Development of a cheminformatics platform for selectivity analyses of carbonic anhydrase inhibitors

New isoxazolidinyl-based N-alkylethanolamines as new activators of human brain carbonic anhydrases

Carbonic anhydrases (CAs) are widespread metalloenzymes which catalyse the reversible hydration of carbon dioxide (CO₂) to bicarbonate (HCO₃^-) and a proton, relevant in many physiological processes. In the last few years, the involvement of CA activation in different metabolic pathways in the human brain addressed the research to the discovery of novel CA activators. Here, a new series of isoxazoline-based amino alcohols as CA activators was investigated. The synthesis and the CA activating effects towards four human CA isoforms expressed in the human brain, that are hCAs I, II, IV and VII, were reported. The best results were obtained for the (methyl)-isoxazoline-amino alcohols 3 and 5 with K_A values in the submicromolar range (0.52-0.86 µM) towards hCA VII, and a good selectivity over hCA I. Being hCA VII involved in brain function and metabolism, the newly identified CA activators might be promising hit compounds with potential therapeutic applications in ageing, epilepsy or neurodegeneration.

A novel class of phenylpyrazolone-sulphonamides rigid synthetic anticancer molecules selectively inhibit the isoform IX of carbonic anhydrases guided by molecular docking and orbital analyses

All the previously reported phenylpyrazoles as carbonic anhydrase inhibitors (CAIs) were found to have small sizes and high levels of flexibility, and hence showed low selectivity profiles toward a particular isoform of CA. Herein, we report the development of a more rigid ring system bearing a sulfonamide hydrophilic head and a lipophilic tail to develop novel molecules that are suggested to have a better selectivity toward a special CA isoform. Accordingly, three novel sets of pyrano[2,3-c]pyrazoles attached with sulfonamide head and aryl hydrophobic tail were synthesized to enhance the selectivity toward a specific isoform of human carbonic anhydrases (hCAs). The impact of both attachments on the potency and selectivity has been extensively discussed in terms of in vitro cytotoxicity evaluation under hypoxic conditions, structure-activity relationship and carbonic anhydrase enzyme assay. All of the new candidates displayed good cytotoxic activities against breast and colorectal carcinomas. Results of the carbonic anhydrase enzyme assay demonstrated the preferential of compounds 22, 24 and 27 to inhibit the isoform IX of hCAs selectively. Wound-healing assay has also been performed and revealed the potential of 27 to decrease the wound closure percentage in MCF-7 cells. Molecular docking and molecular orbital analysis have finally been conducted. Results indicate the potential binding interactions of 24 and 27 with several crucial amino acids of the hCA IX.Communicated by Ramaswamy H. Sarma.

Multiple machine learning methods aided virtual screening of NaV 1.5 inhibitors

Na_v 1.5 sodium channels contribute to the generation of the rapid upstroke of the myocardial action potential and thereby play a central role in the excitability of myocardial cells. At present, the patch clamp method is the gold standard for ion channel inhibitor screening. However, this method has disadvantages such as high technical difficulty, high cost and low speed. In this study, novel machine learning models to screen chemical blockers were developed to overcome the above shortage. The data from the ChEMBL Database were employed to establish the machine learning models. Firstly, six molecular fingerprints together with five machine learning algorithms were used to develop 30 classification models to predict effective inhibitors. A validation and a test set were used to evaluate the performance of the models. Subsequently, the privileged substructures tightly associated with the inhibition of the Na_v 1.5 ion channel were extracted using the bioalerts Python package. In the validation set, the RF-Graph model performed best. Similarly, RF-Graph produced the best result in the test set in which the Prediction Accuracy (Q) was 0.9309 and Matthew's correlation coefficient was 0.8627, further indicating the model had high classification ability. The results of the privileged substructures indicated Sulfa structures and fragments with large Steric hindrance tend to block Na_v 1.5. In the unsupervised learning task of identifying sulfa drugs, MACCS and Graph fingerprints had good results. In summary, effective machine learning models have been constructed which help to screen potential inhibitors of the Na_v 1.5 ion channel and key privileged substructures with high affinity were also extracted.

Insertion of metal carbenes into the anilinic N-H bond of unprotected aminobenzenesulfonamides delivers low nanomolar inhibitors of human carbonic anhydrase IX and XII isoforms

Herein we report the synthesis of a set of thirty-four primary sulfonamides generated via formal N-H-insertion of metal carbenes into anilinic amino group of sulfanilamide and its meta-substituted analog. Obtained compounds were tested in vitro as inhibitors of four physiologically significant isoforms of the metalloenzyme human carbonic anhydrase (hCA, EC 4.2.1.1). Many of the synthesized sulfonamides displayed low nanomolar K_i values against therapeutically relevant hCA II, IX, and XII, whereas they did not potently inhibit hCA I. Provided the promising activity profiles of the substances towards tumor-associated hCA IX and XII isozymes, single-concentration MTT test was performed for the entire set. Disappointingly, most of the discovered hCA inhibitors did not significantly suppress the growth of cancer cells either in normoxia or CoCl₂ induced hypoxic conditions. The only two compounds exerting profound antiproliferative effect turned out to be modest hCA inhibitors. Their out of the range activity in cells is likely attributive to the presence of Michael acceptor substructure which can potentially act either through the inhibition of Thioredoxin reductases (TrxRs, EC 1.8.1.9) or nonspecific covalent binding to cell proteins.

Prediction of activity and selectivity profiles of human Carbonic Anhydrase inhibitors using machine learning classification models

The development of selective inhibitors of the clinically relevant human Carbonic Anhydrase (hCA) isoforms IX and XII has become a major topic in drug research, due to their deregulation in several types of cancer. Indeed, the selective inhibition of these two isoforms, especially with respect to the homeostatic isoform II, holds great promise to develop anticancer drugs with limited side effects. Therefore, the development of in silico models able to predict the activity and selectivity against the desired isoform(s) is of central interest. In this work, we have developed a series of machine learning classification models, trained on high confidence data extracted from ChEMBL, able to predict the activity and selectivity profiles of ligands for human Carbonic Anhydrase isoforms II, IX and XII. The training datasets were built with a procedure that made use of flexible bioactivity thresholds to obtain well-balanced active and inactive classes. We used multiple algorithms and sampling sizes to finally select activity models able to classify active or inactive molecules with excellent performances. Remarkably, the results herein reported turned out to be better than those obtained by models built with the classic approach of selecting an a priori activity threshold. The sequential application of such validated models enables virtual screening to be performed in a fast and more reliable way to predict the activity and selectivity profiles against the investigated isoforms.

Adding high-dose celecoxib to increase effectiveness of standard glioblastoma chemoirradiation

Over one hundred clinical trials since 2005 have failed to significantly improve the prognosis of glioblastoma. Since 2005, the standard of care has been maximal resection followed by 60Gy irradiation over six weeks with daily temozolomide. With this, a median survival of 2 years can be expected. This short paper reviewed how the pharmacodynamic attributes of an EMA/FDA approved, cheap, generic drug to treat pain, celecoxib, intersect with pathophysiological elements driving glioblastoma growth, such that growth drive inhibition can be expected from celecoxib. The two main attributes of celecoxib are carbonic anhydrase inhibition and cyclooxygenase-2 inhibition. Both attributes individually have been in active study as adjuncts during current cancer treatment, including that of glioblastoma. That research is briefly reviewed here. This paper concludes from the collected data, that starting celecoxib, 600 to 800mg twice daily before surgery and continuing it through the chemoirradiation phase of treatment would be a low-risk intervention with sound rationale.

Incorporating structural similarity into a scoring function to enhance the prediction of binding affinities

In this study, we developed a novel algorithm to improve the screening performance of an arbitrary docking scoring function by recalibrating the docking score of a query compound based on its structure similarity with a set of training compounds, while the extra computational cost is neglectable. Two popular docking methods, Glide and AutoDock Vina were adopted as the original scoring functions to be processed with our new algorithm and similar improvement performance was achieved. Predicted binding affinities were compared against experimental data from ChEMBL and DUD-E databases. 11 representative drug receptors from diverse drug target categories were applied to evaluate the hybrid scoring function. The effects of four different fingerprints (FP2, FP3, FP4, and MACCS) and the four different compound similarity effect (CSE) functions were explored. Encouragingly, the screening performance was significantly improved for all 11 drug targets especially when CSE = S⁴ (S is the Tanimoto structural similarity) and FP2 fingerprint were applied. The average predictive index (PI) values increased from 0.34 to 0.66 and 0.39 to 0.71 for the Glide and AutoDock vina scoring functions, respectively. To evaluate the performance of the calibration algorithm in drug lead identification, we also imposed an upper limit on the structural similarity to mimic the real scenario of screening diverse libraries for which query ligands are general-purpose screening compounds and they are not necessarily structurally similar to reference ligands. Encouragingly, we found our hybrid scoring function still outperformed the original docking scoring function. The hybrid scoring function was further evaluated using external datasets for two systems and we found the PI values increased from 0.24 to 0.46 and 0.14 to 0.42 for A2AR and CFX systems, respectively. In a conclusion, our calibration algorithm can significantly improve the virtual screening performance in both drug lead optimization and identification phases with neglectable computational cost.

Predicting Isoform-Selective Carbonic Anhydrase Inhibitors via Machine Learning and Rationalizing Structural Features Important for Selectivity

Carbonic anhydrases (CAs) catalyze the physiological hydration of carbon dioxide and are among the most intensely studied pharmaceutical target enzymes. A hallmark of CA inhibition is the complexation of the catalytic zinc cation in the active site. Human (h) CA isoforms belonging to different families are implicated in a wide range of diseases and of very high interest for therapeutic intervention. Given the conserved catalytic mechanisms and high similarity of many hCA isoforms, a major challenge for CA-based therapy is achieving inhibitor selectivity for hCA isoforms that are associated with specific pathologies over other widely distributed isoforms such as hCA I or hCA II that are of critical relevance for the integrity of many physiological processes. To address this challenge, we have attempted to predict compounds that are selective for isoform hCA IX, which is a tumor-associated protein and implicated in metastasis, over hCA II on the basis of a carefully curated data set of selective and nonselective inhibitors. Machine learning achieved surprisingly high accuracy in predicting hCA IX-selective inhibitors. The results were further investigated, and compound features determining successful predictions were identified. These features were then studied on the basis of X-ray structures of hCA isoform-inhibitor complexes and found to include substructures that explain compound selectivity. Our findings lend credence to selectivity predictions and indicate that the machine learning models derived herein have considerable potential to aid in the identification of new hCA IX-selective compounds.

Probing 4-(diethylamino)-salicylaldehyde-based thiosemicarbazones as multi-target directed ligands against cholinesterases, carbonic anhydrases and α-glycosidase enzymes

With the fading of 'one drug-one target' approach, Multi-Target-Directed Ligands (MTDL) has become a central idea in modern Medicinal Chemistry. The present study aimed to design, develop and characterize a novel series of 4-(Diethylamino)-salicylaldehyde based thiosemicarbazones (3a-p) and evaluates their biological activity against cholinesterase, carbonic anhydrases and α-glycosidase enzymes. The hCA I isoform was inhibited by these novel 4-(diethylamino)-salicylaldehyde-based thiosemicarbazones (3a-p) in low nanomolar levels, the Ki of which differed between 407.73 ± 43.71 and 1104.11 ± 80.66 nM. Against the physiologically dominant isoform hCA II, the novel compounds demonstrated K_is varying from 323.04 ± 56.88 to 991.62 ± 77.26 nM. Also, these novel 4-(diethylamino)-salicylaldehyde based thiosemicarbazones (3a-p) effectively inhibited AChE, with Ki values in the range of 121.74 ± 23.52 to 548.63 ± 73.74 nM. For BChE, Ki values were obtained with in the range of 132.85 ± 12.53 to 618.53 ± 74.23 nM. For α-glycosidase, the most effective Ki values of 3b, 3k, and 3g were with Ki values of 77.85 ± 10.64, 96.15 ± 9.64, and 124.95 ± 11.44 nM, respectively. We have identified inhibition mechanism of 3b, 3g, 3k, and 3n on α-glycosidase AChE, hCA I, hCA II, and BChE enzyme activities. Hydrazine-1-carbothioamide and hydroxybenzylidene moieties of compounds play an important role in the inhibition of AChE, hCA I, and hCA II enzymes. Hydroxybenzylidene moieties are critical for inhibition of both BChE and α-glycosidase enzymes. The findings of in vitro and in silico evaluations indicate 4-(diethylamino)-salicylaldehyde-based thiosemicarbazone scaffold to be a promising hit for drug development for multifactorial diseases like Alzheimer's disease.