How an AI Solution can Design New Tuberculosis Drug Regimens

With a shortage of new tuberculosis drugs in the pipeline, a software tool from the University of Michigan can predict how current drugs - including unlikely candidates - can be combined in new ways to create more effective treatments.

"This could replace our traditional trial-and-error system for drug development that is comparatively slow and expensive," said Sriram Chandrasekaran, U-M assistant professor of biomedical engineering, who leads the research.

Dubbed INDIGO, short for INferring Drug Interactions using chemoGenomics and Orthology, the software tool has shown that the potency of tuberculosis drugs can be amplified when they are teamed with antipsychotics or antimalarials.

"This tool can accurately predict the activity of drug combinations, including synergy - where the activity of the combination is greater than the sum of the individual drugs," said Shuyi Ma, a research scientist at the University of Washington and a first author of the study. "It also accurately predicts antagonism between drugs, where the activity of the combination is lesser. In addition, it also identifies the genes that control these drug responses."

Among the combinations INDIGO identified as showing a strong likelihood of effectiveness against tuberculosis were: A five-drug combination of tuberculosis drugs Bedaquiline, Clofazimine, Rifampicin, Clarithromycin with the antimalarial drug P218. A four-drug combination of Bedaquiline, Clofazimine, Pretomanid and the antipsychotic drug Thioridazine. A combination of antibiotics Moxifloxacin, Spectinomycin--two drugs that are typically antagonistic but can be made highly synergistic by the addition of a third drug, Clofazimine.

All three groupings were in the top .01% of synergistic combinations identified by INDIGO.

"Successful combinations identified by INDIGO, when tested in a lab setting, showed synergy 88.8% of the time," Chandrasekaran said.

Tuberculosis kills 1.8 million people each year and is the world's deadliest bacterial infection. There are 28 drugs currently used to treat tuberculosis, and those can be combined into 24,000 three- or four-drug combinations. If a pair of new drugs is added to the mix, that increases potential combinations to 32,000.

These numbers make developing new treatment regimens time-consuming and expensive, the researchers say. At the same time, multidrug resistant strains are rapidly spreading.

At a time when new drugs are in short supply to deal with old-but-evolving diseases, this tool presents a new way to utilize medicine's current toolbox, they say. Answers may already be out there, and INDIGO's outside-the-box approach represents a faster way of finding them.

INDIGO utilizes a database of previously published research, broken down and quantified by the authors, along with detailed information on the properties of hundreds of drugs.

Shuyi Ma, Suraj Jaipalli, Jonah Larkins-Ford, Jenny Lohmiller, Bree B. Aldridge, David R. Sherman, Sriram Chandrasekaran.
Transcriptomic Signatures Predict Regulators of Drug Synergy and Clinical Regimen Efficacy against Tuberculosis.
mBio Nov 2019, 10 (6). doi: 10.1128/mBio.02627-19.

Most Popular Now

Unlocking the 10 Year Health Plan

The government's plan for the NHS is a huge document. Jane Stephenson, chief executive of SPARK TSL, argues the key to unlocking its digital ambitions is to consider what it...

Alcidion Grows Top Talent in the UK, wit…

Alcidion has today announced the addition of three new appointments to their UK-based team, with one internal promotion and two external recruits. Dr Paul Deffley has been announced as the...

AI can Find Cancer Pathologists Miss

Men assessed as healthy after a pathologist analyses their tissue sample may still have an early form of prostate cancer. Using AI, researchers at Uppsala University have been able to...

AI, Full Automation could Expand Artific…

Automated insulin delivery (AID) systems such as the UVA Health-developed artificial pancreas could help more type 1 diabetes patients if the devices become fully automated, according to a new review...

How AI could Speed the Development of RN…

Using artificial intelligence (AI), MIT researchers have come up with a new way to design nanoparticles that can more efficiently deliver RNA vaccines and other types of RNA therapies. After training...

MIT Researchers Use Generative AI to Des…

With help from artificial intelligence, MIT researchers have designed novel antibiotics that can combat two hard-to-treat infections: drug-resistant Neisseria gonorrhoeae and multi-drug-resistant Staphylococcus aureus (MRSA). Using generative AI algorithms, the research...

AI Hybrid Strategy Improves Mammogram In…

A hybrid reading strategy for screening mammography, developed by Dutch researchers and deployed retrospectively to more than 40,000 exams, reduced radiologist workload by 38% without changing recall or cancer detection...

Penn Developed AI Tools and Datasets Hel…

Doctors treating kidney disease have long depended on trial-and-error to find the best therapies for individual patients. Now, new artificial intelligence (AI) tools developed by researchers in the Perelman School...

New Training Year Starts at Siemens Heal…

In September, 197 school graduates will start their vocational training or dual studies in Germany at Siemens Healthineers. 117 apprentices and 80 dual students will begin their careers at Siemens...

Are You Eligible for a Clinical Trial? C…

A new study in the academic journal Machine Learning: Health discovers that ChatGPT can accelerate patient screening for clinical trials, showing promise in reducing delays and improving trial success rates. Researchers...

Global Study Reveals How Patients View M…

How physicians feel about artificial intelligence (AI) in medicine has been studied many times. But what do patients think? A team led by researchers at the Technical University of Munich...

New AI Tool Addresses Accuracy and Fairn…

A team of researchers at the Icahn School of Medicine at Mount Sinai has developed a new method to identify and reduce biases in datasets used to train machine-learning algorithms...