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The single-nucleotide polymorphism (SNP) rs12979860 near the IL28B gene has been associated with the spontaneous clearance of hepatitis C virus. We sought to determine whether this SNP could be associated with the spontaneous control of human immunodeficiency virus (HIV) infection. We studied the prevalence of the IL28B CC genotype among 53 white HIV controllers, compared with the prevalence among 389 HIV-infected noncontrollers. We found that the IL28B CC genotype was independently associated with spontaneous HIV control (odds ratio [OR], 2.669; P = .017), as were female sex (OR, 7.077; P ≤ .001) and the presence of HLA-B57 and/or B27 (OR, 3.080; P = .017). This result supports the idea that common host mechanisms are involved in the spontaneous control of these 2 chronic infections. [ABSTRACT FROM AUTHOR]

Con el propósito de evaluar el efecto de la selección masal divergente por mazorcas por planta sobre el diagrama deentrenudos en dos subpoblaciones derivadas de la variedad sintética Harinoso Mosquera I Sin.2, se realizó esta investigación en el C.N.1. Tibaitatá en 1980. Los resultados deeste trabajo corresponden a los ciclos de selección 12 a 20 de cada subpoblación. Se empleó el diseño experimental de bloques completos al azar con 22 tratamientos y diez repeticiones. De cada parcela se tomó una muestra de diez plantas, a. cada una de las cuales se les tomó las siguientes características: número total y longitud de los entrenudos,número de entrenudos por debajo y encima de la mazorca superior. Los diagramas de entrenudos por debajo y encima de la mazorca superior. Los diagramas de entrenudos permitieron analizar de una manera integral, las modificaciones en los caracteres medidos. Las plantas de la subpoblación prol ífica resultaron más altas, puesto quetuvieron mayor número de entrenudos y más largos que la subpoblación no prolífica. El aumento del número de entrenudos ocurrió por debajo de la mazorca superior. Seobservó también que el diagrama de entrenudos de los ciclos de selección de la subpoblación no prolífica fue similar al de la variedad original. Por tanto el efecto directo de la selección masal para incrementar la prolificidad en la población estudiada estuvo acompañada de respuestas colaterales en el número total y longitud de los entrenudos.This study was conducted at the C.N.!. Tibaitatá in 1980, to determine the effect of divergent mass selection for earsper plant on internode pattern of two subpopulationsderived from the synthetic Harinoso Mosquera I Sin.2. The results corresponded to the selection cycles from 12 to 20 of each subpopulation. Arando. mized complete block design with 22 treatments, 10 blocks was used. From each plot a random sample of ten plants were taken and the following characteristics were measured: number of internodes, interhode lenght and number of internodes above andbelow the upper ear. The internode patterns allowed to analize in a integral way, the changes in the characteristics considered: the plants of the prolific subpopulation werehigher because they had more and longer internodes than the non-prolific population. The increase of the internode numberocurred below the upper ear. It was also observed that the internode patterns of the non prolific population, were similar to the original variety. Therefore, the response to mass selection to increase prolificacy, was accompanied with a collateral response in the number and lenght of the internodes.

Recent advances in machine learning (ML) are reshaping drug discovery. Structure-based ML methods use physically-inspired models to predict binding affinities from protein:ligand complexes. These methods promise to enable the integration of data for many related targets, which addresses issues related to data scarcity for single targets and could enable generalizable predictions for a broad range of targets, including mutants. In this work, we report our experiences in building KinoML, a novel framework for ML in target-based small molecule drug discovery with an emphasis on structure-enabled methods. KinoML focuses currently on kinases as the relative structural conservation of this protein superfamily, particularly in the kinase domain, means it is possible to leverage data from the entire superfamily to make structure-informed predictions about binding affinities, selectivities, and drug resistance. Some key lessons learned in building KinoML include: the importance of reproducible data collection and deposition, the harmonization of molecular data and featurization, and the choice of the right data format to ensure reusability and reproducibility of ML models. As a result, KinoML allows users to easily achieve three tasks: accessing and curating molecular data; featurizing this data with representations suitable for ML applications; and running reproducible ML experiments that require access to ligand, protein, and assay information to predict ligand affinity. Despite KinoML focusing on kinases, this framework can be applied to other proteins. The lessons reported here can help guide the development of platforms for structure-enabled ML in other areas of drug discovery.

The development of reliable and extensible molecular mechanics (MM) force fields-fast, empirical models characterizing the potential energy surface of molecular systems-is indispensable for biomolecular simulation and computer-aided drug design. Here, we introduce a generalized and extensible machine-learned MM force field, espaloma-0.3, and an end-to-end differentiable framework using graph neural networks to overcome the limitations of traditional rule-based methods. Trained in a single GPU-day to fit a large and diverse quantum chemical dataset of over 1.1 M energy and force calculations, espaloma-0.3 reproduces quantum chemical energetic properties of chemical domains highly relevant to drug discovery, including small molecules, peptides, and nucleic acids. Moreover, this force field maintains the quantum chemical energy-minimized geometries of small molecules and preserves the condensed phase properties of peptides and folded proteins, self-consistently parametrizing proteins and ligands to produce stable simulations leading to highly accurate predictions of binding free energies. This methodology demonstrates significant promise as a path forward for systematically building more accurate force fields that are easily extensible to new chemical domains of interest., Competing Interests: J. D. C. is a current member of the Scientific Advisory Board of OpenEye Scientific Software, Redesign Science, Ventus Therapeutics, and Interline Therapeutics, and has equity interests in Redesign Science and Interline Therapeutics. The Chodera laboratory receives or has received funding from multiple sources, including the National Institutes of Health, the National Science Foundation, the Parker Institute for Cancer Immunotherapy, Relay Therapeutics, Entasis Therapeutics, Silicon Therapeutics, EMD Serono (Merck KGaA), AstraZeneca, Vir Biotechnology, Bayer, XtalPi, Interline Therapeutics, the Molecular Sciences Software Institute, the Starr Cancer Consortium, the Open Force Field Consortium, Cycle for Survival, a Louis V. Gerstner Young Investigator Award, and the Sloan Kettering Institute. A complete funding history for the Chodera lab can be found at http://choderalab.org/funding. Y. W. has limited financial interests in Flagship Pioneering, Inc. and its subsidiaries. M. R. S. is an Open Science Fellow with Psivant Sciences and consults for Relay Therapeutics. D. L. M. serves on the scientific advisory boards of Anagenex and OpenEye Scientific Software, Cadence Molecular Sciences, and is an Open Science Fellow with Psivant., (This journal is © The Royal Society of Chemistry.)

Atomic partial charges are crucial parameters in molecular dynamics simulation, dictating the electrostatic contributions to intermolecular energies and thereby the potential energy landscape. Traditionally, the assignment of partial charges has relied on surrogates of ab initio semiempirical quantum chemical methods such as AM1-BCC and is expensive for large systems or large numbers of molecules. We propose a hybrid physical/graph neural network-based approximation to the widely popular AM1-BCC charge model that is orders of magnitude faster while maintaining accuracy comparable to differences in AM1-BCC implementations. Our hybrid approach couples a graph neural network to a streamlined charge equilibration approach in order to predict molecule-specific atomic electronegativity and hardness parameters, followed by analytical determination of optimal charge-equilibrated parameters that preserve total molecular charge. This hybrid approach scales linearly with the number of atoms, enabling for the first time the use of fully consistent charge models for small molecules and biopolymers for the construction of next-generation self-consistent biomolecular force fields. Implemented in the free and open source package EspalomaCharge, this approach provides drop-in replacements for both AmberTools antechamber and the Open Force Field Toolkit charging workflows, in addition to stand-alone charge generation interfaces. Source code is available at https://github.com/choderalab/espaloma-charge.

Patient-derived organoids (PDOs) serve as invaluable 3D tumor models, retaining the histological complexity and genetic heterogeneity found in primary tumors. However, the limitation of small sample volumes and the lack of tailored platforms have hindered the research using PDOs. Within the tumor microenvironment, cancer-associated fibroblasts play a pivotal role in influencing drug sensitivity. In this study, we introduce an agarose microwell platform designed for PDO-based tumor and tumor microenvironment models, enabling rapid drug screening and resistance studies with small sample volumes. These microwells, constructed using 3D printing molds, feature a U-shaped bottom and 200 μm diameter. We successfully generated co-culture spheroids of non-small cell lung carcinoma (NSCLC) cells, including NCI-H358 or A549, and NSCLC PDOs F231 or F671 with fibroblast cell line, WI-38. Our results demonstrate the production of uniformly-sized spheroids (coefficient of variation <30%), high viability (>80% after 1 week), and fibroblast-induced drug resistance. The PDOs maintained their viability (>81% after 2 weeks) and continued to proliferate. Notably, when exposed to adagrasib, a KRAS G12C inhibitor, we observed reduced cytotoxicity in KRAS G12C -mutant spheroids when co-cultured with fibroblasts or their supernatant. The fibroblast supernatant sustained proliferative signals in tumor models. Taking into account the physical features, viability, and drug resistance acquired through supernatants from the fibroblasts, our platform emerges as a suitable platform for in vitro tumor modeling and the evaluation of drug efficacy using patient-derived tissues.

Introduction: Extravasation is a potentially severe complication of intravenous administration of antineoplastic drugs. The limited data makes it difficult to develop an optimal management scheme. The objective of this study is to describe the clinical practice in the extravasation management of antineoplastic agents in Spanish centers., Methods: An online survey was distributed to oncology pharmacists using the email distribution list of the Spanish Society of Hospital Pharmacists. Respondents were surveyed on the standard operational protocol (SOP) of extravasation, tissue damage risk classification, and specific measures of extravasation management., Results: A total of 68 surveys were completed. A specific extravasation SOP was available in 82.4% centers. The pharmacist participates in the authorship (100%) and actively collaborates in extravasation management (76.5%). A tissue damage risk classification based on the three categories was mostly adopted (48.2%) and 73.2% applied specific criteria based on concentration and/or extravasated volume. Extravasation management was mainly performed with the application of physical measures and/or antidotes (91.2%). High variability in the choices of pharmacological and/or physical measures recommended is outstanding., Conclusion: The results of this study highlight the involvement of Spanish pharmacists in extravasation management, the application of physical measures and/or pharmacological measures as the method of choice in extravasation management, as well as the existing discrepancies in tissue damage risk classification and management recommendations., Competing Interests: Declaration of conflicting interestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

We report the results of the COVID Moonshot, a fully open-science, crowdsourced, and structure-enabled drug discovery campaign targeting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease. We discovered a noncovalent, nonpeptidic inhibitor scaffold with lead-like properties that is differentiated from current main protease inhibitors. Our approach leveraged crowdsourcing, machine learning, exascale molecular simulations, and high-throughput structural biology and chemistry. We generated a detailed map of the structural plasticity of the SARS-CoV-2 main protease, extensive structure-activity relationships for multiple chemotypes, and a wealth of biochemical activity data. All compound designs (>18,000 designs), crystallographic data (>490 ligand-bound x-ray structures), assay data (>10,000 measurements), and synthesized molecules (>2400 compounds) for this campaign were shared rapidly and openly, creating a rich, open, and intellectual property-free knowledge base for future anticoronavirus drug discovery.

Relative alchemical binding free energy calculations are routinely used in drug discovery projects to optimize the affinity of small molecules for their drug targets. Alchemical methods can also be used to estimate the impact of amino acid mutations on protein:protein binding affinities, but these calculations can involve sampling challenges due to the complex networks of protein and water interactions frequently present in protein:protein interfaces. We investigate these challenges by extending a graphics processing unit (GPU)-accelerated open-source relative free energy calculation package (Perses) to predict the impact of amino acid mutations on protein:protein binding. Using the well-characterized model system barnase:barstar, we describe analyses for identifying and characterizing sampling problems in protein:protein relative free energy calculations. We find that mutations with sampling problems often involve charge-changes, and inadequate sampling can be attributed to slow degrees of freedom that are mutation-specific. We also explore the accuracy and efficiency of current state-of-the-art approaches─alchemical replica exchange and alchemical replica exchange with solute tempering─for overcoming relevant sampling problems. By employing sufficiently long simulations, we achieve accurate predictions (RMSE 1.61, 95% CI: [1.12, 2.11] kcal/mol), with 86% of estimates within 1 kcal/mol of the experimentally determined relative binding free energies and 100% of predictions correctly classifying the sign of the changes in binding free energies. Ultimately, we provide a model workflow for applying protein mutation free energy calculations to protein:protein complexes, and importantly, catalog the sampling challenges associated with these types of alchemical transformations. Our free open-source package (Perses) is based on OpenMM and is available at https://github.com/choderalab/perses.

Previous studies demonstrated that enzymatic hydrolysis enhances wheat bran (WB) biological properties. This study evaluated the immunostimulatory effect of a WB hydrolysate (HYD) and a mousse enriched with HYD (MH) before and after in vitro digestion on murine and human macrophages. The antiproliferative activity of the harvested macrophage supernatant on colorectal cancer (CRC) cells was also analyzed. MH showed significantly higher content than control mousse (M) in soluble poly- and oligosaccharides (OLSC), as well as total soluble phenolic compounds (TSPC). Although in vitro gastrointestinal digestion slightly reduced the TSPC bioaccessibility of MH, ferulic acid (FA) levels remained stable. HYD showed the highest antioxidant activity followed by MH, which demonstrated a greater antioxidant activity before and after digestion as compared with M. RAW264.7 and THP-1 cells released the highest amounts of pro-inflammatory cytokines after being treated with 0.5 mg/mL of digested WB samples. Treatment with digested HYD-stimulated RAW264.7 supernatant for 96 h showed the most anticancer effect, and spent medium reduced cancer cell colonies more than direct WB sample treatments. Although a lack of inner mitochondrial membrane potential alteration was found, increased Bax:Bcl-2 ratio and caspase-3 expression suggested activation of the mitochondrial apoptotic pathway when CRC cells were treated with macrophage supernatants. Intracellular reactive oxygen species (ROS) were positively correlated with the cell viability in CRC cells exposed to RAW264.7 supernatants (r = 0.78, p < 0.05) but was not correlated in CRC cells treated with THP-1 conditioned media. Supernatant from WB-stimulated THP-1 cells may be able to stimulate ROS production in HT-29 cells, leading to a decrease of viable cells in a time-dependent manner. Therefore, our present study revealed a novel anti-tumour mechanism of HYD through the stimulation of cytokine production in macrophages and the indirect inhibition of cell proliferation, colony formation, and activation of pro-apoptotic proteins expression in CRC cells.

Molecular mechanics (MM) potentials have long been a workhorse of computational chemistry. Leveraging accuracy and speed, these functional forms find use in a wide variety of applications in biomolecular modeling and drug discovery, from rapid virtual screening to detailed free energy calculations. Traditionally, MM potentials have relied on human-curated, inflexible, and poorly extensible discrete chemical perception rules (atom types) for applying parameters to small molecules or biopolymers, making it difficult to optimize both types and parameters to fit quantum chemical or physical property data. Here, we propose an alternative approach that uses graph neural networks to perceive chemical environments, producing continuous atom embeddings from which valence and nonbonded parameters can be predicted using invariance-preserving layers. Since all stages are built from smooth neural functions, the entire process-spanning chemical perception to parameter assignment-is modular and end-to-end differentiable with respect to model parameters, allowing new force fields to be easily constructed, extended, and applied to arbitrary molecules. We show that this approach is not only sufficiently expressive to reproduce legacy atom types, but that it can learn to accurately reproduce and extend existing molecular mechanics force fields. Trained with arbitrary loss functions, it can construct entirely new force fields self-consistently applicable to both biopolymers and small molecules directly from quantum chemical calculations, with superior fidelity than traditional atom or parameter typing schemes. When adapted to simultaneously fit partial charge models, espaloma delivers high-quality partial atomic charges orders of magnitude faster than current best-practices with low inaccuracy. When trained on the same quantum chemical small molecule dataset used to parameterize the Open Force Field ("Parsley") openff-1.2.0 small molecule force field augmented with a peptide dataset, the resulting espaloma model shows superior accuracy vis-á-vis experiments in computing relative alchemical free energy calculations for a popular benchmark. This approach is implemented in the free and open source package espaloma, available at https://github.com/choderalab/espaloma., Competing Interests: The authors declare no conflicts of interest, but highlight all sources of funding in Disclosures in the interest of full transparency., (This journal is © The Royal Society of Chemistry.)

Objectives: Identifying frequent users' (≥3admissions/year) associated factors in an emergency department (ED), using a comprehensive geriatric assessment (CGA), describing the characteristics of patients over 65 years of age., Methods: A cross-sectional study was performed between August 2017 and June 2018 in an ED in Lisbon, Portugal. CGA was applied and completed with clinical records. Clinical, functional, mental and social scores were created based in Portuguese Society of Internal Medicine, and a statistical model was developed., Results: CGA was applied to 426 patients over 64 years old in an ED. The mean age was 79.3, 84.7% had multimorbidity. 51.2%, 75.6%, and 40% had dependence on basic, instrumental, and walking activities, respectively. 52% had depressive symptoms, 65.7% had cognitive impairment, 63% were undernourished/at risk for malnutrition. 33.1% were socially at risk. Polypharmacy was present with a use on average of 6.5 drugs daily. Social, clinical, functional, and mental scores were unfavourable in 48.6%, 79.6%, 54.9% and 83.1% of the population, respectively. There were 2.7 hospital admissions/year and 39.9% were frequent ED users (≥3/year). The logistic regression model was weak, but showed that patients with polypharmacy, elevated Charlson Comorbidity index and an impairment nutritional status presented higher risk of being frequent users., Conclusions: This study showed that 97.1% of patients had needs that would justify an interventional care plan. This intervention should be extended to primary care and nursing homes. While not providing a robust model, our study has indicated nutritional problems, polypharmacy, and an elevated Charlson index as the features with more weight in frequent users' admissions., (Copyright © 2022 SEGG. Publicado por Elsevier España, S.L.U. All rights reserved.)

Patients with high cholesterol and glucose levels are at high risk for cardiovascular disease. The Sterol Regulatory Element Binding Protein (SREBP) system regulates genes involved in lipid, cholesterol and glucose pathways. Autosomal Dominant Hypercholesterolemias (ADHs) are a group of diseases with increased cholesterol levels. They affect 1 out of every 500 individuals. About 20-30% of patients do not present any mutation in the known genes ( LDLR , APOB and PCSK9 ). ADHs constitute a good model to identify the genes involved in the alteration of lipid levels or possible therapeutic targets. In this paper, we studied whether a mutation in the SREBP system could be responsible for ADH and other metabolic alterations present in these patients. Forty-one ADH patients without mutations in the main responsible genes were screened by direct sequencing of SREBP system genes. A luciferase reporter assay of the found mutation and an oral glucose tolerance test in carriers and non-carriers were performed. We found a novel mutation in the SREBF2 gene that increases transcription levels and cosegregates with hypercholesterolemia, and we found increased glucose levels in one family. SREBP2 is known to be involved in cholesterol synthesis, plasma levels and glucose metabolism in humans. The found mutation may involve the SREBF2 gene in hypercholesterolemia combined with hyperglycemia.

To enlarge the applications of whole wheat grain (WWG) and wheat bran (WB) as functional ingredients in foodstuffs that can promote human health, researchers have explored bioprocessing approaches to improve the bioaccessibility of phenolic compounds from these food matrices and, subsequently, their biological effects. The objective of this study was to compare the composition in nutrients, anti-nutrients, and bioactive compounds of WWG and WB, and their respective bioprocessed products: sprouted wheat (GERM) and WB hydrolysate (stabilized by spray-drying [SPD] and microencapsulated [MEC]). In addition, to evaluate the functional properties of these ingredients, the bioaccessibility of phenolic compounds and their potential antioxidant and anti-inflammatory activities were monitored in different digestion steps. GERM had increased amounts of insoluble dietary fiber, higher diversity of oligosaccharides, and higher concentration of monosaccharides, free phosphorous, and phenolic compounds than WWG. SPD had improved content of soluble dietary fiber, oligosaccharides, monosaccharides, free phosphorous, and phenolic compounds (vs. WB), whereas MEC was mainly composed of protein and had nearly 2-fold lower content of SPD components. All the ingredients showed lower amounts of phytic acid as compared with raw materials. In all samples, hydroxycinnamic acids were the most representative polyphenols followed by minor amounts of hydroxybenzoic acids and flavonoids. Gastrointestinal digestion of GERM, SPD, and MEC revealed high stability of total phenolic compounds in both gastric and intestinal phases. Hydroxycinnamic acids were the most bioaccessible compounds during digestion among the three bioprocessed wheat ingredients studied, although their bioaccessibility varied across ingredients. In this sense, the bioaccessibility of ferulic acid (FA) derivatives increased in GERM with progression of the digestion, while it was reduced in SPD and MEC up to the end of the intestinal phase. Microencapsulation of SPD with pea protein led to generally to lower bioaccessible amounts of phenolic acids. Comparison analysis of biological effects highlighted SPD for its most potent antioxidant effects in the gastrointestinal tract (3 out 4 antioxidant parameters with highest values), while no clear differences were observed with regard to in vitro anti-inflammatory activity. Overall, these results support the potential application of GERM, SPD, and MEC as functional and nutraceutical ingredients., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Tomé-Sánchez, Martín-Diana, Peñas, Frias, Rico, Jiménez-Pulido and Martínez-Villaluenga.)

Objective: To reach at an expert consensus, using the Delphi method, for classifying the tissue-damaging potential of antineoplastic drugs, in  order to facilitate the decision-making process in the event of  extravasations., Method: The panel of expert evaluators was made up of seven  pharmacists belonging to the working group on extravasations. Other  member served as coordinator. The likelihood of tissue damage was  reviewed on the basis of eight reference documents. Four categories of  drugs were established: vesicant (V); high risk irritant (HRI); low risk  irritant (LRI) and non-irritant (NI). Two rounds of surveys were performed. The drugs with an agreement of less than 70% after the two rounds were  discussed non-anonymously by the group. For each of the rounds the  following was analysed: median of the degree of consensus and the  interquartile range (IQR25-75), degree of agreement by tissue damage  category, and percentage of antineoplastics reaching a degree of  consensus of over 85% and of 100%. Drugs whose classification differed in the various reference documents were assessed separately. SPSS v23.0  statistical software was used., Results: Seventy-one antineoplastics were evaluated. In the first round, the median for degree of consensus was 100.0% (IQR25-75: 71.4- 100.0%). In the second round, the median was 100.0% (IQR25-75: 85.7- 100.0%). The percentage of antineoplastics with a consensus of 85.7% or  above increased from 66.7% to 85.9% in the second round. For the 30  antineoplastics whose values differed in the reference documents, the  degree of agreement increased from 71.4% (IQR25-75: 57.1-87.7%) to  100.0% (IQR25-75: 85.7-100.0%) in the second round. The percentage of antineoplastics with a consensus of 85.7% or above increased from 40.0%  to 76.7%. Four antineoplastics had a degree of agreement of less  than 70.0%. The final classification of drugs per category, was: 17  vesicants; 15 HRI; 13 LRI; and 26 NI. The final degree of consensus was  85.7% or above for 90.1% of antineoplastics, and 100.0% for 74.6% of  the same., Conclusions: In this area of scarce evidence and high variability, the Delphi method allows for consensus in classifying tissue damage risk,  thus making it easier to reach clinical decisions. In approximately 90% of  the antineoplastics, the degree of consensus reached by the expert panel  was 85% or above. In 74% of the antineoplastics, it was 100%. This  provides solid ground for management decisions., (Copyright AULA MEDICA EDICIONES 2014. Published by AULA MEDICA. All rights reserved.)