ADAGRASIB (MRTX849) ANALOGLARININ YAPI TEMELLI OPTİMİZASYONU: KRAS G12D İNHİBİTÖR TASARIMI İÇİN GELİŞMİŞ HESAPLAMALI ÇERÇEVE

Öz

KRAS G12D mutasyonu, özellikle mevcut tedavi seçeneklerinin sınırlı olduğu pankreas ve kolorektal kanserlerde önemli bir terapötik zorluk oluşturmaktadır. KRAS G12C mutasyonu için kovalent inhibitörler klinik başarıya ulaşmış olsa da, G12D’yi hedefleyen etkili ajanların geliştirilmesi yapısal ve biyokimyasal farklılıklar nedeniyle güçtür. Bu çalışma, KRAS G12D’yi hedefleyen adagrasib analoglarının yapı temelli optimizasyonu için bir hesaplamalı çerçeve sunmaktadır. Gelişmiş Moleküler Tasarım Platformu kullanılarak, 17–25 pozisyonlarındaki R-gruplarının ilaç kimyası odaklı değiştirilmesiyle elli türev tasarlanmıştır. KRAS G12D (PDB: 7RPZ) üzerinde yapılan moleküler kenetlenme analizleri, adagrasib’e (−7.7 kcal/mol) kıyasla daha güçlü bağlanan adaylar (−6.9 ila −9.6 kcal/mol) belirlemiştir. Yapı-aktivite analizinde 17. pozisyondaki izopropil grubunun en uygun olduğu ve Deriv-34’ün ARG-68, GLU-62 ve TYR-96 ile etkileşimler üzerinden en yüksek bağlanma gücünü (−9.6 kcal/mol) gösterdiği saptanmıştır. Temel bileşen analizi, hidroksillenmiş türevlerin daha yüksek ilaç benzerliği (QED = 0.384) ve sentez kolaylığına sahip olduğunu ortaya koymuştur. Kapsamlı ADME değerlendirmesiyle adayların önceliklendirilmesi, KRAS G12D inhibitör tasarımı için rasyonel bir yol haritası sunmaktadır.

Anahtar Kelimeler

• KRAS G12D mutasyonu
• Adagrasib analogları
• Moleküler kenetlenme
• İlaç-benzerliği
• Hedefe yönelik kanser tedavisi

STRUCTURE-BASED OPTIMIZATION OF ADAGRASIB (MRTX849) ANALOGUES: ADVANCED COMPUTATIONAL FRAMEWORK FOR KRAS G12D INHIBITOR DESIGN

Öz

The KRAS G12D mutation poses a major therapeutic challenge, particularly in pancreatic and colorectal cancers where current treatments are limited. While covalent inhibitors for KRAS G12C have reached clinical success, developing effective G12D-targeted agents remains difficult due to its unique structural and biochemical features. This study introduces a computational framework for structure-based optimization of adagrasib analogues targeting KRAS G12D. Using an Advanced Molecular Design Platform, fifty derivatives were designed by modifying positions 17–25 of the tetracyclic scaffold with medicinal chemistry-guided R-group substitutions. Molecular docking against KRAS G12D (PDB: 7RPZ) identified several high-affinity candidates (−6.9 to −9.6 kcal/mol) outperforming adagrasib (−7.7 kcal/mol). Structure–activity analysis revealed isopropyl substitution at position 17 as optimal, with Deriv-34 achieving the strongest binding (−9.6 kcal/mol) via key interactions with ARG-68, GLU-62, and TYR-96. Principal component analysis highlighted hydroxylated derivatives with superior drug-likeness (QED = 0.384) and synthetic feasibility. Comprehensive ADME profiling guided lead prioritization, defining a rational pipeline for KRAS G12D inhibitor design. This integrated computational approach provides a promising foundation for experimental validation and advances targeted therapy development against KRAS-driven cancers.

Anahtar Kelimeler

• KRAS G12D Mutation
• Adagrasib analogues
• Molecular docking
• Drug-likeness
• Targeted cancer therapy

Kaynakça

1. Bento, A.P., Hersey, A., Félix, E., Landrum, G., Gaulton, A., Atkinson, F., Bellis, L.J., De Veij, M., Leach, A.R., 2020. An open source chemical structure curation pipeline using RDKit. Journal of Cheminformatics 12, 1-16.
2. Bickerton, G.R., Paolini, G.V., Besnard, J., Muresan, S., Hopkins, A.L., 2012. Quantifying the chemical beauty of drugs. Nature chemistry 4, 90-98.
3. Buscail, L., Bournet, B., Cordelier, P., 2020. Role of oncogenic KRAS in the diagnosis, prognosis and treatment of pancreatic cancer. Nature reviews Gastroenterology & hepatology 17, 153-168.
4. Canon, J., Rex, K., Saiki, A.Y., Mohr, C., Cooke, K., Bagal, D., Gaida, K., Holt, T., Knutson, C.G., Koppada, N., 2019. The clinical KRAS (G12C) inhibitor AMG 510 drives anti-tumour immunity. Nature 575, 217-223.
5. Cheng, H., Li, P., Chen, P., Irimia, A., Bae, J.H., Brooun, A., Fagan, P., Lam, R., Lin, B., Zhang, J., 2023. Structure-based design and synthesis of potent and selective KRAS G12D inhibitors. ACS Medicinal Chemistry Letters 14, 1351-1357.
6. Christensen, J.G., Olson, P., Briere, T., Wiel, C., Bergo, M.O., 2020. Targeting Krasg12c‐mutant cancer with a mutation‐specific inhibitor. Journal of internal medicine 288, 183-191.
7. Cox, A.D., Fesik, S.W., Kimmelman, A.C., Luo, J., Der, C.J., 2014. Drugging the undruggable RAS: Mission possible? Nature reviews Drug discovery 13, 828-851.
8. Eberhardt, J., Santos-Martins, D., Tillack, A.F., Forli, S., 2021. AutoDock Vina 1.2. 0: New docking methods, expanded force field, and python bindings. Journal of Chemical Information and Modeling 61, 3891-3898.

9. Fell, J.B., Fischer, J.P., Baer, B.R., Blake, J.F., Bouhana, K., Briere, D.M., Brown, K.D., Burgess, L.E., Burns, A.C., Burkard, M.R., 2020. Identification of the clinical development candidate MRTX849, a covalent KRASG12C inhibitor for the treatment of cancer. Journal of medicinal chemistry 63, 6679-6693.
10. Hallin, J., Engstrom, L.D., Hargis, L., Calinisan, A., Aranda, R., Briere, D.M., Sudhakar, N., Bowcut, V., Baer, B.R., Ballard, J.A., 2020. The KRASG12C inhibitor MRTX849 provides insight toward therapeutic susceptibility of KRAS-mutant cancers in mouse models and patients. Cancer discovery 10, 54-71.
11. Hobbs, G.A., Der, C.J., Rossman, K.L., 2016. RAS isoforms and mutations in cancer at a glance. Journal of cell science 129, 1287-1292.
12. Isert, C., Atz, K., Schneider, G., 2023. Structure-based drug design with geometric deep learning. Current Opinion in Structural Biology 79, 102548.
13. Issahaku, A.R., Mukelabai, N., Agoni, C., Rudrapal, M., Aldosari, S.M., Almalki, S.G., Khan, J., 2022. Characterization of the binding of MRTX1133 as an avenue for the discovery of potential KRASG12D inhibitors for cancer therapy. Scientific Reports 12, 17796.
14. Janes, M.R., Zhang, J., Li, L.-S., Hansen, R., Peters, U., Guo, X., Chen, Y., Babbar, A., Firdaus, S.J., Darjania, L., 2018. Targeting KRAS mutant cancers with a covalent G12C-specific inhibitor. Cell 172, 578-589. e517.
15. Jones, R.P., Sutton, P.A., Evans, J.P., Clifford, R., McAvoy, A., Lewis, J., Rousseau, A., Mountford, R., McWhirter, D., Malik, H.Z., 2017. Specific mutations in KRAS codon 12 are associated with worse overall survival in patients with advanced and recurrent colorectal cancer. British journal of cancer 116, 923-929.
16. Kessler, D., Gmachl, M., Mantoulidis, A., Martin, L.J., Zoephel, A., Mayer, M., Gollner, A., Covini, D., Fischer, S., Gerstberger, T., 2019. Drugging an undruggable pocket on KRAS. Proceedings of the National Academy of Sciences 116, 15823-15829.
17. Lanman, B.A., Allen, J.R., Allen, J.G., Amegadzie, A.K., Ashton, K.S., Booker, S.K., Chen, J.J., Chen, N., Frohn, M.J., Goodman, G., 2019. Discovery of a Covalent Inhibitor of KRASG12C (AMG 510) for the Treatment of Solid Tumors. ACS Publications.
18. Lipinski, C.A., Lombardo, F., Dominy, B.W., Feeney, P.J., 1997. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced drug delivery reviews 23, 3-25.
19. Lito, P., Solomon, M., Li, L.-S., Hansen, R., Rosen, N., 2016. Allele-specific inhibitors inactivate mutant KRAS G12C by a trapping mechanism. Science 351, 604-608.
20. Macindoe, G., Mavridis, L., Venkatraman, V., Devignes, M.-D., Ritchie, D.W., 2010. HexServer: an FFT-based protein docking server powered by graphics processors. Nucleic acids research 38, W445-W449.
21. McFall, T., Diedrich, J.K., Mengistu, M., Littlechild, S.L., Paskvan, K.V., Sisk-Hackworth, L., Moresco, J.J., Shaw, A.S., Stites, E.C., 2019. A systems mechanism for KRAS mutant allele–specific responses to targeted therapy. Science signaling 12, eaaw8288.
22. Mehmood, A., Kaushik, A.C., Wang, Q., Li, C.-D., Wei, D.-Q., 2021. Bringing structural implications and deep learning-based drug identification for KRAS mutants. Journal of Chemical Information and Modeling 61, 571-586.
23. Molina-Arcas, M., Hancock, D.C., Sheridan, C., Kumar, M.S., Downward, J., 2013. Coordinate direct input of both KRAS and IGF1 receptor to activation of PI3 kinase in KRAS-mutant lung cancer. Cancer discovery 3, 548-563.
24. Moore, A.R., Rosenberg, S.C., McCormick, F., Malek, S., 2020. RAS-targeted therapies: is the undruggable drugged? Nature reviews Drug discovery 19, 533-552.
25. Morris, G.M., Huey, R., Lindstrom, W., Sanner, M.F., Belew, R.K., Goodsell, D.S., Olson, A.J., 2009. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. Journal of computational chemistry 30, 2785-2791.
26. Mukherjee, S., Mitra, I., Saha, R., Dodda, S.R., Linert, W., Moi, S.C., 2015. In vitro model reaction of sulfur containing bio-relevant ligands with Pt (ii) complex: Kinetics, mechanism, bioactivity and computational studies. RSC Advances 5, 76987-76999.
27. Ostrem, J.M., Shokat, K.M., 2016. Direct small-molecule inhibitors of KRAS: from structural insights to mechanism-based design. Nature reviews Drug discovery 15, 771-785.
28. Pant, S., Verma, S., Pathak, R.K., Singh, D.B., 2022. Structure-based drug designing, Bioinformatics. Elsevier, pp. 219-231.
29. Pantsar, T., 2020. The current understanding of KRAS protein structure and dynamics. Computational and structural biotechnology journal 18, 189-198.
30. Poorebrahim, M., Abazari, M.F., Moradi, L., Shahbazi, B., Mahmoudi, R., Kalhor, H., Askari, H., Teimoori-Toolabi, L., 2022. Multi-targeting of K-Ras domains and mutations by peptide and small molecule inhibitors. PLoS Computational Biology 18, e1009962.
31. Prior, I.A., Hood, F.E., Hartley, J.L., 2020. The frequency of Ras mutations in cancer. Cancer research 80, 2969-2974.
32. Riniker, S., Landrum, G.A., 2015. Better informed distance geometry: using what we know to improve conformation generation. Journal of Chemical Information and Modeling 55, 2562-2574.
33. Waters, A.M., Der, C.J., 2018. KRAS: the critical driver and therapeutic target for pancreatic cancer. Cold Spring Harbor perspectives in medicine 8, a031435.
34. Yang, J., Anishchenko, I., Park, H., Peng, Z., Ovchinnikov, S., Baker, D., 2020. Improved protein structure prediction using predicted interresidue orientations. Proceedings of the National Academy of Sciences 117, 1496-1503.
35. Yuan, T.L., Amzallag, A., Bagni, R., Yi, M., Afghani, S., Burgan, W., Fer, N., Strathern, L.A., Powell, K., Smith, B., 2018. Differential effector engagement by oncogenic KRAS. Cell reports 22, 1889-1902.
36. Zeng, M., Lu, J., Li, L., Feru, F., Quan, C., Gero, T.W., Ficarro, S.B., Xiong, Y., Ambrogio, C., Paranal, R.M., 2017. Potent and selective covalent quinazoline inhibitors of KRAS G12C. Cell chemical biology 24, 1005-1016. e1003.