Optimization of 5-hydroxytryptamines as dual function inhibitors targeting phospholipase A2 and leukotriene A4 hydrolase

Systematic Investigation of Dual-Target-Directed Ligands

Multitarget-directed ligands (MTDLs) are compounds rationally designed to affect multiple targets, aiming for a better therapeutic profile. For over 20 years, MTDLs have garnered increasing attention, the idea being that their full potential would have been achieved, thanks to unprecedented target combinations and advanced medicinal chemistry strategies. This study presents a literature mining effort resulting in a data set of dual-target-directed ligands (DTDLs), the fundamental example of MTDLs. We used this data set to evaluate the rationale behind target selection and the chemical novelty of DTDLs targeting specific protein combinations. Our analysis focused on DTDL targets in terms of biological function, disease association, structure, and chemogenomic traits. We also compared DTDLs with single-target compounds. We found that well-known target pathology associations often guide DTDL design, leveraging existing chemical scaffolds and binding pocket similarities. These findings highlight the current state of the field and suggest substantial untapped potential for rational polypharmacology.

Discovery of Amino Alcohols as Highly Potent, Selective, and Orally Efficacious Inhibitors of Leukotriene A4 Hydrolase

The discovery of chiral amino alcohols derived from our previously disclosed clinical LTA4H inhibitor LYS006 is described. In a biochemical assay, their optical antipodes showed similar potencies, which could be rationalized by the cocrystal structures of these compounds bound to LTA4H. Despite comparable stabilities in liver microsomes, they showed distinct in vivo PK properties. Selective O-phosphorylation of the (R)-enantiomers in blood led to clearance values above the hepatic blood flow, whereas the (S)-enantiomers were unaffected and exhibited satisfactory metabolic stabilities in vivo. Introduction of two pyrazole rings led to compound (S)-2 with a more balanced distribution of polarity across the molecule, exhibiting high selectivity and excellent potency in vitro and in vivo. Furthermore, compound (S)-2 showed favorable profiles in 16-week IND-enabling toxicology studies in dogs and rats. Based on allometric scaling and potency in whole blood, compound (S)-2 has the potential for a low oral efficacious dose administered once daily.

Classification and QSAR models of leukotriene A4 hydrolase (LTA4H) inhibitors by machine learning methods

Leukotriene A4 hydrolase (LTA4H) is an important anti-inflammatory target which can convert leukotriene A4 (LTA4) into pro-inflammatory substance leukotriene B4 (LTB4). In this paper, we built 18 classification models for 463 LTA4H inhibitors by using support vector machine (SVM), random forest (RF) and K-Nearest Neighbour (KNN). The best classification model (Model 2A) was built from RF and MACCS fingerprints. The prediction accuracy of 88.96% and the Matthews correlation coefficient (MCC) of 0.74 had been achieved on the test set. We also divided the 463 LTA4H inhibitors into six subsets using K-Means. We found that the highly active LTA4H inhibitors mostly contained diphenylmethane or diphenyl ether as the scaffold and pyridine or piperidine as the side chain. In addition, six quantitative structure-activity relationship (QSAR) models for 172 LTA4H inhibitors were built by multiple linear regression (MLR) and SVM. The best QSAR model (Model 6A) was built by using SVM and CORINA Symphony descriptors. The coefficients of determination of the training set and the test set were equal to 0.81 and 0.79, respectively. Classification and QSAR models could be used for subsequent virtual screening, and the obtained fragments that were important for highly active inhibitors would be helpful for designing new LTA4H inhibitors.

Nanosensor for Sensitive Detection of the New Psychedelic Drug 25I-NBOMe

This work reports the synthesis, characterization, and sensing behavior of a hybrid nanodevice for the detection of the potent abuse drug 25I-NBOMe. The system is based on mesoporous silica nanoparticles, loaded with a fluorescent dye, functionalized with a serotonin derivative and capped with the 5-HT_2A receptor antibody. In the presence of 25I-NBOMe the capping antibody is displaced, leading to pore opening and rhodamine B release. This delivery was ascribed to 5-HT_2A receptor antibody detachment from the surface due to its stronger coordination with 25I-NBOMe present in the solution. The prepared nanodevice allowed the sensitive (limit of detection of 0.6 μm) and selective recognition of the 25I-NBOMe drug (cocaine, heroin, mescaline, lysergic acid diethylamide, MDMA, and morphine were unable to induce pore opening and rhodamine B release). This nanodevice acts as a highly sensitive and selective fluorometric probe for the 25I-NBOMe illicit drug in artificial saliva and in sweets.

Computational Multitarget Drug Design

Designing drugs that can simultaneously interact with multiple targets is a promising approach for treating complicated diseases. Compared to using combinations of single target drugs, multitarget drugs have advantages of higher efficacy, improved safety profile, and simpler administration. Many in silico methods have been developed to approach different aspects of this polypharmacology-guided drug design, particularly for drug repurposing and multitarget drug design. In this review, we summarize recent progress in computational multitarget drug design and discuss their advantages and limitations. Perspectives for future drug development will also be discussed.

Comparing structural fingerprints using a literature-based similarity benchmark

Background

The concept of molecular similarity is one of the central ideas in cheminformatics, despite the fact that it is ill-defined and rather difficult to assess objectively. Here we propose a practical definition of molecular similarity in the context of drug discovery: molecules A and B are similar if a medicinal chemist would be likely to synthesise and test them around the same time as part of the same medicinal chemistry program. The attraction of such a definition is that it matches one of the key uses of similarity measures in early-stage drug discovery. If we make the assumption that molecules in the same compound activity table in a medicinal chemistry paper were considered similar by the authors of the paper, we can create a dataset of similar molecules from the medicinal chemistry literature. Furthermore, molecules with decreasing levels of similarity to a reference can be found by either ordering molecules in an activity table by their activity, or by considering activity tables in different papers which have at least one molecule in common.

Results

Using this procedure with activity data from ChEMBL, we have created two benchmark datasets for structural similarity that can be used to guide the development of improved measures. Compared to similar results from a virtual screen, these benchmarks are an order of magnitude more sensitive to differences between fingerprints both because of their size and because they avoid loss of statistical power due to the use of mean scores or ranks. We measure the performance of 28 different fingerprints on the benchmark sets and compare the results to those from the Riniker and Landrum (J Cheminf 5:26, 2013. doi:10.1186/1758-2946-5-26) ligand-based virtual screening benchmark.

Conclusions

Extended-connectivity fingerprints of diameter 4 and 6 are among the best performing fingerprints when ranking diverse structures by similarity, as is the topological torsion fingerprint. However, when ranking very close analogues, the atom pair fingerprint outperforms the others tested. When ranking diverse structures or carrying out a virtual screen, we find that the performance of the ECFP fingerprints significantly improves if the bit-vector length is increased from 1024 to 16,384.Graphical abstract

Diverse ways of perturbing the human arachidonic acid metabolic network to control inflammation

Inflammation and other common disorders including diabetes, cardiovascular disease, and cancer are often the result of several molecular abnormalities and are not likely to be resolved by a traditional single-target drug discovery approach. Though inflammation is a normal bodily reaction, uncontrolled and misdirected inflammation can cause inflammatory diseases such as rheumatoid arthritis and asthma. Nonsteroidal anti-inflammatory drugs including aspirin, ibuprofen, naproxen, or celecoxib are commonly used to relieve aches and pains, but often these drugs have undesirable and sometimes even fatal side effects. To facilitate safer and more effective anti-inflammatory drug discovery, a balanced treatment strategy should be developed at the biological network level. In this Account, we focus on our recent progress in modeling the inflammation-related arachidonic acid (AA) metabolic network and subsequent multiple drug design. We first constructed a mathematical model of inflammation based on experimental data and then applied the model to simulate the effects of commonly used anti-inflammatory drugs. Our results indicated that the model correctly reproduced the established bleeding and cardiovascular side effects. Multitarget optimal intervention (MTOI), a Monte Carlo simulated annealing based computational scheme, was then developed to identify key targets and optimal solutions for controlling inflammation. A number of optimal multitarget strategies were discovered that were both effective and safe and had minimal associated side effects. Experimental studies were performed to evaluate these multitarget control solutions further using different combinations of inhibitors to perturb the network. Consequently, simultaneous control of cyclooxygenase-1 and -2 and leukotriene A4 hydrolase, as well as 5-lipoxygenase and prostaglandin E2 synthase were found to be among the best solutions. A single compound that can bind multiple targets presents advantages including low risk of drug-drug interactions and robustness regarding concentration fluctuations. Thus, we developed strategies for multiple-target drug design and successfully discovered several series of multiple-target inhibitors. Optimal solutions for a disease network often involve mild but simultaneous interventions of multiple targets, which is in accord with the philosophy of traditional Chinese medicine (TCM). To this end, our AA network model can aptly explain TCM anti-inflammatory herbs and formulas at the molecular level. We also aimed to identify activators for several enzymes that appeared to have increased activity based on MTOI outcomes. Strategies were then developed to predict potential allosteric sites and to discover enzyme activators based on our hypothesis that combined treatment with the projected activators and inhibitors could balance different AA network pathways, control inflammation, and reduce associated adverse effects. Our work demonstrates that the integration of network modeling and drug discovery can provide novel solutions for disease control, which also calls for new developments in drug design concepts and methodologies. With the rapid accumulation of quantitative data and knowledge of the molecular networks of disease, we can expect an increase in the development and use of quantitative disease models to facilitate efficient and safe drug discovery.