Pharmacogenetics and personal genomes

Determining the adjusted initial treatment dose of warfarin anticoagulant medicine using kernel-based support vector regression

BACKGROUND AND OBJECTIVE

A novel research field in bioinformatics is pharmacogenomics and the corresponding applications of artificial intelligence tools. Pharmacogenomics is the study of the relationship between genotype and responses to medical measures such as drug use. One of the most effective drugs is warfarin anticoagulant, but determining its initial treatment dose is challenging. Mistakes in the determination of the initial treatment dose can result directly in patient death.

METHODS

Some of the most successful techniques for estimating the initial treatment dose are kernel-based methods. However, all the available studies use pre-defined and constant kernels that might not necessarily address the problem's intended requirements. The present study seeks to define and present a new computational kernel extracted from a data set. This process aims to utilize all the data-related statistical features to generate a dose determination tool proportional to the data set with minimum error rate. The kernel-based version of the least square support vector regression estimator was defined. Through this method, a more appropriate approach was proposed for predicting the adjusted dose of warfarin.

RESULTS AND CONCLUSION

This paper benefits from the International Warfarin Pharmacogenomics Consortium (IWPC) Database. The results obtained in this study demonstrate that the support vector regression with the proposed new kernel can successfully estimate the ideal dosage of warfarin for approximately 68% of patients.

A guide to the current Web-based resources in pharmacogenomics

Human genomics research has produced vast amounts of data that can be applied to or used to inform pharmacogenomic studies. The Internet is an extremely useful resource for pharmacogenomics as many Web sites provide access to data from genomic and clinical studies or host tools which can be used to interpret findings or generate hypotheses. Human genetic variation can now easily be explored or visualized through genome browsers and Web-based repositories which store the details of millions of human germ-line and somatic genetic variants. Gene expression data from many different tissue and cell types are available through Web-based repositories, and human genetic variants that associate with mRNA expression can be identified using Web data portals. Pharmacogenetic associations can be explored through publically available data repositories and the functionality of genetic variants predicted through Web-based bioinformatic tools. Furthermore, resources relating to currently used genetic tests are available online. Large clinical and population studies, many linked to medical records, can be queried for the availability of biospecimens or data. In the future, as the amount of genomic and associated clinical data increases, there is little doubt that Web-based resources will continue to evolve and overcome barriers hindering their efficient use, leading to systems-based approaches to pharmacogenomics.

A new frontier in personalized cancer therapy: mapping molecular changes

Mutations in the genome of a normal cell can affect the function of its many genes and pathways. These alterations could eventually transform the cell from a normal to a malignant state by allowing an uncontrolled proliferation of the cell and formation of a cancer tumor. Each tumor in an individual patient can have hundreds of mutated genes and perturbed pathways. Cancers clinically presenting as the same type or subtype could potentially be very different at the molecular level and thus behave differently in response to therapy. The challenge is to distinguish the key mutations driving the cancer from the background of mutational noise and find ways to effectively target them. The promise is that such a molecular approach to classifying cancer will lead to better diagnostic, prognostic and personalized treatment strategies. This article provides an overview of advances in the molecular characterization of cancers and their applications in therapy.

Genetics and Intervention Research

As part of this special section on genetics and behavioral intervention, we discuss the articles by McGue, Osler, and Christensen (2010, this issue) and by Davey Smith (2010, this issue). In the second half of the present article, we consider the integration of genetics and intervention research more broadly. The two articles describe ways to use genetic controls to infer causation from correlational ("observational") data without intervention. McGue et al. discuss the use of twins discordant for exposure, which is a variant of the cotwin control method. This method can show that the link between an exposure and outcome is not entirely mediated genetically. Davey Smith discusses a method called Mendelian randomization that uses DNA to draw causal inferences without the need for experimental intervention. Despite the possibilities for using genetic controls to infer causation from correlational data in order to attenuate the need for intervention studies, we are most excited about the opportunities for integrating genetics and intervention research, especially as new DNA technologies make it possible to incorporate genetics in any intervention research.