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388 J. E. Gonçalves
Table 13.1 Physicochemical, biological properties, and parameters used in the establishment of in
silico methods with examples of the main platforms available for predictions
Examples of
available
Properties Description
software
Level 1
Water Solubility (Log
)
S
aq
Ability of a compound to dissolve in an aqueous
solvent, important for absorption
ChemAxon [7]
ACD/ Percepta
(ACD Labs) [8]
SwissADME [9]
EPI SUITE™
[10]
Log P - PgK
/ LogDOctanol/water Partition Coefficient. Indicative of the
ow
ability of a molecule to cross cell membranes
ADMETlab [11]
admetSAR [12]
SwissADME [9]
EPI SUITE™
[10]
H
don/Hacc
Ability to donate or receive hydrogen bonds, an
important component of Lipinski’s rule determining
the potential to be absorbed orally
TPSA Topological polar surface area indicates the polarity
of a molecule, the higher the value of this parameter,
the lower the permeability through cell membranes
admetSAR [12]
Corina Symphony [13]
Molinspiration
[14]
Corina Symphony [13]
SwissADME [9]
Level 2
Violation of
Lipinski’s rule
Violation of 2 or more of the characteristics (Log
P > 5; >5 H bond; >10 H bond acceptors; M
SwissADME [9]
wt > 500 Da) associated with low oral absorption
Veber’s rule for good
oral bioavailability
Effective permeability
in humans (Peff)
a drug may be achieved if it possesses 10 or fewer
rotatable bonds (RTB) and a polar surface area
(TPSA) that does not exceed 140 Å
2
Determines the ability of a molecule to permeate
through the human intestine
ADMETlab [11]
SwissADME [9]
Dallphin-AtoM
[15]
Apparent permeability (Papp)
F%—Absolute oral
bioavailability
Indicates the ability of a compound to cross mem-
SwissADME [9]
branes in in vitro or animal models
Fraction effectively absorbed orally SwissADME [9]
Dallphin-AtoM
[15]
PPB Plasma Protein binding, an important determinant of
distribution
ADMETlab [11]
admetSAR [12]
SwissADME [9]
V
d
P-gp substrate,
inducer or inhibitor
Volume of distribution, indicative of the molecule’s
ability to migrate from the blood to different
peripheral tissues
The potential to be a substrate, inhibitor, or inducer
of P-glycoprotein (P-gp), a key efflux transporter that
significantly impacts bioavailability
ADMETlab [11]
admetSAR [12]
SwissADME [9]
ADMETlab [11]
admetSAR [12]
SwissADME [9]
Simcyp [16]
(continued)
13 Challenges Faced in the Development of Computational Methods... 389
Table 13.1 (continued)
Examples of
Properties Description
Blood-brain barrier
(BBB) partitioning
CYP substrate or
inhibitor
Clearance The volume of blood effectively cleared of a mole-
T
½
C
max
T
max
AUC Area under the concentration– time curve (AUC),
Level 3
Target tissue exposure The ability of a drug to reach different tissues PK-SIM®[18]
Physiological absorption model
Enzyme inhibitor
binding process
Adapted from Madden and Thompson [6]
Blood-brain barrier partitioning refers to the ability
of a substance to cross the blood-brain barrier
Predicts the predominant metabolic pathways and
potential sites of metabolism in the liver
Interactions with CYP1A2, CYP3A4, CYP2C9,
CYP2C19, and CYP2D6 most commonly predicted
as these enzymes are responsible for the metabolism
of the majority of drugs. Particularly relevant for
predicting drug–drug interactions
cule per unit time (per unit body weight); total
clearance comprises contributions from renal excretion (Clr), hepatic clearance (Clh), and clearance via
other routes such as metabolism, sweat, secretion
into breast milk, and exhalation
Estimates the time needed for half of the drug to be
eliminated from the plasma
Maximum concentration attained in blood, tissue, or
an organ
Time to reach the maximum concentration in blood
or tissue/organ
indicating overall internal exposure to the drug, in
blood or a specific tissue/organ of interest
Absolute bioavailability and absorption in humans,
animal models, and in vitro
Competitive and mechanism-based inhibition Simcyp [16]
available
software
ADMETlab [11]
admetSAR [12]
SwissADME [9]
Simcyp [16]
ADMETlab [11]
admetSAR [12]
SwissADME [9]
Simcyp [16]
ADMETlab [11]
admetSAR [12]
SwissADME [9]
Simcyp [16]
pkCSM [17]
PK-SIM®[18]
PLETHEM [19]
GastroPlus [20]
PK-SIM®[18]
PLETHEM [19]
GastroPlus [20]
PK-SIM®[18]
PLETHEM [19]
GastroPlus [20]
GastroPlus [20]
GastroPlus [20]
3.1 Data Collection
This step is considered pivotal as it enables a comprehensi ve understanding of the
system’s behavior under investigation. The data that will inform the model can be
obtained through various methods. Primarily, it is sourced from experimental data,
gathered from scientific literature, chemical properties databases, and nonclinical
studies. This dataset may include information on solubility, topology, ionization
390 J. E. Gonçalves
constant (pKa) permeability, metabolism, protein binding, and other relevant properties. This diverse range of data is essential for accurately modeling and predicting
the pharmacokinetic and pharmacodynamic behaviors of compounds, thereby
enhancing the reliability and robustness of the in silico models [22].
Public databases such as PubChem [23], ChEMBL [24], and DrugBank [25]
(please, see Chap. 2) are invaluable resources, providing comprehensive information
on the chemical properties, biological activities, and pharmacokinetic data of various
compounds [21]. These databases serve as crucial repositories of data that support
computational modeling and drug discovery processes.
In specific scenarios, it becomes necessary to generate experimental data through
in vitro and in vivo assays. This approach allows for the incorporation of empirical
data into models, enabling a more accurate characterization of the pharmacokinetics
of particular compounds. By integrating data from both in vitro assays and in vivo
studies, researchers can enhance the predictive power and reliability of pharmacokinetic models, ultimately improving the drug development process [4].
3.2 Data Preprocessing
Given the available data, it is often necessary to undertake a process known as data
cleaning. This involves rigorously analyzing the dataset to identify and eliminate
inconsistencies, incorrect entries, missing values, and outliers [6]. During this stage,
variable coding is also performed, wherein categorical variables are transformed into
numerical formats. This process includes standardizing formats, magnitudes, and
units to ensure consistency across the dataset. This step is crucial for ensuring that
the data are suitable for subsequent modeling and analysis, thereby enhancing the
accuracy and reliability of the computational models.
3.3 Identification of Relevant Variables
During this phase, the relevant variables for the conceptualized model are identified.
Using statistical techniques or machine learning algorithms, the most critical physicochemical properties for predicting pharmacokinetics are determined. In certain
cases, dimensionality reduction may be necessary. This involves applying methods
such as principal component analysis (PCA) to address multicollinearity and
improve the model’sefficiency [6 ].
13 Challenges Faced in the Development of Computational Methods... 391
3.4 Model Choice
The next step involves selecting the most suitable machine learning algorithm. The
choice of model depends on the specific context, the size and nature of the data, and
the research objectives [6]. Often, a trial-and-error approach, combined with crossvalidation, is used to determine which model best fits the data [22]. Each model has
its own set of advantages and limitations, and the selection should be guided by the
specific requirements of the pharmacokinetic prediction problem. Commonly
employed models include:
(a) Linear regression: This is a straightforward and interpretable method that pre-
sumes a linear relationship between the independent variables and the response
[26]. It proves to be useful when a clear linear relationship exists between the
characteristics and pharmacokinetic parameters.
(b) Support vector machines (SVM): SVM proves to be effective in both classifica-
tion and regression problems. It is capable of handling complex and nonlinear
datasets, making it useful when the relationships between variables are not
strictly linear [27 ].
(c) Artificial neural networks (ANN): Neural networks are potent models capable of
learning intricate patterns in data. They are particularly beneficial in scenarios
where the relationship between characteristics and outcomes is nonlinear and
highly complex [28].
(d) Decision Trees: These are easy-to-interpret models that segregate data into
distinct sets based on decision rules . They are applicable in both classification
and regression problems [29].
(e) Random forest: An enhancement of decision trees, random forest constructs
multiple trees, and amalgamates their results to bolster accuracy and mitigate
overfitting [26].
(f) Gradient boosting: Boosting methods like gradient boosting generate a sequence
of weak models that are combined to form a more robust model. They are
effective in enhancing model accuracy [30 ].
(g) Bayesia n models: Bayesian models integrate uncertainties in modeling, proving
useful when it is crucial to consider uncertainty in model parameters [31].
(h) K-nearest neighbors (KNN): KNN is an instance-based learning model that
predicts the class or value of a data point based on the classes or values of its
nearest neighbors [32 ].
(i) Nonlinear regression models: Specific nonlinear regression models, such as
polynomial regression, can be beneficial when the relationship between variables is more intricate than simple linearity [33].
(j) Physiologically based pharmacokinetics models (PBPK): This model is
constructed based on a vast number of drug physicochemical and absorption,
distribution, metabolism, and elimination (ADME) attributes. These include
lipophilicity, solubility, pKa, molecular weight, and plasma unbound fraction,
along with physiological parameters like blood flow, tissue volume, vessel
surface area, transporters, and enzyme expression level [34].
392 J. E. Gonçalves
3.5 Model Training
The model training phase involves using the chosen algorithm on the training data,
allowing the model to discern and internalize patterns and relationships within these
data [35]. Prior to commencing model training, the data are usual ly partitioned into
three distinct subsets: training, validation, and testing. The proportions of these
subsets can vary based on the dataset’s size, but a typical split is 50% for training,
30% for validation, and 20% for testing [36].
During the training phase, the algorithm is provided with data from the training
set. The model adjusts its parameters based on these data, aiming to minimize the
discrepancy between the model’s predictions and the actual values in the training set.
This process allows the model to learn specific patterns and relationships present in
the data [21].
Parameter Tuning: As the model undergoes training, certain algorithms have
parameters that can be fine-tuned to optimize performance. Two common methods
for analyzing the results obtained by the model are residual analysis and goodnessof-fit[35].
In residual analysis, the difference between the values predicted by the model and
those observed in experimental studies is calculated. An adequate model should
exhibit an average residual value close to zero and should not show correlated
residuals when plotting the dispersion diagram with predicted values on the ordinate
and residuals on the abscissa, where no discernible trend in the points should be
observed [35].
Goodness of fit evaluates the correlation between predicted and observed values
using metrics such as the coefficient of determination (R
or coefficient of agreement. Parameter tuning is often performed using the validation
set to avoid overfitting, ensuring that decisions are not based solely on performance
on the train ing data.
Training is considered complete when the model attains acceptable performance
on the validation set. The finalized model is then evaluated on the test set, providing
adefinitive measure of its ability to generalize unseen data [21, 22, 35].
2
), correlation coefficient (r),
3.6 Model Assessment
Once the model is prepared, it will be employed to evaluate a sample or a set of test
samples to predict the desired information. Using these results, performance metrics
such as mean absolute error, mean squared error, and the coefficient of determination
2
(R
) can be obtained to assess the model’s accuracy [35].
Upon analyzing results derived from external validation, it may be necessary to
optimize the model by refining selected features, adjusting hyperparameters, or
opting for a different algorithm [35].
13 Challenges Faced in the Development of Computational Methods... 393
3.7 External Validation
External validation of a pharmacokinetic computational prediction method involves
evaluating the model’s performance on independent datasets that were not used
during model training [21, 22]. The objective is to con firm that the model can
effectively generalize to new, unseen data, providing accurate predictions in various
contexts beyond those used for model development. This validation is essential to
ensure the robustness and applicability of the method in real-world scenarios.
3.8 Implementation and Availability
The modeling process concludes with comprehensive documentation that provides a
detailed description of all stages. This includes the criteria for data collection and the
conditions under which the model should be used [35]. Typically, research groups
submit this documentation for publication in compendia or scientific journals.
Following the publication of the model, the next step is to integrate it with drug
development tools. This integration is accomplished using platforms and software,
whether open-source or commercial [35].
3.9 Continuous Update
As the volume of information about a specific parameter increases, it becomes
essential to update the model to improve its predictive accuracy and robustness .
To achieve this, it is necessary to feed the model with the new data and restart all the
previously outlined steps [35].
In a generalized and simplified manner, these are the steps involved in
constructing a computational model for predicting pharmacokinetics. Each step is
crucial for achieving the desired predictive power. Successful execution of the
project relies on the collaboration between IT specialists and professionals from
the pharmaceutical, chemistry, and biology fields. For more detailed information on
the choice, as well as the implementation and subsequent evaluation of the model, it
is recommended to consult Chap. 4.
394 J. E. Gonçalves
4 Challenges in Achieving Computational Models
with Enhanced Predictive Capability
Numerous challenges are encountered when developing a computational model to
predict the pharmacokinetic behavior of candidate drug molecules. These challenges
can be associated with each of the stages of pharmacokinetics processes and
modeling development.
The initial challenge pertains to the quality of pharmacokinetic data used in
constructing the model. Much of the data utilized is available in publi c databases,
from companies, or published in scientific journal articles. Presently, there is a
significant surge in the availability of databases that can assist in predicting
ADMET, such as the ADME database, SuperToxic, PKKB, and DSSTox [37–
40]. However, the vast amount of information contained in these databases does
not guarantee the necessary quality to enable the models to exhibit the desired
accuracy.
Another potential approach involves generating information through in-house
experimentation. However, it is not always possible to have greater control over the
quality of the data to be generated to feed the model. A limitation of this initiative is
the use of a reduced number and variety of molecules to determine the parameters
that will feed the model [21, 41]. Attempting to conduct a greater number of
experiments to obtain in vivo data can be complex and costly.
Often, in the context of nonclinical studies, predictive models are also utilized,
which can result in computational methods with less predictive power than those
generated with data from in vivo studies. The literature presents discussions related
to the quality of data obtained experimentally in different laboratories. Such inconsistencies may be linked to errors in result acquisition, processing, and manipulation,
as well as the use of inappropriate experimental methods [21] which, in certain
circumstances, is associated with the difficulty due to the inexperience of the
programmer or modeler in critically analyzing the experimental data obtained from
databases or literature.
Among the in vitro models typically used for computational modeling, methods
for evaluating intestinal permeability stand out. These methods use synthetic membrane models, such as PAMPA (Parallel Artificial Membrane Permeability), and
cellular permeability studies, such as Caco-2 cell monolayers [42, 43]. Other studies
include permeability studies across the blood-brain barrier, metabolism studies in
hepatic microsomal systems, and plasma protein binding studies, among others [44–
46]. The heterogeneity of results obtained by different laboratories for these in-house
analyses significantly complicates the comparability of results, as well as subsequent
analyses and predictions.
Recently, a measure has been adopted to address challenges related to data
acquisition by aggregating sources from fields such as biol ogy, chemistry, pha rmacology, and clinical trials to create “big data” sets for medicine research and
development. However, this strategy still faces significant obstacles, including
13 Challenges Faced in the Development of Computational Methods... 395
Table 13.2 Challenges and strategies to mitigate in silico methods applied on pharmacokinetics
predictions
Challenges Strategies
Reduced programming skills for building
model structures
The disparity between existing models and
physicochemical and physiological processes
Difficulties and limitations in obtaining experimental data
Restriction in predictive tools to estimate
desired parameters due to the relative scarcity
of data from available in vivo studies
Challenges in sampling in studies, covering
intra- and inter-subject variability, as well as
physiological differences between species,
normal individuals and special populations
Adapted from Wang and Ouyang [51]
Multidisciplinary training create qualified and
user-friendly platforms for Physiology-Based
Physiological Modeling (PBPK)
Collect or estimate a more comprehensive
range of physiological data and integrate a
greater variety of physiological processes and
tools to build more mechanistic models
Improve the design of in vitro experiments,
carry out preliminary in vivo studies and
establish connections between them
Create additional, user-friendly and qualified in
silico tools for property prediction, and incorporate data from these models
Perform more refined pharmacokinetic studies,
adopt conservative conclusions, and consider
model simplification or exploration in animal
models
missing data, dimensional inaccuracies, and bias control challenges, which add
complexity to big data analysis [48].
It is usually possible to obtain information in the literature about molecules that
have been promising, have advanced in their evaluation or have become drugs. On
the other hand, data on molecules that did not prove to be promising is not publicly
accessible, constituting a private database for pharmaceutical companies [49]. This
fact can harm the robustness of a computational method because it is only fed with a
universe of results from molecules with selected characteristics. Therefore, to
resolve these difficulties, the development of security policies and data and information sharing are essential for building robust big data that takes into account
greater expected variability. In this sense, organizations have worked to promote
guidance by proposing recommendations for formatting standardized data that can
be interchangeable [50].
An added challenge in establishing an in silico method is the necessity of
selecting the most suitable machine learn ing algorithm. This choice calls for an
enhanced understanding of the model to be implemented, necessitating the developer
to possess profound knowledge of machine learning and deep learn ing models,
which are crucial for predicting pharmacokinetic properties [51].
The ceaseless evolution and escalating complexity of AI-based models require
researchers to swiftly comprehend new techniques capable of accurately predicting
outcomes with diverse and large datasets. Table 13.2 presents the main challenges
and possible ways to minimize their occurrence when developing an in silico method
for predicting pharmacokinetics.
396 J. E. Gonçalves
5 Conclusions and Perspectives
The development of a new drug or medicine requires decision-making, which often
entails discontinuing studies with a particular molecule or investing time and
resources to progress through successive stages until an effective and safe medicine
is achieved. These decisions frequently involve substantial uncertainty, posing a
significant challenge. The absence of adequately predictive met hods for pharmacokinetic characterization, target validation, and the identification and optimization of
therapeutic candidates is currently viewed as the primary technical bottleneck in
drug discovery [52].
Despite the advancements made, the evaluation of the predictive accuracy of
existing in silico models continues to face considerable challenges. Numerous
comparative studies have been conducted, as evidenced by the statistical data
available in the scientific literature. However, the disparity in datasets used in
individual research represents a recurring concern, and efforts to minimize it should
be increasingly encouraged through the adoption of Good Laboratory Practices
[53]. This disparity, as presented in this chapter and in various literatures, is
attributed to the diverse origin of the data, which includes information generated
internally, data published in the literature, and information extracted from public
datasets [54]. This difficulty could be minimized by incorporating a broader range of
data sources, including clinical trials, electronic health records, and real-world
evidence [55].
Future research is expected to request time and effort from multidisciplinary
researchers to standardize sets of tests and evaluation criteria. This will facilitate
more robust comparisons between in silico models, significantly contributing to
consistent advancements in the predictability of ADME profiles and promoting the
reliability and applicability of these computational approaches in pharmaceutical
practice.
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