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Read-across, an alternative approach for hazard assessment, has been widely adopted when in vivo data are unavailable for chemicals of interest. Read-across is enabled via in silico tools such as quantitative structure activity relationship (QSAR) modeling. In this study, the current status of structure activity relationship (SAR)-based read-across applications in the Republic of Korea (ROK) was examined considering both chemical risk assessments and chemical registrations from different sectors, including regulatory agencies, industry, and academia. From the regulatory perspective, the Ministry of Environment (MOE) established the Act on Registration and Evaluation of Chemicals (AREC) in 2019 to enable registrants to submit alternative data such as information from read-across instead of in vivo data to support hazard assessment and determine chemical-specific risks. Further, the Ministry of Food and Drug Safety (MFDS) began to consider read-across approaches for establishing acceptable intake (AI) limits of impurities occurring during pharmaceutical manufacturing processes under the ICH M7 guideline. Although read-across has its advantages, this approach also has limitations including (1) lack of standardized criteria for regulatory acceptance, (2) inconsistencies in the robustness of scientific evidence, and (3) deficiencies in the objective reliability of read-across data. The application and acceptance rate of read-across may vary among regulatory agencies. Therefore, sufficient data need to be prepared to verify the hypothesis that structural similarities might lead to similarities in properties of substances (between source and target chemicals) prior to adopting a read-across approach. In some cases, additional tests may be required during the registration process to clarify long-term effects on human health or the environment for certain substances that are data deficient. To improve the quality of read-across data for regulatory acceptance, cooperative efforts from regulatory agencies, academia, and industry are needed to minimize limitations of read-across applications.

Background: Prenatal lead exposure has been reported to affect infant growth and nervous system development, as well as to influence DNA methylation. We conducted an epigenome-wide association study to identify associations between prenatal lead exposure and cord blood DNA methylation in Korean infants., Methods: Cord blood samples were assayed with the Illumina HumanMethylationEPIC BeadChip kits, and maternal blood lead levels during early and late pregnancy, as well as cord blood lead level, were measured. The association between CpG methylation and lead level was analyzed using the limma package, with adjusting for infant sex, maternal pre-pregnancy body mass index, and estimated leukocyte composition., Results: Among 364 blood samples (182 males and 182 females), those for which maternal and cord blood lead concentrations during early and later pregnancy was known were used for analysis. Maternal lead concentration in blood during early pregnancy was significantly associated with the methylation status of specific positions. After data stratification by infant sex, we found that, in males, the level of maternal blood lead was associated with 18 CpG sites during early pregnancy, and with one CpG site near the NBAS gene, during late pregnancy. In female samples, there was no significant association between DNA methylation and lead concentrations., Conclusions: Prenatal lead exposure was associated with altered, gender-specific patterns of DNA methylation in Korean infants., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Background: Several biological age (BA) prediction models have been suggested with a variety of biomarkers. Valid models should be able to measure BA in a relatively short time period and predict subsequent physiological capability. Physiological and physical fitness variables have been shown to be distinctive markers for predicting BA and morbidity. The practical and noninvasive nature of such variables makes them useful as clinical assessment tools in estimating BA for in-depth diagnosis and corresponding intervention., Objective: To identify, develop and evaluate biomarkers and BA prediction models and validate their clinical usefulness for the practical diagnosis of functional aging., Methods: Fourteen variables were measured in 3,112 male and 1,233 female participants aged 30 and older between the years 2004 and 2007. Through a series of parsimonious stepwise elimination processes, two sets of 8 gender-specific variables were selected as candidate biomarkers for 1,604 men and 760 women. Principal component analysis, linear regression analysis and adjustment methods were further applied to obtain two sets of true BA (TBA) prediction models. The TBA models were examined for validity by comparing TBA to the corresponding chronological age (CA) with clinical risk factors., Results: TBA prediction models with r(2) values of 0.638 and 0.672 were developed, each unique to men and women, respectively. The overall mean TBA and CA of the participants were 53.9 and 51.8 years, respectively, with a marginal difference of -2.1 and -1.3 years. The regression slopes or rates of TBA as a function of CA were 1.00 and 1.28 for men and women with r values of 0.799 and 0.820 (p < 0.001), respectively. In comparing TBA to CA rates between healthy and clinical risk groups, both sarcopenic and obese groups showed significant increases in TBA., Conclusions: The selected biomarkers encompass various complex physiopathological factors related to intrinsic and extrinsic physiological and functional aging. The BA prediction models based on the selected biomarkers could be practical in assessing BA for Korean adults., (Copyright © 2012 S. Karger AG, Basel.)