Applying machine learning tools to drug R&D could usher in a new era of effective therapies targeting causative disease drivers, higher success rates in clinical trials and real-world tools to manage adherence, prevent adverse events and reduce total healthcare costs.
Biopharma companies have been working at mining big data since the genomics era began, but traditional data mining parses only one type of data at a time to yield correlations. For example, mining sequencing data can reveal a correlation between a patient population and a disease target or mutation. But it doesn’t reveal whether the mutation is a causative agent in pathogenesis, or its relationship to other factors that affect the safety and efficacy of targeted drugs in different patients.
In contrast, machine learning tools analyze multiple sets of data from disparate sources -- often in wildly different formats -- to sniff out connections among biology, genetics and phenotypic traits.
Using an iterative, learning approach, the technologies have the potential to identify previously unknown pathways, uncover which targets are causing rather than simply correlated with a disease, predict how patients will respond to therapies, and draw conclusions about whether a clinical trial will be successful or how a drug will perform in the real world.
These technologies are created from a series of neural networks -- a computational technique that can be used to discover and predict patterns among a set of inputs and corresponding outcomes.
They employ natural language processing to analyze unstructured data from the literature and physician notes in electronic medical records (EMRs), and combine this information with millions of structured data points to develop hypotheses that can then be tested in preclinical animal models, clinical trials or the real world.
“Our goal is to be at least fourfold more efficient.”
Adoption of these technologies remains modest but could increase over the next few years as early programs deliver successful clinical trials, approved drugs and better outcomes in the real world.
A compound developed for a cancer population selected using GNS Healthcare Inc.’s reverse engineering forward simulation (REFs) technology could be on the market within a few years. The company also has shown its Efficacy to Effectiveness (E2E) tool can accurately predict real-world performance of a new drug prior to its launch.
Many, many more experiments are ongoing via partnerships as drug developers and providers seek to understand whether and