Identification of Mtb GlmU Uridyltransferase Domain Inhibitors by Ligand-Based and Structure-Based Drug Design Approaches

Targeting enzymes that play a role in the biosynthesis of the bacterial cell wall has long been a strategy for antibacterial discovery. In particular, the cell wall of Mycobacterium tuberculosis (Mtb) is a complex of three layers, one of which is Peptidoglycan, an essential component providing rigidity and strength. UDP-GlcNAc, a precursor for the synthesis of peptidoglycan, is formed by GlmU, a bi-functional enzyme. Inhibiting GlmU Uridyltransferase activity has been proven to be an effective anti-bacterial, but its similarity with human enzymes has been a deterrent to drug development. To develop Mtb selective hits, the Mtb GlmU substrate binding pocket was compared with structurally similar human enzymes to identify selectivity determining factors. Substrate binding pockets and conformational changes upon substrate binding were analyzed and MD simulations with substrates were performed to quantify crucial interactions to develop critical pharmacophore features. Thereafter, two strategies were applied to propose potent and selective bacterial GlmU Uridyltransferase domain inhibitors: (i) optimization of existing inhibitors, and (ii) identification by virtual screening. The binding modes of hits identified from virtual screening and ligand growing approaches were evaluated further for their ability to retain stable contacts within the pocket during 20 ns MD simulations. Hits that are predicted to be more potent than existing inhibitors and selective against human homologues could be of great interest for rejuvenating drug discovery efforts towards targeting the Mtb cell wall for antibacterial discovery.     

In Silico Identification of Possible Inhibitors for Protein Kinase B (PknB) of Mycobacterium tuberculosis

With tuberculosis still being one of leading causes of death in the world and the emergence of drug-resistant strains of Mycobacterium tuberculosis (Mtb), researchers have been seeking to find further therapeutic strategies or more specific molecular targets. PknB is one of the 11 Ser/Thr protein kinases of Mtb and is responsible for phosphorylation-mediated signaling, mainly involved in cell wall synthesis, cell division and metabolism. With the amount of structural information available and the great interest in protein kinases, PknB has become an attractive target for drug development. This work describes the optimization and application of an in silico computational protocol to find new PknB inhibitors. This multi-level computational approach combines protein–ligand docking, structure-based virtual screening, molecular dynamics simulations and free energy calculations. The optimized protocol was applied to screen a large dataset containing 129,650 molecules, obtained from the ZINC/FDA-Approved database, Mu.Ta.Lig Virtual Chemotheca and Chimiothèque Nationale. It was observed that the most promising compounds selected occupy the adenine-binding pocket in PknB, and the main interacting residues are Leu17, Val26, Tyr94 and Met155. Only one of the compounds was able to move the active site residues into an open conformation. It was also observed that the P-loop and magnesium position loops change according to the characteristics of the ligand. This protocol led to the identification of six compounds for further experimental testing while also providing additional structural information for the design of more specific and more effective derivatives.     

Macrophage‐Targeted Isoniazid–Selenium Nanoparticles Promote Antimicrobial Immunity and Synergize Bactericidal Destruction of Tuberculosis Bacilli

Pathogenesis hallmarks for tuberculosis (TB) are the Mycobacterium tuberculosis (Mtb) escape from phagolysosomal destruction and limited drug delivery into infected cells. Several nanomaterials can be entrapped in lysosomes, but the development of functional nanomaterials to promote phagolysosomal Mtb clearance remains a big challenge. Here, we report on the bactericidal effects of selenium nanoparticles (Se NPs) against Mtb and further introduce a novel nanomaterial‐assisted anti‐TB strategy manipulating Ison@Man‐Se NPs for synergistic drug‐induced and phagolysosomal destruction of Mtb. Ison@Man‐Se NPs preferentially entered macrophages and accumulated in lysosomes releasing Isoniazid. Surprisingly, Ison@Man‐Se/Man‐Se NPs further promoted the fusion of Mtb into lysosomes for synergistic lysosomal and Isoniazid destruction of Mtb. Concurrently, Ison@Man‐Se/Man‐Se NPs also induced autophagy sequestration of Mtb, evolving into lysosome‐associated autophagosomal Mtb degradation linked to ROS‐mitochondrial and PI3K/Akt/mTOR signaling pathways. This novel nanomaterial‐assisted anti‐TB strategy manipulating antimicrobial immunity and Mtb clearance may potentially serve in more effective therapeutics against TB and drug‐resistant TB.     

Macrophage-Targeted Isoniazid–Selenium Nanoparticles Promote Antimicrobial Immunity and Synergize Bactericidal Destruction of Tuberculosis Bacilli

Pathogenesis hallmarks for tuberculosis (TB) are the Mycobacterium tuberculosis (Mtb) escape from phagolysosomal destruction and limited drug delivery into infected cells. Several nanomaterials can be entrapped in lysosomes, but the development of functional nanomaterials to promote phagolysosomal Mtb clearance remains a big challenge. Here, we report on the bactericidal effects of selenium nanoparticles (Se NPs) against Mtb and further introduce a novel nanomaterial-assisted anti-TB strategy manipulating Ison@Man-Se NPs for synergistic drug-induced and phagolysosomal destruction of Mtb. Ison@Man-Se NPs preferentially entered macrophages and accumulated in lysosomes releasing Isoniazid. Surprisingly, Ison@Man-Se/Man-Se NPs further promoted the fusion of Mtb into lysosomes for synergistic lysosomal and Isoniazid destruction of Mtb. Concurrently, Ison@Man-Se/Man-Se NPs also induced autophagy sequestration of Mtb, evolving into lysosome-associated autophagosomal Mtb degradation linked to ROS-mitochondrial and PI3K/Akt/mTOR signaling pathways. This novel nanomaterial-assisted anti-TB strategy manipulating antimicrobial immunity and Mtb clearance may potentially serve in more effective therapeutics against TB and drug-resistant TB.     

Cavities create a potential back door in epoxide hydrolase Rv1938 from Mycobacterium tuberculosis—A molecular dynamics simulation study

Mycobacterium tuberculosis (Mtb) is the causative organism of tuberculosis. Extensively drug resistant strains and latency have posed formidable challenges in the treatment of tuberculosis. The current study addresses an alpha/beta hydrolase fold bearing enzyme, epoxide hydrolase Rv1938 from Mtb. Epoxide hydrolases are involved in detoxification processes, catabolism and regulation of signaling molecules. Using GROMACS, a 100 ns Molecular Dynamics (MD) simulation was performed for Rv1938. Cavities were identified within the protein at various time frames of the simulation and their volumes were computed. During MD simulation, in addition to the substrate binding cavity, opening of two new cavities located behind the active site was observed. These cavities may be similar to the backdoor proposed for acetylcholinesterase. Structural superimposition of epoxide hydrolase from Mtb with the epoxide hydrolase of Agrobacterium radiobacter1 AD1 (Ephy) indicates that cavity1 in Mtb lies at an identical position to that of the water tunnel in Ephy. Further, docking of the substrate and an inhibitor with protein structures obtained from MD simulation at various time frames was also performed. The potential role of these cavities is discussed.     

Combining ethnopharmacology and virtual screening for lead structure discovery: COX-inhibitors as application example

Virtual screening of large libraries of organic compounds combined with pharmacological high throughput screening is widely used for drug discovery in the pharmaceutical industry. Our aim was to explore the efficiency of using a biased 3D database comprising secondary metabolites from antiinflammatory medicinal plants as a source for the virtual screening. For this study pharmacophore models of cyclooxygenase I and II (COX-1, COX-2), key enzymes in the inflammation process, were generated with structure-based as well as common feature based modeling, resulting in three COX hypotheses. Four different multiconfomational 3D databases limited in molecular weight between 300 and 700 Da were applied to the screening in order to compare and analyze the obtained hit rates. Two of them were created in-house (DIOS, NPD). The database DIOS consists of 2752 compounds from phytochemical reports of antiinflammatory medicinal plants described by the ethnopharmacological source 'De material medica' of Pedanius Dioscorides, whereas NPD contains almost 80,000 compounds gathered arbitrarily from natural sources. In addition, two available multiconformational 3D libraries comprising marketed and development drug substances (DWI and NCI), mainly originating from synthesis, were used for comparison. As a test of the pharmacophore models' capability in natural sources, the models were used to search for known COX inhibitory natural products. This was achieved with some exceptions, which are discussed in the paper. Depending on the hypothesis used, DWI and NCI library searches produced hit rates in the range of 6.6% to 13.7%. A slight increase of the number of molecules assessed for binding was achieved with the database of natural products (NPD). Using the biased 3D database DIOS, however, the average increase of efficiency reached 77% to 133% compared to the hit rates resulting from WDI and NCI. The statistical benefit of a combination of an ethnopharmacological approach with the potential of computer aided drug discovery by in silico screening was demonstrated exemplified on the applied targets COX-1 and COX-2.     

Applications of high throughput microsomal stability assay in drug discovery

High throughput in vitro microsomal stability assays are widely used in drug discovery as an indicator for in vivo stability, which affects pharmacokinetics. This is based on in-depth research involving a limited number of model drug-like compounds that are cleared predominantly by cytochrome P450 metabolism. However, drug discovery compounds are often not drug-like, are assessed with high throughput assays, and have many potential uncharacterized in vivo clearance mechanisms. Therefore, it is important to determine the correlation between high throughput in vitro microsomal stability data and abbreviated discovery in vivo pharmacokinetics study data for a set of drug discovery compounds in order to have evidence for how the in vitro assay can be reliably applied by discovery teams for making critical decisions. In this study the relationship between in vitro single time point high throughput microsomal stability and in vivo clearance from abbreviated drug discovery pharmacokinetics studies was examined using 306 real world drug discovery compounds. The results showed that in vitro Phase I microsomal stability t(1/2) is significantly correlated to in vivo clearance with a p-value<0.001. For compounds with low in vitro rat microsomal stability (t(1/2)<15 min), 87% showed high clearance in vivo (CL>25 mL/min/kg). This demonstrates that high throughput microsomal stability data are very effective in identifying compounds with significant clearance liabilities in vivo. For compounds with high in vitro rat microsomal stability (t(1/2)>15 min), no significant differentiation was observed between high and low clearance compounds. This is likely owing to other clearance pathways, in addition to cytochrome P450 metabolism that enhances in vivo clearance. This finding supports the strategy used by medicinal chemists and drug discovery teams of applying the in vitro data to triage compounds for in vivo PK and efficacy studies and guide structural modification to improve metabolic stability. When in vitro and in vivo data are both available for a compound, potential in vivo clearance pathways can be diagnosed to guide further discovery studies.     

TINS, target immobilized NMR screening: an efficient and sensitive method for ligand discovery

We propose a ligand screening method, called TINS (target immobilized NMR screening), which reduces the amount of target required for the fragment-based approach to drug discovery. Binding is detected by comparing 1D NMR spectra of compound mixtures in the presence of a target immobilized on a solid support to a control sample. The method has been validated by the detection of a variety of ligands for protein and nucleic acid targets (K(D) from 60 to 5000 muM). The ligand binding capacity of a protein was undiminished after 2000 different compounds had been applied, indicating the potential to apply the assay for screening typical fragment libraries. TINS can be used in competition mode, allowing rapid characterization of the ligand binding site. TINS may allow screening of targets that are difficult to produce or that are insoluble, such as membrane proteins.     

Pharmacophore features distributions in different classes of compounds

A pharmacophore analysis approach was used to investigate and compare different classes of compounds relevant to the drug discovery process (specifically, drug molecules, compounds in high throughput screening libraries, combinatorial chemistry building blocks and nondrug molecules). The distributions for a set of pharmacophore features including hydrogen bond acceptors, hydrogen bond donors, negatively ionizable centers, positively ionizable centers and hydrophobic points, were generated and examined. Significant differences were observed between the pharmacophore profiles obtained for the drug molecules and those obtained for the high-throughput screening compounds, which appear to be closely related to the nondrug pharmacophore distribution. It is suggested that the analysis of pharmacophore profiles could be used as an additional tool for the property-based optimization of compound selection and library design processes, thus improving the odds of success in lead discovery projects.     

Hit Compounds and Associated Targets in Intracellular Mycobacterium tuberculosis

Mycobacterium tuberculosis (Mtb), the etiological agent of tuberculosis, is one of the most devastating infectious agents in the world. Chemical-genetic characterization through in vitro evolution combined with whole genome sequencing analysis was used identify novel drug targets and drug resistance genes in Mtb associated with its intracellular growth in human macrophages. We performed a genome analysis of 53 Mtb mutants resistant to 15 different hit compounds. We found nonsynonymous mutations/indels in 30 genes that may be associated with drug resistance acquisitions. Beyond confirming previously identified drug resistance mechanisms such as rpoB and lead targets reported in novel anti-tuberculosis drug screenings such as mmpL3, ethA, and mbtA, we have discovered several unrecognized candidate drug targets including prrB. The exploration of the Mtb chemical mutant genomes could help novel drug discovery and the structural biology of compounds and associated mechanisms of action relevant to tuberculosis treatment.     

Virtual Screening and Structure Generation Applied to Drug Design

The methods of computer-aided drug design can be divided into two categories according to whether or not the structures of receptors are known1, corresponding to two principal strategies: (1) searching the bio-active ligands against virtual combinatorial libraries and calculating the affinity energy between ligand and receptor by docking ; (2) QSAR and 3D-structure data-mining. 3D-QSAR method is now applied widely to drug discovery, but this method is generally limited to refine the structu…     

Structure-based discovery of novel inhibitors of Mycobacterium tuberculosis CYP121 from Indonesian natural products

Tuberculosis (TB) continues to be a serious global health threat with the emergence of multidrug-resistant tuberculosis (MDR-TB) and extremely drug-resistant tuberculosis (XDR-TB). There is an urgent need to discover new drugs to deal with the advent of drug-resistant TB variants. This study aims to find new M. tuberculosis CYP121 inhibitors by the screening of Indonesian natural products using the principle of structure-based drug design and discovery. In this work, eight natural compounds isolated from Rhoeo spathacea and Pluchea indica were selected based on their antimycobacterial activity. Derivatives compound were virtually designed from these natural molecules to improve the interaction of ligands with CYP121. Virtual screening of ligands was carried out using AutoDock Vina followed by 50 ns molecular dynamics simulation using YASARA to study the inhibition mechanism of the ligands. Two ligands, i.e., kaempferol (KAE) and its benzyl derivative (KAE3), are identified as the best CYP121 inhibitors based on their binding affinities and adherence to the Lipinski’s rule. Results of molecular dynamics simulation indicate that KAE and KAE3 possess a unique inhibitory mechanism against CYP121 that is different from GGJ (control ligand). The control ligand alters the overall dynamics of the receptor, which is indicated by changes in residue flexibility away from CYP121 binding site. Meanwhile, the dynamic changes caused by the binding of KAE and KAE3 are isolated around the binding site of CYP121. These ligands can be developed for further potential biological activities.     

Quantum probability ranking principle for ligand-based virtual screening

Chemical libraries contain thousands of compounds that need screening, which increases the need for computational methods that can rank or prioritize compounds. The tools of virtual screening are widely exploited to enhance the cost effectiveness of lead drug discovery programs by ranking chemical compounds databases in decreasing probability of biological activity based upon probability ranking principle (PRP). In this paper, we developed a novel ranking approach for molecular compounds inspired by quantum mechanics, called quantum probability ranking principle (QPRP). The QPRP ranking criteria would make an attempt to draw an analogy between the physical experiment and molecular structure ranking process for 2D fingerprints in ligand based virtual screening (LBVS). The development of QPRP criteria in LBVS has employed the concepts of quantum at three different levels, firstly at representation level, this model makes an effort to develop a new framework of molecular representation by connecting the molecular compounds with mathematical quantum space. Secondly, estimate the similarity between chemical libraries and references based on quantum-based similarity searching method. Finally, rank the molecules using QPRP approach. Simulated virtual screening experiments with MDL drug data report (MDDR) data sets showed that QPRP outperformed the classical ranking principle (PRP) for molecular chemical compounds.     

Why Matter Matters: Fast-Tracking Mycobacterium abscessus Drug Discovery

Unlike Tuberculosis (TB), Mycobacterium abscessus lung disease is a highly drug-resistant bacterial infection with no reliable treatment options. De novo M. abscessus drug discovery is urgently needed but is hampered by the bacterium’s extreme drug resistance profile, leaving the current drug pipeline underpopulated. One proposed strategy to accelerate de novo M. abscessus drug discovery is to prioritize screening of advanced TB-active compounds for anti-M. abscessus activity. This approach would take advantage of the greater chance of homologous drug targets between mycobacterial species, increasing hit rates. Furthermore, the screening of compound series with established structure–activity-relationship, pharmacokinetic, and tolerability properties should fast-track the development of in vitro anti-M. abscessus hits into lead compounds with in vivo efficacy. In this review, we evaluated the effectiveness of this strategy by examining the literature. We found several examples where the screening of advanced TB chemical matter resulted in the identification of anti-M. abscessus compounds with in vivo proof-of-concept, effectively populating the M. abscessus drug pipeline with promising new candidates. These reports validate the screening of advanced TB chemical matter as an effective means of fast-tracking M. abscessus drug discovery.     

Computational validation of the importance of absolute stereochemistry in virtual screening

Consideration of stereochemistry early in the identification and optimization of lead compounds can improve the efficiency and efficacy of the drug discovery process and reduce the time spent on subsequent drug development. These improvements can result by focusing on specific enantiomers that have the desired potential therapeutic effect (eutomers), while removing from consideration enantiomers that may have no, or even undesirable, effects (distomers). A virtual screening campaign that correctly takes stereochemical information into account can, in theory, be utilized to provide information about the relative binding affinities of enantiomers. Thus, the proper enumeration of the relevant stereoisomers in general, and enantiomeric pairs in particular, of chiral compounds is crucial if one is to use virtual screening as an effective drug discovery tool. As is obvious, in cases where no stereochemical information is provided for chiral compounds in a 2D chemical database, then each possible stereoisomer should be generated for construction of the subsequent 3D database to be used for virtual screening. However, acute problems can arise in 3D database construction when relative stereochemistry is encoded in a 2D database for a chiral compound containing multiple stereogenic atoms but absolute stereochemistry is not implied. In this case, we report that generation of enantiomeric pairs is imperative in database development if one is to obtain accurate docking results. A study is described on the impact of the neglect of enantiomeric pairs on virtual screening using the human homolog of murine double minute 2 (MDM2) protein, the product of a proto-oncogene, as the target. Docking in MDM2 with GLIDE 4.0 was performed using the NCI Diversity Set 3D database and, for comparison, a set of enantiomers we created corresponding to mirror image structures of the single enantiomers of chiral compounds present in the NCI Diversity Set. Our results demonstrate that potential lead candidates may be overlooked when databases contain 3D structures representing only a single enantiomer of racemic chiral compounds.     

Computational strategy for intrinsically disordered protein ligand design leads to the discovery of p53 transactivation domain I binding compounds that activate the p53 pathway

Intrinsically disordered proteins or intrinsically disordered regions (IDPs) have gained much attention in recent years due to their vital roles in biology and prevalence in various human diseases. Although IDPs are perceived as attractive therapeutic targets, rational drug design targeting IDPs remains challenging because of their conformational heterogeneity. Here, we propose a hierarchical computational strategy for IDP drug virtual screening (IDPDVS) and applied it in the discovery of p53 transactivation domain I (TAD1) binding compounds. IDPDVS starts from conformation sampling of the IDP target, then it combines stepwise conformational clustering with druggability evaluation to identify potential ligand binding pockets, followed by multiple docking screening runs and selection of compounds that can bind multi-conformations. p53 is an important tumor suppressor and restoration of its function provides an opportunity to inhibit cancer cell growth. TAD1 locates at the N-terminus of p53 and plays key roles in regulating p53 function. No compounds that directly bind to TAD1 have been reported due to its highly disordered structure. We successfully used IDPDVS to identify two compounds that bind p53 TAD1 and restore wild-type p53 function in cancer cells. Our study demonstrates that IDPDVS is an efficient strategy for IDP drug discovery and p53 TAD1 can be directly targeted by small molecules.

A hierarchical computational strategy for IDP drug virtual screening (IDPDVS) was proposed and successfully applied to identify compounds that bind p53 TAD1 and restore wild-type p53 function in cancer cells.     

Determination of brain and plasma drug concentrations by liquid chromatography/tandem mass spectrometry

A method is described for the evaluation of drug concentrations in plasma and brain from treated rats. The analyte is recovered from plasma or brain homogenate by liquid-liquid extraction and subsequently analyzed by liquid chromatography/tandem mass spectrometry (LC/MS/MS). A simple experimental protocol renders the procedure valuable for obtaining information rapidly on brain penetration and plasma exposure of specific classes of compounds. This methodology has been applied to evaluate brain penetration with 30 different compounds from the same discovery program. In an attempt to increase throughput in our screening efforts, mixture dosing was evaluated. Results from single compound administration were compared with results following administration of a mixture of four compounds. Preliminary results, with specific classes of compounds, show no major differences (ranking order) in brain or plasma concentrations between mixture dosing and single compound administration, suggesting that mixture dosing could be applicable to brain penetration studies in the drug discovery phase.