Drug Repurposing against KRAS Mutant G12C: A Machine Learning, Molecular Docking, and Molecular Dynamics Study

Microsecond Molecular Dynamics Simulation to Gain Insight Into the Binding of MRTX1133 and Trametinib With KRASG12D Mutant Protein for Drug Repurposing

The Kirsten Rat Sarcoma (KRAS) G12D mutant protein is a primary driver of pancreatic ductal adenocarcinoma, necessitating the identification of targeted drug molecules. Repurposing of drugs quickly finds new uses, speeding treatment development. This study employs microsecond molecular dynamics simulations to unveil the binding mechanisms of the FDA-approved MEK inhibitor trametinib with KRASG12D, providing insights for potential drug repurposing. The binding of trametinib was compared with clinical trial drug MRTX1133, which demonstrates exceptional activity against KRASG12D, for better understanding of interaction mechanism of trametinib with KRASG12D. The resulting stable MRTX1133-KRASG12D complex reduces root mean square deviation (RMSD) values, in Switch I and II domains, highlighting its potential for inhibiting KRASG12D. MRTX1133's robust interaction with Tyr64 and disruption of Tyr96-Tyr71-Arg68 network showcase its ability to mitigate the effects of the G12D mutation. In contrast, trametinib employs a distinctive binding mechanism involving P-loop, Switch I and II residues. Extended simulations to 1 μs reveal sustained network interactions with Tyr32, Thr58, and GDP, suggesting a role of trametinib in maintaining KRASG12D in an inactive state and impede the further cell signaling. The decomposition binding free energy values illustrate amino acids' contributions to binding energy, elucidating ligand-protein interactions and molecular stability. The machine learning approach reveals that van der Waals interactions among the residues play vital role in complex stability and the potential amino acids involved in drug-receptor interactions of each complex. These details provide a molecular-level understanding of drug binding mechanisms, offering essential knowledge for further drug repurposing and potential drug discovery.

An extensive review on lung cancer therapeutics using machine learning techniques: state-of-the-art and perspectives

There are over 100 types of human cancer, accounting for millions of deaths every year. Lung cancer alone claims over 1.8 million lives per year and is expected to surpass 3.2 million by 2050, which underscores the urgent need for rapid drug development and repurposing initiatives. The application of AI emerges as a pivotal solution to developing anti-cancer therapeutics. This state-of-the-art review aims to explore the various applications of AI in lung cancer therapeutics. Predictive models can analyse large datasets, including clinical data, genetic information, and treatment outcomes, for novel drug design and to generate personalised treatment recommendations, potentially optimising therapeutic strategies, enhancing treatment efficacy, and minimising adverse effects. A thorough literature review study was conducted based on articles indexed in PubMed and Scopus. We compiled the use of various machine learning approaches, including CNN, RNN, GAN, VAEs, and other AI techniques, enhancing efficiency with accuracy exceeding 95%, which is validated through a computer-aided drug design process. AI can revolutionise lung cancer therapeutics, streamlining processes and saving biological scientists' time and effort-however, further research is needed to overcome challenges and fully unlock AI's potential in Lung Cancer Therapeutics.

Insights into the structure-activity relationship of pyrimidine-sulfonamide analogues for targeting BRAF V600E protein

B-rapidly accelerated fibrosarcoma (BRAF) V600E plays a crucial role in the progression of cutaneous melanoma. Core structures of BRAF V600E inhibitors are based on pyrimidine-sulfonamide scaffolds. Exploring the QSAR of these structures can improve our understanding of BRAF V600E inhibitor drug design. This study utilized machine learning-based QSAR to elucidate chemical substructures of pyrimidine-sulfonamide analogues that correlated to the BRAF V600E inhibitory activity. The findings indicate that the support vector regression (SVR) combined with 15 fingerprints achieved the highest statistical performances in terms of goodness-of-fit, robustness, and predictability. Nine key fingerprints from pyrimidine-sulfonamide analogues were identified to exert the BRAF V600E inhibitory activity. These key fingerprints were validated using network-based activity cliff landscape and molecular docking. Together, the developed algorithm can serve as a screening tool for designing BRAF V600E inhibitors. To further utilize this model, we deployed our developed algorithm at https://qsarlabs.com/#braf.

Targeting KRAS in Colorectal Cancer: A Bench to Bedside Review

Colorectal cancer (CRC) is a heterogeneous disease with a myriad of alterations at the cellular and molecular levels. Kristen rat sarcoma (KRAS) mutations occur in up to 40% of CRCs and serve as both a prognostic and predictive biomarker. Oncogenic mutations in the KRAS protein affect cellular proliferation and survival, leading to tumorigenesis through RAS/MAPK pathways. Until recently, only indirect targeting of the pathway had been investigated. There are now several KRAS allele-specific inhibitors in late-phase clinical trials, and many newer agents and targeting strategies undergoing preclinical and early-phase clinical testing. The adequate treatment of KRAS-mutated CRC will inevitably involve combination therapies due to the existence of robust adaptive resistance mechanisms in these tumors. In this article, we review the most recent understanding and findings related to targeting KRAS mutations in CRC, mechanisms of resistance to KRAS inhibitors, as well as evolving treatment strategies for KRAS-mutated CRC patients.

StackBRAF: A Large-Scale Stacking Ensemble Learning for BRAF Affinity Prediction

The B-rapidly accelerated fibrosarcoma (BRAF) is a proto-oncogene that plays a vital role in cell signaling and growth regulation. Identifying a potent BRAF inhibitor can enhance therapeutic success in high-stage cancers, particularly metastatic melanoma. In this study, we proposed a stacking ensemble learning framework for the accurate prediction of BRAF inhibitors. We obtained 3857 curated molecules with BRAF inhibitory activity expressed as a predicted half-maximal inhibitory concentration value (pIC₅₀) from the ChEMBL database. Twelve molecular fingerprints from PaDeL-Descriptor were calculated for model training. Three machine learning algorithms including extreme gradient boosting, support vector regression, and multilayer perceptron were utilized for constructing new predictive features (PFs). The meta-ensemble random forest regression, called StackBRAF, was created based on the 36 PFs. The StackBRAF model achieves lower mean absolute error (MAE) and higher coefficient of determination (R² and Q²) than the individual baseline models. The stacking ensemble learning model provides good y-randomization results, indicating a strong correlation between molecular features and pIC₅₀. An applicability domain of the model with an acceptable Tanimoto similarity score was also defined. Moreover, a large-scale high-throughput screening of 2123 FDA-approved drugs against the BRAF protein was successfully demonstrated using the StackBRAF algorithm. Thus, the StackBRAF model proved beneficial as a drug design algorithm for BRAF inhibitor drug discovery and drug development.