3D-QSAR Design of New Escitalopram Derivatives for the Treatment of Major Depressive Disorders

Antidepressants are psychiatric agents used for the treatment of different types of depression being at present amongst the most commonly prescribed drug, while their effectiveness and adverse effects are the subject of many studies and competing claims. Having studied five QSAR models predicting the biological activities of 18 antidepressants, already approved for clinical treatment, in interaction with the serotonin transporter (SERT), we attempted to establish the membrane ions’ contributions (sodium, potassium, chlorine and calcium) supplied by donor/acceptor hydrogen bond character and electrostatic field to the antidepressant activity. Significant cross-validated correlation q2 (0.5–0.6) and the fitted correlation r2 (0.7–0.82) coefficients were obtained indicating that the models can predict the antidepressant activity of compounds. Moreover, considering the contribution of membrane ions (sodium, potassium and calcium) and hydrogen bond donor character, we have proposed a library of 24 new escitalopram structures, some of them probably with significantly improved antidepressant activity in comparison with the parent compound

Repurposing anti-inflammatory drugs for fighting planktonic and biofilm growth. New carbazole derivatives based on the NSAID carprofen: synthesis, in silico and in vitro bioevaluation

IntroductionOne of the promising leads for the rapid discovery of alternative antimicrobial agents is to repurpose other drugs, such as nonsteroidal anti-inflammatory agents (NSAIDs) for fighting bacterial infections and antimicrobial resistance.MethodsA series of new carbazole derivatives based on the readily available anti-inflammatory drug carprofen has been obtained by nitration, halogenation and N-alkylation of carprofen and its esters. The structures of these carbazole compounds were assigned by NMR and IR spectroscopy. Regioselective electrophilic substitution by nitration and halogenation at the carbazole ring was assigned from H NMR spectra. The single crystal X-ray structures of two representative derivatives obtained by dibromination of carprofen, were also determined. The total antioxidant capacity (TAC) was measured using the DPPH method. The antimicrobial activity assay was performed using quantitative methods, allowing establishment of the minimal inhibitory/bactericidal/biofilm eradication concentrations (MIC/MBC/MBEC) on Gram-positive (Staphylococcus aureus, Enterococcus faecalis) and Gram-negative (Escherichia coli, Pseudomonas aeruginosa) strains. Computational assays have been performed to assess the drug- and lead-likeness, pharmacokinetics (ADME-Tox) and pharmacogenomics profiles.Results and discussionThe crystal X-ray structures of 3,8-dibromocarprofen and its methyl ester have revealed significant differences in their supramolecular assemblies. The most active antioxidant compound was 1i, bearing one chlorine and two bromine atoms, as well as the CO2Me group. Among the tested derivatives, 1h bearing one chlorine and two bromine atoms has exhibited the widest antibacterial spectrum and the most intensive inhibitory activity, especially against the Gram-positive strains, in planktonic and biofilm growth state. The compounds 1a (bearing one chlorine, one NO2 and one CO2Me group) and 1i (bearing one chlorine, two bromine atoms and a CO2Me group) exhibited the best antibiofilm activity in the case of the P. aeruginosa strain. Moreover, these compounds comply with the drug-likeness rules, have good oral bioavailability and are not carcinogenic or mutagenic. The results demonstrate that these new carbazole derivatives have a molecular profile which deserves to be explored further for the development of novel antibacterial and antibiofilm agents

A comprehensive review of swarm optimization algorithms

Many swarm optimization algorithms have been introduced since the early 60’s, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms have demonstrated their potential to solve many optimization problems. This paper provides an in-depth survey of well-known optimization algorithms. Selected algorithms are briefly explained, and compared with each other comprehensively through experiments conducted using thirty well-known benchmark functions. Their advantages and disadvantages are also discussed. A number of statistical tests are then carried out to determine the significant performances. The results indicate the overall advantage of Differential Evolution (DE) and is closely followed by Particle Swarm Optimization (PSO), compared with other considered approaches

Neutralization Data and Aligned ENV Sequences for Predicting Antibody Affinities using Artificial Neural Networks

<p>Sample file with neutralization data (IC₅₀) for different antibodies and viral strains, adapted from J. Huang, G. Ofek, L. Laub, M. K. Louder, N. A. Doria-Rose, N. S. Longo, H. Imamichi, R. T. Bailer, B. Chakrabarti, S. K. Sharma, S.  M. Alam, T. Wang, Y. Yang, B. Zhang, S. A. Migueles, R. Wyatt, B. F. Haynes, P. D. Kwong, J. R. Mascola, and M. Connors, “Broad and potent neutralization of HIV-1 by a gp41-specific human antibody.,” <em>Nature</em>, vol. 491, no. 7424, pp. 406–12, Nov. 2012.</p> <p> </p> <p>Aligned ENV sequences downloaded from the HIV Sequence Database (www.hiv.lanl.gov/content/sequence/HIV/mainpage.html). There are 4907 sequences and the alignment length is 1369.</p

Demonstration videos on using P swarms for deployment tasks in swarm robotics.

In this video we present the capability of the Lulu P colony / P swarn simulator to control a Kilobot robot that is a member of a swarm using a P colony based controller. The swarm application presented here is the dispersion from neighbouring robots, until a certain distance from all neighbours is reached. In the first video entitled kilombo_disperse_1000.avi, we simulated the interactions between 1000 Kilobots using the Kilombo simulator. The i5-4240 CPU used in this test allowed the simulation of 1000 robots, each one controlled by an individual P colony at a peak speed of 29 x real world speed. The source code of the P colony used for the dispersion algorithm is also included. This P colony is defined in the input file format accepted by the Lulu P colony / P swarm simulator

Software system integration of heterogeneous swarms of robots

<p>Paper: Software system integration of heterogeneous swarms of robots</p> <p>Robots> E-puck, E-puck+Raspberry Pi 2 B, Khepera III, Khepera III+Raspberry Pi 2 B, Koala+Laptop.</p> <p>Using the ROS drivers a series of tests were devised to prove that the robots in the swarm can communicate at a software level using ROS. The robots have individual goals, in this case to follow simple movement commands. The robots move after receiving command information from their program whether it is local or from another robot. All programs needed for each test will be called individually. A desktop computer together with the programs SSH and X11VNC were used in the tests to connect remotely to the robots, i.e. the laptop and Raspberry PIs mounted on the robots.</p> <p><em>1. Single-Minded Robot Swarm.</em> A main robot Koala is running all the drivers and programs for each robot. The programs for the robots communicate through the roscore and send commands to their respective robots using Bluetooth.</p> <p><em>2. Swarm with Leader Robot and Standalone Basic Robots. </em>This test is done with three Standalone robots. Each robot runs it’s own drivers and they all connect to a single roscore. In this test a Koala robot is used as a Leader running roscore and rtab map program for ROS (http://wiki.ros.org/rtabmap_ros) to create maps with the Kinect sensor. The mapping information is needed for SLAM. To connect to a single roscore all the robots must have the following two ROS commands run in the terminal to tell the system where the roscore is run and the IP of each robot: <em>export ROS_MASTER_URI=http://Leader_IP:11311 </em>and<em> export ROS_IP=Current_Robot_IP .</em></p> <p><em>3. Swarm with Standalone Robots Connecting to a Central Robot. A</em>ll the robots run their drivers and other high-level programs standalone, but they connect and communicate with each other through a single roscore running on a single robot. In this test, the E-puck robot runs along with its driver and uses gmapping ROS nodes (http://wiki.ros.org/gmapping) for SLAM. The K3 is running the driver and a simple movement program. The Koala robot runs rtab map with the Kinect and has a wireless controller connected via USB for movement commands. The controller Linux drivers are linked to a ROS joy node (http://wiki.ros.org/joy) which will publish the controller data. To prove the communication between robots, the controller on the Koala is used to send movement commands to the Koala and E-puck.</p> <p><em>4. Swarm with Standalone Robots. </em>In this test each robot is completely independent, running roscore and all programs. The robots connect with each other using rosmultimaster nodes (http://wiki.ros.org/multimaster_fkie). All the robots in this test subscribe to the joy node running on the Koala, and thus move receiving commands from the controller.</p> <p><em>5. Combined Swarm - Single-Minded Robot Swarm(s) + Standalone Robots. </em>In the final test all Standalone robots run their own roscore and the rosmultimaster node. The Standalone robots have other simple robots connected to them using Bluetooth and run their own drivers, other more advanced programs and the simple robots’ ROS drivers. A tested goal here is to have simple robots such as those from the first test receive commands from another Standalone robots in the swarm through its Leader robot. The Koala controller joy node was used again publishing data on the ROS network. Any robot driver connected to this network can subscribe to the publishing joy node and thus the simple robots not connected to the Koala with the controller, receive commands from the controller through their own respective Leader robot.</p

Lulu - a software simulator for P colonies. Use case scenarios and demonstration videos

<p>The videos show three different examples of using the Lulu P colony simulator.</p> <p>The Lulu P colony simulator is available under an open-source MIT license at https://github.com/andrei91ro/lulu_pcol_sim. All of the secondary applications, including Lulu_Kilobot are available (also under open-source licenses) at https://github.com/andrei91ro.</p> <p>The first two videos present the simulator running addition (+1) and subtraction (-1). In these two examples, the simulator is ran in a step by step mode in order to clearly visualize the results of running each simulation step. For this reason the total simulation time reported at the end of the simulation is in the order of minutes.</p> <p>The average (of five runs) simulation time for a normal (non-interactive) simulation is 0.0021050 seconds for the addition and 0.0047492 seconds for the subtraction examples.</p> <p>The third example (lulu_kilobot_30_steps) presents the simulator running a more complex P colony that controls a Kilobot robot simulated in V-REP. This P colony is based on the subtraction P colony in the sense that each move the robot makes is marked by the removal of an f object from the environment.</p> <p>The input file used in the addition example (lulu_sim_ag_increment):</p> <p>pi = {<br>         A = {l_p};<br>         e = e;<br>         f = f;<br>         n = 2;<br>         env = {f, f, f, l_p};<br>         B = {AG_1};<br>                 AG_1 = ({e, e}; < e->f, e<->l_p >, < l_p->e, f<->e >);<br> }</p> <p>The input file used in the subtraction example (lulu_sim_ag_decrement):</p> <p>pi = {<br>         A = {l_m, l_p, l_z};<br>         e = e;<br>         f = f;<br>         n = 2;<br>         env = {f, f, f, l_m};<br>         B = {AG_1};<br>                 AG_1 = ({e, e};<br>                                 < e->e, e<->l_m >,<br>                                 < l_m->l_p, e<->f/e<->e >,<br>                                 < f->e, l_p<->e >,<br>                                 < l_p->l_z, e<->e >,<br>                                 < e->e, l_z<->e > );<br> }</p> <p>The input file used in the Kilobot example (lulu_kilobot_30_steps):</p> <p>pi = {<br>         A = {l_m, m_0, m_S, m_L, m_R, c_R, c_G, c_B};<br>         e = e;<br>         f = f;<br>         n = 2;<br>         env = {f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, l_m};<br>         B = {AG_command, AG_motion};<br>                 AG_command = ({e, e};<br>                                 < e->e, e<->l_m >,<br>                                 < l_m->m_S, e<->f/e<->e >,<br>                                 < f->e, m_S<->e >,<br>                                 < m_S->m_0, e<->e >,<br>                                 < e->e, m_0<->e > );</p> <p>                AG_motion = ({e, e};<br>                                 < e->l_m, e<->m_S >,<br>                                 < m_S->e, l_m<->e/e->e ><br>                                 < e->e, e<->m_0 >,<br>                                 < m_0->e, e<->m_0/e->e >);<br> }</p> <p> </p