Improving Manufacturing Processes with Essilor
See how one manufacturing company, Essilor, uses Dataiku to harness large, heterogeneous datasets and develop a robust predictive maintenance solution.
Learn MoreThe pharmaceutical industry is at a crossroads. After a century of rapid progress in the development of new medications, the discovery of new drugs has slowed down significantly and the process of developing new pharmaceuticals has become more expensive. At the same time, the regulatory environment has become more challenging, demanding far more extensive testing before drugs can go to market.
There is major incentive for drug companies to reduce R&D spending, both to free up funds for additional ventures as well as to be able to offer lower prices for their products. There are a number of ways in which sophisticated data science can help researchers save money and time in R&D, supply chain management, and manufacturing.
Identifying patients for clinical trials. Data science, machine learning, and AI can help introduce efficiencies to the clinical trial process in two ways. First, by more quickly and precisely identifying patients who would be a good fit for a particular trial via advanced analysis of medical records through natural language processing (NLP) or by exploring geographically- and symptom-distinct patients at scale. Secondly, these techniques can examine the interactions of potential trial members’ specific biomarkers and current medication to predict the drug’s interactions and side effects, avoiding potential complications.
Identifying compounds. The estimated cost for drug development by U.S. biopharmaceutical companies is nearly $ 1 billion per drug. Instead of throwing darts at the wall and hoping to land on an eventual hit—an expensive and inefficient process—pharmaceutical companies can leverage machine learning techniques to not only cull through literature and journal publications using (again) NLP but also to pre-screen for the most effective potential compounds to prioritize their time.
Pfizer’s researchers use natural language processing to analyze over a million articles in medical journals, 20 million abstracts of journal articles, and 4 million patents.
Medium, interview with Peter Henstock, Machine Learning & AI Technical Lead, Pfizer
The Future of Computational Biochemistry. Computational biochemistry allows drug-makers to cut out a significant portion of the test tube experiments. Instead, a computer simulates the protein and tests all of its atomic interactions. That analysis will yield a far narrower list of “leads” that researchers can take to the next stage of testing.
Recent experimental techniques (including parallel synthesis of drug-like compounds) has drastically increased the amount of available data for deep learning models. This makes such models adept at bioactivity and synthesis predictions, in addition to molecular design and biological image analysis. Deep learning truly has revolutionized drug discovery, as it can factor in everything from possible toxicity risks to new applications for existing drugs, which subsequently are saved the expense of a Stage 1 trial.
Supply chain and Manufacturing. Identifying the most efficient supply system by optimizing and automating steps of production will become even more important as drugs are increasingly customized to small numbers of patients with certain genetic profiles. Data science, machine learning, and AI techniques allow pharmaceutical companies to better forecast demand and to distribute products more efficiently. When it comes to manufacturing, pharmaceuticals can harness machine learning to control rising equipment maintenance costs and pave the way for self-maintenance through artificial intelligence (AI). Predictive maintenance is widely considered to be the obvious next step for any business with high-capital assets.
Dataiku helps streamline the pharmaceutical R&D process and enables robust NLP for clinical trial patient selection and identifying compounds. The platform offers a central, collaborative environment for the major steps including:
Additionally, Dataiku helps pharmaceutical organizations nurture a culture that is receptive to data and willing to put in the process change to incorporate its value into workflows:
Deep learning's main advantage is that it can handle massive amounts of data — particularly unstructured — well. Getting started doesn't have to be hard by leveraging publicly available pre-trained deep learning models to begin.
Read moreSee how one manufacturing company, Essilor, uses Dataiku to harness large, heterogeneous datasets and develop a robust predictive maintenance solution.
Learn MoreThe evolution of AI, machine learning, and data science have been an increasingly integral part of the transformation of many industries, and marketing is no exception.
Learn MoreDataiku lets you interactively clean & enrich your data with a simple visual interface. Use over 90 built-in visual processors for code-free data wrangling.
Learn MoreGet instant insights from your data whatever their size or format, and share with your teams.
Learn More