Discovery of Q203, a potent clinical candidate for the treatment of tuberculosis

New therapeutic strategies are needed to combat the tuberculosis pandemic and the spread of multidrug-resistant (MDR) and extensively drug-resistant (XDR) forms of the disease, which remain a serious public health challenge worldwide1, 2. The most urgent clinical need is to discover potent agents capable of reducing the duration of MDR and XDR tuberculosis therapy with a success rate comparable to that of current therapies for drug-susceptible tuberculosis. The last decade has seen the discovery of new agent classes for the management of tuberculosis3, 4, 5, several of which are currently in clinical trials6, 7, 8. However, given the high attrition rate of drug candidates during clinical development and the emergence of drug resistance, the discovery of additional clinical candidates is clearly needed. Here, we report on a promising class of imidazopyridine amide (IPA) compounds that block Mycobacterium tuberculosis growth by targeting the respiratory cytochrome bc1 complex. The optimized IPA compound Q203 inhibited the growth of MDR and XDR M. tuberculosis clinical isolates in culture broth medium in the low nanomolar range and was efficacious in a mouse model of tuberculosis at a dose less than 1 mg per kg body weight, which highlights the potency of this compound. In addition, Q203 displays pharmacokinetic and safety profiles compatible with once-daily dosing. Together, our data indicate that Q203 is a promising new clinical candidate for the treatment of tuberculosis

Molecular Modeling of Protein Corona Formation and Its Interactions with Nanoparticles and Cell Membranes for Nanomedicine Applications

The conformations and surface properties of nanoparticles have been modified to improve the efficiency of drug delivery. However, when nanoparticles flow through the bloodstream, they interact with various plasma proteins, leading to the formation of protein layers on the nanoparticle surface, called protein corona. Experiments have shown that protein corona modulates nanoparticle size, shape, and surface properties and, thus, influence the aggregation of nanoparticles and their interactions with cell membranes, which can increases or decreases the delivery efficiency. To complement these experimental findings and understand atomic-level phenomena that cannot be captured by experiments, molecular dynamics (MD) simulations have been performed for the past decade. Here, we aim to review the critical role of MD simulations to understand (1) the conformation, binding site, and strength of plasma proteins that are adsorbed onto nanoparticle surfaces, (2) the competitive adsorption and desorption of plasma proteins on nanoparticle surfaces, and (3) the interactions between protein-coated nanoparticles and cell membranes. MD simulations have successfully predicted the competitive binding and conformation of protein corona and its effect on the nanoparticle–nanoparticle and nanoparticle–membrane interactions. In particular, simulations have uncovered the mechanism regarding the competitive adsorption and desorption of plasma proteins, which helps to explain the Vroman effect. Overall, these findings indicate that simulations can now provide predications in excellent agreement with experimental observations as well as atomic-scale insights into protein corona formation and interactions

Molecular Modeling of PEGylated Peptides, Dendrimers, and Single-Walled Carbon Nanotubes for Biomedical Applications

Polyethylene glycol (PEG) has been conjugated to many drugs or drug carriers to increase their solubility and circulating lifetime, and reduce toxicity. This has motivated many experimental studies to understand the effect of PEGylation on delivery efficiency. To complement the experimental findings and uncover the mechanism that cannot be captured by experiments, all-atom and coarse-grained molecular dynamics (MD) simulations have been performed. This has become possible, due to recent advances in simulation methodologies and computational power. Simulations of PEGylated peptides show that PEG chains wrap antimicrobial peptides and weaken their binding interactions with lipid bilayers. PEGylation also influences the helical stability and tertiary structure of coiled-coil peptides. PEGylated dendrimers and single-walled carbon nanotubes (SWNTs) were simulated, showing that the PEG size and grafting density significantly modulate the conformation and structure of the PEGylated complex, the interparticle aggregation, and the interaction with lipid bilayers. In particular, simulations predicted the structural transition between the dense core and dense shell of PEGylated dendrimers, the phase behavior of self-assembled complexes of lipids, PEGylated lipids, and SWNTs, which all favorably compared with experiments. Overall, these new findings indicate that simulations can now predict the experimentally observed structure and dynamics, as well as provide atomic-scale insights into the interactions of PEGylated complexes with other molecules

Molecular Simulations of PEGylated Biomolecules, Liposomes, and Nanoparticles for Drug Delivery Applications

Since the first polyethylene glycol (PEG)ylated protein was approved by the FDA in 1990, PEGylation has been successfully applied to develop drug delivery systems through experiments, but these experimental results are not always easy to interpret at the atomic level because of the limited resolution of experimental techniques. To determine the optimal size, structure, and density of PEG for drug delivery, the structure and dynamics of PEGylated drug carriers need to be understood close to the atomic scale, as can be done using molecular dynamics simulations, assuming that these simulations can be validated by successful comparisons to experiments. Starting with the development of all-atom and coarse-grained PEG models in 1990s, PEGylated drug carriers have been widely simulated. In particular, recent advances in computer performance and simulation methodologies have allowed for molecular simulations of large complexes of PEGylated drug carriers interacting with other molecules such as anticancer drugs, plasma proteins, membranes, and receptors, which makes it possible to interpret experimental observations at a nearly atomistic resolution, as well as help in the rational design of drug delivery systems for applications in nanomedicine. Here, simulation studies on the following PEGylated drug topics will be reviewed: proteins and peptides, liposomes, and nanoparticles such as dendrimers and carbon nanotubes

Minimum distances between hydroxyl groups of different Thr residues as a function of time.

<p>Since the binding sites of TmAFP, TmAFP-m1, TmAFP-m2, and TmAFP-m3 respectively include 11, 7, 6, and 4 Thr residues, the minimum distance for each Thr residue is represented by different colors. Note that the neighboring residues should have the same minimum distance, and thus their values are often overlapped, as highlighted in TmAFP-m2 and TmAFP-m3.</p

Structures, dynamics, and hydrogen-bond interactions of antifreeze proteins in TIP4P/Ice water and their dependence on force fields - Fig 1

<p>Snapshots from the beginning (0 ns, left) to the end (120 ns, right) for the simulation of TmAFP in water. Green ribbons and blue lines respectively represent the backbone and Thr residues of TmAFP, while oxygen and hydrogen atoms of water are represented as red and white dots, respectively. TmAFP and the surrounding water (or ice) region are magnified (top), and the side and bottom views of TmAFP are depicted (bottom). The images were created using Visual Molecular Dynamics [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0198887#pone.0198887.ref056" target="_blank">56</a>].</p

Average diffusion coefficients (<i>D</i>) of TmAFP, TmAFP-m1, TmAFP-m2, and TmAFP-m3.

<p>Average diffusion coefficients were obtained by averaging those from three or five simulations. Error bars indicate standard errors calculated from the standard deviation of three or five samples (diffusion coefficients) with correction by multiplying a factor of 0.886 or 0.940, respectively [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0198887#pone.0198887.ref063" target="_blank">63</a>].</p

List of simulations.

<p>List of simulations.</p

Distances between the center of mass (COM) of TmAFP and its initial COM in the z-direction as a function of time.

<p>Since five simulations were performed for each force field, five values are represented with different colors, where black, red, green, blue, and yellow lines respectively correspond to systems 1, 2, 3, 4, and 5 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0198887#pone.0198887.g002" target="_blank">Fig 2</a>.</p