Exploring the Anti-Hypoxaemia Effect of Hydromethylthionine: A Prospective Study of Phase 3 Clinical Trial Participants.

Methylthioninium chloride (MTC) is a standard treatment for methaemoglobinaemia. A preparation of reduced MTC has been reported to increase blood oxygen saturation (SpO2) and lower respiratory rates in patients with severe COVID-19. We have developed a stable form of reduced methylthionine (hydromethylthionine-mesylate, HMTM) having a benign safety profile in two Phase 3 trials in Alzheimer's disease. The aim of this prospective study was to determine the effects of oral HMTM on SpO2 and methaemoglobin (metHb) levels in a cohort of patients with mild hypoxaemia not due to COVID-19. Eighteen participants randomised to a single dose of 4, 75, 100 or 125 mg doses of HMTM had SpO2 levels below 94% at baseline. Patients were routinely monitored by pulse oximetry after 4 h, and after 2 and 6 weeks of twice daily dosing. Significant ~3% increases in SpO2 occurred within 4 h and were sustained over 2 and 6 weeks with no dose differences. There were small dose-dependent increases (0.060-0.162%) in metHb levels over 2 to 6 weeks. Minimum-energy computational chemistry revealed that HMT can bind within 2.10 Å of heme iron by donating a pair of electrons from the central nitrogen of HMT to d orbitals of heme iron, but with lower affinity than oxygen. In conclusion, HMTM can increase SpO2 without reducing metHb by acting as a strong displaceable field ligand for heme iron. We hypothesise that this facilitates a transition from the low oxygen affinity T-state of heme to the higher affinity R-state. HMTM has potential as an adjunctive treatment for hypoxaemia.

A highly potent maytansinoid analogue and its use as a cytotoxic therapeutic agent in gold nanoparticles for the treatment of hepatocellular carcinoma.

Gold nanoparticles are promising drug delivery agents with the potential to deliver chemotherapeutic agents to tumour sites. The highly cytotoxic maytansinoid tubulin inhibitor DM1 has been attached to gold nanoparticles and shows tumour growth inhibition in mouse models of hepatocellular carcinoma. Attempting to improve the stability of the gold-cytotoxin bond led to the design and synthesis of novel maytansinoids with improved potency in cell viability assays and improved in vivo tolerability compared to the DM1 analogues. These novel maytansines may also have applications in other methods of drug delivery, for example as the cytotoxic component of antibody drug conjugates.

Investigation of the flexibility of protein kinases implicated in the pathology of Alzheimer's disease.

The pathological characteristics of Alzheimer's Disease (AD) have been linked to the activity of three particular kinases--Glycogen Synthase Kinase 3ß (GSK3ß), Cyclin-Dependent Kinase 5 (CDK5) and Extracellular-signal Regulated Kinase 2 (ERK2). As a consequence, the design of selective, potent and drug-like inhibitors of these kinases is of particular interest. Structure-based design methods are well-established in the development of kinase inhibitors. However, progress in this field is limited by the difficulty in obtaining X-ray crystal structures suitable for drug design and by the inability of this method to resolve highly flexible regions of the protein that are crucial for ligand binding. To address this issue, we have undertaken a study of human protein kinases CDK5/p25, CDK5, ERK2 and GSK3ß using both conventional molecular dynamics (MD) and the new Active Site Pressurisation (ASP) methodology, to look for kinase-specific patterns of flexibility that could be leveraged for the design of selective inhibitors. ASP was used to examine the intrinsic flexibility of the ATP-binding pocket for CDK5/p25, CDK5 and GSK3ß where it is shown to be capable of inducing significant conformational changes when compared with X-ray crystal structures. The results from these experiments were used to quantify the dynamics of each protein, which supported the observations made from the conventional MD simulations. Additional information was also derived from the ASP simulations, including the shape of the ATP-binding site and the rigidity of the ATP-binding pocket. These observations may be exploited in the design of selective inhibitors of GSK3ß, CDK5 and ERK2.

Untangling tau hyperphosphorylation in drug design for neurodegenerative diseases.

Aggregation of hyperphosphorylated tau is one of the characteristic neuropathological lesions of Alzheimer's disease and other neurodegenerative disorders. Pharmacological modulation of tau hyperphosphorylation might represent a valid and feasible therapeutic strategy for such disorders. Here, we consider recent evidence supporting the validity of the three most relevant kinases affecting tau hyperphosphorylation - GSK3beta, CDK5 and ERK2 - as drug targets and describe progress in the design of inhibitors for these kinases.

Ligand- and Structure-Based Drug Design and Optimization using KNIME.

In recent years there has been a paradigm shift in how data is being used to progress early drug discovery campaigns from hit identification to candidate selection. Significant developments in data mining methods and the accessibility of tools for research scientists have been instrumental in reducing drug discovery timelines and in increasing the likelihood of a chemical entity achieving drug development milestones. KNIME, the Konstanz Information Miner, is a leading open source data analytics platform and has supported drug discovery endeavours for over a decade. KNIME provides a rich palette of tools supported by an extensive community of contributors to enable ligandand structure-based drug design. This review will examine recent developments within the KNIME platform to support small-molecule drug design and provide a perspective on the challenges and future developments within this field.

Assessment of Hydration Thermodynamics at Protein Interfaces with Grid Cell Theory.

Molecular dynamics simulations have been analyzed with the Grid Cell Theory (GCT) method to spatially resolve the binding enthalpies and entropies of water molecules at the interface of 17 structurally diverse proteins. Correlations between computed energetics and structural descriptors have been sought to facilitate the development of simple models of protein hydration. Little correlation was found between GCT-computed binding enthalpies and continuum electrostatics calculations. A simple count of contacts with functional groups in charged amino acids correlates well with enhanced water stabilization, but the stability of water near hydrophobic and polar residues depends markedly on its coordination environment. The positions of X-ray-resolved water molecules correlate with computed high-density hydration sites, but many unresolved waters are significantly stabilized at the protein surfaces. A defining characteristic of ligand-binding pockets compared to nonbinding pockets was a greater solvent-accessible volume, but average water thermodynamic properties were not distinctive from other interfacial regions. Interfacial water molecules are frequently stabilized by enthalpy and destabilized entropy with respect to bulk, but counter-examples occasionally occur. Overall detailed inspection of the local coordinating environment appears necessary to gauge the thermodynamic stability of water in protein structures.

Evaluation of Host-Guest Binding Thermodynamics of Model Cavities with Grid Cell Theory.

A previously developed cell theory model of liquid water was used to evaluate the excess thermodynamic properties of confined clusters of water molecules. The results are in good agreement with reference thermodynamic integration calculations, suggesting that the model is adequate to probe the thermodynamic properties of water at interfaces or in cavities. Next, the grid cell theory (GCT) method was applied to elucidate the thermodynamic signature of nonpolar association for a range of idealized host-guest systems. Polarity and geometry of the host cavities were systematically varied, and enthalpic and entropic solvent components were spatially resolved for detailed graphical analyses. Perturbations in the thermodynamic properties of water molecules upon guest binding are restricted to the immediate vicinity of the guest in solvent-exposed cavities, whereas longer-ranged perturbations are observed in buried cavities. Depending on the polarity and geometry of the host, water displacement by a nonpolar guest makes a small or large enthalpic or entropic contribution to the free energy of binding. Thus, no assumptions about the thermodynamic signature of the hydrophobic effect can be made in general. Overall the results warrant further applications of GCT to more complex systems such as protein-ligand complexes.

GAMESS as a free quantum-mechanical platform for drug research.

Driven by a steady improvement of computational hardware and significant progress in ab initio method development, quantum-mechanical approaches can now be applied to large biochemical systems and drug design. We review the methods implemented in GAMESS, which are suitable to calculate large biochemical systems. An emphasis is put on the fragment molecular orbital method (FMO) and quantum mechanics interfaced with molecular mechanics (QM/MM). The use of FMO in the protein-ligand binding, structure-activity relationship (SAR) studies, fragment- and structure-based drug design (FBDD/SBDD) is discussed in detail.

Drug discovery applications for KNIME: an open source data mining platform.

Technological advances in high-throughput screening methods, combinatorial chemistry and the design of virtual libraries have evolved in the pursuit of challenging drug targets. Over the last two decades a vast amount of data has been generated within these fields and as a consequence data mining methods have been developed to extract key pieces of information from these large data pools. Much of this data is now available in the public domain. This has been helpful in the arena of drug discovery for both academic groups and for small to medium sized enterprises which previously would not have had access to such data resources. Commercial data mining software is sometimes prohibitively expensive and the alternate open source data mining software is gaining momentum in both academia and in industrial applications as the costs of research and development continue to rise. KNIME, the Konstanz Information Miner, has emerged as a leader in open source data mining tools. KNIME provides an integrated solution for the data mining requirements across the drug discovery pipeline through a visual assembly of data workflows drawing from an extensive repository of tools. This review will examine KNIME as an open source data mining tool and its applications in drug discovery.

Prediction of cyclin-dependent kinase 2 inhibitor potency using the fragment molecular orbital method.

BACKGROUND: The reliable and robust estimation of ligand binding affinity continues to be a challenge in drug design. Many current methods rely on molecular mechanics (MM) calculations which do not fully explain complex molecular interactions. Full quantum mechanical (QM) computation of the electronic state of protein-ligand complexes has recently become possible by the latest advances in the development of linear-scaling QM methods such as the ab initio fragment molecular orbital (FMO) method. This approximate molecular orbital method is sufficiently fast that it can be incorporated into the development cycle during structure-based drug design for the reliable estimation of ligand binding affinity. Additionally, the FMO method can be combined with approximations for entropy and solvation to make it applicable for binding affinity prediction for a broad range of target and chemotypes. RESULTS: We applied this method to examine the binding affinity for a series of published cyclin-dependent kinase 2 (CDK2) inhibitors. We calculated the binding affinity for 28 CDK2 inhibitors using the ab initio FMO method based on a number of X-ray crystal structures. The sum of the pair interaction energies (PIE) was calculated and used to explain the gas-phase enthalpic contribution to binding. The correlation of the ligand potencies to the protein-ligand interaction energies gained from FMO was examined and was seen to give a good correlation which outperformed three MM force field based scoring functions used to appoximate the free energy of binding. Although the FMO calculation allows for the enthalpic component of binding interactions to be understood at the quantum level, as it is an in vacuo single point calculation, the entropic component and solvation terms are neglected. For this reason a more accurate and predictive estimate for binding free energy was desired. Therefore, additional terms used to describe the protein-ligand interactions were then calculated to improve the correlation of the FMO derived values to experimental free energies of binding. These terms were used to account for the polar and non-polar solvation of the molecule estimated by the Poisson-Boltzmann equation and the solvent accessible surface area (SASA), respectively, as well as a correction term for ligand entropy. A quantitative structure-activity relationship (QSAR) model obtained by Partial Least Squares projection to latent structures (PLS) analysis of the ligand potencies and the calculated terms showed a strong correlation (r2 = 0.939, q2 = 0.896) for the 14 molecule test set which had a Pearson rank order correlation of 0.97. A training set of a further 14 molecules was well predicted (r2 = 0.842), and could be used to obtain meaningful estimations of the binding free energy. CONCLUSIONS: Our results show that binding energies calculated with the FMO method correlate well with published data. Analysis of the terms used to derive the FMO energies adds greater understanding to the binding interactions than can be gained by MM methods. Combining this information with additional terms and creating a scaled model to describe the data results in more accurate predictions of ligand potencies than the absolute values obtained by FMO alone.

Using electrophysiology and in silico three-dimensional modeling to reduce human Ether-à-go-go related gene K(+) channel inhibition in a histamine H3 receptor antagonist program.

The histamine H3 receptor (H3R) plays a regulatory role in the presynaptic release of histamine and several other neurotransmitters, and thus, it is an attractive target for central nervous system indications including cognitive disorders, narcolepsy, attention-deficit hyperactivity disorder, and pain. The development of H3R antagonists was complicated by the similarities between the pharmacophores of H3R and human Ether-à-go-go related gene (hERG) channel blockers, a fact that probably prevented promising compounds from being progressed into the clinic. Using a three-dimensional in silico modeling approach complemented with automated and manual patch clamping, we were able to separate these two pharmacophores and to develop highly potent H3R antagonists with reduced risk of hERG liabilities from initial hit series with low selectivity identified in a high-throughput screening campaign.

Exploiting glycogen synthase kinase 3beta flexibility in molecular recognition.

GSK3beta (glycogen synthase kinase 3beta) is involved in the phosphorylation of various important regulatory proteins. Pharmacological inhibition of this enzyme could yield treatments for a variety of diseases including diabetes and Alzheimer's disease. The understanding of events involved in the molecular recognition of inhibitors by the active site of this enzyme is key in structure-based design strategies. The present study deals with the dynamic nature of GSK3beta and highlights the importance of studying protein plasticity in structure-based drug design, exemplified by our method called ASP (active-site pressurization).

Active site pressurization: a new tool for structure-guided drug design and other studies of protein flexibility.

We present a new molecular dynamics methodology to assist in structure-based drug design and other studies that seek to predict protein deformability. Termed Active Site Pressurization (ASP), the new methodology simply injects a resin into the ligand binding-site of a protein during the course of a molecular dynamics simulation such that novel, energetically reasonable protein conformations are generated in an unbiased way that may be better representations of the ligand binding conformation than are currently available. Here we apply two different versions of the ASP methodology to three proteins, cytochrome P450cam, PcrA helicase, and glycogen synthase kinase 3beta (GSK3beta), and show that the method is capable of inducing significant conformational changes when compared to the X-ray crystal structures. Application of the ASP methodology therefore provides a view of binding site flexibility that is a rich source of data for inclusion in a variety of further investigations, including high-throughput virtual screening, lead hopping, revealing alternative modes of deformation, and revealing hidden exit and entrance tunnels.