A 3D in vitro model for assessing the influence of intraocular lens: Posterior lens capsule interactions on lens epithelial cell responses.

Posterior Capsule Opacification (PCO), the most frequent complication of cataract surgery, is caused by the infiltration and proliferation of lens epithelial cells (LECs) at the interface between the intraocular lens (IOL) and posterior lens capsule (PLC). According to the "no space, no cells, no PCO" theory, high affinity (or adhesion force) between the IOL and PLC would decrease the IOL: PLC interface space, hinder LEC migration, and thus reduce PCO formation. To test this hypothesis, an in vitro hemisphere-shaped simulated PLC (sPLC) was made to mimic the human IOL: PLC physical interactions and to assess their influence on LEC responses. Three commercially available IOLs with different affinities/adhesion forces toward the sPLC, including Acrylic foldable IOL, Silicone IOL, and PMMA IOL, were used in this investigation. Using the system, the physical interactions between IOLs and sPLC were quantified by measuring the adhesion force and interface space using an adhesion force apparatus and Optical Coherence Tomography, respectively. Our data shows that high adhesion force and tight binding between IOL and sPLC contribute to a small interface space (or "no space"). By introducing LECs into the in vitro system, we found that, with small interface space, among all IOLs, acrylic foldable IOLs permitted the least extent of LEC infiltration, proliferation, and differentiation (or "no cells"). Further statistical analyses using clinical data revealed that weak LEC responses are associated with low clinical PCO incidence rates (or "no PCO"). The findings support that the in vitro system could simulate IOL: PLC interplays and predict IOLs' PCO potential in support of the "no space, no cells, no PCO" hypothesis.

Role of fibronectin and IOL surface modification in IOL: Lens capsule interactions.

Posterior Capsule Opacification (PCO) is one of the most common complications of cataract surgery. While studies have shown that IOL material properties and fibronectin adsorption may affect IOL-induced PCO in the clinical setting, the mechanism governing such interactions is not totally understood. Since strong adhesion forces between IOLs and posterior capsules (PCs) have been shown to impede cell infiltration and thus reduce PCO formation, this study was designed to assess whether fibronectin adsorption and IOL material properties would impact the IOL:PC adhesion force and cell infiltration using a PCO predictive in vitro model and a macromolecular dye imaging model, respectively. Our results showed that fibronectin adsorption significantly increased the adhesion forces and reduced simulated cell infiltration between acrylic foldable IOLs and the PC at physiological temperature in comparison to fibronectin-free controls. This fibronectin-mediated strong IOL: PC bond may be contributing to low PCO rates in the clinic for acrylic foldable IOLs. In addition, acrylic foldable IOLs coated with Di(ethylene glycol) (Diglyme), a hydrophilic coating known to reduce protein adsorption, was tested for its ability to alter adhesion force and cell infiltration. We observed that IOLs coated with Diglyme coating greatly reduced surface hydrophobicity and fibronectin adsorption of acrylic foldable IOLs. Furthermore, Diglyme coated IOLs showed significantly reduced adhesion force and increased simulated cell infiltration at the IOL:PC interface. The overall results support the hypothesis that IOL surface properties and their ability to adsorb fibronectin may have great impact on the IOL:PC adhesion force. A tight binding between IOLs and PC may contribute to the reduction of cell infiltration and thus the PCO incidence rate in the clinic.

Effect of time and temperature-dependent changes of IOL material properties on IOL: Lens capsule interactions.

Posterior Capsule Opacification (PCO) is the most common complication associated with Intraocular Lens (IOL) implantation. Based on the assumption that the interactions between an IOL and the lens capsule (LC) may influence the extent of PCO formation, a new in vitro model was developed to quantify the adhesion force of an IOL to simulated LC using a custom-designed micro-force tester. Using this system, we examined the influence of temperature (room temperature vs. body temperature) and incubation time (0 vs. 24 h) on the adhesion force between IOLs and LCs. The results show that, in line with clinical observations of PCO incidence, the adhesion force increased at body temperature and with increase in incubation time in the following order, Acrylic foldable IOLs > Silicone IOLs > PMMA IOLs. By examining the changes of surface properties as a function of temperature and incubation time, we found that acrylic foldable IOLs showed the largest increase in their hydrophilicity and reported the lowest surface roughness in comparison to other IOL groups. Coincidentally, using a newly established macromolecular dye imaging system to simulate cell migration between IOLs and LC, we observed that the amount of macromolecular dye infiltration between IOLs and LCs was in the following order: PMMA IOLs > Silicone IOLs > Acrylic foldable IOLs. These results support a new potential mechanism that body temperature, incubation time, surface hydrophilicity and smoothness of IOLs greatly contribute to their tight binding to LCs and such tight binding may lead to reduced IOL: LC space, cell infiltration, and thus PCO formation.

An in vitro system to investigate IOL: Lens capsule interaction.

Posterior capsule opacification (PCO) is the most common complication associated with intraocular lens (IOL) implantation. Unfortunately, current in vitro models cannot be used to assess the potential of PCO due to their failure to simulate the posterior curvature of the lens capsule (LC) and IOL, a factor known to affect PCO pathogenesis in clinic. To overcome such a challenge, a new system to study IOL: LC interaction and potentially predict PCO was developed in this effort. It is believed that the interactions between an IOL and the lens capsule may influence the extent of PCO formation. Specifically, strong adhesion force between an IOL and the LC may impede lens epithelial cell migration and proliferation and thus reduce PCO formation. To assess the adhesion force between an IOL and LC, a new in vitro model was established with simulated LC and a custom-designed micro-force tester. A method to fabricate simulated LCs was developed by imprinting IOLs onto molten gelatin to create simulated three dimensional (3D) LCs with curvature resembling the bag-like structure that collapses on the IOL post implantation. By pushing the LC mold vertically downward, while measuring the change in position of the bending bar with respect to its start position, the adhesion force between the IOLs and LCs was measured. An in vitro system that can measure the adhesion force reproducibly between an IOL and LC with a resolution of ~1 µN was established in this study. During system optimization, the 10% high molecular weight gelatin produced the best LC with the highest IOL: LC adhesion force with all test lenses that were fabricated from acrylic foldable, polymethylmethacrylate (PMMA) and silicone materials. Test IOLs exerted different adhesion force with the 3D simulated LCs in the following sequence: acrylic foldable IOL > silicone IOL > PMMA IOL. These results are in good agreement with the clinical observations associated with PCO performance of IOLs made of the same materials. This novel in vitro system can provide valuable insight on the IOL: LC interplay and its relationship to clinical PCO outcomes.

Insights into nanoparticle toxicity against aquatic organisms using multivariate regression, read-across, and ML algorithms: Predictive models for Daphnia magna and Danio rerio.

The production of nanoparticles (NPs) has recently become more prevalent owing to their numerous applications in the fast-growing nanotechnology industry. Although nanoparticles have growing applications, there is a significant concern over their environmental impact due to their inevitable release into the environment. With the increasing risk to aquatic organisms, D. magna and zebrafish (Danio rerio) have been preferred as important freshwater model organisms for risk assessment and ecotoxicological studies on metal oxide-based nanoparticles (MeOxNPs) in aquatic environments. It is unfeasible to assess the risks associated with every single NP through in vivo or in vitro experiments. As an alternative, in silico approaches are employed to evaluate the NP toxicity. To evaluate such performance, we have collected data from databases and literature reviews to develop models based on multivariate regression, read-across approach (RA), and machine learning (ML) algorithms following the principles of OECD (Organization for Economic Cooperation and Development) for QSAR modeling. This work has aimed to investigate which features are important drivers of nanotoxicity in D. magna and Danio rerio using simple periodic table-derived descriptors. Further, we have examined the effectiveness of read-across-derived similarity measures compared to traditional QSAR models. The results obtained from model 1 infers that nanoparticles' size, the number of metals, the core environment of the metal present in the metal oxide, and the oxidation number of the metal play a key role in the final expression of toxicity of nanoparticles to D. magna. On the other hand, the presence of higher molecular weight, the core of the metal, and the presence of oxygen influence the enzyme inhibition activity. The enzyme inhibition is correlated with the ability of zebrafish embryos to hatch, and therefore, the inhibition of ZHE1 seems to be the factor driving hatch delay. The study emphasized the importance of developing transferable, reproducible, and easily interpretable models for the early identification of nanoparticle features contributing to aquatic toxicity.

Exploring the relationships between physiochemical properties of nanoparticles and cell damage to combat cancer growth using simple periodic table-based descriptors.

A comprehensive knowledge of the physical and chemical properties of nanomaterials (NMs) is necessary to design them effectively for regulated use. Although NMs are utilized in therapeutics, their cytotoxicity has attracted great attention. Nanoscale quantitative structure-property relationship (nano-QSPR) models can help in understanding the relationship between NMs and the biological environment and provide new ways for modeling the structural properties and bio-toxic effects of NMs. The goal of the study is to construct fully validated property-based models to extract relevant features for estimating and influencing the zeta potential and obtaining the toxicity profile regarding cell damage in the treatment of cancer cells. To achieve this, QSPR modeling was first performed with 18 metal oxide (MeOx) NMs to measure their materials properties using periodic table-based descriptors. The features obtained were later applied for zeta potential calculation (imputation for sparse data) for MeOx NMs that lack such information. To further clarify the influence of the zeta potential on cell damage, a QSPR model was developed with 132 MeOx NMs to understand the possible mechanisms of cell damage. The results showed that zeta potential, along with seven other descriptors, had the potential to influence oxidative damage through free radical accumulation, which could lead to changes in the survival rate of cancerous cells. The developed QSPR and quantitative structure-activity relationship models also give hints regarding safer design and toxicity assessment of MeOx NMs.

Prediction of cytotoxicity of heavy metals adsorbed on nano-TiO2 with periodic table descriptors using machine learning approaches.

Nanoparticles with their unique features have attracted researchers over the past decades. Heavy metals, upon release and emission, may interact with different environmental components, which may lead to co-exposure to living organisms. Nanoscale titanium dioxide (nano-TiO2) can adsorb heavy metals. The current idea is that nanoparticles (NPs) may act as carriers and facilitate the entry of heavy metals into organisms. Thus, the present study reports nanoscale quantitative structure-activity relationship (nano-QSAR) models, which are based on an ensemble learning approach, for predicting the cytotoxicity of heavy metals adsorbed on nano-TiO2 to human renal cortex proximal tubule epithelial (HK-2) cells. The ensemble learning approach implements gradient boosting and bagging algorithms; that is, random forest, AdaBoost, Gradient Boost, and Extreme Gradient Boost were constructed and utilized to establish statistically significant relationships between the structural properties of NPs and the cause of cytotoxicity. To demonstrate the predictive ability of the developed nano-QSAR models, simple periodic table descriptors requiring low computational resources were utilized. The nano-QSAR models generated good R2 values (0.99-0.89), Q2 values (0.64-0.77), and Q2F1 values (0.99-0.71). Thus, the present work manifests that ML in conjunction with periodic table descriptors can be used to explore the features and predict unknown compounds with similar properties.

Nano-read-across predictions of toxicity of metal oxide engineered nanoparticles (MeOx ENPS) used in nanopesticides to BEAS-2B and RAW 264.7 cells.

The demand for nutrients and new technologies has increased with population growth. The agro-technological revolution with metal oxide engineered nanoparticles (MeOx ENPs) has the potential to reform the resilient agricultural system while maintaining the security of food. When utilized extensively, MeOx ENPs may have unintended toxicological effects on both target and non-targeted species. Since limited information about nanopesticides' pernicious effects is available, in silico modeling can be done to explore these issues. Hence, in the present work, we have applied computational modeling to explore the influence of metal oxide nanoparticles on the toxicity of bronchial epithelial (BEAS-2B) and murine myeloid (RAW 264.7) cells to bridge the data gap relating to the toxicity of MeOx NPs. Initially, partial least squares (PLS) regression models were developed applying the Small Dataset Modeler software (http://teqip.jdvu.ac.in/QSAR_Tools/DTCLab/) using four datasets having effective concentration (EC50%) as the endpoints and employing only periodic table descriptors. To further explore the predictions, we applied a read-across approach using the descriptors selected in the QSAR models. Also, the inter-endpoint cytotoxicity relationship modeling (quantitative toxicity-toxicity relationship or QTTR) was conducted. It was found that the result obtained by nano-read-across provided a similar level of accuracy as provided by QSAR. The information derived from the PLS models of both the cell lines suggested that metal cation formation, and bond-forming capacity influence the toxicity whereas the presence of metal has an influential impact on the ecotoxicological effects. Thus, it is feasible to design safe nanopesticides that could be more effective than conventional analogs.

Modeling and mechanistic understanding of cytotoxicity of metal oxide nanoparticles (MeOxNPs) to Escherichia coli: categorization and data gap filling for untested metal oxides.

Metal oxide nanoparticles (MeOxNPs) production is expected to increase every year exponentially, and their potential to cause adverse effect to the environment and human health will also expand rapidly. Hence, risk assessment of nanoparticles (NPs) is necessary to design ecosafe products. However, experimental ecotoxicological assessments are time-consuming requiring a lot of resources. Therefore, researchers rely on alternative in silico approaches to predict the behavior of NPs in the biological system. Quantitative structure - toxicity relationship (QSTR) has been adopted as a potential method to predict the cytotoxicity of untested NPs. Hence, in the present study, multiple linear regression (MLR) models were developed using 17 MeOxNPs on Escherichia coli (E. coli) bacteria cells under both light and dark conditions. The models were developed applying Small Dataset Modeler software, version 1.0.0 (http://teqip.jdvu.ac.in/QSAR_Tools/DTCLab/) which generates models with a limited number of data points. Periodic table-based descriptors (both 1st and 2nd generation) were used for the modeling purpose. Two statistically significant MLR models based on photo-induced toxicity (Q(LOO)2= 0.612,R2 = 0.726) and dark-based toxicity (Q(LOO)2= 0.627,R2 = 0.770) were developed. From the developed models, we interpreted that increase in valency and oxidation state of the metal will decrease the cytotoxicity whereas the atomic radius of the metal and electronegativity of MeOxNPs influence the toxicity toward E. coli cells. The MLR models were validated using different internal validation metrics. Additionally, we have collected 42 MeOxNPs as an external set to observe the predictive power of the two developed MLR models and categorize them into toxic and non-toxic classes. The chemical features selected in the developed models are important for understanding the mechanisms of nanotoxicity. Thus, the developed models can be a scientific basis for designing safer NPs.

Recent advances in quantitative structure-activity relationship models of antimalarial drugs.

INTRODUCTION: Due to emerging resistance to the first-line artemisinin-based antimalarials and lack of efficient vaccines and limited chemotherapeutic alternatives, there is an urgent need to develop new antimalarial compounds. In this regard, quantitative structure-activity relationship (QSAR) modeling can provide essential information about required physicochemical properties and structural parameters of antimalarial drug candidates. AREAS COVERED: The authors provide an overview of recent advances of QSAR models covering different classes of antimalarial compounds as well as molecular docking studies of compounds acting on different antimalarial targets reported in the last 5 years (2015-2019) to explore the mode of interactions between the molecules and the receptors. We have tried to cover most of the QSAR models of antimalarials (along with results from some other related computational methods) reported during 2015-2019. EXPERT OPINION: Many QSAR reports for antimalarial compounds are based on small number of data points. This review infers that most of the present work deals with analog-based QSAR approach with a limited applicability domain (a very few cases with wide domain) whereas novel target-based computational approach is reported in very few cases, which leads to huge voids of computational work based on novel antimalarial targets.

First report on a classification-based QSAR model for chemical toxicity to earthworm.

As the use of the pesticides has increased extensively in the farming fields to have a better agricultural production, the negative impacts of such use have also increased exponentially. Hence, the toxic effects of pesticides along with the targeted organisms affect the non-targeted terrestrial organisms such as earthworm. Therefore, in the present work, we have developed a classification-based quantitative structure-activity relationship (QSAR) model using linear discriminant analysis (LDA) to capture the specific information of pesticides / diverse chemicals in order to determine the structural information responsible for toxicity manifestation towards the non-targeted organism, i.e., earthworm (Eisenia foetida). After variable selection, the model was developed using 2D descriptors only and was subjected to rigorous statistical validation. The best discriminant model obtained with 8 descriptors showed appreciable Wilks' λ value of 0.490, F (Fischer's statistics) value of 14.03, χ2 value of 79.098, canonical regression coefficient (R) value of 0.714 and ρ value of 14.63. The sensitivity, specificity, accuracy, precision and F-measure values of the training set are 90.00, 80.52, 83.76, 70.59 and 79.12 respectively whereas for the test set, these are 58.82, 79.31, 71.74, 62.50 and 60.61 respectively. The insights obtained from the LDA model suggested that lipophilicity, electronrichness, and lower degree of branching of the organic compounds are responsible for earthworm toxicity through various mechanisms. On the other hand, polar and bulky diverse chemicals do not have such toxic effects on earthworm. Hence, this model can be an effective tool to tailor molecular structures of the existing pesticides to develop novel compounds or pesticides which would be less toxic to the non-targeted organisms, specifically earthworm.

Injectable Click Chemistry-based Bioadhesives for Accelerated Wound Closure.

Tissue adhesives play a vital role in surgical processes as a substitute for sutures in wound closure. However, several existing tissue adhesives suffer from cell toxicity, weak tissue-adhesive strength, and high cost. In this study, by taking advantage of the fast and specific inverse-demand Diels-Alder cycloaddition reaction, a series of bioadhesives were produced by employing copper-free click chemistry pair trans-cyclooctene (TCO) /tetrazine (Tz) in chitosan. The gelation time of the bioadhesives can be optimized to be less than 2 minutes, which meets the need for surgical wound closure in practice. By adding 4-arm polyethylene glycol propionaldehyde (PEG-PALD) as a co-crosslinker, the adhesive strength of the bioadhesives was optimized to be 2.3 times higher than that of the conventional fibrin glue. Moreover, by adjusting the amount of the co-crosslinker, the swelling ratio and pore size of the chitosan bioadhesives can be tuned to fit the need of drug encapsulation and cell seeding. The chitosan bioadhesives possess no significant in vitro cytotoxicity. Using a mice skin incision wound model, we found that the chitosan bioadhesives were able to close the wound faster and promote wound healing process than the fibrin glue. In conclusion, our results support that the innovative click-chemistry based bioadhesives have been developed with improved physical and biological properties for surgical wound closures. STATEMENT OF SIGNIFICANCE: The manuscript describes a new group of click chemistry-based chitosan bioadhesives fabricated by reacting copper-free click chemistry pair trans-cyclooctene/tetrazine with co-crosslinker PEG-PALD. The new bioadhesives possess the properties of simple preparation, injectability, fast gelation, a minimal cytotoxicity, strong adhesive strength to tissue, and enhanced wound healing responses. This innovative strategy may draw interests of readers from the field of biomaterials, drug delivery, surgical device, and translational medicine.

Risk assessment of heterogeneous TiO2-based engineered nanoparticles (NPs): a QSTR approach using simple periodic table based descriptors.

Nowadays, the risk assessment of engineered nanoparticles (NPs) on human health and animals is of great importance. We have used here simple periodic table based descriptors for mixture compounds to predict the cytotoxicity for the heterogeneous NPs. We have developed mono parametric quantitative structure-toxicity relationship (QSTR) models for 34 TiO2-based NPs modified with (poly) metallic clusters of noble metals (Au, Ag, Pt) to assess the cytotoxicity (-log EC50) towards Chinese Hamster Ovary cell line. After critical statistical analysis of the developed five linear regression (LR) models, we found that the derived models are close to each other in terms of different metric values (R2 = 0.922-0.926; Q2 = 0.907-0.911; R2adj = 0.918-0.922; Q2F1 = 0.930-0.938; Q2F2 = 0.924-0.932). Thus, we have developed a partial least squares (PLS) model using the five descriptors obtained from the five LR models. The developed PLS model showed good predictivity and robustness in terms of both internal (R2 = 0.925; Q2 = 0.911) and external validation (Q2F1 = 0.944; Q2F2 = 0.938) parameters. The descriptors, Electrochemical equivalent (Eq), 2nd ionization potential (2χpi), covalent radius (Rc), amount of Ag (Agamt) and thermal conductivity (Tc) obtained from the final PLS model well explained the cause of cytotoxicity of the heterogeneous NPs without requiring any computationally expensive descriptors. The insights obtained from the developed models suggested that higher electronegativity, lower oxidation state, and release of metal cation from its oxide increase cytotoxicity through various mechanisms. Thus, these models can be used as efficient tools to assess the toxicity with physiological property of the new heterogeneous NPs in the future.

A mild and convenient indium(III) chloride-catalyzed synthesis of thioglycosides.

The efficiency of glycosidation reactions generally involves a high chemical yield, as well as high/complete stereo- and regioselectivity. All these depend on the compatibility of the reactivity of glycosyl donors and acceptors. Among glycosyl donors, thioglycosides are widely used because of their high degree of stability in many organic reactions. Although there are number of methods available for the preparation of thioglycosides, all of them have one or more disadvantages, especially concerning the time factor and cumbersome workup procedures. Here we report a convenient and high-yielding method for the preparation of thioglycosides.