Combining empirical knowledge, in silico molecular docking and ADMET profiling to identify therapeutic phytochemicals from Brucea antidysentrica for acute myeloid leukemia

Abstract

Acute myeloid leukemia (AML) is one of the deadly cancers. Chemotherapy is the first-line treatment and the only curative intervention is stem cell transplantation which are intolerable for aged and comorbid patients. Therefore, finding complementary treatment is still an active research area. For this, empirical knowledge driven search for therapeutic agents have been carried out by long and arduous wet lab processes. Nonetheless, currently there is an accumulated bioinformatics data about natural products that enabled the use of efficient and cost effective in silico methods to find drug candidates. In this work, therefore, we set out to computationally investigate the phytochemicals from Brucea antidysentrica to identify therapeutic phytochemicals for AML. We performed in silico molecular docking of compounds against AML receptors IDH2, MCL1, FLT3 and BCL2. Phytochemicals were docked to AML receptors at the same site where small molecule drugs were bound and their binding affinities were examined. In addition, random compounds from PubChem were docked with AML targets and their docking score was compared with that of phytochemicals using statistical analysis. Then, non-covalent interactions between phytochemicals and receptors were identified and visualized using discovery studio and Protein-Ligand Interaction Profiler web tool (PLIP). From the statistical analysis, most of the phytochemicals exhibited significantly lower (p-value ≤ 0.05) binding energies compared with random compounds. Using cutoff binding energy of less than or equal to one standard deviation from the mean of the phytochemicals' binding energies for each receptor, 12 phytochemicals showed considerable binding affinity. Especially, hydnocarpin (-8.9 kcal/mol) and yadanzioside P (-9.4 kcal/mol) exhibited lower binding energy than approved drugs AMG176 (-8.6 kcal/mol) and gilteritinib (-9.1 kcal/mol) to receptors MCL1 and FLT3 respectively, indicating their potential to be lead molecules. In addition, most of the phytochemicals possessed acceptable drug-likeness and absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. Based on the binding affinities as exhibited by the molecular docking studies supported by the statistical analysis, 12 phytochemicals from Brucea antidysentrica (1,11-dimethoxycanthin-6-one, 1-methoxycanthin-6-one, 2-methoxycanthin-6-one, beta-carboline-1-propionic acid, bruceanol A, bruceanol D, bruceanol F, bruceantarin, bruceantin, canthin-6-one, hydnocarpin, and yadanzioside P) can be considered as candidate compounds to prevent and manage AML. However, the phytochemicals should be further studied using in vivo & in vitro experiments on AML models. Therefore, this study concludes that combination of empirical knowledge, in silico molecular docking and ADMET profiling is useful to find natural product-based drug candidates. This technique can be applied to other natural products with known empirical efficacy.

Fig 1. Schematic representation of the various processes used in the study.

Briefly, AML targets and modern drugs (small molecule drugs) were obtained from drug bank, therapeutic target database (TTD) and/or literatures sources. B. antidysentrica phytochemicals were compiled from Ethiopian traditional medicine database (ETM-DB) and/or literatures. Then, the modern drugs, phytochemicals and random compounds (obtained from PubChem) were docked with AML targets. Binding energies of small molecule drugs and phytochemicals were evaluated against that of random compounds with statistical test. Finally, the candidate therapeutic phytochemicals were selected based on the statistical analysis and cutoff binding energies. In addition, the candidate compounds were further evaluated for drug-likeness, physicochemical and ADMET properties.

Fig 2. 2D structures of the investigated compounds in this study.

(A) Selected B. antidysentrica phytochemicals and (B) Small molecule drugs.

Fig 3. Scatter plots of the docking scores of compound-target interactions for each AML therapeutic targets.

(A) compound—target docking score visualization for each AML targets, (B) compound—target docking scores combined for all the four AML targets.

Fig 4. Molecular docking analysis of enasidenib and selected B. antidysentrica phytochemicals against IDH2 AML receptor.

(1) 3D pose views of interaction of compounds with AML receptor IDH2. (2) 2D pose views of interaction of compounds with AML receptor IDH2.

Fig 5. Molecular docking analysis of AMG176 and selected B. antidysentrica phytochemicals against MCL1 AML receptor.

(1) 3D pose views of interaction of compounds with AML receptor MCL1. (2) 2D pose views of interaction of compounds with AML receptor MCL1.

Fig 6. Molecular docking analysis of gilteritinib and selected B. antidysentrica phytochemicals against FLT3 AML receptor.

(1) 3D pose views of interaction of compounds with AML receptor FLT3. (2) 2D pose views of interaction of compounds with AML receptor FLT3.

Fig 7. Molecular docking analysis of venetoclax and selected B. antidysentrica phytochemicals against BCL2 AML receptor.

(1) 3D pose views of interaction of compounds with AML receptor FLT3. (2) 2D pose views of interaction of compounds with AML receptor BCL2.

Fig 8. BOILED-Egg model of small molecule standard drugs and selected B. antidysentrica phytochemicals.