{"id":79,"date":"2020-08-17T07:54:22","date_gmt":"2020-08-17T07:54:22","guid":{"rendered":"https:\/\/machine-learning.webcloning.com\/2020\/08\/17\/plusvitech-uses-machine-learning-for-drug-treatment-to-win-the-euvsvirus-hackathon\/"},"modified":"2020-08-17T07:54:22","modified_gmt":"2020-08-17T07:54:22","slug":"plusvitech-uses-machine-learning-for-drug-treatment-to-win-the-euvsvirus-hackathon","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2020\/08\/17\/plusvitech-uses-machine-learning-for-drug-treatment-to-win-the-euvsvirus-hackathon\/","title":{"rendered":"PlusVitech Uses Machine Learning for Drug Treatment to Win the #EUvsVirus Hackathon"},"content":{"rendered":"
\n

This guest post is originally authored by Fran Guillen<\/a><\/strong>, CBO of PlusVitech, and Vicente Salinas<\/a><\/strong>, CEO of Plusvitech.<\/p>\n

PlusVitech<\/a><\/strong> is a Spanish company that was founded in 2013 with the key mission to improve people\u2019s quality of life by finding solutions for high-impact diseases such as cancer. In particular, our strategy has always been to search for treatments that already exist in the market, which can be used for cancer. This strategy has many advantages: the cost of development is much lower than that of new drugs, candidate drugs have already been shown to be safe when administered to humans, and they can be immediately available for the new indication after approval. This strategy is called repositioning<\/strong> in the pharma world and it has already taken place with <\/span>Viagra<\/a><\/strong> or <\/span>Propecia<\/a><\/strong>, among others. In our case, we have very promising evidence with complete remissions in different types of cancer, even in more advanced stages.<\/span><\/p>\n

However, this past March, when the COVID-19 epidemic broke out, we realized that some of the solutions we had for cancer could also be useful in treating COVID-19 infections in some people. It is not the virus itself that causes fatalities, but rather the reactions that take place inside our body. After all, the mechanisms activated by the human body for different situations are very similar. In particular, for COVID-19, there is a cascaded lung inflammation, very similar to an allergic reaction, or to the pulmonary inflammation that occurs in lung cancer. Often, it\u2019s this inflammation that generates severe lung damage and pneumonia that leads to death from Coronavirus. Therefore, according to our thesis, if we were able to solve the lung inflammation, we could also stop COVID-19 deaths and any of its mutations, which is what we have patented worldwide.<\/span><\/p>\n

\"PlusVitech<\/p>\n

About a month after our discovery, the European Commission, in collaboration with the EU Member States, held the <\/span>Pan-European Hackathon #EUvsVirus<\/a><\/strong> to identify effective proposals towards curing the adverse effects of the pandemic. We decided to attend this call from the EU with <\/span>our PVT-COVID project<\/a><\/strong>.<\/span><\/p>\n

The weekend was quite intense. For starters, our PlusVitech team and two more people from the hackathon joined the project disinterestedly, as well as various mentors and experts. We developed the business model based on licensing the patent to pharma companies who already produce this type of drug to ensure its availability immediately after obtaining approval by the regulatory agencies of each country. Also, we worked diligently on defining the necessary clinical trial protocol to approve the treatment for COVID-19 and the contacts to be made with hospitals and the Spanish Drug Agency<\/a><\/strong>. <\/span><\/p>\n

However, in the process of designing the protocol, we found that each COVID-19 patient is different from others which means the patient is in a different clinical state. This requires different treatments to address different individual needs. Some of these patients are at home, others hospitalized, and the most severe ones in the ICU, with various levels of oxygen saturation, cough, or fever. This scenario is quite challenging for health professionals, as the treatments need to be personalized in order to be effective. For instance, there are patients so severe that they are intubated thus they can\u2019t take medication orally. This simple idea made us realize that we can do better than just having a single treatment. Instead, we focused on personalized treatments with factors such as dosage, time, and even in combinations with other treatments to play with. Taking this idea into account, ideally, a hospital doctor could enter the patient\u2019s data into an online system and obtain the most appropriate treatment for him in real-time.<\/span><\/p>\n

Preparing such a system, even if it was only a prototype, was too much in the few hours that remained, as we only had a few more hours before the hackathon was to be over at 9:00 AM on Monday morning. At night, while sleeping, Fran Guillen, CBO of PlusVitech,<\/span> had a dream about using BigML to solve this problem! So he got up at 5:00 AM, opened a free account in BigML, and, in a few hours, prepared an initial table in Google Sheets with characteristics and clinical states of patients, crossing it with preliminary results that we have from our treatment. <\/span><\/p>\n

\n\"\"<\/p>\n

Sample of the table of anonymous patient data imported directly from Google Sheets.<\/p>\n<\/div>\n

Fran has almost no background in Machine Learning, but he was able to upload it to BigML, generating a dataset with the 1-click option and, henceforth, a Model, again with the 1-click option. <\/span><\/p>\n

\n\"\"<\/p>\n

One of the COVID-19 treatment models that we generated with the 1-click option.<\/p>\n<\/div>\n

When the rest of the team woke up a few hours later they were amazed! The system allowed us to generate predictions of what would be the best drug treatment to apply for different COVID-19 patient cases<\/strong>, as it considers each of their health characteristics.<\/span><\/p>\n

\n\"\"<\/p>\n

Prediction from the COVID-19 model generated with the 1-click option.<\/p>\n<\/div>\n

Just a few hours later, and after the sleepless night on Sunday, we finally presented the project a couple of hours before the end of the hackathon term, including in the pitch deck the explanation of the work done in the predictive system created using the BigML Dashboard.\u00a0<\/span><\/p>\n