Molecular dissection studies of TAC1, a transcription activator of Candida drug resistance genes of the human pathogenic fungus Candida albicans

The up-regulation of ABC transporters Cdr1p and Cdr2p that efflux antifungal azole drugs are a leading cause of Multi-Drug Resistance (MDR) in the white fungus Candida albicans. C. albicans was reported to infect patients following the recent Covid-19 pandemic after they were given steroids for recovery. Previously, the TAC1 gene was identified as the transcriptional activator of Candida drug resistance genes (CDR1 and CDR2) and has no known human homologs. This makes it a good target for the development of novel antifungals. We, therefore, carried out the molecular dissection study of TAC1 to understand the functional regulation of the ABC transporter genes (CDR1 and CDR2) under its control. The N-terminal DNA Binding Domain (DBD) of Tac1p interacts with the Drug Responsive Element (DRE) present in the upstream promoter region of CDR1 and CDR2 genes of C. albicans. The interaction between DBD and DRE recruits Tac1p to the promoter of CDR genes. The C-terminal Acidic Activation Domain (AAD) of Tac1p interacts with the TATA box Binding Protein (TBP) and thus recruits TBP to the TATA box of CDR1 and CDR2 genes. Taking a cue from a previous study involving a TAC1 deletion strain that suggested that Tac1p acts as a xenobiotic receptor, in this study, we identified that the Middle Homology Region (MHR) of Tac1p acts as a probable xenobiotic binding domain (XBD) which plays an important role in Candida drug resistance. In addition, we studied the role of Tac1p in the regulation of some lipid profiling genes and stress response genes since they also contain the DRE consensus sequence and found that some of them can respond to xenobiotic stimuli.

Acoustic Change Complex as a Neurophysiological Tool to Assess Auditory Discrimination Skill: A Review

Introduction Acoustic change complex (ACC) is a type of event-related potential evoked in response to subtle change(s) in the continuing stimuli. In the presence of a growing number of investigations on ACC, there is a need to review the various methodologies, findings, clinical utilities, and conclusions of different studies by authors who have studied ACC.

Objective The present review article is focused on the literature related to the utility of ACC as a tool to assess the auditory discrimination skill in different populations.

Data Synthesis Various database providers, such as Medline, Pubmed, Google, and Google Scholar, were searched for any ACC-related reference. A total of 102 research papers were initially obtained using descriptors such as acoustic change complex, clinical utility of ACC, ACC in children, ACC in cochlear implant users, and ACC in hearing loss. The titles, authors, and year of publication were examined, and the duplicates were eliminated. A total of 31 research papers were found on ACC and were incorporated in the present review. The findings of these 31 articles were reviewed and have been reported in the present article.

Conclusion The present review showed the utility of ACC as an objective tool to support various subjective tests in audiology.

Identifying developability risks for clinical progression of antibodies using high-throughput in vitro and in silico approaches

With the growing significance of antibodies as a therapeutic class, identifying developability risks early during development is of paramount importance. Several high-throughput in vitro assays and in silico approaches have been proposed to de-risk antibodies during early stages of the discovery process. In this review, we have compiled and collectively analyzed published experimental assessments and computational metrics for clinical antibodies. We show that flags assigned based on in vitro measurements of polyspecificity and hydrophobicity are more predictive of clinical progression than their in silico counterparts. Additionally, we assessed the performance of published models for developability predictions on molecules not used during model training. We find that generalization to data outside of those used for training remains a challenge for models. Finally, we highlight the challenges of reproducibility in computed metrics arising from differences in homology modeling, in vitro assessments relying on complex reagents, as well as curation of experimental data often used to assess the utility of high-throughput approaches. We end with a recommendation to enable assay reproducibility by inclusion of controls with disclosed sequences, as well as sharing of structural models to enable the critical assessment and improvement of in silico predictions.

Multi-state modeling of antibody-antigen complexes with SAXS profiles and deep-learning models

Antibodies are an established class of human therapeutics. Epitope characterization is an important part of therapeutic antibody discovery. However, structural characterization of antibody-antigen complexes remains challenging. On the one hand, X-ray crystallography or cryo-electron microscopy provide atomic resolution characterization of the epitope, but the data collection process is typically long and the success rate is low. On the other hand, computational methods for modeling antibody-antigen structures from the individual components frequently suffer from a high false positive rate, rarely resulting in a unique solution. Recent deep learning models for structure prediction are also successful in predicting protein-protein complexes. However, they do not perform well for antibody-antigen complexes. Small Angle X-ray Scattering (SAXS) is a reliable technique for rapid structural characterization of protein samples in solution albeit at low resolution. Here, we present an integrative approach for modeling antigen-antibody complexes using the antibody sequence, antigen structure, and experimentally determined SAXS profiles of the antibody, antigen, and the complex. The method models antibody structures using a novel deep-learning approach, NanoNet. The structures of the antibodies and antigens are represented using multiple 3D conformations to account for compositional and conformational heterogeneity of the protein samples that are used to collect the SAXS data. The complexes are predicted by integrating the SAXS profiles with scoring functions for protein-protein interfaces that are based on statistical potentials and antibody-specific deep-learning models. We validated the method via application to four Fab:EGFR and one Fab:PCSK9 antibody:antigen complexes with experimentally available SAXS datasets. The integrative approach returns accurate predictions (interface RMSD<4Å) in the top five predictions for four out of five complexes (respective interface RMSD values of 1.95, 2.18, 2.66 and 3.87Å), providing support for the utility of such a computational pipeline for epitope characterization during therapeutic antibody discovery.

Is Early Cochlear Implantation Leads to Better Speech and Language Outcomes?

A cochlear implant is an electronic sensory system that converts mechanical energy into coded electrical signals that directly activate the auditory nerve fibers. Present review article investigates the findings of previous research papers which have assessed speech and language outcome in children who underwent early cochlear implantation. Several databases, including PubMed, Google, Google Scholar, and Medline, were investigated for research papers on the speech and language outcomes of children who had early-life implants. According to previous research, early detection and intervention of hearing loss are critical for the growth of speech and language skills. The effect of early detection of hearing loss and early cochlear implantation on a child's overall speech-language and auditory growth is highlighted in this article.

Comparative study of nutritional and functional characteristics of pearl millet, buckwheat, amaranth and unripe banana flours for gluten-free bakery products

Suburbanization and changes in societal habits have led to a paradigm shift towards products that are nutritionally rich and therapeutic. However, the indispensable wheat flour in the bakery industry is also a trigger for gluten-related disorders. The present research focused on the characterization of nutritional and functional properties of glutenfree cereals-pearl millet, buckwheat, amaranth and unripe banana flours in comparison with wheat flour. The data were evaluated using analysis of variance and the difference between specific pair of means was compared using Duncan’s multiple range test at a 95% confidence interval. Pearson correlation analysed the interaction amongst derived parameters. The results were significantly different (p<0.05) except for oil-absorbing capacity. The gluten-free flours reported higher protein (15.55% for amaranth), carbohydrates (82.69% for unripe banana) and energy content (384.72 kcal/g for pearl millet) compared to wheat flour whereas the moisture (6.26% for pearl millet) and fat content (0.98% for unripe banana) were lower. Amaranth had the highest water holding capacity and foam capacity (3.86 mL/g and 26.62% respectively) but the lowest foam stability (89.20%). Pearson correlation showed significant relations among functional properties and food components. Oil-absorbing capacity was found to be positively correlated to protein and negatively to carbohydrates and foam stability. The foam capacity was strongly correlated with different food components. The protein-rich and high energy gluten-free flours along with their functional properties can aid in the development of composite flour and fill the gap in the demand-supply of the therapeutic gluten-free diet.

Industrial objects recognition in intelligent manufacturing for computer vision

Purpose

The overall goal of this research is to develop algorithms for feature-based recognition of 2D parts from intensity images. Most present industrial vision systems are custom-designed systems, which can only handle a specific application. This is not surprising, since different applications have different geometry, different reflectance properties of the parts.

Design/methodology/approach

Computer vision recognition has attracted the attention of researchers in many application areas and has been used to solve many ranges of problems. Object recognition is a type of pattern recognition. Object recognition is widely used in the manufacturing industry for the purpose of inspection. Machine vision techniques are being applied in areas ranging from medical imaging to remote sensing, industrial inspection to document processing and nanotechnology to multimedia databases. In this work, recognition of objects manufactured in mechanical industry is considered. Mechanically manufactured parts have recognition difficulties due to manufacturing process including machine malfunctioning, tool wear and variations in raw material. This paper considers the problem of recognizing and classifying the objects of such mechanical part. Red, green and blue RGB images of five objects are used as an input. The Fourier descriptor technique is used for recognition of objects. Artificial neural network (ANN) is used for classification of five different objects. These objects are kept in different orientations for invariant rotation, translation and scaling. The feed forward neural network with back-propagation learning algorithm is used to train the network. This paper shows the effect of different network architecture and numbers of hidden nodes on the classification accuracy of objects as well as the effect of learning rate and momentum.

Findings

One important finding is that there is not any considerable change in the network performances after 500 iterations. It has been found that for data smaller network structure, smaller learning rate and momentum are required. The relative sample size also has a considerable effect on the performance of the classifier. Further studies suggest that classification accuracy is achieved with the confusion matrix of the data used. Hence, with these results the proposed system can be used efficiently for more objects. Depending upon the manufacturing product and process used, the dimension verification and surface roughness may be integrated with proposed technique to develop a comprehensive vision system. The proposed technique is also highly suitable for web inspections, which do not require dimension and roughness measurement and where desired accuracy is to be achieved at a given speed. In general, most recognition problems provide identity of object with pose estimation. Therefore, the proposed recognition (pose estimation) approach may be integrated with inspection stage.

Originality/value

This paper considers the problem of recognizing and classifying the objects of such mechanical part. RGB images of five objects are used as an input. The Fourier descriptor technique is used for recognition of objects. ANN is used for classification of five different objects. These objects are kept in different orientations for invariant rotation, translation and scaling. The feed forward neural network with back-propagation learning algorithm is used to train the network. This paper shows the effect of different network architecture and numbers of hidden nodes on the classification accuracy of objects as well as the effect of learning rate and momentum.

The uniqueness of flow in probing the aggregation behavior of clinically relevant antibodies

The development of therapeutic monoclonal antibodies (mAbs) can be hindered by their tendency to aggregate throughout their lifetime, which can illicit immunogenic responses and render mAb manufacturing unfeasible. Consequently, there is a need to identify mAbs with desirable thermodynamic stability, solubility, and lack of self-association. These behaviors are assessed using an array of in silico and in vitro assays, as no single assay can predict aggregation and developability. We have developed an extensional and shear flow device (EFD), which subjects proteins to defined hydrodynamic forces which mimic those experienced in bioprocessing. Here, we utilize the EFD to explore the aggregation propensity of 33 IgG1 mAbs, whose variable domains are derived from clinical antibodies. Using submilligram quantities of material per replicate, wide-ranging EFD-induced aggregation (9-81% protein in pellet) was observed for these mAbs, highlighting the EFD as a sensitive method to assess aggregation propensity. By comparing the EFD-induced aggregation data to those obtained previously from 12 other biophysical assays, we show that the EFD provides distinct information compared with current measures of adverse biophysical behavior. Assessing a candidate's liability to hydrodynamic force thus adds novel insight into the rational selection of developable mAbs that complements other assays.

Molecular characterization and expression profile of interferon-stimulated gene 15 (ISG15) in the endometrium of goat (Capra hircus)

Interferon-stimulated gene 15 (ISG15), a ubiquitin-like protein, is responsible for uterine receptivity, implantation and conceptus development in different ruminant species, but in goat (Capra hircus) its role is yet to be explicated. In the present study, the ISG15 gene was cloned, characterized and its temporal expression profile was examined in the endometrium of caprine (cp). A fragment of cpISG15 gene, 1033 bp in length, was amplified, cloned and sequenced from genomic DNA covering the coding region. Sequence analysis of cpISG15 gene revealed that it was comprised of two exons of 59 bp and 496 bp encoding a peptide of 157 amino acids. Complementary DNA (cDNA) and deduced amino acid sequences of cpISG15 exhibited 99 and 98, 93 and 88, 94 and 89, 76 and 66, and 73 and 62% identity with that of sheep, cattle, buffalo, human and mice, respectively. Further, relative expression of cpISG15 mRNA and protein was determined by quantitative real-time PCR (qPCR) and Western blot, respectively, in the endometrium of pregnant and cyclic does. Both cpISG15 mRNA and protein were expressed maximally (P < 0.05) in the endometrium during early stage of pregnancy (16-24 d) as compared to cyclic does, but no significant difference was observed in cpISG15 mRNA and protein expression in the endometrium between the later stage of pregnancy (25-40 d) and cyclic does. It is concluded that cpISG15 is almost similar in structure and probably in function also to other species as it has been found significantly upregulated during early pregnancy.

Deamidation and isomerization liability analysis of 131 clinical-stage antibodies

Contemporary in vivo and in vitro discovery platform technologies greatly increase the odds of identifying high-affinity monoclonal antibodies (mAbs) towards essentially any desired biologically relevant epitope. Lagging discovery throughput is the ability to select for highly developable mAbs with drug-like properties early in the process. Upstream consideration of developability metrics should reduce the frequency of failures in later development stages. As the field moves towards incorporating biophysical screening assays in parallel to discovery processes, similar approaches should also be used to ensure robust chemical stability. Optimization of chemical stability in the early stages of discovery has the potential to reduce complications in formulation development and improve the potential for successful liquid formulations. However, at present, our knowledge of the chemical stability characteristics of clinical-stage therapeutic mAbs is fragmented and lacks comprehensive comparative assessment. To address this knowledge gap, we produced 131 mAbs with amino acid sequences corresponding to the variable regions of clinical-stage mAbs, subjected these to low and high pH stresses and identified the resulting modifications at amino acid-level resolution via tryptic peptide mapping. Among this large set of mAbs, relatively high frequencies of asparagine deamidation events were observed in CDRs H2 and L1, while CDRs H3, H2 and L1 contained relatively high frequencies of instances of aspartate isomerization.

Unmanned machine vision system for automated recognition of mechanical parts

Purpose

The field of machine vision, or computer vision, has been growing at fast pace. The growth in this field, unlike most established fields, has been both in breadth and depth of concepts and techniques. Machine vision techniques are being applied in areas ranging from medical imaging to remote sensing, industrial inspection to document processing and nanotechnology to multimedia databases. The goal of a machine vision system is to create a model of the real world from images. Computer vision recognition has attracted the attention of researchers in many application areas and has been used to solve many ranges of problems. The purpose of this paper is to consider recognition of objects manufactured in mechanical industry. Mechanically manufactured parts have recognition difficulties due to manufacturing process including machine malfunctioning, tool wear and variations in raw material. This paper considers the problem of recognizing and classifying the objects of such parts. RGB images of five objects are used as an input. The Fourier descriptor technique is used for recognition of objects. Artificial neural network (ANN) is used for classification of five different objects. These objects are kept in different orientations for invariant rotation, translation and scaling. The feed forward neural network with back-propagation learning algorithm is used to train the network. This paper shows the effect of different network architecture and numbers of hidden nodes on the classification accuracy of objects.

Design/methodology/approach

The overall goal of this research is to develop algorithms for feature-based recognition of 2D parts from intensity images. Most present industrial vision systems are custom-designed systems, which can only handle a specific application. This is not surprising, since different applications have different geometry, different reflectance properties of the parts.

Findings

Classification accuracy is affected by the changing network architecture. ANN is computationally demanding and slow. A total of 20 hidden nodes network structure produced the best results at 500 iterations (90 percent accuracy based on overall accuracy and 87.50 percent based on κ coefficient). So, 20 hidden nodes are selected for further analysis. The learning rate is set to 0.1, and momentum term used is 0.2 that give the best results architectures. The confusion matrix also shows the accuracy of the classifier. Hence, with these results the proposed system can be used efficiently for more objects.

Originality/value

After calculating the variation of overall accuracy with different network architectures, the results of different configuration of the sample size of 50 testing images are taken. Table II shows the results of the confusion matrix obtained on these testing samples of objects.

Chaperone proteins as single component reagents to assess antibody nonspecificity

Early stage assays that evaluate monoclonal antibody drug-like properties serve as valuable tools for selection of lead candidates. One liability for clinical development, off-target reactivity, is often assessed by binding to a mixture or panel of noncognate proteins. While robust, these mixes are often ill-defined, and can suffer from issues such as lot-to-lot variability. In this study, we discovered in immunoprecipitation experiments that certain chaperones are present in one of these mixtures;we then explored the use of recombinant chaperone proteins as well-characterized agents to predict antibody nonspecificity. Antibody binding to the heat shock proteins HSP70, HSP90, or trigger factor all served as predictors of cross-interaction propensity, with HSP90 providing the greatest ability to predict antibody clearance rates in mouse. Individual chaperone binding correlates surprisingly closely with binding to complex cell extracts, with the exception of a few “false negatives” (assuming a complex cell extract as the “true” value). As defined reagents, these chaperone reagents present advantages for high throughput assays of nonspecificity.

Rapid assessment of oxidation via middle-down LCMS correlates with methionine side-chain solvent-accessible surface area for 121 clinical stage monoclonal antibodies

Susceptibility of methionine to oxidation is an important concern for chemical stability during the development of a monoclonal antibody (mAb) therapeutic. To minimize downstream risks, leading candidates are usually screened under forced oxidation conditions to identify oxidation-labile molecules. Here we report results of forced oxidation on a large set of in-house expressed and purified mAbs with variable region sequences corresponding to 121 clinical stage mAbs. These mAb samples were treated with 0.1% H₂O₂ for 24 hours before enzymatic cleavage below the hinge, followed by reduction of inter-chain disulfide bonds for the detection of the light chain, Fab portion of heavy chain (Fd) and Fc by liquid chromatography-mass spectrometry. This high-throughput, middle-down approach allows detection of oxidation site(s) at the resolution of 3 distinct segments. The experimental oxidation data correlates well with theoretical predictions based on the solvent-accessible surface area of the methionine side-chains within these segments. These results validate the use of upstream computational modeling to predict mAb oxidation susceptibility at the sequence level.

Human Spermatozoa Quantitative Proteomic Signature Classifies Normo- and Asthenozoospermia

Scarcely understood defects lead to asthenozoospermia, which results in poor fertility outcomes. Incomplete knowledge of these defects hinders the development of new therapies and reliance on interventional therapies, such as in vitro fertilization, increases. Sperm cells, being transcriptionally and translationally silent, necessitate the proteomic approach to study the sperm function. We have performed a differential proteomics analysis of human sperm and seminal plasma and identified and quantified 667 proteins in sperm and 429 proteins in seminal plasma data set, which were used for further analysis. Statistical and mathematical analysis combined with pathway analysis and self-organizing maps clustering and correlation was performed on the data set.It was found that sperm proteomic signature combined with statistical analysis as opposed to the seminal plasma proteomic signature can differentiate the normozoospermic versus the asthenozoospermic sperm samples. This is despite the results that some of the seminal plasma proteins have big fold changes among classes but they fall short of statistical significance. S-Plot of the sperm proteomic data set generated some high confidence targets, which might be implicated in sperm motility pathways. These proteins also had the area under the curve value of 0.9 or 1 in ROC curve analysis.Various pathways were either enriched in these proteomic data sets by pathway analysis or they were searched by their constituent proteins. Some of these pathways were axoneme activation and focal adhesion assembly, glycolysis, gluconeogenesis, cellular response to stress and nucleosome assembly among others. The mass spectrometric data is available via ProteomeXchange with identifier PXD004098.

Target-independent variable region mediated effects on antibody clearance can be FcRn independent

The importance of the neonatal Fc receptor (FcRn) in extending the serum half-life of monoclonal antibodies (mAbs) is well demonstrated, and has led to the development of multiple engineering approaches designed to alter Fc interactions with FcRn. Recent reports have additionally highlighted the effect of nonspecific interactions on antibody pharmacokinetics (PK), suggesting an FcRn-independent mechanism for mAb clearance. In this report we examine a case study of 2 anti-interleukin-12/23 antibodies, ustekinumab and briakinumab, which share the same target and Fc, but differ in variable region sequences. Ustekinumab displayed near baseline signal in a wide range of early stage developability assays for undesirable protein/protein interactions, while briakinumab showed significant propensity for self- and cross-interactions. This phenotypic difference correlates with faster clearance rates for briakinumab in both human FcRn transgenic and FcRn knockout mice. These findings support a dominant contribution for FcRn-independent clearance for antibodies with high nonspecificity, and highlight a key role for early stage developability screening to eliminate clones with such high nonspecific disposition PK.

Follicle-stimulating hormone-induced rescue of cumulus cell apoptosis and enhanced development ability of buffalo oocytes

The effect of follicle-stimulating hormone (FSH) on apoptotic status of cumulus cells, expression of proapoptotic and antiapoptotic genes, and development rate of in vitro fertilization-produced buffalo embryos were investigated. FSH supplementation in in vitro maturation-medium resulted in a dose-dependent reduction in the expression of proapoptotic genes namely, BCL2-associated X protein (BAX), cytochrome c, and caspase-3 and increase in the expression of antiapoptotic genes such as B-cell lymphoma 2 (BCL2) and X-linked inhibitor of apoptosis protein (XIAP) in cumulus cells of mature oocyte. Cumulus expansion, oocyte maturation, cleavage, and blastocyst development rates were significantly higher (P < 0.05) in 5 and 10-μg/mL FSH-supplemented groups as compared with control. Significant increase in the expression of FSH receptor messenger RNA was also found with 5 and 10-μg/mL FSH (P < 0.05). Terminal deoxynucleotidyl transferase dUTP nick end labeling assay confirmed that the population of apoptotic cumulus cells of matured oocytes was reduced in the FSH-treated groups as compared with control (P < 0.05). In conclusion, our data suggest that FSH may attenuate apoptosis in cumulus cells via mitochondria-dependent apoptotic pathway by increasing XIAP expression, resulting in a more favorable ratio of BCL2/BAX expression and decreasing the cytochrome c and caspase-3 expression, eventually contributing to developmental competence of oocytes. The information generated will help in improving the in vitro embryo production program in buffalo.

High throughput cross-interaction measures for human IgG1 antibodies correlate with clearance rates in mice

Although improvements in technology for the isolation of potential therapeutic antibodies have made the process increasingly predictable, the development of biologically active monoclonal antibodies (mAbs) into drugs can often be impeded by developability issues such as poor expression, solubility, and promiscuous cross-reactivity. Establishing early stage developability screening assays capable of predicting late stage behavior is therefore of high value to minimize development risks. Toward this goal, we selected a panel of 16 monoclonal antibodies (mAbs) representing different developability profiles, in terms of self- and cross-interaction propensity, and examined their downstream behavior from expression titer to accelerated stability and pharmacokinetics in mice. Clearance rates showed significant rank-order correlations to 2 cross-interaction related assays, with the closest correlation to a non-specificity assay on the surface of yeast. Additionally, 2 self-association assays correlated with each other but not to mouse clearance rate. This case study suggests that combining assays capable of high throughput screening of self- and cross-interaction early in the discovery stage could significantly lower downstream development risks.

An alternative assay to hydrophobic interaction chromatography for high-throughput characterization of monoclonal antibodies

The effectiveness of therapeutic monoclonal antibodies (mAbs) is governed not only by their bioactivity, but also by their biophysical properties. Assays for rapidly evaluating the biophysical properties of mAbs are valuable for identifying those most likely to exhibit superior properties such as high solubility, low viscosity and slow serum clearance. Analytical hydrophobic interaction chromatography (HIC), which is performed at high salt concentrations to enhance hydrophobic interactions, is an attractive assay for identifying mAbs with low hydrophobicity. However, this assay is low throughput and thus not amenable to processing the large numbers of mAbs that are commonly generated during antibody discovery. Therefore, we investigated whether an alternative, higher throughput, assay could be developed that is based on evaluating antibody self-association at high salt concentrations using affinity-capture self-interaction nanoparticle spectroscopy (AC-SINS). Our approach is to coat gold nanoparticles with polyclonal anti-human antibodies, use these conjugates to immobilize human mAbs, and evaluate mAb self-interactions by measuring the plasmon wavelengths of the antibody conjugates as a function of ammonium sulfate concentration. We find that hydrophobic mAbs, as identified by HIC, generally show significant self-association at low to moderate ammonium sulfate concentrations, while hydrophilic mAbs typically show self-association only at high ammonium sulfate concentrations. The correlation between AC-SINS and HIC measurements suggests that our assay, which can evaluate tens to hundreds of mAbs in a parallel manner and requires only small (microgram) amounts of antibody, will enable early identification of mAb candidates with low hydrophobicity and improved biophysical properties.

Non-articular Felty's syndrome: An uncommon diagnosis

Felty's syndrome is a triad of rheumatoid arthritis, neutropenia, and splenomegaly. We hereby report an unusual case of non-articular Felty's syndrome and its management along with discussing the importance of appropriately ruling out alternate causes of neutropenia with splenomegaly.

High-throughput screening for developability during early-stage antibody discovery using self-interaction nanoparticle spectroscopy

The discovery of monoclonal antibodies (mAbs) that bind to a particular molecular target is now regarded a routine exercise. However, the successful development of mAbs that (1) express well, (2) elicit a desirable biological effect upon binding, and (3) remain soluble and display low viscosity at high concentrations is often far more challenging. Therefore, high throughput screening assays that assess self-association and aggregation early in the selection process are likely to yield mAbs with superior biophysical properties. Here, we report an improved version of affinity-capture self-interaction nanoparticle spectroscopy (AC-SINS) that is capable of screening large panels of antibodies for their propensity to self-associate. AC-SINS is based on concentrating mAbs from dilute solutions around gold nanoparticles pre-coated with polyclonal capture (e.g., anti-Fc) antibodies. Interactions between immobilized mAbs lead to reduced inter-particle distances and increased plasmon wavelengths (wavelengths of maximum absorbance), which can be readily measured by optical means. This method is attractive because it is compatible with dilute and unpurified mAb solutions that are typical during early antibody discovery. In addition, we have improved multiple aspects of this assay for increased throughput and reproducibility. A data set comprising over 400 mAbs suggests that our modified assay yields self-interaction measurements that are well-correlated with other lower throughput assays such as cross-interaction chromatography. We expect that the simplicity and throughput of our improved AC-SINS method will lead to improved selection of mAbs with excellent biophysical properties during early antibody discovery.

Zero order spectrophotometric method for estimation of escitalopram oxalate in tablet formulations

A new, simple, fast and reliable zero order spectrophotometric method has been developed for determination of Escitalopram Oxalate in bulk and tablet dosage forms. The quantitative determination of drug was carried out using the zero order values (absorbance) measured at 238 nm. Calibration graph constructed at 238 nm was linear in concentration range of 2-20 µg/ml with correlation coefficient 0.9999. The method was found to be precise, accurate, specific, and validated as per ICH guidelines and can be used for determination of Escitalopram Oxalate in tablet formulations.

From drug target to leads--sketching a physicochemical pathway for lead molecule design in silico

The discovery of new pharmaceuticals via computer modeling is one of the key challenges in modern medicine. The advent of global networks of genomic, proteomic and metabolomic endeavors is ushering in an increasing number of novel and clinically important targets for screening. Computational methods are anticipated to play a pivotal role in exploiting the structural and functional information to understand specific molecular recognition events of the target macromolecule with candidate hits leading ultimately to the design of improved leads for the target. In this review, we sketch a system independent, comprehensive physicochemical pathway for lead molecule design focusing on the emerging in silico trends and techniques. We survey strategies for the generation of candidate molecules, docking them with the target and ranking them based on binding affinities. We present a molecular level treatment for distinguishing affinity from specificity of a ligand for a given target. We also discuss the significant aspects of drug absorption, distribution, metabolism, excretion and toxicity (ADMET) and highlight improved protocols required for higher quality and throughput of in silico methods employed at early stages of discovery. We present a realization of the various stages in the pathway proposed with select examples from the literature and from our own research to demonstrate the way in which an iterative process of computer design and validation can aid in developing potent leads. The review thus summarizes recent advances and presents a viewpoint on improvements envisioned in the years to come for automated computer aided lead molecule discovery.

Configurational-bias sampling technique for predicting side-chain conformations in proteins

Prediction of side-chain conformations is an important component of several biological modeling applications. In this work, we have developed and tested an advanced Monte Carlo sampling strategy for predicting side-chain conformations. Our method is based on a cooperative rearrangement of atoms that belong to a group of neighboring side-chains. This rearrangement is accomplished by deleting groups of atoms from the side-chains in a particular region, and regrowing them with the generation of trial positions that depends on both a rotamer library and a molecular mechanics potential function. This method allows us to incorporate flexibility about the rotamers in the library and explore phase space in a continuous fashion about the primary rotamers. We have tested our algorithm on a set of 76 proteins using the all-atom AMBER99 force field and electrostatics that are governed by a distance-dependent dielectric function. When the tolerance for correct prediction of the dihedral angles is a <20 degrees deviation from the native state, our prediction accuracies for chi1 are 83.3% and for chi1 and chi2 are 65.4%. The accuracies of our predictions are comparable to the best results in the literature that often used Hamiltonians that have been specifically optimized for side-chain packing. We believe that the continuous exploration of phase space enables our method to overcome limitations inherent with using discrete rotamers as trials.

CIRSE: a solvation energy estimator compatible with flexible protein docking and design applications

We present the Coordinate Internal Representation of Solvation Energy (CIRSE) for computing the solvation energy of protein configurations in terms of pairwise interactions between their atoms with analytic derivatives. Currently, CIRSE is trained to a Poisson/surface-area benchmark, but CIRSE is not meant to fit this benchmark exclusively. CIRSE predicts the overall solvation energy of protein structures from 331 NMR ensembles with 0.951+/-0.047 correlation and predicts relative solvation energy changes between members of individual ensembles with an accuracy of 15.8+/-9.6 kcal/mol. The energy of individual atoms in any of CIRSE's 17 types is predicted with at least 0.98 correlation. We apply the model in energy minimization, rotamer optimization, protein design, and protein docking applications. The CIRSE model shows some propensity to accumulate errors in energy minimization as well as rotamer optimization, but these errors are consistent enough that CIRSE correctly identifies the relative solvation energies of designed sequences as well as putative docked complexes. We analyze the errors accumulated by the CIRSE model during each type of simulation and suggest means of improving the model to be generally useful for all-atom simulations.

A Free Energy Based Computational Pathway from Chemical Templates to Lead Compounds: A Case Study of COX-2 Inhibitors

Automation of lead compound design in silico given the structure of the protein target and a definition of its active site vies for the top of the wish list in any drug discovery programme. We present here an enumeration of steps starting from chemical templates and propose a solution at the state of the art, in the form of a system independent comprehensive computational pathway. This methodology is illustrated with cyclooxygenase-2 (COX-2) as a target. We built candidate molecules including a few Non Steroidal Anti-inflammatory Drugs (NSAIDs) from chemical templates, passed them through empirical filters to assess drug-like properties, optimized their geometries, derived partial atomic charges via quantum calculations, performed Monte Carlo docking, carried out molecular mechanics and developed free energy estimates with Molecular Mechanics Generalized Born Solvent Accessibility (MMGBSA) methodology for each of the candidate molecules. For the case of aspirin, we also conducted molecular dynamics on the enzyme, the drug and the complex with explicit solvent followed by binding free energy analysis. Collectively, the results obtained from the above studies viz. sorting of drugs from non-drugs, semi-quantitative estimates of binding free energies, amply demonstrate the viability of the strategy proposed for lead selection/design for biomolecular targets.

Abdominal pregnancy with lithopedion formation presenting as a pelvic abscess

BACKGROUND

Abdominal pregnancy with lithopedion formation is rare and its presentation as a pelvic abscess has not been reported.

CASE

A 38-year-old, gravida 1, para 0, presented with signs and symptoms of a pelvic abscess, which turned out to be a lithopedion from a 16-week abdominal pregnancy that had been terminated by a potassium chloride injection 2 years earlier. Intravenous antibiotics were followed by surgical removal of the lithopedion.

CONCLUSION

Pelvic abscess is an added complication of a lithopedion. Surgical removal to prevent such complications can be considered, balanced against the high operative risks.