The bioanalysis world has exploded with the need for molecular assays (qPCR, dPCR, NGS, Hybridization technologies) due to the demand for both biodistribution/PK and PD/BM analysis of various drug modalities. Many of these molecular assays have been around for decades and are now routine methods. CLIA and reference labs have offered molecular assays for decades. It might be surprising to learn that while GLP/GCLP molecular assays have also been around for years, there is still a lack of clear regulatory guidelines. There are whitepapers and recommendations, but there are no clear guidelines like there are for PK by LCMS, PK by LBA, and Immunogenicity by LBA guidelines for validation in a regulated lab.
Regulated laboratory support for molecular assays is in the infantile stages. The number of PCR/dPCR specific guidelines and recommendations for how to validate molecular assays in a GLP/GLCP regulated environment is not specific or has yet to be established. To help understand how different it is from the traditional PK, you have performance characteristics like the limit of blank, which uses a no template control (NTC). The most recent and comprehensive recommendations can be found in a publication by the AAPS PCR working group. Click here to read the manuscript.
So, what does this have to do with automation of data analysis? Validation approaches and acceptance criteria lack harmonization. In addition, most PCR data collection systems do not calculate summary statistics to assess assay or batch performance. This increases the risk of inconsistencies in data analysis across and within studies. Additionally, the lack of tools for tabulating the high volumes of raw data used for FDA submissions often leads to transcription errors, data analysis errors, and overburdened quality control staff. To reduce the risk of errors, time-consuming safeguards for data review are required.
To address these concerns, we have partnered with BioData Solutions to utilize automation to improve compliance, consistency, and efficiency. We don’t categorize the solution as AI, but more machine learning. This automated solution improves quality (>99% reduction in transcription & calculation errors), saves time (96.4% time savings) and enhances cost savings (97.3%). Read the poster for complete details.
KCAS Bio is the first CRO to offer this type of automated data analysis, positioning us at the forefront of GLP/GCLP bioanalysis for molecular assays. In addition, we plan to adopt this type of automation to multiplexing Biomarker data to improve quality, save time, and provide cost savings for these services too.
KCAS Bio is pioneering automated data analysis because we are committed to innovation, quality, and cost savings for our clients. By reducing errors and streamlining workflows, our machine learning-powered approach sets a new industry standard.