Neuroblastoma

There are five stages in the clinical neuroblastoma (NB) staging system: 1, 2A, 2B, 3, 4S, and 4 [1]. For those diagnosed with NB stage 4 (NBS4) the treatment options are limited with a survival rate of between 40% to 50% [1]. In a review of drug therapies aimed at treating NBS4, Gerges and Canning [2] (2024) examined the potential for creating multi-targeted drugs to treat high-risk NBS4 using computational methods available to medicinal chemistry. Finding novel therapeutic drug targets by this approach usually involves computational techniques like molecular docking, homology modelling, molecular dynamics, and quantitative structure-activity relationships (QSAR) [3]. The work reported here follows on from that review and investigates two different types of inhibitors for the signalling pathways c-Src kinase (Csk) and retinoic acid (RA) signalling pathways[2]. Six targets were originally identified and investigated by the authors [2] and the results of the Protein Aligner revealed that the receptors for four of the six targets shared similar amino acid sequencing that ranged from 80–100%. The four targets that share similar sequencing included histone deacetylase (HDAC), bromodomain (BRD), hedgehog (HH), and tropomyosin receptor kinase (TRK) and are the focus of a separate paper. The remaining two targets of the original six, Csk and RA signalling pathways showed similarity across their receptors of 80–100% are the focus of this paper. Their potential for multi-targeted drug use was investigated further with four compounds (inhibitors) targeting the Csk and RA pathways identified.

Current treatment for NBS4

With therapeutic agents finding little success in the treatment of NBS4, immunotherapy which has been effective in treating a number of cancers is currently used. This treatment uses monoclonal antibodies and has increased the 5-year survival rate of NBS4 to 40% to 50% [4–6]. Dinutuximab (UNITUXIN), a monoclonal antibody, is an example of cancer immunotherapy and for patients with NBS4, it is used in conjunction with other therapeutic agents. It was approved by the FDA in 2015 for use in the United States [6].

c-Src kinase inhibitors

Proliferation, differentiation, migration, and membrane trafficking are just a few of the cellular functions that are critical to the Src family kinases (SFKs) of non-receptor tyrosine kinases (TK) [7]. TK deregulation has been linked to cancer, with pharmaceutical companies and academic institutions now targeting small molecule TK inhibitors (TKIs) as one of the largest drug families for the treatment of cancer (Figure 1) [8]. TKIs may be associated with fewer harmful side effects during anti-tumor treatments because they target particular molecular targets [8]. As a result, numerous TKIs have undergone testing for their anti-proliferative and anti-cancer properties in vitro and in vivo, with some approved for clinical trials and others currently used in cancer treatment [9]. One subclass of non-receptor TKs targeted in the treatment of human cancers is the SFKs. Of these, c-Src promotes angiogenesis, invasion, migration, and cell proliferation (Figure 1) [10]. c-Src hyperactivation results in abnormal cell activity, which aids in cancer development. High expression levels of c-Src, which have been found in several cancers, are typically associated with a poor overall survival prognosis [11]. More than one study has shown that c-Src inhibitors play an important role in antiproliferative activity in SH-SY5Y cells (NBS4 cell line) [12–23].

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Figure 1. 

c-Src in cancer. VEGF: vascular endothelial growth factor; VEGFR2: VEGF receptor 2; LATS: large tumor suppressor; YAP: Yes-associated protein; TAZ: transcriptional co-activator with PDZ-binding motif; TEAD: transcriptional enhanced associate domain; GAPs: GTPase-activating proteins. Created in BioRender. Gerges, A. (2024) https://BioRender.com/t65n288

Retinoic acid inhibitors

The small lipophilic molecule RA, derived from vitamin A, is essential for axonal growth, neuronal differentiation, neuronal patterning, and embryonic development (Figure 2) [24]. Research on the effect of RA on neuronal development has improved our knowledge of neurodegenerative diseases [25]. It has been noted that the symptoms of neurodegenerative diseases are caused by RA altered signalling, which maintains the differentiated state of adult neurons, neural stem cells, and NB cells in particular (cell line SH-SY5Y) [26–36].

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Figure 2. 

Retinoic Acid receptors and their actions. The three retinoic acid receptor subtypes (RARα, RARβ and RARγ) act as ligand-inducible transcription factors binding to DNA regulatory elements in the promoter regions of target genes by forming heterodimers with the retinoid X receptors (RXRα, RXRβ and RXRγ). RDH: retinol dehydrogenase; PALDH: retinaldehyde dehydrogenase; CRABP: cellular retinoic acid-binding protein; FABP5: fatty acid binding protein 5; PPARβ/δ: peroxisome proliferator-activated receptor beta/delta; ERα: estrogen receptor alpha; ERE: estrogen response elements; E2: estradiol; RARα: retinoic acid receptor-alpha; RXR: retinoid X receptor; PDK-1: phosphoinositide-dependent kinase-1; RA: retinoic acid; RARE: RA responsive element. Created in BioRender. Gerges, A. (2025) https://BioRender.com/v19a099

Medicinal chemistry approach to drug development

In the search for a multi-target drug for NBS4 using advanced medicinal chemistry software, the approach is to assesses the possibility of different targets (Figures 1 and 2) by assessing receptor similarities and selecting two lead compounds for each target (four compounds in total), see Figure 3 [8] and Figure 4 [29], that have demonstrated an inhibitory effect on NBS4 cells to form the basis of a search for a multi-targeted drug [29].

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Figure 3. 

Chemical structure of the lead c-Src inhibitors, compounds 18 and 29

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Figure 4. 

Chemical structure of the lead RA pathway inhibitors, compounds 8A and 8E

For the two targets (Csk and RA) two receptors from each target were selected (Table 1) and their amino acid sequence similarity tested using Protein Aligner [37] (see Figure 5), which reported 80–100% similarity. Protein Aligner checks for amino acid sequence similarity between receptors with high similarity indicating their suitability for use in cross-docking. For the purposes of this work docking involves docking a compound to a receptor known for that target (i.e. Csk 4O2P [8] and 5D10 [38]) whereas cross-docking involves docking a compound to a receptor that belongs to a different target (i.e. RA 1CBQ and 1CBS [39]) and vice versa. The aim of cross-docking is to explore the suitability of selected compounds belonging to different targets to determine their suitability for use as a multi-target drug. This is a process that involves selecting known lead compounds to produce hitlists of compounds, and was done using BROOD [40] as part of the OpenEye suite.

Several BROOD rounds (Figure 6) were completed on the two lead compounds for each target to produce hitlists using “Shape and Color” and “Shape and Electrostatics”. On completion, BROOD ranked each of the hitlists according to BROOD hitlist parameters (see list below) and the top 25 of each hitlist was selected to run on OEDocking, using FRED (Figure 7) [41], ROCS (Figure 8) [42], AFITT (Figure 9) [43], and VIDA [44]. In addition, the Samson docking suite, including AutoDock Vina Extended (Figures 10 and 11) [45, 46] and Fitted (Molecular Forecaster) [47], with another docking program, Molegro [48], acted as confirmation for both the docking and cross-docking procedures. From this process, eight compounds for the Csk target were identified and six from the RA target, making a total of 14 compounds. All 14 compounds showed improved parameters compared to the original lead compounds (see Table 2). The compounds for each individual target were then cross-docked with the receptors identified for the other targets [43]. From the 14 compounds run, four showed potential suitability for multi-target use (two from each list) see Table 2.

To check and compare the compounds (clusters) for toxicity and mutagenicity, the Toxicity Estimation Software Tool (T.E.S.T.) was used to provide the prediction mechanisms of the toxic action of the clusters [49]. The results from T.E.S.T. showed some similarity with the original lead compounds (Table 3), which also confirmed their suitability for use. Further work was done to explore possible synthesis routes for each of the four compounds using the retrosynthesis program Spaya [50].

Materials

Computer programs:

• 1.

  OpenEye Scientific programs, which include various applications. The suite comprises BROOD, VIDA, MakeReceptor, FRED, and AFITT;

• 2.

  Molegro Virtual Docker;

• 3.

  The Samson suite includes Protein Aligner, AutoDock Vina Extended, and the Fitted suite by Molecular Forecaster;

• 4.

  T.E.S.T.;

• 5.

  BIOVIA Discovery Studio Visualizer [51];

• 6.

  Spaya-retrosynthesis software.

Methods

• 1.

  Identifying drug targets;

• 2.

  Selection of two proteins (receptors) for each target;

• 3.

  Download the Protein Data Bank (PDB) files and their electron density map from the PDB database;

• 4.

  Comparing the similarities of the receptors. Run protein similarity on Samson (Protein Aligner) to determine suitability;

• 5.

  Selection of two lead compounds for each type;

• 6.

  Run the lead compounds on BROOD (from the OpenEye suite) and produce hitlists using Shape and Colour and Shape and Electrostatics;

• 7.

  Receptor preparations using MakeReceptor from the OpenEye suite;

• 8.

  Docking the hit compounds with OpenEye suite (FRED), Molegro, and Samson suite (AutoDockVina and Fitted);

• 9.

  Run cross-docking: each hitlist clusters from one target to the other target (using their protein/receptor);

• 10.

  Run hits with AFITT to rank the compounds according to their fitting probabilities;

• 11.

  Run selected clusters on ROCS;

• 12.

  Run selected clusters on T.E.S.T.;

• 13.

  Run clusters on Spaya to find the best synthesis route.

Retrosynthesis results using Spaya

Synthesis of cluster 4, 1 of 1, c-Src inhibitor (Figure 12).

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Figure 12. 

Synthesis of cluster 4, 1 of 1, c-Src inhibitor

Synthesis of cluster 19, 1 of 5, c-Src inhibitor (Figure 13).

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Figure 13. 

Synthesis of cluster 19, 1 of 5, c-Src inhibitor

Synthesis of clusters 18, 1 of 1, RA pathway inhibitor (Figure 14).

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Figure 14. 

Synthesis of clusters 18, 1 of 1, RA pathway inhibitor

Synthesis of cluster 23, 1 of 25, RA pathway inhibitor (Figure 15).

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Figure 15. 

Synthesis of cluster 23, 1 of 25, RA pathway inhibitor