August 14, 2016 – The PubMed-article below illustrates how pharmacogenetics and/or pharmacogenomics-guided drug therapy (i.e., theragenomic medicine) has its place and impact on patients and healthcare systems in African countries when it comes to large disease burdens such as in HIV/AIDS, Tuberculosis (TB), and malaria.
Chaudhry M, Alessandrini M, Pepper MS
Appl Transl Genom 2016 Jun;9:3-5
The rate of mortality in developing countries due to communicable disease remains alarmingly high. The leading contributors to disease burden in these regions are the so-called “big three”, namely HIV/AIDS, TB, and malaria. The global prevalence of these diseases is over 250 million, and it should be noted that 71%, 28% and 88% of these
cases, respectively, occur in sub-Saharan Africa alone (World Health Organization). African countries are continuously struggling to contain infectious diseases, and there is a need for governments to commit resources not only for treatment, but also towards research and development aimed at innovative approaches.
Only 25–60% of patients respond positively to drug therapies (Squassina et al., 2010). This is due to variability in phenotypic and environmental factors, and up to 95% of these variations may be determined by genetic factors alone (Ross et al., 2012). A negative response ranges from the occurrence of adverse drug reactions (ADRs) to complete non-responsiveness to treatment. It has been reported that ADRs account for up to 18% of deaths in hospitalized patients in Norway and the UK (Mouton et al., 2015). In another UK based study, 6.5% of hospital admissions were reported to be due to ADRs (Pirmohamed et al., 2004). And in the USA, fatal ADRs occur in 0.32% of patients, ranking it among the six leading causes of death (Squassina et al., 2010). Given the increased mortality and cost associated with ADRs, they are regarded as major public health and economic problems worldwide. Pharmacogenomics is believed to be a key technology which could alleviate the costs associated with ADR-related hospitalizations and improve dosage optimization. It uses genetic information to predict the efficacy and safety of drugs, and aims specifically to improve treatment decision-making.
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