Integration-mediated prediction enrichment of quantitative model for Hsp90 inhibitors as anti-cancer agents: 3D-QSAR study

Heat Shock Protein 90 (HSP90) Inhibitors as Anticancer Medicines: A Review on the Computer-Aided Drug Discovery Approaches over the Past Five Years

Cancer is a disease caused by the uncontrolled, abnormal growth of cells in different anatomic sites. In 2018, it was predicted that the worldwide cancer burden would rise to 18.1 million new cases and 9.6 million deaths. Anticancer compounds, often known as chemotherapeutic medicines, have gained much interest in recent cancer research. These medicines work through various biological processes in targeting cells at various stages of the cell's life cycle. One of the most significant roadblocks to developing anticancer drugs is that traditional chemotherapy affects normal cells and cancer cells, resulting in substantial side effects. Recently, advancements in new drug development methodologies and the prediction of the targeted interatomic and intermolecular ligand interaction sites have been beneficial. This has prompted further research into developing and discovering novel chemical species as preferred therapeutic compounds against specific cancer types. Identifying new drug molecules with high selectivity and specificity for cancer is a prerequisite in the treatment and management of the disease. The overexpression of HSP90 occurs in patients with cancer, and the HSP90 triggers unstable harmful kinase functions, which enhance carcinogenesis. Therefore, the development of potent HSP90 inhibitors with high selectivity and specificity becomes very imperative. The activities of HSP90 as chaperones and cochaperones are complex due to the conformational dynamism, and this could be one of the reasons why no HSP90 drugs have made it beyond the clinical trials. Nevertheless, HSP90 modulations appear to be preferred due to the competitive inhibition of the targeted N-terminal adenosine triphosphate pocket. This study, therefore, presents an overview of the various computational models implored in the development of HSP90 inhibitors as anticancer medicines. We hereby suggest an extensive investigation of advanced computational modelling of the three different domains of HSP90 for potent, effective inhibitor design with minimal off-target effects.

Recent Progress in the Development of Quinoline Derivatives for the Exploitation of Anti-Cancer Agents

BACKGROUND

Along with the progress in medicine and therapies, the exploitation of anti-cancer agents focused more on the vital signaling pathways and key biological macromolecules. With rational design and advanced synthesis, quinoline derivatives have been utilized frequently in medicinal chemistry, especially in developing anti-cancer drugs or candidates.

METHODS

Using DOI searching, articles published before 2020 all over the world have been reviewed as comprehensively as possible.

RESULTS

In this review, we selected the representative quinoline derivate drugs in market or clinical trials, classified them into five major categories with detailed targets according to their main mechanisms, discussed the relationship within the same mechanism, and generated a summative discussion with prospective expectations. For each mechanism, the introduction of the target was presented, with the typical examples of quinoline derivate drugs.

CONCLUSION

This review has highlighted the quinoline drugs or candidates, suited them into corresponding targets in their pathways, summarized and discussed. We hope that this review may help the researchers who are interested in discovering quinoline derivate anti-cancer agents obtain considerable understanding of this specific topic. Through the flourishing period and the vigorous strategies in clinical trials, quinoline drugs would be potential but facing new challenges in the future.

Deciphering the Inhibition Mechanism of under Trial Hsp90 Inhibitors and Their Analogues: A Comparative Molecular Dynamics Simulation

Heat shock protein 90 (Hsp90) performs functions in cellular activities together with other signaling pathways. Hsp90 is evolutionarily conserved and universally articulated as a human cancer-causing agent involved in lung cancer and breast cancer followed by colon and rectum cancers. It has emerged as an effective drug candidate, and inhibition may affect several signaling pathways associated with cancer spread. Therefore, in-silico approaches, molecular docking, molecular dynamics simulation, and binding free energy calculations were applied to create insights into the inhibition mechanism against Hsp90 to identify new cancer therapeutic drugs. Top-docked Hsp90-inhibitor complexes with their analogues were selected as the best complexes based on the GOLD fitness score and orientation. The significant interaction of Hsp90 inhibitors and their analogues were observed to be bound with active site residues as well as residing within the same cavity region. System stability factors RMSD, RMSF, beta-factor, and radius of gyration were analyzed for top-docked complexes and ensure strong binding interaction between inhibitors and the Hsp90 cavity. Cavity bound inhibitors were found to retain consistent hydrogen bonding during the simulation. The radial distribution function (RDF) illustrated that interacting active site residues drive the binding and stability of the inhibitors. Similarly, the axial frequency distribution, which is an indigenously developed analytical tool, produced noteworthy knowledge of the hydrogen-bonding pattern. Results yielded new insights into the design of cancer therapeutic drugs against Hsp90. This finding suggests that under trial Hsp90 inhibitors MPC-3100 could be a potential starting point into the development of potential anticancer agents with the possibility of future directions for the improvement of early existing Hsp90 inhibitors CNF-2024 and SNX-5422 as an anticancer agent.

Heat-shock protein 90 (Hsp90) as anticancer target for drug discovery: an ample computational perspective

There are over 100 different types of cancer, and each is classified based on the type of cell that is initially affected. If left untreated, cancer can result in serious health problems and eventually death. Recently, the paradigm of cancer chemotherapy has evolved to use a combination approach, which involves the use of multiple drugs each of which targets an individual protein. Inhibition of heat-shock protein 90 (Hsp90) is one of the novel key cancer targets. Because of its ability to target several signaling pathways, Hsp90 inhibition emerged as a useful strategy to treat a wide variety of cancers. Molecular modeling approaches and methodologies have become 'close counterparts' to experiments in drug design and discovery workflows. A wide range of molecular modeling approaches have been developed, each of which has different objectives and outcomes. In this review, we provide an up-to-date systematic overview on the different computational models implemented toward the design of Hsp90 inhibitors as anticancer agents. Although this is the main emphasis of this review, different topics such as background and current statistics of cancer, different anticancer targets including Hsp90, and the structure and function of Hsp90 from an experimental perspective, for example, X-ray and NMR, are also addressed in this report. To the best of our knowledge, this review is the first account, which comprehensively outlines various molecular modeling efforts directed toward identification of anticancer drugs targeting Hsp90. We believe that the information, methods, and perspectives highlighted in this report would assist researchers in the discovery of potential anticancer agents.

Hsp90 inhibitors, part 1: definition of 3-D QSAutogrid/R models as a tool for virtual screening

The multichaperone heat shock protein (Hsp) 90 complex mediates the maturation and stability of a variety of oncogenic signaling proteins. For this reason, Hsp90 has emerged as a promising target for anticancer drug development. Herein, we describe a complete computational procedure for building several 3-D QSAR models used as a ligand-based (LB) component of a comprehensive ligand-based (LB) and structure-based (SB) virtual screening (VS) protocol to identify novel molecular scaffolds of Hsp90 inhibitors. By the application of the 3-D QSAutogrid/R method, eight SB PLS 3-D QSAR models were generated, leading to a final multiprobe (MP) 3-D QSAR pharmacophoric model capable of recognizing the most significant chemical features for Hsp90 inhibition. Both the monoprobe and multiprobe models were optimized, cross-validated, and tested against an external test set. The obtained statistical results confirmed the models as robust and predictive to be used in a subsequent VS.

Chemical Feature-Based Molecular Modeling of Urotensin-II Receptor Antagonists: Generation of Predictive Pharmacophore Model for Early Drug Discovery

For a series of 35 piperazino-phthalimide and piperazino-isoindolinone based urotensin-II receptor (UT) antagonists, a thoroughly validated 3D pharmacophore model has been developed, consisting of four chemical features: one hydrogen bond acceptor lipid (HBA_L), one hydrophobe (HY), and two ring aromatic (RA). Multiple validation techniques like CatScramble, test set prediction, and mapping analysis of advanced known antagonists have been employed to check the predictive power and robustness of the developed model. The results demonstrate that the best model, Hypo 1, shows a correlation (r) of 0.902, a root mean square deviation (RMSD) of 0.886, and the cost difference of 39.69 bits. The model obtained is highly predictive with good correlation values for both internal (r2=0.707) as well as external (r2=0.614) test set compounds. Moreover, the pharmacophore model has been used as a 3D query for virtual screening which served to detect prospective new lead compounds which can be further optimized as UT antagonists with potential for treatment of cardiovascular diseases.

Fragment-based optimization of small molecule CXCL12 inhibitors for antagonizing the CXCL12/CXCR4 interaction

The chemokine CXCL12 and its G protein-coupled receptor (GPCR) CXCR4 are high-priority clinical targets because of their involvement in metastatic cancers (also implicated in autoimmune disease and cardiovascular disease). Because chemokines interact with two distinct sites to bind and activate their receptors, both the GPCRs and chemokines are potential targets for small molecule inhibition. A number of chemokines have been validated as targets for drug development, but virtually all drug discovery efforts focus on the GPCRs. However, all CXCR4 receptor antagonists with the exception of MSX-122 have failed in clinical trials due to unmanageable toxicities, emphasizing the need for alternative strategies to interfere with CXCL12/CXCR4-guided metastatic homing. Although targeting the relatively featureless surface of CXCL12 was presumed to be challenging, focusing efforts at the sulfotyrosine (sY) binding pockets proved successful for procuring initial hits. Using a hybrid structure-based in silico/NMR screening strategy, we recently identified a ligand that occludes the receptor recognition site. From this initial hit, we designed a small fragment library containing only nine tetrazole derivatives using a fragment-based and bioisostere approach to target the sY binding sites of CXCL12. Compound binding modes and affinities were studied by 2D NMR spectroscopy, X-ray crystallography, molecular docking and cell-based functional assays. Our results demonstrate that the sY binding sites are conducive to the development of high affinity inhibitors with better ligand efficiency (LE) than typical protein-protein interaction inhibitors (LE ≤ 0.24). Our novel tetrazole-based fragment 18 was identified to bind the sY21 site with a K(d) of 24 μM (LE = 0.30). Optimization of 18 yielded compound 25 which specifically inhibits CXCL12-induced migration with an improvement in potency over the initial hit 9. The fragment from this library that exhibited the highest affinity and ligand efficiency (11: K(d) = 13 μM, LE = 0.33) may serve as a starting point for development of inhibitors targeting the sY12 site.

Hierarchical virtual screening: identification of potential high-affinity and selective β(3)-adrenergic receptor agonists

The hierarchical virtual screening (HVS) study, consisting of pharmacophore modelling, docking and VS of the generated focussed virtual library, has been carried out to identify novel high-affinity and selective β(3)-adrenergic receptor (β-AR) agonists. The best pharmacophore model, comprising one H-bond donor, two hydrophobes, one positive ionizable and one negative ionizable feature, was developed based on a training set of 51 β(3)-AR agonists using the pharmacophore generation protocol implemented in Discovery Studio. The model was further validated with the test set, external set and ability of the pharmacophoric features to complement the active site amino acids of the homology modelled β(3)-AR developed using MODELLER software. The focussed virtual library was generated using the structure-based insights gained from our earlier reported comprehensive study focussing on the structural basis of β-AR subtype selectivity of representative agonists and antagonists. The HVS with the sequential use of the best pharmacophore model and homology modelled β(3)-AR in the screening of the generated focussed library has led to the identification of potential virtual leads as novel high-affinity and selective β(3)-AR agonists.

CoMFA, CoMSIA, and docking studies on thiolactone-class of potent anti-malarials: identification of essential structural features modulating anti-malarial activity

The integrated ligand- and structure-based drug design techniques have been applied on a homogeneous dataset of thiolactone-class of potent anti-malarials, to explore the essential structural features for the inhibition of Plasmodium falciparum. Developed CoMFA (q(2) = 0.716) and CoMSIA (q(2) = 0.632) models well explained structure-activity variation in both the training (CoMFA R(2) = 0.948 & CoMSIA R(2) = 0.849) and test set (CoMFA R(2) (pred) = 0.789 & CoMSIA R(2) (pred) = 0.733) compounds. The docking and scoring of the most active compound 10 into the active site of high-resolution (2.35 Å) structure of FabB-TLM binary complex (PDB-ID: 1FJ4) indicated that thiolactone core of this compound forms bifurcated H-bonding with two catalytic residues His298 and His333, and its saturated decyl side group is stabilized by hydrophobic interactions with the residues of a small hydrophobic groove, illustrating that the active site architecture, including two catalytic histidines and a small hydrophobic groove, is vital for protein-ligand interaction. In particular, the length and flexibility of the side group attached to the position 5 of thiolactone have been observed to play a significant role in the interaction with FabB enzyme. These results present scope for rational design of thiolactone-class of compounds that could furnish improved anti-malarial activity.

Structural determination of three different series of compounds as Hsp90 inhibitors using 3D-QSAR modeling, molecular docking and molecular dynamics methods

Hsp90 is involved in correcting, folding, maturation and activation of a diverse array of client proteins; it has also been implicated in the treatment of cancer in recent years. In this work, comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), molecular docking and molecular dynamics were performed on three different series of Hsp90 inhibitors to build 3D-QSAR models, which were based on the ligand-based or receptor-based methods. The optimum 3D-QSAR models exhibited reasonable statistical characteristics with averaging internal q² > 0.60 and external r²_pred > 0.66 for Benzamide tetrahydro-4H-carbazol-4-one analogs (BT), AT13387 derivatives (AT) and Dihydroxylphenyl amides (DA). The results revealed that steric effects contributed the most to the BT model, whereas H-bonding was more important to AT, and electrostatic, hydrophobic, H-bond donor almost contributed equally to the DA model. The docking analysis showed that Asp93, Tyr139 and Thr184 in Hsp90 are important for the three series of inhibitors. Molecular dynamics simulation (MD) further indicated that the conformation derived from docking is basically consistent with the average structure extracted from MD simulation. These results not only lead to a better understanding of interactions between these inhibitors and Hsp90 receptor but also provide useful information for the design of new inhibitors with a specific activity.