Vangelis Vergetis, Executive Director
Drug development is not for the faint-hearted. It typically takes more than 10 years and hundreds of millions of dollars for a drug to reach patients. Further, the failure rate of drugs in clinical trials is astonishing – for example, less than 1 in 10 cancer drugs eventually make it to patients. As pharma and biotech companies are pinning their hopes on developing promising technological breakthroughs, their ability to do better than 1 in 10 is critical. While clinical trials are an answer to translate scientific discoveries into clinical therapies and aptly evaluate the effects of a potential drug, their outcome is typically uncertain. As a result, and given the fairly recent availability of data, scientists and researchers have collectively concluded on the need to integrate machine learning (ML) to enhance and enrich the clinical trial process. One company making great strides in this industry using ML is Intelligencia.ai, a data science company that works with its clients in order to properly assess and mitigate every kind of risk associated with drug development. “Our ultimate objective is to change how R&D leaders within pharma and biotech companies think about the risks of drug development and how they can make choices differently using ML,” says Vangelis Vergetis, Executive Director of Intelligencia.
To achieve this feat, Intelligencia has developed a cloud-based AI platform that focuses on assessing the risk of clinical trials and interpreting the multitude of factors that contribute to the risk. “For AI to succeed in this space, it needs to do much better than being a black box that spits out a number,” says Dimitrios Skaltsas, Executive Director of Intelligencia. He continues: “This is why we’ve trained models that not only assess the risk but provide full transparency behind the most important risk factors.” This platform offers a suite of functionalities that provide data clarity and ML-fueled predictions to enable critical decision making within the pre-clinical and clinical space.
Backed by a robust interdisciplinary team of software engineers, AI practitioners, scientists, and drug developers, Intelligencia is capable of bringing together and interpreting disparate data across the life sciences industry. In addition, the company has also extensively curated proprietary data sets that power its predictive models. “AI is only as good as the data that power it. This is why we’ve been very particular in making sure that our platform includes both a large amount of relevant data, but also high-quality data.
Dimitrios Skaltsas, Executive Director
You don’t want to be in a ‘garbage in garbage out’ situation,” says Skaltsas.
Our ultimate objective is to change how pharma and biotech companies think about the risks of drug development, and how they can make choices differently using ML
Intelligencia leverages a proprietary methodology to deliver its solution to the researchers. This platform pulls information from hundreds of characteristics that are fed into various algorithms to pick up trends in order to answer the questions of drug developers. Whether it be to assist in prioritization of pre-clinical assets to take forward to the clinic, help to make critical go/ no-go decisions on development or to evaluate potential compounds to in-license, Intelligencia provides an objective outside view deemed valuable by pharma and biotech today. As the industry continues to seek ways to have an edge with their individual pipelines and develop much needed first-in-class treatments for patients, Intelligencia is also paving the way to make that a reality. “Our ‘hotspots of innovation’ functionality helps to identify high-value and high-quality mechanisms and assets as early as possible, including the optimal indications for those assets,” says Skaltsas.
As an implementation highlight, a particular client was investigating a promising drug for breast cancer. They had almost completed an initial clinical trial (Phase 1), but a few questions held them back from continuing to Phase 2. For instance, how does our program compare to other similar drugs in development? To what degree should we prioritize this program versus others in our portfolio? Working with Intelligencia, the company gained confidence in the quality of their program versus the competition and was also able to make select changes to their program and Phase 2 trial that increased the drug’s chances of approval by more than 40%.
In a world where diseases will impact nearly every individual, Intelligencia works to make a difference. “Although we have some of the top ten or twenty pharma and biotech companies in the world working with us, we continuously seek for more spread and acceptance,” concludes Skaltsas.