Data Mining and in Silico Analysis of Ethiopian Traditional Medicine: Unveiling the Therapeutic Potential of Rumex abyssinicus Jacq.

Multicomponent traditional medicine prescriptions are widely used in Ethiopia for disease treatment. However, inconsistencies across practitioners, cultures, and locations have hindered the development of reliable therapeutic medicines. Systematic analysis of traditional medicine data is crucial for identifying consistent and reliable medicinal materials. In this study, we compiled and analyzed a dataset of 505 prescriptions, encompassing 567 medicinal materials used for treating 106 diseases. Using association rule mining, we identified significant associations between diseases and medicinal materials. Notably, wound healing-the most frequently treated condition-was strongly associated with Rumex abyssinicus Jacq., showing a high support value. This association led to further in silico and network analysis of R. abyssinicus Jacq. compounds, revealing 756 therapeutic targets enriched in various KEGG pathways and biological processes. The Random-Walk with Restart (RWR) algorithm applied to the CODA PPI network identified these targets as linked to diseases such as cancer, inflammation, and metabolic, immune, respiratory, and neurological disorders. Many hub target genes from the PPI network were also directly associated with wound healing, supporting the traditional use of R. abyssinicus Jacq. for treating wounds. In conclusion, this study uncovers significant associations between diseases and medicinal materials in Ethiopian traditional medicine, emphasizing the therapeutic potential of R. abyssinicus Jacq. These findings provide a foundation for further research, including in vitro and in vivo studies, to explore and validate the efficacy of traditional and natural product-derived medicines.

Designing combinational herbal drugs based on target space analysis.

BACKGROUND: Traditional oriental medicines (TOMs) are a medical practice that follows different philosophies to pharmaceutical drugs and they have been in use for many years in different parts of the world. In this study, by integrating TOM formula and pharmaceutical drugs, we performed target space analysis between TOM formula target space and small-molecule drug target space. To do so, we manually curated 46 TOM formulas that are known to treat Anxiety, Diabetes mellitus, Epilepsy, Hypertension, Obesity, and Schizophrenia. Then, we employed Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties such as human ether-a-go-go related gene (hERG) inhibition, Carcinogenicity, and AMES toxicity to filter out potentially toxic herbal ingredients. The target space analysis was performed between TOM formula and small-molecule drugs: (i) both are known to treat the same disease, and (ii) each known to treat different diseases. Statistical significance of the overlapped target space between the TOM formula and small-molecule drugs was measured using support value. Support value distribution from randomly selected target space was calculated to validate the result. Furthermore, the Si-Wu-Tang (SWT) formula and published literature were also used to evaluate our results. RESULT: This study tried to provide scientific evidence about the effectiveness of the TOM formula to treat the main indication with side effects that could come from the use of small-molecule drugs. The target space analysis between TOM formula and small-molecule drugs in which both are known to treat the same disease shows that many targets overlapped between the two medications with a support value of 0.84 and weighted average support of 0.72 for a TOM formula known to treat Epilepsy. Furthermore, support value distribution from randomly selected target spaces in this analysis showed that the number of overlapped targets is much higher between TOM formula and small-molecule drugs that are known to treat the same disease than in randomly selected target spaces. Moreover, scientific literature was also used to evaluate the medicinal efficacy of individual herbs. CONCLUSION: This study provides an evidence to the effectiveness of a TOM formula to treat the main indication as well as side effects associated with the use of pharmaceutical drugs, as demonstrated through target space analysis.

In silico activity and ADMET profiling of phytochemicals from Ethiopian indigenous aloes using pharmacophore models.

In silico profiling is used in identification of active compounds and guide rational use of traditional medicines. Previous studies on Ethiopian indigenous aloes focused on documentation of phytochemical compositions and traditional uses. In this study, ADMET and drug-likeness properties of phytochemicals from Ethiopian indigenous aloes were evaluated, and pharmacophore-based profiling was done using Discovery Studio to predict therapeutic targets. The targets were examined using KEGG pathway, gene ontology and network analysis. Using random-walk with restart algorithm, network propagation was performed in CODA network to find diseases associated with the targets. As a result, 82 human targets were predicted and found to be involved in several molecular functions and biological processes. The targets also were linked to various cancers and diseases of immune system, metabolism, neurological system, musculoskeletal system, digestive system, hematologic, infectious, mouth and dental, and congenital disorder of metabolism. 207 KEGG pathways were enriched with the targets, and the main pathways were metabolism of steroid hormone biosynthesis, lipid and atherosclerosis, chemical carcinogenesis, and pathways in cancer. In conclusion, in silico target fishing and network analysis revealed therapeutic activities of the phytochemicals, demonstrating that Ethiopian indigenous aloes exhibit polypharmacology effects on numerous genes and signaling pathways linked to many diseases.

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

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.