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The influenza A M2 wild-type (WT) proton channel is the target of the anti-influenza drug rimantadine. Rimantadine has two enantiomers, though most investigations into drug binding and inhibition have used a racemic mixture. Solid-state NMR experiments using the full length-M2 WT have shown significant spectral differences that were interpreted to indicate tighter binding for (R)- vs (S)-rimantadine. However, it was unclear if this correlates with a functional difference in drug binding and inhibition. Using X-ray crystallography, we have determined that both (R)- and (S)-rimantadine bind to the M2 WT pore with slight differences in the hydration of each enantiomer. However, this does not result in a difference in potency or binding kinetics, as shown by similar values for kon, koff, and Kd in electrophysiological assays and for EC50 values in cellular assays. We concluded that the slight differences in hydration for the (R)- and (S)-rimantadine enantiomers are not relevant to drug binding or channel inhibition. To further explore the effect of the hydration of the M2 pore on binding affinity, the water structure was evaluated by grand canonical ensemble molecular dynamics simulations as a function of the chemical potential of the water. Initially, the two layers of ordered water molecules between the bound drug and the channel’s gating His37 residues mask the drug’s chirality. As the chemical potential becomes more unfavorable, the drug translocates down to the lower water layer, and the interaction becomes more sensitive to chirality. These studies suggest the feasibility of displacing the upper water layer and specifically recognizing the lower water layers in novel drugs.

Water molecules play a key role in many biomolecular systems, particularly when bound at protein-ligand interfaces. However, molecular simulation studies on such systems are hampered by the relatively long time scales over which water exchange between a protein and solvent takes place. Grand canonical Monte Carlo (GCMC) is a simulation technique that avoids this issue by attempting the insertion and deletion of water molecules within a given structure. The approach is constrained by low acceptance probabilities for insertions in congested systems, however. To address this issue, here, we combine GCMC with nonequilibium candidate Monte Carlo (NCMC) to yield a method that we refer to as grand canonical nonequilibrium candidate Monte Carlo (GCNCMC), in which the water insertions and deletions are carried out in a gradual, nonequilibrium fashion. We validate this new approach by comparing GCNCMC and GCMC simulations of bulk water and three protein binding sites. We find that not only is the efficiency of the water sampling improved by GCNCMC but that it also results in increased sampling of ligand conformations in a protein binding site, revealing new water-mediated ligand-binding geometries that are not observed using alternative enhanced sampling techniques.

Water molecules at protein-ligand interfaces are often of significant pharmaceutical interest, owing in part to the entropy which can be released upon the displacement of an ordered water by a therapeutic compound. Protein structures may not, however, completely resolve all critical bound water molecules, or there may be no experimental data available. As such, predicting the location of water molecules in the absence of a crystal structure is important in the context of rational drug design. Grand canonical Monte Carlo (GCMC) is a computational technique that is gaining popularity for the simulation of buried water sites. In this work, we assess the ability of GCMC to accurately predict water binding locations, using a dataset that we have curated, containing 108 unique structures of complexes between proteins and Food and Drug Administration (FDA)-approved small-molecule drugs. We show that GCMC correctly predicts 81.4% of nonbulk crystallographic water sites to within 1.4 Å. However, our analysis demonstrates that the reported performance of water prediction methods is highly sensitive to the way in which the performance is measured. We also find that crystallographic water sites with more protein/ligand hydrogen bonds and stronger electron density are more reliably predicted by GCMC. An analysis of water networks revealed that more than half of the structures contain at least one ligand-contacting water network. In these cases, displacement of a water site by a ligand modification might yield unexpected results if the larger network is destabilized. Cooperative effects between waters should therefore be explicitly considered in structure-based drug design.

The aim of this study was to characterize the safety of programmed death 1 inhibitors in patients with preexisting autoimmune disease.A medical records review study was conducted on adults with solid tumor malignancies who received ≥1 dose of pembrolizumab or nivolumab at Emory Healthcare from September 4, 2014 until December 31, 2019. All autoimmune patients were included (n = 77), whereas the nonautoimmune patients were randomized and the first 156 patients were included in a 2:1 ratio to autoimmune patients. The primary objective was the comparison of incidence of immune-related adverse events (irAEs) between patients with preexisting autoimmune disease and those without. Secondary objectives included irAE characterization, irAE treatment, and survival analyses.Preexisting autoimmune disease was controlled in all of the autoimmune patients before immunotherapy initiation. The rate of irAE was 32.7% in the nonautoimmune group and 42.9% in the autoimmune group (odds ratio, 0.65; 95% confidence interval, 0.37-1.14; p = 0.130). In the patient population diagnosed with a rheumatologic autoimmune disease, 23.81% of irAEs were considered to be a flare of their preexisting autoimmune disease. Less patients in the autoimmune group experienced a grade ≥3 irAE (21.21% vs 37.25%, p = 0.379) and received systemic corticosteroids (54.55% vs 67.35%, p = 0.241) for the treatment of the irAE.These results suggest that pembrolizumab and nivolumab can be safely administered in patients with controlled preexisting autoimmune diseases without a significant increase in irAE compared with patients without autoimmune diseases. Inclusion of patients with preexisting autoimmune diseases in prospective clinical trials is warranted.

The fundamental importance of water molecules at drug-protein interfaces is now widely recognised and a significant feature in structure-based drug design. Experimental methods for analysing the role of water in drug binding have many challenges, including the accurate location of bound water molecules in crystal structures, and problems in resolving specific water contributions to binding thermodynamics. Computational analyses of binding site water molecules provide an alternative, and in principle complete, structural and thermodynamic picture, and their use is now commonplace in the pharmaceutical industry. In this review, we describe the computational methodologies that are available and discuss their strengths and weaknesses. Additionally, we provide a critical analysis of the experimental data used to validate the methods, regarding the type and quality of experimental structural data. We also discuss some of the fundamental difficulties of each method and suggest directions for future study.

Water often plays a key role in protein structure, molecular recognition, and mediating protein-ligand interactions. Thus, free energy calculations must adequately sample water motions, which often proves challenging in typical MD simulation time scales. Thus, the accuracy of methods relying on MD simulations ends up limited by slow water sampling. Particularly, as a ligand is removed or modified, bulk water may not have time to fill or rearrange in the binding site. In this work, we focus on several molecular dynamics (MD) simulation-based methods attempting to help rehydrate buried water sites: BLUES, using nonequilibrium candidate Monte Carlo (NCMC); grand, using grand canonical Monte Carlo (GCMC); and normal MD. We assess the accuracy and efficiency of these methods in rehydrating target water sites. We selected a range of systems with varying numbers of waters in the binding site, as well as those where water occupancy is coupled to the identity or binding mode of the ligand. We analyzed the rehydration of buried water sites in binding pockets using both clustering of trajectories and direct analysis of electron density maps. Our results suggest both BLUES and grand enhance water sampling relative to normal MD and grand is more robust than BLUES, but also that water sampling remains a major challenge for all of the methods tested. The lessons we learned for these methods and systems are discussed.

Purpose The primary objective of the study described here was to compare rates of patient adherence to anticancer medications filled at an internal health system specialty pharmacy (HSSP) vs external specialty pharmacies. The primary outcome was the medication possession ratio (MPR), and the secondary outcomes included proportion of days covered (PDC), and time to treatment (TTT). Methods A retrospective chart review was conducted to compare the MPR, PDC, and TTT for patients who received oral anticancer therapy using prescriptions claim data. A t test or Wilcoxon test was used to explore the effect of demographic and other factors on adherence and TTT. A multiple regression model with backward elimination was used to analyze significant factors to identify covariates significantly associated with the outcomes. Results Of the 300 patients screened for study inclusion, 204 patients whose records had complete MPR and PDC data and 164 whose records had TTT data were included in the analysis. There were significant between-group differences in mean MPR and mean PDC with patient use of the HSSP vs external pharmacies (1.00 vs 0.75 [P < 0.001] and 0.95 vs 0.7 [P < 0.001], respectively). Pharmacy type (P = 0.024) and tumor type (P = 0.048) were significantly associated with TTT. Conclusion The multiple regression analysis indicated that oncology patients who filled their anticancer medication precriptions at an internal HSSP at an academic medical center had significantly higher adherence, as measured by MPR and PDC, and quicker TTT than those who filled their prescriptions at an external specialty pharmacy.

Whilst the quality of force fields and sampling methodologies has improved significantly over the years, along with the accessible lengths of simulations, there still remain a number of factors which limit the reproducibility of relative binding free energy calculations. In this chapter, we discuss a number of the choices affecting the ability of independent research teams to obtain statistically equivalent results for a given free energy calculation, and also those factors which affect the precision of a given set of calculations. We also discuss some of the published methods which are able to resolve or minimize these issues, and suggest future directions as to how researchers can maximize the reproducibility of their work.

1ABSTRACTWater often plays a key role in protein structure, molecular recognition, and mediating protein-ligand interactions. Thus, free energy calculations must adequately sample water motions, which often proves challenging in typical MD simulation timescales. Thus, the accuracy of methods relying on MD simulations ends up limited by slow water sampling. Particularly, as a ligand is removed or modified, bulk water may not have time to fill or rearrange in the binding site. In this work, we focus on several molecular dynamics (MD) simulation-based methods attempting to help rehydrate buried water sites: BLUES, using nonequilibrium candidate Monte Carlo (NCMC); grand, using grand canonical Monte Carlo (GCMC); and normal MD. We assess the accuracy and efficiency of these methods in rehydrating target water sites. We selected a range of systems with varying numbers of waters in the binding site, as well as those where water occupancy is coupled to the identity or binding mode of the ligand. We analyzed rehydration of buried water sites in binding pockets using both clustering of trajectories and direct analysis of electron density maps. Our results suggest both BLUES and grand enhance water sampling relative to normal MD and grand is more robust than BLUES, but also that water sampling remains a major challenge for all of the methods tested. The lessons we learned for these methods and systems are discussed.

Current computer architectures, coupled with state-of-the-art molecular dynamics simulation software, facilitate the in-depth study of large biomolecular systems at high levels of detail. However, biological phenomena take place at various time and length scales and as a result a multiscale approach must be adopted. One such approach is coarse-graining, where biochemical accuracy is sacrificed for computational efficiency. Here, we present a practical guide to setting up and carrying out coarse-grained molecular dynamics simulations.

Current computer architectures, coupled with state-of-the-art molecular dynamics simulation software, facilitate the in-depth study of large biomolecular systems at high levels of detail. However, biological phenomena take place at various time and length scales and as a result a multiscale approach must be adopted. One such approach is coarse-graining, where biochemical accuracy is sacrificed for computational efficiency. Here, we present a practical guide to setting up and carrying out coarse-grained molecular dynamics simulations.

This is a review of current literature published between January 2018 and January 2019 including any publications regarding adverse effects related to corticotrophins, corticosteroids, and prostaglandins. The following medications are included: corticotrophins, glucocorticoids, and the following prostaglandins alprostadil (PGE1), epoprostenol (PGI1 analogue), iloprost (PGI2 analogue), misoprostol (PGE1 analogue), and treprostinil (PGI2 analogue).

This study is aimed at (i) estimating the angular leaf spot (ALS) disease severity in common beans crops in Brazil, caused by the fungus Pseudocercospora griseola, employing leaf and canopy spectral reflectance data, (ii) evaluating the informative spectral regions in the detection, and (iii) comparing the estimation accuracy when the reflectance or the first derivative reflectance (FDR) is employed. Three data sets of useful spectral reflectance measurements in the 440 to 850 nm range were employed; measurements were taken over the leaves and canopy of bean crops with different levels of disease. A system based in Principal Component Analysis (PCA) and Artificial Neural Networks (ANN) was developed to estimate the disease severity from leaf and canopy hyperspectral reflectance spectra. Levels of disease to be taken as true reference were determined from the proportion of the total leaf surface covered by necrotic lesions on RGB images. When estimating ALS disease severity in bean crops by using hyperspectral reflectance spectrometry, this study suggests that (i) successful estimations with coefficients of determination up to 0.87 can be achieved if the spectra is acquired by the spectroradiometer in contact with the leaves, (ii) unsuccessful estimations are obtained when the spectra are acquired by the spectroradiometer from one or more meters above the crop, (iii) the red to near-infrared spectral region (630–850 nm) offers the same precision in the estimation as the blue to near-infrared spectral region (440–850), and (iv) neither significant improvements nor significant detriments are achieved when the input data to the estimation processing system are the FDR spectra, instead of the reflectance spectra.

Side Effects of Drugs Annual is an annual publication including the most recently published adverse effects and toxicities associated with medications. The series supplements the contents of Meyler's Side Effects of Drugs: The International Encyclopedia of Adverse Drug Reactions and Interactions. This is a review of current literature published between January 2017 and January 2018 including any publications regarding immunosuppressive and immunostimulatory agents, specifically focusing on the following medications: belatacept, cyclosporine (ciclosporin), everolimus, fingolimod, glatiramer acetate, leflunomide, mycophenolic acid (mycophenolate mofetil), sirolimus, tacrolimus, temsirolimus, thiopurines, and levamisole. Included is a special review on vasculopathy/vasculitis associated with levamisole and adultered cocaine.

Neste estudo objetivou-se identificar comprimentos de onda e faixas espectrais provenientes de reflectâncias hiper e multiespectrais utilizando regressão PLS e promover avaliação comparativa desses métodos e de dez índices de vegetação, para determinar aqueles que melhor estimam níveis de severidade de mofo-branco em feijão. Foram implantados experimentos nos municípios de Viçosa e de Oratórios, estado de Minas Gerais. Reflectâncias hiperespectrais foram obtidas com espectroradiômetro cuja faixa útil de leitura adotada foi entre 440 e 900 nm. Reflectâncias multiespectrais foram obtidas de imagens de câmara constituídas de cinco bandas (vermelho, verde, azul, Red-edge e infravermelho). Os índices de severidade da doença foram baixos; em Viçosa a média foi de 5,8% e em Oratórios, 7,4%. Modelos matemáticos utilizando reflectâncias hiperespectrais tiveram melhor desempenho para estimar mofo-branco; a banda do red-edge apresentou os comprimentos de onda que melhor estimam a severidade do mofo-branco. Índices de vegetação resistentes a efeitos da reflectância de solo estimaram melhor o mofo-branco do que os demais índices. This study aimed to identify wavelengths and spectral ranges from hyper and multispectral reflectance using PLS regression; and to promote comparative evaluation of these methods and ten vegetation indices to determine those that best estimate levels of white mold severity in common beans. Experiments were implemented in the municipalities of Viçosa and Oratorios, Minas Gerais state. Hyperspectral reflectance measurements were acquired with the spectroradiometer, whose useful reading range was between 440 and 900 nm. Multispectral reflectance measurements were obtained from camera images comprising five bands (red, green, blue, red-edge and infrared). The indexes of disease severity were low. In Viçosa the average was 5.8% and in Oratorios, 7.4%. Mathematical models using hyperspectral reflectance performed better for estimating white mold. The red-edge band presented the wavelengths that best estimate the severity of white mold. Vegetation index resistant to the soil reflectance effects was better to estimate white mold than the other indices.