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As part of our work to develop small-molecule inhibitors (SMIs) of the CD40-CD40L(CD154) costimulatory protein-protein interaction, here, we describe the ability of two of our most promising SMIs, DRI-C21041 and DRI-C21095, to prolong the survival and function of islet allografts in two murine models of islet transplantation (under the kidney capsule and in the anterior chamber of the eye) and to prevent autoimmune type 1 diabetes (T1D) onset in NOD mice. In both transplant models, a significant portion of islet allografts (50%-80%) remained intact and functional long after terminating treatment, suggesting the possibility of inducing operational immune tolerance via inhibition of the CD40-CD40L axis. SMI-treated mice maintained the structural integrity and function of their islet allografts with concomitant reduction in immune cell infiltration as evidenced by direct longitudinal imaging in situ. Furthermore, in female NODs, three-month SMI treatment reduced the incidence of diabetes from 80% to 60% (DRI-C21041) and 25% (DRI-C21095). These results (i) demonstrate the susceptibility of this TNF superfamily protein-protein interaction to small-molecule inhibition, (ii) confirm the in vivo therapeutic potential of these SMIs of a critical immune checkpoint, and (iii) reaffirm the therapeutic promise of CD40-CD40L blockade in islet transplantation and T1D prevention. Thus, CD40L-targeting SMIs could ultimately lead to alternative immunomodulatory therapeutics for transplant recipients and prevention of autoimmune diseases that are safer, less immunogenic, more controllable (shorter half-lives), and more patient-friendly (i.e., suitable for oral administration, which makes them easier to administer) than corresponding antibody-based interventions. [ABSTRACT FROM AUTHOR]

Background: Type 1 diabetes (T1D) is a devastating autoimmune disease, and its rising prevalence in the United States and around the world presents a critical problem in public health. While some treatment options exist for patients already diagnosed, individuals considered at risk for developing T1D and who are still in the early stages of their disease pathogenesis without symptoms have no options for any preventive intervention. This is because of the uncertainty in determining their risk level and in predicting with high confidence who will progress, or not, to clinical diagnosis. Biomarkers that assess one's risk with high certainty could address this problem and will inform decisions on early intervention, especially in children where the burden of justifying treatment is high. Single omics approaches (e.g., genomics, proteomics, metabolomics, etc.) have been applied to identify T1D biomarkers based on specific disturbances in association with the disease. However, reliable early biomarkers of T1D have remained elusive to date. To overcome this, we previously showed that parallel multi-omics provides a more comprehensive picture of the disease-associated disturbances and facilitates the identification of candidate T1D biomarkers. Methods: This paper evaluated the use of machine learning (ML) using data augmentation and supervised ML methods for the purpose of improving the identification of salient patterns in the data and the ultimate extraction of novel biomarker candidates in integrated parallel multi-omics datasets from a limited number of samples. We also examined different stages of data integration (early, intermediate, and late) to assess at which stage supervised parametric models can learn under conditions of high dimensionality and variation in feature counts across different omics. In the late integration scheme, we employed a multi-view ensemble comprising individual parametric models trained over single omics to address the computational challenges posed by the high dimensionality and variation in feature counts across the different yet integrated multi-omics datasets. Results: the multi-view ensemble improves the prediction of case vs. control and finds the most success in flagging a larger consistent set of associated features when compared with chance models, which may eventually be used downstream in identifying a novel composite biomarker signature of T1D risk. Conclusions: the current work demonstrates the utility of supervised ML in exploring integrated parallel multi-omics data in the ongoing quest for early T1D biomarkers, reinforcing the hope for identifying novel composite biomarker signatures of T1D risk via ML and ultimately informing early treatment decisions in the face of the escalating global incidence of this debilitating disease. [ABSTRACT FROM AUTHOR]

Introduction Essential tremor (ET) is a common movement disorder, with 3 of 4 patients reporting impaired activities of daily living (1). Less than 50% note tremor improvement with current pharmacotherapy, [...], BACKGROUND. Recently, 1-octanol has been shown to have efficacy in treating patients with essential tremor (ET). The primary metabolite of 1-octanol is octanoic acid (OA), which is now thought to be the active substance that mediates tremor suppression. Our aim was to describe the maximum tolerated dose (MTD) of oral OA in patients with ET and assess the pharmacokinetics (PK) and pharmacodynamics (PD) profile of OA. METHODS. The MTD was studied using an open-label, single-ascending 3 + 3 dose-escalation design. Predefined single doses ranged from 8 to 128 mg/kg, with grade 2 adverse events (AEs) defined as dose-limiting toxicity. Tremor was assessed using accelerometry, digital spiral analysis, and a standard clinical rating scale at baseline and up to 600 minutes after intake. Safety assessments and PK sampling were also performed. RESULTS. Dose-limiting toxicity was not reached. The most frequent AE was mild abdominal discomfort. Exposure (AUC) increased linearly with the dose. Secondary efficacy measures suggested a dose-dependent reduction of tremor. Accordingly, a single unified PK/PD model with an effect compartment and sigmoid maximum effect ([E.sub.max]) response could be built that accounted well for the time profiles of plasma concentrations as well as effects on tremor severity across the 5 dose levels. CONCLUSION. Although our trial did not reach an MTD, a dose-dependent effect was demonstrated in the PK/PD model as well as in secondary efficacy outcomes. Future studies are needed to explore the safety in higher dose ranges and to confirm dose-dependent efficacy in a placebo-controlled design. TRIAL REGISTRATION. Clinicaltrials.gov NCT01468948 FUNDING. NINDS Intramural Research Program; TG Therapeutics Inc.