Phase 1 Safety and Immunogenicity Evaluation of ADVAX, a Multigenic, DNA-Based Clade C/B' HIV-1 Candidate Vaccine

BACKGROUND: We conducted a Phase I dose escalation trial of ADVAX, a DNA-based candidate HIV-1 vaccine expressing Clade C/B' env, gag, pol, nef, and tat genes. Sequences were derived from a prevalent circulating recombinant form in Yunnan, China, an area of high HIV-1 incidence. The objective was to evaluate the safety and immunogenicity of ADVAX in human volunteers. METHODOLOGY/PRINCIPAL FINDINGS: ADVAX or placebo was administered intramuscularly at months 0, 1 and 3 to 45 healthy volunteers not at high risk for HIV-1. Three dosage levels [0.2 mg (low), 1.0 mg (mid), and 4.0 mg (high)] were tested. Twelve volunteers in each dosage group were assigned to receive ADVAX and three to receive placebo in a double-blind design. Subjects were followed for local and systemic reactogenicity, adverse events, and clinical laboratory parameters. Study follow up was 18 months. Humoral immunogenicity was evaluated by anti-gp120 binding ELISA. Cellular immunogenicity was assessed by a validated IFNgamma ELISpot assay and intracellular cytokine staining. ADVAX was safe and well-tolerated, with no vaccine-related serious adverse events. Local and systemic reactogenicity events were reported by 64% and 42% of vaccine recipients, respectively. The majority of events were mild. The IFNgamma ELISpot response rates to any HIV antigen were 0/9 (0%) in the placebo group, 3/12 (25%) in the low-dosage group, 4/12 (33%) in the mid-dosage group, and 2/12 (17%) in the high-dosage group. Overall, responses were generally transient and occurred to each gene product, although volunteers responded to single antigens only. Binding antibodies to gp120 were not detected in any volunteers, and HIV seroconversion did not occur. CONCLUSIONS/SIGNIFICANCE: ADVAX delivered intramuscularly is safe, well-tolerated, and elicits modest but transient cellular immune responses. TRIAL REGISTRATION: Clinicaltrials.gov NCT00249106.published_or_final_versio

Towards Universal Structure-Based Prediction of Class II MHC Epitopes for Diverse Allotypes

The binding of peptide fragments of antigens to class II MHC proteins is a crucial step in initiating a helper T cell immune response. The discovery of these peptide epitopes is important for understanding the normal immune response and its misregulation in autoimmunity and allergies and also for vaccine design. In spite of their biomedical importance, the high diversity of class II MHC proteins combined with the large number of possible peptide sequences make comprehensive experimental determination of epitopes for all MHC allotypes infeasible. Computational methods can address this need by predicting epitopes for a particular MHC allotype. We present a structure-based method for predicting class II epitopes that combines molecular mechanics docking of a fully flexible peptide into the MHC binding cleft followed by binding affinity prediction using a machine learning classifier trained on interaction energy components calculated from the docking solution. Although the primary advantage of structure-based prediction methods over the commonly employed sequence-based methods is their applicability to essentially any MHC allotype, this has not yet been convincingly demonstrated. In order to test the transferability of the prediction method to different MHC proteins, we trained the scoring method on binding data for DRB1*0101 and used it to make predictions for multiple MHC allotypes with distinct peptide binding specificities including representatives from the other human class II MHC loci, HLA-DP and HLA-DQ, as well as for two murine allotypes. The results showed that the prediction method was able to achieve significant discrimination between epitope and non-epitope peptides for all MHC allotypes examined, based on AUC values in the range 0.632–0.821. We also discuss how accounting for peptide binding in multiple registers to class II MHC largely explains the systematically worse performance of prediction methods for class II MHC compared with those for class I MHC based on quantitative prediction performance estimates for peptide binding to class II MHC in a fixed register

Minimal Disclosure in Hierarchical Hippocratic Databases with Delegation

Hippocratic Databases have been proposed as a mechanism to guarantee the respect of privacy principles in data management. We argue that three major principles are missing from the proposed mechanism: hierarchies of purposes, delegation of tasks and authorizations (i.e. outsourcing), and the minimal disclosure of private information. In this paper, we propose a flexible framework for the negotiation of personal information among customers and (possibly virtual) enterprises based on user preferences when enterprises may adopt different processes to provide the same service. We use a goal-oriented approach to analyze the purposes of a Hippocratic system and derive a purpose and delegation hierarchy. Based on this hierarchy, effective algorithms are given to determine the minimum set of authorizations needed for a service. In this way, the minimal authorization table of a global business process can be automatically constructed from the collection of privacy policy tables associated with the collaborating enterprises. By using effective on-line algorithms, the derivation of such minimal information can also be done on-the-fly by the customer wishing to use the services of a virtual organization

Performance Modeling for the Panda Array I/O Library

We present an analytical performance model for Panda, a library for synchronized i/o of large multidimensional arrays on parallel and sequential platforms, and show how the Panda developers use this model to evaluate Panda's parallel i/o performance and guide future Panda development. The model validation shows that system developers can simplify performance analysis, identify potential performance bottlenecks, and study the design trade-offs for Panda on massively parallel platforms more easily than by conducting empirical experiments. More importantly, we show that the outputs of the performance model can be used to help make optimal plans for handling application i/o requests, the first step toward our long-term goal of automatically optimizing i/o request handling in Panda. This research was supported by an ARPA Fellowship in High Performance Computing administered by the Institute for Advanced Computer Studies, University of Maryland, by NSF under PYI grant IRI 89 58582, and by N..

Exploiting Local Data in Parallel Array I/O on a Practical Network of Workstations

A cost-effective way to run a parallel application is to use existing workstations connected by a local area network such as Ethernet or FDDI. In this paper, we present an approach for parallel I/O of multidimensional arrays on small networks of workstations with a shared-media interconnect, using the Panda I/O library. In such an environment, the message passing throughput per node is lower than the throughput obtainable from a fast disk and it is not easy for users to determine the configuration which will yield the best I/O performance. We introduce an I/O strategy that exploits local data to reduce the amount of data that must be shipped across the network, present experimental results, and analyze the results using an analytical performance model and predict the best choice of I/O parameters. Our experiments show that the new strategy results in a factor of 1.2--2.1 speedup in response time compared to the Panda version originally developed for the IBM SP2, depending on the array..