Ligand-based computer aided drug design reveals new tropomycin receptor kinase a (TrkA) inhibitors

Cancer on motors: How kinesins drive prostate cancer progression?

Prostate cancer causes numerous male deaths annually. Although great progress has been made in the diagnosis and treatment of prostate cancer during the past several decades, much about this disease remains unknown, especially its pathobiology. The kinesin superfamily is a pivotal group of motor proteins, that contains a microtubule-based motor domain and features an adenosine triphosphatase activity and motility characteristics. Large-scale sequencing analyses based on clinical samples and animal models have shown that several members of the kinesin family are dysregulated in prostate cancer. Abnormal expression of kinesins could be linked to uncontrolled cell growth, inhibited apoptosis and increased metastasis ability. Additionally, kinesins may be implicated in chemotherapy resistance and escape immunologic cytotoxicity, which creates a barrier to cancer treatment. Here we cover the recent advances in understanding how kinesins may drive prostate cancer progression and how targeting their function may be a therapeutic strategy. A better understanding of kinesins in prostate cancer tumorigenesis may be pivotal for improving disease outcomes in prostate cancer patients.

Advances in Drug Discovery and Design using Computer-aided Molecular Modeling

Computer-aided molecular modeling is a rapidly emerging technology that is being used to accelerate the discovery and design of new drug therapies. It involves the use of computer algorithms and 3D structures of molecules to predict interactions between molecules and their behavior in the body. This has drastically improved the speed and accuracy of drug discovery and design. Additionally, computer-aided molecular modeling has the potential to reduce costs, increase the quality of data, and identify promising targets for drug development. Through the use of sophisticated methods, such as virtual screening, molecular docking, pharmacophore modeling, and quantitative structure-activity relationships, scientists can achieve higher levels of efficacy and safety for new drugs. Moreover, it can be used to understand the activity of known drugs and simplify the process of formulating, optimizing, and predicting the pharmacokinetics of new and existing drugs. In conclusion, computer-aided molecular modeling is an effective tool to rapidly progress drug discovery and design by predicting the interactions between molecules and anticipating the behavior of new drugs in the body.

Kinesin spindle protein inhibitors in cancer: from high throughput screening to novel therapeutic strategies

Bringing to a halt the cell cycle in mitosis and interfering with its normal progression is one of the most successful anti-cancer strategies used nowadays. Classically, several kinds of anti-cancer drugs like taxanes and vinca alkaloids directly inhibit microtubules during cell division. These drugs exhibit serious side effects, most importantly, severe peripheral neuropathies. Alternatively, KSP inhibitors are grasping a lot of research attention as less toxic mitotic inhibitors. In this review, we track the medicinal chemistry developmental stages of KSP inhibitors. Moreover, we address the challenges that are faced during the development of KSP inhibitor therapy for cancer and future insights for the latest advances in research that are directed to find active KSP inhibitor drugs.

Scientists have recognized the importance of selective KSP inhibitors in the early 2000s and so various KSP protein inhibitors have been investigated. Only ten of these have been clinically evaluated for cancer treatment. Ispinesib (SB-715992) and filanesib (Arry-520) were the most promising small molecules in clinical trials against the KSP protein. Many challenges are faced during the development of an active anti-KSP drug; most importantly are the unsatisfactory clinical trial results. Designing dual inhibitors, antibody–drug conjugates, combination therapy and gene therapy approach are among the main strategies that are being investigated nowadays to find new effective KSP inhibitors. The scientific research efforts are still devoted to find an effective and tolerable KSP inhibitor drug that can gain US FDA approval.

QSAR-guided pharmacophoric modeling reveals important structural requirements for Polo kinase 1 (Plk1) inhibitors

Targeting Polo-like kinase 1 (Plk1) by molecular inhibitors is being a promising approach for tumor therapy. Nevertheless, insufficient methodical analyses have been done to characterize the interactions inside the Plk1 binding pocket. In this study, an extensive combined ligand and structure-based drug design workflow was conducted to data-mine the structural requirements for Plk1 inhibition. Consequently, the binding modes of 368 previously known Plk1 inhibitors were investigated by pharmacophore generation technique. The resulted pharmacophores were engaged in the context of Genetic function algorithm (GFA) and Multiple linear regression (MLR) analyses to search for a prognostic QSAR model. The most successful QSAR model was with statistical criteria of (r2277 = 0.76, r2adj = 0.76, r2pred = 0.75, Q2 = 0.73). Our QSAR-selected pharmacophores were validated by Receiver Operating Characteristic (ROC) curve analysis. Later on, the best QSAR model and its associated pharmacophoric hypotheses (HypoB-T4-5, HypoI-T2-7, HypoD-T4-3, and HypoC-T3-3) were used to identify new Plk1 inhibitory hits retrieved from the National Cancer Institute (NCI) database. The most potent hits exhibited experimental anti-Plk1 IC50 of 1.49, 3.79. 5.26 and 6.35 μM. Noticeably, our hits, were found to interact with the Plk1 kinase domain through some important amino acid residues namely, Cys67, Lys82, Cys133, Phe183, and Asp194.

Ligand Based Pharmacophore Modeling Followed by Biological Screening Lead to Discovery of Novel PDK1 Inhibitors as Anticancer Agents

BACKGROUND

Phosphoinositide-Dependent Kinase-1 (PDK1) is a serine/threonine kinase, which belongs to AGC kinase family required by cancer cells.

METHODS

Pharmacophoric space of 86 PDK1 inhibitors using six diverse sets of inhibitors was explored to identify high-quality pharmacophores. The best combination of pharmacophoric models and physicochemical descriptors was selected by genetic algorithm-based QSAR analysis that can elucidate the variation of bioactivity within the training inhibitors. Two successful orthogonal pharmacophores emerged in the optimum QSAR equation (r2 69 = 0.90, r2 LOO= 0.86, F= 51.92, r2 PRESS against 17 test inhibitors = 0.79). Receiver Operating Characteristic (ROC) curve analyses were used to estimate the QSAR-selected pharmacophores.

RESULTS

5 out of 11 compounds tested had shown potential intracellular PDK1 inhibition with the highest inhibition percent for compounds 92 and 93 as follows; 90 and 92% PDK1 inhibition, respectively.

CONCLUSION

PDK1 inhibitors are potential anticancer agents that can be discovered by combination method of ligand based design with QSAR and ROC analysis.

Construction of Quantitative Structure Activity Relationship (QSAR) Models to Predict Potency of Structurally Diversed Janus Kinase 2 Inhibitors

Janus kinase 2 (JAK2) inhibitors represent a promising therapeutic class of anticancer agents against many myeloproliferative disorders. Bioactivity data on pIC50 of 2229 JAK2 inhibitors were employed in the construction of quantitative structure-activity relationship (QSAR) models. The models were built from 100 data splits using decision tree (DT), support vector machine (SVM), deep neural network (DNN) and random forest (RF). The predictive power of RF models were assessed via 10-fold cross validation, which afforded excellent predictive performance with R2 and RMSE of 0.74 ± 0.05 and 0.63 ± 0.05, respectively. Moreover, test set has excellent performance of R2 (0.75 ± 0.03) and RMSE (0.62 ± 0.04). In addition, Y-scrambling was utilized to evaluate the possibility of chance correlation of the predictive model. A thorough analysis of the substructure fingerprint count was conducted to provide insights on the inhibitory properties of JAK2 inhibitors. Molecular cluster analysis revealed that pyrazine scaffolds have nanomolar potency against JAK2.

Investigation of Binding Characteristics of Phosphoinositide-dependent Kinase-1 (PDK1) Co-crystallized Ligands Through Virtual Pharmacophore Modeling Leading to Novel Anti-PDK1 Hits

BACKGROUND

3-Phosphoinositide Dependent Protein Kinase-1 (PDK1) is being lately considered as an attractive and forthcoming anticancer target. A Protein Data Bank (PDB) cocrystallized crystal provides not only rigid theoretical data but also a realistic molecular recognition data that can be explored and used to discover new hits.

OBJECTIVE

This incited us to investigate the co-crystallized ligands' contacts inside the PDK1 binding pocket via a structure-based receptor-ligand pharmacophore generation technique in Discovery Studio 4.5 (DS 4.5).

METHODS

Accordingly, 35 crystals for PDK1 were collected and studied. Every single receptorligand interaction was validated and the significant ones were converted into their corresponding pharmacophoric features. The generated pharmacophores were scored by the Receiver Operating Characteristic (ROC) curve analysis.

RESULTS

Consequently, 169 pharmacophores were generated and sorted, 11 pharmacophores acquired good ROC-AUC results of 0.8 and a selectivity value above 8. Pharmacophore 1UU3_2_01 was used in particular as a searching filter to screen NCI database because of its acceptable validity criteria and its distinctive positive ionizable feature. Several low micromolar PDK1 inhibitors were revealed. The most potent hit illustrated anti-PDK1 IC50 values of 200 nM with 70% inhibition against SW480 cell lines.

CONCLUSION

Eventually, the active hits were docked inside the PDK1 binding pocket and the recognition points between the active hits and the receptor were analyzed that led to the discovery of new scaffolds as potential PDK1 inhibitors.