This document discusses best practices for determining blood alcohol concentration (BAC) at specific time points using ante-mortem alcohol pharmacokinetic modeling and post-mortem considerations. It describes how human alcohol metabolism works and factors that influence BAC levels over time. It also evaluates approaches for modeling ante-mortem BAC levels using the Widmark equation and discusses factors that affect post-mortem BAC measurements. The goal is to standardize the determination of BAC levels before and after death to accurately assess impairment.

1. Best-practices approach to determination of blood alcohol
concentration (BAC) at specific time points: Combination of ante-
mortem alcohol pharmacokinetic modeling and post-mortem alcohol
generation and transport considerations
Dallas M. Cowan a, *
, Joshua R. Maskrey b
, Ernest S. Fung a
, Tyler A. Woods a
,
Lisa M. Stabryla b
, Paul K. Scott b
, Brent L. Finley c
a
Cardno ChemRisk, LLC, Aliso Viejo, CA, United States
b
Cardno ChemRisk, LLC, Pittsburgh, PA, United States
c
Cardno ChemRisk, LLC, Brooklyn, NY, United States
a r t i c l e i n f o
Article history:
Received 5 October 2015
Received in revised form
24 March 2016
Accepted 29 March 2016
Available online 1 April 2016
KEYWORDS:
Alcohol
Ethanol
Blood alcohol concentration (BAC)
Pharmacokinetic model
Post-mortem neoformation
Forensic toxicology
a b s t r a c t
Alcohol concentrations in biological matrices offer information regarding an individual's intoxication
level at a given time. In forensic cases, the alcohol concentration in the blood (BAC) at the time of death is
sometimes used interchangeably with the BAC measured post-mortem, without consideration for
alcohol concentration changes in the body after death. However, post-mortem factors must be taken into
account for accurate forensic determination of BAC prior to death to avoid incorrect conclusions. The
main objective of this work was to describe best practices for relating ante-mortem and post-mortem
alcohol concentrations, using a combination of modeling, empirical data and other qualitative consid-
erations. The Widmark modeling approach is a best practices method for superimposing multiple alcohol
doses ingested at various times with alcohol elimination rate adjustments based on individual body
factors. We combined the selected ante-mortem model with a suggestion for an approach used to
roughly estimate changes in BAC post-mortem, and then analyzed the available data on post-mortem
alcohol production in human bodies and potential markers for alcohol production through decompo-
sition and putrefaction. Hypothetical cases provide best practice approaches as an example for deter-
mining alcohol concentration in biological matrices ante-mortem, as well as potential issues encountered
with quantitative post-mortem approaches. This study provides information for standardizing BAC
determination in forensic toxicology, while minimizing real world case uncertainties.
© 2016 Elsevier Inc. All rights reserved.
1. Introduction
Alcohol (e.g., ethanol or ethyl alcohol), one of the most
commonly consumed psychoactive drugs in the world, is often used
to promote social interaction, is generally accepted and legal in
many countries. However, alcohol is a depressant that can impair a
person's ability to operate a motor vehicle; determining blood
alcohol concentration (BAC) is therefore one of the most prevalent
forensic chemical analyses performed for criminal and medical
purposes (Robinson and Harris, 2011). For example, a recent review
article evaluating 69 epidemiological studies found that 52% of
driving-related fatalities and 35% of driving-related injuries were
associated with positive alcohol tests (Schalast et al., 2011).
Although alcohol metabolism has been studied for over 100
years, accurately predicting BAC following alcohol consumption
remains an active scientific research area (Nicloux, 1899; Hamill,
1910). Precise estimation of the BAC at a given time point is
complicated by individual variability in body and metabolism
characteristics (e.g., age, body mass index, liver health, state of
nourishment, state of hydration and basal metabolic rate), vari-
ability in mass or concentration of alcohol present in beverages
(e.g., beer, wine, spirits), and the biological matrices sampled to
determine the BAC.
Determining BAC is particularly challenging when an impaired
driver is fatally injured in an accident. In such instances, the BAC
* Corresponding author. Cardno ChemRisk, LLC, 130 Vantis Suite 170, Aliso Viejo,
CA, USA.
E-mail address: dallas.cowan@cardno.com (D.M. Cowan).
Contents lists available at ScienceDirect
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Regulatory Toxicology and Pharmacology 78 (2016) 24e36

2. measured in a blood sample collected from the driver post-mortem
is used to determine the level of the driver's impairment. However,
various factors can affect post-mortem BAC measurements that do
not typically affect ante-mortem measurements: alcohol meta-
bolism phase, presence of a preservative in the collected sample,
sample storage condition, variation in sampling media, putrefac-
tion, and post-mortem alcohol neoformation. These factors are
particularly important in accident situations in which the body is
not recovered and promptly refrigerated. A direct post-mortem BAC
measurement may not accurately characterize a driver's impair-
ment level at the time of death. In many instances, the BAC
measured after an accident is much higher than the level predicted
by simple reconstruction of the driver's recent alcohol and food
consumption (Wigmore, 2011).
The purpose of this paper is to present a best-practices ante-
mortem alcohol modeling approach combined with a simple post-
mortem alcohol concentration analysis to generate accurate BAC
predictions before and after the time of death, thereby optimizing
and standardizing forensic approaches in real world cases. The
objectives of this study were to: 1) evaluate the relationships be-
tween alcohol concentrations in various biological matrices; 2)
generate an empirical modeling approach for correlating post-
mortem alcohol concentrations with pharmacokinetic (PK)
modeled ante-mortem concentrations up until the time of death; 3)
describe factors associated with determining whether alcohol
concentrations measured post-mortem are due to ante-mortem
ingestion of alcohol or post-mortem synthesis of alcohol by mi-
croorganisms; and 4) describe best practices for determining ante-
and post-mortem alcohol concentrations with a focus on potential
sources of error.
2. Background
2.1. Human metabolism of alcohol
Alcohol (CH3CH2OH) is a small, polar molecule that accumulates
in water-rich areas of the body, and does not significantly diffuse
into fatty tissues. Following ingestion, alcohol is absorbed slowly in
the stomach and rapidly in the small intestines. The rate of alcohol
absorption is affected by the rate of gastric emptying, which in turn
is influenced by various factors such as food ingestion (Holt, 1981;
Holt et al., 1980; Sedman et al., 1976; Lin et al., 1976).
Various enzymes are responsible for alcohol metabolism
including alcohol dehydrogenase (ADH) in the liver, and aldehyde
dehydrogenase (ALDH) and CYP2E1 in the brain and liver (Fig. 1)
(Matsumoto and Fukui, 2002; Israel et al., 2013). Approximately
90e98% of ingested alcohol is metabolized through the alcohol
dehydrogenase þ aldehyde dehydrogenase pathway and other
phase II metabolic pathways, while the remaining 2e10% is
excreted un-modified in breath, sweat and urine (Jones, 2010). In
cases of low exposure, alcohol is metabolized and eliminated
without significant physiological effects. The body's first-pass
metabolism can prevent small doses of alcohol from reaching sys-
temic circulation (Jones, 2010). However, once a threshold exposure
is reached (which varies among individuals), the metabolic en-
zymes are saturated, and excess alcohol begins to accumulate in the
bloodstream. Alcohol in the blood will diffuse across the blood
brain barrier, causing inebriation and impairment of physiological
responses. Alcohol's progressive physiological effects follow a dose-
response relationship with respect to physiological effects in
drinkers who do not suffer from alcoholism (Table 1) (Chong, 2014;
Dubowski, 2006).
Alcohol concentration in the body changes as a function of time.
BAC generally increases following an exponential curve to a
maximum after initial alcohol ingestion as it is absorbed by the
body, then decreases linearly as it is eliminated until very low levels
(<0.01e0.02%) of BAC, at which point the decrease becomes
exponential (Jones, 2010). The increasing BAC phase is generally
called the “absorption phase”, while the decreasing phase is called
the “elimination phase”. The mass of alcohol ingested is important
in determining BAC, and the alcohol content varies widely by type
of drink. Additionally, the percentage of alcohol by volume (ABV)
impacts the rate of absorption; drinks with 10e30% ABV are
absorbed the fastest; stronger or weaker drinks are absorbed more
slowly (Kelly and Mozayani, 2012). Also, during the absorption
phase, equilibrium is not reached, and the blood alcohol concen-
tration may not fully reflect an individual's intoxication state
(Wigmore, 2011). In the elimination phase, equilibrium is reached,
and BAC is on the decline, thereby better reflecting the biological
influence of alcohol (Wigmore, 2011).
2.2. Ante-mortem alcohol pharmacokinetic modeling approaches
Widmark presented an empirically-based formula in 1932 that
considered the exponential metabolic absorption rate constant, the
zero-order elimination rate for alcohol, and the Widmark Factor
(WF), an empirical rate constant accounting for the body's water
content and volume alcohol distribution into body compartments
as described below (inverse first-order dependence) (Posey and
Mozayani, 2007).
BAC ¼
Aingested
1 À eÀkt
rW
À ðbtÞ
where,
BAC ¼ Blood alcohol concentration (g/L)
t ¼ Time since ingestion of alcohol (h)
Aingested ¼ Mass of alcohol contained in the drink (g)
r ¼ Widmark Factor (unitless)
W ¼ Body weight (kg)
k ¼ Absorption rate constant (hÀ1
)
b ¼ Elimination rate ((g/L)/h)
The Widmark Equation remains the “gold standard” approach
for retrospectively estimating BAC (Posey and Mozayani, 2007;
Widmark, 1932). Further developments in BAC estimation in
recent years included Derr's 1993 development of compartmental
physiologically-based pharmacokinetic (PBPK) models for four
different ethnicities, and Umulis et al., 2005 addition of reversible
enzyme kinetics (Derr, 1993; Umulis et al., 2005). PBPK models can
Ethanol Acetaldehyde
CytosolMicrosomesPeroxisomes
Alcohol
Dehydrogenase
Catalase
CYP2E1
Mitochondria
Circula on
NAD+ NADH
H2O2 H2O
NADPH + H+ + O2 NADP+ + H2O
Acetate
NAD+ NADH + H+
Aldehyde
Dehydrogenase 2
Fig. 1. Metabolic pathway for elimination of alcohol in humans.
D.M. Cowan et al. / Regulatory Toxicology and Pharmacology 78 (2016) 24e36 25

3. accurately estimate ethanol concentration over time in the blood
compartment; however, they require complex solutions to differ-
ential equations for little gain in accuracy over simple empirical
models, such as the Widmark Model (Derr, 1993).
The original WF was determined by averaging measured BAC
results from a large group of individuals using a standard collection
of variables (e.g., age, sex, height, and weight) (Widmark,1932). The
WF has been modified and improved over the past 80 years to
include more descriptive variables, such as body mass index (BMI),
blood water content, and total body water (Watson et al., 1981;
Posey and Mozayani, 2007; Forrest, 1986;Ulrich et al., 1987; Seidl
et al., 2000). Posey and Mozayani (2007) recently modified the
Widmark Model by including an empirical first order rate constant
for alcohol absorption in the GI tract, an average WF over multiple
calculation approaches, and an empirical elimination rate. The
combination of these approaches allows the model to better
describe a specific individual's BAC using data specific to the indi-
vidual (Posey and Mozayani, 2007).
The Widmark Equation, like any modeling approach, is limited
by input parameter accuracy. The magnitude of the WF is related to
the volume distribution of water in the body, which is a function of
gender and body weight. Body weight and BAC are inversely pro-
portional; an individual with a greater body weight will therefore
have a lower BAC at a given dose (Kwo et al., 1998; Jones, 2010).
Blood alcohol elimination rates (normalized over body weight)
depend on metabolic rate and gender; male elimination rates tend
to be slightly lower than female elimination rates (Dettling et al.,
2009; Pavlic et al., 2007). Also, the elimination rate of alcohol
within persons of the same gender can vary: empirically measured
values have ranged between 0.096 and 0.241 g/kg/h in males and
0.015e0.260 g/kg/h in females (Dettling et al., 2009; Pavlic et al.,
2007). Another study measured similar elimination rates and
found a range of 0.106e0.217 g/L/h in males and 0.103e0.254 g/L/h
in females (Pavlic et al., 2007). The accuracy of the Widmark model
can be affected by the variability of elimination rates within the
population.
Also, elimination rates are not truly linear (or, zero-order with
respect to concentration for all alcohol concentrations). Indeed,
alcohol elimination rates follow Michaelis-Menten enzyme kinetics
(Wagner, 1973; Mullen, 1977). For all concentrations greater than
0.015e0.020 g%, the linear elimination assumption has very low
error (Wagner, 1973; Posey and Mozayani, 2007). Most forensic
cases involve BAC levels much greater than 0.02 g%; therefore, this
simplification is appropriate. However, in low-dose ante-mortem
modeling, we recommend use of a case-by-case Michaelis-Menten
kinetics approach such as those presented by Wagner and Patel or
Mullen (Wagner and Patel, 1972; Mullen, 1977).
Clearance rates of alcohol are a function of blood flow and
metabolic efficiency. Alcohol clearance rates are higher for older
populations than younger populations because elderly persons
Table 1
Relationship between blood alcohol concentration and reported physiological and behavioral effectsa
.
BAC (%) Physiological effect
0.01e0.05 - Increased heart and respiration rates
- Decreased functions in brain center
- Slightly impaired judgment
- Decreased inhibition
- Mild euphoria
- For some, effects are not apparent or obvious by ordinary observation
- Inconsistent performance on special tests
0.06e0.10 (Legal limit ¼ 0.08) - Euphoria
- Sociability, increased self-confidence
- Decreased attention and alertness
- Slowed reactions, impaired coordination, and reduced muscle strength
- Reduced ability to make rational decisions and exercise good judgment
- Increased anxiety and depression
- Decrease in patience
0.11e0.15 - Emotional instability, loss of judgment
- Dramatic slowing of reactions
- Impairment of balance and movement
- Impairment of some visual functions
- Slurred speech
- Vomiting
- Drowsiness
0.16e0.29 - Severe sensory impairment, including reduced awareness of external stimulation
- Increased pain threshold
- Severe motor impairment (staggering gait)
- Double vision and vertigo
- Exaggerated emotional states and mental confusion
- Lethargy
0.30e0.39 - Non-responsive stupor
- Inability to stand or walk
- No control of bladder and bowels
- Vomiting
- Loss of consciousness
- Anesthesia comparable to that for surgery
- Death
0.40 or above - Unconsciousness, coma-like state
- Cessation of breathing
- Death, usually due to respiratory arrest
a
Adapted from Chong 2014 and Dubowski 2006.
D.M. Cowan et al. / Regulatory Toxicology and Pharmacology 78 (2016) 24e3626

4. have less volume of distribution for alcohol (Fiorentino and
Moskowitz, 2013). Thierauf et al. compared theoretical Widmark
BAC calculations to observed BAC for persons of multiple ages and
found that a 75 year-old male reached a BAC of over 150% the
intended endpoint; this unexpectedly high BAC was likely due to
less total body water related to age (Thierauf et al., 2013). A more
recent study was performed to assess the accuracy of the Widmark
Model for elderly persons. The study included 51 individuals aged
60 or over who had abstained from alcohol for two days. Their
blood alcohol concentrations were generally higher than predicted,
and corrections to the WF for those in the study were suggested
based on differences in body water volume (Bielefeld et al., 2015).
The absorption rate constant is affected by the presence of food
in an individual's stomach. The absorption rate constant for an
empty stomach is roughly 2.3 hÀ1
compared to roughly 6.5 hÀ1
for a
full stomach (Posey and Mozayani, 2007). Alcohol absorption is
affected significantly by fasting since alcohol is trapped in ingested
food; the average absorption availability of alcohol was 97% with
fasting, 94% with a light snack, and 72% and 66% with a meal in men
and women, respectively (Sadler and Fox, 2011). Also, the absorp-
tion of alcohol as a percentage can vary: the range for the fasting
value was 87e108%, the range for the light snack value was
81e112%, and the range for the gender-specific meal values were
61e93% and 54e78% in men and women, respectively (Sadler and
Fox, 2011). The accuracy of the Widmark model can be affected
by varying absorption efficiencies (Sadler and Fox, 2011).
Other modifying factors such as race are not explicitly obvious in
the Widmark Model. Individuals from different racial backgrounds,
may contain different levels of alcohol dehydrogenase, affecting
their ability to eliminate alcohol (Ehlers et al., 2012). Individuals of
Hispanic descent generally have a higher alcohol tolerance
compared to other populations because of a heightened alcohol
metabolic rate (Caetano and Clark, 2000; Schwartz et al., 1996).
Individuals of Asians and Native Americans do not produce suffi-
cient ADH, therefore, the duration necessary for them to metabo-
lize alcohol is longer compared to those of Caucasian origin (Israel
et al., 2013). Individuals from these backgrounds therefore often
experience tachycardia, headache, nausea and facial flushing
following alcohol consumption.
Individual alcohol tolerance affects total alcohol metabolism,
but the metabolic alcohol oxidation rate is not significantly affected
(Palmer and Jenkins, 1982). Increased blood acetaldehyde levels are
observed in alcoholics after alcohol ingestion, however, the mean
rate of alcohol oxidation (the first step of alcohol metabolism) did
not differ between alcoholics and non-alcoholics (Palmer and
Jenkins, 1982). Though alcohol dehydrogenase accounts for the
greater part of alcohol oxidation, other enzymes such as CYP2E1
and catalase may be induced at high alcohol concentrations or after
long term alcohol intake; though CYP2E1 and catalase may only
account for a relatively small part of the total alcohol metabolism, it
likely contributes to the general variability of ethanol metabolism
due to alcohol tolerance (Quertemont, 2004). However, the activity
of acetaldehyde dehydrogenase (the second step) was significantly
lower in alcoholics; thus the Widmark Equation may be used for
alcoholics with low error (Palmer and Jenkins, 1982).
2.3. Post-mortem BAC determination
Retrospectively determining BAC at the time of death can be
challenging because many additional variables must be considered.
One such variable is the initial level of alcohol-generating microbial
contamination and potential for environmental contamination over
time prior to collection of the sample by a forensic examiner. Blood
and other biological matrices can be potentially contaminated with
some species of bacteria, fungi and other agents capable of
generating alcohol (mostly from glucose) via putrefaction. This
contamination and neoformation of alcohol can confound identi-
fication of BAC at time of death when derived from post-mortem
blood samples.
2.3.1. Biological matrices for post-mortem alcohol determination
Testing multiple matrices to determine alcohol concentration is
common practice in forensic analyses. The most common biological
matrices tested include: blood, urine, and vitreous humor. In gen-
eral, BAC is higher than urine alcohol concentration (UAC) and
vitreous humor alcohol concentration (VAC) during the absorption
phase, and the reverse is true during elimination (Kelly and
Mozayani, 2012). Additionally, arterial blood may exhibit up to
40% higher alcohol concentrations compared to venous blood
during the absorption phase (Kelly and Mozayani, 2012).
A number of biological matrices including blood, vitreous hu-
mor, muscle, urine, and internal organs have been previously
evaluated forensically to determine level of alcohol intoxication
and cause of death. Bodily fluids such as bile, vitreous humor, urine,
and synovial fluid have been studied to determine their accuracy in
estimating BAC. Winek et al. (1993) determined the BAC:synovial
fluid alcohol concentration ratio to be 0.98; however, variability
existed in samples with higher alcohol concentration, leading to a
ratio range of 0.4e1.72 (Winek et al., 1993). Stone and Rooney
(1984) studied the viability of using bile, urine, and vitreous hu-
mor to accurately determine BAC. The authors found that VAC:BAC
ratios were consistently 0.77 for BAC 0.10% and 0.63 for BAC
0.10%; however, BAC: bile alcohol concentration and UAC: BAC
ratios had larger variation for BAC 0.10% (Stone and Rooney, 1984).
Additionally, Kugelberg and Jones, 2007 found insignificant varia-
tion in VAC between the left and right eyes of one individual
(Kugelberg and Jones, 2007). Blood, bile, vitreous humor, and urine
are the four most common biological matrices used to determine
BAC (Stone and Rooney, 1984).
Generally, whole blood is used in post-mortem analysis due to
the difficulty in separating serum or plasma fractions post-mortem,
while serum or plasma are used in ante-mortem analysis. The ratio
of alcohol concentrations in serum to those in whole blood gener-
ally ranges between 1.12 and 1.24, and the ratio of alcohol con-
centrations in plasma to whole blood can range between 1.1 and
1.35 (O'Neal and Poklis, 1996; Kelly and Mozayani, 2012; Bielefeld
et al., 2015).
2.3.2. Sources of post-mortem alcohol neoformation
Alcohol neoformation may result from microbial growth in
improperly stored samples. Post-mortem neoformation of alcohol
by microorganisms can complicate analytical results from biolog-
ical matrices, although it still may be possible to accurately inter-
pret forensic results using best-practices approaches, such as those
presented in this paper. Although vitreous humor and urine are less
likely substrates than blood for microbial growth, such growth
cannot be ruled out (Kelly and Mozayani, 2012).
The vitreous alcohol concentration (VAC) to BAC ratio generally
provides valuable information regarding the alcohol metabolic
state, especially in forensic-related cases. Table 2 summarizes
literature-reported VAC:BAC ratios, along with the anatomical
source of blood in which the BAC was measured. A VAC:BAC ratio of
less than one implies that the individual was in the absorption
phase prior to equilibrium; a ratio greater than one implies that the
elimination phase was reached prior to death (Boonyoung et al.,
2008). Deviation from typical VAC:BAC ratios (generally consid-
ered 0.5 to 1.5) suggests alcohol consumption or production by
microorganisms (de Lima et al., 1999). However, authors of some
studies have concluded that the VAC:BAC ratio is unreliable for
determining the source of alcohol (Jollymore et al., 1984). Indeed,
D.M. Cowan et al. / Regulatory Toxicology and Pharmacology 78 (2016) 24e36 27

5. autopsy analyses have led to the conclusion that extrapolation of
BAC from VAC results is a rough estimate at best, and that such
results should be considered cautiously (Neil et al., 1985; Jollymore
et al., 1984).
Alcohol is generated in vitro via the glycolytic pathway utilized
by many microorganisms during fermentation (Kugelberg and
Jones, 2007; Skopp, 2009). High yields of alcohol can be produced
from carbohydrates, with glucose being the substrate of choice
where 1 mol of glucose is metabolized to 2 mol of alcohol (Sutlovic
et al., 2013). Glucose content is thus often a primary determinant of
the amount of alcohol produced (Boumba et al., 2012). The absence
of excess glucose, however, does not exclude post-mortem alcohol
production, since alcohol may be produced from other substrates,
such as mannitol, sucrose, mannose, lactose, ribose, and various
amino acids (O'Neal and Poklis, 1996; Skopp, 2009; Canfield et al.,
2007). Substrate composition differences result in the production
of different amounts of alcohol from the same microorganism
(Boumba et al., 2012). Generally, at least 58 species of bacteria, 17
species of yeast, and 24 species of molds are capable of producing
alcohol from sugars (O'Neal and Poklis, 1996). Information on 18
species relevant to post-mortem alcohol generation in human
bodies is presented in Table 3. Twelve of the 18 species occur
naturally in the intestines, skin, mouth, and sputum (Corry, 1978).
Post-mortem alcohol generation has been observed in blood, urine
or vitreous humor samples as fast as 7 h after death and two days
after sample collection, with levels ranging from 0.16 to 10.63 mg/
mL (Boumba et al., 2012; Sutlovic et al., 2013; Corry, 1978).
Generally, alcohol generation occurred in samples stored at room
temperature and lacking preservatives. Additionally, low levels of
various biological indicators of putrefaction, such as short-chain
alcohols, including 1-propanol, 2-propanol, 1-butanol, 3-methyl-1-
butanol, 2-methyl-1-propanol, acetone, diethyl ether, acetalde-
hyde, and formaldehyde, were identified (O'Neal and Poklis, 1996;
Chikasue et al., 1988; Skopp, 2009; Sutlovic et al., 2013; Corry,
1978). Specifically, 0.003e0.105 mg/mL of 1-propanol and
0.002e0.119 mg/mL of 1-butanol was detected from various bio-
logical matrices where putrefaction was suspected (Yajima et al.,
2006; Boumba et al., 2012).
Microorganism presence in samples with detectable alcohol
concentration but no evidence of alcohol consumption suggests
post-mortem neoformation as the potential alcohol source. O'Neal
and Poklis (1996) reported that 12% of alcohol detections in au-
topsies were attributed to post-mortem generation; however, this
figure rises to 40e50% in accident-related deaths (O'Neal and
Poklis, 1996). Additionally, BAC values of up to 0.19% were
measured in sailors killed in an explosion who had not been
drinking alcohol (Mayes et al., 1992). Studies suggest that sample
handling, storage conditions, and time between death and analysis
contribute to the potential for post-mortem neoformation alcohol
production (Kugelberg and Jones, 2007).
Storage condition of the body or biological matrix sample is a
key factor affecting post-mortem alcohol generation. Higher tem-
peratures produce higher alcohol yields; refrigeration of the body
therefore helps to prevent alcohol synthesis (Kugelberg and Jones,
2007; Nanikawa et al., 1982). It is standard forensic practice to add
sodium fluoride (NaF), a preservative with proven anti-microbial
activity, to blood samples in order to prevent microbial growth and
degradation. However Sutlovic et al. (2013), reported that alcohol
concentration continued to increase in blood and urine samples
stored at 4 C despite adding NaF (Sutlovic et al., 2013). Alcohol
production occurs at a higher rate under anaerobic conditions, such
as submersion in water (Hadley and Smith, 2003). Multiple studies
have found that alcohol concentrations are higher and may change
over time in submerged bodies (Kugelberg and Jones, 2007;
Gonzales et al., 1954; Waller, 1972; Tomita, 1975). Post-mortem
alcohol generation in submerged subjects started between 12 and
24 h after submersion depending on the temperature (Kugelberg
and Jones, 2007; Hadley and Smith, 2003; Wintemute et al., 1990).
Various biological indicators of putrefaction have been identi-
fied, such as short-chain alcohols including n-propanol, n-butanol,
3-methyl-1-butanol, and isopropanol, along with other non-
alcohol indicators, such as acetone, diethyl ether, acetaldehyde,
and formaldehyde (O'Neal and Poklis, 1996; Chikasue et al., 1988;
Skopp, 2009; Sutlovic et al., 2013; Corry, 1978). Forensic labora-
tories often use 1-propanol as a standard, making it invalid as a
marker for post-mortem alcohol production in the cases where it is
used; 2-methyl-2-propanol, which is not a natural indicator of
putrefaction, is also used as a standard by some laboratories (O'Neal
and Poklis, 1996). The concentration of these indicators can be used
to quantitatively determine the alcohol concentration generated by
microbes with considerable reliability.
Loss of alcohol concentration from blood samples may also
occur during storage. Alcohol loss from samples occurs under
various conditions, such as evaporation due to improper sample
storage, enzyme-mediated oxidation, and microorganism action
(Brown et al., 1973;Dick and Stone, 1987). Brown et al. (1973) found
that a temperature increase led to loss of alcohol content via
oxidation of alcohol to aldehyde, and Jones (2007) reported loss of
alcohol concentration in samples stored at 4 C (Brown et al., 1973;
Jones, 2007). Smalldon and Brown (1973) attributed the loss of
alcohol concentration from non-enzymatic oxidation involving
oxyhemoglobin (Smalldon and Brown, 1973).
Table 2
Reported ratios of blood and vitreous alcohol concentration.
Reference Blood source Sample
size
VAC:BAC mean
ratio
VAC:BAC ratio
SD
VAC:BAC
ratio range
Elapsed time between death
and sample collection
Winek and Esposito, 1981 NR 30 0.94 0.17 NR NR
Budd, 1982 NR 15 1.3 0.6 NR NR
Caplan and Levine, 1990 NR 205 1.19 NR 0.10e1.91 NR
Chao and Lo, 1993 Femoral 68 1.06 0.23 NR Within 28 h
Sylvester et al., 1998 Femoral 9 1.06 0.10 NR NR
Jones and Holmgren, 2001 Femoral Vein 672 1.19 0.29 NR NR
Honey et al., 2005 Femoral 203 1.24
Heart 5 1.19 NR 1.01e2.20 NR
Pleural Cavity 1 1.18
Boonyoung et al., 2008 Femoral 25 0.963e1.206 NR NR Within 24 h
Hassan, 2011 Femoral 43 1.13 0.43 NR NR
Hoffman et al., 2011 NR TBD 1.10e1.20 NR NR NR
NR ¼ Not reported; SD ¼ Standard deviation; BAC ¼ Blood alcohol concentration; VAC ¼ Vitreous alcohol concentration.
D.M. Cowan et al. / Regulatory Toxicology and Pharmacology 78 (2016) 24e3628

6. Trauma is often observed in forensic cases; introduction of mi-
croorganisms from the environment or from within the gut into
various biological matrices has been observed following trauma
(references). Putrefaction and post-mortem alcohol production
may occur more rapidly with trauma (Kugelberg and Jones, 2007;
Canfield et al., 2007). Direct trauma to the gut is likely to result in
stomach rupture, leading to the spread of gastric contents,
including ingested alcohol and gut flora (Kugelberg and Jones,
2007;Plueckhahn and Ballard, 1968; Winek et al., 1995). BACs of
5 mg/dL were observed in blood samples with gastrointestinal
fluid contamination (Winek et al., 1995). Additionally, agonal
events resulting from trauma may lead to pulmonary aspiration of
stomach contents into the lungs, leading to direct alcohol diffusion
into the bloodstream (Kugelberg and Jones, 2007). Vitreous humor
Table 3
Reported Cases where Alcohol was Generated Post-Mortem.
Reference Microorganism presentA
Biological
matrixB
Sample
size
Storage duration
after death to
analysis (days)
Blood
glucose
level (mg/
mL)
Highest
ethanol
concentration
(mg/mL)
Indicators of putrefaction
Other
volatiles and
gases
present
Max. concentration
detected (mg/mL)
No Evidence of Alcohol Consumption
Hoiseth
et al.,
2008
Escherichia coliNO
, Clostridium bacteroidesNO
,
Prevotella species Escherichia coliNO
, Clostridium
bacteroidesNO
, Prevotella species
BloodFL,
RFBlood FL, RF
1 11 days (10 days
after autopsy)
9.96 in VH 3.50 NR NR
Appenzeller
et al.,
2008
Streptococcus familyNO
, Lactococcus garvieae,
Streptococcus familyNO
, Lactococcus garvieae
BloodRTBlood
RT
3 7 days 4.00 1.21 NR NR
Antonides
and
Marinetti,
2011
Staphylococcal aureusNO
, Candida
albicansNOStaphylococcal aureusNO
, candida albicansNO
BloodRFBlood
RF
1 30 þ days after
death
2.17 in VH NR Acetone 0.540
Carbon
dioxide
NR
Boumba
et al.,
2012
Clostridia perfrigensNOClostridia perfrigensNO
BloodRTBlood
RT
NR 29 days 2.00 0.16 1-propanol 0.007
1-butanol 0.019
2-methyl-1-
propanol
0.002
3-methyl-1-
butanol
0.001
Boumba
et al.,
2012
Clostridia sporogenesNOClostridia sporogenesNO
BloodRTBlood
RT
NR 5 days 2.00 0.89 1-propanol 0.105
1-butanol 0.119
2-methyl-1-
propanol
0.061
3-methyl-1-
butanol
0.006
Boumba
et al.,
2012
Escherichia coliNO
BloodRT
NR 22 days 2.00 0.56 1-propanol 0.010
1-butanol 0.002
2-methyl-1-
propanol
0.001
3-methyl-1-
butanol
0.001
Sutlovic
et al.,
2013
Citrobacter freundii, Enterococcus faecalis, Serratia
marcescensNO
, Candida glabrata
BloodRF
1 17 days (14 days
after autopsy)
5.85 2.34 Acetone 0.510
Antonides
and
Marinetti,
2011
Staphylococcal aureusNO
, candida albicansNO
UrineRF
1 30 þ days after
death
2.17 in VH 0.28 Acetone 8.020
Carbon
dioxide
NR
Sutlovic
et al.,
2013
Candida glabrata, Enterococcus faecalis, Escherichia
coliNO
, Morganella morganii, Klebsiella
pneumoniaeNO
UrineRF
1 17 days (14 days
after autopsy)
0.07 10.63 Acetone 0.630
Evidence of Alcohol Consumption
Yajima et al.,
2006
Candida albicansNO
, Candida parapsilosisNO
BloodRF
1 23 days (22 days
after autopsy)
NR 4.90 1-propanol 0.002e0.003
Yajima et al.,
2006
Corynebacterium sp.NO
, Escherichia coliNO
, Candida
tropicalisNO
BloodNR
1 20 days NR 9.60 1-propanol 0.004e0.03
Yajima et al.,
2006
Candida albicansNO
BloodRT
4 2 days 7.00 2.10 1-propanol 0.030
A: NO - Naturally occurring in intestines, skin, mouth and sputum according to Corry (1978).
B: FL - 0.21% w/v Fluoride ions added; NR - Not reported; RF - Stored at 2e10
C; RT - Stored at room temperature; VH - Vitreous humor.
D.M. Cowan et al. / Regulatory Toxicology and Pharmacology 78 (2016) 24e36 29

7. is often cited as the matrix least prone to microbial contamination
because of its biological remoteness and seclusion from the rest of
the body, and is therefore also often cited as the most suitable
matrix for determining BAC (de Lima et al., 1999; O'Neal and Poklis,
1996; Jenkins et al., 1995; Hoffman et al., 2011). Furthermore, vit-
reous humor is often preserved, even in the case of severe trauma
(Coe and Sherman, 1970). Trauma to the eye itself, however, could
open a pathway for microbial contamination of the vitreous humor.
3. Methods
3.1. Literature review
To more accurately estimate BAC at various time points ante-
mortem and post-mortem (e.g., 2 h after two drinks; 12 h after
an alcohol related death, etc.), we evaluated the existing litera-
ture and available methodologies regarding pharmacokinetic
modeling of BAC and the empirical estimation of post-mortem
BAC. A comprehensive literature search was performed using
Pubmed, Elsevier, and Google Scholar to identify peer-reviewed
studies that evaluated relationships between alcohol concentra-
tions in various biological matrices, applications of the Widmark
Model for modeling of alcohol concentrations in humans, and
evidence of putrefaction and post-mortem alcohol generation by
various microorganism species. The literature search helped us
identify a best-practices Widmark Model that was accurate in a
number of alcohol consumption scenarios. This best-practices
Widmark Model was then used to calculate BAC values in hy-
pothetical scenarios that highlight its utility.
Particular emphasis was placed on studies containing infor-
mation regarding the dependence of alcohol concentration in
bodily matrices on gender, body weight, body mass index (BMI),
height, age, ethnicity, elimination rate, absorption rate and
tolerance for alcohol. The reliability of methods used to deter-
mine alcohol concentration in post-mortem samples was also
evaluated.
3.2. Specific parameter selections for case studies and modeling
approach
We next devised three hypothetical examples to demonstrate
the Widmark Model's important features. In our first example, we
evaluated the differences in pharmacokinetics between the two
genders after ingesting three alcoholic drinks. In our second
example, we performed several adjustments to the traditional
Widmark Equation to account for multiple drinks consumed over
time. In our third example, we combined ante-mortem modeling
with post-mortem considerations to estimate the BAC of an indi-
vidual at the time of death, and the amount of alcohol generated
post-mortem. Current literature is lacking in examples where ante-
mortem modeling is combined with post-mortem considerations
regarding alcohol generation. As previously described, post-mortem
generation is a concern in many forensic matters; we thus present a
third example to address this concern and literature gap.
All ante-mortem Widmark parameters and equations were
solved as a function of time and displayed graphically using
Microsoft Excel (Ver. 2013, Microsoft Corporation, Redmond,
WA). Data were extracted from graphs presented in Yajima et al.,
2006 using an open source digitization program. Microsoft Excel
was used to determine functions of best fit for post-mortem
alcohol generation rates by particular species or groups of spe-
cies using the empirical data presented in each of the following
studies, where applicable: Yajima et al., 2006, Boumba et al.,
2012,Antonides and Marinetti, 2011, Sutlovic et al., 2013,
Hoiseth et al., 2008 and Appenzeller et al., 2008.
3.2.1. Example 1: application of the existing Widmark Modeling
approaches – gender and body weight differences
To illustrate the differences in BAC predicted by the Widmark
Equation between genders, a hypothetical case study was created
with heights and weights representative of the general population,
where a 1.8 m, 70 kg, 30 year old male and a 1.7 m, 55 kg, 30 year
old female both drink three shots (133.2 mL) of 80-proof liquor. The
Widmark Model was applied to estimating the BAC of these two
individuals over a 180 min time period. The WF was calculated
using the average of the approaches presented by Watson et al.
(1981),Forrest (1986), Seidl et al. (2000) and Ulrich et al. (1987)
as presented in Table 4 (Posey and Mozayani, 2007) was pre-
sented in Table 4. The absorption rate constant was set equal to
5 hÀ1
for both individuals to simulate a semi-full stomach (Posey
and Mozayani, 2007). Elimination rates were assumed to be
0.162 g/L/h for the male and 0.179 g/L/h for the female (Pavlic et al.,
2007).
Table 4
Explanation of the Widmark model and contributing factors.
Parameter Symbol Value range Unit Value factors
Ethanol absorption rate constant k 2.1e6.5 h-1
Depends mainly on BMI and personal metabolic factors (Posey and
Mozayani, 2007)
Ethanol elimination rate constant b 0.13e0.25 (g/L)/hr Depends on BMI, age, water content of body, fullness of stomach (Pavlic
et al., 2007) Generally linear at BAC 0.015e0.02%
Body weight W weight kg Dependent on the person's body weight
Height H height m Dependent on the person's height
Age G age years Dependent on the person's age
Widmark factor - Watson et al., (1981) estimate rWatson See below e See below
Widmark factor - Forrest (1986) estimate rForrest See below e See below
Widmark factor - Seidl et al. (2000) estimate rSeidl See below e See below
Widmark factor - Ulrich et al. (1987) estimate rUlrich See below e See below
rWatsonðmalesÞ ¼ 0:39834 þ 12:725H
W À 0:11275G
W þ 2:8993
W
rForrestðmalesÞ ¼ 1:0178 À 0:012127W
H2
rSeidlðmalesÞ ¼ 0:31608 À 0:004821W þ 0:4632H
rUlrichðmalesÞ ¼ 0:715 À 0:00462W þ 0:22H
rWatsonðfemalesÞ ¼ 0:29218 þ 12:666H
W À 2:4846
W
rForrestðfemalesÞ ¼ 0:8736 À 0:0124W
H2
rSeidlðfemalesÞ ¼ 0:31223 À 0:006446W þ 0:4466H
rUlrichðfemalesÞ NA
D.M. Cowan et al. / Regulatory Toxicology and Pharmacology 78 (2016) 24e3630

8. 3.2.2. Example 2: modified Widmark Model e superimposition of
multiple BAC curves
A second hypothetical case study was created to illustrate the
more precise BAC estimation over time by superimposition of
multiple Widmark curves. In this case study, a 1.8 m, 70 kg, 30
year old male consumes three shots (44.4 mL each) of 80-proof
liquor over a 45 min period. The absorption rate constant rate
was set at 5 hÀ1
(Posey and Mozayani, 2007). The elimination
rate was assumed to be 0.162 g/L/h (Pavlic et al., 2007). The
male's BAC was modeled for 90 min. One model was run
assuming that all three shots were consumed immediately (using
the traditional Widmark Model), and the other model was run
assuming that one shot was consumed every 15 min (using the
modified Widmark Model).
3.2.3. Example 3: combination of post-mortem alcohol generation
considerations with the modified Widmark Model
To illustrate alcohol generation in the body after death, both
ante-mortem and post-mortem BAC was estimated for a hypo-
thetical scenario in which a 1.8 m, 70 kg, 30 year old male was
assumed to have consumed three shots (44.4 mL each) of 80
proof liquor 15 min apart with all parameters the same as pre-
sented in Example 2 before dying in an accident that occurred
75 min after the first point of alcohol ingestion. For this example,
microorganism-based neoformation of ethanol was assumed to
begin immediately at death. For simplicity, the broad range of
microorganisms present in the human gut was assumed to be
well represented by the 18 species presented in Table 3. The
blood-specific results for each of the six individual studies pre-
sented in Table 3 were analyzed for the following: initial glucose
concentration, temperature of study, real body or in vitro envi-
ronment, and ethanol concentration in the medium as a function
of time. Following this analysis, studies identified as containing
sufficient information to estimate an ethanol generation or loss
rate were used to estimate the change in ethanol concentration
post-mortem at 1 day after death, 7 days after death, and 14
hours after death. It has been demonstrated in the literature that
a logistic equation can be used to describe fermentation and
ethanol production processes by microbes (Wang and Liu,
2014;Olaoye and Kolawole, 2013). A least-squares regression
calculation was used to determine the logistical equation of best-
fit for each data set of the form demonstrated below using
Microsoft Excel (Ver. 2013, Microsoft Corporation, Redmond,
WA).
BAC
hmg
mL
i
¼ A À
A
1 þ
time½dayŠ
C
B
where A, B, and C are constants. Results obtained by Hoiseth et al.
(2008) were not modeled using the logistic model due to insuffi-
cient sample size (n ¼ 3, resulting in 0 degrees of freedom). The
post-mortem approximations for each data set were then added to
the existing BAC starting at the time of death to determine the
ethanol concentration over time. All regression values were
confirmed, and equation coefficients were tested for statistical
significance using SYSTAT 11 (SYSTAT Software Inc, San Jose, CA).
The data and parameters were input into a nonlinear regression
model, and the 95% Wald confidence intervals were evaluated to
determine if the model parameters A and B were significantly
different than 0, and if the model parameter C was significantly
different than 1.
4. Results
4.1. Identification, evaluation and modification of the standard
Widmark Model
The Widmark Model appears to be the gold standard for
quantitatively determining BAC at a given time point in a living
human. More complicated models are available in the literature
(e.g., compartmental PBPK models). As previously stated, however,
the pharmacokinetics of alcohol in humans is fairly simple, so the
traditional Widmark empirical model is sufficiently accurate to
estimate BAC under most conditions. It has also been extensively
studied and applied, and is simple to implement, making it a best
practices approach for estimating BAC. Specifically, we used the
adjusted empirical pharmacokinetic model presented by Posey and
Mozayani (2007), a Widmark-style approach adapted to account for
multiple drinks over time.
4.2. The Widmark Model accurately predicts BAC
The standard and modified Widmark Model was used with
input parameters specific to the individual's body characteristics for
Examples 1 and 2 described in the methods section. The results of
the calculated BACs are described in detail below.
4.2.1. Gender and weight significantly affect BAC
The difference between gender and body weight is demon-
strated in Fig. 2, where the BAC of a male and female individual is
estimated after ingesting three shots (133.2 mL) of 80-proof liquor.
The model predicts that the female reaches a maximum BAC of
0.10%, while the male only reaches a maximum BAC of 0.064%,
which is only 64% of the female maximum BAC. Additionally, the
male reaches the maximum BAC at 36 min, while the female rea-
ches the maximum at 40 min. The time to 0.02%, which is both the
threshold of non-linear elimination and a concentration at which
physiological effects would be minimal (Table 1), was also deter-
mined from the model. The time for BAC to return to 0.02% is
212 min in the male, compared to 321 min in the female. Together,
these findings indicate the gender-specific differences in elimina-
tion rates and WFs affected by body mass and volume of
distribution.
4.2.2. The modified Widmark Model accurately estimates BAC
We compare two modeling approaches for an individual who
consumed three shots (44.4 mL each) of 80-proof liquor over a
45 min period (Fig. 3). Fig. 3 contains the comparison of the
instantaneous dose and time-modified dose Widmark approaches.
The instantaneous drinking approach resulted in a maximum BAC
of 0.064% at 36 min after the first drink, while the time-modified
drinking approach resulted in a maximum of 0.043% within
44 min after the first drink. In this case study, the traditional
Widmark Model predicted a maximum BAC that was 150% larger
than the maximum BAC predicted by the modified Widmark Model
that accounts for drinks consumed at various times. The time
required to reach the maximum BAC is 22% shorter in the original
approach. The time-modified drinking approach results in a faster
decline of BAC in the 60e90 min time range compared to the im-
mediate consumption approach, because the non-linear term of the
Widmark equation has larger magnitude over any specific time
period if the initial dose was larger. Specifically, in the instanta-
neous approach, the BAC returned to 0.02% in 212 min, compared to
85 min with the time-modified drinking approach. After 85 min,
Michaelis-Menten elimination kinetics should be considered. These
were not reflected in Fig. 3.
D.M. Cowan et al. / Regulatory Toxicology and Pharmacology 78 (2016) 24e36 31

9. 4.3. Alcohol produced by microorganisms in biological matrices
Alcohol present in biological matrices obtained during forensic
investigations may signify alcohol consumption or post-mortem
production by microorganisms. Information on 18 species rele-
vant to post-mortem alcohol generation in human bodies were
collected (Table 3). Samples were collected between one to three
days after death, and analyzed between two to more than 30 days
after death.
4.3.1. Combining the Widmark Model with post-mortem microbial
generation considerations can roughly indicate whether microbial
generation occurred
The three-parameter logistic model approximations for the data
collected in each study are described below. Additionally, the 95%
Wald confidence intervals are presented in parentheses beside each
parameter and the overall coefficient of determination (R2
) is
presented.
Based on Boumba et al.’s results for C. sporogenes, C. perfrigens,
and E. coli in vitro, the rate of alcohol generation after death was
modeled with logistic functions of time as described above:
C. sporogenes (n ¼ 31): A ¼ 0.77 (0.74e0.79), B ¼ 7.0 (1.5e11),
C ¼ 1.3 (1.0e1.6), R2
¼ 0.92.
E. coli (n ¼ 31): A ¼ 0.5 (0.48e0.52), B ¼ 1.5 (1.0e2.0), C ¼ 1.3
(1.0e1.5), R2
¼ 0.94.
C. perfrigens (n ¼ 31): A ¼ 0.16 (0.14e0.17), B ¼ 1.8 (1.2e2.4),
C ¼ 5.1 (4.2e6.0), R2
¼ 0.94.
Notably, E. faecalis was also tested, with no generation of
ethanol.
Based on Yajima et al.’s results (n ¼ 5) for C. albicans in vitro, the
concentration increased for two days and the rate of alcohol
generation after death was modeled as a logistic function of
time as described above with parameters A ¼ 1.6 (1.2e2.0),
B ¼ 8.3 (À9.8e26), C ¼ 1.1 (0.87e1.2), R2
¼ 0.99. For this dataset,
B and C are not statistically significant. Yajima et al. also noted
that the concentration began to decrease after two days and
described results for the concentration (n ¼ 4) over time; the
0.000%
0.020%
0.040%
0.060%
0.080%
0.100%
0.120%
0 20 40 60 80 100 120 140 160 180
TotalBAC%(g%)
Time Since 1st Drink (min)
Male
*Male is 1.8 m, 70 kg, 30 years old
**Female is 1.7 m, 55 kg, 30 years old
Female
Fig. 2. Example comparison of BAC% Responses to Ingestion of Three Shots (44.4 mL) of 80-Proof Liquor in a Male and a Female.
0.000%
0.010%
0.020%
0.030%
0.040%
0.050%
0.060%
0.070%
0 10 20 30 40 50 60 70 80 90 100
TotalBAC%(g%)
Time Since 1st Drink (min)
1 Shot Consumed Every 15 Min
All 3 Shots Consumed Immediately
*Male is 1.8 m, 70 kg, 30 years old
Fig. 3. Example comparison of BAC% Responses to Ingestion of Three Shots (44.4 mL) of 80-Proof Liquor at different time points.
D.M. Cowan et al. / Regulatory Toxicology and Pharmacology 78 (2016) 24e3632

10. rate of loss of ethanol was unable to be fit to a logistic decay
model. This model only had one degree of freedom.
Hoiseth et al.’s measured results were not modeled using the
three parameter logistic model due to insufficient sample size.
Based on Sutlovic et al.’s measured results (n ¼ 6) for repeated
analysis of a blood sample from a deceased 75-year old female
which contained C. freundii, E. faecalis, S. marcesens and
C. glabrata, the rate of alcohol generation after death was
modeled as a logistic function of time as described above, with
parameters: A ¼ 2.5 (2.3e2.8), B ¼ 6.0 (3.9e8.0), C ¼ 11 (10e12)
(R2
¼ 1.0).
Antonides et al. measured no ethanol generation over 5 days in a
sample which contained S. aureus and C. albicans.
The data presented by Appenzeller et al., 2008 showed that the
blood concentration of ethanol in a sample of heart blood from a
14-month old child who died suddenly was initially at 2.00 mg/
mL decreased to 1.21 mg/mL after 7 days and 0.48 mg/mL of
in vitro fermentation.
Biological matrices are occasionally obtained a considerable
period of time after an individual's death, making it difficult to
accurately determine the alcohol concentration attributed to post-
mortem generation and the actual BAC at the time of death. Here,
we estimated the BAC at various times post-mortem using best-fit
equations based on the six studies presented in Table 3. The BAC at
time of death is predicted by the Widmark Model to be 0.027%
(75 min after consuming consumption the first drink, see Fig. 3),
which is less than the US legal driving limit of 0.08%. The results of
application of the various lines of best fit cause a number of
different results. For example, application of the C. albicans gen-
eration/consumption curve demonstrates that the BAC can be ex-
pected to reach a maximum at 2 days after death of 0.19% (0.027% at
death þ 0.16% of generation), well above the US legal driving limit,
but that it can be expected to decrease after two days (Yajima et al.,
2006). Interpretation with respect to Boumba's results for the four
organism studies over 30 days indicates a concentration that
logistically increases for two to three days and then stabilizes.
Individually, application of the three logistic equations of best fit for
a time period of 5 days gives 0.10% (C. sporogenes
generation þ ante-mortem), 0.071% (E. coli generation þ ante-
mortem), or 0.035% (C. perfrigens generation þ ante-mortem),
while selection of E. faecalics would imply a concentration of
0.027%, or no change from the concentration at time of death
(Boumba et al., 2012). Any other time period could be used in the
suggested logistic growth models. These data did not match the
experimentally determined values well; the Hoiseth et al., 2008
measurements continued to increase over time, the Sutlovic et al.,
2013 values demonstrated no decrease even after 10 days, unlike
the Yajima et al., 2006 curve with a decreasing portion, and the
other cited values showed either no increase or a decrease in
ethanol concentration over time (Hoiseth et al., 2008; Sutlovic
et al., 2013; Antonides and Marinetti, 2011; Appenzeller et al.,
2008). Regardless, at 5 days, the fitted logistic equation for the
Sutlovic et al., 2013 data suggest an expected BAC of 0.12%.
5. Discussion
The three examples presented in this analysis serve as a guide to
demonstrate how BAC results should be interpreted with consid-
eration for individual body parameters, superposition of multiple
alcoholic beverages, and postmortem generation of alcohol if
applicable. Selection of individual parameters and interpretation of
post-mortem considerations depends on the details of individual
forensic cases. In real-world scenarios, parameter selections and/or
principles applied should vary based on the details of the case,
which is not always done. Example 3 demonstrates how best
practices ante-mortem modeling can be combined with post-
mortem considerations to determine whether alcohol found in
samples was generated by microorganisms or was ingested prior to
death. The ante-mortem Widmark model was adapted from Posey
and Mozayani (2007) and utilized the time-modified drinking
approach, allowing for more accurate modeling of BAC over
extended periods of alcohol ingestion. Also, the adapted ante-
mortem Widmark Model used an average WF from four empiri-
cally determined relationships (Watson et al., 1981; Forrest, 1986;
Ulrich et al., 1987; Seidl et al., 2000). This approach averages out
any biases present in each group, such as a narrow age range or a
narrow body weight range. For example, the Watson et al. (1981)
WF approach for males included a slight dependence on age due
to the wide age range in their sample, while the other three ap-
proaches did not. We evaluated the applicability of these models for
gender differences and time-stepped modeling differences using
the first two hypothetical examples.
This analysis of the Widmark Model indicates that it is a rela-
tively accurate approach for determining BAC as a function of time
for an individual until the time of death, provided that adjustments
are made for body mass index, age, individual metabolic rate, ab-
sorption of ethanol and elimination of ethanol above 0.02% BAC.
The variation between BAC predictions made with general pa-
rameters and time-adjusted parameters highlights the importance
of proper adjustment of input parameters on a case by case basis.
Adjustments for these factors are well described, yet few forensic
investigators have incorporated these parameters together. Addi-
tionally, superimposition of multiple Widmark BAC curves is a
reasonable method for estimating BAC over time for a person
consuming multiple alcohol-containing beverages at different
times, and this approach results in a more precise estimate of
maximum BAC and the time to maximum BAC than does assuming
all drinks were consumed at once. A key difference between the
traditional and modified superimposition approach is an in-
dividual's maximum BAC. The traditional Widmark Model may
predict a maximum BAC above the legal limit, whereas the modi-
fied superimposition approach may predict a maximum BAC below
legal limits.
It is common knowledge among forensic investigators that
alcohol present in post-mortem body matrices originates from
alcohol consumption prior to death, post-mortem alcohol produc-
tion by microorganisms, or some combination of both (Posey and
Mozayani, 2007; Kelly and Mozayani, 2012). The phenomena
where cadaver BAC was elevated in the apparent absence of ante-
mortem alcohol ingestion has been noted (Mayes et al., 1992;
Boumba et al., 2012; Antonides and Marinetti, 2011). Further-
more, putrefaction or decomposition follows shortly after death,
depending on conditions surrounding the body, and autolysis oc-
curs during putrefaction. During autolysis, tissue decomposition
occurs and endogenous enzymes are released. Further decompo-
sition by endogenous enzymes lead to putrefactive bacteria release
from the gut (Boumba et al., 2012). In total, we highlight 18 species
of bacteria or fungi with demonstrated ability to generate alcohol in
body matrices. Indicators or biomarkers are produced in concert
with alcohol during alcohol neoformation. Key indicators of post-
mortem neoformation of alcohol from the processes of putrefac-
tion include: 1-propanol, n-butanol, and 2-propanol. Concentration
of these indicators can be used to quantitatively estimate the
concentration of alcohol produced, as demonstrated by Boumba
et al. for 1-propanol and n-butanol (Boumba et al., 2012). However,
confounding is possible, as these substances may be ingested as
well. For example, some researchers have estimated the half-life of
2-propanol in blood following isopropanol ingestion in attempted
suicides, and others have described a case of ingestion of acetone
D.M. Cowan et al. / Regulatory Toxicology and Pharmacology 78 (2016) 24e36 33

11. (Ramu et al., 1978; Daniel et al., 1981). All cases described involved
alcohol dependent or alcoholic persons, which may indicate that
alcoholism is a risk factor for ingestion of other solvents.
No quantitative models for post-mortem neoformation of
ethanol in human blood existed in the literature using input pa-
rameters such as time since death, initial ethanol concentration,
etc. However, empirical data existed, and models of best fit for in-
dividual species did not match experimental data well. Addition-
ally, experimental data did not match other sets of experimental
data well, as evidenced by the widely different predictions for BAC
(modeled to start at death at 0.027%) between the Hoiseth et al.,
2008 data (which were not approximated by a model, but
increased rapidly) and the Sutlovic et al., 2013 fitted equation
(0.028%). The use of the empirical equations based on the literature
was meant to correct the concentration measured during the au-
topsy for post-mortem neoformation or loss, but the results were
often different on a case-by-case basis. This exercise demonstrated
the massive variety of potential results that can be estimated using
literature: the measured BAC within 11 days of death can peak up to
0.38% based on the values observed by Hoiseth et al. (2008), and
may decrease to 0 within the same time period based on Yajima
et al., 2006. Therefore, it is recommended that the forensic practi-
tioner rely more heavily on ante-mortem modeling if the amount
consumed is determinable, and qualitatively measures the poten-
tial for post-mortem generation of alcohol by sampling the blood or
other medium for: 1) presence of microorganisms, 2) amount of
preservative and 3) presence of other markers of putrefaction
(which can be used semi-quantitatively to estimate ethanol pro-
duction, per Boumba et al., 2012).
Similar pharmacokinetic modeling to that presented in this
study for ethanol can be performed for other alcohols and indus-
trial solvents such as methanol, acetone, 2-propanol, or acetalde-
hyde. Bouchard et al. (2001) developed and validated a biologically
based dynamic model that described the time trajectories of
methanol and its metabolites in whole blood and other biological
matrices (e.g.,urine; expired air) (Bouchard et al., 2001). The model
can quantitatively relate the parent compound or metabolites in
the biological matrix to the absorbed dose and tissue burdens at
any point in time for different exposure scenarios (Bouchard et al.,
2001). In a separate study, the coherence between occupational
exposure limits and biological limit values was evaluated, and
blood and urine concentrations of 2-propanol and acetone were
determined after inhalation exposure by human pharmacokinetic
modeling. The acetone and 2-propanol model could be used to
create a time profile of the commonly used solvents in different
biological matrices for various exposure scenarios (Huizer et al.,
2014). Umulis et al., 2005 developed a compartmental PBPK
model that predicts the time evolution for ethanol's major
metabolite, acetaldehyde, in the blood by having derived average
enzymatic rate laws for alcohol dehydrogenase and acetaldehyde
dehydrogenase. This approach was novel in that it combined
ethanol and acetaldehyde PBPK modeling, which correlated
strongly with the experimentally observed ethanol and acetalde-
hyde concentration results for healthy individuals and those with
reduced acetaldehyde dehydrogenase activity. This model also
accounted for the reverse reaction of acetaldehyde back into
ethanol, keeping acetaldehyde levels 10-fold lower than if irre-
versible (Umulis et al., 2005). All of the aforementioned solvents
could serve as a starting point for additional areas of research in
combining ante- and post-mortem approaches in order to deter-
mine a time profile for solvent concentration before or at the time
of death.
This research did not focus on modeling of VAC. However, in
forensic investigations, common practice is to obtain blood from a
femoral source, determine its BAC, and compare it to VAC to
determine an individual's intoxication and alcohol metabolic state.
Our literature review yielded 10 peer-reviewed papers that re-
ported both BAC and VAC values, as well as VAC:BAC ratios
(Table 2). Sample sizes ranged from one sample to 672 samples,
with one paper not reporting the sample size. The mean VAC:BAC
ratio ranged from 0.94 to 1.30, with standard deviations ranging
from 0.10 to 0.60. Multiple studies have reported that VACs are
typically 10e20% higher than BACs during the elimination phase; a
BAC larger than or equal to VACs is expected before equilibrium or
signifies post-mortem production of alcohol. Interestingly, the
duration between death and sample collection was not reported in
all but two studies. The time to collection is specifically important
because potential for microorganisms to generate alcohol in the
blood exists post-mortem. Two studies indicated the time from
death to collection, which was only listed as within 24 and 28 h,
respectively. According to this body of literature, all 1276 subjects
included in these ten papers had some exposure to alcohol through
ingestion, and the mean VAC:BAC ratio ranged from 0.94 to 1.30.
These results indicate that the VAC:BAC ratio can vary significantly
based on the specific ingestion aspects, particularly by metabolism
phase (e.g., absorption or elimination). Therefore, caution must be
exerted when comparing VAC to BAC results.
The models and equations used in this study are likely appli-
cable to a wide range of scenarios associated with alcohol con-
sumption and BAC determination. The modifications to the
traditional Widmark Model allow for a better reflection of BAC, and
a better understanding of an individual's intoxication state by more
accurately estimating BAC at the time of death. Thus, the combi-
nation of the modified Widmark Model and qualitative consider-
ations for post-mortem alcohol generation allows for a more
comprehensive interpretation of BAC obtained in forensic
investigations.
6. Best practices for determination of BAC at various time
points
Based on our analysis, the best methods for determining BAC at
a given time point and a given set of parameters are described
below:
Matrices Collection:
Sample immediately to avoid any microbial contamination
Collect liquids in glass tubes
Addition of 1e2% of NaF
Store at 0e4
C
Modeling Parameters:
Use the modified Widmark Model proposed by Posey and
Mozayani (2007), which allows for superimposition of mul-
tiple drinks consumed at various times.
Use a WF approach specific to gender, weight, height, and age.
Calculate the WF (r) from multiple empirical approaches and
use the average value.
Use gender-specific and weight-specific empirical data for
elimination rate (b), which is generally available in peer-
reviewed literature.
For cases involving low-BAC modeling (0.02%), use a
Michaelis-Menten kinetics approach to estimation of the
elimination rate (b).
Combine the ante-mortem approach with post-mortem
ethanol generation considerations. If available, use data
regarding other markers of putrefaction in combination with
the methodology presented by Boumba et al. (2012) to gauge
ethanol production. Also, check the sample for microbial
contamination, and concentration of preservative.
D.M. Cowan et al. / Regulatory Toxicology and Pharmacology 78 (2016) 24e3634

12. If multiple measurements are taken from various media
(whole blood, serum, plasma, vitreous humor, urine) correct
the concentrations for variability between media using pub-
lished literature values.
Conflict of interest
The authors report no conflicts of interest. Funding for this
manuscript was provided entirely by Cardno ChemRisk, LLC, a
consulting firm that provides scientific advice to the government,
corporations, law firms, and various scientific/professional organi-
zations. This paper was prepared and written exclusively by the
authors without review or input by any outside sources. Two of the
authors (DMC, BLF) have served as an expert witness regarding
alcohol toxicology and PBPK modeling of alcohol.
Acknowledgments
The authors wish to thank Nekisa Heghitat for referencing
assistance.
Transparency document
Transparency document related to this article can be found
online at http://dx.doi.org/10.1016/j.yrtph.2016.03.020.
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