Phlorotannins as HIV Vpu inhibitors, an in silico virtual screening study of marine natural products.

The importance of new effective treatment methodologies for human immunodeficiency virus (HIV) is undeniable for the medical society. Viral protein U (Vpu), one of the disparaged accessory proteins of HIV, is responsible for the dissemination of viral particles, and HIV mutants lacking Vpu protein have remarkably reduced pathogenicity. Here, we explored the marine natural products to find the leading structures which can potentially inhibit the activity of Vpu in silico. To fulfill this goal, we set up a virtual screening based on molecular docking to evaluate the binding capacity of different marine products to Vpu. For validation, we used molecular dynamics simulation and monitored the root mean square deviation value and binding interactions. The results were intriguing when we realized that the hit compounds (phlorotannins) had previously been identified as reverse transcriptase and HIV protease inhibitors. This research inaugurates a new road to combat HIV by multifaceted mode of action of these marine natural products without putting the normal cells in jeopardy (with their safe toxicological profile).

Hydroxyapatite as a biomaterial - a gift that keeps on giving.

The synthetic analogue to biogenic apatite, hydroxyapatite (HA) has a number of physicochemical properties that make it an attractive candidate for diagnosis, treatment of disease and augmentation of biological tissues. Here we describe some of the recent studies on HA, which may provide bases for a number of new medical applications. The content of this review is divided to different medical application modes utilizing HA, including tissue engineering, medical implants, controlled drug delivery, gene therapies, cancer therapies and bioimaging. A number of advantages of HA over other biomaterials emerge from this discourse, including (i) biocompatibility, (ii) bioactivity, (iii) relatively simple synthesis protocols for the fabrication of nanoparticles with specific sizes and shapes, (iv) smart response to environmental stimuli, (v) facile functionalization and surface modification through noncovalent interactions, and (vi) the capacity for being simultaneously loaded with a wide range of therapeutic agents and switched to bioimaging modalities for uses in theranostics. A special section is dedicated to analysis of the safety of particulate HA as a component of parenterally administrable medications. It is concluded that despite the fact that many benefits come with the usage of HA, its deficiencies and potential side effects must be addressed before the translation to the clinical domain is pursued. Although HA has been known in the biomaterials world as the exemplar of safety, this safety proves to be the function of size, morphology, surface ligands and other structural and compositional parameters defining the particles. For this reason, each HA, especially when it comes in a novel structural form, must be treated anew from the safety research angle before being allowed to enter the clinical stage.

Nickel Ferrite (NiFe2O4) Nanoparticles as Magnetically Recyclable Nanocatalyst for Highly Efficient Synthesis of 4H-Chromene Derivatives.

An adapted one-pot route to nanocatalyst-assisted synthesis of 4H-chromenes via three component condensation reaction between dimedone, malononitrile, and a broad range of aryl aldehydes by the use of magnetic nickel ferrite nanoparticles is described. By this achievement, not only a novel route to highly efficient synthesis of these series of heterocycles was introduced but also the scope of these medicinally important products was developed via preparation of some novel products. Above all, a new application of nickel ferrite nanoparticles (NiFe2O4 NPs) as highly efficient, green and magnetically recyclable catalyst has been introduced. Overall, obtaining good to excellent yields of products, environmentally and economic benign procedure, easy handling, availability of starting materials, use of non-toxic solvents, and high recyclability of nano-catalyst could be countered as most important advantages of this methodology.

Design, synthesis, biological evaluation, and molecular dynamics of novel cholinesterase inhibitors as anti-Alzheimer's agents.

A series of novel chroman-4-one derivatives were designed and synthesized successfully with good to excellent yield (3a-l). In addition, the obtained products were evaluated for their cholinesterase (ChE) inhibitory activities. The results show that among the various synthesized compounds, analogs bearing the piperidinyl ethoxy side chain with 4-hydroxybenzylidene on the 3-positions of chroman-4-one (3l) showed the most potent activity with respect to acetylcholinesterase (anti-AChE activity; IC50 = 1.18 µM). In addition, the structure-activity relationship was studied and the results revealed that the electron-donating groups on the aryl ring of the 3-benzylidene fragment (3k, 3l) resulted in the designed compounds to be more potent ChE inhibitors in comparison with those having electron-withdrawing groups (3h). In this category, the strongest ChE inhibition was found for the compound containing piperidine as cyclic amine, and a hydroxyl group (for AChE, compound 3l) and fluoro group (for butyrylcholinesterase (BuChE, compound 3i) on the para-position of the aryl ring of the benzylidene group. The molecular docking and dynamics studies of the most potent compounds (3i and 3l against BuChE and AChE, respectively) demonstrated remarkable interactions with the binding pockets of the ChE enzymes and confirmed the results obtained through in vitro experiments.

Metronidazole aryloxy, carboxy and azole derivatives: Synthesis, anti-tumor activity, QSAR, molecular docking and dynamics studies.

A series of novel metronidazole aryloxy, carboxy and azole derivatives has been synthesized and their cytotoxic activities on three cancer cell lines were evaluated by MTT assay. Compounds 4m, 4l and 4d showed the most potent cytotoxic activity (IC50s less than 100 µg/mL). Apoptosis was also detected for these compounds by flow cytometry. Docking studies were performed in order to propose the probable target protein. In the next step, molecular dynamics simulation was carried out on the proposed target protein, focal adhesion kinase (FAK, PDB code: 2ETM), bound to compound 4m. As, 4m showed a potent cytotoxic activity and an acceptable apoptotic effect, it can be a potential anticancer candidate that may work through inhibition of FAK.

The detour problem in a stochastic environment: Tolman revisited.

We designed a grid world task to study human planning and re-planning behavior in an unknown stochastic environment. In our grid world, participants were asked to travel from a random starting point to a random goal position while maximizing their reward. Because they were not familiar with the environment, they needed to learn its characteristics from experience to plan optimally. Later in the task, we randomly blocked the optimal path to investigate whether and how people adjust their original plans to find a detour. To this end, we developed and compared 12 different models. These models were different on how they learned and represented the environment and how they planned to catch the goal. The majority of our participants were able to plan optimally. We also showed that people were capable of revising their plans when an unexpected event occurred. The result from the model comparison showed that the model-based reinforcement learning approach provided the best account for the data and outperformed heuristics in explaining the behavioral data in the re-planning trials.

Learning to allocate limited time to decisions with different expected outcomes.

The goal of this article is to investigate how human participants allocate their limited time to decisions with different properties. We report the results of two behavioral experiments. In each trial of the experiments, the participant must accumulate noisy information to make a decision. The participants received positive and negative rewards for their correct and incorrect decisions, respectively. The stimulus was designed such that decisions based on more accumulated information were more accurate but took longer. Therefore, the total outcome that a participant could achieve during the limited experiments' time depended on her "decision threshold", the amount of information she needed to make a decision. In the first experiment, two types of trials were intermixed randomly: hard and easy. Crucially, the hard trials were associated with smaller positive and negative rewards than the easy trials. A cue presented at the beginning of each trial would indicate the type of the upcoming trial. The optimal strategy was to adopt a small decision threshold for hard trials. The results showed that several of the participants did not learn this simple strategy. We then investigated how the participants adjusted their decision threshold based on the feedback they received in each trial. To this end, we developed and compared 10 computational models for adjusting the decision threshold. The models differ in their assumptions on the shape of the decision thresholds and the way the feedback is used to adjust the decision thresholds. The results of Bayesian model comparison showed that a model with time-varying thresholds whose parameters are updated by a reinforcement learning algorithm is the most likely model. In the second experiment, the cues were not presented. We showed that the optimal strategy is to use a single time-decreasing decision threshold for all trials. The results of the computational modeling showed that the participants did not use this optimal strategy. Instead, they attempted to detect the difficulty of the trial first and then set their decision threshold accordingly.

Learning to maximize reward rate: a model based on semi-Markov decision processes.

WHEN ANIMALS HAVE TO MAKE A NUMBER OF DECISIONS DURING A LIMITED TIME INTERVAL, THEY FACE A FUNDAMENTAL PROBLEM: how much time they should spend on each decision in order to achieve the maximum possible total outcome. Deliberating more on one decision usually leads to more outcome but less time will remain for other decisions. In the framework of sequential sampling models, the question is how animals learn to set their decision threshold such that the total expected outcome achieved during a limited time is maximized. The aim of this paper is to provide a theoretical framework for answering this question. To this end, we consider an experimental design in which each trial can come from one of the several possible "conditions." A condition specifies the difficulty of the trial, the reward, the penalty and so on. We show that to maximize the expected reward during a limited time, the subject should set a separate value of decision threshold for each condition. We propose a model of learning the optimal value of decision thresholds based on the theory of semi-Markov decision processes (SMDP). In our model, the experimental environment is modeled as an SMDP with each "condition" being a "state" and the value of decision thresholds being the "actions" taken in those states. The problem of finding the optimal decision thresholds then is cast as the stochastic optimal control problem of taking actions in each state in the corresponding SMDP such that the average reward rate is maximized. Our model utilizes a biologically plausible learning algorithm to solve this problem. The simulation results show that at the beginning of learning the model choses high values of decision threshold which lead to sub-optimal performance. With experience, however, the model learns to lower the value of decision thresholds till finally it finds the optimal values.