In Silico Mixed Ligand/Structure-Based Design of New CDK-1/PARP-1 Dual Inhibitors as Anti-Breast Cancer Agents.

CDK-1 and PARP-1 play crucial roles in breast cancer progression. Compounds acting as CDK-1 and/or PARP-1 inhibitors can induct cell death in breast cancer with a selective synthetic lethality mechanism. A mixed treatment by means of CDK-1 and PARP-1 inhibitors resulted in radical breast cancer cell growth reduction. Inhibitors with a dual target mechanism of action could arrest cancer progression by simultaneously blocking the DNA repair mechanism and cell cycle, resulting in advantageous monotherapy. To this aim, in the present work, we identified compound 645656 with a significant affinity for both CDK-1 and PARP-1 by a mixed ligand- and structure-based virtual screening protocol. The Biotarget Predictor Tool was used at first in a Multitarget mode to filter the large National Cancer Institute (NCI) database. Then, hierarchical docking studies were performed to further screen the compounds and evaluate the ligands binding mode, whose putative dual-target mechanism of action was investigated through the correlation between the antiproliferative activity data and the target proteins' (CDK-1 and PARP-1) expression pattern. Finally, a Molecular Dynamics Simulation confirmed the high stability of the most effective selected compound 645656 in complex with both PARP-1 and CDK-1.

Wearable Multisensor Ring-Shaped Probe for Assessing Stress and Blood Oxygenation: Design and Preliminary Measurements.

The increasing interest in innovative solutions for health and physiological monitoring has recently fostered the development of smaller biomedical devices. These devices are capable of recording an increasingly large number of biosignals simultaneously, while maximizing the user's comfort. In this study, we have designed and realized a novel wearable multisensor ring-shaped probe that enables synchronous, real-time acquisition of photoplethysmographic (PPG) and galvanic skin response (GSR) signals. The device integrates both the PPG and GSR sensors onto a single probe that can be easily placed on the finger, thereby minimizing the device footprint and overall size. The system enables the extraction of various physiological indices, including heart rate (HR) and its variability, oxygen saturation (SpO2), and GSR levels, as well as their dynamic changes over time, to facilitate the detection of different physiological states, e.g., rest and stress. After a preliminary SpO2 calibration procedure, measurements have been carried out in laboratory on healthy subjects to demonstrate the feasibility of using our system to detect rapid changes in HR, skin conductance, and SpO2 across various physiological conditions (i.e., rest, sudden stress-like situation and breath holding). The early findings encourage the use of the device in daily-life conditions for real-time monitoring of different physiological states.

Antiproliferative Activity Predictor: A New Reliable In Silico Tool for Drug Response Prediction against NCI60 Panel.

In vitro antiproliferative assays still represent one of the most important tools in the anticancer drug discovery field, especially to gain insights into the mechanisms of action of anticancer small molecules. The NCI-DTP (National Cancer Institute Developmental Therapeutics Program) undoubtedly represents the most famous project aimed at rapidly testing thousands of compounds against multiple tumor cell lines (NCI60). The large amount of biological data stored in the National Cancer Institute (NCI) database and many other databases has led researchers in the fields of computational biology and medicinal chemistry to develop tools to predict the anticancer properties of new agents in advance. In this work, based on the available antiproliferative data collected by the NCI and the manipulation of molecular descriptors, we propose the new in silico Antiproliferative Activity Predictor (AAP) tool to calculate the GI50 values of input structures against the NCI60 panel. This ligand-based protocol, validated by both internal and external sets of structures, has proven to be highly reliable and robust. The obtained GI50 values of a test set of 99 structures present an error of less than ±1 unit. The AAP is more powerful for GI50 calculation in the range of 4-6, showing that the results strictly correlate with the experimental data. The encouraging results were further supported by the examination of an in-house database of curcumin analogues that have already been studied as antiproliferative agents. The AAP tool identified several potentially active compounds, and a subsequent evaluation of a set of molecules selected by the NCI for the one-dose/five-dose antiproliferative assays confirmed the great potential of our protocol for the development of new anticancer small molecules. The integration of the AAP tool in the free web service DRUDIT provides an interesting device for the discovery and/or optimization of anticancer drugs to the medicinal chemistry community. The training set will be updated with new NCI-tested compounds to cover more chemical spaces, activities, and cell lines. Currently, the same protocol is being developed for predicting the TGI (total growth inhibition) and LC50 (median lethal concentration) parameters to estimate toxicity profiles of small molecules.

Time-Constrained Node Visit Planning for Collaborative UAV-WSN Distributed Applications.

Unmanned Aerial Vehicles (UAVs) are often studied as tools to perform data collection from Wireless Sensor Networks (WSNs). Path planning is a fundamental aspect of this endeavor. Works in the current literature assume that data are always ready to be retrieved when the UAV passes. This operational model is quite rigid and does not allow for the integration of the UAV as a computational object playing an active role in the network. In fact, the UAV could begin the computation on a first visit and retrieve the data later. Potentially, the UAV could orchestrate the distributed computation to improve its performance, change its parameters, and even upload new applications to the sensor network. In this paper, we analyze a scenario where a UAV plays an active role in the operation of multiple sensor networks by visiting different node clusters to initiate distributed computation and collect the final outcomes. The experimental results validate the effectiveness of the proposed method in optimizing total flight time, Average Age of Information, Average cluster computation end time, and Average data collection time compared to prevalent approaches to UAV path-planning that are adapted to the purpose.

Identification of biological targets through the correlation between cell line chemosensitivity and protein expression pattern.

Matching biological data sequences is one of the most interesting ways to discover new bioactive compounds. In particular, matching cell chemosensitivity with a protein expression profile can be a useful approach to predict the activity of compounds against definite biological targets. In this review, we discuss this correlation. First, we analyze case studies in which some known drugs, acting on known targets, show a good correlation between their antiproliferative activities and protein expression when a large panel of tumor cells is considered. Then, we highlight how the application of in silico methods based on the correlation between cell line chemosensitivity and gene/protein expression patterns might be a quick, cheap, and interesting approach to predict the biological activity of investigated molecules.

In Silico Identification of Small Molecules as New Cdc25 Inhibitors through the Correlation between Chemosensitivity and Protein Expression Pattern.

The cell division cycle 25 (Cdc25) protein family plays a crucial role in controlling cell proliferation, making it an excellent target for cancer therapy. In this work, a set of small molecules were identified as Cdc25 modulators by applying a mixed ligand-structure-based approach and taking advantage of the correlation between the chemosensitivity of selected structures and the protein expression pattern of the proposed target. In the first step of the in silico protocol, a set of molecules acting as Cdc25 inhibitors were identified through a new ligand-based protocol and the evaluation of a large database of molecular structures. Subsequently, induced-fit docking (IFD) studies allowed us to further reduce the number of compounds biologically screened. In vitro antiproliferative and enzymatic inhibition assays on the selected compounds led to the identification of new structurally heterogeneous inhibitors of Cdc25 proteins. Among them, J3955, the most active inhibitor, showed concentration-dependent antiproliferative activity against HepG2 cells, with GI50 in the low micromolar range. When J3955 was tested in cell-cycle perturbation experiments, it caused mitotic failure by G2/M-phase cell-cycle arrest. Finally, Western blotting analysis showed an increment of phosphorylated Cdk1 levels in cells exposed to J3955, indicating its specific influence in cellular pathways involving Cdc25 proteins.

Knowledge-Based Verification of Concatenative Programming Patterns Inspired by Natural Language for Resource-Constrained Embedded Devices.

We propose a methodology to verify applications developed following programming patterns inspired by natural language that interact with physical environments and run on resource-constrained interconnected devices. Natural language patterns allow for the reduction of intermediate abstraction layers to map physical domain concepts into executable code avoiding the recourse to ontologies, which would need to be shared, kept up to date, and synchronized across a set of devices. Moreover, the computational paradigm we use for effective distributed execution of symbolic code on resource-constrained devices encourages the adoption of such patterns. The methodology is supported by a rule-based system that permits runtime verification of Software Under Test (SUT) on board the target devices through automated oracle and test case generation. Moreover, verification extends from syntactic and semantic checks to the evaluation of the effects of SUT execution on target hardware. Additionally, by exploiting rules tying sensors and actuators to physical quantities, the effects of code execution on the physical environment can be verified. The system is also able to build test code to highlight software issues that may arise during repeated SUT execution on the target hardware.

Numerical optimization of plasmid DNA delivery combined with hyaluronidase injection for electroporation protocol.

BACKGROUND AND OBJECTIVE: The paper focuses on the numerical strategies to optimize a plasmid DNA delivery protocol, which combines hyaluronidase and electroporation. METHODS: A well-defined continuum mechanics model of muscle porosity and advanced numerical optimization strategies have been used, to propose a substantial improvement of a pre-existing experimental protocol of DNA transfer in mice. Our work suggests that a computational model might help in the definition of innovative therapeutic procedures, thanks to the fine tuning of all the involved experimental steps. This approach is particularly interesting in optimizing complex and costly protocols, to make in vivo DNA therapeutic protocols more effective. RESULTS: Our preliminary work suggests that computational model might help in the definition of innovative therapeutic protocol, thanks to the fine tuning of all the involved operations. CONCLUSIONS: This approach is particularly interesting in optimizing complex and costly protocols for which the number of degrees of freedom prevents a experimental test of the possible configuration.

DRUDIT: web-based DRUgs DIscovery Tools to design small molecules as modulators of biological targets.

MOTIVATION: New in silico tools to predict biological affinities for input structures are presented. The tools are implemented in the DRUDIT (DRUgs DIscovery Tools) web service. The DRUDIT biological finder module is based on molecular descriptors that are calculated by the MOLDESTO (MOLecular DEScriptors TOol) software module developed by the same authors, which is able to calculate more than one thousand molecular descriptors. At this stage, DRUDIT includes 250 biological targets, but new external targets can be added. This feature extends the application scope of DRUDIT to several fields. Moreover, two more functions are implemented: the multi- and on/off-target tasks. These tools applied to input structures allow for predicting the polypharmacology and evaluating the collateral effects. RESULTS: The applications described in the article show that DRUDIT is able to predict a single biological target, to identify similarities among biological targets, and to discriminate different target isoforms. The main advantages of DRUDIT for the scientific community lie in its ease of use by worldwide scientists and the possibility to be used also without specific, and often expensive, hardware and software. In fact, it is fully accessible through the WWW from any device to perform calculations. Just a click or a tap can start tasks to predict biological properties for new compounds or repurpose drugs, lead compounds, or unsuccessful compounds. To date, DRUDIT is supported by four servers each able to execute 8 jobs simultaneously. AVAILABILITY AND IMPLEMENTATION: The web service is accessible at the www.drudit.com URL and its use is free of charge. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Three-dimensional geometrical models of the liver.

In this work we used a virtual approach to study the human liver by three-dimensional geometrical models. We built the models through computer aided geometric modelling techniques starting from pictures taken during both real dissections and diagnostic medical imaging. The results show in a complete modular synthesis and with a schematic iconology the structural organization of this organ in a logic sequence of layers and topographic and spatial relationship among its components. This approach represents an amazing support to clinical anatomy for teaching and research.

Three-dimensional geometrical models of the inguinal region. Towards a new stereology.

In this work we studied the inguinal-abdominal region and the inguinal canal using three-dimensional geometrical models. We built the models through computer aided geometric modeling techniques on the basis of observations during real dissections, operations and diagnostic medical imaging. The obtained models show in a complete modular synthesis and with a schematic iconology the structural organization of the anatomical districts in a logic sequence of layers and topographic and spatial relationships among its components. The models represent an amazing support to anatomy and clinical anatomy for teaching and research purposes on organogenesis, surgery and diagnosis.