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Nominated by: Tina Cowan, Stanford Health Care (CA, USA); email@example.com
“I am delighted to support Justin Mak’s nomination for the Bioanalysis Rising Star Award. Justin is a bright, self-motivated and independent researcher who consistently seeks out ways to push clinical metabolomics testing to the next level. His work focuses on new methods for screening, diagnosis and monitoring patients with inborn errors of metabolism. Justin has developed, validated and implemented process improvements for a number of small-molecule assays including methylmalonic acid (Mak et al, 2018) and carnitine, as well as a novel LC—MS/MS method for quantitative amino acid analysis (Mak et al, in press), and its expansion to additional analytes (e.g., sulfocysteine, guanidinoacetate) and classes of compounds (arylglycines, acylcarnitines). He is now focusing on novel methods for targeted and untargeted broad-scale metabolomics testing using LC-QTOF-MS, representing a paradigm shift for our field. His innovations include designing and optimizing chromatographic methods to capture all clinically relevant compounds from the most polar to non-polar, and developing rigorous procedures for sample preparation, quality control and statistical analysis appropriate for clinical testing. Along the way he has independently reached out to industry and academic leaders in analytical hardware, software, and statistical methods, and is an invaluable resource and mentor to our entire team.”
1Describe the main highlights of your bioanalytical work.
The main focus of my work has been to improve patient care by developing and implementing novel, rapid, and underivatized LC—MS assays that assess metabolic health.
It is known that matrix effects diminish assay accuracy and sensitivity, both of which are unacceptable for patient testing. Rather than using laborious and slow extraction methods, such as solid-phase extraction (SPE), my approach has been to fully leverage the principles of chromatography. For example, serum methylmalonic acid samples are traditionally processed by SPE and derivatized, totaling 4-6 hours. I was able to minimize sample preparation using ultrafiltration, a 15-minute process, while maintaining assay precision, accuracy, and sensitivity by developing a chromatographic method that consistently excluded matrix effects (Mak et al, JALM, 2018).
Second, I have implemented a 14.5 minute, underivatized amino acid method (Mak et al, in press). While derivatization kits are available, result turnaround is slower and these methods cannot baseline separate clinically significant isobars or isomers, such as alloisoleucine from isoleucine and leucine.
Lastly, I am validating a newly developed chromatographic method that may supplant the traditional reverse-phase and HILIC methods used in clinical metabolomics. In conjunction, I have programmed the necessary data analysis tools in Python and R.