Artenimol

Artemisinin and 1-oleoyl-2-acetyl-sn-glycerol (OAG) were purchased from Sigma Aldrich (Munich, Germany). All other Artemisinin derivatives (artemether, artenimol, arteether, and artesunate) were from Santa Cruz Biotechnology (Heidelberg, Germany). GSK1702934A and GSK-417651A were obtained from Tebubio (Offenbach, Germany), SAR7334 hydrochloride was from MedChemexpress (Monmouth Junction, NJ, U.S.A.), and CBN 2910-0498 and BTD were from ChemDiv (San Diego, CA, U.S.A.). All other reagents were of analytical grade.

Analyzing potential drug repurposing opportunities for Familial Melanoma (FM) revealed 8.8% overlap and 14.3% complementary overlap between drug targets identified by DTSEA and drexml.
EGFR stands again as a highly ranked overlapping target. The dual focus on EGFR, targeted by EGFR inhibitors such as Alvocidib and CUDC-101, reflects its critical role in cancer biology, particularly in signaling pathways that drive tumor growth and survival. The EGFR ranking in both approximations highlights its potential as a candidate for FM treatment, suggesting that interventions aimed at this receptor could impact the course of the disease.
The voltage-dependent calcium channel subunit CACNA2D1, associated with drugs such as Pregabalin and Gabapentin enacarbil, also stands out as significantly distant from the disease. The high NES score of these drugs indicates a concentrated interest in modulating calcium channel activity as a therapeutic avenue. Given the role of calcium channels in cellular processes, including proliferation and apoptosis, targeting CACNA2D1 offers a promising strategy for influencing FM pathology, as highlighted in previous research [68] (link).
Additional targets such as LDHB, which is targeted by Artenimol, and PDGFRB, targeted by Trapidil, Erdafitinib, and Ilorasertib, present promising therapeutic options for Familial Melanoma. These drugs, commonly used or in trials for advanced cancers and solid tumors, indicate various mechanisms within pathways relevant to FM. This, combined with the variability in target rankings among the pathways, underscores the intricate nature of the disease's progression.
The combined analysis of DTSEA and drexml highlights the complexity of FM's molecular landscape while identifying key targets and drugs. EGFR and CACNA2D1 emerge as central players in the disease network with their high ranking and significant DREXML scores. Targeting these genes offers the potential for therapeutic intervention to revert or mitigate disease traits in Familial Melanoma.
The analysis further highlights the importance of considering the network context and mechanistic insights when evaluating drug repositioning opportunities. By focusing on genes that play a pivotal role in different signaling circuits, as indicated by the DREXML scores, researchers can prioritize targets that are likely to have a substantial impact on disease progression.
See Table 2 for a summary table of the intersection of both approaches.

The following compounds were used: Dihydroartemisinin (dihydroqinghaosu, artenimol, DHA) was a kind gift from Dominic Hoepfner (Novartis); Delta‐aminolaevulinic acid hydrochloride, 5‐ALA (Sigma Aldrich A3785‐500MG); acifluorfen, protoporphyrinogen oxidase (Ppox) inhibitor (Sigma Aldrich N11027‐250MG); piperlongumine, induces ROS (Sigma Aldrich SML0221‐5MG). For dosage responses, cells were seeded in 96‐wells (25,000/96‐well, in triplicate—where indicated) and subjected to compounds for 48 h (or 72 h if indicated in the Figure). Cell viability was assessed using automated cell counting (high‐throughput flow cytometry), Alamar Blue staining (Invitrogen, DAL1100) or CellTiter‐Glo Luminescent Assay (Promega, G7570, according to the manufacturer's protocol), respectively. For the detection of ROS, treated cells were collected, washed with 1x HBSS (without Ca²⁺ and Mg²⁺), incubated with 1 mM DHE (dihydroethidium (hydroethidine), Invitrogen, D11347) in 1× HBSS for 45 min at 37°C, washed twice and counterstained with DAPI or a viability dye (eBioscience™ Fixable Viability Dye eFluor™ 780, 65‐0865‐18) for 10 min on ice. Cells were then collected, strained, and analyzed using flow cytometry for DHE in the red fluorescent spectrum (PE channel 582/15 nm). For JC‐1 staining, treated cells were collected, washed with 1xPBS, incubated with 2 μM JC‐1 in 1xPBS (MitoProbe JC‐1 Assay Kit‐1, Invitrogen, M34152) for 35 min at 37°C, washed twice, and analyzed using flow cytometry in the red (PE channel) and green (FITC channel) fluorescence spectrum.

To select potential hits, the ligand-binding affinity was assessed using Autodock Vina and Autodock 4, and at least three replicates were performed. The docking protocol for each software was validated using previously known protein–ligand interactions and performing the redocking among P. falciparum plasmepsin I (PfPMI) (PDB:1LEE) and PfPMII (PDB:3QS1) and the following ligands: androstan-17-one, ethyl-3-hydroxy-(5-alpha)-torreyol, delta-cadinene, alpha-cadinol, neoclovenoxid, guaicoal, and artemisinin (Fatimawali et al. 2021 ). Predicted binding energies obtained using our docking protocol were similar to those redocking scores previously reported, for example, the binding energy variation for PfPMII ranged from − 0.8 to 0.8 (SD 0.596), whereas for PfPMI ranged from − 0.2 to 0.2 (SD 0.310). Artenimol (PubChem CID 6918483) also known as dihydroartemisinin (DHA), the active metabolite of artemisinin, was included as a control for docking simulations with all protein structures. Binding energy values were used to choose the best receptor structure when more than one structure from each protein was available in PDB (Bhojwani and Joshi 2019 (link)). A consensus docking method based on the ranking of binding energy values was used to improve the docking analysis and predictions as stated by Triches et al. (2022 (link)). Briefly, the average of the binding energy scores obtained from Autodock Vina and Autodock 4 were ranked; for Autodock 4, the binding poses based on the root-mean-square deviation (RMSD) were considered for analysis. An exponential consensus ranking (ECR) was calculated, and ECR values for the compounds with each protein structure obtained using both software tools were combined and ranked again to identify the top 10 ligands (Triches et al. 2022 (link)). The best 10 molecules from the final ranking were selected.