HIV resistance testing in the USA – a model for the application of pharmacogenomics in the clinical setting

Pharmacogenomics of Viral Diseases

Viral diseases are leading cause of deaths worldwide as WHO report suggests that hepatitis A virus (HAV) infects more than 80 % of the population of many developing countries. Viral hepatitis B (HBV) affects an estimated 360 million people, whereas hepatitis C affects 123 million people worldwide, and last but not least, at current, India has an HIV/AIDS population of approximately 2.4 million people and more than 30 million in whole world and now it has become a reason for 1.8 million death globally; thus, millions of people still struggle for their lives.

The progress in medical science has made it possible in overcoming the various fatal diseases such as small pox, chicken pox, dengue, etc., but human immunodeficiency viruses, influenza, and hepatitis virus have renewed challenge surprisingly. The obstacles and challenges in therapy include existence of antibiotic resistance strains of common organisms due to overuse of antibiotics, lack of vaccines, adverse drug reaction, and last but not least the susceptibility concerns. Emergence of pharmacogenomics and pharmacogenetics has shown some promises to take challenges. The discovery of human genome project has opened new vistas to understand the behaviors of genetic makeup in development and progression of diseases and treatment in various viral diseases. Current and previous decade have been engaged in making repositories of polymorphisms (SNPs) of various genes including drug-metabolizing enzymes, receptors, inflammatory cells related with immunity, and antigen-presenting cells, along with the prediction of risks. The genetic makeup alone is most likely an adequate way to handle the therapeutic decision-making process for previous regimen failure. With the introduction of new antiviral therapeutic agents, a significant improvement in progression and overall survival has been achieved, but these drugs have shown several adverse responses in some individuals, so the success is not up to the expectations. Research and acquisition of new knowledge of pharmacogenomics may help in overcoming the prevailing burden of viral diseases. So it will definitely help in selecting the most effective therapeutic agents, effective doses, and drug response for the individuals. Thus, it will be able to transform the laboratory research into the clinical bench side and will also help in understanding the pathogenesis of viral diseases with drug action, so the patients will be managed more properly and finally become able to fulfill the promise of the future.

A national clinical decision support infrastructure to enable the widespread and consistent practice of genomic and personalized medicine

BACKGROUND

In recent years, the completion of the Human Genome Project and other rapid advances in genomics have led to increasing anticipation of an era of genomic and personalized medicine, in which an individual's health is optimized through the use of all available patient data, including data on the individual's genome and its downstream products. Genomic and personalized medicine could transform healthcare systems and catalyze significant reductions in morbidity, mortality, and overall healthcare costs.

DISCUSSION

Critical to the achievement of more efficient and effective healthcare enabled by genomics is the establishment of a robust, nationwide clinical decision support infrastructure that assists clinicians in their use of genomic assays to guide disease prevention, diagnosis, and therapy. Requisite components of this infrastructure include the standardized representation of genomic and non-genomic patient data across health information systems; centrally managed repositories of computer-processable medical knowledge; and standardized approaches for applying these knowledge resources against patient data to generate and deliver patient-specific care recommendations. Here, we provide recommendations for establishing a national decision support infrastructure for genomic and personalized medicine that fulfills these needs, leverages existing resources, and is aligned with the Roadmap for National Action on Clinical Decision Support commissioned by the U.S. Office of the National Coordinator for Health Information Technology. Critical to the establishment of this infrastructure will be strong leadership and substantial funding from the federal government.

SUMMARY

A national clinical decision support infrastructure will be required for reaping the full benefits of genomic and personalized medicine. Essential components of this infrastructure include standards for data representation; centrally managed knowledge repositories; and standardized approaches for leveraging these knowledge repositories to generate patient-specific care recommendations at the point of care.

Integrating genomics and neuromarkers for the era of brain-related personalized medicine

The harsh reality is that many medical treatments do not work as expected in a significant percentage of patients, and occasionally there are serious side effects. A new paradigm of personalized medicine is emerging, which proactively tailors treatment to each individual’s biological and psychological profile. The first proof-of-concept phase of personalized medicine has now been achieved. However, it has thus far focused on the use of genomic markers and on disorders of the body. The complexity of the brain is likely to require a shift from a single genetic marker focus to a more integrated approach in which additional brain-related information (neuromarkers) is taken into account. Codevelopment of genomic neuromarkers with new compounds in a personalized medicine approach will lead to increased drug R&D and treatment benefits. The emerging genomic neuromarker potential has begun to be incorporated into the template for the next version of the Diagnostic and Statistical Manual (DSM-V). The statistical power of large subject numbers in databases in general (and standardized databases in particular) provides an ideal source for elucidating the best genomic–neuromarker profiles (explaining most of the main-effects variance), which will empower a brain-related personalized medicine into mainstream clinical practice.

A Longitudinal Analysis of Healthcare Costs after Treatment Optimization following Genotypic Antiretroviral Resistance Testing: Does Resistance Testing pay off?

Objective

To assess the impact of antiretroviral therapy optimized by genotypic antiretroviral resistance testing (GRT) on healthcare costs over a 2-year period in patients after antiretroviral treatment failure.

Study design

Non-randomized, prospective, tertiary care, clinic-based study.

Patients

One-hundred and forty-two HIV patients enrolled in the ‘ZIEL’ study and the Swiss HIV Cohort Study who experienced virological treatment failure.

Methods

For all patients GRT was used to optimize the antiretroviral treatment regimen. All healthcare costs during 2 years following GRT were assessed using micro-costing. Costs were separated into ART medication costs and healthcare costs other than ART medication (that is, non-ART medication costs, in-patient costs and ambulatory [out-patient] costs). These cost estimates were then split into four consecutive 6-month periods (period 1–4) and the accumulated cost for each period was calculated. Univariate and multivariate regression modelling techniques for repeated measurements were applied to assess the changes of healthcare costs over time and factors associated with healthcare costs following GRT.

Results

Overall healthcare costs after GRT decreased over time and were significantly higher in period 1 (32%; 95% confidence interval [CI]: 18–47) compared with period 4. ART medication costs significantly increased by 1,017 (95% CI: 22–2,014) Swiss francs (CHF) from period 1–4, whereas healthcare costs other than ART medication costs decreased substantially by a factor of 3.1 (95% CI: 2.6–3.7) from period 1 to period 4. Factors mostly influencing healthcare costs following GRT were AIDS status, costs being 15% (95% CI: 6–24) higher in patients with AIDS compared with patients without AIDS, and baseline viral load, costs being 12% (95% CI: 6–17) higher in patients with each log increase in plasma RNA.

Conclusions

Optimized antiretroviral treatment regimens following GRT lead to a reduction of healthcare costs in patients with treatment failure over 2 years. Patients in a worse health state (that is, a positive AIDS status and high baseline viral load) will experience higher overall costs.