Monday August 20, 2018

Genetic differences lead to failure of anti-HIV drug

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Washington, Genetic variations and not complying with treatment regimens may account for some failures of an anti-HIV drug to treat and prevent the infection.

335238FA-F319-41E1-A55AB94B28EB1600The drug Tenofovir, marketed as Viread, is processed differently according to cell locations, the study said.

This is to see if the drug is eventually marketed as a topical gel, it can work differently depending on whether it is applied to the vagina or the rectum.

“Our results suggest that in future, before prescribing tenofovir to a patient, a doctor could order genetic testing and know in advance if it works, and prescribe a different drug if it won’t,” said Namandje Bumpus, associate professor of medicine at the Johns Hopkins University School of Medicine.

In the study described in the journal EBio-Medicine, the team focused on a search for the human enzymes that convert tenofovir from its original form to an activated one that combats HIV.

The team “knocked out” genes for phosphate-adding enzymes one by one, then exposed the tissues’ cells to tenofovir.

They found that the enzyme called pyruvate kinase was different from that which performed the second activation step in the colorectal tissues.

The team sequenced the genes of 142 women who had participated in a clinical trial of tenofovir to look for genetic variations that might have affected the function of the enzyme.

They found 71 such variants, several of which a computer model predicted would make the enzyme ineffective.

Altogether, eight percent of the women had genetic variants that were likely to make them unable to convert tenofovir to its activated form.

“Tenofovir has been shown in trials to be very effective, so when it does not work, researchers and clinicians tend to assume the individual just was not taking the drug as directed,” Bumpus said.

(IANS)

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Computer Simulations can Predict HIV Spread

The researchers also plan to develop public health computational tools to help the agencies track the disease and allocate resources for targeted prevention campaigns

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HIV Aids
Computer simulations can predict the spread of HIV: Study. Flickr

Researchers have found that computer simulations can accurately predict the transmission of Human Immunodeficiency Virus (HIV) across populations, aiding in preventing the disease.

The study, published in the journal Nature Microbiology, found that the simulations were consistent with actual DNA data obtained from a global public HIV database.

“We looked for special genetic patterns that we had seen in the simulations, and we can confirm that these patterns also hold for real data covering the entire epidemic,” said lead author Thomas Leitner from the Los Alamos National Laboratory in the US.

HIV is particularly interesting to study in this manner as the virus mutates rapidly and constantly within each infected individual, the researcher said.

The changing “genetic signatures” of its code provide a path that can be followed in determining the origin and time frame of an infection, the study found.

HIV
For the study, the researchers used phylogenetic methods, examining evolutionary relationships in the virus’s genetic code to evaluate how HIV is transmitted. Pixabay

The rapid mutational capability of the virus is useful for the epidemiological sleuthing, but is also one of the features that makes it so difficult to tackle with a vaccine.

For the study, the researchers used phylogenetic methods, examining evolutionary relationships in the virus’s genetic code to evaluate how HIV is transmitted.

The research team found that certain phylogenetic “family tree” patterns correlated to the DNA data from 955 pairs of people, in which the transmitter and recipient of the virus were known.

Also Read: Every Three Minutes a Teenage Girl is Infected by HIV — UNICEF

“These HIV transmissions had known linkage based on epidemiological information such as partner studies, mother-to-child transmission, pairs identified by contact tracing, and criminal cases,” the researchers said.

The researchers also plan to develop public health computational tools to help the agencies track the disease and allocate resources for targeted prevention campaigns. (IANS)

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