Repurposing of Fluindione an Anti-Coagulant Drug Against Thymondylate Synthetase For Skin Cancer Management

Fluindione, a vitamin K antagonist which has traditionally been used for its anticoagulant properties, is clinically deemed useful for treatment of thromboembolic diseases due to its effect upon inhibiting vitamin K epoxide reductase and other enzymes responsible for the production of essential platelet reliant clotting factors [1]. Although fluindione is effective as an anticoagulant, clinical application is limited by its narrow therapeutic range and high sensitivity to drug and dietary binding [2]. Recently, repurposing fluindione for oncological indications has arisen given evidence that fluindione may exert non-anticoagulant effects [3]. This is especially relevant when considering skin cancers, the most qualified cancer globally, with NMSC including basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), and melanoma being the most challenging skin cancer [4]. While surgically removable early-detected NMSCs are considered treatable, advanced melanoma poses a significant treatment hurdle due to its high metastatic potential and de nova resistance to standard chemotherapy regimens and immunotherapy [5]. With this in mind, TS (thymidylate synthase) has become an attractive target as an enzyme important in the synthesis of DNA, expressed in excess by rapidly proliferating cancer cells [6]. Inhibition of TS interrupts DNA replication, thereby forcing cell cycle arrest or apoptosis. The present TS inhibitors, 5-fluorouracil, for example, show the principle, but we still have issues of efficacy and toxicity [7]. Fluindione has the potential to inhibit TS, and therefore provides a new option for skin cancers, especially when current treatments are inadequate [8]. Drug repurposing can provide a cost and time advantage to research in cancer in general, and fluindione has an existing safety profile that makes it a good candidate [9]. In this review, we examine the new evidence suggesting repurposing fluindione in skin cancer therapy focusing on the mechanisms of actions, particularly TS inhibition, and the designed repair of improved treatment outcomes in melanoma and skin cancer [10].

MATERIALS AND METHODS:

Data Collection: Sources of Fluindione Analogs

We started with compounds from a few reliable databases: PubChem, very large and comprehensive, harboring all chemical information available, including a wide array of derivatives [11], ChEMBL for molecules with drug-like properties and established biological activities, and the ZINC Database, a free source of commercially available compounds that provides a large collection of Fluindione analogs [12]. Besides, we carried out a review of published articles and patents to collect even more derivatives of Fluindione, other than those mentioned in the above databases [13].

Ligand – Based virtual screening protocol.

In ligand-based virtual screening, compounds were selected on multiple criteria to highlight potential drug candidates[14]. Chemical diversity was ensured through the assessment of structural variety with Tanimoto similarity coefficients ADMET properties were assessed to rank compounds according to favorable pharmacokinetic profiles for oral bioavailability[15]; biological relevance was taken into account by considering compounds with reported activity against the target of interest[16]; molecular weight between 300-500 Da and lipophilicity within LogP values 2-5 as optimized for pharmacological properties, and availability that allowed ease of experimental validation[17]. Candidates for drug repurposing were tested on Ligand-based Screen by using ligand-based virtual screening [18]. The approach made use of a known active ligand of an exemplified target as a template compound, and it was used as a template for screening by different similarity measures like LigMate, FitDock-align, Morgan Fingerprint, LSalign, FP2, and FP4, to rank compounds based on their similarity scores and then to pick the top-ranked compounds for further studies in drug repurposing [19].

 Docking studies

In one previous drug repurposing project, I attempted to generate a compound library from databases like ZINC and ChEMBL [20]. Subsequently, I constructed a pharmacophore model using the active ligand in order to carry out virtual screening using molecular docking and pharmacophore-based methods [21]. After post-docking analysis was done for evaluation of binding modes and interactions, I prioritized high-scoring compounds further for evaluation [22].

RESULTS AND DISCUSSION

Results of ligand-based drug repurposing

Table 1: justification for selecting fluorouracil as the primary ligand for designing new agents targeting skin cancer.

Table (1) gives the justification for selecting fluorouracil as the primary ligand for designing new agents targeting skin cancer.

Result of ligand-based screening using the drug rep platform

Table 2: matching scores and target interactions of various compounds

The table (2) The data indicates that the highest scoring compound in this dataset is DB00544 (Fluorouracil) with a score of 0.263, this compound has the highest score in this dataset. This means that Fluorouracil has the highest binding affinity or binding interaction according to the scoring system used in this thesis. Second place is DB12466 (Favipiravir) with a score of 0.205 and third place is DB01 099 (Flucytosine) with a score of 0.200. The lowest scoring compound in this dataset is DB00347 (Trimethadione) with a score of 0.140. Overall, the compounds vary by binding strength, while Fluorouracil clearly stood out as the highest scoring compound in table. This means Fluorouracil likely had the most favorable interaction in whatever context is being examined.                                        

Table 3: Validation metrics from PDB-REDO

Comparisons of crystallographic refinement information supports relief in the overall structures for both PDB-REDO models. The R factor shows that the original model was originally 0.2197, and the PDB-REDO model is 0.1702 (which is a better fit to the model to the observed data). The R-free value also decreased from 0.2621 to 0.2233 compared to the PDB-REDO model which will also indicate better refinement and validation of the model. The Bond length RMS Z-Score also shows a substantial improvement