STRUCTURE-BASED VIRTUAL SCREENING STUDY OF FDA‑APPROVED DRUGS TO INHIBIT TP53 72 ARG/PRO VARIANT IDENTIFIED IN ACUTE LYMPHOCYTIC PATIENT VIA WHOLE EXOME SEQUENCING

Main Article Content

Shahid Ullah
Alex Tonks
Abdulsalam M Alruwaili
Hedib Alkoumi H Alrawili
Asifullah Khan
Ahmad Salem Alanazi
Amirah Sayah S Alkuwaykibi
Carlos Eliel Maya-Ramírez
Muhammad Arif Lodhi

Keywords

TP53, Messense Mutation, Acute lymphocytic leukemia, Docking.

Abstract

Acute lymphoblastic leukaemia (ALL) is a significant threat to global health. Tumour suppressor gene TP53 mutations are often associated with a more aggressive form of leukaemia and poorer prognoses. This study conducted whole-exome sequencing of leukaemia patients at various treatment stages, including early diagnosis, relapse, and remission. We identified a missense 72 Arg/Pro (rs1042522) homozygous and heterozygous variant along with indel novel intron variant 376-158delAAAAAAA and 993+409delT in TP53. This mutational profile may serve as a predictor of poor treatment success in the Pakistani Pathan (Pakhtun) Population. Theoretical study explores the virtual repurposing of the FDA-approved drugs as inhibitors against these mutant TP53 cancers. The crystal structure of the TP53 proteins was downloaded from Alpha fold database and PDB and subjected to virtual screening by the DrugRep web server while using an FDA-approved drugs library as a ligands database. Our study revealed that Duvelisib and Robinin herb are the top-ranked inhibitors of MUT TP53 as compared to the reference chemotherapy. Duvelisib exhibited a docking score of −10.6 kcal/mol while Robinin herb scored –10.4 kcal/mol. In conclusion the two drugs deserve further consideration as possible cancer treatment option.

Abstract 125 | PDF Downloads 47

References

1. Bhojwani D, Pei D, Sandlund JT, Jeha S, Onciu M, Cheng C, et al. NIH Public Access. 2013;26(2):265–70.
2. Roberts KG, Gu Z, Payne-Turner D, McCastlain K, Harvey RC, Chen IM, et al. High Frequency and Poor Outcome of Philadelphia Chromosome-Like Acute Lymphoblastic Leukemia in Adults. J Clin Oncol. 2017;35(4):394–401.
3. Paulsson K, Jonson T, Øra I, Olofsson T, Panagopoulos I, Johansson B. Characterisation of genomic translocation breakpoints and identification of an alternative TCF3/PBX1 fusion transcript in t(1;19)(q23;p13)-positive acute lymphoblastic leukaemias. Br J Haematol. 2007;138(2):196–201.
4. Leitão LPC, de Carvalho DC, Rodrigues JCG, Fernandes MR, Wanderley A V., Vinagre LWMS, et al. Identification of Genomic Variants Associated with the Risk of Acute Lymphoblastic Leukemia in Native Americans from Brazilian Amazonia. J Pers Med. 2022;12(6):856.
5. Awan T, Iqbal Z, Aleem A, Sabir N, Absar M, Rasool M, et al. Five most common Prognostically important fusion oncogenes are detected in the majority of Pakistani pediatric Acute lymphoblastic leukemia patients and are strongly associated with disease biology and treatment outcome. Asian Pacific J Cancer Prev. 2012;13(11):5469–75.
6. Schrappe M. Detection and management of minimal residual disease in acute lymphoblastic leukemia. Hematol (United States). 2014;2014(1):244–9.
7. Iacobucci I, Iraci N, Messina M, Lonetti A, Chiaretti S, Valli E, et al. IKAROS deletions dictate a unique gene expression signature in patients with adult B-cell acute lymphoblastic Leukemia. PLoS One. 2012;7(7).
8. Pillai PM, Carroll WL. Acute lymphoblastic leukemia. Lanzkowsky’s Man Pediatr Hematol Oncol. 2021;381(9881):413–38.
9. Rasul HO, Aziz BK, Ghafour DD, Kivrak A. Screening the possible anti-cancer constituents of Hibiscus rosa-sinensis flower to address mammalian target of rapamycin: an in silico molecular docking, HYDE scoring, dynamic studies, and pharmacokinetic prediction. Mol Divers [Internet]. 2023;27(5):2273–96. Available from: https://doi.org/10.1007/s11030-022-10556-9
10. Astalakshmi D., T G, K B GS, M N, M R HHS, S G, et al. Over View on Molecular Docking: A Powerful Approach for Structure Based Drug Discovery. Int J Pharm Sci Rev Res. 2022;77(2):146–57.
11. Winkler DG, Faia KL, Dinitto JP, Ali JA, White KF, Brophy EE, et al. PI3K-δ and PI3K-γ inhibition by IPI-145 abrogates immune responses and suppresses activity in autoimmune and inflammatory disease models. Chem Biol [Internet]. 2013;20(11):1364–74. Available from: http://dx.doi.org/10.1016/j.chembiol.2013.09.017
12. McPherson A, Chen K, Wu C, Wallis JW, Wyatt AW, McLellan MD, et al. BreakDancer: an algorithm for high-resolution mapping of genomic structural variation : Article : Nature Methods. Nat Methods [Internet]. 2009;6(9):677–81. Available from: papers2://publication/doi/10.1038/nmeth.1363
13. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25(14):1754–60.
14. McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GRS, Thormann A, et al. The Ensembl Variant Effect Predictor. Genome Biol [Internet]. 2016;17(1):1–14. Available from: http://dx.doi.org/10.1186/s13059-016-0974-4
15. Ng PC, Henikoff S. Predicting deleterious amino acid substitutions. Genome Res. 2001;11(5):863–74.
16. Wishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A, Grant JR, et al. DrugBank 5.0: A major update to the DrugBank database for 2018. Nucleic Acids Res. 2018;46(D1):D1074–82.
17. Gan J hong, Liu J xiang, Liu Y, Chen S wen, Dai W tao, Xiao ZX, et al. DrugRep: an automatic virtual screening server for drug repurposing. Acta Pharmacol Sin. 2023;44(4):888–96.
18. Li AP. Screening for human ADME/Tox drug properties in drug discovery. Drug Discov Today. 2001;6(7):357–66.
19. Rasul HO, Aziz BK, Ghafour DD, Kivrak A. Discovery of potential mTOR inhibitors from Cichorium intybus to find new candidate drugs targeting the pathological protein related to the breast cancer: an integrated computational approach. Mol Divers [Internet]. 2023;27(3):1141–62. Available from: https://doi.org/10.1007/s11030-022-10475-9
20. Izraeli S, Shochat C, Tal N, Geron I. Towards precision medicine in childhood leukemia - Insights from mutationally activated cytokine receptor pathways in acute lymphoblastic leukemia. Cancer Lett [Internet]. 2014;352(1):15–20. Available from: http://dx.doi.org/10.1016/j.canlet.2014.02.009
21. Chiaretti S, Vitale A, Cazzaniga G, Orlando SM, Silvestri D, Fazi P, et al. Clinico-biological features of 5202 patients with acute lymphoblastic leukemia enrolled in the Italian AIEOP and GIMEMA protocols and stratified in age cohorts. Haematologica. 2013;98(11):1702–10.
22. Olivier M, Hollstein M, Hainaut P. TP53 mutations in human cancers: origins, consequences, and clinical use. Cold Spring Harb Perspect Biol. 2010;2(1):1–17.
23. Salmoiraghi S, Montalvo MLG, Ubiali G, Tosi M, Peruta B, Zanghi P, et al. Mutations of TP53 gene in adult acute lymphoblastic leukemia at diagnosis do not affect the a

Most read articles by the same author(s)