Attribute Analytics Performance Metrics from the MAM Consortium Interlaboratory Study.
Trina MouchahoirJohn E SchielRich RogersN Alan HeckertBenjamin J PlaceAaron AmmermanXiaoxiao LiTom RobinsonBrian SchmidtChris M ChumsaeXinbi LiAnton V ManuilovBo YanGregory O StaplesDa RenAlexander J VeachDongdong WangWael YaredZoran SosicYan WangLi ZangAnthony M LeonePeiran LiuRichard LudwigLi TaoWei WuAhmet CansizogluAndrew HannemanGreg W AdamsIrina PerdivaraHunter WalkerMargo WilsonArnd BrandenburgNick DeGraan-WeberStefano GottaJoe ShambaughMelissa AlvarezX Christopher YuLi CaoChun ShaoAndrew MahanHirsh NandaKristen NieldsNancy NightlingerBen NiuJihong WangWei XuGabriella LeoNunzio SepeYan-Hui LiuBhumit A PatelDouglas D RichardsonYi WangDaniela TizabiOleg V BorisovYali LuErnest L MaynardAlbrecht GruhlerKim F HaselmannThomas N KroghCarsten P SönksenSimon LetarteSean ShenKristin BoggioKeith JohnsonWenqin NiHimakshi PatelDavid RipleyJason C RouseYing ZhangCarly DanielsAndrew DawdyOlga FrieseThomas W PowersJustin B SperryJosh WoodsEric CarlsonK Ilker SenSt John SkiltonMichelle BuschAnders LundMartha StapelsXu GuoSibylle HeidelbergerHarini KaluarachchiSean McCarthyJohn KimJing ZhenYing ZhouSarah RogstadXiaoshi WangJing FangWeibin ChenYing Qing YuJohn G HoogerheideRebecca ScottHua YuanPublished in: Journal of the American Society for Mass Spectrometry (2022)
The multi-attribute method (MAM) was conceived as a single assay to potentially replace multiple single-attribute assays that have long been used in process development and quality control (QC) for protein therapeutics. MAM is rooted in traditional peptide mapping methods; it leverages mass spectrometry (MS) detection for confident identification and quantitation of many types of protein attributes that may be targeted for monitoring. While MAM has been widely explored across the industry, it has yet to gain a strong foothold within QC laboratories as a replacement method for established orthogonal platforms. Members of the MAM consortium recently undertook an interlaboratory study to evaluate the industry-wide status of MAM. Here we present the results of this study as they pertain to the targeted attribute analytics component of MAM, including investigation into the sources of variability between laboratories and comparison of MAM data to orthogonal methods. These results are made available with an eye toward aiding the community in further optimizing the method to enable its more frequent use in the QC environment.
Keyphrases
- mass spectrometry
- big data
- healthcare
- quality control
- high resolution
- multiple sclerosis
- mental health
- high throughput
- liquid chromatography
- cancer therapy
- machine learning
- artificial intelligence
- high performance liquid chromatography
- high density
- sensitive detection
- loop mediated isothermal amplification
- clinical evaluation