Constructing and Validating 3D-pharmacophore Models to a Set of MMP-9 Inhibitors for Designing Novel Anti-melanoma Agents

Novel Matrix Metalloproteinase-9 (MMP-9) Inhibitors in Cancer Treatment

Matrix metalloproteinases (MMPs) belong to a family of zinc-dependent proteolytic metalloenzymes. MMP-9, a member of the gelatinase B family, is characterized as one of the most intricate MMPs. The crucial involvement of MMP-9 in extracellular matrix (ECM) remodeling underscores its significant correlation with each stage of cancer pathogenesis and progression. The design and synthesis of MMP-9 inhibitors is a potentially attractive research area. Unfortunately, to date, there is no effective MMP-9 inhibitor that passes the clinical trials and is approved by the FDA. This review primarily focuses on exploring the diverse strategies employed in the design and advancement of MMP-9 inhibitors, along with their anticancer effects and selectivity. To illuminate the essential structural characteristics necessary for the future design of novel MMP-9 inhibitors, the current narrative review highlights several recently discovered MMP-9 inhibitors exhibiting notable selectivity and potency.

Melanoma 2.0. Skin cancer as a paradigm for emerging diagnostic technologies, computational modelling and artificial intelligence

We live in an unprecedented time in oncology. We have accumulated samples and cases in cohorts larger and more complex than ever before. New technologies are available for quantifying solid or liquid samples at the molecular level. At the same time, we are now equipped with the computational power necessary to handle this enormous amount of quantitative data. Computational models are widely used helping us to substantiate and interpret data. Under the label of systems and precision medicine, we are putting all these developments together to improve and personalize the therapy of cancer. In this review, we use melanoma as a paradigm to present the successful application of these technologies but also to discuss possible future developments in patient care linked to them. Melanoma is a paradigmatic case for disruptive improvements in therapies, with a considerable number of metastatic melanoma patients benefiting from novel therapies. Nevertheless, a large proportion of patients does not respond to therapy or suffers from adverse events. Melanoma is an ideal case study to deploy advanced technologies not only due to the medical need but also to some intrinsic features of melanoma as a disease and the skin as an organ. From the perspective of data acquisition, the skin is the ideal organ due to its accessibility and suitability for many kinds of advanced imaging techniques. We put special emphasis on the necessity of computational strategies to integrate multiple sources of quantitative data describing the tumour at different scales and levels.

The Challenging Melanoma Landscape: From Early Drug Discovery to Clinical Approval

Melanoma is recognized as the most dangerous type of skin cancer, with high mortality and resistance to currently used treatments. To overcome the limitations of the available therapeutic options, the discovery and development of new, more effective, and safer therapies is required. In this review, the different research steps involved in the process of antimelanoma drug evaluation and selection are explored, including information regarding in silico, in vitro, and in vivo experiments, as well as clinical trial phases. Details are given about the most used cell lines and assays to perform both two- and three-dimensional in vitro screening of drug candidates towards melanoma. For in vivo studies, murine models are, undoubtedly, the most widely used for assessing the therapeutic potential of new compounds and to study the underlying mechanisms of action. Here, the main melanoma murine models are described as well as other animal species. A section is dedicated to ongoing clinical studies, demonstrating the wide interest and successful efforts devoted to melanoma therapy, in particular at advanced stages of the disease, and a final section includes some considerations regarding approval for marketing by regulatory agencies. Overall, considerable commitment is being directed to the continuous development of optimized experimental models, important for the understanding of melanoma biology and for the evaluation and validation of novel therapeutic strategies.

Two Decades of 4D-QSAR: A Dying Art or Staging a Comeback?

A key question confronting computational chemists concerns the preferable ligand geometry that fits complementarily into the receptor pocket. Typically, the postulated ‘bioactive’ 3D ligand conformation is constructed as a ‘sophisticated guess’ (unnecessarily geometry-optimized) mirroring the pharmacophore hypothesis—sometimes based on an erroneous prerequisite. Hence, 4D-QSAR scheme and its ‘dialects’ have been practically implemented as higher level of model abstraction that allows the examination of the multiple molecular conformation, orientation and protonation representation, respectively. Nearly a quarter of a century has passed since the eminent work of Hopfinger appeared on the stage; therefore the natural question occurs whether 4D-QSAR approach is still appealing to the scientific community? With no intention to be comprehensive, a review of the current state of art in the field of receptor-independent (RI) and receptor-dependent (RD) 4D-QSAR methodology is provided with a brief examination of the ‘mainstream’ algorithms. In fact, a myriad of 4D-QSAR methods have been implemented and applied practically for a diverse range of molecules. It seems that, 4D-QSAR approach has been experiencing a promising renaissance of interests that might be fuelled by the rising power of the graphics processing unit (GPU) clusters applied to full-atom MD-based simulations of the protein-ligand complexes.

Ligand-based pharmacophore model for the discovery of novel CXCR2 antagonists as anti-cancer metastatic agents

Metastatic cancer is considered a fatal progression of cancer worldwide. It has been shown that a key player in this scenario is the CXC chemokine receptor 2 (CXCR2). To identify novel CXCR2 antagonists, a pharmacophore model was built with the HipHop program by screening a database containing compounds which were designed based on the known structure-activity relationship (SAR) of the diarylurea series CXCR2 antagonists. Compound 1a bearing the novel skeleton was selected from database screening and subjected to the in vitro biological test which showed a moderate CXCR2 antagonist potential. With further modification and exploration of SAR, compound 1e demonstrated improved CXCR2 antagonist activity with an IC₅₀ value of 14.8 µM. Furthermore, wound healing assay using the NCI-H1299 cell line indicated that 1e showed an excellent anti-cancer metastatic effect (72% inhibition in cell migration at 50 µg ml^-1).

ADME/toxicity prediction and antitumor activity of novel nitrogenous heterocyclic compounds designed by computer targeting of alkylglycerone phosphate synthase

Alkylglycerone phosphate synthase (AGPS) is an oncogene and can be considered as an antitumor drug target. The aim of the present study was to design novel nitrogenous heterocyclic compound improving targetability by computer-aided drug design technology targeting AGPS. A total of 12 nitrogenous heterocyclic compounds were designed and predicted the absorption, distribution, metabolism and excretion parameters/toxicity. Their activity in terms of proliferation inhibition, cell cycle arrest and apoptosis induction was then measured using an MTS assay and a high-content screening system in U251 cells. The results showed that anti-glioma activity was present in compounds N4, N5, N6, N7, N8 and N12, which was in accordance with the computer prediction. Therefore, these compounds may be suitable for the development of a novel glioma therapeutic drug.

Computer-aided design of temozolomide derivatives based on alkylglycerone phosphate synthase structure with isothiocyanate and their pharmacokinetic/toxicity prediction and anti-tumor activity in vitro

Despite the development of temozolomide (TMZ), a novel type of glioma therapeutic drug, malignant glioma remains to cause severe damage to human health. The present study aimed to utilize the molecular biological differences between tumor and normal cells to design TMZ derivatives with improved selectivity and targeting using computer-aided drug design (CADD). Taking alkylglycerone phosphate synthase (AGPS) as a target, a 3D structure-activity relationship model was built using CADD technology; molecular docking of isothiocyanate (ITC) and TMZ compounds was conducted; ITC-TMZ derivatives were designed; and predictions on the absorption, distribution, metabolism and excretion (ADME) processes and toxicity of the ITC-TMZ derivatives were established in order to obtain improved understanding of the structure-activity relationship of the candidate compounds. Using these techniques, it was identified that the docking scores of the structural derivatives S1-9 were higher than that of TMZ. Additionally, S3, −6, −7, −8, −9 and −10 exhibited enhanced ADME and similar toxicity to that of TMZ. The half maximal inhibitory concentrations of the CADD derivatives were also assessed in the glioma U87MG and U251 cell lines, and the activities of S1, −3, −8 and −10 were determined to be greater than that of TMZ, suggesting their potential as anti-cancer drugs with adequate AGPS targeting, ADME/toxicity and anti-tumor activity.

Genetic variants in the metzincin metallopeptidase family genes predict melanoma survival

Metzincins are key molecules in the degradation of the extracellular matrix and play an important role in cellular processes such as cell migration, adhesion, and cell fusion of malignant tumors, including cutaneous melanoma (CM). We hypothesized that genetic variants of the metzincin metallopeptidase family genes would be associated with CM-specific survival (CMSS). To test this hypothesis, we first performed Cox proportional hazards regression analysis to evaluate the associations between genetic variants of 75 metzincin metallopeptidase family genes and CMSS using the dataset from the genome-wide association study (GWAS) from The University of Texas MD Anderson Cancer Center (MDACC) which included 858 non-Hispanic white patients with CM, and then validated using the dataset from the Harvard GWAS study which had 409 non-Hispanic white patients with invasive CM. Four independent SNPs (MMP16 rs10090371 C>A, ADAMTS3 rs788935 T>C, TLL2 rs10882807 T>C and MMP9 rs3918251 A>G) were identified as predictors of CMSS, with a variant-allele attributed hazards ratio (HR) of 1.73 (1.32-2.29, 9.68E-05), 1.46 (1.15-1.85, 0.002), 1.68 (1.31-2.14, 3.32E-05) and 0.67 (0.51-0.87, 0.003), respectively, in the meta-analysis of these two GWAS studies. Combined analysis of risk genotypes of these four SNPs revealed a decreased CMSS in a dose-response manner as the number of risk genotypes increased (P_trend < 0.001). An improvement was observed in the prediction model (area under the curve [AUC] = 81.4% vs. 78.6%), when these risk genotypes were added to the model containing non-genotyping variables. Our findings suggest that these genetic variants may be promising prognostic biomarkers for CMSS.