Current Strategies and Applications for Precision Drug Design

Antibiotics and Antibiotic Resistance-Mur Ligases as an Antibacterial Target

The emergence of Multidrug Resistance (MDR) strains of bacteria has accelerated the search for new antibacterials. The specific bacterial peptidoglycan biosynthetic pathway represents opportunities for the development of novel antibacterial agents. Among the enzymes involved, Mur ligases, described herein, and especially the amide ligases MurC-F are key targets for the discovery of multi-inhibitors, as they share common active sites and structural features.

Tadalafil Rescues the p.M325T Mutant of Best1 Chloride Channel

Bestrophin 1 (Best1) is a chloride channel that localises to the plasma membrane of retinal pigment epithelium (RPE) cells. Mutations in the BEST1 gene are associated with a group of untreatable inherited retinal dystrophies (IRDs) called bestrophinopathies, caused by protein instability and loss-of-function of the Best1 protein. 4PBA and 2-NOAA have been shown to rescue the function, expression, and localisation of Best1 mutants; however, it is of interest to find more potent analogues as the concentration of the drugs required is too high (2.5 mM) to be given therapeutically. A virtual docking model of the COPII Sec24a site, where 4PBA has been shown to bind, was generated and a library of 1416 FDA-approved compounds was screened at the site. The top binding compounds were tested in vitro in whole-cell patch-clamp experiments of HEK293T cells expressing mutant Best1. The application of 25 μM tadalafil resulted in full rescue of Cl^- conductance, comparable to wild type Best1 levels, for p.M325T mutant Best1 but not for p.R141H or p.L234V mutants.

The molecular interplay of known phytochemicals as Culex pipiens and Rift Valley fever virus inhibitors through molecular docking

Infectious diseases transmitted by vectors have claimed millions of lives. The mosquito Culex pipiens is a main vector species of Rift Valley Fever virus (RVFV) transmission. RVFV is an arbovirus that infects both people and animals. No effective vaccine or drugs are available for RVFV. Therefore, it is vital to find effective therapies for this viral infection. Because of their critical roles in transmission and infection, acetylcholinesterase 1 (AChE1) of Cx. Pipiens and RVFV glycoproteins, and nucleocapsid proteins are appealing protein targets. To understand intermolecular interactions, computational screening was carried out using molecular docking. More than 50 compounds were tested against different target proteins in the current study. Anabsinthin (-11.1 kcal/mol), zapoterin (-9.4 kcal/mol), porrigenin A (-9.4 kcal/mol), and 3-Acetyl-11-keto-beta-boswellic acid (AKBA) (-9.4 kcal/mol) were the top hit compounds for Cx. Pipiens. Similarly, the top hit compounds for RVFV were zapoterin, porrigenin A, anabsinthin, and yamogenin. The toxicity of Rofficerone is predicted as fatal (Class II), whereas Yamogenin is safe (Class VI). Further investigations are needed to validate the selected promising candidates against Cx. pipiens and RVFV infection using in-vitro and in-vivo methods.

Drug delivery targets and strategies to address mast cell diseases

INTRODUCTION

Current and developing mast cell therapeutics are reliant on small molecule drugs and biologics, but few are truly selective for mast cells. Most have cellular and disease-specific limitations that require innovation to overcome longstanding challenges to selectively targeting and modulating mast cell behavior. This review is designed to serve as a frame of reference for new approaches that utilize nanotechnology or combine different drugs to increase mast cell selectivity and therapeutic efficacy.

AREAS COVERED

Mast cell diseases include allergy and related conditions as well as malignancies. Here, we discuss the targets of existing and developing therapies used to treat these disease pathologies, classifying them into cell surface, intracellular, and extracellular categories. For each target discussed, we discuss drugs that are either the current standard of care, under development, or have indications for potential use. Finally, we discuss how novel technologies and tools can be used to take existing therapeutics to a new level of selectivity and potency against mast cells.

EXPERT OPINION

There are many broadly and very few selectively targeted therapeutics for mast cells in allergy and malignant disease. Combining existing targeting strategies with technology like nanoparticles will provide novel platforms to treat mast cell disease more selectively.

Ethanolic Extracts of Datura innoxia Have Promising Acaricidal Activity against Rhipicephalus microplus as It Blocks the Glutathione S-Transferase Activity of the Target Tick

Rhipicephalus microplus is a major bovine ectoparasite that negatively impacts the cattle industry. The acaricidal activity of Datura innoxia ethanolic plant extract against R. microplus, compared with trichlorfon, was examined using the adult immersion test (AIT), and larval packet test (LPT). In vitro acaricidal activity of the selected plant extract against R. microplus engorged females was evaluated at different concentrations (2.5, 5, 10, 20, and 40 mg/mL), and was the same for AIT and LPT. It was further supported by in silico molecular docking of D. innoxia's 21 phytochemicals against the R. microplus Glutathione S-transferases (RmGST) protein's three-dimensional (3D) structure predicted by the trRosetta server. The modeled 3D structure was then evaluated and confirmed with PROCHECK, ERRAT, and Verify3D online servers. To predict the binding mechanisms of these compounds, molecular docking was performed using Auto dock Vina software, and molecular dynamic (MD) simulations were used to investigate the protein atom's dynamic motion. D. innoxia has a relatively higher inhibitory effect on oviposition (from 9.81% to 45.37%) and total larval mortality (42.33% at 24 h and 93.67% at 48 h) at 40 mg/mL. Moreover, the docking results showed that the chemicals norapoatropine and 7-Hydroxyhyoscyamine have strong interactions with active site residues of the target protein, with a docking score of -7.3 and -7.0 Kcal/mol, respectively. The current work also provided a computational basis for the inhibitors of Glutathione S-transferases that were studied in this research work, and this new knowledge should aid in creating new and effective acaricidal chemicals. Furthermore, this plant extract's acaricide activity and its effect on oviposition and larval mortality were established in this work for the first time, indicating the possible use of this extract in the management of ticks.

Discovery of (5-(Benzylthio)-4-(3-(trifluoromethyl)phenyl)-4H-1,2,4-triazol-3-yl) Methanols as Potent Phytoene Desaturase Inhibitors through Virtual Screening and Structure Optimization

Phytoene desaturase (PDS) is not only an important enzyme in the biosynthesis of carotenoids but also a promising target for herbicide discovery. However, in recent years, no expected PDS inhibitors with new scaffolds have been reported. Hence, a solution for developing PDS inhibitors is to search for new compounds with novel chemotypes based on the PDS protein structure. In this work, we integrated structure-based virtual screening, structure-guided optimization, and biological evaluation to discover some PDS inhibitors with novel chemotypes. It is noteworthy that the highly potent compound 1b, 1-(4-chlorophenyl)-2-((5-(hydroxymethyl)-4-(3-(trifluoromethyl)phenyl)-4H-1,2,4-triazol-3-yl)thio)ethan-1-one, exhibited a broader spectrum of post-emergence herbicidal activity at 375-750 g/ha against six kinds of weeds than the commercial PDS inhibitor diflufenican. Surface plasmon resonance (SPR) assay showed that the affinity of our compound 1b (K_D = 65.9 μM) to PDS is slightly weaker but at the same level as diflufenican (K_D = 38.3 μM). Meanwhile, determination of the phytoene content and PDS mRNA quantification suggested that 1b could induce PDS mRNA reduction and phytoene accumulation. Moreover, 1b also caused the increase of reactive oxygen species (ROS) and the change of ROS-associated enzyme activity in albino leaves. Hence, all these results indicated the feasibility of PDS protein structure-based virtual screen and structure optimization to search for highly potent PDS inhibitors with novel chemotypes for weed control.

Computer-aided discovery of pleiotropic effects: Anti-inflammatory action of dithioloquinolinethiones as a case study

Most of pharmaceutical agents exhibit several or even many biological activities. It is clear that testing even one compound for thousands of biological activities is a practically not feasible task. Therefore, computer-aided prediction is the method-of-the-choice to select the most promising bioassays for particular compounds. Using PASS Online software, we determined the likely anti-inflammatory action of the 13 dithioloquinolinethione derivatives with antimicrobial activities. Chemical similarity search in the Cortellis Drug Discovery Intelligence database did not reveal close structural analogues with anti-inflammatory action. Experimental testing of anti-inflammatory activity of the synthesized compounds in carrageenan-induced inflammation mouse model confirmed the computational predictions. The anti-inflammatory activity of the studied compounds was comparable with or higher than the reference drug Indomethacin. Thus, based on the in silico predictions, novel class of the anti-inflammatory agents was discovered.

Challenges in Discovering Drugs That Target the Protein-Protein Interactions of Disordered Proteins

Protein–protein interactions (PPIs) outnumber proteins and are crucial to many fundamental processes; in consequence, PPIs are associated with several pathological conditions including neurodegeneration and modulating them by drugs constitutes a potentially major class of therapy. Classically, however, the discovery of small molecules for use as drugs entails targeting individual proteins rather than targeting PPIs. This is largely because discovering small molecules to modulate PPIs has been seen as extremely challenging. Here, we review the difficulties and limitations of strategies to discover drugs that target PPIs directly or indirectly, taking as examples the disordered proteins involved in neurodegenerative diseases.

Discovery of Biphenyl-Sulfonamides as Novel β-N-Acetyl-d-Hexosaminidase Inhibitors via Structure-Based Virtual Screening

Novel insecticidal targets are always in demand due to the development of resistance. OfHex1, a β-N-acetyl-d-hexosaminidase identified in Ostrinia furnacalis (Asian corn borer), is involved in insect chitin catabolism and has proven an ideal target for insecticide development. In this study, structure-based virtual screening, structure simplification, and biological evaluation are used to show that compounds with a biphenyl-sulfonamide skeleton have great potential as OfHex1 inhibitors. Specifically, compounds 10k, 10u, and 10v have K_i values of 4.30, 3.72, and 4.56 μM, respectively, and thus, they are more potent than some reported nonglycosyl-based inhibitors such as phlegmacin B₁ (K_i = 26 μM), berberine (K_i = 12 μM), 2 (K_i = 11.2 μM), and 3 (K_i = 28.9 μM). Furthermore, inhibitory kinetic assessments reveal that the target compounds are competitive inhibitors with respect substrate, and based on toxicity predictions, most of them have potent drug properties. The obtained results indicate that the biphenyl-sulfonamide skeleton characterized by simple chemical structure, synthetic tractability, potent activity, and low toxicity has potential for further development in pest management targeting OfHex1.

Silver(I)-N-heterocyclic carbene complexes challenge cancer; evaluation of their anticancer properties and in silico studies

Because of the continuous need for efficient therapeutic agents against various kinds of cancers and infectious diseases, the pharmaceutical industry has to find new candidates and strategies to develop novel and efficient drugs. They increasingly use computational tools in R&D stages for screening extensive sets of drug candidates before starting pre-clinical and clinical trials. N-Heterocyclic carbenes (NHCs) can be evaluated as good drug candidates because they offer both anti-cancer and anti-inflammatory features with their general low-toxicity profiles. To date, different kinds of NHCs (Cu, Co, Ni, Au, Ag, Ru, etc.) have been synthesized and their therapeutic uses has been shown. Here, we have reviewed the recent studies focused on Ag(I)-NHC complexes and their anti-cancer activities. Also, existing examples of the usage of density functional theory and structure-activity relationship have been evaluated.

Can Deep Learning Hit a Moving Target? A Scoping Review of Its Role to Study Neurological Disorders in Children

Neurological disorders dramatically impact patients of any age population, their families, and societies. Pediatrics are among vulnerable age populations who differently experience the devastating consequences of neurological conditions, such as attention-deficit hyperactivity disorders (ADHD), autism spectrum disorders (ASD), cerebral palsy, concussion, and epilepsy. System-level understanding of these neurological disorders, particularly from the brain networks' dynamic perspective, has led to the significant trend of recent scientific investigations. While a dramatic maturation in the network science application domain is evident, leading to a better understanding of neurological disorders, such rapid utilization for studying pediatric neurological disorders falls behind that of the adult population. Aside from the specific technological needs and constraints in studying neurological disorders in children, the concept of development introduces uncertainty and further complexity topping the existing neurologically driven processes caused by disorders. To unravel these complexities, indebted to the availability of high-dimensional data and computing capabilities, approaches based on machine learning have rapidly emerged a new trend to understand pathways better, accurately diagnose, and better manage the disorders. Deep learning has recently gained an ever-increasing role in the era of health and medical investigations. Thanks to its relatively more minor dependency on feature exploration and engineering, deep learning may overcome the challenges mentioned earlier in studying neurological disorders in children. The current scoping review aims to explore challenges concerning pediatric brain development studies under the constraints of neurological disorders and offer an insight into the potential role of deep learning methodology on such a task with varying and uncertain nature. Along with pinpointing recent advancements, possible research directions are highlighted where deep learning approaches can assist in computationally targeting neurological disorder-related processes and translating them into windows of opportunities for interventions in diagnosis, treatment, and management of neurological disorders in children.

Synthetic Biology and Computer-Based Frameworks for Antimicrobial Peptide Discovery

Antibiotic resistance is one of the greatest challenges of our time. This global health problem originated from a paucity of truly effective antibiotic classes and an increased incidence of multi-drug-resistant bacterial isolates in hospitals worldwide. Indeed, it has been recently estimated that 10 million people will die annually from drug-resistant infections by the year 2050. Therefore, the need to develop out-of-the-box strategies to combat antibiotic resistance is urgent. The biological world has provided natural templates, called antimicrobial peptides (AMPs), which exhibit multiple intrinsic medical properties including the targeting of bacteria. AMPs can be used as scaffolds and, via engineering, can be reconfigured for optimized potency and targetability toward drug-resistant pathogens. Here, we review the recent development of tools for the discovery, design, and production of AMPs and propose that the future of peptide drug discovery will involve the convergence of computational and synthetic biology principles.

Lithium modulates striatal reward anticipation and prediction error coding in healthy volunteers

Lithium is one of the most effective mood-stabilizing medications in bipolar disorder. This study was designed to test whether lithium administration may stabilize mood via effects on reward processing. It was hypothesized that lithium administration would modulate reward processing in the striatum and affect both anticipation and outcome computations. Thirty-seven healthy human participants (18 males, 33 with suitable fMRI data) received 11 (±1) days of lithium carbonate or placebo intervention (double-blind), after which they completed the monetary incentive delay task while fMRI data were collected. The monetary incentive delay task is a robust task with excellent test-retest reliability and is well suited to investigate different phases of reward processing within the caudate and nucleus accumbens. To test for correlations with prediction error signals a Rescorla-Wagner reinforcement-learning model was applied. Lithium administration enhanced activity in the caudate during reward anticipation compared to placebo. In contrast, lithium administration reduced caudate and nucleus accumbens activity during reward outcome. This latter effect seems related to learning as reward prediction errors showed a positive correlation with caudate and nucleus accumbens activity during placebo, which was absent after lithium administration. Lithium differentially modulates the anticipation relative to the learning of rewards. This suggests that lithium might reverse dampened reward anticipation while reducing overactive reward updating in patients with bipolar disorder. This specific effect of lithium suggests that a targeted modulation of reward learning may be a viable approach for novel interventions in bipolar disorder.

N-Heterocycles Scaffolds as Quorum Sensing Inhibitors. Design, Synthesis, Biological and Docking Studies

Quorum sensing is a communication system among bacteria to sense the proper time to express their virulence factors. Quorum sensing inhibition is a therapeutic strategy to block bacterial mechanisms of virulence. The aim of this study was to synthesize and evaluate new bioisosteres of N-acyl homoserine lactones as Quorum sensing inhibitors in Chromobacterium violaceum CV026 by quantifying the specific production of violacein. Five series of compounds with different heterocyclic scaffolds were synthesized in good yields: thiazoles, 16a-c, thiazolines 17a-c, benzimidazoles 18a-c, pyridines 19a-c and imidazolines 32a-c. All 15 compounds showed activity as Quorum sensing inhibitors except 16a. Compounds 16b, 17a-c, 18a, 18c, 19c and 32b exhibited activity at concentrations of 10 µM and 100 µM, highlighting the activity of benzimidazole 18a (IC50 = 36.67 µM) and 32b (IC50 = 85.03 µM). Pyridine 19c displayed the best quorum sensing inhibition activity (IC50 = 9.66 µM). Molecular docking simulations were conducted for all test compounds on the Chromobacterium violaceum CviR protein to gain insight into the process of quorum sensing inhibition. The in-silico data reveal that all 15 the compounds have higher affinity for the protein than the native AHL ligand (1). A strong correlation was found between the theoretical and experimental results.

Current Advancements in Addressing Key Challenges of Therapeutic Antibody Design, Manufacture, and Formulation

Therapeutic antibody technology heavily dominates the biologics market and continues to present as a significant industrial interest in developing novel and improved antibody treatment strategies. Many noteworthy advancements in the last decades have propelled the success of antibody development; however, there are still opportunities for improvement. In considering such interest to develop antibody therapies, this review summarizes the array of challenges and considerations faced in the design, manufacture, and formulation of therapeutic antibodies, such as stability, bioavailability and immunological engagement. We discuss the advancement of technologies that address these challenges, highlighting key antibody engineered formats that have been adapted. Furthermore, we examine the implication of novel formulation technologies such as nanocarrier delivery systems for the potential to formulate for pulmonary delivery. Finally, we comprehensively discuss developments in computational approaches for the strategic design of antibodies with modulated functions.

Toward computer-made artificial antibiotics

Merging concepts from synthetic biology and computational biology may yield antibiotics that are less likely to elicit resistance than existing drugs and that yet can fight drug-resistant infections. Indeed, computer-guided strategies coupled with massively parallel high-throughput experimental methods represent a new paradigm for antibiotic discovery. Infections caused by multidrug-resistant microorganisms are increasingly deadly. In the current post-antibiotic era, many of these infections cannot be treated with our existing antimicrobial arsenal. Furthermore, we may have already exhausted the category of large molecules produced in nature having antimicrobial activity: the antibiotic scaffolds we have discovered so far may represent the majority of those that exist. The rise in drug-resistant bacteria and lack of new antibiotic classes clearly call for out-of-the-box strategies. Recent advances in computational synthetic biology have enabled the development of antimicrobials. New molecular descriptors and genetic and pattern recognition algorithms are powerful tools that bring us a step closer to developing efficient antibiotics. We review several computational tools for drug design and a number of recently generated antibiotic candidates, with an emphasis on peptide-based molecules. Design strategies can generate a diversity of synthetic antimicrobial peptides, which may help to mitigate the spread of resistance and combat multidrug-resistant microorganisms.

Pharmacological and molecular dynamics analyses of differences in inhibitor binding to human and nematode PDE4: Implications for management of parasitic nematodes

Novel chemical controls are needed that selectively target human, animal, and plant parasitic nematodes with reduced adverse effects on the host or the environment. We hypothesize that the phosphodiesterase (PDE) enzyme family represents a potential target for development of novel nematicides and anthelmintics. To test this, we identified six PDE families present in the nematode phylum that are orthologous to six of the eleven human PDE families. We characterized the binding interactions of family-selective PDE inhibitors with human and C. elegans PDE4 in conjunction with molecular dynamics (MD) simulations to evaluate differences in binding interactions of these inhibitors within the PDE4 catalytic domain. We observed that roflumilast (human PDE4-selective inhibitor) and zardaverine (selective for human PDE3 and PDE4) were 159- and 77-fold less potent, respectively, in inhibiting C. elegans PDE4. The pan-specific PDE inhibitor isobutyl methyl xanthine (IBMX) had similar affinity for nematode and human PDE4. Of 32 residues within 5 Å of the ligand binding site, five revealed significant differences in non-bonded interaction energies (van der Waals and electrostatic interaction energies) that could account for the differential binding affinities of roflumilast and zardaverine. One site (Phe506 in the human PDE4D3 amino acid sequence corresponding to Tyr253 in C. elegans PDE4) is predicted to alter the binding conformation of roflumilast and zardaverine (but not IBMX) into a less energetically favorable state for the nematode enzyme. The pharmacological differences in sensitivity to PDE4 inhibitors in conjunction with differences in the amino acids comprising the inhibitor binding sites of human and C. elegans PDE4 catalytic domains together support the feasibility of designing the next generation of anthelmintics/nematicides that could selectively bind to nematode PDEs.

Assessing the public landscape of clinical-stage pharmaceuticals through freely available online databases

Several public databases have emerged over the past decade to enable chemo- and bio-informatics research in the field of drug development. To a naive observer, as well as many seasoned professionals, the differences among many drug databases are unclear. We assessed the availability of all pharmaceuticals with evidence of clinical testing (i.e., been in at least a Phase I clinical trial) and highlight the major differences and similarities between public databases containing clinically tested pharmaceuticals. We review a selection of the most recent and prominent databases including: ChEMBL, CRIB NME, DrugBank, DrugCentral, PubChem, repoDB, SuperDrug2 and WITHDRAWN, and found that ∼11700 unique active pharmaceutical ingredients are available in the public domain, with evidence of clinical testing.

Analysis of Sustained Release Behavior of Drug-Containing Tablet Prepared by CO₂-Assisted Polymer Compression

A controlled-release system for drug delivery allows the continuous supply of a drug to the target region at a predetermined rate for a specified period of time. Herein, the sustained release behavior of a drug-containing tablet fabricated through CO₂-assisted polymer compression (CAPC) was investigated. CAPC involves placing the drug in the center of a nonwoven fabric, sandwiching this fabric between an integer number of nonwoven fabrics, and applying pressure bonding. An elution test, in which the drug-carrying tablet was immersed in water, showed that sustained-release performance can be controlled by the number of nonwoven fabrics covering the top and bottom of the drug-loaded fabric and compression conditions. A model of sustained drug release was formulated to estimate the effective diffusion coefficient in the porous material. Comparative analysis of the bulk diffusion coefficient revealed that the change in diffusion volume due to change in porosity predominates. The tortuosity of the diffusion path was 3⁻4, and tended to remain almost constant or increase only slightly when the compression rate was increased. These findings show that sustained drug release can be controlled by incorporating the drug into a nonwoven fabric and using the same raw material to encapsulate it.

Homology Model and Docking-Based Virtual Screening for Ligands of Human Dyskerin as New Inhibitors of Telomerase for Cancer Treatment

Immortality is one of the main features of cancer cells. Tumor cells have an unlimited replicative potential, principally due to the holoenzyme telomerase. Telomerase is composed mainly by dyskerin (DKC1), a catalytic retrotranscriptase (hTERT) and an RNA template (hTR). The aim of this work is to develop new inhibitors of telomerase, selecting the interaction between hTR⁻DKC1 as a target. We designed two models of the human protein DKC1: homology and ab initio. These models were evaluated by different procedures, revealing that the homology model parameters were the most accurate. We selected two hydrophobic pockets contained in the PUA (pseudouridine synthase and archaeosine transglycosylase) domain, using structural and stability analysis. We carried out a docking-based virtual screen on these pockets, using the reported mutation K314 as the center of the docking. The hDKC1 model was tested against a library of 450,000 drug-like molecules. We selected the first 10 molecules that showed the highest affinity values to test their inhibitory activity on the cell line MDA MB 231 (Monroe Dunaway Anderson Metastasis Breast cancer 231), obtaining three compounds that showed inhibitory effect. These results allowed us to validate our design and set the basis to continue with the study of telomerase inhibitors for cancer treatment.