Modeling the permeability of drug-like molecules through the cell wall of Mycobacterium tuberculosis: an analogue based approach

Free Energy Barriers for Passive Drug Transport through the Mycobacterium tuberculosis Outer Membrane: A Molecular Dynamics Study

The emergence of multi-drug-resistant tuberculosis strains poses a significant challenge to modern medicine. The development of new antituberculosis drugs is hindered by the low permeability of many active compounds through the extremely strong bacterial cell wall of mycobacteria. In order to estimate the ability of potential antimycobacterial agents to diffuse through the outer mycolate membrane, the free energy profiles, the corresponding activation barriers, and possible permeability modes of passive transport for a series of known antibiotics, modern antituberculosis drugs, and prospective active drug-like molecules were determined using molecular dynamics simulations with the all-atom force field and potential of mean-force calculations. The membranes of different chemical and conformational compositions, density, thickness, and ionization states were examined. The typical activation barriers for the low-mass molecules penetrating through the most realistic membrane model were 6-13 kcal/mol for isoniazid, pyrazinamide, and etambutol, and 19 and 25 kcal/mol for bedaquilin and rifampicin. The barriers for the ionized molecules are usually in the range of 37-63 kcal/mol. The linear regression models were derived from the obtained data, allowing one to estimate the permeability barriers from simple physicochemical parameters of the diffusing molecules, notably lipophilicity and molecular polarizability.

Machine Learning Prediction of Mycobacterial Cell Wall Permeability of Drugs and Drug-like Compounds

The cell wall of Mycobacterium tuberculosis and related organisms has a very complex and unusual organization that makes it much less permeable to nutrients and antibiotics, leading to the low activity of many potential antimycobacterial drugs against whole-cell mycobacteria compared to their isolated molecular biotargets. The ability to predict and optimize the cell wall permeability could greatly enhance the development of novel antitubercular agents. Using an extensive structure-permeability dataset for organic compounds derived from published experimental big data (5371 compounds including 2671 penetrating and 2700 non-penetrating compounds), we have created a predictive classification model based on fragmental descriptors and an artificial neural network of a novel architecture that provides better accuracy (cross-validated balanced accuracy 0.768, sensitivity 0.768, specificity 0.769, area under ROC curve 0.911) and applicability domain compared with the previously published results.

New Screening Protocol for Effective Green Solvents Selection of Benzamide, Salicylamide and Ethenzamide

New protocol for screening efficient and environmentally friendly solvents was proposed and experimentally verified. The guidance for solvent selection comes from computed solubility via COSMO-RS approach. Furthermore, solute-solvent affinities computed using advanced quantum chemistry level were used as a rationale for observed solvents ranking. The screening protocol pointed out that 4-formylomorpholine (4FM) is an attractive solubilizer compared to commonly used aprotic solvents such as DMSO and DMF. This was tested experimentally by measuring the solubility of the title compounds in aqueous binary mixtures in the temperature range between 298.15 K and 313.15 K. Additional measurements were also performed for aqueous binary mixtures of DMSO and DMF. It has been found that the solubility of studied aromatic amides is very high and quite similar in all three aprotic solvents. For most aqueous binary mixtures, a significant decrease in solubility with a decrease in the organic fraction is observed, indicating that all systems can be regarded as efficient solvent-anti-solvent pairs. In the case of salicylamide dissolved in aqueous-4FM binary mixtures, a strong synergistic effect has been found leading to the highest solubility for 0.6 mole fraction of 4-FM.

Study of lipid heterogeneity on bilayer membranes using molecular dynamics simulations

Human cell membranes consist of various lipids that are essential for their structure and function. It typically comprises phosphatidylcholine (POPC), phosphatidylethanolamine (POPE), phosphatidylserine (POPS), sphingomyelin (PSM), and cholesterol (CHL). Several experimental and computational techniques have been employed to characterize the composition of human cell membranes, however, CHL enriched membrane is still not clearly understood through these techniques. Molecular dynamics simulation results illustrated the biophysical properties of heterogeneous membranes based on the lipid composition as well as the concentration of lipids, exclusively for CHL and PSM. Herein, we have investigated the structure-function relationships of lipids comparatively to delineate the effect of heterogeneity on the biophysical properties of different membranes. It has been observed that the significant fraction of CHL (i.e., ~33% in ternary, ~25% in quaternary, and ~16% in senary type bilayers) in combination with other lipids introduced compactness, and increased the thickness of the membrane. The analysis of lipid mass density stated that the density of lipid head group, phosphate_, and glycerol-ester in presence of CHL with or without PSM is an underlying reason for membrane ordering. Results also revealed that the presence of POPI and POPS are the reasons for an adequate drop in the ordering of lipid chain, particularly on POPE chain. The self-interaction of CHL, PSM, POPE and the interaction of CHL and POPC with POPE seem to determine the structure and function of the heterogeneous membrane. Our findings provide a qualitative understanding of the effect of membrane heterogeneity on the physiological properties of membranes. The structures inspected in this study would help to select the heterogeneous bilayer model to mimic the human cell membranes to analyse or characterize the membrane-associated phenomena.

Mycobacterium tuberculosis Cell Wall Permeability Model Generation Using Chemoinformatics and Machine Learning Approaches

The drug-resistant strains of Mycobacterium tuberculosis (M.tb) are evolving at an alarming rate, and this indicates the urgent need for the development of novel antitubercular drugs. However, genetic mutations, complex cell wall system of M.tb, and influx-efflux transporter systems are the major permeability barriers that significantly affect the M.tb drugs activity. Thus, most of the small molecules are ineffective to arrest the M.tb cell growth, even though they are effective at the cellular level. To address the permeability issue, different machine learning models that effectively distinguish permeable and impermeable compounds were developed. The enzyme-based (IC₅₀) and cell-based (minimal inhibitory concentration) data were considered for the classification of M.tb permeable and impermeable compounds. It was assumed that the compounds that have high activity in both enzyme-based and cell-based assays possess the required M.tb cell wall permeability. The XGBoost model was outperformed when compared to the other models generated from different algorithms such as random forest, support vector machine, and naïve Bayes. The XGBoost model was further validated using the validation data set (21 permeable and 19 impermeable compounds). The obtained machine learning models suggested that various descriptors such as molecular weight, atom type, electrotopological state, hydrogen bond donor/acceptor counts, and extended topochemical atoms of molecules are the major determining factors for both M.tb cell permeability and inhibitory activity. Furthermore, potential antimycobacterial drugs were identified using computational drug repurposing. All the approved drugs from DrugBank were collected and screened using the developed permeability model. The screened compounds were given as input in the PASS server for the identification of possible antimycobacterial compounds. The drugs that were retained after two filters were docked to the active site of 10 different potential antimycobacterial drug targets. The results obtained from this study may improve the understanding of M.tb permeability and activity that may aid in the development of novel antimycobacterial drugs.

Structure-based in silico approaches for drug discovery against Mycobacterium tuberculosis

Mycobacterium tuberculosis is the causative agent of TB and was estimated to cause 1.4 million death in 2019, alongside 10 million new infections. Drug resistance is a growing issue, with multi-drug resistant infections representing 3.3% of all new infections, hence novel antimycobacterial drugs are urgently required to combat this growing health emergency. Alongside this, increased knowledge of gene essentiality in the pathogenic organism and larger compound databases can aid in the discovery of new drug compounds. The number of protein structures, X-ray based and modelled, is increasing and now accounts for greater than > 80% of all predicted M. tuberculosis proteins; allowing novel targets to be investigated. This review will focus on structure-based in silico approaches for drug discovery, covering a range of complexities and computational demands, with associated antimycobacterial examples. This includes molecular docking, molecular dynamic simulations, ensemble docking and free energy calculations. Applications of machine learning onto each of these approaches will be discussed. The need for experimental validation of computational hits is an essential component, which is unfortunately missing from many current studies. The future outlooks of these approaches will also be discussed.

Physicochemical properties and Mycobacterium tuberculosis transporters: keys to efficacious antitubercular drugs?

Securing novel, safe, and effective medicines to treat Mycobacterium tuberculosis remains an elusive goal, particularly influenced by the largely impervious Mtb envelope that limits exposure and thus efficacy of inhibitors at their cellular and periplasmic targets. The impact of physicochemical properties on pharmacokinetic parameters that govern oral absorption and exposure at sites of infection is considered alongside how these properties influence penetration of the Mtb envelope, with the likely influence of transporter proteins. The findings are discussed to benchmark current drugs and the emerging pipeline, whilst considering tactics for future rational and targeted design strategies, based around emerging data on Mtb transporters and their structures and functions.

Drug-Resistant Tuberculosis 2020: Where We Stand

The control of tuberculosis (TB) is hampered by the emergence of multidrug-resistant (MDR) Mycobacterium tuberculosis (Mtb) strains, defined as resistant to at least isoniazid and rifampin, the two bactericidal drugs essential for the treatment of the disease. Due to the worldwide estimate of almost half a million incident cases of MDR/rifampin-resistant TB, it is important to continuously update the knowledge on the mechanisms involved in the development of this phenomenon. Clinical, biological and microbiological reasons account for the generation of resistance, including: (i) nonadherence of patients to their therapy, and/or errors of physicians in therapy management, (ii) complexity and poor vascularization of granulomatous lesions, which obstruct drug distribution to some sites, resulting in resistance development, (iii) intrinsic drug resistance of tubercle bacilli, (iv) formation of non-replicating, drug-tolerant bacilli inside the granulomas, (v) development of mutations in Mtb genes, which are the most important molecular mechanisms of resistance. This review provides a comprehensive overview of these issues, and releases up-dated information on the therapeutic strategies recently endorsed and recommended by the World Health Organization to facilitate the clinical and microbiological management of drug-resistant TB at the global level, with attention also to the most recent diagnostic methods.

Design, Synthesis and Biological Evaluation of Some Triazole Schiff's Base Derivatives as Potential Antitubercular Agents

Background:

Tuberculosis (TB) is the second important cause of death worldwide caused by a bacterium called Mycobacterium tuberculosis. There is a need to find and develop new Anti-TB medications that are effective, inexpensive and suitable with human immunodeficiency virus and other anti-TB drugs used in many countries and mainly the developing countries where the disease is widespread. These drugs must be designed to shorten treatment time and to be active against resistant forms of the mycobacteria that will help to increase the patients compliance. A key compound which could be used as a lead to meet these requirements, is the thiolactomycin (TLM). This antibiotic which is naturally available has an ability to treat M. tuberculosis by inhibiting condensing enzymes called FAS II (mtFabH, KasA and KasB) which are related to biosynthesis of mycolic acid.

Methods:

Our main aims are to design and synthesize analogues of TLM as new lead molecules which could be a possible anti–TB candidate. To overcome the synthetic challenges associated with preparing the chiral TLM analogues; we synthesized and investigated a series of triazole analogues as inhibitors of KasA enzyme and the whole cell Mycobacteria. A series of twelve compounds were synthesized, purified and fully characterized using several spectroscopic techniques. Molecular modelling studies for our synthesised compounds were achieved by using a modelling program called AutoDock 4.2 utilising rigid docking.

Results:

Our results indicate that analogues of TLM show a good activity as compared to TLM.

Conclusion:

The activity obtained for the synthesized compounds against Mycobacteria tuberculosis indicate that the synthesised compounds 1, 2, 6 and 9 are pharmacologically active as they restrained the growth of the Mycobacteria bacteria.

Antimicrobial resistance in Mycobacterium tuberculosis: mechanistic and evolutionary perspectives

Antibiotic-resistant Mycobacterium tuberculosis strains are threatening progress in containing the global tuberculosis epidemic. Mycobacterium tuberculosis is intrinsically resistant to many antibiotics, limiting the number of compounds available for treatment. This intrinsic resistance is due to a number of mechanisms including a thick, waxy, hydrophobic cell envelope and the presence of drug degrading and modifying enzymes. Resistance to the drugs which are active against M. tuberculosis is, in the absence of horizontally transferred resistance determinants, conferred by chromosomal mutations. These chromosomal mutations may confer drug resistance via modification or overexpression of the drug target, as well as by prevention of prodrug activation. Drug resistance mutations may have pleiotropic effects leading to a reduction in the bacterium's fitness, quantifiable e.g. by a reduction in the in vitro growth rate. Secondary so-called compensatory mutations, not involved in conferring resistance, can ameliorate the fitness cost by interacting epistatically with the resistance mutation. Although the genetic diversity of M. tuberculosis is low compared to other pathogenic bacteria, the strain genetic background has been demonstrated to influence multiple aspects in the evolution of drug resistance. The rate of resistance evolution and the fitness costs of drug resistance mutations may vary as a function of the genetic background.

A QSAR and molecular modelling study towards new lead finding: polypharmacological approach to Mycobacterium tuberculosis

Developing effective inhibitors against Mycobacterium tuberculosis (Mtb) is a challenging task, primarily due to the emergence of resistant strains. In this study, we have proposed and implemented an in silico guided polypharmacological approach, which is expected to be effective against resistant strains by simultaneously inhibiting several potential Mtb drug targets. A combination of pharmacophore and QSAR based virtual screening strategy taking three key targets such as InhA (enoyl-acyl-carrier-protein reductase), GlmU (N-acetyl-glucosamine-1-phosphate uridyltransferase) and DapB (dihydrodipicolinate reductase) have resulted in initial 784 hits from Asinex database of 435,000 compounds. These hits were further subjected to docking with 33 Mtb druggable targets. About 110 potential polypharmacological hits were taken by integrating the aforementioned screening protocols. Further screening was conducted by taking various parameters and properties such as cell permeability, drug-likeness, drug-induced phospholipidosisand structural alerts. A consensus analysis has yielded 59 potential hits that pass through all the filters and can be prioritized for effective drug-resistant tuberculosis. This study proposes about nine potential hits which are expected to be promising molecules, having not only drug-like properties, but also being effective against multiple Mtb targets.