Addressing analytical control challenges for different plasmid DNA products

There are approximately 7,000 distinct rare diseases affecting 350 million people worldwide, approximately 80 percent of which are caused by faulty genes. The US Food and Drug Administration (FDA) has more than 700 active Investigational New Drug Applications (INDs) for gene and cell therapies and in 2017, the FDA approved two cell-based gene therapies and it is anticipated that gene therapy will become a mainstay treatment for many rare diseases.

Gain an understanding of how to help maintain the proper ethical pain management of patients. We are practicing in the midst of a national crisis in which every day over 130 people in the US die of overdosing from narcotic prescriptions overdose, heroin, and other related drugs. The pain was described as the "5th vital sign" by some drug companies in the 1990s and stated that patients would not be addicted to pain killers and providers began to overprescribe them in order to adequately treat pain. Approximately one-third of the chronic opioid user for none malignant pains misuse them. The incidence of neonatal abstinence syndrome has been on the rise due to widespread opioid use. CDC has published new guidelines as for how to counteract this crisis and regulatory agencies including states medical boards and DEA has increased enforcement against irresponsible and fraudulent opioid prescription writers to include license revocations and jail terms.

Establishing comparability in manufacturing cell therapies is a significant challenge for the field due to the complexity of the products, limited material availability, and inherent donor-to-donor variability. As products progress through their lifecycles, organizations need to focus on their ultimate manufacturing end goal. In addition, commercialization and application of cell therapy products to larger patient populations drive the need to switch from small-scale, manual, or semi-automated single function-based production models to more robust and efficient commercial-scale manufacturing using closed and automated end-to-end platforms.

Big datasets are a challenge to analyze. While all the “omics” crank out data by the terabyte, it hardly gets any bigger and messier than metagenomics data.