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Two stereocontrolled, efficient, and modular syntheses of eicosanoid lipoxin B4 (LXB4) are reported. One features a stereoselective reduction followed by an asymmetric epoxidation sequence to set the vicinal diol stereocentres. The dienyne was installed via a one‐pot Wittig olefination and base‐mediated epoxide ring opening cascade. The other approach installed the diol through an asymmetric dihydroxylation reaction followed by a Horner‐Wadsworth‐Emmons olefination to afford the common dienyne intermediate. Finally, a Sonogashira coupling and an alkyne hydrosilylation/proto‐desilylation protocol furnished LXB4 in 25 % overall yield in just 10 steps. For the first time, LXB4 has been fully characterized spectroscopically with its structure confirmed as previously reported. We have demonstrated that the synthesized LXB4 showed similar biological activity to commercial sources in a cellular neuroprotection model. This synthetic route can be employed to synthesize large quantities of LXB4, enable synthesis of new analogs, and chemical probes for receptor and pathway characterization. [ABSTRACT FROM AUTHOR]

Background: With the first positive phase III trials for anti‐amyloid approaches in MCI participants, the drug evaluation during the asymptomatic Alzheimer's disease (AD) stage is now more relevant than ever. Currently, cognitively unimpaired participants are included in preventive trials based on risk factors such as amyloid positivity. The lifetime risks for an individual without cognitive impairment does not exceed 30% for a 65‐year‐old with amyloidosis alone (Brookmeyer et al., 2018). Based on this risk factor, most participants included will never develop AD and therefore will not respond to anti‐AD therapies. There is a need to develop new biomarkers with high specificity predicting which individuals in the cognitively unimpaired population will exhibit AD symptoms and which will not to conduct effective preventive clinical trials. Method: Targeted mass spectrometry assays were developed for 81 blood biomarkers (45 proteins and 36 metabolites) pre‐identified in AAV‐AD rats (Audrain et al. 2018). 287 participants were collected in plasma at baseline and followed clinically from 1 to 18 years. Participants' diagnosis (AD, or Healthy Controls) was established at the last clinical visit based on reference diagnosis standard. 73.9% of participants had available amyloid status, 42.9% were determined as amyloid positive. Predictive machine learning models for asymptomatic AD participants (n = 48, followed for an average of 6.1 years) among individuals without cognitive decline (HC, n = 239, followed for an average of 4.6 years) was developed. The training dataset (70%) aimed to select the biomarkers subset, train the algorithm, and define the cut‐off. External test dataset (30%) aimed to validate in blind the locked predictive model. Result: With 20 blood biomarkers and 2 covariates (age and gender), the model predicted asymptomatic AD (n = 16) from HC participants (n = 72) with 81.9% specificity and 56.3% sensitivity during external validation (AUROC = 69.1%, p = 0.02). When current amyloid tests (CSF or PET) and this predictive ML model were applied in series, and participants positive for both were predicted as asymptomatic AD, 90.9% specificity and 54.5% sensitivity were achieved. Conclusion: Multiomics blood peripheral biomarkers predict asymptomatic AD patients among cognitively unimpaired individuals with high specificity. This could constitute an additional tool for the inclusion of participants in preventive clinical trials. [ABSTRACT FROM AUTHOR]

Background: The core criteria for mild cognitive impairment (MCI) or dementia due to Alzheimer's disease (AD) is based in part on cognitive criteria and biological evidence of at least moderately frequent amyloid plaques (Albert et al., 2011; McKhann et al., 2011). They predict AD among cognitively impaired patients with high sensitivity but low specificity (Kokkinou et al. 2021; Martinez et al. 2017; Ritchie et al. 2014): up to 25% of amyloid‐positive patients would be misdiagnosed as AD while suffering from another brain disorder. Therefore, there is a need to develop new biomarkers with high specificity to facilitate earlier diagnosis, rapid therapeutic intervention and monitoring of clinical trials. Method: Targeted mass spectrometry assays were developed for 81 blood biomarkers (45 proteins and 36 metabolites) pre‐identified in AAV‐AD rats (Audrain et al. 2018). 345 cognitively impaired participants (193 MCI and 152 demented) were collected in plasma at baseline and followed clinically from 1 to 13 years. Participants' diagnosis (AD, or non‐AD) was established at the last clinical visit based on cognitive diagnosis standard for each brain disorder subtype. 82.9% of participants had available amyloid status at baseline, 61.9% were determined as amyloid positive. Each clinical group contained amyloid positive and negative participants. Predictive machine learning models for AD participants (n = 123 Prodromal AD, n = 126 AD dementia) among individuals with Non‐AD brain disorder (Non‐AD, n = 96) was developed. The training dataset (70%) aimed to select the biomarkers subset, train the algorithm, and define the cut‐off. External test dataset (30%) aimed to validate in blind the locked predictive model. Result: With 19 blood biomarkers and age, the model predicted AD participants (n = 41 Prodromal AD, n = 43 AD dementia) from non‐AD participants (n = 25) with 92.0% specificity and 52.4% sensitivity during external validation (AUROC = 71.8%, p = 0.001). When amyloid status (CSF or PET) and this predictive ML model were applied in series, and participants positive for both were predicted as AD, 100% specificity and 39.7% sensitivity were achieved. Conclusion: Multiomics blood peripheral biomarkers predicted AD patients among cognitively impaired population with low FPR. In combination with amyloid screening, these biomarkers could identify a nearly pure AD population. [ABSTRACT FROM AUTHOR]

Glioblastoma multiform (GBM) remains clinical indication with significant “unmet medical need”. Innovative new therapy to eliminate residual tumor cells and prevent tumor recurrences is critically needed for this deadly disease. A major challenge of GBM research has been the identification of novel molecular therapeutic targets and accurate diagnostic/prognostic biomarkers. Many of the current clinical therapeutic targets of immunotoxins and ligand-directed toxins for high-grade glioma (HGG) cells are surface sialylated glycoproteins. Therefore, methods that systematically and quantitatively analyze cell surface sialoglycoproteins in human clinical tumor samples would be useful for the identification of potential diagnostic markers and therapeutic targets for malignant gliomas. In this study, we used the bioorthogonal chemical reporter strategy (BOCR) in combination with label-free quantitative mass spectrometry (LFQ-MS) to characterize and accurately quantify the individual cell surface sialoproteome in human GBM tissues, in fetal, adult human astrocytes, and in human neural progenitor cells (NPCs). We identified and quantified a total of 843 proteins, including 801 glycoproteins. Among the 843 proteins, 606 (72%) are known cell surface or secreted glycoproteins, including 156 CD-antigens, all major classes of cell surface receptor proteins, transporters, and adhesion proteins. Our findings identified several known as well as new cell surface antigens whose expression is predominantly restricted to human GBM tumors as confirmed by microarray transcription profiling, quantitative RT-PCR and immunohistochemical staining. This report presents the comprehensive identification of new biomarkers and therapeutic targets for the treatment of malignant gliomas using quantitative sialoglycoproteomics with clinically relevant, patient derived primary glioma cells. [ABSTRACT FROM AUTHOR]