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Combining cutting edge computational and experimental methods for targeting KRAS mutations in non-small cell lung
Ram Samudrala1, Liana Bruggemann1, Zackary Falls1
1Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA.
Introduction:
Historically, KRAS mutations have been notoriously difficult to target despite their status as the most commonly mutated oncogene in the RAS gene family. Pioneering work by Shokat and colleagues has led to the discovery of KRAS G12C-GDP mutant-specific inhibitors, with two such inhibitors adagrasib and sotorasib now FDA approved for treatment of non-small cell lung cancer (NSCLC). Unfortunately, several patients did not achieve full treatment response. Further drug discovery is urgently needed to identify compounds capable of synergizing with available KRAS G12C inhibitors to prevent drug resistance, pan-KRAS inhibitors capable of binding multiple KRAS mutations, and KRAS-GTP inhibitors.
Areas Covered:
This review encompasses the development of the first KRAS G12C inhibitors to recent advances in precision oncology utilizing artificial intelligence (AI) to identify compounds capable of targeting KRAS G12C, D, and V individually, as well as pan-KRAS and SOS1 inhibitors.
Expert Opinion:
Recent studies support the view that integration of AI algorithms with experimental methods is a key aspect in stream-lining the drug discovery process and identifying molecules with greater structural diversity, less off-target effects than traditional screening methods. Furthermore, the authors believe that AI will eventually become standardized in drug discovery for aggressive driver oncogenes across multiple cancers.
Insights
Targeting KRAS mutations in cancer remains challenging. New artificial intelligence (AI) approaches are accelerating the discovery of novel KRAS G12C inhibitors and pan-KRAS inhibitors for improved cancer treatment.
Area of Science:
- Oncology
- Drug Discovery
- Computational Biology
Background:
- KRAS mutations are common oncogenic drivers, historically difficult to target.
- KRAS G12C inhibitors (adagrasib, sotorasib) are approved for non-small cell lung cancer (NSCLC), but treatment responses vary.
- There is an urgent need for novel inhibitors to overcome resistance, target multiple KRAS mutations (pan-KRAS), and inhibit KRAS-GTP.
Purpose of the Study:
- To review the development of KRAS G12C inhibitors.
- To explore recent advances in precision oncology using artificial intelligence (AI) for KRAS inhibitor discovery.
- To discuss the potential of AI in identifying novel KRAS G12C, G12D, G12V, pan-KRAS, and SOS1 inhibitors.
Main Methods:
- Literature review using PubMed and Google Scholar.
- Keywords: "KRAS G12C inhibitors," "NSCLC," "pan-KRAS inhibitors," "AI," "drug discovery" (2013-2025).
- Focus on rational drug design and AI-driven compound identification.
Main Results:
- AI integration with experimental methods streamlines drug discovery.
- AI identifies molecules with greater structural diversity and fewer off-target effects.
- AI facilitates the development of inhibitors targeting specific KRAS mutations and pan-KRAS.
Conclusions:
- AI is revolutionizing drug discovery, offering more efficient and effective methods.
- AI is expected to become standard in drug discovery pipelines.
- AI will expand inhibitor design beyond KRAS G12C to other KRAS mutations and oncogenes.
