Biophysical properties of the clinical-stage antibody landscape.

Antibodies are a highly successful class of biological drugs, with over 50 such molecules approved for therapeutic use and hundreds more currently in clinical development. Improvements in technology for the discovery and optimization of high-potency antibodies have greatly increased the chances for finding binding molecules with desired biological properties; however, achieving drug-like properties at the same time is an additional requirement that is receiving increased attention. In this work, we attempt to quantify the historical limits of acceptability for multiple biophysical metrics of "developability." Amino acid sequences from 137 antibodies in advanced clinical stages, including 48 approved for therapeutic use, were collected and used to construct isotype-matched IgG1 antibodies, which were then expressed in mammalian cells. The resulting material for each source antibody was evaluated in a dozen biophysical property assays. The distributions of the observed metrics are used to empirically define boundaries of drug-like behavior that can represent practical guidelines for future antibody drug candidates.

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.

Prediction of delayed retention of antibodies in hydrophobic interaction chromatography from sequence using machine learning.

MOTIVATION: The hydrophobicity of a monoclonal antibody is an important biophysical property relevant for its developability into a therapeutic. In addition to characterizing heterogeneity, Hydrophobic Interaction Chromatography (HIC) is an assay that is often used to quantify the hydrophobicity of an antibody to assess downstream risks. Earlier studies have shown that retention times in this assay can be correlated to amino-acid or atomic propensities weighted by the surface areas obtained from protein 3-dimensional structures. The goal of this study is to develop models to enable prediction of delayed HIC retention times directly from sequence. RESULTS: We utilize the randomforest machine learning approach to estimate the surface exposure of amino-acid side-chains in the variable region directly from the antibody sequence. We obtain mean-absolute errors of 4.6% for the prediction of surface exposure. Using experimental HIC data along with the estimated surface areas, we derive an amino-acid propensity scale that enables prediction of antibodies likely to have delayed retention times in the assay. We achieve a cross-validation Area Under Curve of 0.85 for the Receiver Operating Characteristic curve of our model. The low computational expense and high accuracy of this approach enables real-time assessment of hydrophobic character to enable prioritization of antibodies during the discovery process and rational engineering to reduce hydrophobic liabilities. AVAILABILITY AND IMPLEMENTATION: Structure data, aligned sequences, experimental data and prediction scores for test-cases, and R scripts used in this work are provided as part of the Supplementary Material. CONTACT: tushar.jain@adimab.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Biofilm inhibition and anti-Candida activity of a cationic lipo-benzamide molecule with twin-nonyl chain.

A series of cationic lipo-benzamide compounds with varying lengths of hydrocarbon chains (C2M-C18M) were evaluated for anti-Candida activity. Four compounds harbouring 8-11 hydrocarbon chains demonstrated concentration-dependent inhibition of fungal cell growth with Minimum Inhibitory Concentration (MIC) of ≤6.2 µg ml-1. The most active compound (C9M) inhibited growth of both Candida albicans and non-albicans strains and is equally active against pairs of azole sensitive and resistant clinical isolates of C. albicans. Compound C9M also inhibited different stages of Candida biofilms. Scanning Electron Microscopy (SEM) of Candida cells after C9M treatment was also done and no significant cell lysis was observed. Hemolysis assay was performed and only 2.5% haemolysis was observed at MIC concentration.

Assessment and incorporation of in vitro correlates to pharmacokinetic outcomes in antibody developability workflows.

In vitro assessments for the prediction of pharmacokinetic (PK) behavior of biotherapeutics can help identify corresponding liabilities significantly earlier in the discovery timeline. This can minimize the need for extensive early in vivo PK characterization, thereby reducing animal usage and optimizing resources. In this study, we recommend bolstering classical developability workflows with in vitro measures correlated with PK. In agreement with current literature, in vitro measures assessing nonspecific interactions, self-interaction, and FcRn interaction are demonstrated to have the highest correlations to clearance in hFcRn Tg32 mice. Crucially, the dataset used in this study has broad sequence diversity and a range of physicochemical properties, adding robustness to our recommendations. Finally, we demonstrate a computational approach that combines multiple in vitro measurements with a multivariate regression model to improve the correlation to PK compared to any individual assessment. Our work demonstrates that a judicious choice of high throughput in vitro measurements and computational predictions enables the prioritization of candidate molecules with desired PK properties.

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.

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.

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.

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.

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.

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.

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

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

Pyranocarbazoles from Murraya koenigii (L.) Spreng. as antimicrobial agents.

The bioassay guided fractionation of methanolic extract of Murraya koenigii (L.) Spreng. leaves resulted in the isolation of seven pyranocarbazoles. These were evaluated against four bacterial strains and ten Candida sp. including two matched pair of fluconazole sensitive/resistant clinical isolates. Out of seven, three i.e. Koenine (mk279), Koenigine (mk309) and Mahanine (mk347) exhibited significant antibacterial activity MIC90 3.12-12.5 µg/mL against bacterial strains Streptococcus aureus and Klebsiella pneumonia compared with standard drug Kanamycin MIC90 12.5 µg/mL. However, only mk309 was found active against variety of Candida species MIC90 12.5-100 µg/mL. It was observed that hydroxylation at C-6 and C-7 positions in the studied pyranocarbazoles activate the bioactivity. Simultaneously, decrease in Log P value compares with -H and -O-CH3 substituted derivatives. The study is focused on selective antifungal and antibacterial activity of pyranocarbazoles on bacterial strains S. aureus, K. pneumonia and variety of Candida species with structure activity relationship observations.

Chain-length-specific anti-Candida activity of cationic lipo-oxazoles: a new class of quaternary ammonium compounds.

PURPOSE: Candida species have become resistant to commonly used anti-fungal drugs like fluconazole and echinocandins. In our screen, a series of quaternary ammonium compounds (QACs) emerged as an alternative treatment choice for drug-resistant Candida infections. METHODOLOGY: Medium alkyl chain cationic lipo-oxazoles comprising six to thirteen twin carbon chains and a quaternary ammonium unit were synthesized and evaluated for their in vitro anti-Candida and biofilm inhibition activity. SEM was performed to visualize membrane distortion.Results/Key findings. Heptyl and octyl chain analogues (5c, 6b and 6c) showed promising anti-fungal activity. Compound 5c was active against both fluconazole-sensitive and resistant clinical isolates of Candida albicans as well as non-albicans Candida strains. 5c also inhibited the adhesion of C. albicans cells to a polystyrene surface and restricted biofilm formation. SEM further confirmed Candida cell membrane distortion by 5c. CONCLUSION: A novel class of QACs, called cationic lipo-oxazoles, was tested and found to exhibit anti-fungal activity against planktonic cells as well as biofilms of Candida.

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.

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.

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.