Forensic science meets clinical pharmacology: pharmacokinetic model based estimation of alcohol concentration of a defendant as requested by a local prosecutor's office.
- Author:
Hyeong Seok LIM
1
;
Jea Hyen SOUNG
;
Kyun Seop BAE
Author Information
- Publication Type:Case Report
- Keywords: Alcohol; Estimation; Defendant; NONMEM; Bayesian
- MeSH: Bayes Theorem; Blood Alcohol Content; Driving Under the Influence; Forensic Sciences*; Korea; Pharmacology, Clinical*; Social Problems; Uncertainty
- From:Translational and Clinical Pharmacology 2017;25(1):5-9
- CountryRepublic of Korea
- Language:English
- Abstract: Drunk driving is a serious social problem. We estimated the blood alcohol concentration of a defendant on the request of local prosecutor's office in Korea. Based on the defendant's history, and a previously constructed pharmacokinetic model for alcohol, we estimated the possible alcohol concentration over time during his driving using a Bayesian method implemented in NONMEM®. To ensure generalizability and to take the parameter uncertainty of the alcohol pharmacokinetic models into account, a non-parametric bootstrap with 1,000 replicates was applied to the Bayesian estimations. The current analysis enabled the prediction of the defendant's possible blood alcohol concentrations over time with a 95% prediction interval. The results showed a high probability that the alcohol concentration was ≥ 0.05% during driving. The current estimation of the alcohol concentration during driving by the Bayesian method could be used as scientific evidence during court trials.
