Hologram QSAR models of a series of 6-arylquinazolin-4-amine inhibitors of a new Alzheimer's disease target: dual specificity tyrosine-phosphorylation-regulated kinase-1A enzyme

Identification of Pharmacophoric Fragments of DYRK1A Inhibitors Using Machine Learning Classification Models

Dual-specific tyrosine phosphorylation regulated kinase 1 (DYRK1A) has been regarded as a potential therapeutic target of neurodegenerative diseases, and considerable progress has been made in the discovery of DYRK1A inhibitors. Identification of pharmacophoric fragments provides valuable information for structure- and fragment-based design of potent and selective DYRK1A inhibitors. In this study, seven machine learning methods along with five molecular fingerprints were employed to develop qualitative classification models of DYRK1A inhibitors, which were evaluated by cross-validation, test set, and external validation set with four performance indicators of predictive classification accuracy (CA), the area under receiver operating characteristic (AUC), Matthews correlation coefficient (MCC), and balanced accuracy (BA). The PubChem fingerprint-support vector machine model (CA = 0.909, AUC = 0.933, MCC = 0.717, BA = 0.855) and PubChem fingerprint along with the artificial neural model (CA = 0.862, AUC = 0.911, MCC = 0.705, BA = 0.870) were considered as the optimal modes for training set and test set, respectively. A hybrid data balancing method SMOTETL, a combination of synthetic minority over-sampling technique (SMOTE) and Tomek link (TL) algorithms, was applied to explore the impact of balanced learning on the performance of models. Based on the frequency analysis and information gain, pharmacophoric fragments related to DYRK1A inhibition were also identified. All the results will provide theoretical supports and clues for the screening and design of novel DYRK1A inhibitors.

Discovery of novel 6-hydroxybenzothiazole urea derivatives as dual Dyrk1A/α-synuclein aggregation inhibitors with neuroprotective effects

A role of Dyrk1A in the progression of Down syndrome-related Alzheimer's disease (AD) is well supported. However, the involvement of Dyrk1A in the pathogenesis of Parkinson's disease (PD) was much less studied, and it is not clear whether it would be promising to test Dyrk1A inhibitors in relevant PD models. Herein, we modified our previously published 1-(6-hydroxybenzo[d]thiazol-2-yl)-3-phenylurea scaffold of Dyrk1A inhibitors to obtain a new series of analogues with higher selectivity for Dyrk1A on the one hand, but also with a novel, additional activity as inhibitors of α-synuclein (α-syn) aggregation, a major pathogenic hallmark of PD. The phenyl acetamide derivative b27 displayed the highest potency against Dyrk1A with an IC50 of 20 nM and high selectivity over closely related kinases. Furthermore, b27 was shown to successfully target intracellular Dyrk1A and to inhibit SF3B1 phosphorylation in HeLa cells with an IC50 of 690 nM. In addition, two compounds among the Dyrk1A inhibitors, b1 and b20, also suppressed the aggregation of α-synuclein (α-syn) oligomers (with IC50 values of 10.5 μM and 7.8 μM, respectively). Both compounds but not the Dyrk1A reference inhibitor harmine protected SH-SY5Y neuroblastoma cells against α-syn-induced cytotoxicity, with b20 exhibiting a higher neuroprotective effect. Compound b1 and harmine were more efficient in protecting SH-SY5Y cells against 6-hydroxydopamine-induced cell death, an effect that was previously correlated to Dyrk1A inactivation in cells but not yet verified using chemical inhibitors. The presented dual inhibitors exhibited a novel activity profile encouraging for further testing in neurodegenerative disease models.

Topomer CoMFA and HQSAR Study on Benzimidazole Derivative as NS5B Polymerase Inhibitor

Background:

In recent years, the number of people infected with the hepatitis C virus (HCV) is increasing rapidly. This has become a major threat to global health, therefore, new anti- HCV drugs are urgently needed. HCV NS5B polymerase is an RNA-dependent RNA polymerase (RdRp), which plays an important role in virus replication, and can effectively prevent the replication of HCV sub-genomic RNA in daughter cells. It is considered a very promising HCV therapeutic target for the design of anti-HCV drugs.

Methods:

In order to explore the relationship between the structure of benzimidazole derivative and its inhibitory activity on NS5B polymerase, holographic quantitative structure-activity relationship (HQSAR) and Topomer comparative molecular field analysis (CoMFA) were used to establish benzimidazole QSAR model of derivative inhibitors.

Results:

The results show that for the Topomer CoMFA model, the cross-validation coefficient q2 value is 0.883, and the non-cross-validation coefficient r2 value is 0.975. The model is reasonable, reliable, and has a good predictive ability. For the HQSAR model, the cross-validated q2 value is 0.922, and the uncross-validated r2 value is 0.971, indicating that the model data fit well and has a high predictive ability. Through the analysis of the contour map and color code diagram, 40 new benzimidazole inhibitor molecules were designed, and all of them have higher activity than template molecules, and the new molecules have significant interaction sites with protein 3SKE.

Conclusion:

The 3D-QSAR model established by Topomer CoMFA and HQSAR has good prediction results and the statistical verification is valid. The newly designed molecules and docking results provide theoretical guidance for the synthesis of new NS5B polymerase inhibitors and for the identification of key residues that the inhibitors bind to NS5B, which helps to better understand their inhibitory mechanism. These findings are helpful for the development of new anti-HCV drugs.

Discovery of Hydroxybenzothiazole Urea Compounds as Multitargeted Agents Suppressing Major Cytotoxic Mechanisms in Neurodegenerative Diseases

Multiple factors are causally responsible and/or contribute to the progression of Alzheimer's and Parkinson's diseases. The protein kinase Dyrk1A was identified as a promising target as it phosphorylates tau protein, α-synuclein, and parkin. The first goal of our study was to optimize our previously identified Dyrk1A inhibitors of the 6-hydroxy benzothiazole urea chemotype in terms of potency and selectivity. Our efforts led to the development of the 3-fluorobenzyl amide derivative 16b, which displayed the highest potency against Dyrk1A (IC₅₀ = 9.4 nM). In general, the diversification of the benzylamide moiety led to an enhanced selectivity over the most homologous isoform, Dyrk1B, which was a meaningful indicator, as the high selectivity could be confirmed in an extended selectivity profiling of 3b and 16b. Eventually, we identified the novel phenethyl amide derivative 24b as a triple inhibitor of Dyrk1A kinase activity (IC₅₀ = 119 nM) and the aggregation of tau and α-syn oligomers. We provide evidence that the novel combination of selective Dyrk1A inhibition and suppression of tau and α-syn aggregations of our new lead compound confers efficacy in several established cellular models of neurotoxic mechanisms relevant to neurodegenerative diseases, including α-syn- and 6-hydroxydopamine-induced cytotoxicities.

Small Molecule Inhibitors of DYRK1A Identified by Computational and Experimental Approaches

Dual-specificity tyrosine phosphorylation-regulated kinase 1A (DYRK1A) is a protein kinase with diverse functions in cell regulation. Abnormal expression and activity of DYRK1A contribute to numerous human malignancies, Down syndrome, and Alzheimer's disease. Notably, DYRK1A has been proposed as a potential therapeutic target for the treatment of diabetes because of its key role in pancreatic β-cell proliferation. Consequently, DYRK1A is an attractive drug target for a variety of diseases. Here, we report the identification of several DYRK1A inhibitors using our in-house topological water network-based approach. All inhibitors were further verified by in vitro assay.

In Silico Studies in Drug Research Against Neurodegenerative Diseases

Background

Neurodegenerative diseases such as Alzheimer's disease (AD), amyotrophic lateral sclerosis, Parkinson's disease (PD), spinal cerebellar ataxias, and spinal and bulbar muscular atrophy are described by slow and selective degeneration of neurons and axons in the central nervous system (CNS) and constitute one of the major challenges of modern medicine. Computer-aided or in silico drug design methods have matured into powerful tools for reducing the number of ligands that should be screened in experimental assays.

Methods

In the present review, the authors provide a basic background about neurodegenerative diseases and in silico techniques in the drug research. Furthermore, they review the various in silico studies reported against various targets in neurodegenerative diseases, including homology modeling, molecular docking, virtual high-throughput screening, quantitative structure activity relationship (QSAR), hologram quantitative structure activity relationship (HQSAR), 3D pharmacophore mapping, proteochemometrics modeling (PCM), fingerprints, fragment-based drug discovery, Monte Carlo simulation, molecular dynamic (MD) simulation, quantum-mechanical methods for drug design, support vector machines, and machine learning approaches.

Results

Detailed analysis of the recently reported case studies revealed that the majority of them use a sequential combination of ligand and structure-based virtual screening techniques, with particular focus on pharmacophore models and the docking approach.

Conclusion

Neurodegenerative diseases have a multifactorial pathoetiological origin, so scientists have become persuaded that a multi-target therapeutic strategy aimed at the simultaneous targeting of multiple proteins (and therefore etiologies) involved in the development of a disease is recommended in future.

Anti-Mycobacterial Evaluation of 7-Chloro-4-Aminoquinolines and Hologram Quantitative Structure-Activity Relationship (HQSAR) Modeling of Amino-Imino Tautomers

In an ongoing research program for the development of new anti-tuberculosis drugs, we synthesized three series (A, B, and C) of 7-chloro-4-aminoquinolines, which were evaluated in vitro against Mycobacterium tuberculosis (MTB). Now, we report the anti-MTB and cytotoxicity evaluations of a new series, D (D01–D21). Considering the active compounds of series A (A01–A13), B (B01–B13), C (C01–C07), and D (D01–D09), we compose a data set of 42 compounds and carried out hologram quantitative structure–activity relationship (HQSAR) analysis. The amino–imino tautomerism of the 4-aminoquinoline moiety was considered using both amino (I) and imino (II) forms as independent datasets. The best HQSAR model from each dataset was internally validated and both models showed significant statistical indexes. Tautomer I model: leave-one-out (LOO) cross-validated correlation coefficient (q²) = 0.80, squared correlation coefficient (r²) = 0.97, standard error (SE) = 0.12, cross-validated standard error (SE_cv) = 0.32. Tautomer II model: q² = 0.77, r² = 0.98, SE = 0.10, SE_cv = 0.35. Both models were externally validated by predicting the activity values of the corresponding test set, and the tautomer II model, which showed the best external prediction performance, was used to predict the biological activity responses of the compounds that were not evaluated in the anti-MTB trials due to poor solubility, pointing out D21 for further solubility studies to attempt to determine its actual biological activity.

QSPR models for predicting generator-column-derived octanol/water and octanol/air partition coefficients of polychlorinated biphenyls

Octanol/water (K(OW)) and octanol/air (K(OA)) partition coefficients are two important physicochemical properties of organic substances. In current practice, K(OW) and K(OA) values of some polychlorinated biphenyls (PCBs) are measured using generator column method. Quantitative structure-property relationship (QSPR) models can serve as a valuable alternative method of replacing or reducing experimental steps in the determination of K(OW) and K(OA). In this paper, two different methods, i.e., multiple linear regression based on dragon descriptors and hologram quantitative structure-activity relationship, were used to predict generator-column-derived log K(OW) and log K(OA) values of PCBs. The predictive ability of the developed models was validated using a test set, and the performances of all generated models were compared with those of three previously reported models. All results indicated that the proposed models were robust and satisfactory and can thus be used as alternative models for the rapid assessment of the K(OW) and K(OA) of PCBs.