**3. Results**

#### **3.1 ADMET profiling**

ADMET analysis is a challenging process in drug discovery. The SwissADME tool was applied to predict the pharmacokinetic properties of potato glycoalkaloids. Pharmacokinetic factors can be utilized to predict the absorption, distribution, metabolism, and elimination (ADME), as well as the toxicity of potential novel therapeutic compounds. The glycoalkaloids also have more rotatable bonds, hydrogen-bond acceptors and donors, and a larger TPSA than solanidine. These properties could be attributed to the polar nature of the glycosidic moiety, which can participate in hydrogen bonding and increase solubility in water. The lower lipophilicity of glycoalkaloids could also make them less permeable through biological membranes, as evidenced by the lower log Kp values.

The results show that the solubility and lipophilicity of the solanine and chaconine and their hydrolysis products vary based on their glycosylation state. In general, the glycosylated forms of solanine and chaconine have higher molecular weights and lower LogSw values compared to the aglycone solanidine. This suggests that the glycosylation of the compounds may affect their solubility and lipophilicity. Gastrointestinal (GI) absorption was found to be negatively correlated with the glycosylation states. Solanidine and the least glycosylated forms were found to be the higher GI absorption. Similarly, only solanidine is permeable across the blood-brain barrier (BBB), while none of the glycosylated solanines or chaconines can penetrate the BBB. This could be due to the larger size of the glycosylated molecules. All of the compounds, except for solanidine, are P-glycoprotein (Pgp) substrates, meaning they are recognized and effluxed by this membrane transporter, which might be the cause of their anticancer activity. None of the compounds are inhibitors of the various cytochrome P450 enzymes (**Table 2**).

Compounds with fewer Lipinski and Leadlikeness violations, higher bioavailability scores, are more likely to become successful drugs. Alpha and beta forms of solanine and chaconine have more Lipinski and Leadlikeness violations, lower bioavailability scores, compared to the gamma form and solanidine. This suggests that solanine and chaconine may have lower drug-likeness compared to solanidine. It seems that the presence of multiple glycosylation moieties in solanine and chaconine may contribute to their lower drug-likeness properties. The glycosidic moiety can influence the pharmacokinetics of the compound by affecting its absorption, distribution, metabolism, and excretion (ADME) properties (**Table 2**).

#### **3.2 Drug targets in cancer**

We aimed to identify potential drug targets for α-solanine and α-chaconine in cancer using SEA, SwissTargetPrediction, and Superpred online tools. We applied specific criteria and identified a total of 59 α-solanine targets and 62 α-chaconine targets. 46 targets were shared between both drugs (**Figure 3A**), so we focused our analysis on these genes to explore the common mechanism. We further researched


**Table 2.**

*ADME profile of main potato glycoalkaloids.*

*Perspective Chapter: Integrated Network Pharmacology and Multiomics Approach to Elucidate… DOI: http://dx.doi.org/10.5772/intechopen.112789*

#### **Figure 3.**

*(A) Common targets between alpha-solanine and alpha chaconine and (B) neoplasm related targets shared between alpha-solanine and alpha-chaconine.*

these targets by looking at disease-related genes from Genecards, which resulted in 1458 genes, and using DisGeNet, we found 2510 targets related to cancer. After eliminating duplicates, we were left with 2925 targets that were relevant to cancer. These targets were then cross-referenced with known drug targets, resulting in a final list of 26 targets that were considered as potential drug targets for cancer treatment (**Figure 3B**). These targets were then chosen for further examination.

#### **3.3 PPI network construction and topology analysis**

We used the 26 drugs predicted targets in cancer to build a protein-protein interaction (PPI) network using the STRING database. The resulting PPI network consisted of 26 nodes and 38 edges, with an average node score of 2.92. PPI enrichment analysis revealed a P value of 3.65E−05 (**Figure 4A**). We then applied the MCC algorithm in Cytohubba to identify the top 10 hub genes in the network, which were found to be STAT3, TLR4, FGF2, IL2, NFKB1, AR, CHUK, TRIM24, NOS3 and KDM1A (**Figure 4B**).

#### **3.4 Gene ontology and pathway analysis**

We performed KEGG and GO pathway analysis on drug predicted targets imported to WebGesTalt. For KEGG analysis, PI3K-Akt1 pathway, HIF-1 pathway, and Th17 cell differentiation were among the most enriched pathways that are closely related to cancer development, progression, and drug resistance [83–90] (**Figure 5A**). Potential target genes in Pi3k-Akt1 and HIF-1 signaling pathways are visualized in **Figure 5B** and **C**. Among the enriched BP related to cancer, cellular response to oxygen-containing compound, cellular response to endogenous stimulus, regulation of cell death, regulation of programmed cell death, and regulation of intracellular signal transduction (**Figure 5D**). In addition, three molecular functions were identified, namely, signaling receptor binding, transcription factor binding, and chromatin binding (**Figure 5E**).

#### **Figure 4.**

*PPI and topology analysis. (A) Original PPI obtained from STRING and (B) top 10 hub genes according to MCC algorithm in cytohubba.*

#### **Figure 5.**

*KEGG pathway and gene ontology analysis of common target genes. (A) Enriched KEGG pathways according to WebG2estalt database. Darker node colors indicate higher significance, (B) target genes mapped to the PI3K-Akt1 pathway, (C) target genes mapped to the HIF-1 pathway, (D) enriched gene ontology biological processes and (E) enriched gene ontology molecular function.*

*Perspective Chapter: Integrated Network Pharmacology and Multiomics Approach to Elucidate… DOI: http://dx.doi.org/10.5772/intechopen.112789*

**Figure 6.**

*Survival analysis of hub genes. (A) Pan-cancer overall survival and (B) pan-cancer disease free survival.*

## **3.5 Survival analysis**

To investigate the prognostic significance of hub genes identified from the network analysis, we performed pan-cancer survival analysis using the GEPIA2 database. We found that expression levels of several hub genes were significantly associated with patient survival in multiple cancer types. Notably, low expression levels of seven out of the 10 hub genes were associated with poor overall survival (OS) in kidney renal carcinoma (KIRC) (**Figure 6**). These findings suggest that the drug predicted targets may have potential therapeutic implications in cancer, and the enriched terms and pathways may provide insights into the underlying biological mechanisms.
