One and done? Optimal decisions from very few samples

In many learning or inference tasks human behavior approximates that of a Bayesian ideal observer, suggesting that, at some level, cognition can be described as Bayesian inference. However, a number of findings have highlighted an intriguing mismatch between human behavior and standard assumptions about optimality: People often appear to make decisions based on just one or a few samples from the appropriate posterior probability distribution, rather than using the full distribution. Although sampling-based approximations are a common way to implement Bayesian inference, the very limited numbers of samples often used by humans seem insufficient to approximate the required probability distributions very accurately. Here, we consider this discrepancy in the broader framework of statistical decision theory, and ask: If people are making decisions based on samples--but as samples are costly--how many samples should people use to optimize their total expected or worst-case reward over a large number of decisions? We find that under reasonable assumptions about the time costs of sampling, making many quick but locally suboptimal decisions based on very few samples may be the globally optimal strategy over long periods. These results help to reconcile a large body of work showing sampling-based or probability matching behavior with the hypothesis that human cognition can be understood in Bayesian terms, and they suggest promising future directions for studies of resource-constrained cognition.

Mathematical models of tumor cell proliferation: A review of the literature

INTRODUCTION

A defining hallmark of cancer is aberrant cell proliferation. Efforts to understand the generative properties of cancer cells span all biological scales: from genetic deviations and alterations of metabolic pathways to physical stresses due to overcrowding, as well as the effects of therapeutics and the immune system. While these factors have long been studied in the laboratory, mathematical and computational techniques are being increasingly applied to help understand and forecast tumor growth and treatment response. Advantages of mathematical modeling of proliferation include the ability to simulate and predict the spatiotemporal development of tumors across multiple experimental scales. Central to proliferation modeling is the incorporation of available biological data and validation with experimental data. Areas covered: We present an overview of past and current mathematical strategies directed at understanding tumor cell proliferation. We identify areas for mathematical development as motivated by available experimental and clinical evidence, with a particular emphasis on emerging, non-invasive imaging technologies. Expert commentary: The data required to legitimize mathematical models are often difficult or (currently) impossible to obtain. We suggest areas for further investigation to establish mathematical models that more effectively utilize available data to make informed predictions on tumor cell proliferation.

The Flatland Fallacy: Moving Beyond Low-Dimensional Thinking

Psychology is a complicated science. It has no general axioms or mathematical proofs, is rarely directly observable, and is the only discipline in which the subject matter (i.e., human psychological phenomena) is also the tool of investigation. Like the Flatlanders in Edwin Abbot's famous short story (), we may be led to believe that the parsimony offered by our low-dimensional theories reflects the reality of a much higher-dimensional problem. Here we contend that this "Flatland fallacy" leads us to seek out simplified explanations of complex phenomena, limiting our capacity as scientists to build and communicate useful models of human psychology. We suggest that this fallacy can be overcome through (a) the use of quantitative models, which force researchers to formalize their theories to overcome this fallacy, and (b) improved quantitative training, which can build new norms for conducting psychological research.

Population-level mathematical modeling of antimicrobial resistance: a systematic review

BACKGROUND

Mathematical transmission models are increasingly used to guide public health interventions for infectious diseases, particularly in the context of emerging pathogens; however, the contribution of modeling to the growing issue of antimicrobial resistance (AMR) remains unclear. Here, we systematically evaluate publications on population-level transmission models of AMR over a recent period (2006-2016) to gauge the state of research and identify gaps warranting further work.

METHODS

We performed a systematic literature search of relevant databases to identify transmission studies of AMR in viral, bacterial, and parasitic disease systems. We analyzed the temporal, geographic, and subject matter trends, described the predominant medical and behavioral interventions studied, and identified central findings relating to key pathogens.

RESULTS

We identified 273 modeling studies; the majority of which (> 70%) focused on 5 infectious diseases (human immunodeficiency virus (HIV), influenza virus, Plasmodium falciparum (malaria), Mycobacterium tuberculosis (TB), and methicillin-resistant Staphylococcus aureus (MRSA)). AMR studies of influenza and nosocomial pathogens were mainly set in industrialized nations, while HIV, TB, and malaria studies were heavily skewed towards developing countries. The majority of articles focused on AMR exclusively in humans (89%), either in community (58%) or healthcare (27%) settings. Model systems were largely compartmental (76%) and deterministic (66%). Only 43% of models were calibrated against epidemiological data, and few were validated against out-of-sample datasets (14%). The interventions considered were primarily the impact of different drug regimens, hygiene and infection control measures, screening, and diagnostics, while few studies addressed de novo resistance, vaccination strategies, economic, or behavioral changes to reduce antibiotic use in humans and animals.

CONCLUSIONS

The AMR modeling literature concentrates on disease systems where resistance has been long-established, while few studies pro-actively address recent rise in resistance in new pathogens or explore upstream strategies to reduce overall antibiotic consumption. Notable gaps include research on emerging resistance in Enterobacteriaceae and Neisseria gonorrhoeae; AMR transmission at the animal-human interface, particularly in agricultural and veterinary settings; transmission between hospitals and the community; the role of environmental factors in AMR transmission; and the potential of vaccines to combat AMR.

Does interaction matter? Testing whether a confidence heuristic can replace interaction in collective decision-making

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We tested whether a confidence heuristic could replace interaction in a collective perceptual decision-making task.

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For individuals of nearly equal reliability, the confidence heuristic is just as accurate as interaction.

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For individuals with different reliabilities, the confidence heuristic is less accurate than interaction.

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Interacting individuals use the credibility of each other’s confidence estimates to guide their joint decisions.

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Interacting individuals face a problem of how to map ‘internal’ variables onto ‘external’ (shareable) variables.

In a range of contexts, individuals arrive at collective decisions by sharing confidence in their judgements. This tendency to evaluate the reliability of information by the confidence with which it is expressed has been termed the ‘confidence heuristic’. We tested two ways of implementing the confidence heuristic in the context of a collective perceptual decision-making task: either directly, by opting for the judgement made with higher confidence, or indirectly, by opting for the faster judgement, exploiting an inverse correlation between confidence and reaction time. We found that the success of these heuristics depends on how similar individuals are in terms of the reliability of their judgements and, more importantly, that for dissimilar individuals such heuristics are dramatically inferior to interaction. Interaction allows individuals to alleviate, but not fully resolve, differences in the reliability of their judgements. We discuss the implications of these findings for models of confidence and collective decision-making.

Adverse outcome pathways: From research to regulation scientific workshop report

An adverse outcome pathway (AOP) helps to organize existing knowledge on chemical mode of action, starting with a molecular initiating event such as receptor binding, continuing through key events, and ending with an adverse outcome such as reproductive impairment. AOPs can help identify knowledge gaps where more research is needed to understand the underlying mechanisms, aid in chemical hazard characterization, and guide the development of new testing approaches that use fewer or no animals. A September 2014 workshop in Bethesda, Maryland considered how the AOP concept could improve regulatory assessments of chemical toxicity. Scientists from 21 countries, representing industry, academia, regulatory agencies, and special interest groups, attended the workshop, titled Adverse Outcome Pathways: From Research to Regulation. Workshop plenary presentations were followed by breakout sessions that considered regulatory acceptance of AOPs and AOP-based tools, criteria for building confidence in an AOP for regulatory use, and requirements to build quantitative AOPs and AOP networks. Discussions during the closing session emphasized a need to increase transparent and inclusive collaboration, especially with disciplines outside of toxicology. Additionally, to increase impact, working groups should be established to systematically prioritize and develop AOPs. Multiple collaborative projects and follow-up activities resulted from the workshop.

Rewiring the connectome: Evidence and effects

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Rewiring is a plasticity mechanism that alters connectivity between neurons.

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Evidence for rewiring has been difficult to obtain.

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New evidence indicates that local circuitry is rewired during learning.

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Harnessing rewiring offers new ways to treat psychiatric and neurological diseases.

Neuronal connections form the physical basis for communication in the brain. Recently, there has been much interest in mapping the “connectome” to understand how brain structure gives rise to brain function, and ultimately, to behaviour. These attempts to map the connectome have largely assumed that connections are stable once formed. Recent studies, however, indicate that connections in mammalian brains may undergo rewiring during learning and experience-dependent plasticity. This suggests that the connectome is more dynamic than previously thought. To what extent can neural circuitry be rewired in the healthy adult brain? The connectome has been subdivided into multiple levels of scale, from synapses and microcircuits through to long-range tracts. Here, we examine the evidence for rewiring at each level. We then consider the role played by rewiring during learning. We conclude that harnessing rewiring offers new avenues to treat brain diseases.

Magnetic molecularly imprinted polymer nanoparticles for simultaneous extraction and determination of 6-mercaptopurine and its active metabolite thioguanine in human plasma

Cytotoxic drugs used in cancer chemotherapy require the continuous monitoring of their plasma concentration levels for dose adjustment purposes. Such condition necessitates the presence of a sensitive technique for accurate extraction and determination of these drugs together with their active metabolites. In this study a novel solid phase extraction technique using magnetic molecularly imprinted nanoparticles (MMI-SPE) is combined with liquid chromatography tandem mass spectrometry (LC-MS/MS) to extract and determine the anti-leukemic agent; 6-mercaptopurine (6-MP) and its active metabolite thioguanine (TG) in human plasma. The magnetic molecularly imprinted nanoparticles (Fe₃O₄@MIP NPs) were synthesized via precipitation polymerization technique and were characterized using different characterization methods A computational approach was adopted to help in the choice of the monomer used in the fabrication process. The Fe₃O₄@MIPs NPs possessed a highly improved imprinting efficiency, fast adsorption kinetics following 2nd order kinetics and good adsorption capacity of 1.0 mg/g. The presented MMI-SPE provided the optimum approach in comparison to other reported ones to achieve good extraction recovery and matrix effect of trace levels of 6-MP and TG from plasma. Chromatographic separation was carried out using a validated LC-MS/MS assay and recovery, matrix effect and process efficiency were evaluated. Recovery of 6-MP and TG was in the range of 85.94-103.03%, while, matrix effect showed a mean percentage recovery of 85.94-97.62% and process efficiency of 85.54-96.18%. The proposed extraction technique is simple, effective and can be applicable to the extraction and analysis of other pharmaceutical compounds in complex matrices for therapeutic drug monitoring applications.

Computational Identification of Ventricular Arrhythmia Risk in Pediatric Myocarditis

Children with myocarditis have increased risk of ventricular tachycardia (VT) due to myocardial inflammation and remodeling. There is currently no accepted method for VT risk stratification in this population. We hypothesized that personalized models developed from cardiac late gadolinium enhancement magnetic resonance imaging (LGE-MRI) could determine VT risk in patients with myocarditis using a previously-validated protocol. Personalized three-dimensional computational cardiac models were reconstructed from LGE-MRI scans of 12 patients diagnosed with myocarditis. Four patients with clinical VT and eight patients without VT were included in this retrospective analysis. In each model, we incorporated a personalized spatial distribution of fibrosis and myocardial fiber orientations. Then, VT inducibility was assessed in each model by pacing rapidly from 26 sites distributed throughout both ventricles. Sustained reentrant VT was induced from multiple pacing sites in all patients with clinical VT. In the eight patients without clinical VT, we were unable to induce sustained reentry in our simulations using rapid ventricular pacing. Application of our non-invasive approach in children with myocarditis has the potential to correctly identify those at risk for developing VT.

In silico methods for predicting drug-drug interactions with cytochrome P-450s, transporters and beyond

Drug-drug interactions (DDIs) are associated with severe adverse effects that may lead to the patient requiring alternative therapeutics and could ultimately lead to drug withdrawal from the market if they are severe. To prevent the occurrence of DDI in the clinic, experimental systems to evaluate drug interaction have been integrated into the various stages of the drug discovery and development process. A large body of knowledge about DDI has also accumulated through these studies and pharmacovigillence systems. Much of this work to date has focused on the drug metabolizing enzymes such as cytochrome P-450s as well as drug transporters, ion channels and occasionally other proteins. This combined knowledge provides a foundation for a hypothesis-driven in silico approach, using either cheminformatics or physiologically based pharmacokinetics (PK) modeling methods to assess DDI potential. Here we review recent advances in these approaches with emphasis on hypothesis-driven mechanistic models for important protein targets involved in PK-based DDI. Recent efforts with other informatics approaches to detect DDI are highlighted. Besides DDI, we also briefly introduce drug interactions with other substances, such as Traditional Chinese Medicines to illustrate how in silico modeling can be useful in this domain. We also summarize valuable data sources and web-based tools that are available for DDI prediction. We finally explore the challenges we see faced by in silico approaches for predicting DDI and propose future directions to make these computational models more reliable, accurate, and publically accessible.

Spectroscopic, Computational, Antimicrobial, DNA Interaction, In Vitro Anticancer and Molecular Docking Properties of Biochemically Active Cu(II) and Zn(II) Complexes of Pyrimidine-Ligand

Biochemically active Cu(II) and Zn(II) complexes [CuL(ClO₄)₂(1) and ZnL(ClO₄)₂(2)] have been synthesized from N,N donor Schiff base ligand L derived from4,6-dichloropyrimdine-5-carboxaldehyde with 4-(2-aminoethyl)morpholine. The L, complexes 1 and 2 have been structurally characterized by elemental analysis, ¹H-NMR, FTIR, MS, UV-Visible and ESR techniques. The results obtained from the spectral studies supports the complexes 1 and 2 are coordinated with L through square planar geometry. DFT calculations results supports, the ligand to metal charge transfer mechanism can occur between L and metal(II) ions. The antimicrobial efficacy results have been recommended that, complexes 1 and 2 are good anti-pathogenic agents than ligand L. The interaction of complexes 1 and 2 with calf thymus (CT) DNA has been studied by electronic absorption, viscometric, fluorometric and cyclic voltammetric measurements. The calculated K_b values for L, complexes 1 and 2 found from absorption titrations was 4.45 × 10⁴, L; 1.92 × 10⁵, 1 and 1.65 × 10⁵, 2. The Ksv values were found to be 3.0 × 10³, 3.68 × 10³and 3.52 × 10³ for L, complexes 1 and 2 by using competitive binding with ethidium bromide (EB). These results suggest that, the compounds are interacted with DNA may be electrostatic binding. The molecular docking studies have been carried out to confirm the interaction of compounds with DNA. Consequently, in vitro anticancer activities of L, complexes 1 and 2 against selected cancer (lung cancer A549, liver cancer HepG2 and cervical carcinoma HeLa) and normal (NHDF) cell lines were assessed by MTT assay.

Recent Advances in Synthesis and Identification of Cyclic Peptides for Bioapplications

Cyclic peptides, owing to their good stability, high resistance to exo- and to some extent endo-peptidases, enhanced binding affinity and selectivity towards target biomolecules, are actively investigated as biochemical tools and therapeutic agents. In this review, we discuss various commonly utilized synthetic strategies for cyclic peptides and peptoids (peptidomimetics), their important screening methods to identify the bioactive cyclic peptides and peptoids such as combinatorial beadbased peptide library, phage display, mRNA display etc. and recent advances in their applications as bioactive compounds. Lastly, we also make a summary and provide an outlook of the research area.

A computational model of the Cambridge gambling task with applications to substance use disorders

BACKGROUND

Impulsivity is central to all forms of externalizing psychopathology, including problematic substance use. The Cambridge Gambling task (CGT) is a popular neurocognitive task used to assess impulsivity in both clinical and healthy populations. However, the traditional methods of analysis in the CGT do not fully capture the multiple cognitive mechanisms that give rise to impulsive behavior, which can lead to underpowered and difficult-to-interpret behavioral measures.

OBJECTIVES

The current study presents the cognitive modeling approach as an alternative to traditional methods and assesses predictive and convergent validity across and between approaches.

METHODS

We used hierarchical Bayesian modeling to fit a series of cognitive models to data from healthy controls (N = 124) and individuals with histories of substance use disorders (Heroin: N = 79; Amphetamine: N = 76; Polysubstance: N = 103; final total across groups N = 382). Using Bayesian model comparison, we identified the best fitting model, which was then used to identify differences in cognitive model parameters between groups.

RESULTS

The cognitive modeling approach revealed differences in quality of decision making and impulsivity between controls and individuals with substance use disorders that traditional methods alone did not detect. Crucially, convergent validity between traditional measures and cognitive model parameters was strong across all groups.

CONCLUSION

The cognitive modeling approach is a viable method of measuring the latent mechanisms that give rise to choice behavior in the CGT, which allows for stronger statistical inferences and a better understanding of impulsive and risk-seeking behavior.

Therapeutics for COVID-19: from computation to practices-where we are, where we are heading to

After the 1918 Spanish Flu pandemic caused by the H1N1 virus, the recent coronavirus disease 2019 (COVID-19) brought us to the time of serious global health catastrophe. Although no proven therapies are identified yet which can offer a definitive treatment of the COVID-19, a series of antiviral, antibacterial, antiparasitic, immunosuppressant drugs have shown clinical benefits based on repurposing theory. However, these studies are made on small number of patients, and, in majority of the cases, have been carried out as nonrandomized trials. As society is running against the time to combat the COVID-19, we present here a comprehensive review dealing with up-to-date information of therapeutics or drug regimens being utilized by physicians to treat COVID-19 patients along with in-depth discussion of mechanism of action of these drugs and their targets. Ongoing vaccine trials, monoclonal antibodies therapy and convalescent plasma treatment are also discussed. Keeping in mind that computational approaches can offer a significant insight to repurposing based drug discovery, an exhaustive discussion of computational modeling studies is performed which can assist target-specific drug discovery.

Functionalized magnetic nanomaterials for rapid and effective adsorptive removal of fluoroquinolones: Comprehensive experimental cum computational investigations

Alarming growth of pharmaceutical residues in aquatic environment has elevated concerns about their potential impact on human health. Taking cognizance of this, the present study is focussed on the coating of cobalt ferrite nanoparticles with different functionalities and to use them as adsorbents for pharmaceutical waste. The thickness of the coating was analysed using Small angle X-ray scattering technique. Thorough study of the isotherms and kinetics were performed suggesting monolayer adsorption and pseudo kinetic order model, respectively. To get an insight of the interactions liable for adsorption of fluoroquinolones over the functionalized magnetic nanoparticles computational studies were undertaken. The results demonstrated substantial evidence proposing remarkable potential of these nanostructures as adsorbents for different pollutants with an additional advantage of stability and facile recoverability with a view to treat wastewater.

Machine learning guided structure function predictions enable in silico nanoparticle screening for polymeric gene delivery

Developing highly efficient non-viral gene delivery reagents is still difficult for many hard-to-transfect cell types and, to date, has mostly been conducted via brute force screening routines. High throughput in silico methods of evaluating biomaterials can enable accelerated optimization and development of devices or therapeutics by exploring large chemical design spaces quickly and at low cost. This work reports application of state-of-the-art machine learning algorithms to a dataset of synthetic biodegradable polymers, poly(beta-amino ester)s (PBAEs), which have shown exciting promise for therapeutic gene delivery in vitro and in vivo. The data set includes polymer properties as inputs as well as polymeric nanoparticle transfection performance and nanoparticle toxicity in a range of cells as outputs. This data was used to train and evaluate several state-of-the-art machine learning algorithms for their ability to predict transfection and understand structure-function relationships. By developing an encoding scheme for vectorizing the structure of a PBAE polymer in a machine-readable format, we demonstrate that a random forest model can satisfactorily predict DNA transfection in vitro based on the chemical structure of the constituent PBAE polymer in a cell line dependent manner. Based on the model, we synthesized PBAE polymers and used them to form polymeric gene delivery nanoparticles that were predicted in silico to be successful. We validated the computational predictions in two cell lines in vitro, RAW 264.7 macrophages and Hep3B liver cancer cells, and found that the Spearman's R correlation between predicted and experimental transfection was 0.57 and 0.66 respectively. Thus, a computational approach that encoded chemical descriptors of polymers was able to demonstrate that in silico computational screening of polymeric nanomedicine compositions had utility in predicting de novo biological experiments. STATEMENT OF SIGNIFICANCE: Developing highly efficient non-viral gene delivery reagents is difficult for many hard-to-transfect cell types and, to date, has mostly been explored via brute force screening routines. High throughput in silico methods of evaluating biomaterials can enable accelerated optimization and development for therapeutic or biomanufacturing purposes by exploring large chemical design spaces quickly and at low cost. This work reports application of state-of-the-art machine learning algorithms to a large compiled PBAE DNA gene delivery nanoparticle dataset across many cell types to develop predictive models for transfection and nanoparticle cytotoxicity. We develop a novel computational pipeline to encode PBAE nanoparticles with chemical descriptors and demonstrate utility in a de novo experimental context.

Screening possible drug molecules for Covid-19. The example of vanadium (III/IV/V) complex molecules with computational chemistry and molecular docking

We are still facing a Covid-19 pandemic these days and after the aggressively infection control measures taken by the governments in the whole world, there is a need of a rapid pharmaceutical solution in order to control this crisis. The computer aided chemistry and molecular docking is a rapid tool for drug screening and investigation. Moreover, more metal-based drugs are tested daily by research institutes for their antiviral activity. Here, we make use of theoretical studies on previously published biological active complex molecules of vanadium as an example of evaluating possible drug candidates before entering the laboratory. We used DFT calculation studies for structural elucidation and optimization of the molecules and molecular docking studies on several Covid-19 related proteins. Our findings suggest that drug discovery should always be computer -aided. Additionally, it is found that Vtocdea and VXn molecules are seem to be good candidates for further studies as antiviral agents.

Molecular dynamics simulation for membrane separation and porous materials: A current state of art review

Computational frameworks have been under specific attention within the last two decades. Molecular Dynamics (MD) simulations, identical to the other computational approaches, try to address the unknown question, lighten the dark areas of unanswered questions, to achieve probable explanations and solutions. Owing to their complex microporous structure on one side and the intricate biochemical nature of various materials used in the structure, separative membrane materials possess peculiar degrees of complications. More notably, as nanocomposite materials are often integrated into separative membranes, thin-film nanocomposites and porous separative nanocomposite materials could possess an additional level of complexity with regard to the nanoscale interactions brought to the structure. This critical review intends to cover the recent methods used to assess membranes and membrane materials. Incorporation of MD in membrane technology-related fields such as desalination, fuel cell-based energy production, blood purification through hemodialysis, etc., were briefly covered. Accordingly, this review could be used to understand the current extent of MD applications for separative membranes. The review could also be used as a guideline to use the proper MD implementation within the related fields.

Mathematical models for dynamic, multisensory spatial orientation perception

Mathematical models have been proposed for how the brain interprets sensory information to produce estimates of self-orientation and self-motion. This process, spatial orientation perception, requires dynamically integrating multiple sensory modalities, including visual, vestibular, and somatosensory cues. Here, we review the progress in mathematical modeling of spatial orientation perception, focusing on dynamic multisensory models, and the experimental paradigms in which they have been validated. These models are primarily "black box" or "as if" models for how the brain processes spatial orientation cues. Yet, they have been effective scientifically, in making quantitative hypotheses that can be empirically assessed, and operationally, in investigating aircraft pilot disorientation, for example. The primary family of models considered, the observer model, implements estimation theory approaches, hypothesizing that internal models (i.e., neural systems replicating the behavior/dynamics of physical systems) are used to produce expected sensory measurements. Expected signals are then compared to actual sensory afference, yielding sensory conflict, which is weighted to drive central perceptions of gravity, angular velocity, and translation. This approach effectively predicts a wide range of experimental scenarios using a small set of fixed free parameters. We conclude with limitations and applications of existing mathematical models and important areas of future work.

Facile synthesis of N- (4-bromophenyl)-1- (3-bromothiophen-2-yl)methanimine derivatives via Suzuki cross-coupling reaction: their characterization and DFT studies

A variety of imine derivatives have been synthesized via Suzuki cross coupling of N-(4-bromophenyl)-1-(3-bromothiophen-2-yl)methanimine with various arylboronic acids in moderate to good yields (58–72%). A wide range of electron donating and withdrawing functional groups were well tolerated in reaction conditions. To explore the structural properties, Density functional theory (DFT) investigations on all synthesized molecules (3a–3i) were performed. Conceptual DFT reactivity descriptors and molecular electrostatic potential analyses were performed by using B3LYP/6-31G(d,p) method to explore the reactivity and reacting sites of all derivatives (3a–3i).

Toxicophore exploration as a screening technology for drug design and discovery: techniques, scope and limitations

Toxicity is a common drawback of newly designed chemotherapeutic agents. With the exception of pharmacophore-induced toxicity (lack of selectivity at higher concentrations of a drug), the toxicity due to chemotherapeutic agents is based on the toxicophore moiety present in the drug. To date, methodologies implemented to determine toxicophores may be broadly classified into biological, bioanalytical and computational approaches. The biological approach involves analysis of bioactivated metabolites, whereas the computational approach involves a QSAR-based method, mapping techniques, an inverse docking technique and a few toxicophore identification/estimation tools. Being one of the major steps in drug discovery process, toxicophore identification has proven to be an essential screening step in drug design and development. The paper is first of its kind, attempting to cover and compare different methodologies employed in predicting and determining toxicophores with an emphasis on their scope and limitations. Such information may prove vital in the appropriate selection of methodology and can be used as screening technology by researchers to discover the toxicophoric potentials of their designed and synthesized moieties. Additionally, it can be utilized in the manipulation of molecules containing toxicophores in such a manner that their toxicities might be eliminated or removed.

Synthetic biology design to display an 18 kDa rotavirus large antigen on a modular virus-like particle

Virus-like particles are an established class of commercial vaccine possessing excellent function and proven stability. Exciting developments made possible by modern tools of synthetic biology has stimulated emergence of modular VLPs, whereby parts of one pathogen are by design integrated into a less harmful VLP which has preferential physical and manufacturing character. This strategy allows the immunologically protective parts of a pathogen to be displayed on the most-suitable VLP. However, the field of modular VLP design is immature, and robust design principles are yet to emerge, particularly for larger antigenic structures. Here we use a combination of molecular dynamic simulation and experiment to reveal two key design principles for VLPs. First, the linkers connecting the integrated antigenic module with the VLP-forming protein must be well designed to ensure structural separation and independence. Second, the number of antigenic domains on the VLP surface must be sufficiently below the maximum such that a "steric barrier" to VLP formation cannot exist. This second principle leads to designs whereby co-expression of modular protein with unmodified VLP-forming protein can titrate down the amount of antigen on the surface of the VLP, to the point where assembly can proceed. In this work we elucidate these principles by displaying the 18.1 kDa VP8* domain from rotavirus on the murine polyomavirus VLP, and show functional presentation of the antigenic structure.

GPCRs: What Can We Learn from Molecular Dynamics Simulations?

Advances in the structural biology of G-protein Coupled Receptors have resulted in a significant step forward in our understanding of how this important class of drug targets function at the molecular level. However, it has also become apparent that they are very dynamic molecules, and moreover, that the underlying dynamics is crucial in shaping the response to different ligands. Molecular dynamics simulations can provide unique insight into the dynamic properties of GPCRs in a way that is complementary to many experimental approaches. In this chapter, we describe progress in three distinct areas that are particularly difficult to study with other techniques: atomic level investigation of the conformational changes that occur when moving between the various states that GPCRs can exist in, the pathways that ligands adopt during binding/unbinding events and finally, the influence of lipids on the conformational dynamics of GPCRs.

Smart biomaterial platforms: Controlling and being controlled by cells

Across diverse research and application areas, dynamic functionality-such as programmable changes in biochemical property, in mechanical property, or in microscopic or macroscopic architecture-is an increasingly common biomaterials design criterion, joining long-studied criteria such as cytocompatibility and biocompatibility, drug release kinetics, and controlled degradability or long-term stability in vivo. Despite tremendous effort, achieving dynamic functionality while simultaneously maintaining other desired design criteria remains a significant challenge. Reversible dynamic functionality, rather than one-time or one-way dynamic functionality, is of particular interest but has proven especially challenging. Such reversible functionality could enable studies that address the current gap between the dynamic nature of in vivo biological and biomechanical processes, such as cell traction, cell-extracellular matrix (ECM) interactions, and cell-mediated ECM remodeling, and the static nature of the substrates and ECM constructs used to study the processes. This review assesses dynamic materials that have traditionally been used to control cell activity and static biomaterial constructs, experimental and computational techniques, with features that may inform continued advances in reversible dynamic materials. Taken together, this review presents a perspective on combining the reversibility of smart materials and the in-depth dynamic cell behavior probed by static polymers to design smart bi-directional ECM platforms that can reversibly and repeatedly communicate with cells.

Pesticide remediation with cyclodextrins: a review

The aim of this review is to highlight and provide an update on the current development of pesticide remediation methods, focusing on the utilization of different cyclodextrin (CD) molecules. Because of less environmental impact and non-toxic nature, CDs are beneficial for pesticide remediation, reducing environmental risk and health hazards. They are advantageous for the removal of pesticides from contaminated areas, as well as for better pesticide formulation and, posing significant effects on the hydrolysis or degradation of pesticides. The review focuses on the current trend and innovations regarding the methods and strategies employed for using CDs in designing pesticide remediation. Nowadays, in addition to the conventional experimental techniques, molecular simulation approaches are significantly contributing to the study of such phenomena and hence are recognized as a widely used tool.