IVIVE-PBPK based new approach methodology for addressing early life toxicity induced by Bisphenol A

Bisphenol A (BPA) is a known endocrine disruptor mimicking natural estrogens with the potential to affect human health, especially during prenatal and postnatal exposure at or below current acceptable daily intake levels. Different adverse effects of BPA are still under investigation, and multiple mechanisms of action remain unexplored. This may be one of the reasons for the continuously changing tolerable daily intake (TDI) of BPA with the emergence of new adverse health effects over time. In addition, translational modelling through in vitro-in vivo extrapolation (IVIVE) can act as prerequisite bridge for translating in-vitro finding into human risk assessment. The objective of this study was to conduct in-vitro experiments and utilize an IVIVE-pregnancy physiologically based pharmacokinetic (P-PBPK) modeling to investigate developmental neurotoxicity and embryotoxicity in humans. The data obtained from human embryonic stem cells-based assays (study conducted between October 2020-2021) were used for the IVIVE-P-PBPK models to obtain the human equivalent doses (HEDs) which were further extrapolated to reference doses (RfDs). The results showed that simulated mean RfDs (μg/kg/day) derived from the HSD3B1 and NFATC2 gene of embryotoxicity and neurodevelopmental toxicity tests, respectively, were 4.94 and 5.18. The simulated RfDs were close to the temporary-tolerable daily intake (t-TDI) recommended by European Food Safety Authority (EFSA) in 2015 (t-TDI: 4 μg/kg·bw) and higher than the TDI of 2023 (0.2 ng/kg·bw). In conclusion, in-vitro toxicogenomics dose-response data combined with PBPK modeling can become a promising alternative new approach methodology (NAM) to support decision-making in chemical risk assessment. Based on the simulated RfDs derived from this NAM, the t-TDI set by EFSA in 2015 may be considered a safe exposure limit for mothers and fetuses at the current BPA intake levels in Chinese mothers. This study provided an animal-free new strategy for NAMs based risk assessment by combining toxicogenomics and computational toxicology.

Development and Evaluation of Amorphous Solid Dispersion of Riluzole with PBPK Model to Simulate the Pharmacokinetic Profile

In the current work, screening of polymers viz. polyacrylic acid (PAA), polyvinyl pyrrolidone vinyl acetate (PVP VA), and hydroxypropyl methyl cellulose acetate succinate (HPMC AS) based on drug-polymer interaction and wetting property was done for the production of a stable amorphous solid dispersion (ASD) of a poorly water-soluble drug Riluzole (RLZ). PAA showed maximum interaction and wetting property hence, was selected for further studies. Solid state characterization studies confirmed the formation of ASD with PAA. Saturation solubility, dissolution profile, and in vivo pharmacokinetic data of the ASD formulation were generated in rats against its marketed tablet Rilutor. The RLZ:PAA ASD showed exponential enhancement in the dissolution of RLZ. Predicted and observed pharmacokinetic data in rats showed enhanced area under curve (AUC) and Cmax in plasma and brain with respect to Rilutor. Furthermore, a physiologically based pharmacokinetic (PBPK) model of rats for Rilutor and RLZ ASD was developed and then extrapolated to humans where physiological parameters were changed along with a biochemical parameter. The partition coefficient was kept similar in both species. The model was used to predict different exposure scenarios, and the simulated data was compared with observed data points. The PBPK model simulated Cmax and AUC was within two times the experimental data for plasma and brain. The Cmax and AUC in the brain increased with ASD compared to Rilutor for humans showing its potential in improving its biopharmaceutical performance and hence enhanced therapeutic efficacy. The model can predict the RLZ concentration in multiple compartments including plasma and liver.

Development of a Rat Physiologically Based Kinetic Model (PBK) for three Organophosphate Flame Retardants (TDCIPP, TCIPP, TCEP)

Tris(1,3-dichloro-2-propyl) phosphate (TDCIPP), Tris (1-chloro-2-propyl) phosphate (TCIPP) and tris (2-chloroethyl) phosphate (TCEP) are three widely used organophosphate flame retardants (OPFRs) being frequently detected in human body fluids. Although OPFRs are being detected in human beings, the toxicological effects of their exposure are not clearly understood due to limited data. For this, a physiologically based kinetic model (PBK) was developed in MCSIM integrated with R studio and validated in rats to understand the toxicokinetics of OPFRs for the first time. The model required the enterohepatic recirculation (EHR) mechanism which was included to explain the non-linear data. Model parameters were optimized using the Bayesian framework (Markov Chain Monte Carlo) along with a visual fitting to explain toxicokinetic data. Goodness-of-fit was calculated to evaluate model predictability power in Rstudio. The model can appropriately predict the concentration of OPFRs in several organs like plasma, urine, kidney, etc. within 1-2-fold of experimental data. Slow elimination of OPFRs was observed from adipose tissue and brain at late time points, showing their potential to accumulate upon daily exposure. The use of PBK was demonstrated by reconstructing the oral exposure equivalent to the in-vitro toxic dose to support neurotoxic risk assessment. This version of PBK can be extrapolated to human for toxicological risk assessment. Nonetheless, further investigation is required to understand whether these chemicals follow similar kinetics in humans, which could lead to a greater risk to human health. CODE AVAILABILITY: The model will be available to access through Rshiny using GIThub soon, InSilicoVida/Flame-Retardant-PBPK-Model: It contains organophosphate flame retardant (OPFRs) PBK for TDCIPP, TCIPP and TCEP (github.com).

The Role of "Physiologically Based Pharmacokinetic Model (PBPK)" New Approach Methodology (NAM) in Pharmaceuticals and Environmental Chemical Risk Assessment

Physiologically Based Pharmacokinetic (PBPK) models are mechanistic tools generally employed in the pharmaceutical industry and environmental health risk assessment. These models are recognized by regulatory authorities for predicting organ concentration-time profiles, pharmacokinetics and daily intake dose of xenobiotics. The extension of PBPK models to capture sensitive populations such as pediatric, geriatric, pregnant females, fetus, etc., and diseased populations such as those with renal impairment, liver cirrhosis, etc., is a must. However, the current modelling practices and existing models are not mature enough to confidently predict the risk in these populations. A multidisciplinary collaboration between clinicians, experimental and modeler scientist is vital to improve the physiology and calculation of biochemical parameters for integrating knowledge and refining existing PBPK models. Specific PBPK covering compartments such as cerebrospinal fluid and the hippocampus are required to gain mechanistic understanding about xenobiotic disposition in these sub-parts. The PBPK model assists in building quantitative adverse outcome pathways (qAOPs) for several endpoints such as developmental neurotoxicity (DNT), hepatotoxicity and cardiotoxicity. Machine learning algorithms can predict physicochemical parameters required to develop in silico models where experimental data are unavailable. Integrating machine learning with PBPK carries the potential to revolutionize the field of drug discovery and development and environmental risk. Overall, this review tried to summarize the recent developments in the in-silico models, building of qAOPs and use of machine learning for improving existing models, along with a regulatory perspective. This review can act as a guide for toxicologists who wish to build their careers in kinetic modeling.

Unravelling sex-specific BPA toxicokinetics in children using a pediatric PBPK model

Bisphenol A (BPA) is a widely known endocrine disruptor (ED) found in many children's products such as toys, feeding utensils, and teething rings. Recent epidemiology association studies have shown postnatal BPA exposure resulted in developing various diseases such as diabetes, obesity, and neurodegeneration, etc., later in their lives. However, little is known about its sex-specific metabolism and consequently internal exposure. The aim of this study was to develop a sex-specific pediatric physiologically based pharmacokinetic model (PBPK) for BPA to compare their toxicokinetic differences. First, the published adult PBPK model was re-validated, and then this model was extended by interpolation to incorporate pediatric sex specific physiological and biochemical parameters. We used both the classical body weight and ontogeny-based scaling approach to interpolate the metabolic process. Then, the pharmacokinetic attributes of the models using the two-scaling approach mentioned above were compared with adult model. Further, a sex-specific PBPK model with an ontogeny scaling approach was preferred to evaluate the pharmacokinetic differences. Moreover, this model was used to reconstruct the BPA exposure from two cohorts (Helix and PBAT Cohort) from 7 EU countries. The half-life of BPA was found to be almost the same in boys and girls at the same exposure levels. Our model estimated BPA children's exposure to be about 1500 times higher than the tolerable daily intake (TDI) recently set by European Food Safety Authority (EFSA) i.e., 0.04 ng/kg BW/day. The model demonstrated feasibility of extending the adult PBPK to sex-specific pediatric, thus investigate a gender-specific health risk assessment.

Pharmacophore Modeling Using Machine Learning for Screening the Blood-Brain Barrier Permeation of Xenobiotics

Daily exposure to xenobiotics affects human health, especially the nervous system, causing neurodegenerative diseases. The nervous system is protected by tight junctions present at the blood-brain barrier (BBB), but only molecules with desirable physicochemical properties can permeate it. This is why permeation is a decisive step in avoiding unwanted brain toxicity and also in developing neuronal drugs. In silico methods are being implemented as an initial step to reduce animal testing and the time complexity of the in vitro screening process. However, most in silico methods are ligand based, and consider only the physiochemical properties of ligands. However, these ligand-based methods have their own limitations and sometimes fail to predict the BBB permeation of xenobiotics. The objective of this work was to investigate the influence of the pharmacophoric features of protein-ligand interactions on BBB permeation. For these purposes, receptor-based pharmacophore and ligand-based pharmacophore fingerprints were developed using docking and Rdkit, respectively. Then, these fingerprints were trained on classical machine-learning models and compared with classical fingerprints. Among the tested footprints, the ligand-based pharmacophore fingerprint achieved slightly better (77% accuracy) performance compared to the classical fingerprint method. In contrast, receptor-based pharmacophores did not lead to much improvement compared to classical descriptors. The performance can be further improved by considering efflux proteins such as BCRP (breast cancer resistance protein), as well as P-gp (P-glycoprotein). However, the limited data availability for other proteins regarding their pharmacophoric interactions is a bottleneck to its improvement. Nonetheless, the developed models and exploratory analysis provide a path to extend the same framework for environmental chemicals, which, like drugs, are also xenobiotics. This research can help in human health risk assessment by a priori screening for neurotoxicity-causing agents.

Chlorpyrifos, permethrin and cyfluthrin effect on cell survival, permeability, and tight junction in an in-vitro model of the human blood-brain barrier (BBB)

The blood-brain barrier (BBB) is a structural and functional interface between the plasma and the human brain. Predictive BBB in-vitro models like immortalized human capillary microvascular endothelial cells (HCMEC/D3) can be used to explore the BBB disruption potential of daily exposed chemicals. The present study was focused on investigating the human BBB permeation potential of one organophosphate pesticide, chlorpyrifos (CPF), and two pyrethroids, permethrin (PMT) and cyfluthrin (CFT). HCMEC/D3 cells were exposed to the chemical and the time-dependent pass across BBB along with permeation coefficient (Papp) was calculated. Transendothelial electrical resistance (TEER) was measured for the cells to check the monolayer formation and later to check the reduction in integrity after chemical exposure. Real time PCR was conducted to investigate the effect of chemicals on the expression BBB´s tight and adherens junction proteins. Calculated Papp value for three chemicals was in the following order: CPF>CFT>PMT, where CPF showed the highest permeation coefficient. TEER calculation showed that the integrity decreased after CPF exposure which was in concordance with Papp value whereas for other chemicals, no change in TEER after exposure was observed. In addition, the transwell study showed a higher efflux ratio (ER) (>2) of CFT indicating that CFT could be a substrate for active transport. For CPF and PMT, ER was less than 2, so no active transport seems to be involved. The evaluation of the mRNA expression analysis revealed a statistically significant decrease in Occludin (OCLN) gene expression for CPF, VE-Cadherin (CDH5) for PMT and Zonula Occludens (ZO1) expression for CFT. Our study showed that CPF has the highest potential for inducing cell death, higher permeation, and capability to induce BBB dysfunction than among the above-mentioned chemicals. Additionally, the results of the permeation study could be useful to build a human PBPK model using in vitro-to-in vivo extrapolation approach.

Effective heart disease prediction with Grey-wolf with Firefly algorithm-differential evolution (GF-DE) for feature selection and weighted ANN classification

In recent time, heart disease has become common leading to mortality of many individuals. Hence, early and accurate prediction of this disease is vital to reduce death rate and enhance people's lives. Concurrently, Artificial Intelligence has gained more attention at present as it permits deeper understanding of the healthcare data thereby providing accurate prediction results. This efficient prediction will solve complicated queries regarding heart diseases and hence assists clinical practitioners to adopt smart medical decisions. Hence, this study intends to predict heart disease with high accuracy by proposing an improved feature selection and enhanced classification approach. The paper employs Grey-wolf with Firefly algorithm for effective feature selection and using Differential Evolution Algorithm for tuning the hyper parameters of Artificial Neural Network (ANN). Hence, it is named as Grey Wolf Firefly algorithm with Differential Evolution (GF-DE) for better classification of the selected features. This proposed classification model trains the neural network to obtain optimal weights and tunes huge number of hyper parameters in an efficiently. To prove this, the proposed system is comparatively analysed with existing methods in terms of performance metrics like accuracy, precision, recall and F1 score for Cleveland and Statlog dataset. In addition, statistical analysis is also undertaken to analyse the significance of proposed system. Outcomes revealed the efficiency of proposed method which makes it highly suitable for heart disease prediction in an efficient manner.

Framework for risk assessment of PFAS utilizing experimental studies and in-silico models

Perfluoroalkyl substances (PFAS), especially PFOS and PFOA, are two widely used synthetic chemicals that can impact human health based on evidence from animal and epidemiologic studies. In this paper, we have reviewed and summarized the influence of PFAS exposure on health, pointing the quality of evidence, and applied translational techniques to integrate evidence for PFAS policy making. This is the first review where highly referred articles on PFAS used for policymaking by several regulatory agencies were collected and evaluated based on the review guidelines developed by the US National Toxicology Program's Office of Health Assessment and Translation (OHAT) review guidelines. Several limitations were observed, including co-exposure to multiple chemicals and limited measurement of primary and secondary outcomes related to specific toxicity. However, data from all the studies provided a moderate to strong level of confidence for link between PFAS exposure and different adverse outcomes. Secondly, for translating the risk to humans, an in-silico model and scaling approach was utilized. Physiologically based pharmacokinetic model (PBPK) was used to calculate the human equivalent dose (HED) from two widely accepted studies and compared with tolerable daily intakes (TDIs) established by various regulatory agencies. Inter-species dose extrapolation was done to compare with human the relevance of dosing scenarios used in animals. Overall, a framework for translation of risk was proposed based on the conclusions of this review with the goal of improving policymaking. The current paper can improve the methodological protocols for PFAS experimental studies and encourage the utilization of in-silico models for translating risk.

Molecular Characterization Reveals the Involvement of Calcium Dependent Protein Kinases in Abiotic Stress Signaling and Development in Chickpea (Cicer arietinum)

Calcium-dependent protein kinases (CDPKs) are a major group of calcium (Ca²⁺⁾ sensors in plants. CDPKs play a dual function of "Ca²⁺ sensor and responder." These sensors decode the "Ca²⁺ signatures" generated in response to adverse growth conditions such as drought, salinity, and cold and developmental processes. However, knowledge of the CDPK family in the legume crop chickpea is missing. Here, we have identified a total of 22 CDPK genes in the chickpea genome. The phylogenetic analysis of the chickpea CDPK family with other plants revealed their evolutionary conservation. Protein homology modeling described the three-dimensional structure of chickpea CDPKs. Defined arrangements of α-helix, β-strands, and transmembrane-helix represent important structures like kinase domain, inhibitory junction domain, N and C-lobes of EF-hand motifs. Subcellular localization analysis revealed that CaCDPK proteins are localized mainly at the cytoplasm and in the nucleus. Most of the CaCDPK promoters had abiotic stress and development-related cis-regulatory elements, suggesting the functional role of CaCDPKs in abiotic stress and development-related signaling. RNA sequencing (RNA-seq) expression analysis indicated the role of the CaCDPK family in various developmental stages, including vegetative, reproductive development, senescence stages, and during seed stages of early embryogenesis, late embryogenesis, mid and late seed maturity. The real-time quantitative PCR (qRT-PCR) analysis revealed that several CaCDPK genes are specifically as well as commonly induced by drought, salt, and Abscisic acid (ABA). Overall, these findings indicate that the CDPK family is probably involved in abiotic stress responses and development in chickpeas. This study provides crucial information on the CDPK family that will be utilized in generating abiotic stress-tolerant and high-yielding chickpea varieties.

Hyperbolic uncertainty estimator based fractional order sliding mode control framework for uncertain fractional order chaos stabilization and synchronization

This paper formulates a new fractional order (FO) integral terminal sliding mode control algorithms for the stabilization and synchronization of N-dimensional FO chaotic/hyper-chaotic systems, which are perturbed with unknown uncertainties. In order to render closed loop robustness, a novel efficient double hyperbolic functions based uncertainty estimator is developed for the estimation and mitigation of unknown uncertainties. Moreover, a double hyperbolic reaching law comprising of tangent hyperbolic and inverse sine hyperbolic functions is incorporated in the presented control techniques for the practical convergence of various chaotic system states and tracking errors to infinitesimally close to equilibrium. Examples such as FO Lu, FO Chen and FO Lorenz systems are taken to investigate robustness, finite time convergence, tracking accuracy and closed loop stability properties of the devised methodologies. Last but not least, comparative analysis is also carried out between the proposed and prior control techniques through various time domain performances such as settling time, error indices and measure of control energy.

Impact of Contaminants on Microbiota: Linking the Gut-Brain Axis with Neurotoxicity

Over the last years, research has focused on microbiota to establish a missing link between neuronal health and intestine imbalance. Many studies have considered microbiota as critical regulators of the gut–brain axis. The crosstalk between microbiota and the central nervous system is mainly explained through three different pathways: the neural, endocrine, and immune pathways, intricately interconnected with each other. In day-to-day life, human beings are exposed to a wide variety of contaminants that affect our intestinal microbiota and alter the bidirectional communication between the gut and brain, causing neuronal disorders. The interplay between xenobiotics, microbiota and neurotoxicity is still not fully explored, especially for susceptible populations such as pregnant women, neonates, and developing children. Precisely, early exposure to contaminants can trigger neurodevelopmental toxicity and long-term diseases. There is growing but limited research on the specific mechanisms of the microbiota–gut–brain axis (MGBA), making it challenging to understand the effect of environmental pollutants. In this review, we discuss the biological interplay between microbiota–gut–brain and analyse the role of endocrine-disrupting chemicals: Bisphenol A (BPA), Chlorpyrifos (CPF), Diethylhexyl phthalate (DEHP), and Per- and polyfluoroalkyl substances (PFAS) in MGBA perturbations and subsequent neurotoxicity. The complexity of the MGBA and the changing nature of the gut microbiota pose significant challenges for future research. However, emerging in-silico models able to analyse and interpret meta-omics data are a promising option for understanding the processes in this axis and can help prevent neurotoxicity.

EDC-induced mechanisms of immunotoxicity: a systematic review

Endocrine-disrupting chemicals (EDCs) refer to a group of chemicals that cause adverse effects in human health, impairing hormone production and regulation, resulting in alteration of homeostasis, reproductive, and developmental, and immune system impairments. The immunotoxicity of EDCs involves many mechanisms altering gene expression that depend on the activation of nuclear receptors such as the aryl hydrocarbon receptor (AHR), the estrogen receptor (ER), and the peroxisome proliferator-activated receptor (PPAR), which also results in skin and intestinal disorders, microbiota alterations and inflammatory diseases. This systematic review aims to review different mechanisms of immunotoxicity and immunomodulation of T cells, focusing on T regulatory (Treg) and Th17 subsets, B cells, and dendritic cells (DCs) caused by specific EDCs such as 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), bisphenols (BPs) and polyfluoroalkyl substances (PFASs). To achieve this objective, a systematic study was conducted searching various databases including PubMed and Scopus to find in-vitro, in-vivo, and biomonitoring studies that examine EDC-dependent mechanisms of immunotoxicity. While doing the systematic review, we found species- and cell-specific outcomes and a translational gap between in-vitro and in-vivo experiments. Finally, an adverse outcome pathway (AOP) framework is proposed, which explains mechanistically toxicity endpoints emerging from different EDCs having similar key events and can help to improve our understanding of EDCs mechanisms of immunotoxicity. In conclusion, this review provides insights into the mechanisms of immunotoxicity mediated by EDCs and will help to improve human health risk assessment.

Risk Assessment of Perfluorooctane Sulfonate (PFOS) using Dynamic Age Dependent Physiologically based Pharmacokinetic Model (PBPK) across Human Lifetime

The widespread use of Perfluorooctane sulfonate (PFOS) in everyday life, its long half-life, and the lipophilicity that makes it easily accumulate in the body, raises the question of its safe exposure among different population groups. There are currently enough epidemiological studies showing evidence of PFOS exposure and its associated adverse effects on humans. Moreover, it is already known that physiological changes along with age e.g. organ volume, renal blood flow, cardiac output and albumin concentrations affect chemicals body burden. Human biomonitoring cohort studies have reported PFOS concentrations in blood and autopsy tissue data with PFOS present in sensitive organs across all human lifespan. However, to interpret such biomonitoring data in the context of chemical risk assessment, it is necessary to have a mechanistic framework that explains show the physiological changes across age affects the concentration of chemical inside different tissues of the human body. PBPK model is widely and successfully used in the field of risk assessment. The objective of this manuscript is to develop a dynamic age-dependent PBPK model as an extension of the previously published adult PFOS model and utilize this model to predict and compare the PFOS tissue distribution and plasma concentration across different age groups. Different cohort study data were used for exposure dose reconstruction and evaluation of time-dependent concentration in sensitive organs. Predicted plasma concentration followed trends observed in biomonitoring data and model predictions showed the increased disposition of PFOS in the geriatric population. PFOS model is sensitive to parameters governing renal resorption and elimination across all ages, which is related to PFOS half-life in humans. This model provides an effective framework for improving the quantitative risk assessment of PFOS throughout the human lifetime, particularly in susceptible age groups. The dynamic age-dependent PBPK model provides a step forward for developing such kind of dynamic model for other perfluoroalkyl substances.

Binding of noble gas atoms by superhalogens

Because of their closed shells, noble gas (Ng) atoms (Ng = Ne, Ar, Kr, and Xe) seldom take part in chemical reactions, yet finding such mechanisms not only is of scientific interest but also has practical significance. Following a recent work by Mayer et al. [Proc. Natl. Acad. Sci. U. S. A. 116, 8167-8172 (2019)] on the room temperature binding of Ar to a superelectrophilic boron site embedded in a negative ion complex, B₁₂(CN)₁₁ ^-, we have systematically studied the effect of cluster size and terminal ligands on the interaction of Ng by focusing on B₁₂X₁₁(Ng) (X = H, CN, and BO) and B₁₂X₁₀(Ng)₂ (X = CN and BO) whose stabilities are governed by the Wade-Mingos rule and on C₅BX₅(Ng) (X = H, F, and CN) and C₄B₂(CN)₄(Ng)₂ whose stabilities are governed by the Huckel's aromaticity rule. Our conclusion, based on density functional theory, is that both the cluster size and the terminal ligands matter-the interaction between the cluster and the Ng atoms becomes stronger with increasing cluster size and the electron affinity of the terminal ligands. Our studies also led to a counter-intuitive finding-removing multiple terminal ligands can enable electrophilic centers to bind multiple Ng atoms simultaneously without compromising their binding strength.

Plant phospholipase D: novel structure, regulatory mechanism, and multifaceted functions with biotechnological application

Phospholipases D (PLDs) are important membrane lipid-modifying enzymes in eukaryotes. Phosphatidic acid, the product of PLD activity, is a vital signaling molecule. PLD-mediated lipid signaling has been the subject of extensive research leading to discovery of its crystal structure. PLDs are involved in the pathophysiology of several human diseases, therefore, viewed as promising targets for drug design. The availability of a eukaryotic PLD crystal structure will encourage PLD targeted drug designing. PLDs have been implicated in plants response to biotic and abiotic stresses. However, the molecular mechanism of response is not clear. Recently, several novel findings have shown that PLD mediated modulation of structural and developmental processes, such as: stomata movement, root growth and microtubule organization are crucial for plants adaptation to environmental stresses. Involvement of PLDs in regulating membrane remodeling, auxin mediated alteration of root system architecture and nutrient uptake to combat nitrogen and phosphorus deficiencies and magnesium toxicity is established. PLDs via vesicle trafficking modulate cytoskeleton and exocytosis to regulate self-incompatibility (SI) signaling in flowering plants, thereby contributes to plants hybrid vigor and diversity. In addition, the important role of PLDs has been recognized in biotechnologically important functions, including oil/TAG synthesis and maintenance of seed quality. In this review, we describe the crystal structure of a plant PLD and discuss the molecular mechanism of catalysis and activity regulation. Further, the role of PLDs in regulating plant development under biotic and abiotic stresses, nitrogen and phosphorus deficiency, magnesium ion toxicity, SI signaling and pollen tube growth and in important biotechnological applications has been discussed.

Integral terminal sliding mode control unified with UDE for output constrained tracking of mismatched uncertain non-linear systems

This paper presents a novel integral terminal sliding mode control (ITSMC) algorithm compounded with Barrier Lyapunov's Function (BLF) for high precision output tracking objective of uncertain N-dimensional strict feedback class of non-linear systems subjected to non-infringement of system output constraint. Besides, the proposed algorithm also employs uncertainty and disturbance estimator (UDE) technique for abating the existence of unknown mismatched and matched uncertainties. Most of the preliminary SMC schemes encompass the UDE technique to cope with unknown matched uncertainties only. However, the presented method embodies the dynamic surface control (DSC) architecture to get rid of the matching architectural constraint in UDE approach as well as 'complexity explosion' concern in backstepping approach. Additionally, the introduced method also applies the notion of symmetric BLF to enforce strict bounds on output tracking deviation from its reference value. To emphasize on the strengths of devised methodology, two examples are simulated by comparing the derived outcomes with already existing techniques in literature.

An Ex Vivo Evaluation of Moxifloxacin Nanostructured Lipid Carrier Enriched In Situ Gel for Transcorneal Permeation on Goat Cornea

The study was designed to fabricate the moxifloxacin nanostructured lipid carriers (MOX-NLCs) loaded in situ gel for opthalmic application to improve the corneal permeation and retention and also subside the toxic effect associated with intracameral injection of moxifloxacin in endophthalmitis treatment. Initially, Box-Behnken design was used to optimize the various factors significantly affecting the final formulation attributes. MOX-NLCs with particle size 232.1 ± 9.2 nm, polydispersity index 0.247 ± 0.031, zeta potential -16.3 ± 1.6 mV, entrapment efficiency 63.1 ± 2.4%, and spherical shape was achieved. The optimized MOX-NLCs demonstrated the Higuchi release kinetics with highest regression coefficient. Besides this, FTIR, differential scanning calorimetry, and X-ray diffraction results suggested that MOX had excellent compatibility with excipients. Furthermore, the results of ex-vivo permeation study demonstrated 2-fold higher permeation (208.7 ± 17.6 μg), retention (37.26 ± 2.83 μg), and flux (9.57 ± 0.73 μg/cm² h) compared with free MOX in situ gel. In addition, MOX-NLCs exhibited normal corneal hydration and did not show any sign of structural damage to the corneal tissue as confirmed by histology. Therefore, the findings strongly suggest that MOX-NLCs in situ gel with higher permeation and retention can be a better alternative strategy to prevent and treat the endophthalmitis infection.

Intranasal Drug Delivery of Frovatriptan Succinate-Loaded Polymeric Nanoparticles for Brain Targeting

The objective of the present study was to develop polymeric nanoparticles (PNPs) of frovatriptan succinate for brain targeting by nasal route. Double emulsion method was used to increase the entrapment efficiency of hydrophilic drug, and formulation was optimized by central composite design to achieve critical quality attributes namely particle size, zeta potential, and entrapment efficiency. Optimized batch was evaluated for surface morphology, in vitro release, permeation across nasal mucosa, stability, histopathology, and brain tissue uptake study. Prepared PNPs were found to be smooth with particle size of 264.4 ± 0.04 nm, zeta potential -35.17 ± 0.07 mV, and 65.2 ± 0.06% entrapment efficiency. PNPs showed biphasic release pattern, initial burst release followed by sustained release up to 72 h. Ex vivo diffusion study using goat nasal mucosa at pH 6.8 revealed that PNPs permeation across nasal mucosa was about 3 times more than the pure drug solution, and quick delivery of PNPs in brain region was confirmed by fluorescence microscopic evaluation in male Wistar rats after intranasal administration. Histopathology studies further revealed integrity of nasal mucosa after treatment with PNPs. The investigation indicated that hydrophilic drug, frovatriptan succinate can be successfully entrapped in PNPs to target brain via nasal delivery, and thus it could be an effective approach for nose to brain delivery.

A surfactant-assisted high energy ball milling technique to produce colloidal nanoparticles and nanocrystalline flakes in Mn–Al alloys

A surfactant assisted-high energy ball milling technique was employed to process Mn₅₄Al₄₆ alloy in the form of colloidal nanoparticles with non-magnetic nature and high-aspect ratio nanocrystalline flakes with interesting hard magnetic properties.

A Group Intervention for HIV/STI Risk Reduction among Indian Couples

BACKGROUND

HIV in India is transmitted primarily by heterosexual contact. The present study sought to test the feasibility of a group HIV/STI risk re-duction intervention among heterosexual couples in India.

METHODS

Focus groups and key informant interviews were used in 2008 to cul-turally tailor the intervention. Thirty sexually active and HIV/STI negative cou-ples were enrolled and assessed regarding risk behavior and sexual barrier accept-ability. Gender-concordant group sessions used cognitive behavioral strategies for HIV/STI prevention.

RESULTS

At baseline, male condom use was low (36%); no participants re-ported use of female condoms or vaginal gels. HIV knowledge was low; women had more HIV knowledge and more positive attitudes towards con-dom use than men. Post-intervention, willingness to use all barrier products (t = 10.0, P< .001) and intentions to avoid risk behavior increased (t = 5.62, P< .001).

CONCLUSION

This study illustrates the feasibility of utilizing a group interven-tion to enhance HIV/STI risk reduction among Indian couples.