This work provides a perspective on the qualification and verification of physiologically based pharmacokinetic (PBPK) platforms/models intended for regulatory submission based on the collective experience of the Simcyp Consortium members. Examples of regulatory submission of PBPK analyses across various intended applications are presented and discussed. European Medicines Agency (EMA) and US Food and Drug Administration (FDA) recent draft guidelines regarding PBPK analyses and reporting are encouraging, and to advance the use and acceptability of PBPK analyses, more clarity and flexibility are warranted.

Ethyl glucuronide (EtG) is a direct ethanol biomarker and U.S. Department of Health and Human Services has advised that specificity studies at low EtG levels are needed for distinction of ethanol consumption and incidental exposure. The authors report urinary EtG excretion with ethanol abstinence, dermal exposure and oral consumption. EtG concentration by sensitive liquid chromatography-tandem mass spectrometry measurement in 39 urine specimens from adult alcohol abstainers (< 10-62 microg/L) and in urine from 13 children (< 10-80 microg/L) indicates either unrecognized ethanol exposure or endogenous ethanol metabolism. With repetitive daily dermal exposure to hand sanitizer (60% ethanol) by 9 adults, EtG concentration ranged from < 10 to 114 microg/L in 88 first-morning void specimens. EtG excretion following a 24 g ethanol drink by 4 adults revealed maximum urine EtG concentration (12,200-83,200 microg/L) at 3 to 8 h postdose and an EtG detection window up to 25-39 h, compared to an ethanol window of only 2 to 4 h. Oral ethanol use also showed an increase in the percent (molar equivalent) ethanol excreted as EtG with increasing oral ethanol doses. Human excretion studies show 1. EtG detectable at low concentration (< 100 microg/L) when ethanol use or exposures is not evident, 2. EtG concentration less than 120 microg/L in first morning specimens from adults with repeated dermal exposure to ethanol, 3. EtG levels maximally elevated within 3-8 h and above baseline for up to 39 h after a 24 g ethanol drink, and 4. a dose-dependent increase in the percentage of ethanol excreted as EtG with increasing oral ethanol use.

Autism spectrum disorder (ASD) is a type of neurodevelopmental disorder that has been diagnosed in an increasing number of children around the world. Existing data suggest that early diagnosis and intervention can improve ASD outcomes. However, the causes of ASD remain complex and unclear, and there are currently no clinical biomarkers for autism spectrum disorder. More mechanisms and biomarkers of autism have been found with the development of advanced technology such as mass spectrometry. Many recent studies have found a link between ASD and elevated oxidative stress, which may play a role in its development. ASD is caused by oxidative stress in several ways, including protein post-translational changes (e.g., carbonylation), abnormal metabolism (e.g., lipid peroxidation), and toxic buildup [e.g., reactive oxygen species (ROS)]. To detect elevated oxidative stress in ASD, various biomarkers have been developed and employed. This article summarizes recent studies about the mechanisms and biomarkers of oxidative stress. Potential biomarkers identified in this study could be used for early diagnosis and evaluation of ASD intervention, as well as to inform and target ASD pharmacological or nutritional treatment interventions.

Background: Autism spectrum disorder (ASD) has become one of the most common neurological developmental disorders in children. However, the study of ASD diagnostic markers faces signi cant challenges due to the existence of heterogeneity.Methods: In this study, genetic testing was performed on children who were clinically diagnosed with ASD. Children with ASD susceptibility genes and healthy controls were studied. The proteomics of plasma and peripheral blood mononuclear cells (PBMCs) as well as plasma metabolomics were carried out.Results: The results showed that although there was genetic heterogeneity in children with ASD, the differentially expressed proteins (DEPs) in plasma, PBMCs, and differential metabolites in plasma could still effectively distinguish autistic children from controls. The mechanism associated with them focus on several common and previously reported mechanisms of ASD.Limitations: The number of samples carrying risk genes in omics research is limited. In further research, a large sample size is required. A group of children who have been diagnosed with ASD but have not been detected to carry risk genes should also be included. Conclusion:The biomarkers for ASD diagnosis could be found by taking DEPs and differential metabolites into consideration. Integrating omics data, glycerophospholipid metabolism and N-glycan biosynthesis might play a critical role in the pathogenesis of ASD.