Testing Unconventional Matrices to Monitor for Prenatal Exposure to Heroin, Cocaine, Amphetamines, Synthetic Cathinones, and Synthetic Opioids

摘要

Background: The prevalence of drug use during pregnancy continues to increase despite the associated serious adverse obstetrical outcomes, including increased risk of miscarriage, fetal growth restriction, brain development impairment, neonatal abstinence syndrome, preterm delivery, and stillbirths. Monitoring drug use during pregnancy is crucial to limit prenatal exposure and provide suitable obstetrical health care. The authors reviewed published literature reporting the concentrations of common drugs of abuse and new psychoactive substances (NPS), such as synthetic cathinones and synthetic opioids, NPS, and their metabolites using unconventional matrices to identify drug use during pregnancy and improve data interpretation. Methods: A literature search was performed from 2010 to July 2019 using PubMed, Scopus, Web of Science scientific databases, and reports from international institutions to review recently published articles on heroin, cocaine, amphetamine, methamphetamine, synthetic cathinone, and synthetic opioid monitoring during pregnancy. Results: Meconium has been tested for decades to document prenatal exposure to drugs, but data regarding drug concentrations in amniotic fluid, the placenta, the umbilical cord, and neonatal hair are still lacking. Data on prenatal exposure to NPS are limited. Conclusions: Maternal hair testing is the most sensitive alternative matrix for identifying drug use during pregnancy, while drug concentrations in the meconium, placenta, and umbilical cord offer the identification of prenatal drug exposure at birth. Adverse developmental outcomes for the infant make it critical to promptly identify maternal drug use to limit fetal exposure or, if determined at birth, to provide resources to the exposed child and family. Alternative matrices offer choices for monitoring and challenge laboratories to deliver highly sensitive and specific analytical methods for detection.