Effective Drug Combinations To Treat Tuberculosis Discovered

Effective Drug Combinations To Treat Tuberculosis Discovered

As per WHO reports Worldwide, Tuberculosis is one of the Top 10 reasons for death globally, caused by a single infectious agent and ranks above HIV/AIDS. Millions of people suffer from TB every year. Tuberculosis results in the death of approx 1.6 million people per year out of 10 million patients who struggle with this potentially lethal yet curable disease.

Till date, people suffering from tuberculosis have to take medications for continuous six to eight months. Due to the extreme length of treatment, some patients do not stick with the treatment schedule or might develop adverse effects from drug toxicity. Some even develop resistance to the drugs and require a change in the drug regime, thus lengthening the treatment schedule further.

UCLA researchers have now reported the finding of a novel way to significantly reduce the duration of treatment by using an approach called “artificial intelligence-parabolic response surface.” It is a data analysis method that identifies drug combinations which will work synergistically – that is, individual drugs working together in a way that is more potent than the sum of their individual potencies.

The method was tested on mouse models of TB, via which the scientists identified three- or four-drug combinations among billions of possible

combinations of drugs and doses, that significantly reduces the duration of TB therapy. These drug combos are suitable for treating both drug-sensitive TB and most cases of drug-resistant TB. The insight of the research was published in PLOS One.

“If our findings are replicated in human studies, patients will be cured much faster, be more likely to adhere to the drug regimen, suffer less drug toxicity, and be less likely to develop drug-resistant TB,” said Dr. Marcus Horwitz, distinguished professor of medicine and microbiology, immunology & molecular genetics at the David Geffen School of Medicine at UCLA, and the study’s senior author.

Co-author – Chih-Ming Ho, a renowned research professor of mechanical and aerospace engineering at the UCLA Henry Samueli School of Engineering, developed the artificial intelligence-parabolic response surface platform which has been applied to infectious diseases, cancers, and organ transplants as well.

The researchers assessed 15 drugs to spot the best four-drug combinations. The two most powerful regimens included bedaquiline clofazimine, pyrazinamide, and either amoxicillin/clavulanate or delamanid. Two of those medication regimens achieved a 100 percent cure rate, in mice in three weeks, with no relapse. Another regimen treated the mice in five weeks. Both of the drug combinations included approved medications, Horwitz said.

Mice who were treated with a standard drug therapy all still had TB bacteria in their lungs following six weeks, and in companion studies, these mice took 16 to 20 months to achieve a 100 percent relapse-free cure.

The parabolic response surface regimens don’t include rifampin and isoniazid, the two drugs in the standard regimen to which people with TB develop resistance. They do not include drugs classified as fluoroquinolones and aminoglycosides, to which immunity is additionally developed by people with TB. This means that the reaction surface regimens are acceptable for treating the very drug-resistant cases of TB’s vast majority.

The following step will be to test the drug regimens in humans with tuberculosis that are immune to the conventional drugs used in the treatment of TB. The researchers also aim to expand the therapy platform to include TB drugs that are experimental to develop combinations that are even more potent.

The study was supported by a subgrant from Shanghai Jiao Tong University, a grantee of the Bill & Melinda Gates Foundation.

Additional study co-authors are Daniel Clemens, Bai-Yu Lee, Aleidy Silva, Barbara Jane Dillon, Saša Masleša-Galić and Susana Nava of UCLA; and Xianting Ding of Shanghai Jiao Tong University in China.

The above article has been adapted from the original article published by UCLA.

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