Algorithm ‘can detect illicit online pharmacies’

Researchers in the US say they have developed an algorithm that could spot rogue online pharmacies offering substandard, fake and illicit medicines more quickly.

The team from Penn State University have developed web analytics models that can be used to predict whether an online pharmacy is operating illegally, making it easier to spot the bad apples among the estimated 32,000-35,000 pharmacy website operating on the Internet at any one time.

That’s a task made much harder by rapid-fire opening and closing of rogue pharmacies, with those taken down by enforcement actions often springing up again almost immediately with a different URL.

The researchers designed the computer model to approach the problem of weeding out good online pharmacies from bad in much the same way that people make comparisons, according to Soundar Kumara, a professor at Penn State’s industrial engineering department.

“The essential question in this study is, how do you know what is good or bad – you create a baseline of what is good and then you compare that baseline with anything else you encounter, which normally tells you whether something is not good,” he said.

"This is how we recognise things that might be out of the norm. The same thing applies here. You look at a good online pharmacy and find out what the features are of that site and then you collect the features of other online pharmacies and do a comparison.”

Policy makers, government agencies, patient advocacy groups and drug manufacturers could use such a system to identify, monitor, curb illicit online pharmacies and educate consumers, according to the researchers.

The team writes in the Journal of Medical Internet Research that current tools to detect rogue pharmacies have limitations.

That includes the Canadian International Pharmacy Association and Pharmacychecker – which have been criticised for not classifying pharmacies correctly and are not recommended by the authors – and systems run by the National Association Board of Pharmacies (NABP) and Legitscript that are recommended by the FDA.

The latter options require customers to take the initiative and look up pharmacies, however, and according to the Alliance for Safe Online Pharmacies (ASOP) 95 per cent of the public are unaware these certification schemes exist.

According to Sowmyasri Muthupandi, a former research assistant in industrial engineering and now a data engineer at Facebook, the team looked at several attributes of online pharmacies but identified the relationships between the pharmacies and other sites as a critical attribute in determining whether the business was legitimate, or not.

"One novelty of the algorithm is that we focused mostly on websites that link to these particular pharmacies," said Muthupandi. "And among all the attributes we found that it's these referral websites that paint a clearer picture when it comes to classifying online pharmacies."

She added that if a pharmacy is mainly reached from referral websites that mostly link to or refer illicit pharmacies, then this pharmacy is more likely to be illicit.

“Given that this is a critical area of concern to patients’ health and the integrity of the drug supply chain, we hope this study will inspire additional efficient and effective prediction models or additional applications for the prediction models developed,” they conclude.