Page 92 - ITPS-7-1

INNOSC Theranostics and
Pharmacological Sciences Plants immunoactivity: In silico study

2.1. Chemical composition of each herbal plant 2.4. Related targets in the immune system and the
involved pathways
An exhaustive literature search was conducted for each
herbal remedy, aiming to capture all available information Related targets in the immune system were searched
regarding their constituents. The compound composition using the innate immune database . Subsequently, the
[25]
of these plants was further extracted from Dr. Duke’s STRING database and the pathway database were utilized
Phytochemical Database (https://phytochem.nal.usda. to determine the different pathways associated with
gov/) and the Indian Medicinal Plants, Phytochemistry, the immune system. The I-PW were then evaluated and
and Therapeutics (IMPPAT) (https://cb.imsc.res.in/ analyzed.
imppat/) . Structure files of molecules, provided in mol Moreover, the STRING database was used to identify
[15]
format, were retrieved from PubChem and ChEMBL , the interacting partners of these bioactive targets searched
[16]
[17]
including their canonical SMILES. in the KEGG pathway to find IA-IP.
2.2. Screening of potential active phytocompounds 2.5. Targets of multiple myeloma and control drugs
The attributes of absorption, distribution, metabolism, The multiple myeloma-associated human genes/proteins
and excretion (ADME) were recognized as pivotal were obtained from two different databases: GeneCards
[26]
indicators of herb or potential medication potency. and DisGeNET . The search query employed the specific
[27]
To unveil the possible bioactive components within term “multiple myeloma,” with the search parameters
each of the two herbal plants, three ADME-related confined to the species “Homo sapiens.” The targets of
models were used, including the evaluation of oral control IMiDs (Lenalidomide and Thalidomide) were also
bioavailability (OB) and drug-likeness (DL). The retrieved from GeneCards and Disgenet, subsequently
technique also includes the identification and inclusion overlapping with the targets of the identified bioactives.
of the main components present within the plants [17] .
OB refers to the percentage of an orally administered 2.6. PPi
dose of a medication that enters the systemic circulation The overlap of related gene targets of the bioactives and
unaltered. High OB is frequently indicative of bioactive the immune system was regarded as the bioactives’
compounds possessing drug-like properties suitable for immune targets at the interactome level. Similarly, the
medicinal applications [18] . DL is a qualitative concept overlap of related gene targets of the bioactives’ immune
employed in drug design to gauge the “drug-likeness” targets and multiple myeloma was regarded as bioactive-
of a prospective product. This assessment aids in multiple myeloma’s immune gene targets at the diseasome
the optimization of pharmacokinetic and medicinal level. These interactions were obtained using Venny
characteristics, including solubility and chemical 2.0 . Subsequently, the STRING database was employed
[28]
stability. Typically, variables such as OB ≤30 – 33% and/ to identify the possible inter-protein interactions .
[29]
or DL ≤0.1 are widely adopted criteria [19] . This database compiles both established and predicted
The ADMET profiles of all potential substances were PPi. STRING uses five sources to uncover relationships
estimated using SwissADME and AdmetSar 2.0. In within the database: genomic context predictions, high-
addition, the DL score was estimated based on “Lipinski’s throughput lab trials, co-expression, automated text
Rule of Five” parameters (molecular weight, Log P, mining, and existing database information. Several
hydrogen bond donors, and hydrogen bond acceptors) variables for STRING-based PPi identification can be
using the Molinspiration online web server [20,21] . used to ensure the reliability of the generated data. These
variables include interactions derived exclusively from
2.3. Targets of the bioactives high-throughput laboratory experiments, a minimum
required interaction score of 0.7 (a high confidence score
Target genes/proteins in humans that interact with active according to STRING), and the highest score of interactors.
phytochemical substances from two herbal plants were
investigated using the Similarity Ensemble Approach 2.7. Network construction
(SEA) , SwissTargetPrediction , and PharmMapper .
[24]
[22]
[23]
A network is a diagrammatic representation that depicts
Data for each protein, including its standard protein the interactions between numerous components known as
name, gene ID and organism (set to Homo sapiens), were nodes. These nodes are interconnected by edges, which are
derived from UniProt (“UniProt: a hub for protein lines that connect them. In this study, the nodes encompass
[25]
information,” 2014) using the UniProt ID provided in the herbal plants under investigation, the bioactives
BindingDB. within these herbal plants, the bioactives’ targets, and the
Volume 7 Issue 1 (2024) 3 https://doi.org/10.36922/itps.1076