A canonical cation-π interaction stabilizes the agonist conformation of estrogen-like nuclear receptors

Representative crystal structures of the ligand-binding domain for the majority of nuclear receptors are currently available. A systematic comparative analysis of these structures identified an energetically favorable cation-π interaction that involves an amino acid located at the extreme C-terminal end and appears to form only in the agonist conformation of the estrogen receptor α, glucocorticoid, mineralocorticoid, progesterone, and androgen receptors. It is postulated that this cation-π interaction is used by members of the estrogen-like subfamily to provide additional stabilization to the transcriptional active conformation upon ligand binding.

Comparative electronic analysis between hydrogen transfers in the CH4/CH3+, CH4/CH3•, and CH4/CH3− systems: on the electronic nature of the hydrogen (H−, H•, and H+) being transferred

A comparative electronic analysis of the generally termed hydrogen transfers between CH4 and the CH3+, CH3•, and CH3− fragments is presented. These systems are taken as simple models of hydride (H−), hydrogen (H•), and proton (H+) transfers between two carbon fragments (in these simple cases being modelized by two CH3+, CH3•, and CH3− fragments, respectively). The study is mainly focused on analysis of the electronic nature of the type of hydrogen being transferred in each system, and for this reason a topological analysis of charge density distributions was performed. Computation of Bader atomic charges and construction of the charge density, gradient vector field, and Appalachian of the charge density maps reveal the specific features of the electronic nature of the transferring H−, H•, and H+. Moreover, characterization of the bond critical points on the charge density surface permits clarification of the differences in atomic interactions between H−, H•, and H+ and the carbon belonging to each CH3+, CH3•, and CH3− fragment, respectively. A charge density redistribution analysis is also performed to quantify the reorganization of the electron density when going from the reactant complex to the transition state. Finally, effects of inclusion of the correlation energy at the MP2 and CISD levels are also discussed. Key words: electron density, hydrogen transfer, topological density analysis, molecular similarity, Bader density analysis.

Abstract 2983: In silico prescription of anticancer drugs to cohorts of 28 tumor types reveals novel targeting opportunities

Recent advances in DNA sequencing technologies provide unprecedented capacity to comprehensively identify the alterations, genes, and pathways involved in the tumorigenic process, raising the hope of extending targeted therapies against the drivers of cancer from a few successful examples to a broader personalized medicine strategy. However, high intertumor heterogeneity is a major obstacle to develop and apply therapeutic targeted agents to treat most cancer patient. In addition, advances in our ability to precisely assign the most effective targeted therapy to each patient based on the genome events driving the tumor are urgently needed.

The present study offers the first comprehensive assessment of the scope of targeted drugs in a large pan-cancer cohort. To pursue this goal, we developed a three-step in silico drug prescription strategy. We first identified the driver genes acting across 6792 tumor samples from 28 different cancer types via an integrated analysis of their mutations, copy number alterations and gene fusions. All information pertaining these driver genes has been compiled in a publicly available Drivers Database. Next, following the rationale that targeted therapies are effective only if they are administered to treat tumors driven by the alterations they are aimed at, we collected all therapeutic agents capable of targeting altered driver genes either directly, indirectly or through gene therapies. The catalog of available therapeutic agents and ancillary information on their application, referred here as Drivers Actionability Database, included FDA (Foods and Drugs Administration Agency) approved drugs, agents undergoing clinical trials, and ligands in pre-clinical stages. Finally, based on the driver alterations in each tumor in the cohort and the rules in the Drivers Actionability Database, we connected each patient to all targeted therapies that could benefit them, thus producing the landscape of utility of targeted therapeutic agents in the cohort.

We found that only a minority of patients could benefit from approved targeted therapy interventions following clinical guidelines (5.9%), while up to 40% could benefit from different types of repurposing opportunities of approve drugs, and up to 78% considering treatments currently under investigation. In addition, we identified 16 therapeutically unexploited cancer genes targeted by small molecules currently in pre-clinical stages, and 66 others structurally suitable for small molecule binding or accessible by antibody targeting. These results highlight the current scope of targeted anti-cancer therapies and its prospects for growth. The application of the strategy to larger cohorts and the continuous update of drug-target interactions information, will improve the in silico prescription rules contained within the two databases, thus enhancing its usefulness within personalized cancer medicine.

Citation Format: Carlota Rubio-Perez, David Tamborero, Michael P. Schroeder, Albert A. Antolin, Jordi Deu-Pons, Christian Perez-Llamas, Jordi Mestres, Abel Gonzalez-Perez, Nuria Lopez-Bigas. In silico prescription of anticancer drugs to cohorts of 28 tumor types reveals novel targeting opportunities. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2983. doi:10.1158/1538-7445.AM2015-2983

Evaluation of QSAR models for predicting mutagenicity: outcome of the Second Ames/QSAR international challenge project

Quantitative structure-activity relationship (QSAR) models are powerful in silico tools for predicting the mutagenicity of unstable compounds, impurities and metabolites that are difficult to examine using the Ames test. Ideally, Ames/QSAR models for regulatory use should demonstrate high sensitivity, low false-negative rate and wide coverage of chemical space. To promote superior model development, the Division of Genetics and Mutagenesis, National Institute of Health Sciences, Japan (DGM/NIHS), conducted the Second Ames/QSAR International Challenge Project (2020-2022) as a successor to the First Project (2014-2017), with 21 teams from 11 countries participating. The DGM/NIHS provided a curated training dataset of approximately 12,000 chemicals and a trial dataset of approximately 1,600 chemicals, and each participating team predicted the Ames mutagenicity of each trial chemical using various Ames/QSAR models. The DGM/NIHS then provided the Ames test results for trial chemicals to assist in model improvement. Although overall model performance on the Second Project was not superior to that on the First, models from the eight teams participating in both projects achieved higher sensitivity than models from teams participating in only the Second Project. Thus, these evaluations have facilitated the development of QSAR models.

Comparative electronic analysis between hydrogen transfers in the CH4/CH3+, CH4/CH3•, and CH4/CH3- systems: on the electronic nature of the hydrogen (H-, H•, H+) being transferred. II. Analysis of electron-pair interactions from intracule and extracule densities

The nature of the hydrogen transferred in the CH3/CH4+, CH3/CH4·, and CH3/CH4- systems is investigated by analyzing the topology of the contracted intracule and extracule electron-pair densities and their respective Laplacians. The CH3/CH4+, CH3/CH4·, and CH3/CH4- systems are taken as simple models for the study of hydride (H-), hydrogen (H·), and proton (H+) transfer reactions, respectively, under a constrained C-C distance. The study is focused on the comparison of the intracule and extracule densities at the intermediate structures for the three H-transfer reactions, complementing a previous investigation of the same model reactions based on the analysis of one-electron densities. The results obtained by analyzing the contracted electron-pair densities are consistent with those obtained from the analysis of one-electron densities. The electronic nature of the H atom being transferred in the three systems can be differentiated by the topologies of the corresponding intracule and extracule densities. However, the analysis underlies also the difficulties to interpretation of the topologies of contracted electron-pair densities, as different electron-electron interactions may contribute to the same point in the intracule or extracule spaces. In particular, for the systems studied, the contribution of the electron-electron interaction associated to the probability of having two electrons on the H being transferred is not reflected separately neither in the intracule nor in the extracule distributions. Nevertheless, the nature of the H being transferred can still be studied by comparing the importance of the electron-electron interactions associated to the probability of having one electron in C and one in the transferring H. The effects of inclusion of electron correlation are also discussed by means of (HF-CISD//HF) intracule and extracule density difference maps.Key words: hydrogen transfer, electron-pair density, intracule density, extracule density, topological density analyisis.

Vitamin E serum levels in patients with leukemia, lymphoma and myeloma

The serum alpha-tocopherol levels were determined in a group of 182 patients with hematological neoplasms: 87 lymphoid or myeloid leukemias, 65 lymphomas and 30 myelomas. The levels did not differ from those of controls, when compared either globally or for diagnosis. Low alpha-tocopherol serum levels were observed in 6 patients (3.3%).

A Workflow of Integrated Resources to Catalyze Network Pharmacology Driven COVID-19 Research

In the event of an outbreak due to an emerging pathogen, time is of the essence to contain or to mitigate the spread of the disease. Drug repositioning is one of the strategies that has the potential to deliver therapeutics relatively quickly. The SARS-CoV-2 pandemic has shown that integrating critical data resources to drive drug-repositioning studies, involving host-host, host-pathogen, and drug-target interactions, remains a time-consuming effort that translates to a delay in the development and delivery of a life-saving therapy. Here, we describe a workflow we designed for a semiautomated integration of rapidly emerging data sets that can be generally adopted in a broad network pharmacology research setting. The workflow was used to construct a COVID-19 focused multimodal network that integrates 487 host-pathogen, 63 278 host-host protein, and 1221 drug-target interactions. The resultant Neo4j graph database named "Neo4COVID19" is made publicly accessible via a web interface and via API calls based on the Bolt protocol. Details for accessing the database are provided on a landing page (https://neo4covid19.ncats.io/). We believe that our Neo4COVID19 database will be a valuable asset to the research community and will catalyze the discovery of therapeutics to fight COVID-19.

In Silico Approaches In Carcinogenicity Hazard Assessment: Current Status and Future Needs

Historically, identifying carcinogens has relied primarily on tumor studies in rodents, which require enormous resources in both money and time. In silico models have been developed for predicting rodent carcinogens but have not yet found general regulatory acceptance, in part due to the lack of a generally accepted protocol for performing such an assessment as well as limitations in predictive performance and scope. There remains a need for additional, improved in silico carcinogenicity models, especially ones that are more human-relevant, for use in research and regulatory decision-making. As part of an international effort to develop in silico toxicological protocols, a consortium of toxicologists, computational scientists, and regulatory scientists across several industries and governmental agencies evaluated the extent to which in silico models exist for each of the recently defined 10 key characteristics (KCs) of carcinogens. This position paper summarizes the current status of in silico tools for the assessment of each KC and identifies the data gaps that need to be addressed before a comprehensive in silico carcinogenicity protocol can be developed for regulatory use.

A Workflow of Integrated Resources to Catalyze Network Pharmacology Driven COVID-19 Research

MOTIVATION

In the event of an outbreak due to an emerging pathogen, time is of the essence to contain or to mitigate the spread of the disease. Drug repositioning is one of the strategies that has the potential to deliver therapeutics relatively quickly. The SARS-CoV-2 pandemic has shown that integrating critical data resources to drive drug-repositioning studies, involving host-host, hostpathogen and drug-target interactions, remains a time-consuming effort that translates to a delay in the development and delivery of a life-saving therapy.

RESULTS

Here, we describe a workflow we designed for a semi-automated integration of rapidly emerging datasets that can be generally adopted in a broad network pharmacology research setting. The workflow was used to construct a COVID-19 focused multimodal network that integrates 487 host-pathogen, 74,805 host-host protein and 1,265 drug-target interactions. The resultant Neo4j graph database named "Neo4COVID19" is accessible via a web interface and via API calls based on the Bolt protocol. We believe that our Neo4COVID19 database will be a valuable asset to the research community and will catalyze the discovery of therapeutics to fight COVID-19.

AVAILABILITY

https://neo4covid19.ncats.io.

Closing the Gap Between Therapeutic Use and Mode of Action in Remedial Herbs

The ancient tradition of taking parts of a plant or preparing plant extracts for treating certain discomforts and maladies has long been lacking a scientific rationale to support its preparation and still widespread use in several parts of the world. In an attempt to address this challenge, we collected and integrated data connecting metabolites, plants, diseases, and proteins. A mechanistic hypothesis is generated when a metabolite is known to be present in a given plant, that plant is known to be used to treat a certain disease, that disease is known to be linked to the function of a given protein, and that protein is finally known or predicted to interact with the original metabolite. The construction of plant–protein networks from mutually connected metabolites and diseases facilitated the identification of plausible mechanisms of action for plants being used to treat analgesia, hypercholesterolemia, diarrhea, catarrh, and cough. Additional concrete examples using both experimentally known and computationally predicted, and subsequently experimentally confirmed, metabolite–protein interactions to close the connection circle between metabolites, plants, diseases, and proteins offered further proof of concept for the validity and scope of the approach to generate mode of action hypotheses for some of the therapeutic uses of remedial herbs.

Abstract A1-45: In silico prescription of anticancer drugs to cohorts of 28 tumor types reveals novel targeting opportunities

The development of targeted therapies against altered driver proteins holds the promise of selectively and efficiently eliminating cancer cells. However, high intertumor heterogeneity is a major obstacle to develop and apply therapeutic targeted agents to treat most cancer patients. Here, we present the first large-scale therapeutic landscape of cancer as it stands today in a 6.792 sample cohort covering 28 tumor types.

To pursue this goal, we developed a three-step in silico drug prescription strategy. 1) To discover actionable driver events, we first comprehensively identified mutational cancer driver genes by detecting complementary signals of positive selection in the pattern of their mutations across the tumor cohorts. We also identified actionable copy number alteration (CNA) and fusion cancer driver genes. Second, we detected which of these driver genes would have an oncogenic role in the tumor and which ones would lose their function. With these two steps we generated the Drivers Database. 2) Next, we systematically gathered all information available on therapeutic agents; FDA approved and in clinical or pre-clinical stages. We considered three different types of targeting strategies for the cancer driver genes: direct targeting, indirect targeting and gene therapies in clinical trials. Moreover, we designed a set of rules for assigning therapeutic agents to specific genomic alterations beard for the driver genes. By doing this last step, we generated the Drivers Actionability Database. 3) Finally, by combining data of Drivers Database, Drivers Actionability Database and sample data, we developed in silico drug prescription, a novel approach to determine which of the drugs could benefit each of the tumor individuals.

In all, in the Driver Database we identified 460 mutational cancer driver genes acting in one or more of the tumor types along with 39 driver genes acting via CNAs or fusions. Fifty of these cancer driver genes are targeted by FDA approved agents, 63 by molecules currently in clinical trials and 74 are bound by pre-clinical ligands. We also identified 81 therapeutically unexploited targetable cancer genes. Lastly, by applying in silico drug prescription we found that only 6.7% of the patients could be treated following clinical guidelines, and were concentrated in only 6 tumor types. Moreover, considering repurposing strategies the fraction of patients that could benefit from FDA approved drugs would increase up to 40%, increasing remarkably the fraction of targetable patients in some tumor types like glioblastoma and thyroid cancer, and up to 72% if considering targeted therapies in clinical trials.

In summary, the in silico drug prescription based on Drivers and Drivers Actionability Databases was tested on one of the largest cohorts of tumor samples currently collected for research. The main result highlights the current scope of targeted anti-cancer therapies and its prospects for growth in view of the drugs that are currently in clinical trials or at pre-clinical stages. Additionally, another important output of this work is a ranked list of novel target opportunities for anticancer drug development. Continuous update of drug-target interactions information, and the application of the strategy to larger cohorts, will improve the in silico prescription rules contained within the two databases, thus enhancing its usefulness within personalized cancer medicine.

Citation Format: Carlota Rubio-Perez, David Tamborero, Michael P. Schroeder, Albert A. Antolín, Jordi Deu-Pons, Christian Perez-Llamas, Jordi Mestres, Abel Gonzalez-Perez, Nuria Lopez-Bigas. In silico prescription of anticancer drugs to cohorts of 28 tumor types reveals novel targeting opportunities. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A1-45.

Primary and Community Care Transformation in Post-COVID Era: Nationwide General Practitioner Survey

Introduction: The health emergency caused by COVID-19 has led to substantial changes in the usual working system of primary healthcare centers and in relations with users. The Catalan Society of Family and Community Medicine designed a survey that aimed to collect the opinions and facilitate the participation of its partners on what the future work model of general practitioners (GPs) should look like post-COVID-19. Methodology: Online survey of Family and Community Medicine members consisting of filiation data, 22 Likert-type multiple-choice questions grouped in five thematic axes, and a free text question. Results: The number of respondents to the questionnaire was 1051 (22.6% of all members): 83.2% said they spent excessive time on bureaucratic tasks; 91.8% were against call center systems; 66% believed that home care is the responsibility of every family doctor; 77.5% supported continuity of care as a fundamental value of patient-centered care; and >90% defended the contracting of complementary tests and first hospital visits from primary healthcare (PHC). Conclusions: The survey responses describe a strong consensus on the identity and competencies of the GP and on the needs of and the threats to the PHC system. The demand for an increase in health resources, greater professional leadership, elimination of bureaucracy, an increase in the number of health professionals, and greater management autonomy, are the axes towards which a new era in PHC should be directed.

CIPSI: An open chemical intellectual property service for medicinal chemists

The availability of patent chemical data offers public access to a chemical space that is not well covered by other sources collecting small molecules from scholarly literature. However, open applications to facilitate the search and analysis of biologically-relevant molecular structures present in patents are still largely missing. We have developed CIPSI, an open Chemical Intellectual Property Service @ IMIM to assist medicinal chemists in searching and analysing molecules in SureChEMBL patents. The current version contains 6,240,500 molecules from 236,689 pharmacological patents, of which 5,949,214 are confidently assigned to core chemical structures reminiscent of the Markush structure in the patent claim. The platform includes some graphical tools to facilitate comparative patent analyses between drugs, chemical substructures, and company assignees. CIPSI is available at https://cipsi.org.