Drug Integrating Amphiphilic Nano-Assemblies: 2. Spatiotemporal Distribution within Inflammation Sites.

The need for chronic systemic immunosuppression, which is associated with unavoidable side-effects, greatly limits the applicability of allogeneic cell transplantation for regenerative medicine applications including pancreatic islet cell transplantation to restore insulin production in type 1 diabetes (T1D). Cell transplantation in confined sites enables the localized delivery of anti-inflammatory and immunomodulatory drugs to prevent graft loss by innate and adaptive immunity, providing an opportunity to achieve local effects while minimizing unwanted systemic side effects. Nanoparticles can provide the means to achieve the needed localized and sustained drug delivery either by graft targeting or co-implantation. Here, we evaluated the potential of our versatile platform of drug-integrating amphiphilic nanomaterial assemblies (DIANAs) for targeted drug delivery to an inflamed site model relevant for islet transplantation. We tested either passive targeting of intravenous administered spherical nanomicelles (nMIC; 20-25 nm diameter) or co-implantation of elongated nanofibrils (nFIB; 5 nm diameter and >1 µm length). To assess the ability of nMIC and nFIB to target an inflamed graft site, we used a lipophilic fluorescent cargo (DiD and DiR) and evaluated the in vivo biodistribution and cellular uptake in the graft site and other organs, including draining and non-draining lymph nodes, after systemic administration (nMIC) and/or graft co-transplantation (nFIB) in mice. Localized inflammation was generated either by using an LPS injection or by using biomaterial-coated islet-like bead implantation in the subcutaneous site. A cell transplant inflammation model was used as well to test nMIC- and nFIB-targeted biodistribution. We found that nMIC can reach the inflamed site after systemic administration, while nFIB remains localized for several days after co-implantation. We confirmed that DIANAs are taken up by different immune cell populations responsible for graft inflammation. Therefore, DIANA is a useful approach for targeted and/or localized delivery of immunomodulatory drugs to decrease innate and adaptive immune responses that cause graft loss after transplantation of therapeutic cells.

Supervised Parametric Learning in the Identification of Composite Biomarker Signatures of Type 1 Diabetes in Integrated Parallel Multi-Omics Datasets.

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.

A Composite Biomarker Signature of Type 1 Diabetes Risk Identified via Augmentation of Parallel Multi-Omics Data from a Small Cohort.

Background: Biomarkers of early pathogenesis of type 1 diabetes (T1D) are crucial to enable effective prevention measures in at-risk populations before significant damage occurs to their insulin producing beta-cell mass. We recently introduced the concept of integrated parallel multi-omics and employed a novel data augmentation approach which identified promising candidate biomarkers from a small cohort of high-risk T1D subjects. We now validate selected biomarkers to generate a potential composite signature of T1D risk. Methods: Twelve candidate biomarkers, which were identified in the augmented data and selected based on their fold-change relative to healthy controls and cross-reference to proteomics data previously obtained in the expansive TEDDY and DAISY cohorts, were measured in the original samples by ELISA. Results: All 12 biomarkers had established connections with lipid/lipoprotein metabolism, immune function, inflammation, and diabetes, but only 7 were found to be markedly changed in the high-risk subjects compared to the healthy controls: ApoC1 and PON1 were reduced while CETP, CD36, FGFR1, IGHM, PCSK9, SOD1, and VCAM1 were elevated. Conclusions: Results further highlight the promise of our data augmentation approach in unmasking important patterns and pathologically significant features in parallel multi-omics datasets obtained from small sample cohorts to facilitate the identification of promising candidate T1D biomarkers for downstream validation. They also support the potential utility of a composite biomarker signature of T1D risk characterized by the changes in the above markers.

Quantitative receptor model for responses that are left- or right-shifted versus occupancy (are more or less concentration sensitive): the SABRE approach.

Simple one-to three-parameter models routinely used to fit typical dose-response curves and calculate EC50 values using the Hill or Clark equation cannot provide the full picture connecting measured response to receptor occupancy, which can be quite complex due to the interplay between partial agonism and (pathway-dependent) signal amplification. The recently introduced SABRE quantitative receptor model is the first one that explicitly includes a parameter for signal amplification (γ) in addition to those for binding affinity (K d), receptor-activation efficacy (ε), constitutive activity (ε R0), and steepness of response (Hill slope, n). It can provide a unified framework to fit complex cases, where fractional response and occupancy do not match, as well as simple ones, where parameters constrained to specific values can be used (e.g., ε R0 = 0, γ = 1, or n = 1). Here, it is shown for the first time that SABRE can fit not only typical cases where response curves are left-shifted compared to occupancy (κ = K d/EC50 > 1) due to signal amplification (γ > 1), but also less common ones where they are right-shifted (i.e., less concentration-sensitive; κ = K d/EC50 < 1) by modeling them as apparent signal attenuation/loss (γ < 1). Illustrations are provided with µ-opioid receptor (MOPr) data from three different experiments with one left- and one right-shifted response (G protein activation and ß-arrestin2 recruitment, respectively; EC50,Gprt < K d < EC50,ßArr). For such cases of diverging pathways with differently shifted responses, partial agonists can cause very weak responses in the less concentration-sensitive pathway without having to be biased ligands due to the combination of low ligand efficacy and signal attenuation/loss-an illustration with SABRE-fitted oliceridine data is included.

Quantification of receptor binding from response data obtained at different receptor levels: a simple individual sigmoid fitting and a unified SABRE approach.

Methods that allow quantification of receptor binding (occupancy) by measuring response (effect) data only are of interest as they can be used to allow characterization of binding properties (e.g., dissociation constant, Kd) without having to perform explicit ligand binding experiments that require different setups (e.g., use of labeled ligands). However, since response depends not just on the binding affinity-determined receptor occupancy, but also on receptor activation, which is affected by ligand efficacy (plus constitutive activity, if present), and downstream pathway amplification, this requires the acquisition and fitting of multiple concentration-response data. Here, two alternative methods, which both are straightforward to implement using nonlinear regression software, are described to fit such multiple responses measured at different receptor levels that can be obtained, for example, by partial irreversible receptor inactivation (i.e., Furchgott method) or different expression levels. One is a simple method via straightforward fitting of each response with sigmoid functions and estimation of Kd from the obtained Emax and EC50 values as Kd = (Emax·EC'50 - E'max·EC50)/(Emax - E'max). This is less error-prone than the original Furchgott method of double-reciprocal fit and simpler than alternatives that require concentration interpolations, thus, should allow more widespread use of this so-far underutilized approach to estimate binding properties. Relative efficacies can then be compared using Emax·Kd/EC50 values. The other is a complex method that uses the SABRE receptor model to obtain a unified fit of the multiple concentration-response curves with a single set of parameters that include binding affinity Kd, efficacy ε, amplification γ, and Hill coefficient n. Illustrations with simulated and experimental data are presented including with activity data of three muscarinic agonists measured in rabbit myocardium.

Exploring Computational Data Amplification and Imputation for the Discovery of Type 1 Diabetes (T1D) Biomarkers from Limited Human Datasets.

BACKGROUND: Type 1 diabetes (T1D) is a devastating disease with serious health complications. Early T1D biomarkers that could enable timely detection and prevention before the onset of clinical symptoms are paramount but currently unavailable. Despite their promise, omics approaches have so far failed to deliver such biomarkers, likely due to the fragmented nature of information obtained through the single omics approach. We recently demonstrated the utility of parallel multi-omics for the identification of T1D biomarker signatures. Our studies also identified challenges. METHODS: Here, we evaluated a novel computational approach of data imputation and amplification as one way to overcome challenges associated with the relatively small number of subjects in these studies. RESULTS: Using proprietary algorithms, we amplified our quadra-omics (proteomics, metabolomics, lipidomics, and transcriptomics) dataset from nine subjects a thousand-fold and analyzed the data using Ingenuity Pathway Analysis (IPA) software to assess the change in its analytical capabilities and biomarker prediction power in the amplified datasets compared to the original. These studies showed the ability to identify an increased number of T1D-relevant pathways and biomarkers in such computationally amplified datasets, especially, at imputation ratios close to the "golden ratio" of 38.2%:61.8%. Specifically, the Canonical Pathway and Diseases and Functions modules identified higher numbers of inflammatory pathways and functions relevant to autoimmune T1D, including novel ones not identified in the original data. The Biomarker Prediction module also predicted in the amplified data several unique biomarker candidates with direct links to T1D pathogenesis. CONCLUSIONS: These preliminary findings indicate that such large-scale data imputation and amplification approaches are useful in facilitating the discovery of candidate integrated biomarker signatures of T1D or other diseases by increasing the predictive range of existing data mining tools, especially when the size of the input data is inherently limited.

Broad-Spectrum Small-Molecule Inhibitors of the SARS-CoV-2 Spike-ACE2 Protein-Protein Interaction from a Chemical Space of Privileged Protein Binders.

Therapeutically useful small-molecule inhibitors (SMIs) of protein−protein interactions (PPIs) initiating the cell attachment and entry of viruses could provide novel alternative antivirals that act via mechanisms similar to that of neutralizing antibodies but retain the advantages of small-molecule drugs such as oral bioavailability and low likelihood of immunogenicity. From screening our library, which is focused around the chemical space of organic dyes to provide good protein binders, we have identified several promising SMIs of the SARS-CoV-2 spike­ACE2 interaction, which is needed for the attachment and cell entry of this coronavirus behind the COVID-19 pandemic. They included organic dyes, such as Congo red, direct violet 1, and Evans blue, which seem to be promiscuous PPI inhibitors, as well as novel drug-like compounds (e.g., DRI-C23041). Here, we show that in addition to the original SARS-CoV-2 strain, these SMIs also inhibit this PPI for variants of concern including delta (B.1.617.2) and omicron (B.1.1.529) as well as HCoV-NL63 with low- or even sub-micromolar activity. They also concentration-dependently inhibited SARS-CoV-2-S expressing pseudovirus entry into hACE2-expressing cells with low micromolar activity (IC50 < 10 µM) both for the original strain and the delta variant. DRI-C23041 showed good therapeutic (selectivity) index, i.e., separation between activity and cytotoxicity (TI > 100). Specificities and activities require further optimization; nevertheless, these results provide a promising starting point toward novel broad-spectrum small-molecule antivirals that act via blocking the interaction between the spike proteins of coronaviruses and their ACE2 receptor initiating cellular entry.

Parallel Evaluation of Polyethylene Glycol Conformal Coating and Alginate Microencapsulation as Immunoisolation Strategies for Pancreatic Islet Transplantation.

Pancreatic islet transplantation improves metabolic control and prevents complications in patients with brittle type 1 diabetes (T1D). However, chronic immunosuppression is required to prevent allograft rejection and recurrence of autoimmunity. Islet encapsulation may eliminate the need for immunosuppression. Here, we analyzed in parallel two microencapsulation platforms that provided long-term diabetes reversal in preclinical T1D models, alginate single and double capsules versus polyethylene glycol conformal coating, to identify benefits and weaknesses that could inform the design of future clinical trials with microencapsulated islets. We performed in vitro and in vivo functionality assays with human islets and analyzed the explanted grafts by immunofluorescence. We quantified the size of islets and capsules, measured capsule permeability, and used these data for in silico simulations of islet functionality in COMSOL Multiphysics. We demonstrated that insulin response to glucose stimulation is dependent on capsule size, and the presence of permselective materials augments delays in insulin secretion. Non-coated and conformally coated islets could be transplanted into the fat pad of diabetic mice, resulting in comparable functionality and metabolic control. Mac-2+ cells were found in conformally coated grafts, indicating possible host reactivity. Due to their larger volume, alginate capsules were transplanted in the peritoneal cavity. Despite achieving diabetes reversal, changes in islet composition were found in retrieved capsules, and recipient mice experienced hypoglycemia indicative of hyperinsulinemia induced by glucose retention in large capsules as the in silico model predicted. We concluded that minimal capsule size is critical for physiological insulin secretion, and anti-inflammatory modulation may be beneficial for small conformal capsules.

Methylene Blue Is a Nonspecific Protein-Protein Interaction Inhibitor with Potential for Repurposing as an Antiviral for COVID-19.

We have previously identified methylene blue, a tricyclic phenothiazine dye approved for clinical use for the treatment of methemoglobinemia and for other medical applications as a small-molecule inhibitor of the protein-protein interaction (PPI) between the spike protein of the SARS-CoV-2 coronavirus and ACE2, the first critical step of the attachment and entry of this coronavirus responsible for the COVID-19 pandemic. Here, we show that methylene blue concentration dependently inhibits this PPI for the spike protein of the original strain as well as for those of variants of concern such as the D614G mutant and delta (B.1.617.2) with IC50 in the low micromolar range (1-5 µM). Methylene blue also showed promiscuous activity and inhibited several other PPIs of viral proteins (e.g., HCoV-NL63-ACE2, hepatitis C virus E-CD81) as well as others (e.g., IL-2-IL-2Rα) with similar potency. This nonspecificity notwithstanding, methylene blue inhibited the entry of pseudoviruses bearing the spike protein of SARS-CoV-2 in hACE2-expressing host cells, both for the original strain and the delta variant. It also blocked SARS-CoV-2 (B.1.5) virus replication in Vero E6 cells with an IC50 in the low micromolar range (1.7 µM) when assayed using quantitative PCR of the viral RNA. Thus, while it seems to be a promiscuous PPI inhibitor with low micromolar activity and has a relatively narrow therapeutic index, methylene blue inhibits entry and replication of SARS-CoV-2, including several of its mutant variants, and has potential as a possible inexpensive, broad-spectrum, orally bioactive small-molecule antiviral for the prevention and treatment of COVID-19.

RNA aptamers specific for transmembrane p24 trafficking protein 6 and Clusterin for the targeted delivery of imaging reagents and RNA therapeutics to human ß cells.

The ability to detect and target ß cells in vivo can substantially refine how diabetes is studied and treated. However, the lack of specific probes still hampers a precise characterization of human ß cell mass and the delivery of therapeutics in clinical settings. Here, we report the identification of two RNA aptamers that specifically and selectively recognize mouse and human ß cells. The putative targets of the two aptamers are transmembrane p24 trafficking protein 6 (TMED6) and clusterin (CLUS). When given systemically in immune deficient mice, these aptamers recognize the human islet graft producing a fluorescent signal proportional to the number of human islets transplanted. These aptamers cross-react with endogenous mouse ß cells and allow monitoring the rejection of mouse islet allografts. Finally, once conjugated to saRNA specific for X-linked inhibitor of apoptosis (XIAP), they can efficiently transfect non-dissociated human islets, prevent early graft loss, and improve the efficacy of human islet transplantation in immunodeficient in mice.

Cdc42 GTPase-activating proteins (GAPs) regulate generational inheritance of cell polarity and cell shape in fission yeast.

The highly conserved small GTPase Cdc42 regulates polarized cell growth and morphogenesis from yeast to humans. We previously reported that Cdc42 activation exhibits oscillatory dynamics at cell tips of Schizosaccharomyces pombe cells. Mathematical modeling suggests that this dynamic behavior enables a variety of symmetric and asymmetric Cdc42 activation distributions to coexist in cell populations. For individual wild-type cells, however, Cdc42 distribution is initially asymmetrical and becomes more symmetrical as cell volume increases, enabling bipolar growth activation. To explore whether different patterns of Cdc42 activation are possible in vivo, we examined S. pombe rga4∆ mutant cells, lacking the Cdc42 GTPase-activating protein (GAP) Rga4. We found that monopolar rga4∆ mother cells divide asymmetrically leading to the emergence of both symmetric and asymmetric Cdc42 distributions in rga4∆ daughter cells. Motivated by different hypotheses that can mathematically reproduce the unequal fate of daughter cells, we used genetic screening to identify mutants that alter the rga4∆ phenotype. We found that the unequal distribution of active Cdc42 GTPase is consistent with an unequal inheritance of another Cdc42 GAP, Rga6, in the two daughter cells. Our findings highlight the crucial role of Cdc42 GAP localization in maintaining consistent Cdc42 activation and growth patterns across generations.

Small-Molecule Inhibitors of the Coronavirus Spike: ACE2 Protein-Protein Interaction as Blockers of Viral Attachment and Entry for SARS-CoV-2.

Inhibitors of the protein-protein interaction (PPI) between the SARS-CoV-2 spike protein and human ACE2 (hACE2), which acts as a ligand-receptor pair that initiates the viral attachment and cellular entry of this coronavirus causing the ongoing COVID-19 pandemic, are of considerable interest as potential antiviral agents. While blockade of such PPIs with small molecules is more challenging than that with antibodies, small-molecule inhibitors (SMIs) might offer alternatives that are less strain- and mutation-sensitive, suitable for oral or inhaled administration, and more controllable/less immunogenic. Here, we report the identification of SMIs of this PPI by screening our compound library focused around the chemical space of organic dyes. Among promising candidates identified, several dyes (Congo red, direct violet 1, Evans blue) and novel druglike compounds (DRI-C23041, DRI-C91005) inhibited the interaction of hACE2 with the spike proteins of SARS-CoV-2 as well as SARS-CoV with low micromolar activity in our cell-free ELISA-type assays (IC50's of 0.2-3.0 µM), whereas control compounds, such as sunset yellow FCF, chloroquine, and suramin, showed no activity. Protein thermal shift assays indicated that the SMIs of interest identified here bind SARS-CoV-2-S and not hACE2. While dyes seemed to be promiscuous inhibitors, DRI-C23041 showed some selectivity and inhibited the entry of two different SARS-CoV-2-S expressing pseudoviruses into hACE2-expressing cells in a concentration-dependent manner with low micromolar IC50's (6-7 µM). This provides proof-of-principle evidence for the feasibility of small-molecule inhibition of PPIs critical for SARS-CoV-2 attachment/entry and serves as a first guide in the search for SMI-based alternative antiviral therapies for the prevention and treatment of diseases caused by coronaviruses in general and COVID-19 in particular.

Parallel Multi-Omics in High-Risk Subjects for the Identification of Integrated Biomarker Signatures of Type 1 Diabetes.

BACKGROUND: Biomarkers are crucial for detecting early type-1 diabetes (T1D) and preventing significant ß-cell loss before the onset of clinical symptoms. Here, we present proof-of-concept studies to demonstrate the potential for identifying integrated biomarker signature(s) of T1D using parallel multi-omics. METHODS: Blood from human subjects at high risk for T1D (and healthy controls; n = 4 + 4) was subjected to parallel unlabeled proteomics, metabolomics, lipidomics, and transcriptomics. The integrated dataset was analyzed using Ingenuity Pathway Analysis (IPA) software for disturbances in the at-risk subjects compared to controls. RESULTS: The final quadra-omics dataset contained 2292 proteins, 328 miRNAs, 75 metabolites, and 41 lipids that were detected in all samples without exception. Disease/function enrichment analyses consistently indicated increased activation, proliferation, and migration of CD4 T-lymphocytes and macrophages. Integrated molecular network predictions highlighted central involvement and activation of NF-κB, TGF-ß, VEGF, arachidonic acid, and arginase, and inhibition of miRNA Let-7a-5p. IPA-predicted candidate biomarkers were used to construct a putative integrated signature containing several miRNAs and metabolite/lipid features in the at-risk subjects. CONCLUSIONS: Preliminary parallel quadra-omics provided a comprehensive picture of disturbances in high-risk T1D subjects and highlighted the potential for identifying associated integrated biomarker signatures. With further development and validation in larger cohorts, parallel multi-omics could ultimately facilitate the classification of T1D progressors from non-progressors.

Growth hormone-releasing hormone agonists ameliorate chronic kidney disease-induced heart failure with preserved ejection fraction.

Therapies for heart failure with preserved ejection fraction (HFpEF) are lacking. Growth hormone-releasing hormone agonists (GHRH-As) have salutary effects in ischemic and nonischemic heart failure animal models. Accordingly, we hypothesized that GHRH-A treatment ameliorates chronic kidney disease (CKD)-induced HFpEF in a large-animal model. Female Yorkshire pigs (n = 16) underwent 5/6 nephrectomy via renal artery embolization and 12 wk later were randomized to receive daily subcutaneous injections of GHRH-A (MR-409; n = 8; 30 µg/kg) or placebo (n = 8) for 4 to 6 wk. Renal and cardiac structure and function were serially assessed postembolization. Animals with 5/6 nephrectomy exhibited CKD (elevated blood urea nitrogen [BUN] and creatinine) and faithfully recapitulated the hemodynamic features of HFpEF. HFpEF was demonstrated at 12 wk by maintenance of ejection fraction associated with increased left ventricular mass, relative wall thickness, end-diastolic pressure (EDP), end-diastolic pressure/end-diastolic volume (EDP/EDV) ratio, and tau, the time constant of isovolumic diastolic relaxation. After 4 to 6 wk of treatment, the GHRH-A group exhibited normalization of EDP (P = 0.03), reduced EDP/EDV ratio (P = 0.018), and a reduction in myocardial pro-brain natriuretic peptide protein abundance. GHRH-A increased cardiomyocyte [Ca2+] transient amplitude (P = 0.009). Improvement of the diastolic function was also evidenced by increased abundance of titin isoforms and their ratio (P = 0.0022). GHRH-A exerted a beneficial effect on diastolic function in a CKD large-animal model as demonstrated by improving hemodynamic, structural, and molecular characteristics of HFpEF. These findings have important therapeutic implications for the HFpEF syndrome.

Drug-Integrating Amphiphilic Nanomaterial Assemblies: 1. Spatiotemporal control of cyclosporine delivery and activity using nanomicelles and nanofibrils.

Immunomodulatory therapies are limited by unavoidable side effects as well as poor solubility, stability, and pharmacokinetic properties. Nanomaterial-based drug delivery may overcome these limitations by increasing drug solubility, site-targeting, and duration of action. Here, we prepared innovative drug-integrating amphiphilic nanomaterial assemblies (DIANA) with tunable hydrophobicity, size, and morphology, and we evaluated their ability to deliver cyclosporine A (CsA) for immunomodulatory applications. We synthesized amphiphilic block copolymers made of poly(ethylene glycol)-poly(propylene sulfide) (PEG-PPS) and poly(ethylene glycol)-oligo(ethylene sulfide) (PEG-OES) that can self-assemble into solid core nanomicelles (nMIC, with ≈20 nm diameter) and nanofibrils (nFIB, with ≈5 nm diameter and > 500 nm length), respectively. nMIC and nFIB displayed good CsA encapsulation efficiency (up to 4.5 and 2 mg/mL, respectively in aqueous solution), superior to many other solubilization methods, and provided sustained release (>14 and > 7 days for the nMIC and nFIB) without compromising CsA's pharmacological activity. Treatment of insulin-secreting cells with unloaded DIANAs did not impair cell viability and functionality. Both CsA-loaded DIANAs inhibited the proliferation and activation of insulin-reactive cytotoxic T cells in vitro. Subcutaneous injections of CsA-loaded DIANAs in mice provided CsA sustained release, decreasing alloantigen-induced immune responses in the draining lymph node at lower doses and reduced administration frequency than unformulated CsA. While nMIC solubilized higher amounts and provided more sustained release of CsA in vitro, nFIB enhanced cellular uptake and promoted local retention due to slower trafficking in vivo. DIANAs provide a versatile platform for a local immune suppression regimen that can be applied to allogeneic cell transplantation.

A Collagen Based Cryogel Bioscaffold that Generates Oxygen for Islet Transplantation.

The aim of this work was to develop, characterize and test a novel 3D bioscaffold matrix which can accommodate pancreatic islets and provide them with a continuous, controlled and steady source of oxygen to prevent hypoxia-induced damage following transplantation. Hence, we made a collagen based cryogel bioscaffold which incorporated calcium peroxide (CPO) into its matrix. The optimal concentration of CPO integrated into bioscaffolds was 0.25wt.% and this generated oxygen at 0.21±0.02mM/day (day 1), 0.19±0.01mM/day (day 6), 0.13±0.03mM/day (day 14), and 0.14±0.02mM/day (day 21). Accordingly, islets seeded into cryogel-CPO bioscaffolds had a significantly higher viability and function compared to islets seeded into cryogel alone bioscaffolds or islets cultured alone on traditional cell culture plates; these findings were supported by data from quantitative computational modelling. When syngeneic islets were transplanted into the epididymal fat pad (EFP) of diabetic mice, our cryogel-0.25wt.%CPO bioscaffold improved islet function with diabetic animals re-establishing glycemic control. Mice transplanted with cryogel-0.25wt.%CPO bioscaffolds showed faster responses to intraperitoneal glucose injections and had a higher level of insulin content in their EFP compared to those transplanted with islets alone (P<0.05). Biodegradability studies predicted that our cryogel-CPO bioscaffolds will have long-lasting biostability for approximately 5 years (biodegradation rate: 16.00±0.65%/year). Long term implantation studies (i.e. 6 months) showed that our cryogel-CPO bioscaffold is biocompatible and integrated into the surrounding fat tissue with minimal adverse tissue reaction; this was further supported by no change in blood parameters (i.e. electrolyte, metabolic, chemistry and liver panels). Our novel oxygen-generating bioscaffold (i.e. cryogel-0.25wt.%CPO) therefore provides a biostable and biocompatible 3D microenvironment for islets which can facilitate islet survival and function at extra-hepatic sites of transplantation.

Controlled Nutrient Delivery to Pancreatic Islets Using Polydopamine-Coated Mesoporous Silica Nanoparticles.

In the present study, we created a nanoscale platform that can deliver nutrients to pancreatic islets in a controlled manner. Our platform consists of a mesoporous silica nanoparticle (MSNP), which can be loaded with glutamine (G: an essential amino acid required for islet survival and function). To control the release of G, MSNPs were coated with a polydopamine (PD) layer. With the optimal parameters (0.5 mg/mL and 0.5 h), MSNPs were coated with a layer of PD, which resulted in a delay of G release from MSNPs over 14 d (57.4 ± 4.7% release). Following syngeneic renal subcapsule islet transplantation in diabetic mice, PDG-MSNPs improved the engraftment of islets (i.e., enhanced revascularization and reduced inflammation) as well as their function, resulting in re-establishment of glycemic control. Collectively, our data show that PDG-MSNPs can support transplanted islets by providing them with a controlled and sustained supply of nutrients.

A single unified model for fitting simple to complex receptor response data.

The fitting of complex receptor-response data where fractional response and occupancy do not match is challenging. They encompass important cases including (a) the presence of "receptor reserve" and/or partial agonism, (b) multiple responses assessed at different vantage points along a pathway, (c) responses that are different along diverging downstream pathways (biased agonism), and (d) constitutive activity. For these, simple models such as the well-known Clark or Hill equations cannot be used. Those that can, such as the operational (Black&Leff) model, do not provide a unified approach, have multiple nonintuitive parameters that are challenging to fit in well-defined manner, have difficulties incorporating binding data, and cannot be reduced or connected to simpler forms. We have recently introduced a quantitative receptor model (SABRE) that includes parameters for Signal Amplification (γ), Binding affinity (Kd), Receptor activation Efficacy (ε), and constitutive activity (εR0). It provides a single equation to fit complex cases within a full two-state framework with the possibility of incorporating receptor occupancy data (i.e., experimental Kds). Simpler cases can be fit by using consecutively reduced forms obtained by constraining parameters to specific values, e.g., εR0 = 0: no constitutive activity, γ = 1: no amplification (Emax-type fitting), and ε = 1: no partial agonism (Clark equation). Here, a Hill-type extension is introduced (n ≠ 1), and simulated and experimental receptor-response data from simple to increasingly complex cases are fitted within the unified framework of SABRE with differently constrained parameters.