250
P. Volarath et al.
on a case-by-case basis. Furthermore, the utilization of TK modeling and simulation requires a level of proficiency that is achieved by receiving appropriate training and access to resources that are necessary for applying these approaches for
safety evaluation of food ingredients. Although there are vast resources available
for developing TK/PBTK models, translating or transferring a model from one platform to another may be challenging due to unique applications of the models and
lack of sufficient familiarity with all available platforms [70]. Several attempts have
been made to develop open-source packages to support TK modeling and simulation
for high-throughput TK, such as the “R-package” [71] and “Population Lifecourse
Exposure-To-Health-Effects Model” (PLETHEM) [72]. A consistent data exchange
and information sharing process for TK model development and quality control for
safety evaluation can provide opportunities for exporting the TK models from one
platform to another, thereby improving the accessibility of the developed TK models
[70].
In recent years, additional in silico approaches based on TK have been developed
by integration with other computational methods to facilitate advancement in the
field of food ingredient safety assessment. One example is the integration of QSAR
with PBTK which has been described in Sect. 12.3.1 of this chapter. Another example of such an integration is the use of in vitro to in vivo extrapolation (IVIVE)
for TK modeling that allows utilization of data from in vitro systems for examining individual processes that could be integrated to determine effects in an intact
organism [73]. If it is assumed that the toxic response of a substance is a function of its concentration in the target tissue, in vitro systems can be used to estimate
parameters that can be further extrapolated to in vivo systems. Some commonly used
in vitro systems for examining pharmacokinetic processes include isolated perfused
liver, tissue slices, hepatocytes, subcellular fractions (microsomes and cytosol), and
recombinantly expressed enzymes [73]. Over the years, several methods of performing IVIVE based on the use of different scaling factors have been proposed [73–75].
One of the most common applications of IVIVE for safety assessment is its integration with PBTK modeling [76–80]. IVIVE can be used to estimate physiological and
pharmacokinetic parameters, such as metabolic rate parameters (e.g., Vmax and Km
values for Michaelis–Menten kinetics) that can further be included in PBTK model
development for safety assessment [76–78].
12.3.3 Bioinformatic Approaches for Allergenicity
Assessment
A limitation of bioinformatic methods is that they are unable to predict de novo
food sensitization as they rely on existing known allergens, IgE epitopes, or even
sequence motifs leading to non-IgE mediated food allergy (such as gluten sensitivity).
In addition, the AA sequence alignment only helps to identify linear epitopes but is not
very useful in identifying conformational epitopes. Although efforts have been made
P. Volarath et al.
on a case-by-case basis. Furthermore, the utilization of TK modeling and simulation requires a level of proficiency that is achieved by receiving appropriate training and access to resources that are necessary for applying these approaches for
safety evaluation of food ingredients. Although there are vast resources available
for developing TK/PBTK models, translating or transferring a model from one platform to another may be challenging due to unique applications of the models and
lack of sufficient familiarity with all available platforms [70]. Several attempts have
been made to develop open-source packages to support TK modeling and simulation
for high-throughput TK, such as the “R-package” [71] and “Population Lifecourse
Exposure-To-Health-Effects Model” (PLETHEM) [72]. A consistent data exchange
and information sharing process for TK model development and quality control for
safety evaluation can provide opportunities for exporting the TK models from one
platform to another, thereby improving the accessibility of the developed TK models
[70].
In recent years, additional in silico approaches based on TK have been developed
by integration with other computational methods to facilitate advancement in the
field of food ingredient safety assessment. One example is the integration of QSAR
with PBTK which has been described in Sect. 12.3.1 of this chapter. Another example of such an integration is the use of in vitro to in vivo extrapolation (IVIVE)
for TK modeling that allows utilization of data from in vitro systems for examining individual processes that could be integrated to determine effects in an intact
organism [73]. If it is assumed that the toxic response of a substance is a function of its concentration in the target tissue, in vitro systems can be used to estimate
parameters that can be further extrapolated to in vivo systems. Some commonly used
in vitro systems for examining pharmacokinetic processes include isolated perfused
liver, tissue slices, hepatocytes, subcellular fractions (microsomes and cytosol), and
recombinantly expressed enzymes [73]. Over the years, several methods of performing IVIVE based on the use of different scaling factors have been proposed [73–75].
One of the most common applications of IVIVE for safety assessment is its integration with PBTK modeling [76–80]. IVIVE can be used to estimate physiological and
pharmacokinetic parameters, such as metabolic rate parameters (e.g., Vmax and Km
values for Michaelis–Menten kinetics) that can further be included in PBTK model
development for safety assessment [76–78].
12.3.3 Bioinformatic Approaches for Allergenicity
Assessment
A limitation of bioinformatic methods is that they are unable to predict de novo
food sensitization as they rely on existing known allergens, IgE epitopes, or even
sequence motifs leading to non-IgE mediated food allergy (such as gluten sensitivity).
In addition, the AA sequence alignment only helps to identify linear epitopes but is not
very useful in identifying conformational epitopes. Although efforts have been made