Re-positioning of known drugs for Pim-1 kinase target using molecular docking analysis

A PIM-1 Kinase Inhibitor Docking Optimization Study Based on Logistic Regression Models and Interaction Analysis

PIM-1 kinase is a serine-threonine phosphorylating enzyme with implications in multiple types of malignancies, including prostate, breast, and blood cancers. Developing better search methodologies for PIM-1 kinase inhibitors may be a good strategy to speed up the discovery of an oncological drug approved for targeting this specific kinase. Computer-aided screening methods are promising approaches for the discovery of novel therapeutics, although certain limitations should be addressed. A frequent omission that is encountered in molecular docking is the lack of proper implementation of scoring functions and algorithms on the post-docking results, which usually alters the outcome of the virtual screening. The current study suggests a method for post-processing docking results, expressed either as binding affinity or score, that considers different binding modes of known inhibitors to the studied targets while making use of in vitro data, where available. The docking protocol successfully discriminated between known PIM-1 kinase inhibitors and decoy molecules, although binding energies alone were not sufficient to ensure a successful prediction. Logistic regression models were trained to predict the probability of PIM-1 kinase inhibitory activity based on binding energies and the presence of interactions with identified key amino acid residues. The selected model showed 80.9% true positive and 81.4% true negative rates. The discussed approach can be further applied in large-scale molecular docking campaigns to increase hit discovery success rates.

DockingApp RF: A State-of-the-Art Novel Scoring Function for Molecular Docking in a User-Friendly Interface to AutoDock Vina

Motivation: Bringing a new drug to the market is expensive and time-consuming. To cut the costs and time, computer-aided drug design (CADD) approaches have been increasingly included in the drug discovery pipeline. However, despite traditional docking tools show a good conformational space sampling ability, they are still unable to produce accurate binding affinity predictions. This work presents a novel scoring function for molecular docking seamlessly integrated into DockingApp, a user-friendly graphical interface for AutoDock Vina. The proposed function is based on a random forest model and a selection of specific features to overcome the existing limits of Vina’s original scoring mechanism. A novel version of DockingApp, named DockingApp RF, has been developed to host the proposed scoring function and to automatize the rescoring procedure of the output of AutoDock Vina, even to nonexpert users. Results: By coupling intermolecular interaction, solvent accessible surface area features and Vina’s energy terms, DockingApp RF’s new scoring function is able to improve the binding affinity prediction of AutoDock Vina. Furthermore, comparison tests carried out on the CASF-2013 and CASF-2016 datasets demonstrate that DockingApp RF’s performance is comparable to other state-of-the-art machine-learning- and deep-learning-based scoring functions. The new scoring function thus represents a significant advancement in terms of the reliability and effectiveness of docking compared to AutoDock Vina’s scoring function. At the same time, the characteristics that made DockingApp appealing to a wide range of users are retained in this new version and have been complemented with additional features.

PIM-1 Is Overexpressed at a High Frequency in Circulating Tumor Cells from Metastatic Castration-Resistant Prostate Cancer Patients

PIM-1 is an oncogene involved in cell cycle progression, cell growth, cell survival and therapy resistance, activated in many types of cancer, and is now considered as a very promising target for cancer therapy. We report for the first time that PIM-1 is overexpressed in circulating tumor cells (CTCs) from metastatic castration-resistant prostate cancer patients (mCRPC). We first developed and validated a highly sensitive RT-qPCR assay for quantification of PIM-1 transcripts. We further applied this assay to study PIM-1 expression in EpCAM⁽⁺⁾ CTC fraction isolated from 64 peripheral blood samples of 50 mCRPC patients. CTC enumeration in all samples was performed using the FDA-cleared CellSearch^® system. PIM-1 overexpression was detected in 24/64 (37.5%) cases, while in 20/24 (83.3%) cases that were positive for PIM-1 expression, at least one CTC/7.5 mL PB was detected in the CellSearch^®. Our data indicate that PIM-1 overexpression is observed at high frequency in CTCs from mCRPC patients and this finding, in combination with androgen receptor splice variant 7 (AR-V7) expression in CTCs, suggest its potential role as a very promising target for cancer therapy. We strongly believe that PIM-1 overexpression in EpCAM⁽⁺⁾ CTC fraction merits to be further evaluated and validated as a non-invasive circulating tumor biomarker in a large and well-defined patient cohort with mCRPC.

Current perspectives on targeting PIM kinases to overcome mechanisms of drug resistance and immune evasion in cancer

PIM kinases are a class of serine/threonine kinases that play a role in several of the hallmarks of cancer including cell cycle progression, metabolism, inflammation and immune evasion. Their constitutively active nature and unique catalytic structure has led them to be an attractive anticancer target through the use of small molecule inhibitors. This review highlights the enhanced activity of PIM kinases in cancer that can be driven by hypoxia in the tumour microenvironment and the important role that aberrant PIM kinase activity plays in resistance mechanisms to chemotherapy, radiotherapy, anti-angiogenic therapies and targeted therapies. We highlight an interaction of PIM kinases with numerous major oncogenic players, including but not limited to, stabilisation of p53, synergism with c-Myc, and notable parallel signalling with PI3K/Akt. We provide a comprehensive overview of PIM kinase's role as an escape mechanism to targeted therapies including PI3K/mTOR inhibitors, MET inhibitors, anti-HER2/EGFR treatments and the immunosuppressant rapamycin, providing a rationale for co-targeting treatment strategies for a more durable patient response. The current status of PIM kinase inhibitors and their use as a combination therapy with other targeted agents, in addition to the development of novel multi-molecularly targeted single therapeutic agents containing a PIM kinase targeting moiety are discussed.