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Conjugating drugs to therapeutic antibodies is a promising strategy to increase their therapeutic efficacy. Shen et al. show that the local chemical environment of the conjugation site influences the in vivo stability and efficacy of the modified antibodies. The reactive thiol in cysteine is used for coupling maleimide linkers in the generation of antibody conjugates. To assess the impact of the conjugation site, we engineered cysteines into a therapeutic HER2/neu antibody at three sites differing in solvent accessibility and local charge. The highly solvent-accessible site rapidly lost conjugated thiol-reactive linkers in plasma owing to maleimide exchange with reactive thiols in albumin, free cysteine or glutathione. In contrast, a partially accessible site with a positively charged environment promoted hydrolysis of the succinimide ring in the linker, thereby preventing this exchange reaction. The site with partial solvent-accessibility and neutral charge displayed both properties. In a mouse mammary tumor model, the stability and therapeutic activity of the antibody conjugate were affected positively by succinimide ring hydrolysis and negatively by maleimide exchange with thiol-reactive constituents in plasma. Thus, the chemical and structural dynamics of the conjugation site can influence antibody conjugate performance by modulating the stability of the antibody-linker interface.

The diagnostic classification of digitized tissue images based on histopathologic lesions present in whole slide images (WSI) is a significant task that eludes modern image classification techniques. Even with advanced methods designed for digital histopathology, the domain of toxicologic pathology presents challenges in that histopathologic features may be at times complex, subtle, and/or rare. We propose an innovative weakly supervised learning method that leverages minimal annotations, a state-of-the-art self-supervised vision transformer for embedding extraction, and a novel guided attention mechanism that is better suited for heavily imbalanced datasets typical in toxicologic pathology. Our model demonstrates improvements in diagnostic classification and attention heatmap quality over the previously described clustering-constrained-attention multiple-instance learning method on several lesion classes in rat livers (38% improvement in AUC). We also demonstrate how an ensemble of binary classifiers improves interpretability and allows for multiclass classification and the classification of diagnostic regions of interest in each slide. The improved classification performance and higher contrast heatmaps better support toxicologic pathologists’ histopathology analysis and will enable more efficient workflows as they are further refined and integrated into routine use.

Background We investigated the effect of crenezumab, a humanized anti-amyloid-beta (Aβ) immunoglobulin (Ig)G4 monoclonal antibody, on biomarkers of amyloid pathology, neurodegeneration, and disease progression in patients with mild-to-moderate Alzheimer’s disease (AD). Methods This double-blind, placebo-controlled, randomized phase II study enrolled patients with mild-to-moderate AD and a Mini-Mental State Examination (MMSE) score of 18–26. In part 1 of the study, patients were 2:1 randomized to receive low-dose subcutaneous (SC) 300 mg crenezumab every 2 weeks (q2w) or placebo for 68 weeks; in part 2, patients were 2:1 randomized to receive high-dose intravenous (IV) 15 mg/kg crenezumab every 4 weeks (q4w) or placebo for 68 weeks. The primary endpoint was change in amyloid burden from baseline to week 69 assessed by florbetapir positron emission tomography (PET) in the modified intent-to-treat population. Secondary endpoints were change from baseline to week 69 in cerebrospinal fluid (CSF) biomarkers and fluorodeoxyglucose PET, and change from baseline to week 73 in 12-point Alzheimer’s Disease Assessment Scale cognitive subscale (ADAS-Cog12) and Clinical Dementia Rating Sum of Boxes (CDR-SB). Safety was assessed in patients who received at least one dose of study treatment. Results From August 2011 to September 2012, 91 patients were enrolled and randomized (low-dose SC cohort: crenezumab ( n  = 26) or placebo ( n  = 13); high-dose IV cohort: crenezumab ( n  = 36) or placebo ( n  = 16)). The primary endpoint was not met using a prespecified cerebellar reference region to calculate standard uptake value ratios (SUVRs) from florbetapir PET. Exploratory analyses using subcortical white matter reference regions showed nonsignificant trends toward slower accumulation of plaque amyloid in the high-dose IV cohort. In both cohorts, a significant mean increase from baseline in CSF Aβ(1–42) levels versus placebo was observed. Nonsignificant trends toward ADAS-Cog12 and CDR-SB benefits were identified in a mild (MMSE 20–26) subset of the high-dose IV cohort. No amyloid-related imaging abnormalities due to edema/effusion were observed. Conclusion The primary endpoint was not met. Exploratory findings suggest potential Aβ target engagement with crenezumab and possible slower accumulation of plaque amyloid. Studies investigating the effects of higher doses of crenezumab on amyloid load and disease progression are ongoing. Trial registration ClinicalTrials.gov, NCT01397578 . Registered on 18 July 2011.

Automated anomaly detection is an important tool that has been developed for many real-world applications, including security systems, industrial inspection, and medical diagnostics. Despite extensive use of machine learning for anomaly detection in these varied contexts, it is challenging to generalize and apply these methods to complex tasks such as toxicologic histopathology (TOXPATH) assessment (i.e.,finding abnormalities in organ tissues). In this work, we introduce an anomaly detection method using deep learning that greatly improves model generalizability to TOXPATH data. We evaluated a one-class classification approach that leverages novel regularization and perceptual techniques within generative adversarial network (GAN) and autoencoder architectures to accurately detect anomalous histopathological findings of varying degrees of complexity. We also utilized multiscale contextual data and conducted a thorough ablation study to demonstrate the efficacy of our method. We trained our models on data from normal whole slide images (WSIs) of rat liver sections and validated on WSIs from three anomalous classes. Anomaly scores are collated into heatmaps to localize anomalies within WSIs and provide human-interpretable results. Our method achieves 0.953 area under the receiver operating characteristic on a real-worldTOXPATH dataset. The model also shows good performance at detecting a wide variety of anomalies demonstrating our method’s ability to generalize to TOXPATH data. Anomalies in both TOXPATH histological and non-histological datasets were accurately identified with our method, which was only trained with normal data.

Background Accumulation of amyloid β (Aβ) in the brain is proposed as a cause of Alzheimer’s disease (AD), with Aβ oligomers hypothesized to be the primary mediators of neurotoxicity. Crenezumab is a humanized immunoglobulin G4 monoclonal antibody that has been shown to bind to synthetic monomeric and aggregated Aβ in vitro; however, less is known about the binding characteristic in vivo. In this study, we evaluated the binding patterns of crenezumab to synthetic and native forms of Aβ both in vitro and in vivo. Methods Crenezumab was used to immunoprecipitate Aβ from synthetic Aβ preparations or brain homogenates from a PS2APP mouse model of AD to determine the forms of Aβ that crenezumab interacts with. Following systemic dosing in PS2APP or nontransgenic control mice, immunohistochemistry was used to localize crenezumab and assess its relative distribution in the brain, compared with amyloid plaques and markers of neuritic dystrophies (BACE1; LAMP1). Pharmacodynamic correlations were performed to investigate the relationship between peripheral and central target engagement. Results In vitro, crenezumab immunoprecipitated Aβ oligomers from both synthetic Aβ preparations and endogenous brain homogenates from PS2APP mice. In vivo studies in the PS2APP mouse showed that crenezumab localizes to regions surrounding the periphery of amyloid plaques in addition to the hippocampal mossy fibers. These regions around the plaques are reported to be enriched in oligomeric Aβ, actively incorporate soluble Aβ, and contribute to Aβ-induced neurotoxicity and axonal dystrophy. In addition, crenezumab did not appear to bind to the dense core region of plaques or vascular amyloid. Conclusions Crenezumab binds to multiple forms of amyloid β (Aβ), particularly oligomeric forms, and localizes to brain areas rich in Aβ oligomers, including the halo around plaques and hippocampal mossy fibers, but not to vascular Aβ. These insights highlight a unique mechanism of action for crenezumab of engaging Aβ oligomers.

Despite considerable investment into potential therapeutic approaches for Alzheimer's disease (AD), currently approved treatment options are limited. Predictive modeling using quantitative systems pharmacology (QSP) can be used to guide the design of clinical trials in AD. This study developed a QSP model representing amyloid beta (Aβ) pathophysiology in AD. The model included mechanisms of Aβ monomer production and aggregation to form insoluble fibrils and plaques; the transport of soluble species between the compartments of brain, cerebrospinal fluid (CSF), and plasma; and the pharmacokinetics, transport, and binding of monoclonal antibodies to targets in the three compartments. Ordinary differential equations were used to describe these processes quantitatively. The model components were calibrated to data from the literature and internal studies, including quantitative data supporting the underlying AD biology and clinical data from clinical trials for anti‐Aβ monoclonal antibodies (mAbs) aducanumab, crenezumab, gantenerumab, and solanezumab. The model was developed for an apolipoprotein E (APOE) ɛ4 allele carrier and tested for an APOE ɛ4 noncarrier. Results indicate that the model is consistent with data on clinical Aβ accumulation in untreated individuals and those treated with monoclonal antibodies, capturing increases in Aβ load accurately. This model may be used to investigate additional AD mechanisms and their impact on biomarkers, as well as predict Aβ load at different dose levels for mAbs with known targets and binding affinities. This model may facilitate the design of scientifically enriched and efficient clinical trials by enabling a priori prediction of biomarker dynamics in the brain and CSF.

INTRODUCTION Triggering receptor expressed on myeloid cells 2 (TREM2) agonists are being clinically evaluated as disease‐modifying therapeutics for Alzheimer's disease. Clinically translatable pharmacodynamic (PD) biomarkers are needed to confirm drug activity and select the appropriate therapeutic dose in clinical trials. METHODS We conducted multi‐omic analyses on paired non‐human primate brain and cerebrospinal fluid (CSF), and stimulation of human induced pluripotent stem cell–derived microglia cultures after TREM2 agonist treatment, followed by validation of candidate fluid PD biomarkers using immunoassays. We immunostained microglia to characterize proliferation and clustering. RESULTS We report CSF soluble TREM2 (sTREM2) and CSF chitinase‐3‐like protein 1 (CHI3L1/YKL‐40) as PD biomarkers for the TREM2 agonist hPara.09. The respective reduction of sTREM2 and elevation of CHI3L1 in brain and CSF after TREM2 agonist treatment correlated with transient microglia proliferation and clustering. DISCUSSION CSF CHI3L1 and sTREM2 reflect microglial TREM2 agonism and can be used as clinical PD biomarkers to monitor TREM2 activity in the brain. Highlights CSF soluble triggering receptor expressed on myeloid cells 2 (sTREM2) reflects brain target engagement for a novel TREM2 agonist, hPara.09. CSF chitinase‐3‐like protein 1 reflects microglial TREM2 agonism. Both can be used as clinical fluid biomarkers to monitor TREM2 activity in brain.

Background Accumulation of amyloid [beta] (A[beta]) in the brain is proposed as a cause of Alzheimer's disease (AD), with A[beta] oligomers hypothesized to be the primary mediators of neurotoxicity. Crenezumab is a humanized immunoglobulin G4 monoclonal antibody that has been shown to bind to synthetic monomeric and aggregated A[beta] in vitro; however, less is known about the binding characteristic in vivo. In this study, we evaluated the binding patterns of crenezumab to synthetic and native forms of A[beta] both in vitro and in vivo. Methods Crenezumab was used to immunoprecipitate A[beta] from synthetic A[beta] preparations or brain homogenates from a PS2APP mouse model of AD to determine the forms of A[beta] that crenezumab interacts with. Following systemic dosing in PS2APP or nontransgenic control mice, immunohistochemistry was used to localize crenezumab and assess its relative distribution in the brain, compared with amyloid plaques and markers of neuritic dystrophies (BACE1; LAMP1). Pharmacodynamic correlations were performed to investigate the relationship between peripheral and central target engagement. Results In vitro, crenezumab immunoprecipitated A[beta] oligomers from both synthetic A[beta] preparations and endogenous brain homogenates from PS2APP mice. In vivo studies in the PS2APP mouse showed that crenezumab localizes to regions surrounding the periphery of amyloid plaques in addition to the hippocampal mossy fibers. These regions around the plaques are reported to be enriched in oligomeric A[beta], actively incorporate soluble A[beta], and contribute to A[beta]-induced neurotoxicity and axonal dystrophy. In addition, crenezumab did not appear to bind to the dense core region of plaques or vascular amyloid. Conclusions Crenezumab binds to multiple forms of amyloid [beta] (A[beta]), particularly oligomeric forms, and localizes to brain areas rich in A[beta] oligomers, including the halo around plaques and hippocampal mossy fibers, but not to vascular A[beta]. These insights highlight a unique mechanism of action for crenezumab of engaging A[beta] oligomers. Keywords: Crenezumab, Amyloid [beta], Alzheimer's disease, Oligomeric, Mossy fiber, Vascular amyloid