In an effort to increase the return on investment of research and development, pharmaceutical companies are constantly looking for ways to reduce costs. One way to do this is to incorporate technology and minimize human interaction. As a result, scientists will have more time to focus on the design and analysis of experiments. How will the Lab of the Future reflect these changes?
Drug discovery and development are both time and resource intensive since a lot of different compounds need to be screened across a lot of different targets. See how machine learning models can be used to predict screening results and how active learning algorithms can be used to efficiently select which experiments to perform in order to build those models.
Over the past several decades, research informatics (RIS) environments within BioPharma organizations have evolved dramatically, increasing the demand for simple and easy data integration and visualization. Learn about the tools available to address this need and what the future could hold.
Many BioPharma organizations are moving to the cloud in order to improve the performance and speed, cost, physical and personnel resources, security, back up, and sharing/collaboration associated with large, complex datasets. Learn about the different strategies used in migrating to the cloud.
The advent of CRISPR as a gene editing tool appears to have revolutionized drug discovery and development in a very short time. CRISPR-Cas9 technology is also being utilized to target HIV, to mutate or cut out the provirus. Read about what’s new in potential treatments for HIV and the challenges facing the industry.