LC-MS Assessment of Human Metabolism Compliance with "MIST" Guidance
Tuesday afternoon's session titled "LC?MS Assessment of Human Metabolism Compliance with "MIST" Guidance" plans to tackle the topic of the key challenges for drug development as a result of the FDA?s MIST guidance.
Tuesday afternoon's session titled "LC–MS Assessment of Human Metabolism Compliance with "MIST" Guidance" plans to tackle the topic of the key challenges for drug development as a result of the FDA’s MIST guidance.
The first presentation, "A New Paradigm for Metabolite Profiling and Bioanalysis to Identify and Manage Metabolite Safety Concerns," will be given by Scott W. Grimm of AstraZeneca Pharmaceuticals. This presentation will reportedly provide an overview of the new challenges facing biotransformation and bioanalytical scientists in the pharmaceutical industry.
Following AstraZeneca's presentation will be, "A Methodology for Complete Plasma Metabolite Profiling and Identification with High-Resolution LC/MS to Address MIST Issues in Early Clinical Studies," by Haiying Zhang and a team from Bristol-Myers Squibb. The application presented will reportedly demonstrate the practicality of this complete metabolite profiling methodology for assessment of exposures of human metabolites with non-radiolabeled compounds.
The next presentation scheduled is from Natalia Penner of Schering-Plough and is called "Metabolite Profiling Challenges in the First-in-Human Study. Identification of Two Novel Metabolites of a Nociceptin Agonist." This presentation will focus on the structural elucidation of two unique metabolites detected in the process of metabolite characterization in plasma and urine from first-in-human study.
Following up this presentation will be one from Richard Clayton of Covance Laboratories, Ltd. entitled, "A Review of Accurate Mass LC–MS Applications for Compliance with MIST Guidelines." This talk will cover samples from a variety of early development studies that can be used to address questions raised by publication of FDA guidelines.
"A Retention-Time-Shift-Tolerant Background-Subtraction and Noise-Reduction Algorithm (BgS-NoRA) for Extraction of Drug Metabolites in LC–MS Data," will be the second to last presentation of the session given by Peijuan Penny Zhu of Schering-Plough Research Institute. This talk will address issues with the MIST guidelines, a retention-time-shift-tolerant background subtraction, and the noise reduction algorithm that was implemented using R to remove matrix ions from accurate mass liquid chromatography mass spectrometry data.
The final presentation from this session, "Rapid Detection and Characterization of N-acetyl-L-Cysteine Conjugates in Human Urine Using Polarity Switching of Quadrupole-Linear Ion Trap Mass Spectrometry," will be given by Wenying Jian from Bristol-Myers Squibb. The talk will discuss a novel methodology for rapid and sensitive detection and structure characterization of NAC conjugates using polarity switching of Q-Trap.
AI and GenAI Applications to Help Optimize Purification and Yield of Antibodies From Plasma
October 31st 2024Deriving antibodies from plasma products involves several steps, typically starting from the collection of plasma and ending with the purification of the desired antibodies. These are: plasma collection; plasma pooling; fractionation; antibody purification; concentration and formulation; quality control; and packaging and storage. This process results in a purified antibody product that can be used for therapeutic purposes, diagnostic tests, or research. Each step is critical to ensure the safety, efficacy, and quality of the final product. Applications of AI/GenAI in many of these steps can significantly help in the optimization of purification and yield of the desired antibodies. Some specific use-cases are: selecting and optimizing plasma units for optimized plasma pooling; GenAI solution for enterprise search on internal knowledge portal; analysing and optimizing production batch profitability, inventory, yields; monitoring production batch key performance indicators for outlier identification; monitoring production equipment to predict maintenance events; and reducing quality control laboratory testing turnaround time.
