A physiological “digital twin” model may help evaluate whether reported alcohol consumption scenarios are consistent with observed biomarker patterns, according to a study published in Scientific Reports.

Accurately determining the timing and quantity of alcohol intake remains a challenge in health care and forensic settings. Traditional markers such as blood alcohol concentration (BAC) and breath alcohol concentration (BrAC) decline rapidly, limiting their usefulness in retrospective analyses. This limitation is particularly relevant in legal cases involving the “hipflask” defense, in which individuals claim alcohol consumption occurred following an incident.

To address these gaps, researchers developed a unified physiological model that integrates both short- and longer-term biomarkers, including BAC, BrAC, ethyl glucuronide (EtG), ethyl sulphate (EtS), and urine alcohol concentration (UAC). The framework is a mechanistic, equation-based pharmacokinetic model parameterized using characteristics such as age, weight, height, and sex, rather than a continuously learning patient-specific system. The model also simulates BrAC as a distinct output, reflecting differences between central and peripheral alcohol compartments.

Model Development and Scope of Validation

The researchers trained the model using data from 10 prior experimental studies, many of which involved controlled drinking protocols with sequential alcohol intake and standardized meal conditions. The model was then validated against a single independent data set involving a fixed drinking scenario (0.119 L of vodka consumed with a 500 kcal meal).

Following validation, the model was applied to predict biomarker profiles in two individuals (one male and one female) from a controlled experimental study using a sequential wine-then-vodka protocol. Although predicted trajectories aligned with observed data, this limited and highly standardized scenario highlights the early-stage nature of individual-level validation and restricts generalizability across broader drinking patterns.

The model’s statistical evaluation included a chi-squared test assessing whether residual differences between model predictions and observed data were consistent with expected variability. However, this approach evaluates model fit to existing data rather than prospective predictive accuracy. The researchers also reported several systematic discrepancies, including differences in peak BAC for certain drinking conditions and mismatches in EtG and EtS elimination rates.

Individualized Simulations and Scenario Testing

The model incorporates physiologic processes such as alcohol absorption, metabolism, and excretion, including a gastric emptying component that accounts for the effects of food intake on alcohol absorption. These features allow simulation of alcohol kinetics under varying conditions.

In simulation studies, reliance on BAC alone was insufficient to distinguish between certain drinking patterns. However, combining multiple biomarkers improved differentiation between similar intake scenarios. This multimarker approach may provide additional context when evaluating disputed alcohol consumption claims.

Decision-Support Role in Forensic Contexts

The researchers emphasized that the model is intended as a decision-support tool rather than a definitive reconstruction method. In the study, forensic application was demonstrated using simulated (in silico) comparisons between alternative drinking scenarios, rather than reconstruction of real-world cases. Within this context, the model is designed to assess whether observed biomarker data are compatible with a reported sequence of alcohol intake.

“The primary purpose of the framework is to evaluate the plausibility of claimed scenarios rather than to provide a single deterministic reconstruction of events,” the researchers wrote.

Uncertainty in inputs—such as timing of alcohol intake, drink composition, or physiologic characteristics—results in broader prediction ranges. This feature may reduce overconfidence in interpretations but also reflects the model’s dependence on input assumptions.

Practical Considerations and Limitations

The model requires detailed input data, including drinking timing, volume, alcohol concentration, caloric intake, and anthropometric characteristics. In real-world clinical or forensic settings, these variables are often incomplete or disputed, which may limit applicability.

Several limitations are particularly relevant:

• Limited validation and narrow scenarios: The model has been validated against a single independent drinking scenario and tested in two individuals under a controlled, sequential drinking protocol, limiting generalizability across diverse real-world patterns

• Biomarker variability: The model does not fully account for interindividual variability in EtG and EtS, which are influenced by differences in enzymatic activity. The researchers noted that improving personalization would likely require enzymatic data that are not routinely available

• Model discrepancies: Observed mismatches in certain biomarker dynamics, including EtG and EtS elimination patterns, indicate incomplete representation of underlying physiology

Research Implications

Although developed for forensic applications, the framework may inform future research into alcohol metabolism and biomarker interpretation, including potential future approaches to analyzing alcohol exposure in complex scenarios.

However, the model remains a research tool and has not been validated for clinical decision-making or legal use.

An accompanying web-based interface allows users to simulate drinking scenarios and visualize biomarker trajectories. The tool is publicly accessible (https://alcohol.streamlit.app/) and may support further research and methodological development.

The researchers reported no competing interests.

Source: Scientific Reports