## 4 Discussion  
Cancer is one of the greatest causes of death worldwide, with estimates pointing to a significant increase in the number of new cases, which is expected to reach 35 million by 2050 [38]. Chemotherapy remains the primary therapeutic strategy in the fight against cancer, but multidrug resistance (MDR) compromises its effectiveness in around 90% of cases. The reason for this is that tumor cells develop resistance to antitumor compounds, leading to tumor cell invasion and metastasizing [39, 40]. The overexpression of P-glycoprotein (P-gp) promotes the efflux of chemotherapeutic drugs from tumor cells, reducing their intracellular concentrations and efficacy. This MDR mechanism, which has already been identified in various types of cancer, impairs the efficacy of routinely used drugs such as doxorubicin, 5-fluorouracilacil, paclitaxel, and cisplatin [40, 41]. Searching for natural compounds, such as those derived from mushrooms, is promising because they exhibit an MDR inhibition capacity while maintaining low toxicity, indicating potential to enhance the efficacy of cancer treatments. Molecular docking is a valuable tool for predicting interactions between bioactive substances and molecular targets, offering insights into their mechanisms of action. This method efficiently predicts ligand-receptor affinities by evaluating the binding energy. Lower binding energies indicate stronger and more stable interactions, which are crucial in identifying promising compounds [42, 43, 44]. This method was first validated by redocking tariquidar against P-gp (PDB: 7A6E), which produced an RMSD value of 1.60 This result confirms the docking predictions' accuracy and the methodology's robustness. RMSD values less than 2 A are typically ideal in verifying the conformance and guaranteeing the reliability of the used model [45]. After validation, the first screening stage of this work was to apply the molecular docking technique to predict the P-gp inhibition of the 211 compounds present in the mushroom database [21, 22, 23]. The analysis results are summarized in Figure 6, which presents an integrated heat map showcasing the predicted binding energy, toxicity prediction, and ADME performance for the top-ranking tested compounds. The results showed a wide range of potential interactions with P-gp, with binding energies ranging from \(-\)13.19 to 0.07 kcal/mol. The compounds with the strongest among those analyzed were lanostanoid 4 (\(-\)13.19 kcal/mol), lanostanoid 3 (\(-\)12.673 kcal/mol), and \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Compound** & **Name** & **P-gp Substrate** & **GI Absorption\({}_{\text{50}}\)** & **BBB Permanent** &
\begin{tabular}{c} **Lipinski’s** \\ **Rule of Five** \\ \end{tabular} & **ADME Score** \\ \hline
**26** & Antcin K & Yes & High & No & Yes; & 0 & 4 \\  & (22\(E\),24S)- & & & & & Yes; & 1 & \\
**4** & Ergosta-4,22- & No & High & Yes & violation: & 4 \\  & dien-3-one & & & & & MLOGP \(>\) 4.15 & \\  & 3,11- & & & & & & \\
**154** & Dioxolanosta-8, & & & & & & \\  & 24(\(Z\))-diene-26- & & & & & & \\  & oic acid & (22\(E\))-19- & & & & & \\
**7** & Norergosta- & & & & & & \\  & 5,7,9,22-tetraen- & & & & & & \\  & 3\(\beta\)-ol & & & & & & \\
**Control** & Tariquidar & No & Low & No & No &
\begin{tabular}{c} Yes; \\ violation: \\ \(\text{MW}>\) 500 \\ \end{tabular} & 4 \\ \hline \hline \end{tabular} Only the compounds with the best classification are shown in this table. Complete table available in Supplementary Materials.  
\end{table} 
Table 3: _Cont._(22E,24S)-ergosta-4,22-dien-3-one (\(-\)12.31 kcal/mol). Notably, of the 211 compounds present in the database, 59 compounds showed lower binding energies compared to the positive control, ratiquadr (\(-\)10.78 kcal/mol), a strong and well-known P-gp inhibitor [44], indicating greater potential for these compounds. Figure 6 presents the results of 31 compounds that presented lower predicted binding energies and better predicted toxicity profiles.