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10.4 Distribution parameters of the drug molecule
After the absorption of the drug inside the systemic circulation, its distribution inside
the body comes into effect. Distribution, the second component of ADME is a pivotal
phase in a drug’s journey through the body. It involves the movement of a drug from
the bloodstream to various tissues and compartments, impacting its efficacy and
safety. Distribution is influenced by several factors, including a drug’s physicochemi-
cal properties, such as size, lipophilicity, and charge. Lipophilic drugs tend to pene-
trate cell membranes more easily, while charged molecules may face barriers. Protein
binding also plays a significant role; drugs bound to plasma proteins have a limited
distribution, while those unbound are more readily available for action. Tissues vary
in blood flow and permeability, affecting drug distribution. Organs with high blood
flow, like the heart and liver, receive drugs more rapidly, while those with low blood
flow, such as fat tissue, may accumulate lipophilic drugs. Distribution can impact a
drug’s therapeutic window and potential side effects. Understanding these factors is
crucial in drug development to optimize dosing regimens and ensure the drug reaches
its target site effectively while minimizing unwanted distribution to other tissues [25–28].
There are certain parameters that determine the distribution of the drug inside the body
and can be calculated through in silico approaches.
10.4.1 Apparent volume of distribution (V
d
)
Apparent volume of distribution (V
d
) is a pharmacokinetic parameter that reflects the
extent of a drug’s distribution within the body relative to its concentration in the
bloodstream. It helps assess a drug’s distribution characteristics and is a key factor in
dosing and drug design. In silico prediction of Vd involves computational models that
use physicochemical and pharmacological properties to estimate this parameter.
These models leverage data on a drug’s lipophilicity, molecular weight, protein bind-
Tab. 10.2 (continued)
S. no. Name of drug Calculated F value
Captopril .
Rosuvastatin .
Cetirizine .
Pantoprazole .
Glimepride .
Donepezil .
Quinine .
Lidocaine .
10.4 Distribution parameters of the drug molecule 229
ing, and tissue-specific factors to simulate its distribution behavior. By predicting Vd,
researchers can anticipate a drug’s distribution profile before conducting costly and
time-consuming in vivo experiments, aiding in drug development and optimizing dos-
ing strategies for therapeutic efficacy. Apparent volume of distribution is an impor-
tant parameter that is defined as the volume required to dissolve any drug inside the
body to reach a steady state concentration in the blood plasma at equilibrium. Mathe-
matically, it can be expressed as the following equation:
V
d
=
The dose of the drug administered
Steady-state concentration of the drug in plasma
(10:4)
A drug will have a high value of V
d
if a larger fraction of it remains inside the tissues
as compared to plasma due to certain factors such as high lipophilicity. Therefore, a
larger dose of such drugs is required to attain a s teady-state plasma concentration.
Similarly, if the major fraction of the drug remains in the blood plasma due to high
plasma protein binding or high molecular weight, then the drug will have a low value
of V
d
[29–32].
10.4.2 Plasma protein binding
Plasma protein binding is a crucial pharmacokinetic parameter that influences a drug’s
distribution, efficacy, and safety within the body. It refers to the degree to which a drug
molecule binds to proteins found in the bloodstream, primarily albumin and alpha-1
acid glycoprotein. Only the unbound, or free fraction of a drug is pharmacologically ac-
tive and able to distribute to target tissues. The bound fraction acts as a reservoir, grad-
ually releasing the drug over time. In silico prediction methods are essential tools in
drug discovery and development for estimating plasma protein binding. These compu-
tational approaches leverage the drug’s physicochemical properties, such as molecular
weight, lipophilicity, hydrogen bonding capacity, and 3D molecular structure, to predict
its binding affinity for specific plasma proteins. Several methods, including molecular
docking, QSAR modeling, and machine learning algorithms, are employed for this pur-
pose. Accurate prediction of plasma protein binding is invaluable for several reasons. It
helps researchers understand a drug’s distribution characteristics, assists in optimizing
dosage regimens, and can flag potential drug–drug interactions. Moreover, it aids in
prioritizing drug c andidates early in the development process, ensuring that com-
pounds with appropriate binding profiles progress through the pipeline. Therefore, in
silico prediction of plasma protein binding is a vital step in drug discovery and develop-
ment, providing valuable insights into a drug’s pharmacokinetic behavior and support-
ing informed decision-making throughout the drug development process.
Drugs that show high affinity to the plasma proteins tend to have a low apparent
volume of distribution. The fraction of the drug that binds with the plasma protein
cannot cross the lipid membrane and hence does not reach the tissues to show their
230 Chapter 10 ADMET analysis in CADD
desirable pharmacological action. This plasma protein binding is a reversible phe-
nomenon and it is important to mention that the presence of multiple drugs in the
systemic circulation can lead to altered plasma protein binding of drugs as some
drugs do displace other plasma protein-bound drugs. Albumin is one of the most com-
mon proteins to which most of the drugs bind in the blood plasma [33–36]. Table 10.3
depicts the volume of distribution (V
d
) and plasma protein binding values of the com-
monly marketed drugs calculated from the ADMETlab 2.0 online server [37].
10.4.3 CNS MPO score for the distribution of the drugs in the brain
CNS-active drugs for the management of disorders like Alzheimer’sdisease,Parkinson’s
disorder, depression, anxiety, etc. can show their pharmacological action only when
they cross the blood–brain barrier (BBB) and reach the brain. Therefore, the first pre-
requisite for any drug to show its activity in the brain is that it should cross the BBB.
For assessing this ability of any drug molecule, the Central Nervous System Multiparam-
eter Optimization (CNS MPO) score was developed by Wager et al. The CNS MPO score
for any drug can range from 0 to 6 and for any drug to exert its CNS pharmacological
activity, it is desired that its CNS MPO score should be greater or equal to 4.
The CNS MPO score is a valuable tool in drug development, specifically designed
to predict and optimize the distribution of drug s within the brain. As the brain is a
highly specialized and protected organ with a complex BBB, assessing a drug’s ability
to reach its target site within the CNS is critical for neuropharmacology. The CNS MPO
score incorporates multiple parameters, such as molecular size, lipophilicity, polarity,
and the presence of specific functional groups, to evaluate a drug candidate’s poten-
tial for effective brain penetration. Here is how the CNS MPO score aids in the distri-
bution of drugs in the brain:
Tab. 10.3: Values of bioavailability of the commonly marketed drugs calculated through
the SwissADME server.
S. no. Name of drug Calculated V
d
value (liters) Plasma protein binding (%)
Paracetamol . .
Clonazepam . .
Captopril . .
Rosuvastatin . .
Cetirizine . .
Pantoprazole . .
Glimepride . .
Donepezil . .
Quinine . .
Lidocaine . .
10.4 Distribution parameters of the drug molecule 231
– Size and lipophilicity: Small, lipophilic molecules tend to cross the BBB more ef-
ficiently. The CNS MPO score assesses these properties to determine a drug’s like-
lihood of reaching its intended site of action.
– Polarity: Polar molecules generally have more difficulty crossing the BBB. The
score considers the balance between lipophilicity and polarity to optimize brain
distribution.
– Functional groups: Certain functional groups can enhance or inhibit brain pene-
tration. The CNS MPO score evaluates the presence of these groups to predict a
drug’s BBB permeability.
– Optimization: Researchers can use the CNS MPO score to fine-tune drug candi-
dates, modifying their chemical structures to improve brain distribution while
maintaining their pharmacological activity.
By employing the CNS MPO score, pharmaceutical scientists can make informed deci-
sions during drug design and development, leading to more effective CNS drugs with
improved brain distribution, potentially increasing therapeutic efficacy, and minimiz-
ing side effects in neurological disorders and conditions [38–40].
10.4.4 Blood–brain barrier (BBB) score
Blood–brain barrier (BBB) score is a critical parameter in drug development and dis-
tribution within the brain. The BBB is a specialized barrier that separates the circulat-
ing blood from the brain’s extracellular fluid, ensuring the brain’s microenvironment
remains stable and protected. However, it also poses a significant challenge for drug
delivery to the central nervous system. The BBB score is a computational tool used in
silico to predict a drug’s ability to penetrate the BBB. It takes into account various
physicochemical properties of a compound, such as molecular size, lipophilicity,
charge, and hydrogen bonding capacity, to estimate its likelihood of crossing the BBB.
A high BBB score indicates a drug with a better chance of penetrating this barrier and
reaching its target within the brain. Accurate BBB score prediction is crucial in drug
development for neurological and psychiatric conditions, as it helps identify promis-
ing drug candidates with optimal brain distribution. Drugs designed to treat diseases
of the CNS must possess the ability to cross the BBB effectively to exert their therapeu-
tic effects without causing unwanted side effects. Therefore, the BBB score is a valu-
able computational tool that aids in the early screening and prioritization of drug
candidates for CNS-related conditions. It plays a pivotal role in optimizing drug design
and dosage regimens, ultimately contributing to the development of safer and more
effective treatments for brain-related disorders.
This parameter was also developed with the same aim to identify the drugs that
can cross the BBB and act on the CNS. BBB score was calculated by involving 5 param-
eters of the drug molecule; its value lies between 0 and 6. Any drug having a value
232 Chapter 10 ADMET analysis in CADD
less than 4 is considered as non-CNS drugs and those having a score equal to or
greater than 4 are considered as CNS drugs [41]. The CNS MPO and BBB scores can be
calculated by in si lico methods using many tools. One such tool is Marvin Sketch of
the ChemAxon [24]. Table 10.4 illustrates the CNS MPO and BBB scores of certain
drugs calculated through this tool.
10.5 Metabolism parameters of the drug molecule
After the distribution of the drug inside the body, it undergoes one or more of the
following changes inside the body:
– Excreted, unchanged
– Remains unchanged inside the body
– Chemical modification occurs to the drug moiety
– Enzymatic biotransformation
Drugs that are highly polar in nature are excreted unchanged through the urine as
they are not absorbed through the lipid membrane and not distribu ted much to the
tissues. On the contrary, highly polar drugs are retained inside the tissues of the body
through the lipid membranes. Certain drugs also undergo chemical modification such
as hydrolysis inside the body. These chemical modifications are done to impart polar-
ity to the drug molecules so that they can be excreted from the body. It is the enzy-
matic metabolism of the drugs that predominates over all these processes and occurs
mostly inside the liver. Besides the liver, this enzymatic metabolism also occurs to a
smaller extent inside the kidney, lungs, skin, and gastrointestinal mucosa. For enzy-
matic metabolism, the cytochrome-P450 enzyme superfamily plays a very important
role as most of the enzymatic metabolism is performed by these enzymes inside the
body. The most important enzymes of the cytochrome-P450 family that are involved
in drug metabolism are CYP1A, CYP2C, CYP2D, and CYP3A subfamilies.
Tab. 10.4: CNS MPO and BBB score of certain drugs calculated through the Marvin Sketch of ChemAxon.
S. no. Name of drug Calculated CNS MPO score value Calculated BBB score value
Paracetamol ..
Clonazepam ..
Captopril ..
Rosuvastatin . .
Cetirizine . .
Pantoprazole ..
Glimepride . .
Donepezil . .
Quinine . .
Lidocaine . .
10.5 Metabolism parameters of the drug molecule 233
Metabolism is a critical parameter in ADME studies, particularly in the context of
in silico drug design. In silico methods harness computational techniques to pre dict
and understand a drug candidate’s metabolic fate within the body, providing essential
insights to streamline drug development. In silico metabolism and prediction involves
modeling the enzymatic transformations that drugs u ndergo, primarily in the liver.
Cytochrome P450 enzymes (CYPs) play a central role in drug metabolism, and in silico
tools predict how these enzymes may modify a drug’s chemical structure. Understand-
ing the metabolic pathways a drug may follow helps identify potential metabolites
and assess their pharmacological activity, toxicity, and impact on bioavailability.
Moreover, in silico metabolism analysis aids in optimizing a drug’s chemical structure
to minimize susceptibility to metabol ic processes that could render it ineffective or
harmful. Researchers can explore structural modifications to enhance a drug’s meta-
bolic stability and bioavailability. In the early stages of drug design, in silico metabo-
lism predictions allow for the prioritization of lead compounds that are less likely to
be extensively metabolized or exhibit toxic metabolites [42–45]. This reduces the cost
and time associated with experimental studies. Table 10.5 depicts the details of the
cytochrome-P450 enzymes inhibited by some of the marketed drugs, assessed through
the SwissADME online server [24].
10.6 Excretion parameters of the drug molecule
Excretion is the last step in the fate of any drug molecule that is present in the body.
It is simply defined as the process of removing the drug that is absorbed systemically.
The two most common routes of excretion of the drugs from the body are renal excre-
tion and biliary excretion. Renal excretion is the most common type of excretion of
the drug molecules from the body. If a drug is highly polar, it is readily excreted from
Tab. 10.5: Details of the cytochrome-P450 enzymes inhibited by some of the marketed drugs,
assessed through the SwissADME online server.
S. no. Name of
drug
CYPA
inhibitor
CYPC
inhibitor
CYPC
inhibitor
CYPD
inhibitor
CYPA
inhibitor
Paracetamol No No No No No
Clonazepam Yes Yes No No Yes
Captopril No No No No No
Rosuvastatin No No No No No
Cetirizine No No No Yes No
Pantoprazole Yes Yes Yes Yes Yes
Glimepride No No Yes No Yes
Donepezil No No No Yes Yes
Quinine No No No Yes No
Lidocaine No No No Yes No
234 Chapter 10 ADMET analysis in CADD
the urine. For a lipophilic drug, it is first metabolized to a polar form and then ex-
creted through the urine. Biliary excretion is the most preferable route for the excre-
tion of large lipophilic drugs, such as steroids, which have high protein binding. Some
other routes of excretion of less importance to the drug molecules also include respi-
ration, saliva, and sweat.
In the realm of in silico drug design, the consideration of excretion is a pivotal as-
pect of ADME analysis. Understanding and predicting a drug’s excretion profile is essen-
tial for optimizing its pharmacokinetics and ensuring safety. In silico methods are
instrumental in assessing excretion parameters. Computational models take into ac-
count various factors, including a drug’s molecular weight, lipophilicity, polarity, and
metabolic stability, to estimate its propensity for renal or hepatic clearance. These mod-
els can predict the likelihood of a drug being excreted unchanged or as metabolites,
providing critical insights into its elimination pathways. Accurate in silico predictions
of excretion parameters aid in rational drug design by allowing researchers to:
– Optimize dosage regimens: Understanding a drug’s excretion rate helps deter-
mine the appropriate dosing frequency and amount to maintain therapeutic lev-
els in the body.
– Minimize toxicity: Predicting metabolites and their toxicity profiles helps iden-
tify and design drugs with safer excretion profiles.
– Identify potential drug–drug interactions: By assessing excretion pathways, in
silico methods can flag potential interactions that might affect a drug’s clearance,
leading to adverse effects or reduced efficacy.
Therefore, the in silico prediction of excretion parameters in the context of ADME is a
crucial step in drug design and development. It enables the creation of compounds
with optimal pharmacokinetic profiles, enhancing the chances of success in clinical
trials and ultimately improving patient outcomes [46–48]. The two most important pa-
rameters that are to be considered for designing any drug molecule include:
10.6.1 Clearance (CL) of the drug
Clearance (CL) of the drug is defined as the volume of blood cleared of a drug in a
given period. Drug CL refers to the pharmacokinetic process by which the body elimi-
nates a drug or its metabolites from the bloodstream. It represents the rate at which
the drug is removed and is typically measured in units of volume per unit of time
(e.g., milliliters per minute). CL is a critical parameter that influences a drug ’s concen-
tration in the body over time, which in turn affects its efficacy and safety. CL can
occur through various mechanisms, including renal CL (excretion of drugs or metabo-
lites via the kidneys into urine), hepatic CL (metabolism and elimination of drugs by
the liver), and other nonrenal and non-hepatic routes, such as metabolism in other
tissues or excretion in feces, sweat, or saliva.
10.6 Excretion parameters of the drug molecule 235
The concept of CL is central to pharmacokinetics, as it helps determine the appro-
priate dosing regimen for a drug. The CL rate, in combination with the volume of distri-
bution (V
d
), allows pharmacokinetic equations to estimate the drug’s half-life, which is
the time it takes for half of the drug concentration to be eliminated from the body. By
understanding drug CL, healthcare professionals and researchers can tailor drug dosing
to achieve desired therapeutic levels while minimizing the risk of toxicity or inadequate
efficacy. Mathematically, CL can be defined through the following equation:
CL =
Rate of elimination
Plasma concentration of a drug
(10:5)
The value of CL greater than 15 is considered high, the value of CL between 5 and 15 is
considered moderate, and the drug molecules possessing CL value less than 5 are con-
sidered to have low clearance inside the body [49, 50].
10.6.2 Plasma half-life of the drug (t
1/2
)
Plasma half-life (t
1/2
) is also a very important parameter for studying the excretion of
the drug from the body. Plasma half-life is defined as the time taken by a drug to re-
duce to its half of initial concentration in the blood plasma. Mathematically, it is de-
fined by the following equation:
t
1=2
=
0.693
k
(10:6)
where k is the elimination rate constant of the drug.
In the context of in silico drug design, understanding the plasma half-life of a
drug is a crucial aspect of ADME analysis. This parameter is a key determinant of a
drug’s duration of action and dosing frequency. In silico methods are instrumental in
predicting and optimizing the plasma half-life of potential drug candidates. Computa-
tional models take into account various factors such as the drug’s chemical structure,
physicochemical properties, metabolic stability, and clearance mechanisms to esti-
mate its half-life. Machine learning algorithms and quantitative structure–activity re-
lationship (QSAR) modeling are commonly employed for these predictions. A longer
plasma half-life can be desirable for drugs intended for chronic conditions, as it re-
duces the frequency of administration and enhances patient compliance. Conversely,
for drugs with potential safety concerns or narrow therapeutic windows, a shorter
half-life may be preferred to allow for rapid elimination and better control of drug
levels. By accurately predicting and manipulating the plasma half-life during the drug
design phase, in silico methods help researchers make informed decisions about lead
optimization, dosing regimens, and overall drug development strategies. This contrib-
utes to the creation of more efficacious and safer pharmaceuticals, speeding up the
drug discovery process and reducing costs associated with experimental trials.
236 Chapter 10 ADMET analysis in CADD
For any drug molecule to exert its desired pharmacological action, it must have
the desired concentration inside the plasma, below which it cannot show its action.
Hence, the t
1/2
value of any drug gives us an idea about the dosing duration of that
drug moiety [51, 52]. Table 10.6 depicts the clearance value and t
1/2
values of some of
the marketed drugs, calculated from the ADMETlabs 2.0 server [37].
10.7 Toxicity aspects of the drug molecule
Besides the pharmacological and pharmacokinetic profile of the drug, its toxicity pro-
file should also be assessed before designing any drug molecule. The drug molecules
that are highly potent and have a good pharmacokinetic profile may not be suitable
drug candidates if they show high toxicity. Toxicity is a critical parameter within the
ADMET framework in the context of in silico drug designing. In silico methods play a
crucial role in predicting and evaluating drug toxicity , offering a cost-effective and
time-efficient way to identify potential safety concerns early in the drug development
process. In silico drug, design employs various computational techniques to assess
toxicity, including QSAR modeling, molecular docking, and machine learning algo-
rithms. These methods utilize information abou t a drug candidate’s chemica l stru c-
ture, physicochemical properties, and interactions with biological targets to make
predictions about its potential toxic effects.
Toxicity prediction encompasses the following aspects:
– Predicting specific toxicity: In silico tools can estimate the likelihood of a drug
candidate causing specific types of toxicity, such as hepatotoxicity (liver damage),
cardiotoxicity (heart toxicity), or nephrotoxicity (kidney toxicity).
– Dose-response modeling: Computational models can provide insights into the
dose-response relationship, helping researchers determine safe dosage levels for
a drug candidate.
Tab. 10.6: Clearance (CL) value and t
1/2
values of some of the marketed drugs
calculated from the ADMETlabs 2.0 server.
S. no. Name of drug Calculated CL value Calculated t
/
value
Paracetamol . .
Clonazepam . .
Captopril . .
Rosuvastatin . .
Cetirizine . .
Pantoprazole . .
Glimepride . .
Donepezil . .
Quinine . .
Lidocaine . .
10.7 Toxicity aspects of the drug molecule 237
– Structural alerts: Virtual screening can identify structural features or functional
groups associated with toxicity, allowing for structural modifications to mitigate
adverse effects.
– Prediction of metabolite toxicity: In silico methods can also predict the toxicity
of metabolites produced during drug metabolism, aiding in the selection of candi-
dates with safer metabolic profiles.
By integrating toxicity predictions into the drug design process, researchers can prior-
itize compounds with lower toxicity risks, optimize molecular structures to reduce
toxic potential, and ultimately accelerate the development of safer and more effective
drugs. This proactive approach to toxicity assessment is invaluable in reducing the
attrition rate of drug candidates in later stages of development and improving patient
safety [53–56].
There are numerous parameters available that should be checked for evaluating
the toxicity of any new drug molecule. Table 10.7 depicts the important toxicity pa-
rameters obtained for some commonly marketed drugs calculated through the AD-
METlabs 2.0 online server [37].
Tab. 10.7: Important toxicity parameters obtained for some commonly marketed drugs calculated
through the ADMETlabs 2.0 online server.
S. no. Name of
drug
hERG
blocker
Human
hepatotoxicity
Skin
sensitization
Carcinogenicity Respiratory
toxicity
Paracetamol –
(May not
interfere)
–
(Nontoxic)
+
(Little irritant to
the skin)
+
(Little
probability of
being
carcinogenic)
–
(Nontoxic)
Clonazepam –
(Very little
probability
of being a
blocker)
–
(Very little
probability of
being toxic)
+
(Little irritant to
the skin)
–
(Very little
probability of
being
carcinogenic)
–
(Nontoxic)
Captopril –
(May not
interfere)
+++
(Probably
hepatotoxic)
++
(Little high
probability of
being an irritant
to the skin)
–
(Not a probable
carcinogenic
agent)
–
(Little
probability of
being
respiratory
toxic)
238 Chapter 10 ADMET analysis in CADD
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