Computational Modelling of Drug disposition, modelling techniques, drug absorption, drug distribution, drug Excretion, quantitative approach, qualitative approach, in silico models, blood brain barrier, plasma protein binding, QSAR, QSPR, Volume of distribution

1. Computational Modelling
of Drug Disposition

2. CONTENTS:
INTRODUCTION
MODELLING TECHNIQUES
DRUG ABSORPTION
DRUG DISTRIBUTION
DRUG EXCRETION

3. INTRODUCTION
*In the past, drug discovery has focused almost exclusively on efficacy and
selectivity against the biological target. As a result, nearly half of drug candidates
fail at phase II and phase III clinical trials because of undesirable drug
pharmacokinetics properties, including absorption, distribution,
metabolism,excretion, and toxicity (ADMET).
*To reduce the cost of long term testing in in-vitro ADMET properties,
computational modelling has been adopted. For example, Caco-2 and MDCK cell
monolayers are widely used to simulate membrane permeability as an in vitro
estimation of in vivo absorption. These in vitro results have enabled the training of
in silico models, which could be applied to predict the ADMET properties of
compounds even before they are synthesized.
NOTE: Recent advancements in modeling a diverse array of active transporters
as well as their impact on drug pharmacokinetic profiles are also reviewed.

4. Quantitative
approach
*It is represented by pharmacophore modeling and flexible docking studies
investigate the structural requirements for the interaction between drugs and the
targets that are involved in ADMET processes.
*These are especially useful when there is an accumulation of knowledge against a
certain target.
*For example, a set of drugs known to be transported by a transporter would
enable a pharmacophore study to elucidate the minimum required structural
features for transport.
*The availability of a protein’s three dimensional structure, from either X-ray
crystallization or homology modeling, would assist flexible docking of the active
ligand to derive important interactions between the protein and the ligand.
1. Quantitative approaches.
2. Qualitative approaches.

5. Qualitative approach
*It is represented by quantitative structure-activity relationship (QSAR) and
quantitative structure-property relationship (QSPR) studies utilize multivariate analysis
to correlate molecular descriptors with ADMET-related properties.
*A diverse range of molecular descriptors can be calculated based on the drug
structure
*Some of these descriptors are closely related to a physical property and are easy to
comprehend (e.g., molecular weight), whereas the majority of the descriptors are of
quantum mechanical concepts or interaction energies at dispersed space points that
a) DISCO (Distance Comparisons)
b) GASP (Genetic Algorithm Similarity Program)
c) Catalyst/HIPHOP
All three programs attempt to determine common features based on
the superposition of active compounds with different algorithms.

6. *When calculating correlations, it is important to select the molecular descriptors that
represent the type of interactions contributing to the targeted biological property’
*In fact, a set of descriptors that specifically target ADME related properties has been
proposed by Cruciani and colleagues.
*The majority of published ADMET models are generated based on 2D descriptors.
*Even though the alignment dependent 3D descriptors that are relevant to the targeted
biological activity tend to generate the most predictive models, the difficulties inherent in
structure alignment thwart attempts to apply this type of modeling in a high-throughput
manner.
*A wide selection of statistical algorithms is available to researchers for correlating field
descriptors with ADMET properties including simple multiple linear regression (MLR),
multivariate partial least-squares (PLS), and the nonlinear regression-type algorithms
such as artificial neural networks(ANN) and support vector machine (SVM).

8. 1) DRUG ABSORPTION
Because of its convenience and good patient compliance, oral administration is
the most preferred drug delivery form. As a result, much of the attention of in silico
approaches is focused on modeling drug oral absorption, which mainly occurs in
the human intestine. In general, drug bioavailability and absorption is the result of
the interplay between drug solubility and intestinal permeability.
Solubility
*A drug generally must dissolve before it can be absorbed from the intestinal
lumen.
* Direct measurement of solubility is time-consuming.
*By measuring a drug’s logP value (log of the partition coefficient of the
compound between water and n-octanol) and its melting point, one could
indirectly estimate solubility using the “general solubility equation”.

9. *To predict the solubility of the compound even before synthesizing it, in silico
modeling can be implemented.
*There are mainly two approaches to modeling solubility.
1)based on the underlying physiological processes,
2)the other is an empirical approach.
The dissolution process involves the breaking up of the solute from its crystal
lattice and the association of the solute with solvent molecules.
*For drugs like molecules, solvent-solute interaction has been the major determinant
of solubility and its prediction attracts most efforts. LogP is the simplest estimation of
solvent-solute interaction and can be readily predicted with commercial programs
such as CLogP (Daylight Chemical Information Systems, Aliso Viejo, CA), which
utilizes a fragment-based approach.
Intestinal Permeation
*Intestinal permeation describes the ability of drugs to cross the intestinal mucosa
separating the gut lumen from the portal circulation.
*It is an essential process for drugs to pass the intestinal membrane before entering
the systemic circulation to reach their target site of action.

10. The ion state will affect both solubility and permeability and, as a result, influence the
absorption profile of a compound. Given the environmental pH, the charge of a
molecule can be determined using the compound’s ionization constant value (pKa),
which indicates the strength of an acid or a base. Several commercially and publicly
available programs provide pKa estimation based on the input structure, including
SCS pKa (ChemSilico, Tewksbury, MA), Pallas/pKalc (CompuDrug, Sedona, AZ),
ACD/pKa (ACD, Toronto, ON, Canada), and SPARC online calculators.
Note: Many other considerations are also calculated by using in silico considerations
such as influx and efflux proteins,cytochrome enzymes etc......
*The process involves both passive diffusion and active transport. It is a complex
process that is difficult to predict solely based on molecular mechanism.
*As a result, most current models aim to simulate in vitro membrane permeation of
Caco-2, MDCK, or PAMPA , which have been a useful indicator of in vivo drug
absorption.

11. *It is an important aspect of a drug’s pharmacokinetic profile.
*The structural and physiochemical properties of a drug determine the extent of its
distribution, which is mainly reflected by three parameters:
a)volume of distribution (VD),
b)plasma-protein binding (PPB),
c)Blood-brain barrier (BBB) permeability.
a)Volume of distribution:
VD is a measure of relative partitioning of drug between plasma and
tissue, an important proportional constant that, when combined with drug
clearance, could be used to predict drug half-life.
*The half-life of a drug is a major determinant of how often the drug should be
administered.
*However, because of the scarcity of in vivo data and the complexity of the
underlying processes, computational models that are capable of predicting VD
based solely on computed descriptors are still under development.

12. b)Plasma protein binding:
Drugs bind to a variety of plasma proteins such as serum albumin. As
unbound drug primarily contributes to pharmacological efficacy, the effect of PPB is
an important consideration when evaluating the effective (unbound) drug plasma
concentration.
*Several models have been proposed to predict PPB.
*The model should not rely on the binding data of only one protein when predicting
plasma protein binding because it is a composite parameter reflecting interactions
with multiple proteins.
*Recently, scientists applied a nonlinear regression analysis over 300 drugs with
experimental human PPB percent data.
*For neutral and basic drugs they found a sigmoidal correlation between logD
(distribution coefficient) and PPB, and for acidic drugs the same sigmoidal correlation
between logP and PPB.
*The model was validated with an external test set of 20 compounds.

13. C)Blood Brain Barrier:
The BBB maintains the restricted extracellular environment in the
central nerve system (CNS).
*For drugs that target the CNS, it is imperative they cross the BBB to reach
their targets.
*Conversely, for drugs with peripheral targets, it is desirable to restrict their
passage through the BBB to avoid CNS side effects.
*Again, because of the few experimental data derived from inconsistent
protocols, most BBB permeation prediction models are of limited practical use
despite intensive efforts.
*Most approaches model log blood/brain (logBB), which is a measurement of
the drug partitioning between blood and brain tissue.
*This measurement is an indirect implication of the BBB permeability, which
does not discriminate between free and plasma protein-bound solute.

14. *The excretion or clearance of a drug is quantified by plasma clearance, which
is defined as plasma volume that has been cleared completely free of drug per
unit of time.
*Together with VD, it can assist in the calculation of drug half-life, thus
determining dosage regime.
*Hepatic and renal clearances are the two main components of plasma
clearance.
*No model has been reported that is capable of predicting plasma clearance
solely from computed drug structures.
*Current modeling efforts are mainly focused on estimating in vivo clearance
from in vitro data. Just like other pharmacokinetic aspects, the hepatic and renal
clearance process is also complicated by the presence of active transporters.
Drug Excretion

17. References:
*Computer Applications in Pharmaceutical Research and Development,
Sean Ekins, 2006, John Wiley & Sons, (page number -495 to 502)
*Modeling of interactions between xenobiotics and cytochrome P450 (CYP)
enzymes – Scientific Figure on ResearchGate. Available from:[accessed 17
May, 2022]
*A.G. Atanasov et al. / Biotechnology Advances 33 (2015) 1582–1614 –
science direct.

18. Thank you