Tuberculosis Treatment

Researchers use AI to improve tuberculosis treatment

Business Healthcare Technology

If there were 20 new compounds that showed effectiveness to treat a disease, such as tuberculosis that affects `10 million people worldwide and is responsible for 1.5 million deaths each year. For effective treatment, patients will require three or four drugs in combination for months or even years because tuberculosis bacteria behaves differently in different environments in cells, and in some cases have developed drug resistance. Twenty compounds in three and four drug combinations offer almost 6,000 possible combinations.

A recent study published in Cell Reports Medicine involved using data from large studies that contained laboratory values of two-drug combinations of `12 anti-tuberculosis drugs. Employing mathematical model, the team found a set of instructions that drug pairs need to meet to possibly serve as good treatments as part of three-and four-drug combinations.

The use of drug pairs cuts down significantly from three-and four-drug combination due to the volume of testing that is required before moving a drug combination for further study.

With the help of design rules that are established and tested, one drug pair can be substituted with another and know with a high degree of confidence that the drug pair should work in association with other drug pair to destroy in the rodent model.

The selection process that is developed is more streamlined and accurate to predict success than prior processes, which involved fewer combinations.

Previously, the corresponding author of the paper developed and uses diagonal measurement of n-way interactions (DiaMOND), a method that systematically examines pairwise and high-order interactions of drug combinations to detect shorter, more efficient treatment order for TB and possibly other bacterial diseases.

Edward Turner

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