L
,
Guallar
V
,
Ray
SS
.
Are deep learning structural models sufficiently accurate for free-energy calculations? Application of FEP+ to AlphaFold2-predicted structures
.
J Chem Inf Model
2022
;
62
:
4351
60
.
135.
Borkakoti
N
,
Thornton
JM
.
AlphaFold2 protein structure prediction: implications for drug discovery
.
Curr Opin Struct Biol
2023
;
78
:
102526
.
136.
O'Brien
SG
,
Guilhot
F
,
Larson
RA
,
Gathmann
I
,
Baccarani
M
,
Cervantes
F
, et al
.
Imatinib compared with interferon and low-dose cytarabine for newly diagnosed chronic-phase chronic myeloid leukemia
.
N Engl J Med
2003
;
348
:
994
1004
.
137.
Weisberg
E
,
Manley
PW
,
Breitenstein
W
,
Brüggen
J
,
Cowan-Jacob
SW
,
Ray
A
, et al
.
Characterization of AMN107, a selective inhibitor of native and mutant Bcr-Abl
.
Cancer Cell
2005
;
7
:
129
41
.
138.
Kantarjian
HM
,
Hochhaus
A
,
Saglio
G
,
De Souza
C
,
Flinn
IW
,
Stenke
L
, et al
.
Nilotinib versus imatinib for the treatment of patients with newly diagnosed chronic phase, Philadelphia chromosome-positive, chronic myeloid leukaemia: 24-month minimum follow-up of the phase 3 randomised ENESTnd trial
.
Lancet Oncol
2011
;
12
:
841
51
.
139.
Braun
TP
,
Eide
CA
,
Druker
BJ
.
Response and resistance to BCR-ABL1-targeted therapies
.
Cancer Cell
2020
;
37
:
530
42
.
140.
Adrián
FJ
,
Ding
Q
,
Sim
T
,
Velentza
A
,
Sloan
C
,
Liu
Y
, et al
.
Allosteric inhibitors of Bcr-abl-dependent cell proliferation
.
Nat Chem Biol
2006
;
2
:
95
102
.
141.
Wylie
AA
,
Schoepfer
J
,
Jahnke
W
,
Cowan-Jacob
SW
,
Loo
A
,
Furet
P
, et al
.
The allosteric inhibitor ABL001 enables dual targeting of BCR-ABL1
.
Nature
2017
;
543
:
733
7
.
142.
Baccarani
M
,
Gale
RP
.
Why chronic myeloid leukaemia cannot be cured by tyrosine kinase-inhibitors
.
Leukemia
2021
;
35
:
2199
204
.
143.
Solomon
BJ
,
Mok
T
,
Kim
DW
,
Wu
YL
,
Nakagawa
K
,
Mekhail
T
, et al
.
First-line crizotinib versus chemotherapy in ALK-positive lung cancer
.
N Engl J Med
2014
;
371
:
2167
77
.
144.
Yoda
S
,
Lin
JJ
,
Lawrence
MS
,
Burke
BJ
,
Friboulet
L
,
Langenbucher
A
, et al
.
Sequential ALK inhibitors can select for lorlatinib-resistant compound ALK mutations in ALK-positive lung cancer
.
Cancer Discov
2018
;
8
:
714
29
.
145.
Hida
T
,
Nokihara
H
,
Kondo
M
,
Kim
YH
,
Azuma
K
,
Seto
T
, et al
.
Alectinib versus crizotinib in patients with ALK-positive non-small-cell lung cancer (J-ALEX): an open-label, randomised phase 3 trial
.
Lancet
2017
;
390
:
29
39
.
146.
Nakagawa
K
,
Hida
T
,
Nokihara
H
,
Morise
M
,
Azuma
K
,
Kim
YH
, et al
.
Final progression-free survival results from the J-ALEX study of alectinib versus crizotinib in ALK-positive non-small-cell lung cancer
.
Lung Cancer
2020
;
139
:
195
9
.
147.
Shaw
AT
,
Bauer
TM
,
de Marinis
F
,
Felip
E
,
Goto
Y
,
Liu
G
, et al
.
First-line lorlatinib or crizotinib in advanced ALK-positive lung cancer
.
N Engl J Med
2020
;
383
:
2018
29
.
148.
Solomon
BJ
,
Bauer
TM
,
Mok
TSK
,
Liu
G
,
Mazieres
J
,
de Marinis
F
, et al
.
Efficacy and safety of first-line lorlatinib versus crizotinib in patients with advanced, ALK-positive non-small-cell lung cancer: updated analysis of data from the phase 3, randomised, open-label CROWN study
.
Lancet Respir Med
2023
;
11
:
354
66
.
149.
Lynch
TJ
,
Bell
DW
,
Sordella
R
,
Gurubhagavatula
S
,
Okimoto
RA
,
Brannigan
BW
, et al
.
Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib
.
N Engl J Med
2004
;
350
:
2129
39
.
150.
Paez
JG
,
Jänne
PA
,
Lee
JC
,
Tracy
S
,
Greulich
H
,
Gabriel
S
, et al
.
EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy
.
Science
2004
;
304
:
1497
500
.
151.
Maemondo
M
,
Inoue
A
,
Kobayashi
K
,
Sugawara
S
,
Oizumi
S
,
Isobe
H
, et al
.
Gefitinib or chemotherapy for non–small-cell lung cancer with mutated EGFR
.
N Engl J Med
2010
;
362
:
2380
8
.
152.
Rosell
R
,
Carcereny
E
,
Gervais
R
,
Vergnenegre
A
,
Massuti
B
,
Felip
E
, et al
.
Erlotinib versus standard chemotherapy as first-line treatment for European patients with advanced EGFR mutation-positive non-small-cell lung cancer (EURTAC): a multicentre, open-label, randomised phase 3 trial
.
Lancet Oncol
2012
;
13
:
239
46
.
153.
Oxnard
GR
,
Arcila
ME
,
Sima
CS
,
Riely
GJ
,
Chmielecki
J
,
Kris
MG
, et al
.
Acquired resistance to EGFR tyrosine kinase inhibitors in EGFR-mutant lung cancer: distinct natural history of patients with tumors harboring the T790M mutation
.
Clin Cancer Res
2011
;
17
:
1616
22
.
154.
Pao
W
,
Miller
VA
,
Politi
KA
,
Riely
GJ
,
Somwar
R
,
Zakowski
MF
, et al
.
Acquired resistance of lung adenocarcinomas to gefitinib or erlotinib is associated with a second mutation in the EGFR kinase domain
.
PLoS Med
2005
;
2
:
e73
.
155.
Mok
TS
,
Wu
YL
,
Ahn
MJ
,
Garassino
MC
,
Kim
HR
,
Ramalingam
SS
, et al
.
Osimertinib or platinum-pemetrexed in EGFR T790M-positive lung cancer
.
N Engl J Med
2017
;
376
:
629
40
.
156.
Ramalingam
SS
,
Vansteenkiste
J
,
Planchard
D
,
Cho
BC
,
Gray
JE
,
Ohe
Y
, et al
.
Overall survival with osimertinib in untreated, EGFR-mutated advanced NSCLC
.
N Engl J Med
2020
;
382
:
41
50
.
157.
Soria
JC
,
Ohe
Y
,
Vansteenkiste
J
,
Reungwetwattana
T
,
Chewaskulyong
B
,
Lee
KH
, et al
.
Osimertinib in untreated EGFR-mutated advanced non-small-cell lung cancer
.
N Engl J Med
2018
;
378
:
113
25
.
158.
Passaro
A
,
Jänne
PA
,
Mok
T
,
Peters
S
.
Overcoming therapy resistance in EGFR-mutant lung cancer
.
Nat Cancer
2021
;
2
:
377
91
.
159.
Eno
MS
,
Brubaker
JD
,
Campbell
JE
,
De Savi
C
,
Guzi
TJ
,
Williams
BD
, et al
.
Discovery of BLU-945, a reversible, potent, and wild-type-sparing next-generation EGFR mutant inhibitor for treatment-resistant non-small-cell lung cancer
.
J Med Chem
2022
;
65
:
9662
77
.
160.
Jänne
PA
,
Riely
GJ
,
Gadgeel
SM
,
Heist
RS
,
Ou
SI
,
Pacheco
JM
, et al
.
Adagrasib in non-small-cell lung cancer harboring a KRAS(G12C) mutation
.
N Engl J Med
2022
;
387
:
120
31
.
161.
Hallin
J
,
Bowcut
V
,
Calinisan
A
,
Briere
DM
,
Hargis
L
,
Engstrom
LD
, et al
.
Anti-tumor efficacy of a potent and selective non-covalent KRAS(G12D) inhibitor
.
Nat Med
2022
;
28
:
2171
82
.
162.
Zhang
Z
,
Morstein
J
,
Ecker
AK
,
Guiley
KZ
,
Shokat
KM
.
Chemoselective covalent modification of K-Ras(G12R) with a small molecule electrophile
.
J Am Chem Soc
2022
;
144
:
15916
21
.
163.
Kim
D
,
Herdeis
L
,
Rudolph
D
,
Zhao
Y
,
Böttcher
J
,
Vides
A
, et al
.
Pan-KRAS inhibitor disables oncogenic signalling and tumour growth
.
Nature
2023
;
619
:
160
6
.
164.
Lee
BJ
,
Boyer
JA
,
Burnett
GL
,
Thottumkara
AP
,
Tibrewal
N
,
Wilson
SL
, et al
.
Selective inhibitors of mTORC1 activate 4EBP1 and suppress tumor growth
.
Nat Chem Biol
2021
;
17
:
1065
74
.
165.
Zhang
Z
,
Shokat
KM
.
Bifunctional small-molecule ligands of K-Ras induce its association with immunophilin proteins
.
Angew Chem Int Ed Engl
2019
;
58
:
16314
9
.
166.
Fischer
ES
,
Böhm
K
,
Lydeard
JR
,
Yang
H
,
Stadler
MB
,
Cavadini
S
, et al
.
Structure of the DDB1-CRBN E3 ubiquitin ligase in complex with thalidomide
.
Nature
2014
;
512
:
49
53
.
167.
Sasso
JM
,
Tenchov
R
,
Wang
D
,
Johnson
LS
,
Wang
X
,
Zhou
QA
.
Molecular glues: the adhesive connecting targeted protein degradation to the clinic
.
Biochemistry
2023
;
62
:
601
23
.
168.
Juric
D
,
Rodon
J
,
Tabernero
J
,
Janku
F
,
Burris
HA
,
Schellens
JHM
, et al
.
Phosphatidylinositol 3-kinase α-selective inhibition with alpelisib (BYL719) in PIK3CA-altered solid tumors: results from the first-in-human study
.
J Clin Oncol
2018
;
36
:
1291
9
.
169.
Nichols
CA
,
Gibson
WJ
,
Brown
MS
,
Kosmicki
JA
,
Busanovich
JP
,
Wei
H
, et al
.
Loss of heterozygosity of essential genes represents a widespread class of potential cancer vulnerabilities
.
Nat Commun
2020
;
11
:
2517
.
170.
Patel
MN
,
Halling-Brown
MD
,
Tym
JE
,
Workman
P
,
Al-Lazikani
B
.
Objective assessment of cancer genes for drug discovery
.
Nat Rev Drug Discov
2013
;
12
:
35
50
.
171.
DeVita
VT
Jr
,
Rosenberg
SA
.
Two hundred years of cancer research
.
N Engl J Med
2012
;
366
:
2207
14
.
172.
Druker
BJ
,
Talpaz
M
,
Resta
DJ
,
Peng
B
,
Buchdunger
E
,
Ford
JM
, et al
.
Efficacy and safety of a specific inhibitor of the BCR-ABL tyrosine kinase in chronic myeloid leukemia
.
N Engl J Med
2001
;
344
:
1031
7
.
173.
Cerami
E
,
Gao
J
,
Dogrusoz
U
,
Gross
BE
,
Sumer
SO
,
Aksoy
BA
, et al
.
The cBio Cancer Genomics Portal: an open platform for exploring multidimensional cancer genomics data
.
Cancer Discov
2012
;
2
:
401
4
.
174.
Gao
J
,
Aksoy
BA
,
Dogrusoz
U
,
Dresdner
G
,
Gross
B
,
Sumer
SO
, et al
.
Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal
.
Sci Signal
2013
;
6
:
pl1
.
175.
Chakravarty
D
,
Gao
J
,
Phillips
SM
,
Kundra
R
,
Zhang
H
,
Wang
J
, et al
.
OncoKB: a precision oncology knowledge base
.
JCO Precis Oncol
2017
;
2017
:
PO.17.00011
.
176.
Tsherniak
A
,
Vazquez
F
,
Montgomery
PG
,
Weir
BA
,
Kryukov
G
,
Cowley
GS
, et al
.
Defining a cancer dependency map
.
Cell
2017
;
170
:
564
76
.
This open access article is distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license.