DiversityNursing Blog

By Darius Tahir

www.modernhealthcare.com 

In the fall of 2012, Nick Valilis was diagnosed with leukemia just as he was starting medical school. In treatment he found it difficult to remember to take his medications at the proper time and in the right order.

“He struggled handling the sheer complexity,” said Rahul Jain, Valilis' classmate at Duke University. “He went from no meds to 10 meds a day. How is an 85-year-old cancer patient supposed to handle that same regimen?” 

Since then, Jain, Valilis and a few other Duke classmates have formed a startup company called TowerView Health with the goal of making it easier for patients to manage their medication regimens. Jain is CEO of the company, which was incorporated last year; Valilis is chief medical officer. They are about to launch a clinical trial, in partnership with Independence Blue Cross and Penn Medicine in Philadelphia, to test whether their technological solution helps patients understand and comply with their drug regimens.

That could be an important innovation. Poor medication adherence is estimated to cause as much as $290 billion a year in higher U.S. medical costs, as well as a big chunk of medication-related hospital admissions.

TowerView has developed software and hardware that reminds patients and their clinicians about medication schedules, and warns them when a patient is falling off track.

Dr. Ron Brooks, senior medical director for clinical services at Independence Blue Cross, said he thinks TowerView's solution is a notable improvement over previous medication-adherence technology. “Most of the apps I've seen are reminder apps,” he said. “It might remind you to take a medication, but you have to input that you actually take it. There's no closing of the loop.” By contrast, TowerView automatically provides reminders and tracking, with the opportunity for clinician follow-up.  

Here's how TowerView's system works. When clinicians prescribe drugs and develop a medications schedule for a patient, the scrips and schedule are sent to a mail-order pharmacy that has partnered with TowerView. The pharmacy splits the medications into the scheduled dosages on a prescription-drug tray. The tray is labeled with the schedule and sent to the patient, who places the tray into an electronic pillbox, which senses when pills are taken out of each tray compartment. 

The pillbox sensors communicate with connected software through a cellular radio when patients have taken their pills and when it's time to remind them—either through a text message, phone call or the pillbox lighting up—that they've missed a dose. The system also compiles information for providers about the patient's history of missed doses, enabling the provider to personally follow up with the patient.

But some question whether tech solutions are the most effective way to improve medication adherence. A 2013 literature review in the Journal of the American Pharmacists Association identified nearly 160 medication-adherence apps and found poor-quality research evidence supporting their use.

Experts say it's not clear whether apps and devices can address the underlying reasons why patients don't comply with their drug regimens. For instance, patients simply might not like taking their drugs because of side effects or other issues. “I'd wager that improved adherence—and a range of other health benefits—are ultimately more likely to be achieved not by clever apps and wireless gadgets, but rather by an empathetic physician who understands, listens and is trusted by her patients,” Dr. David Shaywitz, chief medical officer at DNAnexus, a network for sharing genomic data, recently wrote.

Jain doesn't disagree. He notes that his firm's system empowers empathetic clinicians to provide better care. “This solution allows more of a communication element,” he said. “We'll be able to understand why patients don't take their meds.” 

That system soon will be put to the test in a randomized clinical trial. TowerView and Independence Blue Cross are enrolling 150 diabetic patients who are noncompliant with their medication regimens; half of those participants will receive usual care. The goal is to improve compliance by at least 10% over six months.

If it works, Jain and his company hope to sell the product to insurers and integrated healthcare providers working under risk-based contracts. The idea is that patients' improved adherence will reduce providers' hospitalization and other costs and boost their financial performance.

By Catharine Paddock PhD

The rise of drug-resistant bacteria - such as MRSA - is making it increasingly difficult to control even common infections like pneumonia or urinary tract infections with standard antibiotics. After repeated exposure, the bugs mutate into strains that are immune to the drugs that once killed them.

There is clearly a desperate need for new drugs to fight these superbugs. But there is also another option - to extend the useful life of a drug. Now, researchers have developed a computer algorithm that can help in this area.

Imagine the war against a superbug as a chess game, with each move that your opponent makes being a mutation in the superbug that makes it more drug-resistant. 

To stand a good chance of winning, it helps to anticipate your opponent's most likely counter-moves.

Now, a team of researchers - including members from Duke University in Durham, NC - has developed a computer algorithm that stands a good chance of beating a superbug at its own game.

The software - called OSPREY - predicts the most likely mutations that a bug develops in response to a new drug before the drug is even given to patients.

Writing in the Proceedings of the National Academy of Sciences, the team describes how they tested OSPREY with the superbug MRSA (methicillin-resistant Staphylococcus aureus). 

The researchers programmed the algorithm to identify the genetic changes that MRSA would have to undergo in order to become resistant to a promising new class of experimental drug. And when they exposed MRSA to the new drugs, they found some of the genetic changes the software had predicted actually arose.

"This gives us a window into the future to see what bacteria will do to evade drugs that we design before a drug is deployed," says author Bruce Donald, a professor of computer science and biochemistry at Duke.

The team hopes the approach they are developing will give drug designers a head start in the race against superbugs, as co-author and Duke graduate student Pablo Gainza-Cirauqui explains:

"If we can somehow predict how bacteria might respond to a particular drug ahead of time, we can change the drug, or plan for the next one, or rule out therapies that are unlikely to remain effective for long."

Resistant forms of Staphylococcus aureus now kill 11,000 people in the US every year - more than HIV. In 1975, around 2% of infections caused by the bacterium were resistant to treatment - rising to 29% in 1991 - and now the proportion is 55%.

Depending on the drug, it can take up to 20 years for resistant strains to emerge. Sometimes it only takes 1 year.

Ability to anticipate new mutations beats searching 'libraries' of known mutations

The team believes approaches like OSPREY beat the current method where scientists have to look up "libraries" of previously observed resistance mutations - an approach that is not necessarily satisfactory for predicting future mutations. Prof. Donald explains:

"With a new drug, there is always the possibility that the organism will develop different mutations that had never been seen before. This is what really worries physicians."

OSPREY - which stands for Open Source Protein REdesign for You - is based on a protein design algorithm. It identifies changes to DNA sequences in the bacteria that would enable the resulting protein to block the drug while still being able to work normally.

The team tested OSPREY with a new class of drugs called propargyl-linked antifolates that attack a bacterial enzyme called dihydrofolate reductase (DHFR), used for building DNA and other tasks. The drugs - still to be tested in humans - are showing promise as a new treatment for MRSA infections.

Using OSPREY, the team came up with a ranked list of possible mutations. They picked out four - none of which had been seen before.

One predicted mutation reduced drug effectiveness by 58%

When they treated MRSA with the new drugs, they found more than half of the bacteria that survived carried the mutation they predicted would give the organism the greatest amount of resistance: a tiny change in the bacterial DNA that reduced the effectiveness of the new drugs by 58%.

"The fact that we actually found the new predicted mutations in bacteria is very exciting," Prof. Donald says, adding that the approach could be expanded to anticipate the bug's responses more than one move ahead:

"We might even be able to coax a pathogen into developing mutations that enable it to evade one drug, but that then make it particularly susceptible to a second drug, like a one-two punch."

The team is now enhancing OSPREY to predict resistance mutations to drugs designed to treat E. coli and Enterococcus infections.

They believe OSPREY will be useful for predicting drug resistance in cancer, HIV, flu and other diseases where culturing resistant strains is harder than it is with bacteria.

Prof. Donald and colleagues are developing OSPREY in open source format so it is freely available for any researcher to use.

In September 2014, Medical News Today learned about a study that showed how an  old drug may lead to a potential new class of antibiotics . The study showed that lamotrigine - currently used as an anticonvulsant - can inhibit the assembly of ribosomes in bacteria.

Source: www.medicalnewstoday.com