Performance evaluation of the Sysmex XN-1000V in side-by-side comparison with the Siemens ADVIA 120 and manual methods for healthy CD Sprague-Dawley rats and CD-1 mice.

BACKGROUND: The Sysmex XN-1000V automated hematology analyzer with multispecies software was released in June 2017 for use in research laboratories. Laser light, impedance, fluorescent staining, and fluorescent flow cytometry are used to analyze whole blood for CBC, reticulocyte counts, and WBC counts, including a 5-part differential leukocyte analysis. OBJECTIVES: A side-by-side comparison of the Sysmex XN-1000V with the Siemens ADVIA 120 in analyzing blood from healthy mice and rats will provide insight into the performance of the new analyzer and its capabilities for use in drug development studies. Method correlation analyses on normal mouse and rat hematology data collected with both analyzers and manual reference methods will help determine the reliability of the data produced using the Sysmex XN-1000V analyzer. METHODS: Whole blood samples collected in K2 EDTA from healthy CD-1 mice and CD Sprague-Dawley rats were analyzed in parallel with the XN-1000V and ADVIA 120 analyzers. Male and female mice, approximately 6-9 weeks old, and male and female rats, approximately 7-9 weeks old, were included in this study. Manual reference methods for WBC differential leukocyte analysis and packed cell volume (PCV) measurements were also performed. EP Evaluator version 11.2 (Data Innovations LLC, South Burlington, VT, USA) was used for method comparison statistical analysis. RESULTS: Most hematologic parameters for naïve mice and rats achieved correlation in the fair to excellent range, with the majority showing very good to excellent correlation with low biases (<11.0%) for cohorts analyzed separately and when cohort data were combined. CONCLUSIONS: The Sysmex XN-1000V Hematology Analyzer provided comparable results to those obtained from the Siemens ADVIA 120. We found the Sysmex XN-1000V Hematology Analyzer to be acceptable for use in drug development studies for rats and mice.

Deciphering ADME genetic data using an automated haplotype approach.

OBJECTIVE: To investigate the utility of statistical tools in translating Affymetrix Drug Metabolizing Enzyme and Transporter (DMET) Assay single-nucleotide polymorphisms (SNPs) into common consensus star alleles. METHODS: DMET SNP data from clinical trials in different ethnicities were pooled for analyses. Three different statistical methods, PHASE, Bayesian, and expectation-maximization (EM), were first assessed by comparing the consistency of calling CYP2D6 alleles among 1108 Asians and 55 Caucasians. Subsequently, the performance of EM in deriving haplotype calls was evaluated against the Affymetrix Translation Table for CYPs 2B6, 2C19, 2C9, and 3A4/5 in 582 Asians, 296 Caucasians, and 369 Africans. Selected DNA samples were sequenced to verify the EM-predicted haplotype calls. RESULTS: PHASE, Bayesian, and EM methods showed a similar CYP2D6 star allele call rate. The EM method, with a 0.99 posterior probability cutoff, was chosen for further evaluation because of its low false-positive call rate. Haplotype calls obtained with the EM method were consistent with the Affymetrix Translation Table more than 95% of the time for all five CYPs, except for the CYP2B6 calls in the African descents (83%). In addition, the EM method was superior to the Translation Table-only approach in resolving complex haplotype patterns, identifying novel haplotypes in CYP2B6 and CYP3A5, and determining genotype calls in the presence of missing SNP data. CONCLUSION: A statistical method such as EM could be used to augment the translation of DMET assay SNP data into star alleles, especially for complex genes, to facilitate full utilization and interpretation of clinical pharmacogenetics data.