biopharma

The CAR T-cell manufacturing process involves high potential for variability. Learn how sensitive ddPCR assays can be used to ensure safety and efficacy.

Whether you are part of a small academic lab or a multinational corporation, upgrading to a new quantitative PCR (qPCR) system can be both exciting and daunting. How will the new system perform compared to your current platform? Will your existing workflows translate smoothly? Read on to learn about our recent bridging study for users considering upgrading to the new CFX Opus Real-Time PCR System.

As more of the cell and gene therapies revolutionizing healthcare are approved for use, innovator companies usually get the credit. But many therapies would never make it to market without the expertise and capabilities of contract development and manufacturing organizations (CDMOs) operating behind the scenes. Learn more about how CDMOs enable new cell and gene therapies to come to market, and how Droplet Digital PCR can help.

Precision diagnostics for cancer. COVID-19 surveillance in wastewater. Quality control of cell and gene therapies. These were just a few of the cutting-edge applications on display at the Droplet Digital PCR (ddPCR) World 2021 symposium. Recapping the event in 5 takeaways, moderator Francisco Bizouarn shares how international experts are leveraging ddPCR technology to solve today’s scientific and public health problems.

While the FDA has approved Abecma, the first CAR-T cell therapy in its class, regulatory approval is just the beginning for cell and gene therapies. Such therapies’ safety and financial viability rest on a robust manufacturing and quality control program with the flexibility to scale up and out. Droplet Digital PCR is a sensitive biopharmaceutical quality control tool, offering absolute quantification of nucleic acids and other molecules in a sample.

Increasingly, biopharma research & development experimental data are siloed and stored in varying formats. This makes it difficult to search, retrieve and share data. Furthermore, these data are often not interoperable, making it impossible to compare. Data needs to be better managed to be more collaborative, shareable, and interoperable, in order to accelerate research and continue making breakthroughs. Data standardization aims to forge a path toward machine-readable and actionable data that can be found, accessed, and reused with minimal human intervention.

The Human Genome Project was completed nearly two decades ago, in 2003. The primary vision of this project was to produce a human reference genome, a representative example of the set of genes in one idealized individual human. However, the currently available reference genomes do not represent the genetic diversity found across different human populations. As such, any downstream products (drugs therapies, personalized medicine, etc.) derived from the human genome will not accurately represent the vast majority of the human population.

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.