Drugs that are taken orally need to pass through the lining of the gastrointestinal (GI) to be taken up. Transporter proteins found on cells that line the GI tract help with this process, but for many drugs scientists don’t know exactly which transporters are involved. If two oral drugs that rely on the same transporter are taken together, the absorption of each could be affected by the other. Identifying which transporters are used by specific drugs could help to indicate which drugs should not be prescribed together, and so feasibly lead to improved treatment for patients.
Researchers at MIT, Brigham and Women’s Hospital, and Duke University have now developed a multipronged strategy to identify the transporters that are used by different drugs. A study detailing their approach, which makes use of both tissue models and machine-learning algorithms, revealed that a commonly prescribed antibiotic and a blood thinner can interfere with each other.
Learning more about which transporters help drugs pass through the digestive tract could also help drug developers improve the absorbability of new drugs by adding excipients that enhance their interactions with transporters. “One of the challenges in modeling absorption is that drugs are subject to different transporters,” said research lead Giovanni Traverso, PhD, an associate professor of mechanical engineering at MIT, a gastroenterologist at Brigham and Women’s Hospital. “This study is all about how we can model those interactions, which could help us make drugs safer and more efficacious, and predict potential toxicities that may have been difficult to predict until now.”
Senior author Traverso and first authors, MIT postdocs Yunhua Shi, PhD, and Daniel Reker, PhD, reported on their findings in Nature Biomedical Engineering, in a paper titled “Screening oral drugs for their interactions with the intestinal transportome via porcine tissue explants and machine learning.” In their report the team commented, “Approaches such as this show that combinations of technologies including machine learning and tissue engineering can accelerate drug and formulation development by prioritizing drug candidates and formulations while mitigating the risks of the development of drug resistance and of unexpected drug–drug interactions.”
Drug transporters are membrane proteins that have a key impact on the pharmacokinetics, biodistribution and efficacy of drugs, the authors wrote. “It is currently under-recognized that drugs are substrates for multiple drug transporters, leading to complex transporter–drug interaction patterns that can drastically reduce bioavailability, increase the risk of drug resistance and exponentially increase the number of drug–drug interactions …A major focus in drug discovery and development is to understand the transportome and the interaction between drugs and their transporters, specifically in the context of their role in determining intestinal absorption of orally administered medications.”
Previous studies have identified several transporters in the GI tract that help drugs pass through the intestinal lining. The team focused their study on three: BCRP, MRP2, and PgP. “We concentrated our machine-learning efforts on the three efflux transporters P-gp, BCRP and MRP2 given their significance for clinical drug transport as supported by having the greatest number of annotated substrates,” the investigators explained.
To carry out their study, Traverso and colleagues adapted a tissue model they had developed in 2020 to measure a given drug’s absorbability. This experimental setup, based on pig intestinal tissue grown in the laboratory, can be used to systematically expose tissue to different drug formulations and measure how well they are absorbed. “Our system uses intact, ex vivo porcine tissue to model intestinal drug transport in a physiologically relevant context with similar cellular structure and protein expression to what would be found in humans,” the investigators noted.
To study the role of individual transporters within the tissue, the researchers generated small interfering RNAs (siRNA) to knock down the expression of each transporter. “To establish a drug transporter model using porcine small intestine tissue, we developed a small interfering RNA (siRNA) knock-down protocol for each transporter in our ex vivo culture system,” they continued. In each section of tissue, they knocked down different combinations of transporters, which enabled them to study how each transporter interacts with many different drugs. “There are a few roads that drugs can take through tissue, but you don’t know which road. We can close the roads separately to figure out, if we close this road, does the drug still go through? If the answer is yes, then it’s not using that road,” Traverso said.
Using this system the researchers tested 23 commonly used drugs, and were able to identify transporters used by each of the compounds. The team then trained a machine learning model on that data, as well as on data from several drug databases. The model learned to make predictions of which drugs would interact with which transporters, based on similarities between the chemical structures of the drugs.
Using this model, the researchers analyzed a new set of 28 currently used drugs, as well as 1,595 experimental small molecule drugs. This screen yielded nearly two million predictions of potential drug interactions. Among them was the prediction that the antibiotic doxycycline could interact with warfarin, a commonly prescribed blood thinner. Doxycycline was also predicted to interact with digoxin, which is used to treat heart failure, levetiracetam, an antiseizure medication, and tacrolimus, an immunosuppressant. “We chose doxycycline as the primary test compound given its broad clinical use and that we had here identified and in vivo validated as a novel BCRP and MRP2 substrate,” they commented. “We then manually selected four candidate drugs that are known substrates of BCRP and MRP2 to study whether they could potentially interact with doxycycline.”
To test their predictions on interactions between doxycycline and the four drugs, the researchers looked at data from about 50 patients who had been taking either warfarin, digoxin or levetiracetam when they were prescribed doxycycline. The data, which came from a patient database at Massachusetts General Hospital and Brigham and Women’s Hospital, showed that when doxycycline was given to patients already taking warfarin, the level of warfarin in the patients’ bloodstream went up, then went back down again after they stopped taking doxycycline. That data also confirmed the model’s predictions that the absorption of doxycycline is affected by digoxin, levetiracetam, and tacrolimus. “We found a significant increase of all four tested drugs when co-administrated with doxycycline (P = 0.0001, 0.0413, 0.0004 and 0.0152), while levels returned to baseline after completion of doxycycline therapy,” they commented. “Although we cannot exclude that other unknown enzymes may play a role in this drug–drug interaction, we believe that the clinical data combined with our ex vivo data provide good evidence for transporter-driven interactions.”
Only one of those drugs, tacrolimus, had been previously suspected to interact with doxycycline. As the authors noted, “… while for tacrolimus there have been previous suggestions of potential interactions with doxycycline without a clearly identified mechanism, the other three (warfarin, digoxin, levetiracetam) represent currently unknown interactions with doxycycline … these cases highlight the potential of our system to identify clinically relevant and previously unknown drug–drug interactions with immediate implications for clinical practice.”
Traverso further stated, “These are drugs that are commonly used, and we are the first to predict this interaction using this accelerated in silico and in vitro model. This kind of approach gives you the ability to understand the potential safety implications of giving these drugs together.”
In addition to identifying potential interactions between drugs that are already in use, the same approach could also be applied to drugs now in development. Drug developers could feasibly use the technology to help tune the formulation of new drug molecules so as to prevent interactions with other drugs, or improve their absorbability. In their paper the team wrote, “Our data suggest that complex transport profiles involving more than one transporter are in fact common among both approved and investigational drugs (compare Figs. 1a,b and 3d). Therefore, understanding the effects of multiple drug transporters on a single compound and characterizing the transportome is critical for preclinical drug development and clinical decision making.” In 2018 Traverso and MIT colleagues co-founded Vivtex, a biotech company focused on develop new oral drug delivery systems, which is now pursuing that kind of drug tuning.
