Current Topics in Medicinal Chemistry - Volume 20, Issue 18, 2020

Background: Graph edit distance is a methodology used to solve error-tolerant graph matching. This methodology estimates a distance between two graphs by determining the minimum number of modifications required to transform one graph into the other. These modifications, known as edit operations, have an edit cost associated that has to be determined depending on the problem. Objective: This study focuses on the use of optimization techniques in order to learn the edit costs used when comparing graphs by means of the graph edit distance. Methods: Graphs represent reduced structural representations of molecules using pharmacophore-type node descriptions to encode the relevant molecular properties. This reduction technique is known as extended reduced graphs. The screening and statistical tools available on the ligand-based virtual screening benchmarking platform and the RDKit were used. Results: In the experiments, the graph edit distance using learned costs performed better or equally good than using predefined costs. This is exemplified with six publicly available datasets: DUD-E, MUV, GLL&GDD, CAPST, NRLiSt BDB, and ULS-UDS. Conclusion: This study shows that the graph edit distance along with learned edit costs is useful to identify bioactivity similarities in a structurally diverse group of molecules. Furthermore, the target-specific edit costs might provide useful structure-activity information for future drug-design efforts.

Introduction: Monoamine oxidase inhibitors (MAOIs) are compounds largely used in the treatment of Parkinson’s disease (PD), Alzheimer’s disease and other neuropsychiatric disorders since they are closely related to the MAO enzymes activity. The two isoforms of the MAO enzymes, MAO-A and MAO-B, are responsible for the degradation of monoamine neurotransmitters and due to this, relevant efforts have been devoted to finding new compounds with more selectivity and less side effects. One of the most used approaches is based on the use of computational approaches since they are time and money-saving and may allow us to find a more relevant structure-activity relationship. Objective: In this manuscript, we will review the most relevant computational approaches aimed at the prediction and development of new MAO inhibitors. Subsequently, we will also introduce a new multitask model aimed at predicting MAO-A and MAO-B inhibitors. Methods: The QSAR multi-task model herein developed was based on the use of the linear discriminant analysis. This model was developed gathering 5,759 compounds from the public dataset Chembl. The molecular descriptors used was calculated using the Dragon software. Classical statistical tests were performed to check the validity and robustness of the model. Results: The herein proposed model is able to correctly classify all the 5,759 compounds. All the statistical performed tests indicated that this model is robust and reproducible. Conclusion: MAOIs are compounds of large interest since they are largely used in the treatment of very serious illness. These inhibitors may lose efficacy and produce severe side effects. Due to this, the development of selective MAO-A or MAO-B inhibitors is crucial for the treatment of these diseases and their effects. The herein proposed multi-target QSAR model may be a relevant tool in the development of new and more selective MAO inhibitors.

Background: Alzheimer’s disease (AD), a neurological disorder, is the most common cause of senile dementia. Butyrylcholinesterase (BuChE) enzyme plays a vital role in regulating the brain acetylcholine (ACh) neurotransmitter, but in the case of Alzheimer’s disease (AD), BuChE activity gradually increases in patients with a decrease in the acetylcholine (ACh) concentration via hydrolysis. ACh plays an essential role in regulating learning and memory as the cortex originates from the basal forebrain, and thus, is involved in memory consolidation in these sites. Methods: In this work, we have developed a partial least squares (PLS)-regression based two dimensional quantitative structure-activity relationship (2D-QSAR) model using 1130 diverse chemical classes of compounds with defined activity against the BuChE enzyme. Keeping in mind the strict Organization for Economic Co-operation and Development (OECD) guidelines, we have tried to select significant descriptors from the large initial pool of descriptors using multi-layered variable selection strategy using stepwise regression followed by genetic algorithm (GA) followed by again stepwise regression technique and at the end best subset selection prior to development of final model thus reducing noise in the input. Partial least squares (PLS) regression technique was employed for the development of the final model while model validation was performed using various stringent validation criteria. Results: The results obtained from the QSAR model suggested that the quality of the model is acceptable in terms of both internal (R2= 0.664, Q2= 0.650) and external (R2 Pred= 0.657) validation parameters. The QSAR studies were analyzed, and the structural features (hydrophobic, ring aromatic and hydrogen bond acceptor/donor) responsible for enhancement of the activity were identified. The developed model further suggests that the presence of hydrophobic features like long carbon chain would increase the BuChE inhibitory activity and presence of amino group and hydrazine fragment promoting the hydrogen bond interactions would be important for increasing the inhibitory activity against BuChE enzyme. Conclusion: Furthermore, molecular docking studies have been carried out to understand the molecular interactions between the ligand and receptor, and the results are then correlated with the structural features obtained from the QSAR models. The information obtained from the QSAR models are well corroborated with the results of the docking study.

Background: The Epidermal Growth Factor Receptor (EGFR) is a transmembrane protein that acts as a receptor of extracellular protein ligands of the epidermal growth factor (EGF/ErbB) family. It has been shown that EGFR is overexpressed by many tumours and correlates with poor prognosis. Therefore, EGFR can be considered as a very interesting therapeutic target for the treatment of a large variety of cancers such as lung, ovarian, endometrial, gastric, bladder and breast cancers, cervical adenocarcinoma, malignant melanoma and glioblastoma. Methods: We have followed a structure-based virtual screening (SBVS) procedure with a library composed of several commercial collections of chemicals (615,462 compounds in total) and the 3D structure of EGFR obtained from the Protein Data Bank (PDB code: 1M17). The docking results from this campaign were then ranked according to the theoretical binding affinity of these molecules to EGFR, and compared with the binding affinity of erlotinib, a well-known EGFR inhibitor. A total of 23 top-rated commercial compounds displaying potential binding affinities similar or even better than erlotinib were selected for experimental evaluation. In vitro assays in different cell lines were performed. A preliminary test was carried out with a simple and standard quick cell proliferation assay kit, and six compounds showed significant activity when compared to positive control. Then, viability and cell proliferation of these compounds were further tested using a protocol based on propidium iodide (PI) and flow cytometry in HCT116, Caco-2 and H358 cell lines. Results: The whole six compounds displayed good effects when compared with erlotinib at 30 μM. When reducing the concentration to 10μM, the activity of the 6 compounds depends on the cell line used: the six compounds showed inhibitory activity with HCT116, two compounds showed inhibition with Caco-2, and three compounds showed inhibitory effects with H358. At 2 μM, one compound showed inhibiting effects close to those from erlotinib. Conclusion: Therefore, these compounds could be considered as potential primary hits, acting as promising starting points to expand the therapeutic options against a wide range of cancers.

Heart rate variability (HRV) signals are reported to be associated with the personalized drug response in many diseases such as major depressive disorder, epilepsy, chronic pain, hypertension, etc. But the relationships between HRV signals and the personalized drug response in different diseases and patients are complex and remain unclear. With the fast development of modern smart sensor technologies and the popularization of big data paradigm, more and more data on the HRV and drug response will be available, it then provides great opportunities to build models for predicting the association of the HRV with personalized drug response precisely. We here review the present status of the HRV data resources and models for predicting and evaluating of personalized drug responses in different diseases. The future perspectives on the integration of knowledge and personalized data at different levels such as, genomics, physiological signals, etc. for the application of HRV signals to the precision prediction of drug therapy and their response will be provided.