Training and Comparison of nnU-Net and DeepMedic Methods for Autosegmentation of Pediatric Brain Tumors.

BACKGROUND AND PURPOSE: Tumor segmentation is essential in surgical and treatment planning and response assessment and monitoring in pediatric brain tumors, the leading cause of cancer-related death among children. However, manual segmentation is time-consuming and has high interoperator variability, underscoring the need for more efficient methods. After training, we compared 2 deep-learning-based 3D segmentation models, DeepMedic and nnU-Net, with pediatric-specific multi-institutional brain tumor data based on multiparametric MR images. MATERIALS AND METHODS: Multiparametric preoperative MR imaging scans of 339 pediatric patients (n = 293 internal and n = 46 external cohorts) with a variety of tumor subtypes were preprocessed and manually segmented into 4 tumor subregions, ie, enhancing tumor, nonenhancing tumor, cystic components, and peritumoral edema. After training, performances of the 2 models on internal and external test sets were evaluated with reference to ground truth manual segmentations. Additionally, concordance was assessed by comparing the volume of the subregions as a percentage of the whole tumor between model predictions and ground truth segmentations using the Pearson or Spearman correlation coefficients and the Bland-Altman method. RESULTS: The mean Dice score for nnU-Net internal test set was 0.9 (SD, 0.07) (median, 0.94) for whole tumor; 0.77 (SD, 0.29) for enhancing tumor; 0.66 (SD, 0.32) for nonenhancing tumor; 0.71 (SD, 0.33) for cystic components, and 0.71 (SD, 0.40) for peritumoral edema, respectively. For DeepMedic, the mean Dice scores were 0.82 (SD, 0.16) for whole tumor; 0.66 (SD, 0.32) for enhancing tumor; 0.48 (SD, 0.27) for nonenhancing tumor; 0.48 (SD, 0.36) for cystic components, and 0.19 (SD, 0.33) for peritumoral edema, respectively. Dice scores were significantly higher for nnU-Net (P ≤ .01). Correlation coefficients for tumor subregion percentage volumes were higher (0.98 versus 0.91 for enhancing tumor, 0.97 versus 0.75 for nonenhancing tumor, 0.98 versus 0.80 for cystic components, 0.95 versus 0.33 for peritumoral edema in the internal test set). Bland-Altman plots were better for nnU-Net compared with DeepMedic. External validation of the trained nnU-Net model on the multi-institutional Brain Tumor Segmentation Challenge in Pediatrics (BraTS-PEDs) 2023 data set revealed high generalization capability in the segmentation of whole tumor, tumor core (a combination of enhancing tumor, nonenhancing tumor, and cystic components), and enhancing tumor with mean Dice scores of 0.87 (SD, 0.13) (median, 0.91), 0.83 (SD, 0.18) (median, 0.89), and 0.48 (SD, 0.38) (median, 0.58), respectively. CONCLUSIONS: The pediatric-specific data-trained nnU-Net model is superior to DeepMedic for whole tumor and subregion segmentation of pediatric brain tumors.

A multi-institutional pediatric dataset of clinical radiology MRIs by the Children's Brain Tumor Network.

Pediatric brain and spinal cancers remain the leading cause of cancer-related death in children. Advancements in clinical decision-support in pediatric neuro-oncology utilizing the wealth of radiology imaging data collected through standard care, however, has significantly lagged other domains. Such data is ripe for use with predictive analytics such as artificial intelligence (AI) methods, which require large datasets. To address this unmet need, we provide a multi-institutional, large-scale pediatric dataset of 23,101 multi-parametric MRI exams acquired through routine care for 1,526 brain tumor patients, as part of the Children's Brain Tumor Network. This includes longitudinal MRIs across various cancer diagnoses, with associated patient-level clinical information, digital pathology slides, as well as tissue genotype and omics data. To facilitate downstream analysis, treatment-naïve images for 370 subjects were processed and released through the NCI Childhood Cancer Data Initiative via the Cancer Data Service. Through ongoing efforts to continuously build these imaging repositories, our aim is to accelerate discovery and translational AI models with real-world data, to ultimately empower precision medicine for children.

Streamlining pipeline efficiency: a novel model-agnostic technique for accelerating conditional generative and virtual screening pipelines.

The discovery of potential therapeutic agents for life-threatening diseases has become a significant problem. There is a requirement for fast and accurate methods to identify drug-like molecules that can be used as potential candidates for novel targets. Existing techniques like high-throughput screening and virtual screening are time-consuming and inefficient. Traditional molecule generation pipelines are more efficient than virtual screening but use time-consuming docking software. Such docking functions can be emulated using Machine Learning models with comparable accuracy and faster execution times. However, we find that when pre-trained machine learning models are employed in generative pipelines as oracles, they suffer from model degradation in areas where data is scarce. In this study, we propose an active learning-based model that can be added as a supplement to enhanced molecule generation architectures. The proposed method uses uncertainty sampling on the molecules created by the generator model and dynamically learns as the generator samples molecules from different regions of the chemical space. The proposed framework can generate molecules with high binding affinity with [Formula: see text]a 70% improvement in runtime compared to the baseline model by labeling only [Formula: see text]30% of molecules compared to the baseline oracle.

Development of an antibody fused with an antimicrobial peptide targeting Pseudomonas aeruginosa: A new approach to prevent and treat bacterial infections.

The increase in emerging drug resistant Gram-negative bacterial infections is a global concern. In addition, there is growing recognition that compromising the microbiota through the use of broad-spectrum antibiotics can impact long term patient outcomes. Therefore, there is the need to develop new bactericidal strategies to combat Gram-negative infections that would address these specific issues. In this study, we report and characterize one such approach, an antibody-drug conjugate (ADC) that combines (i) targeting the surface of a specific pathogenic organism through a monoclonal antibody with (ii) the high killing activity of an antimicrobial peptide. We focused on a major pathogenic Gram-negative bacterium associated with antibacterial resistance: Pseudomonas aeruginosa. To target this organism, we designed an ADC by fusing an antimicrobial peptide to the C-terminal end of the VH and/or VL-chain of a monoclonal antibody, VSX, that targets the core of P. aeruginosa lipopolysaccharide. This ADC demonstrates appropriately minimal levels of toxicity against mammalian cells, rapidly kills P. aeruginosa strains, and protects mice from P. aeruginosa lung infection when administered therapeutically. Furthermore, we found that the ADC was synergistic with several classes of antibiotics. This approach described in this study might result in a broadly useful strategy for targeting specific pathogenic microorganisms without further augmenting antibiotic resistance.

Automated tumor segmentation and brain tissue extraction from multiparametric MRI of pediatric brain tumors: A multi-institutional study.

Background: Brain tumors are the most common solid tumors and the leading cause of cancer-related death among all childhood cancers. Tumor segmentation is essential in surgical and treatment planning, and response assessment and monitoring. However, manual segmentation is time-consuming and has high interoperator variability. We present a multi-institutional deep learning-based method for automated brain extraction and segmentation of pediatric brain tumors based on multi-parametric MRI scans. Methods: Multi-parametric scans (T1w, T1w-CE, T2, and T2-FLAIR) of 244 pediatric patients ( n = 215 internal and n = 29 external cohorts) with de novo brain tumors, including a variety of tumor subtypes, were preprocessed and manually segmented to identify the brain tissue and tumor subregions into four tumor subregions, i.e., enhancing tumor (ET), non-enhancing tumor (NET), cystic components (CC), and peritumoral edema (ED). The internal cohort was split into training ( n = 151), validation ( n = 43), and withheld internal test ( n = 21) subsets. DeepMedic, a three-dimensional convolutional neural network, was trained and the model parameters were tuned. Finally, the network was evaluated on the withheld internal and external test cohorts. Results: Dice similarity score (median ± SD) was 0.91 ± 0.10/0.88 ± 0.16 for the whole tumor, 0.73 ± 0.27/0.84 ± 0.29 for ET, 0.79 ± 19/0.74 ± 0.27 for union of all non-enhancing components (i.e., NET, CC, ED), and 0.98 ± 0.02 for brain tissue in both internal/external test sets. Conclusions: Our proposed automated brain extraction and tumor subregion segmentation models demonstrated accurate performance on segmentation of the brain tissue and whole tumor regions in pediatric brain tumors and can facilitate detection of abnormal regions for further clinical measurements.

Automated Tumor Segmentation and Brain Tissue Extraction from Multiparametric MRI of Pediatric Brain Tumors: A Multi-Institutional Study.

Background: Brain tumors are the most common solid tumors and the leading cause of cancer-related death among all childhood cancers. Tumor segmentation is essential in surgical and treatment planning, and response assessment and monitoring. However, manual segmentation is time-consuming and has high interoperator variability. We present a multi-institutional deep learning-based method for automated brain extraction and segmentation of pediatric brain tumors based on multi-parametric MRI scans. Methods: Multi-parametric scans (T1w, T1w-CE, T2, and T2-FLAIR) of 244 pediatric patients (n=215 internal and n=29 external cohorts) with de novo brain tumors, including a variety of tumor subtypes, were preprocessed and manually segmented to identify the brain tissue and tumor subregions into four tumor subregions, i.e., enhancing tumor (ET), non-enhancing tumor (NET), cystic components (CC), and peritumoral edema (ED). The internal cohort was split into training (n=151), validation (n=43), and withheld internal test (n=21) subsets. DeepMedic, a three-dimensional convolutional neural network, was trained and the model parameters were tuned. Finally, the network was evaluated on the withheld internal and external test cohorts. Results: Dice similarity score (median±SD) was 0.91±0.10/0.88±0.16 for the whole tumor, 0.73±0.27/0.84±0.29 for ET, 0.79±19/0.74±0.27 for union of all non-enhancing components (i.e., NET, CC, ED), and 0.98±0.02 for brain tissue in both internal/external test sets. Conclusions: Our proposed automated brain extraction and tumor subregion segmentation models demonstrated accurate performance on segmentation of the brain tissue and whole tumor regions in pediatric brain tumors and can facilitate detection of abnormal regions for further clinical measurements. Key Points: We proposed automated tumor segmentation and brain extraction on pediatric MRI.The volumetric measurements using our models agree with ground truth segmentations. Importance of the Study: The current response assessment in pediatric brain tumors (PBTs) is currently based on bidirectional or 2D measurements, which underestimate the size of non-spherical and complex PBTs in children compared to volumetric or 3D methods. There is a need for development of automated methods to reduce manual burden and intra- and inter-rater variability to segment tumor subregions and assess volumetric changes. Most currently available automated segmentation tools are developed on adult brain tumors, and therefore, do not generalize well to PBTs that have different radiological appearances. To address this, we propose a deep learning (DL) auto-segmentation method that shows promising results in PBTs, collected from a publicly available large-scale imaging dataset (Children's Brain Tumor Network; CBTN) that comprises multi-parametric MRI scans of multiple PBT types acquired across multiple institutions on different scanners and protocols. As a complementary to tumor segmentation, we propose an automated DL model for brain tissue extraction.

Automated Quantification of Abnormal QRS Peaks From High-Resolution ECGs Predicts Late Ventricular Arrhythmias in Hypertrophic Cardiomyopathy: A 5-Year Prospective Multicenter Study.

Background Patients with hypertrophic cardiomyopathy (HCM) are at risk of ventricular arrhythmia (VA) attributed to abnormal electrical activation arising from myocardial fibrosis and myocyte disarray. We sought to quantify intra-QRS peaks (QRSp) in high-resolution ECGs as a measure of abnormal activation to predict late VA in patients with HCM. Methods and Results Prospectively enrolled patients with HCM (n=143, age 53±14 years) with prophylactic implantable cardioverter-defibrillators had 3-minute, high-resolution (1024 Hz), digital 12-lead ECGs recorded during intrinsic rhythm. For each precordial lead, QRSp was defined as the total number of peaks detected in the QRS complex that deviated from a smoothing filtered version of the QRS. The VA end point was appropriate implantable cardioverter-defibrillator therapy during 5-year prospective follow-up. After 5 years, 21 (16%) patients had VA. Patients who were VA positive had greater QRSp (6.0 [4.0-7.0] versus 4.0 [2.0-5.0]; P<0.01) and lower left ventricular ejection fraction (57±11 versus 62±9; P=0.038) compared with patients who were VA negative, but had similar established HCM risk metrics. Receiver operating characteristic analysis revealed that QRSp discriminated VA (area under the curve=0.76; P<0.001), with a QRSp ≥4 achieving 91% sensitivity and 39% specificity. The annual VA rate was greater in patients with QRSp ≥4 versus QRSp <4 (4.4% versus 0.98%; P=0.012). In multivariable Cox regression, age <50 years (hazard ratio [HR], 2.53; P=0.009) and QRSp (HR per QRS peak, 1.41; P=0.009) predicted VA after adjusting for established HCM risk metrics. In patients aged <50 years, the annual VA rate was 0.0% for QRSp <4 compared with 6.9% for QRSp ≥4 (P=0.012). Conclusions QRSp predicted VA in patients with HCM who were eligible for an implantable cardioverter-defibrillator after adjusting for established HCM risk metrics, such that each additional QRS peak increases VA risk by 40%. QRSp <4 was associated with a <1% annual VA risk in all patients, and no VA risk among those aged <50 years. This novel ECG metric may improve patient selection for prophylactic implantable cardioverter-defibrillator therapy by identifying those with low VA risk. These findings require further validation in a lower risk HCM cohort. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT02560844.

Microvolt QRS Alternans in Hypertrophic Cardiomyopathy: A Novel Risk Marker of Late Ventricular Arrhythmias.

Background Unlike T-wave alternans (TWA), the relation between QRS alternans (QRSA) and ventricular arrhythmia (VA) risk has not been evaluated in hypertrophic cardiomyopathy (HCM). We assessed microvolt QRSA/TWA in relation to HCM risk factors and late VA outcomes in HCM. Methods and Results Prospectively enrolled patients with HCM (n=130) with prophylactic implantable cardioverter-defibrillators underwent digital 12-lead ECG recordings during ventricular pacing (100-120 beats/min). QRSA/TWA was quantified using the spectral method. Patients were categorized as QRSA+ and/or TWA+ if sustained alternans was present in ≥2 precordial leads. The VA end point was appropriate implantable cardioverter-defibrillator therapy over 5 years of follow-up. QRSA+ and TWA+ occurred together in 28% of patients and alone in 7% and 7% of patients, respectively. QRSA magnitude increased with pacing rate (1.9±0.6 versus 6.2±2.0 µV; P=0.006). Left ventricular thickness was greater in QRSA+ than in QRSA- patients (22±7 versus 20±6 mm; P=0.035). Over 5 years follow-up, 17% of patients had VA. The annual VA rate was greater in QRSA+ versus QRSA- patients (5.8% versus 2.0%; P=0.006), with the QRSA+/TWA- subgroup having the greatest rate (13.3% versus 2.6%; P<0.001). In those with <2 risk factors, QRSA- patients had a low annual VA rate compared QRSA+ patients (0.58% versus 7.1%; P=0.001). Separate Cox models revealed QRSA+ (hazard ratio [HR], 2.9 [95% CI, 1.2-7.0]; P=0.019) and QRSA+/TWA- (HR, 7.9 [95% CI, 2.9-21.7]; P<0.001) as the most significant VA predictors. TWA and HCM risk factors did not predict VA. Conclusions In HCM, microvolt QRSA is a novel, rate-dependent phenomenon that can exist without TWA and is associated with greater left ventricular thickness. QRSA increases VA risk 3-fold in all patients, whereas the absence of QRSA confers low VA risk in patients with <2 risk factors. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT02560844.

VIS832, a novel CD138-targeting monoclonal antibody, potently induces killing of human multiple myeloma and further synergizes with IMiDs or bortezomib in vitro and in vivo.

Therapeutically targeting CD138, a define multiple myeloma (MM) antigen, is not yet approved for patients. We here developed and determined the preclinical efficacy of VIS832, a novel therapeutic monoclonal antibody (MoAb) with differentiated CD138 target binding to BB4 that is anti-CD138 MoAb scaffold for indatuximab ravtansine (BT062). VIS832 demonstrated enhanced CD138-binding avidity and significantly improved potency to kill MM cell lines and autologous patient MM cells regardless of resistance to current standard-of-care therapies, via robust antibody-dependent cellular cytotoxicity and phagocytosis mediated by NK and macrophage effector cells, respectively. Specifically, CD38-targeting daratumumab-resistant MM cells were highly susceptible to VIS832 which, unlike daratumumab, spares NK cells. Superior maximal cytolysis of VIS832 vs. daratumumab corresponded to higher CD138 vs. CD38 levels in MM cells. Furthermore, VIS832 acted synergistically with lenalidomide or bortezomib to deplete MM cells. Importantly, VIS832 at a sub-optimal dose inhibited disseminated MM1S tumors in vivo as monotherapy (P < 0.0001), and rapidly eradicated myeloma burden in all mice concomitantly receiving bortezomib, with 100% host survival. Taken together, these data strongly support clinical development of VIS832, alone and in combination, for the therapeutic treatment of MM in relapsed and refractory patients while pointing to its potential therapeutic use earlier in disease intervention.

Characterization of an Antibody Recognizing the Conserved Inner Core of Pseudomonas aeruginosa Lipopolysaccharides.

Bacterial infections are a growing public health threat with carbapenem-resistant Pseudomonas aeruginosa being classified as a Priority 1 critical threat by the World Health Organization. Antibody-based therapeutics can serve as an alternative and in some cases supplement antibiotics for the treatment of bacterial infections. The glycans covering the bacterial cell surface have been proposed as intriguing targets for binding by antibodies; however, antibodies that can engage with high affinity and specificity with glycans are much less common compared to antibodies that engage with protein antigens. In this study, we sought to characterize an antibody that targets a conserved glycan epitope on the surface of Pseudomonas. First, we characterized the breadth of binding of VSX, demonstrating that the VSX is specific to Pseudomonas but can bind across multiple serotypes of the organism. Next, we provide insight into how VSX engages with its target epitope, using a combination of biolayer interferometry and nuclear magnetic resonance, and verify our results using site-directed mutagenesis experiments. We demonstrate that the antibody, with limited somatic hypermutation of the complementarity-determining regions (CDRs) and with a characteristic set of arginines within the CDRs, specifically targets the conserved inner core of Pseudomonas lipopolysaccharides. Our results provide important additional context to antibody-glycan contacts and provide insight useful for the construction of vaccines and therapeutics against Pseudomonas aeruginosa, an important human pathogen.

An Interventional Study on Comprehensive Emergency Care and Trauma Registry for Road Traffic Injuries in India: A Protocol.

Road traffic injuries (RTIs) stands as one of the leading causes of mortality and morbidity across the globe. Effective injury surveillance systems and pre-hospital and in-hospital interventions set up in developing countries have shown promising results in controlling the problem. This study aimed to standardise and evaluate an evidence-based intervention for safety, efficacy and quality of post-crash pre-hospital and in-hospital trauma care services to improve the outcome in RTI victims. In addition, it establishes the android-based trauma registry for effective RTI surveillance. This multi-centric, prospective, observational study is commissioned by the Indian Council of Medical Research (ICMR) as a National Task Force Project. This study is being conducted in five sites, viz., Anand, Bengaluru, Delhi, Lucknow and Thrissur located across India. Each centre will have a level I, two level II and three level III trauma hospitals. The study will be carried out in four phases namely: i) preparatory phase, ii) trauma registry establishment and pre-intervention data collection, iii) intervention and iv) impact evaluation. The preparatory phase, which lasts for four months includes the situational analysis pertaining to managing RTIs. Trauma registry will be initiated from the fifth month. Pre-intervention data will be collected for six months. The intervention will be conducted for six months in the form of prehospital notification, training for trauma care providers and trauma care quality improvement. Post-intervention data collection will continue for 12 months and the impact of the intervention will be assessed. The primary outcome measure will be early preventable mortality, defined as death at 24 hours after admission for patients with a calculated probability of survival >50% based on their injury severity score.

Quantifying the nativeness of antibody sequences using long short-term memory networks.

Antibodies often undergo substantial engineering en route to the generation of a therapeutic candidate with good developability properties. Characterization of antibody libraries has shown that retaining native-like sequence improves the overall quality of the library. Motivated by recent advances in deep learning, we developed a bi-directional long short-term memory (LSTM) network model to make use of the large amount of available antibody sequence information, and use this model to quantify the nativeness of antibody sequences. The model scores sequences for their similarity to naturally occurring antibodies, which can be used as a consideration during design and engineering of libraries. We demonstrate the performance of this approach by training a model on human antibody sequences and show that our method outperforms other approaches at distinguishing human antibodies from those of other species. We show the applicability of this method for the evaluation of synthesized antibody libraries and humanization of mouse antibodies.

Structural prediction of antibody-APRIL complexes by computational docking constrained by antigen saturation mutagenesis library data.

IgA nephropathy (IgAN) is the most prevalent cause of primary glomerular disease worldwide, and the cytokine A PRoliferation-Inducing Ligand (APRIL) is emerging as a key player in IgAN pathogenesis and disease progression. For a panel of anti-human APRIL antibodies with known antagonistic activity, we sought to define their structural mode of engagement to understand molecular mechanisms of action and aid rational antibody engineering. Reliable computational prediction of antibody-antigen complexes remains challenging, and experimental methods such as X-ray co-crystallography and cryoEM have considerable technical, resource, and throughput barriers. To overcome these limitations, we implemented an integrated and accessible experimental-computational workflow to more accurately predict structures of antibody-APRIL complexes. Specifically, a yeast surface display library encoding site-saturation mutagenized surface positions of APRIL was screened against a panel of anti-APRIL antibodies to rapidly obtain a comprehensive biochemical profile of mutational impact on binding for each antibody. The experimentally derived mutational profile data were used as quantitative constraints in a computational docking workflow optimized for antibodies, resulting in robust structural models of antibody-antigen complexes. The model results were confirmed by solving the cocrystal structure of one antibody-APRIL complex, which revealed strong agreement with our model. The models were used to rationally select and engineer one antibody for cross-species APRIL binding, which quite often aids further testing in relevant animal models. Collectively, we demonstrate a rapid, integrated computational-experimental approach to robustly predict antibody-antigen structures information, which can aid rational antibody engineering and provide insights into molecular mechanisms of action.

Multicenter Study of Ischemic Ventricular Tachycardia Ablation With Decrement-Evoked Potential (DEEP) Mapping With Extra Stimulus.

OBJECTIVES: The authors conducted a multicenter study of decrement-evoked potential (DEEP)-based functional ventricular tachycardia (VT) substrate modification to evaluate if such a mechanistic and physiological strategy is feasible and efficient in clinical practice and provides reduction in the VT burden. BACKGROUND: Only a fraction of the myocardium targeted in current VT substrate modification procedures is involved in the initiation and perpetuation of VT. The physiological basis of the DEEP strategy for identification of areas of initiation and maintenance of VT was recently established. METHODS: We included 20 consecutive patients with ischemic cardiomyopathy. During substrate mapping, fractionated and late potentials (LPs) were tagged, and an extra stimulus was performed to determine which LPs displayed decrement (DEEPs). All patients underwent DEEP-focused ablation: elimination of DEEP + further radiofrequency (RF) if VT was still inducible. Patients were followed during 6 months. RESULTS: Patients were predominantly male (95%), and their mean age was 64.6 ± 17.1 years. Mean left ventricular ejection fraction was 33.4 ± 11.4%. Mean ablation time was 30.6 ± 20.4 min. Specificity of DEEPs to detect the isthmus of VT was better than that of LPs (0.97 [95% confidence interval [CI]: 0.95 to 0.98] vs. 0.82 [95% CI: 0.73 to 0.89]), without significant differences in terms of sensitivity (0.61 [95% CI: 0.52 to 0.69] vs. 0.60 [95% CI: 0.44 to 0.74], respectively). Fifteen of 20 (75%) patients were free of any VT after DEEP-RF at 6 months of follow-up and there was a strong reduction in VT burden compared to 6 months pre-ablation. CONCLUSIONS: In a multicenter prospective study, DEEP substrate mapping identified the functional substrate critical to the VT circuit with high specificity. DEEP-guided VT ablation, by its physiological nature, may enable greater access to focused ablation therapy for patients requiring VT treatment.

Antibody-Bactericidal Macrocyclic Peptide Conjugates To Target Gram-Negative Bacteria.

To combat antimicrobial infections, new active molecules are needed. Antimicrobial peptides, ever abundant in nature, are a fertile starting point to develop new antimicrobial agents but suffer from low stability, low specificity, and off-target toxicity. These drawbacks have limited their development. To overcome some of these limitations, we developed antibody-bactericidal macrocyclic peptide conjugates (ABCs), in which the antibody directs the bioactive macrocyclic peptide to the targeted Gram-negative bacteria. We used cysteine SN Ar chemistry to synthesize and systematically study a library of large (>30-mer) macrocyclic antimicrobial peptides (mAMPs) to discover variants with extended proteolytic stability in human serum and low hemolytic activity while maintaining bioactivity. We then conjugated, by using sortase A, these bioactive variants onto an Escherichia coli targeted monoclonal antibody. We found that these ABCs had minimized hemolytic activity and were able to kill E. coli at nanomolar concentrations. Our findings suggest macrocyclic peptides if fused to antibodies may facilitate the discovery of new agents to treat bacterial infections.

Extending human IgG half-life using structure-guided design.

Engineering of antibodies for improved pharmacokinetics through enhanced binding to the neonatal Fc receptor (FcRn) has been demonstrated in transgenic mice, non-human primates and humans. Traditionally, such approaches have largely relied on random mutagenesis and display formats, which fail to address related critical attributes of the antibody, such as effector functions or biophysical stability. We have developed a structure- and network-based framework to interrogate the engagement of IgG with multiple Fc receptors (FcRn, C1q, TRIM21, FcγRI, FcγRIIa/b, FcγRIIIa) simultaneously. Using this framework, we identified features that govern Fc-FcRn interactions and identified multiple distinct pathways for enhancing FcRn binding in a pH-specific manner. Network analysis provided a novel lens to study the allosteric impact of half-life-enhancing Fc mutations on FcγR engagement, which occurs distal to the FcRn binding site. Applying these principles, we engineered a panel of unique Fc variants that enhance FcRn binding while maintaining robust biophysical properties and wild type-like binding to activating receptors. An antibody harboring representative Fc designs demonstrates a half-life improvement of > 9 fold in transgenic mice and > 3.5 fold in cynomolgus monkeys, and maintains robust effector functions such as antibody-dependent cell-mediated cytotoxicity and complement-dependent cytotoxicity.

Automated Quantification of Low-Amplitude Abnormal QRS Peaks From High-Resolution ECG Recordings Predicts Arrhythmic Events in Patients With Cardiomyopathy.

BACKGROUND: Cardiomyopathy patients are at risk of sudden death, typically from scar-related abnormalities of electrical activation that promote ventricular tachyarrhythmias. Abnormal intra-QRS peaks may provide a measure of altered activation. We hypothesized that quantification of such QRS peaks (QRSp) in high-resolution ECGs would predict arrhythmic events in implantable cardioverter-defibrillator (ICD)-eligible cardiomyopathy patients. METHODS AND RESULTS: Ninety-nine patients with ischemic or non-ischemic dilated cardiomyopathy undergoing prophylactic ICD implantation were prospectively enrolled (age 62±11 years, left ventricular ejection fraction 27±7%). High-resolution (1024 Hz) digital 12-lead ECGs were recorded during intrinsic rhythm. QRSp was quantified for each precordial lead as the total number of low-amplitude deflections that deviated from their respective naive QRS template. The primary end point of arrhythmic events was defined as appropriate ICD therapy or sustained ventricular tachyarrhythmias. After a median follow-up of 24 (15-43) months, 20 (20%) patients had arrhythmic events. Both QRSp and QRS duration were greater in those with arrhythmic events (both P<0.001) and this was consistent for QRSp for both cardiomyopathy types. In a multivariable Cox regression model that included age, left ventricular ejection fraction, QRS duration, and QRSp, only QRSp was an independent predictor of arrhythmic events (hazard ratio, 2.1; P<0.001). Receiver operating characteristic analysis revealed that a QRSp ≥2.25 identified arrhythmic events with greater sensitivity (100% versus 70%, P<0.05) and negative predictive value (100% versus 89%, P<0.05) than QRS duration ≥120 ms. CONCLUSIONS: QRSp measured from high-resolution digital 12-lead ECGs independently predicts ventricular tachyarrhythmias in ICD-eligible cardiomyopathy patients. This novel QRS morphology index has the potential to improve sudden death risk stratification and patient selection for prophylactic ICD therapy.

Evaluation of a novel high-resolution mapping system for catheter ablation of ventricular arrhythmias.

BACKGROUND: The mapping of ventricular arrhythmias in humans using a minibasket 64-electrode catheter paired with a novel automatic mapping system (Rhythmia) has not been evaluated. OBJECTIVE: The purpose of this study was to evaluate the safety and efficacy of mapping ventricular arrhythmias and clinical outcomes after ablation using this system. METHODS: Electroanatomic maps for ventricular arrhythmias were obtained during 20 consecutive procedures in 19 patients (12 with ventricular tachycardia [VT] and 2 with ventricular ectopy [VE]). High-density maps were acquired using automatic beat acceptance and automatic system annotation of electrograms. RESULTS: Forty-seven electroanatomic maps (including 3 right ventricular and 9 epicardial maps) were obtained. Left ventricular endocardial mapping by transseptal (n = 13) and/or transaortic (n = 11) access was safe with no complications related to the minibasket catheter. VT substrate maps (n = 14; median 10,184 points) consistently demonstrated late potentials with high resolution. VT activation maps (n = 25; median 6401 points) obtained by automatic annotation included 7 complete maps (covering ≥90% of the tachycardia cycle length) in 5 patients in whom the entire VT circuit was accurately visualized. VE timing maps (n = 8) successfully localized the origin of VEs in all, with all accepted beats consistent with clinical VEs. Over a median follow-up of 10 months, no arrhythmia recurrence was noted in 75% after VT ablation and 86% after VE ablation. CONCLUSION: In this first human experience for ventricular arrhythmias using this system, ultra-high-density maps were created rapidly and safely, with a reliable automatic annotation of VT and consistent recording of abnormal electrograms. Medium-term outcomes after ablation were encouraging. Further larger studies are needed to validate these findings.

A Structural and Mathematical Modeling Analysis of the Likelihood of Antibody-Dependent Enhancement in Influenza.

Broadly neutralizing monoclonal antibodies (bNAbs) for viral infections, such as HIV, respiratory syncytial virus (RSV), and influenza, are increasingly entering clinical development. For influenza, most neutralizing antibodies target influenza virus hemagglutinin. These bNAbs represent an emerging, promising modality for treatment and prophylaxis of influenza due to their multiple mechanisms of antiviral action and generally safe profile. Preclinical work in other viral diseases, such as dengue, has demonstrated the potential for antibody-based therapies to enhance viral uptake, leading to enhanced viremia and worsening of disease. This phenomenon is referred to as antibody-dependent enhancement (ADE). In the context of influenza, ADE has been used to explain several preclinical and clinical phenomena. Using structural and viral kinetics modeling, we assess the role of ADE in the treatment of influenza with a bNAb.

Safety and Upper Respiratory Pharmacokinetics of the Hemagglutinin Stalk-Binding Antibody VIS410 Support Treatment and Prophylaxis Based on Population Modeling of Seasonal Influenza A Outbreaks.

BACKGROUND: Seasonal influenza is a major public health concern in vulnerable populations. Here we investigated the safety, tolerability, and pharmacokinetics of a broadly neutralizing monoclonal antibody (VIS410) against Influenza A in a Phase 1 clinical trial. Based on these results and preclinical data, we implemented a mathematical modeling approach to investigate whether VIS410 could be used prophylactically to lessen the burden of a seasonal influenza epidemic and to protect at-risk groups from associated complications. METHODS: Using a single-ascending dose study (n = 41) at dose levels from 2 mg/kg-50 mg/kg we evaluated the safety as well as the serum and upper respiratory pharmacokinetics of a broadly-neutralizing antibody (VIS410) against influenza A (ClinicalTrials.gov identifier NCT02045472). Our primary endpoints were safety and tolerability of VIS410 compared to placebo. We developed an epidemic microsimulation model testing the ability of VIS410 to mitigate attack rates and severe disease in at risk-populations. FINDINGS: VIS410 was found to be generally safe and well-tolerated at all dose levels, from 2-50 mg/kg. Overall, 27 of 41 subjects (65.9%) reported a total of 67 treatment emergent adverse events (TEAEs). TEAEs were reported by 20 of 30 subjects (66.7%) who received VIS410 and by 7 of 11 subjects (63.6%) who received placebo. 14 of 16 TEAEs related to study drug were considered mild (Grade 1) and 2 were moderate (Grade 2). Two subjects (1 subject who received 30 mg/kg VIS410 and 1 subject who received placebo) experienced serious AEs (Grade 3 or 4 TEAEs) that were not related to study drug. VIS410 exposure was approximately dose-proportional with a mean half-life of 12.9 days. Mean VIS410 Cmax levels in the upper respiratory tract were 20.0 and 25.3 µg/ml at the 30 mg/kg and 50 mg/kg doses, respectively, with corresponding serum Cmax levels of 980.5 and 1316 µg/mL. Using these pharmacokinetic data, a microsimulation model showed that median attack rate reductions ranged from 8.6% (interquartile range (IQR): 4.7%-11.0%) for 2% coverage to 22.6% (IQR: 12.7-30.0%) for 6% coverage. The overall benefits to the elderly, a vulnerable subgroup, are largest when VIS410 is distributed exclusively to elderly individuals, resulting in reductions in hospitalization rates between 11.4% (IQR: 8.2%-13.3%) for 2% coverage and 30.9% (IQR: 24.8%-35.1%) for 6% coverage among those more than 65 years of age. INTERPRETATION: VIS410 was generally safe and well tolerated and had good relative exposure in both serum and upper respiratory tract, supporting its use as either a single-dose therapeutic or prophylactic for influenza A. Including VIS410 prophylaxis among the public health interventions for seasonal influenza has the potential to lower attack rates and substantially reduce hospitalizations in individuals over the age of 65. FUNDING: Visterra, Inc.