Small molecular inhibitors for KRAS-mutant cancers

Wu, Xuan; Song, Wenping; Cheng, Cheng; Liu, Ziyang; Li, Xiang; Cui, Yu; Gao, Yao; Li, Ding

doi:10.3389/fimmu.2023.1223433

Cited by 7 publications

(3 citation statements)

References 87 publications

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“…However, as a pan-KRAS inhibitor, BI-2865 also inactivates WT KRAS. Presently, no clinical data demonstrating the consequences of blocking WT KRAS have been reported ( 46 ). Thus, it would be of great interest to see how cancer patients can tolerate potential toxicity of this pan-KRAS inhibitor.…”

Section: Pan Kras/ras Inhibitors- Targeting All Ras Isoforms and Muta...mentioning

confidence: 99%

Structural perspectives on recent breakthrough efforts toward direct drugging of RAS and acquired resistance

Lokhandwala,

Smalley,

Tran

2024

Front. Oncol.

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The Kirsten rat sarcoma viral oncoprotein homolog (KRAS) is currently a primary focus of oncologists and translational scientists, driven by exciting results with KRAS-targeted therapies for non-small cell lung cancer (NSCLC) patients. While KRAS mutations continue to drive high cancer diagnosis and death, researchers have developed unique strategies to target KRAS variations. Having been investigated over the past 40 years and considered “undruggable” due to the lack of pharmacological binding pockets, recent breakthroughs and accelerated FDA approval of the first covalent inhibitors targeting KRASG12C, have largely sparked further drug development. Small molecule development has targeted the previously identified primary location alterations such as G12, G13, Q61, and expanded to address the emerging secondary mutations and acquired resistance. Of interest, the non-covalent KRASG12D targeting inhibitor MRTX-1133 has shown promising results in humanized pancreatic cancer mouse models and is seemingly making its way from bench to bedside. While this manuscript was under review a novel class of first covalent inhibitors specific for G12D was published, These so-called malolactones can crosslink both GDP and GTP bound forms of G12D. Inhibition of the latter state suppressed downstream signaling and cancer cell proliferation in vitro and in mouse xenografts. Moreover, a non-covalent pan-KRAS inhibitor, BI-2865, reduced tumor proliferation in cell lines and mouse models. Finally, the next generation of KRAS mutant-specific and pan-RAS tri-complex inhibitors have revolutionized RAS drug discovery. This review will give a structural biology perspective on the current generation of KRAS inhibitors through the lens of emerging secondary mutations and acquired resistance.

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Section: Pan Kras/ras Inhibitors- Targeting All Ras Isoforms and Muta...mentioning

confidence: 99%

Structural perspectives on recent breakthrough efforts toward direct drugging of RAS and acquired resistance

Lokhandwala,

Smalley,

Tran

2024

Front. Oncol.

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“…Kirsten rat sarcoma (KRAS) gene is one of the most frequently mutated oncogenes, and it regulates cell signaling through downstream mitogen activated protein kinase (MAPK) and phosphatidylinositol 3-kinase (PI3K) pathways by encoding its protein [ 1 ]. KRAS mutations can cause active state of tyrosine kinase and constant activation of downstream signaling which lead to cell proliferation, migration, and tumorigenesis [ 2 – 4 ]. Despite KRAS mutations may play an important role in tumorigenesis, there has been no meaningful progress in targeted therapy against these mutations [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Efficacy and toxicity of KRASG12C inhibitors in advanced solid tumors: a meta-analysis

Dang,

Zhang,

Zhao

et al. 2024

World J Surg Onc

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Background The efficacy and toxicity of KRASG12C inhibitors were evaluated for advanced solid tumors in several studies; however, the results were not fully consistent. Methods Clinical trials evaluating KRASG12C inhibitors for advanced solid tumors were searched from PubMed, Embase, and Cochrane Library online databases up to 31st December 2023. The characteristics of the studies and the results of objective response rate (ORR), disease control rate (DCR), duration of response (DoR), progression-free survival (PFS) rate, overall survival (OS) rate, and treatment-related adverse events (trAEs) were extracted. Results Ten studies with 925 heavily pretreated advanced patients harboring KRASG12C mutation were included. For total population, the pooled analysis of ORR was 28.6% (95%CI, 21.2-36.6%), DCR was 85.5% (95%CI, 82.2-88.6%), PFS rate at 6 months (PFS6) was 49.6% (95%CI, 41.4-57.9%), PFS rate at 12 months (PFS12) was 26.7% (95%CI, 19.8-34.1%), OS rates at 6 months (OS6) was 76.2% (95%CI, 68.8-82.9%), OS rates at 12 months (OS12) was 47.8% (95%CI, 38.6-57.0%). The pooled analysis of any grade trAEs was 79.3% (95%CI, 66.2-90.0%) and grade three or more trAEs was 24.4% (95%CI, 16.7-32.9%). The median time to response and DoR results from individual data were 1.39 months (95%CI, 1.37–1.41 months) and 10.54 months (95%CI, 7.72–13.36 months). Sotorasib had significantly lower pooled incidences of any trAEs (OR, 0.07, 95%CI, 0.03–0.14) and grade three or more trAES (OR, 0.34, 95%CI, 0.24–0.49) compared with adagrasib. Conclusions KRASG12C inhibitors have good ORR, DCR, PFS rate, OS rate, tolerable trAEs, and early response with long duration in advanced solid tumors; however, most of the pooled results were heterogeneous. Sotorasib has shown better safety results.

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“…KRAS is a challenging target in oncology because it lacks a deep, well-defined pocket for smallmolecule inhibition27 . Recently, Popow et al created a KRAS-degrading PROTAC using the VHL E3 Ligase (Figure…”

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confidence: 99%

Interpretable deep-learning pKa prediction for small molecule drugs via atomic sensitivity analysis

DeCorte,

Brown,

Meiler

2024

Preprint

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Machine learning (ML) models play a crucial role in predicting properties essential to drug development, such as a drug’s logscale acid-dissociation constant (pKa). Despite recent architectural advances, these models often generalize poorly to novel compounds due to a scarcity of ground-truth data. Further, these models lack interpretability, in part due to a dependence on explicit encodings of input molecules’ molecular substructures. To this end, atomic-resolution information is accessible in chemical structures by observing model response to atomic perturbations of an input molecule; however, no methods exist that systematically utilize this information for model and molecular analysis. Here, we present BCL-XpKa, a substructure-independent, deep neural network (DNN)-based pKa predictor that generalizes well to novel small molecules. BCL-XpKa discretizes pKa prediction from a regression problem into a multitask-classification problem, which accumulates data for prediction at biologically relevant pH values and records the model’s uncertainty in its prediction as a discrete distribution for each pKa prediction. BCL-XpKa outperforms modern ML pKa predictors and accurately models the effects of common molecular modifications on a molecule’s ionizability. We then leverage BCL-XpKa’s substructure independence to introduce atomic sensitivity analysis (ASA), which quickly decomposes a molecule’s predicted pKa value into its respective atomic contributions without model retraining. When paired with BCL-XpKa, ASA informs that BCL-XpKa has implicitly learned high-resolution information about molecular substructures. We further demonstrate ASA’s utility in structure preparation for protein-ligand docking by identifying ionization sites in 97.8% and 83.4% of complex small molecule acids and bases. We then apply ASA with BCL-XpKa to understand the physicochemical liabilities and guide optimization of a recently published KRAS-degrading PROTAC.

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Small molecular inhibitors for KRAS-mutant cancers

Cited by 7 publications

References 87 publications

Structural perspectives on recent breakthrough efforts toward direct drugging of RAS and acquired resistance

Structural perspectives on recent breakthrough efforts toward direct drugging of RAS and acquired resistance

Efficacy and toxicity of KRASG12C inhibitors in advanced solid tumors: a meta-analysis

Interpretable deep-learning pKa prediction for small molecule drugs via atomic sensitivity analysis

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