omicsMIC: a comprehensive benchmarking platform for robust comparison of imputation methods in mass spectrometry-based omics data

Mass spectrometry is a powerful and widely used tool for generating proteomics, lipidomics and metabolomics profiles, which is pivotal for elucidating biological processes and identifying biomarkers. However, missing values in mass spectrometry-based omics data may pose a critical challenge for the comprehensive identification of biomarkers and elucidation of the biological processes underlying human complex disorders. To alleviate this issue, various imputation methods for mass spectrometry-based omics data have been developed. However, a comprehensive comparison of these imputation methods is still lacking, and researchers are frequently confronted with a multitude of options without a clear rationale for method selection. To address this pressing need, we developed omicsMIC (mass spectrometry-based omics with Missing values Imputation methods Comparison platform), an interactive platform that provides researchers with a versatile framework to evaluate the performance of 28 diverse imputation methods. omicsMIC offers a nuanced perspective, acknowledging the inherent heterogeneity in biological data and the unique attributes of each dataset. Our platform empowers researchers to make data-driven decisions in imputation method selection based on real-time visualizations of the outcomes associated with different imputation strategies. The comprehensive benchmarking and versatility of omicsMIC make it a valuable tool for the scientific community engaged in mass spectrometry-based omics research. omicsMIC is freely available at https://github.com/WQLin8/omicsMIC.

Simultaneous determination of tramadol and paracetamol in human plasma using LC-MS/MS and application in bioequivalence study of -fixed-dose combination

The study aimed to develop a sensitive and high-throughput liquid chromatography coupled with tandem mass spectrometry method to quantify concentrations of tramadol and paracetamol simultaneously in human plasma. Sample preparation involved single-step protein precipitation using methanol and two deuterated internal standards, tramadol D6 and paracetamol D4. Agilent Poroshell 120 EC-C18 (100 × 2.1 mm, 2.1 µm) analytical column was employed to achieve chromatographic separation. Detection was in positive ion multiple reaction monitoring mode. A tailing factor (Tf) of <1.2, separation factor (K prime) of >1.5 from the column dead time and signal-to-noise (S/N) ratio >10, were obtained for analytes and internal standards. The standard curve was linear over the concentration range of 2.5-500.00 ng/mL for tramadol and 0.025-20.00 μg/mL for paracetamol. A small injection volume of 1 µL, low flow rate of 440 µL/min and short analysis time of 3.5 min reduced the solvent consumption, analysis cost and system contamination. The results of method validation parameters fulfilled the acceptance criteria of bioanalytical guidelines. The method was successfully applied to a bioequivalence study of fixed-dose combination products of tramadol and paracetamol in Malaysian healthy subjects.

omicsMIC: a Comprehensive Benchmarking Platform for Robust Comparison of Imputation Methods in Mass Spectrometry-based Omics Data

Mass spectrometry is a powerful and widely used tool for generating proteomics, lipidomics, and metabolomics profiles, which is pivotal for elucidating biological processes and identifying biomarkers. However, missing values in spectrometry-based omics data may pose a critical challenge for the comprehensive identification of biomarkers and elucidation of the biological processes underlying human complex disorders. To alleviate this issue, various imputation methods for mass spectrometry-based omics data have been developed. However, a comprehensive and systematic comparison of these imputation methods is still lacking, and researchers are frequently confronted with a multitude of options without a clear rationale for method selection. To address this pressing need, we developed omicsMIC (mass spectrometry-based omics with Missing values Imputation methods Comparison platform), an interactive platform that provides researchers with a versatile framework to simulate and evaluate the performance of 28 diverse imputation methods. omicsMIC offers a nuanced perspective, acknowledging the inherent heterogeneity in biological data and the unique attributes of each dataset. Our platform empowers researchers to make data-driven decisions in imputation method selection based on real-time visualizations of the outcomes associated with different imputation strategies. The comprehensive benchmarking and versatility of omicsMIC make it a valuable tool for the scientific community engaged in mass spectrometry-based omics research. OmicsMIC is freely available at https://github.com/WQLin8/omicsMIC.

Dixon's Q-test and Student's t-test to assess analog internal standard response in nonregulated LC-MS/MS bioanalysis

Aim: In bioanalytical assays, analyte response is normalized to an internal standard response. When the internal standard works well, it compensates for processing and detection variability. However, in case the internal standard introduces additional variability, due to addition errors or other issues, scientists need to identify this. Results: A new method, using a Q-test for outliers and a t-test to compare internal standard response from different sample types, is applied to 15 cases. The results show that the Q-test/t-test, which uses confidence level rather than arbitrary cut-points, is more discerning of deviations compared with widely used methods. Conclusion: This work may improve the quality of and rationale for the internal standard response monitoring method.

What the lab can and cannot do: clinical interpretation of drug testing results

Urine drug testing is one of the objective tools available to assess adherence. To monitor adherence, quantitative urinary results can assist in differentiating "new" drug use from "previous" (historical) drug use. "Spikes" in urinary concentration can assist in identifying patterns of drug use. Coupled chromatographic-mass spectrometric methods are capable of identifying very small amounts of analyte and can make clinical interpretation rather challenging, specifically for drugs that have a longer half-life. Polypharmacy is common in treatment and rehabilitation programs because of co-morbidities. Medications prescribed for comorbidities can cause drug-drug interaction and phenoconversion of genotypic extensive metabolizers into phenotypic poor metabolizers of the treatment drug. This can have significant impact on both pharmacokinetic (PK) and pharmacodynamic properties of the treatment drug. Therapeutic drug monitoring (TDM) coupled with PKs can assist in interpreting the effects of phenoconversion. TDM-PKs reflects the cumulative effects of pathophysiological changes in the patient as well as drug-drug interactions and should be considered for treatment medications/drugs used to manage pain and treat substance abuse. Since only a few enzyme immunoassays for TDM are available, this is a unique opportunity for clinical laboratory scientists to develop TDM-PK protocols that can have a significant impact on patient care and personalized medicine. Interpretation of drug screening results should be done with caution while considering pharmacological properties and the presence or absence of the parent drug and its metabolites. The objective of this manuscript is to review and address the variables that influence interpretation of different drugs analyzed from a rehabilitation and treatment programs perspective.

Signal Drift in Liquid Chromatography Tandem Mass Spectrometry and Its Internal Standard Calibration Strategy for Quantitative Analysis

The present project studied the signal drift in liquid chromatography tandem mass spectrometry (LC-MS/MS) and proposed a strategy for compensating such drift. In the study, four 4-component groups were repeatedly run on different LC-MS/MS systems for over 12 h to investigate the dependence of signal drift on time and hardware systems. The 4-component groups each consisted of (1) an analyte, (2) a stable isotope labeled analyte, (3) a compound with similar structure to the analyte, and (4) a compound with dissimilar structure. All of the species showed significant signal drift, generally more than 25% over 12 h. The analyte and its stable isotope labeled analog always have the same drifting pattern including the trends and direction from one LC-MS/MS system to another. Signal drift was also found to be concentration dependent. Our experiments further proved that a conventional stable isotope labeled internal standard in LC-MS/MS quantification would not compensate the variations caused by concentration-dependent signal drift. An ideal internal standard for LC-MS/MS has both identical structure and similar concentration to the analyte. For that, we proposed a new internal standard strategy, pseudo internal standard (Pseudo IS), for LC-MS/MS quantification. Pseudo IS could effectively compensate signal drift in spite of its significant time, system, and concentration dependencies.

Evaluation, identification and impact assessment of abnormal internal standard response variability in regulated LC-MS bioanalysis

Internal standard (IS) plays an important role in LC-MS bioanalysis by compensating for the variability of the analyte of interest in bioanalytical workflow. Due to the complexity of biological sample compositions and bioanalytical processes, a certain level of IS response variability across a run or a study is anticipated. However, an extensive variability may raise doubts to the accuracy of the measured results and also suggest nonoptimal analytical method. In this current paper, recent publications and guidelines regarding IS response in LC-MS bioanalysis were thoroughly reviewed with focus on the evaluation, identification and impact assessment of 'abnormal' IS response variability. A systematic decision tree was proposed to facilitate investigation into abnormal IS response variability after each run.