Pharmacokinetics, safety, and efficacy of 20% subcutaneous immunoglobulin (Ig20Gly) administered weekly or every 2 weeks in Japanese patients with primary immunodeficiency diseases: a phase 3, open-label study.

This phase 3, open-label, multidose study (NCT04346108) evaluated the pharmacokinetics, safety, tolerability, and efficacy of immunoglobulin subcutaneous (human) 20% solution (Ig20Gly) administered weekly and every 2 weeks in Japanese patients with primary immunodeficiency diseases (PIDs). The study was conducted at eight study sites in Japan and enrolled patients aged ≥2 years with PIDs treated using a stable intravenous immunoglobulin dose for ≥3 months prior to the study. Patients received intravenous immunoglobulin every 3 or 4 weeks at pre-study dose (200-600 mg/kg) for 13 weeks (Epoch 1), subcutaneous Ig20Gly (50-200 mg/kg) once weekly for 24 weeks (Epoch 2), and Ig20Gly (100-400 mg/kg) every 2 weeks for 12 weeks (Epoch 3). The primary endpoint was serum total immunoglobulin G (IgG) trough levels during Epochs 2 and 3. Overall, 17 patients were enrolled (median [range] age: 24 [5-69] years; 59% male) and participated in Epochs 1 and 2; seven patients entered Epoch 3. Serum total IgG trough levels were maintained at >8 g/l: geometric means (95% confidence intervals) at the end of Epochs 2 and 3 were 8.56 (8.03-9.12) g/l and 8.39 (7.89-8.91) g/l, respectively. Related treatment-emergent adverse events were all mild in severity; the most common treatment-emergent adverse events (excluding infections) in Epochs 2 and 3 were injection site swelling (24%) and injection site erythema (18%). This is the first trial to demonstrate the efficacy and favourable safety profile of 20% subcutaneous immunoglobulin administered every 2 weeks in adult and paediatric Japanese patients with PIDs.

A multiscale computational model predicts distribution of anti-angiogenic isoform VEGF165b in peripheral arterial disease in human and mouse.

Angiogenesis is the growth of new blood vessels from pre-existing microvessels. Peripheral arterial disease (PAD) is caused by atherosclerosis that results in ischemia mostly in the lower extremities. Clinical trials including VEGF-A administration for therapeutic angiogenesis have not been successful. The existence of anti-angiogenic isoform (VEGF165b) in PAD muscle tissues is a potential cause for the failure of therapeutic angiogenesis. Experimental measurements show that in PAD human muscle biopsies the VEGF165b isoform is at least as abundant if not greater than the VEGF165a isoform. We constructed three-compartment models describing VEGF isoforms and receptors, in human and mouse, to make predictions on the secretion rate of VEGF165b and the distribution of various isoforms throughout the body based on the experimental data. The computational results are consistent with the data showing that in PAD calf muscles secrete mostly VEGF165b over total VEGF. In the PAD calf compartment of human and mouse models, most VEGF165a and VEGF165b are bound to the extracellular matrix. VEGF receptors VEGFR1, VEGFR2 and Neuropilin-1 (NRP1) are mostly in 'Free State'. This study provides a computational model of VEGF165b in PAD supported by experimental measurements of VEGF165b in human and mouse, which gives insight of VEGF165b in therapeutic angiogenesis and VEGF distribution in human and mouse PAD model.

Computational drug repositioning for peripheral arterial disease: prediction of anti-inflammatory and pro-angiogenic therapeutics.

Peripheral arterial disease (PAD) results from atherosclerosis that leads to blocked arteries and reduced blood flow, most commonly in the arteries of the legs. PAD clinical trials to induce angiogenesis to improve blood flow conducted in the last decade have not succeeded. We have recently constructed PADPIN, protein-protein interaction network (PIN) of PAD, and here we combine it with the drug-target relations to identify potential drug targets for PAD. Specifically, the proteins in the PADPIN were classified as belonging to the angiome, immunome, and arteriome, characterizing the processes of angiogenesis, immune response/inflammation, and arteriogenesis, respectively. Using the network-based approach we predict the candidate drugs for repositioning that have potential applications to PAD. By compiling the drug information in two drug databases DrugBank and PharmGKB, we predict FDA-approved drugs whose targets are the proteins annotated as anti-angiogenic and pro-inflammatory, respectively. Examples of pro-angiogenic drugs are carvedilol and urokinase. Examples of anti-inflammatory drugs are ACE inhibitors and maraviroc. This is the first computational drug repositioning study for PAD.

PADPIN: protein-protein interaction networks of angiogenesis, arteriogenesis, and inflammation in peripheral arterial disease.

Peripheral arterial disease (PAD) results from an obstruction of blood flow in the arteries other than the heart, most commonly the arteries that supply the legs. The complexity of the known signaling pathways involved in PAD, including various growth factor pathways and their cross talks, suggests that analyses of high-throughput experimental data could lead to a new level of understanding of the disease as well as novel and heretofore unanticipated potential targets. Such bioinformatic analyses have not been systematically performed for PAD. We constructed global protein-protein interaction networks of angiogenesis (Angiome), immune response (Immunome), and arteriogenesis (Arteriome) using our previously developed algorithm GeneHits. The term "PADPIN" refers to the angiome, immunome, and arteriome in PAD. Here we analyze four microarray gene expression datasets from ischemic and nonischemic gastrocnemius muscles at day 3 posthindlimb ischemia (HLI) in two genetically different C57BL/6 and BALB/c mouse strains that display differential susceptibility to HLI to identify potential targets and signaling pathways in angiogenesis, immune, and arteriogenesis networks. We hypothesize that identification of the differentially expressed genes in ischemic and nonischemic muscles between the strains that recovers better (C57BL/6) vs. the strain that recovers more poorly (BALB/c) will help for the prediction of target genes in PAD. Our bioinformatics analysis identified several genes that are differentially expressed between the two mouse strains with known functions in PAD including TLR4, THBS1, and PRKAA2 and several genes with unknown functions in PAD including EphA4, TSPAN7, SLC22A4, and EIF2a.

Computational systems biology approaches to anti-angiogenic cancer therapeutics.

Angiogenesis is an exquisitely regulated process that is required for physiological processes and is also important in numerous diseases. Tumors utilize angiogenesis to generate the vascular network needed to supply the cancer cells with nutrients and oxygen, and many cancer drugs aim to inhibit tumor angiogenesis. Anti-angiogenic therapy involves inhibiting multiple cell types, molecular targets, and intracellular signaling pathways. Computational tools are useful in guiding treatment strategies, predicting the response to treatment, and identifying new targets of interest. Here, we describe progress that has been made in applying mathematical modeling and bioinformatics approaches to study anti-angiogenic therapeutics in cancer.

Angiogenesis interactome and time course microarray data reveal the distinct activation patterns in endothelial cells.

Angiogenesis involves stimulation of endothelial cells (EC) by various cytokines and growth factors, but the signaling mechanisms are not completely understood. Combining dynamic gene expression time-course data for stimulated EC with protein-protein interactions associated with angiogenesis (the "angiome") could reveal how different stimuli result in different patterns of network activation and could implicate signaling intermediates as points for control or intervention. We constructed the protein-protein interaction networks of positive and negative regulation of angiogenesis comprising 367 and 245 proteins, respectively. We used five published gene expression datasets derived from in vitro assays using different types of blood endothelial cells stimulated by VEGFA (vascular endothelial growth factor A). We used the Short Time-series Expression Miner (STEM) to identify significant temporal gene expression profiles. The statistically significant patterns between 2D fibronectin and 3D type I collagen substrates for telomerase-immortalized EC (TIME) show that different substrates could influence the temporal gene activation patterns in the same cell line. We investigated the different activation patterns among 18 transmembrane tyrosine kinase receptors, and experimentally measured the protein level of the tyrosine-kinase receptors VEGFR1, VEGFR2 and VEGFR3 in human umbilical vein EC (HUVEC) and human microvascular EC (MEC). The results show that VEGFR1-VEGFR2 levels are more closely coupled than VEGFR1-VEGFR3 or VEGFR2-VEGFR3 in HUVEC and MEC. This computational methodology can be extended to investigate other molecules or biological processes such as cell cycle.

Constructing the angiome: a global angiogenesis protein interaction network.

Angiogenesis is the formation of new blood vessels from pre-existing microvessels. Excessive and insufficient angiogenesis have been associated with many diseases including cancer, age-related macular degeneration, ischemic heart, brain, and skeletal muscle diseases. A comprehensive understanding of angiogenesis regulatory processes is needed to improve treatment of these diseases. To identify proteins related to angiogenesis, we developed a novel integrative framework for diverse sources of high-throughput data. The system, called GeneHits, was used to expand on known angiogenesis pathways to construct the angiome, a protein-protein interaction network for angiogenesis. The network consists of 478 proteins and 1,488 interactions. The network was validated through cross validation and analysis of five gene expression datasets from in vitro angiogenesis assays. We calculated the topological properties of the angiome. We analyzed the functional enrichment of angiogenesis-annotated and associated proteins. We also constructed an extended angiome with 1,233 proteins and 5,726 interactions to derive a more complete map of protein-protein interactions in angiogenesis. Finally, the extended angiome was used to identify growth factor signaling networks that drive angiogenesis and antiangiogenic signaling networks. The results of this analysis can be used to identify genes and proteins in different disease conditions and putative targets for therapeutic interventions as high-ranked candidates for experimental validation.

Unraveling virus identity by detection of depleted probes with capillary electrophoresis.

With the emergence of new viral infections and pandemics, there is a need to develop faster methods to unravel the virus identities in a large number of clinical samples. This report describes a virus identification method featuring high throughput, high resolution, and high sensitivity detection of viruses. Identification of virus is based on liquid hybridization of different lengths of virus-specific probes to their corresponding viruses. The probes bound to target sequences are removed by a biotin-streptavidin pull-down mechanism and the supernatant is analyzed by capillary electrophoresis. The probes depleted from the sample appear as diminished peaks in the electropherograms and the remaining probes serve as calibrators to align peaks in different capillaries. The virus identities are unraveled by a signal processing and peak detection algorithm developed in-house. Nine viruses were used in the study to demonstrate how the system works to unravel the virus identity in single and double virus infections. With properly designed probes, the system is able to distinguish closely related viruses. The system takes advantage of the high resolution feature of capillary electrophoresis to resolve probes that differ by length. The method may facilitate virus identity screen from more candidate viruses with an automated 4-color DNA sequencer.

Construction of a cancer-perturbed protein-protein interaction network for discovery of apoptosis drug targets.

BACKGROUND: Cancer is caused by genetic abnormalities, such as mutations of oncogenes or tumor suppressor genes, which alter downstream signal transduction pathways and protein-protein interactions. Comparisons of the interactions of proteins in cancerous and normal cells can shed light on the mechanisms of carcinogenesis. RESULTS: We constructed initial networks of protein-protein interactions involved in the apoptosis of cancerous and normal cells by use of two human yeast two-hybrid data sets and four online databases. Next, we applied a nonlinear stochastic model, maximum likelihood parameter estimation, and Akaike Information Criteria (AIC) to eliminate false-positive protein-protein interactions in our initial protein interaction networks by use of microarray data. Comparisons of the networks of apoptosis in HeLa (human cervical carcinoma) cells and in normal primary lung fibroblasts provided insight into the mechanism of apoptosis and allowed identification of potential drug targets. The potential targets include BCL2, caspase-3 and TP53. Our comparison of cancerous and normal cells also allowed derivation of several party hubs and date hubs in the human protein-protein interaction networks involved in caspase activation. CONCLUSION: Our method allows identification of cancer-perturbed protein-protein interactions involved in apoptosis and identification of potential molecular targets for development of anti-cancer drugs.

Comparisons of robustness and sensitivity between cancer and normal cells by microarray data.

Robustness is defined as the ability to uphold performance in face of perturbations and uncertainties, and sensitivity is a measure of the system deviations generated by perturbations to the system. While cancer appears as a robust but fragile system, few computational and quantitative evidences demonstrate robustness tradeoffs in cancer. Microarrays have been widely applied to decipher gene expression signatures in human cancer research, and quantification of global gene expression profiles facilitates precise prediction and modeling of cancer in systems biology. We provide several efficient computational methods based on system and control theory to compare robustness and sensitivity between cancer and normal cells by microarray data. Measurement of robustness and sensitivity by linear stochastic model is introduced in this study, which shows oscillations in feedback loops of p53 and demonstrates robustness tradeoffs that cancer is a robust system with some extreme fragilities. In addition, we measure sensitivity of gene expression to perturbations in other gene expression and kinetic parameters, discuss nonlinear effects in feedback loops of p53 and extend our method to robustness-based cancer drug design.