Resource Modelling: The Missing Piece of the HTA Jigsaw?

Within health technology assessment (HTA), cost-effectiveness analysis and budget impact analyses have been broadly accepted as important components of decision making. However, whilst they address efficiency and affordability, the issue of implementation and feasibility has been largely ignored. HTA commonly takes place within a deliberative framework that captures issues of implementation and feasibility in a qualitative manner. We argue that only through a formal quantitative assessment of resource constraints can these issues be fully addressed. This paper argues the need for resource modelling to be considered explicitly in HTA. First, economic evaluation and budget impact models are described along with their limitations in evaluating feasibility. Next, resource modelling is defined and its usefulness is described along with examples of resource modelling from the literature. Then, the important issues that need to be considered when undertaking resource modelling are described before setting out recommendations for the use of resource modelling in HTA

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Preclinical Evaluation of Genomic-Based Therapies in Pancreatic Cancer and Glioblastoma

INTRODUCTION: The focus of this study is the testing of biomarker-driven analytical methods to identify targeted therapies in pancreatic cancer and glioblastoma, which are highly invasive and metastatic cancers with poor outcomes and few treatment options. The objective was to make treatment predictions based on the molecular signatures of pancreatic cancer and glioblastoma samples, then to evaluate the efficacy of these therapies using preclinical models. METHODS AND MATERIALS: XenoBase Bio-Integration Suite (XB-BIS) in an informatics platform for the analysis of molecular data using Personalized Medicine (PMED) algorithms. PMED applies four independent methods (Drug Target Expression, Connectivity Map, Parametric Gene Set Enrichment, and GeneGo Network Topological Enrichment Analysis) to a genomic dataset to identify targeted therapies. Affymetrix data was collected from panels of pancreatic cancer cell lines and human glioblastoma specimens and analyzed in XB-BIS to predict therapies, which were evaluated in vivo. RESULTS: Treatment of mice with subcutaneous pancreatic tumors with Chlorpromazine, predicted by CMAP, resulted in a decrease in tumor volume and extended survival compared to control animals. Predictive algorithms identified BCNU, Doxorubicin, and Marimastat as potential treatments for glioblastoma. Combination treatment of mice implanted intracranially with U251 glioblastoma cells showed extended survival compared to control mice and similar survival to standard-of-care treatment, Temozolomide. CONCLUSIONS: We have demonstrated efficacy of therapies identified by the PMED approach in relevant models of pancreatic cancer and glioblastoma. While further investigation is needed, these therapies could prove to be a great resource against two devastating human diseases