Optimizing Excipient Properties to Prevent Aggregation in Biopharmaceutical Formulations.

Excipients are included within protein biotherapeutic solution formulations to improve colloidal and conformational stability but are generally not designed for the specific purpose of preventing aggregation and improving cryoprotection in solution. In this work, we have explored the relationship between the structure and antiaggregation activity of excipients by utilizing coarse-grained molecular dynamics modeling of protein-excipient interaction. We have studied human serum albumin as a model protein, and we report the interaction of 41 excipients (polysorbates, fatty alcohol ethoxylates, fatty acid ethoxylates, phospholipids, glucosides, amino acids, and others) in terms of the reduction of solvent accessible surface area of aggregation-prone regions, proposed as a mechanism of aggregation prevention. Polyoxyethylene sorbitan had the greatest degree of interaction with aggregation-prone regions, decreasing the solvent accessible surface area of APRs by 20.7 nm2 (40.1%). Physicochemical descriptors generated by Mordred are employed to probe the structure-property relationship using partial least-squares regression. A leave-one-out cross-validated model had a root-mean-square error of prediction of 4.1 nm2 and a mean relative error of prediction of 0.077. Generally, longer molecules with a large number of alcohol-terminated PEG units tended to interact more, with qualitatively different protein interactions, wrapping around the protein. Shorter or less ethoxylated compounds tend to form hemimicellar clusters at the protein surface. We propose that an improved design would feature many short chains of 5 to 10 PEG units in many distinct branches and at least some hydrophobic content in the form of medium-length or greater aliphatic chains (i.e., six or more carbon atoms). The combination of molecular dynamics simulation and quantitative modeling is an important first step in an all-purpose protein-independent model for the computer-aided design of stabilizing excipients.

Design, Synthesis, and Application of Fluorescent Ligands Targeting the Intracellular Allosteric Binding Site of the CXC Chemokine Receptor 2.

The inhibition of CXC chemokine receptor 2 (CXCR2), a key inflammatory mediator, is a potential strategy in the treatment of several pulmonary diseases and cancers. The complexity of endogenous chemokine interaction with the orthosteric binding site has led to the development of CXCR2 negative allosteric modulators (NAMs) targeting an intracellular pocket near the G protein binding site. Our understanding of NAM binding and mode of action has been limited by the availability of suitable tracer ligands for competition studies, allowing direct ligand binding measurements. Here, we report the rational design, synthesis, and pharmacological evaluation of a series of fluorescent NAMs, based on navarixin (2), which display high affinity and preferential binding for CXCR2 over CXCR1. We demonstrate their application in fluorescence imaging and NanoBRET binding assays, in whole cells or membranes, capable of kinetic and equilibrium analysis of NAM binding, providing a platform to screen for alternative chemophores targeting these receptors.

Linear Binary Classifier to Predict Bacterial Biofilm Formation on Polyacrylates.

Bacterial infections are increasingly problematic due to the rise of antimicrobial resistance. Consequently, the rational design of materials naturally resistant to biofilm formation is an important strategy for preventing medical device-associated infections. Machine learning (ML) is a powerful method to find useful patterns in complex data from a wide range of fields. Recent reports showed how ML can reveal strong relationships between bacterial adhesion and the physicochemical properties of polyacrylate libraries. These studies used robust and predictive nonlinear regression methods that had better quantitative prediction power than linear models. However, as nonlinear models' feature importance is a local rather than global property, these models were hard to interpret and provided limited insight into the molecular details of material-bacteria interactions. Here, we show that the use of interpretable mass spectral molecular ions and chemoinformatic descriptors and a linear binary classification model of attachment of three common nosocomial pathogens to a library of polyacrylates can provide improved guidance for the design of more effective pathogen-resistant coatings. Relevant features from each model were analyzed and correlated with easily interpretable chemoinformatic descriptors to derive a small set of rules that give model features tangible meaning that elucidate relationships between the structure and function. The results show that the attachment of Pseudomonas aeruginosa and Staphylococcus aureus can be robustly predicted by chemoinformatic descriptors, suggesting that the obtained models can predict the attachment response to polyacrylates to identify anti-attachment materials to synthesize and test in the future.

Development of fluorescent peptide G protein-coupled receptor activation biosensors for NanoBRET characterization of intracellular allosteric modulators.

G protein-coupled receptors (GPCRs) are widely therapeutically targeted, and recent advances in allosteric modulator development at these receptors offer further potential for exploitation. Intracellular allosteric modulators (IAM) represent a class of ligands that bind to the receptor-effector interface (e.g., G protein) and inhibit agonist responses noncompetitively. This potentially offers greater selectivity between receptor subtypes compared to classical orthosteric ligands. However, while examples of IAM ligands are well described, a more general methodology for assessing compound interactions at the IAM site is lacking. Here, fluorescent labeled peptides based on the Gα peptide C terminus are developed as novel binding and activation biosensors for the GPCR-IAM site. In TR-FRET binding studies, unlabeled peptides derived from the Gαs subunit were first characterized for their ability to positively modulate agonist affinity at the ß2 -adrenoceptor. On this basis, a tetramethylrhodamine (TMR) labeled tracer was synthesized based on the 19 amino acid Gαs peptide (TMR-Gαs19cha18, where cha = cyclohexylalanine). Using NanoBRET technology to detect binding, TMR-Gαs19cha18 was recruited to Gs coupled ß2 -adrenoceptor and EP2 receptors in an agonist-dependent manner, but not the Gi-coupled CXCR2 receptor. Moreover, NanoBRET competition binding assays using TMR-Gαs19cha18 enabled direct assessment of the affinity of unlabeled ligands for ß2 -adrenoceptor IAM site. Thus, the NanoBRET platform using fluorescent-labeled G protein peptide mimetics offers novel potential for medium-throughput screens to identify IAMs, applicable across GPCRs coupled to a G protein class. Using the same platform, Gs peptide biosensors also represent useful tools to probe orthosteric agonist efficacy and the dynamics of receptor activation.

In Vitro Anticancer Properties of Novel Bis-Triazoles.

Here, we describe the anticancer activity of our novel bis-triazoles MS47 and MS49, developed previously as G-quadruplex stabilizers, focusing specifically upon the human melanoma MDA-MB-435 cell line. At the National Cancer Institute (NCI), USA, bis-triazole MS47 (NCS 778438) was evaluated against a panel of sixty human cancer cell lines, and showed selective, distinct multi-log differential patterns of activity, with GI50 and LC50 values in the sub-micromolar range against human cancer cells. MS47 showed highly selective cytotoxicity towards human melanoma, ovarian, CNS and colon cancer cell lines; in contrast, the leukemia cell lines interestingly showed resistance to MS47 cytotoxic activity. Further studies revealed the potent cell growth inhibiting properties of MS47 and MS49 against the human melanoma MDA-MB-435 cell line, as verified by MTT assays; both ligands were more potent against cancer cells than MRC-5 fetal lung fibroblasts (SI > 9). Melanoma colony formation was significantly suppressed by MS47 and MS49, and time- and dose-dependent apoptosis induction was also observed. Furthermore, MS47 significantly arrested melanoma cells at the G0/G1 cell cycle phase. While the expression levels of Hsp90 protein in melanoma cells were significantly decreased by MS49, corroborating its binding to the G4-DNA promoter of the Hsp90 gene. Both ligands failed to induce senescence in the human melanoma cells after 72 h of treatment, corroborating their weak stabilization of the telomeric G4-DNA.

GLIMPS: A Machine Learning Approach to Resolution Transformation for Multiscale Modeling.

We describe a general approach to transforming molecular models between different levels of resolution, based on machine learning methods. The approach uses a matched set of models at both levels of resolution for training, but requires only the coordinates of their particles and no side information (e.g., templates for substructures, defined mappings, or molecular mechanics force fields). Once trained, the approach can transform further molecular models of the system between the two levels of resolution in either direction with equal facility.

Sequence-dependent structural properties of B-DNA: what have we learned in 40 years?

The structure of B-DNA, the physiological form of the DNA molecule, has been a central topic in biology, chemistry and physics. Far from uniform and rigid, the double helix was revealed as a flexible and structurally polymorphic molecule. Conformational changes that lead to local and global changes in the helix geometry are mediated by a complex choreography of base and backbone rearrangements affecting the ability of the B-DNA to recognize ligands and consequently on its functionality. In this sense, the knowledge obtained from the sequence-dependent structural properties of B-DNA has always been thought crucial to rationalize how ligands and, most notably, proteins recognize B-DNA and modulate its activity, i.e. the structural basis of gene regulation. Honouring the anniversary of the first high-resolution X-ray structure of a B-DNA molecule, in this contribution, we present the most important discoveries of the last 40 years on the sequence-dependent structural and dynamical properties of B-DNA, from the early beginnings to the current frontiers in the field.

Molecular design aided by random forests and synthesis of potent trypanocidal agents as cruzain inhibitors for Chagas disease treatment.

Cruzain is an established target for the identification of novel trypanocidal agents, but how good are in vitro/in vivo correlations? This work describes the development of a random forests model for the prediction of the bioavailability of cruzain inhibitors that are Trypanosoma cruzi killers. Some common properties that characterize drug-likeness are poorly represented in many established cruzain inhibitors. This correlates with the evidence that many high-affinity cruzain inhibitors are not trypanocidal agents against T. cruzi. On the other hand, T. cruzi killers that present typical drug-like characteristics are likely to show better trypanocidal action than those without such features. The random forests model was not outperformed by other machine learning methods (such as artificial neural networks and support vector machines), and it was validated with the synthesis of two new trypanocidal agents. Specifically, we report a new lead compound, Neq0565, which was tested on T. cruzi Tulahuen (ß-galactosidase) with a pEC50 of 4.9. It is inactive in the host cell line showing a selectivity index (SI = EC50cyto /EC50T. cruzi ) higher than 50.

Nanoformulation-by-design: an experimental and molecular dynamics study for polymer coated drug nanoparticles.

The formulation of drug compounds into nanoparticles has many potential advantages in enhancing bioavailability and improving therapeutic efficacy. However, few drug molecules will assemble into stable, well-defined nanoparticulate structures. Amphiphilic polymer coatings are able to stabilise nanoparticles, imparting defined surface properties for many possible drug delivery applications. In the present article we explore, both experimentally and in silico, a potential methodology to coat drug nanoparticles with an amphiphilic co-polymer. Monomethoxy polyethylene glycol-polycaprolactone (mPEG-b-PCL) diblock copolymers with different mPEG lengths (M w 350, 550, 750 and 2000), designed to give different levels of colloidal stability, were used to coat the surface of indomethacin nanoparticles. Polymer coating was achieved by a flow nanoprecipitation method that demonstrated excellent batch-to-batch reproducibility and resulted in nanoparticles with high drug loadings (up to 78%). At the same time, in order to understand this modified nanoprecipitation method at an atomistic level, large-scale all-atom molecular dynamics simulations were performed in parallel using the GROMOS53a6 forcefield parameters. It was observed that the mPEG-b-PCL chains act synergistically with the acetone molecules to dissolve the indomethacin nanoparticle while after the removal of the acetone molecules (mimicking the evaporation of the organic solvent) a polymer-drug nanoparticle was formed (yield 99%). This work could facilitate the development of more efficient methodologies for producing nanoparticles of hydrophobic drugs coated with amphiphilic polymers. The atomistic insight from the MD simulations in tandem with the data from the drug encapsulation experiments thus leads the way to a nanoformulation-by-design approach for therapeutic nanoparticles.

Ligand-induced conformational selection predicts the selectivity of cysteine protease inhibitors.

Cruzain, a cysteine protease of Trypanosoma cruzi, is a validated target for the treatment of Chagas disease. Due to its high similarity in three-dimensional structure with human cathepsins and their sequence identity above 70% in the active site regions, identifying potent but selective cruzain inhibitors with low side effects on the host organism represents a significant challenge. Here a panel of nitrile ligands with varying potencies against cathepsin K, cathepsin L and cruzain, are studied by molecular dynamics simulations as both non-covalent and covalent complexes. Principal component analysis (PCA), identifies and quantifies patterns of ligand-induced conformational selection that enable the construction of a decision tree which can predict with high confidence a low-nanomolar inhibitor of each of three proteins, and determine the selectivity for one against others.

Tios: The Internet of Simulations. Turning Molecular Dynamics into a Data Streaming Web Application.

The configuration of most current academic high-performance computing (HPC) resources tends to enforce ways of working with, and thinking about, molecular dynamics (MD) simulations that are not always optimal. For example, when the aim of the simulation(s) is to produce a representative sample of a Boltzmann weighted ensemble, the ideal scenario would be to be able to do just that-i.e. to tap into a running simulation of indefinite length, collect data from it in real time, and only terminate the simulation once the quality of a sample was assured. Current approaches, based on batch jobs of proscribed maximum length, and a postprocessing style of data analysis, inhibit this. In the spirit of the Internet of Things, we have developed Tios, a Python application that turns MD simulations into remotely discoverable and accessible streaming web applications to which researchers can connect and download data as they please. Tios is freely available, works with standard MD codes, and requires no modifications to them. In this paper we outline how Tios works and present a number of test cases that demonstrate its capabilities.

Dipeptidyl nitrile derivatives have cytostatic effects against Leishmania spp. promastigotes.

Cysteine proteases are involved in critical cell processes to the protozoa from Leishmania genus, and their inhibition is a therapeutic alternative to treat the disease. In this work, derivatives of dipeptidyl nitriles acting as reversible covalent inhibitors of cysteine proteases were studied as cytostatic agents. The proteolytic activity inside the living and lysed parasite cells was quantified using a selective substrate for cysteine proteases (Z-FR-MCA) from Leishmania amazonensis and L. infantum. The overall proteolytic activity of intact cells and even cell extracts was only marginally affected at high concentrations, with the observation of cytostatic activity and cell cycle arrest of promastigotes. However, the cytotoxic effects were only observed for infected J774 macrophages, which impaired further analysis of the amastigote infection. Therefore, the proteolytic inhibition in intact L. amazonensis and L. infantum promastigotes had no relationship to the cytostatic activity, which emphasizes that these dipeptidyl nitriles act through another mechanism of action.

CoCo-MD: A Simple and Effective Method for the Enhanced Sampling of Conformational Space.

CoCo ("complementary coordinates") is a method for ensemble enrichment based on principal component analysis (PCA) that was developed originally for the investigation of NMR data. Here we investigate the potential of the CoCo method, in combination with molecular dynamics simulations (CoCo-MD), to be used more generally for the enhanced sampling of conformational space. Using the alanine penta-peptide as a model system, we find that an iterative workflow, interleaving short multiple-walker MD simulations with long-range jumps through conformational space informed by CoCo analysis, can increase the rate of sampling of conformational space up to 10 times for the same computational effort (total number of MD timesteps). Combined with the reservoir-REMD method, free energies can be readily calculated. An alternative, approximate but fast and practically useful, alternative approach to unbiasing CoCo-MD generated data is also described. Applied to cyclosporine A, we can achieve far greater conformational sampling than has been reported previously, using a fraction of the computational resource. Simulations of the maltose binding protein, begun from the "open" state, effectively sample the "closed" conformation associated with ligand binding. The PCA-based approach means that optimal collective variables to enhance sampling need not be defined in advance by the user but are identified automatically and are adaptive, responding to the characteristics of the developing ensemble. In addition, the approach does not require any adaptations to the associated MD code and is compatible with any conventional MD package.

Experimental study and computational modelling of cruzain cysteine protease inhibition by dipeptidyl nitriles.

Chagas disease affects millions of people in Latin America. This disease is caused by the protozoan parasite Trypanossoma cruzi. The cysteine protease cruzain is a key enzyme for the survival and propagation of this parasite lifecycle. Nitrile-based inhibitors are efficient inhibitors of cruzain that bind by forming a covalent bond with this enzyme. Here, three nitrile-based inhibitors dubbed Neq0409, Neq0410 and Neq0570 were synthesized, and the thermodynamic profile of the bimolecular interaction with cruzain was determined using isothermal titration calorimetry (ITC). The result suggests the inhibition process is enthalpy driven, with a detrimental contribution of entropy. In addition, we have used hybrid Quantum Mechanical/Molecular Mechanical (QM/MM) and Molecular Dynamics (MD) simulations to investigate the reaction mechanism of reversible covalent modification of cruzain by Neq0409, Neq0410 and Neq0570. The computed free energy profile shows that the nucleophilic attack of Cys25 on the carbon C1 of inhibitiors and the proton transfer from His162 to N1 of the dipeptidyl nitrile inhibitor take place in a single step. The calculated free energy of the inhibiton reaction is in agreement with covalent experimental binding. Altogether, the results reported here suggests that nitrile-based inhibitors are good candidates for the development of reversible covalent inhibitors of cruzain and other cysteine proteases.

In Silico Screening for Solid Dispersions: The Trouble with Solubility Parameters and χFH.

The problem of predicting small molecule-polymer compatibility is relevant to many areas of chemistry and pharmaceutical science but particularly drug delivery. Computational methods based on Hildebrand and Hansen solubility parameters, and the estimation of the Flory-Huggins parameter, χ, have proliferated across the literature. Focusing on the need to develop amorphous solid dispersions to improve the bioavailability of poorly soluble drug candidates, an innovative, high-throughput 2D printing method has been employed to rapidly assess the compatibility of 54 drug-polymer pairings (nine drug compounds in six polymers). In this study, the first systematic assessment of the in silico methods for this application, neither the solubility parameter approach nor the calculated χ, correctly predicted drug-polymer compatibility. The theoretical limitations of the solubility parameter approach are discussed and used to explain why this approach is fundamentally unsuitable for predicting polymer-drug interactions. Examination of the original sources describing the method for calculating χ shows that only the enthalpic contributions to the term have been included, and the corrective entropic term is absent. The development and application of new in silico techniques, that consider all parts of the free energy of mixing, are needed in order to usefully predict small molecule-polymer compatibility and to realize the ambition of a drug-polymer screening method.

Correction: Unveiling a novel transient druggable pocket in BACE-1 through molecular simulations: Conformational analysis and binding mode of multisite inhibitors.

[This corrects the article DOI: 10.1371/journal.pone.0177683.].

Unveiling a novel transient druggable pocket in BACE-1 through molecular simulations: Conformational analysis and binding mode of multisite inhibitors.

The critical role of BACE-1 in the formation of neurotoxic ß-amyloid peptides in the brain makes it an attractive target for an efficacious treatment of Alzheimer's disease. However, the development of clinically useful BACE-1 inhibitors has proven to be extremely challenging. In this study we examine the binding mode of a novel potent inhibitor (compound 1, with IC50 80 nM) designed by synergistic combination of two fragments-huprine and rhein-that individually are endowed with very low activity against BACE-1. Examination of crystal structures reveals no appropriate binding site large enough to accommodate 1. Therefore we have examined the conformational flexibility of BACE-1 through extended molecular dynamics simulations, paying attention to the highly flexible region shaped by loops 8-14, 154-169 and 307-318. The analysis of the protein dynamics, together with studies of pocket druggability, has allowed us to detect the transient formation of a secondary binding site, which contains Arg307 as a key residue for the interaction with small molecules, at the edge of the catalytic cleft. The formation of this druggable "floppy" pocket would enable the binding of multisite inhibitors targeting both catalytic and secondary sites. Molecular dynamics simulations of BACE-1 bound to huprine-rhein hybrid compounds support the feasibility of this hypothesis. The results provide a basis to explain the high inhibitory potency of the two enantiomeric forms of 1, together with the large dependence on the length of the oligomethylenic linker. Furthermore, the multisite hypothesis has allowed us to rationalize the inhibitory potency of a series of tacrine-chromene hybrid compounds, specifically regarding the apparent lack of sensitivity of the inhibition constant to the chemical modifications introduced in the chromene unit. Overall, these findings pave the way for the exploration of novel functionalities in the design of optimized BACE-1 multisite inhibitors.

Rapid Nanogram Scale Screening Method of Microarrays to Evaluate Drug-Polymer Blends Using High-Throughput Printing Technology.

A miniaturized, high-throughput assay was optimized to screen polymer-drug solid dispersions using a 2-D Inkjet printer. By simply printing nanoliter amounts of polymer and drug solutions onto an inert surface, drug/polymer microdots of tunable composition were produced in an easily addressable microarray format. The amount of material printed for each dried spot ranged from 25 ng to 650 ng. These arrays were used to assess the stability of drug/polymer dispersions with respect to recrystallization, using polarized light microscopy. One array with a panel of 6 drugs formulated at different ratios with a poly(vinylpyrrolidone-vinyl acetate) (PVPVA) copolymer was developed to estimate a possible bulk (gram-scale) approximation threshold from the final printed nanoamount of formulation. Another array was printed at a fixed final amount of material to establish a literature comparison of one drug formulated with different commercial polymers for validation. This new approach may offer significant efficiency in pharmaceutical formulation screening, with each experiment in the nanomicro-array format requiring from 3 up to 6 orders of magnitude lower amounts of sample than conventional screening methods.

GPCRs through the keyhole: the role of protein flexibility in ligand binding to ß-adrenoceptors.

G protein-coupled receptors (GPCRs) are proteins of pharmaceutical importance, with over 30% of all drugs in clinical use targeting them. Increasing numbers of X-ray crystal (XRC) structures of GPCRs offer a wealth of data relating to ligand binding. For the ß-adrenoceptors (ß-ARs), XRC structures are available for human ß2- and turkey ß1-subtypes, in complexes with a range of ligands. While these structures provide insight into the origins of ligand structure-activity relationships (SARs), questions remain. The ligands in all published complexed XRC structures lack extensive substitution, with no obvious way the ligand-binding site can accommodate ß1-AR-selective antagonists with extended side-chains para- to the common aryloxypropanolamine pharmacophore. Using standard computational docking tools with such ligands generally returns poses that fail to explain known SARs. Application of our Active Site Pressurisation modelling method to ß-AR XRC structures and homology models, however, reveals a dynamic area in the ligand-binding pocket that, through minor changes in amino acid side chain orientations, opens a fissure between transmembrane helices H4 and H5, exposing intra-membrane space. This fissure, which we term the "keyhole", is ideally located to accommodate extended moieties present in many high-affinity ß1-AR-selective ligands, allowing the rest of the ligand structure to adopt a canonical pose in the orthosteric binding site. We propose the keyhole may be a feature of both ß1- and ß2-ARs, but that subtle structural differences exist between the two, contributing to subtype-selectivity. This has consequences for the rational design of future generations of subtype-selective ligands for these therapeutically important targets.

Long-range correlations in the mechanics of small DNA circles under topological stress revealed by multi-scale simulation.

It is well established that gene regulation can be achieved through activator and repressor proteins that bind to DNA and switch particular genes on or off, and that complex metabolic networks determine the levels of transcription of a given gene at a given time. Using three complementary computational techniques to study the sequence-dependence of DNA denaturation within DNA minicircles, we have observed that whenever the ends of the DNA are constrained, information can be transferred over long distances directly by the transmission of mechanical stress through the DNA itself, without any requirement for external signalling factors. Our models combine atomistic molecular dynamics (MD) with coarse-grained simulations and statistical mechanical calculations to span three distinct spatial resolutions and timescale regimes. While they give a consensus view of the non-locality of sequence-dependent denaturation in highly bent and supercoiled DNA loops, each also reveals a unique aspect of long-range informational transfer that occurs as a result of restraining the DNA within the closed loop of the minicircles.