This document discusses in silico modeling techniques for predicting absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of drug candidates. It describes quantitative approaches like pharmacophore modeling and docking studies, as well as qualitative approaches like quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) studies. Specific techniques are discussed for modeling various ADMET properties like solubility, permeability, plasma protein binding, blood-brain barrier penetration, and clearance. Transporters, ionization, and data quality are also mentioned as important factors. Commercial software packages are noted that can simulate these processes.

1. Presented By
Dr. Bhupen Kalita
GIPS, Guwahati.

2. Introduction
 Efficacy and selectivity against biological targets are two important
aspects of drug discovery
 Many drug candidates in spite of having significant efficacy, fail in clinical
trial due to unfavorable pharmacokinetic properties (ADMET).
 To reduce the cost of drug discovery, in vitro evaluation of ADMET
properties in the early phase of drug discovery has been widely 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.
 Success in the in vitro studies are the motivation behind development of in
silico models, which could be applied to study the ADMET of a compound
even before it is synthesized.

3. Modeling Techniques
 There are mainly two types of modeling approaches.
 1st-The quantitative approaches represented by pharmacophore
modeling .
 2nd -Flexible docking studies investigate the structural
requirements for the interaction between drugs and the targets.
•These are useful when there is an accumulation of knowledge
against a certain target.
•For ex: transporter involved in the transportation of a drug
would help in pharmacophore study to interpret minimum
structural requirements for transport.

4. Modeling techniques…
 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.
 The three widely used pharmacophore perception tools -
DISCO (DIStance Comparisons)
GASP (Genetic Algorithm Similarity Program)
Catalyst/HIPHOP
 All three programs attempt to determine common features
based on the superposition of active compounds with
different algorithms.

5. Modeling techniques…
 The qualitative approaches represented by (QSAR) and
(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, ex-molecular
weight, interaction energies, etc.
 When calculating correlations, it is important to select the
molecular descriptors that represent the type of
interactions contributing to the targeted biological
property.

6. Modeling techniques…
 A wide range statistical algorithms are available to researchers for
correlating field descriptors with ADMET properties including
Simple multiple linear regression (MLR),
Multivariate partial least-squares (PLS)
Nonlinear regression-type algorithms such as artificial
neural networks (ANN) and support vector machine
(SVM).
 Just like descriptor selection, it is essential to select the right
mathematical tool for most effective ADMET modeling.
 Sometimes it is necessary to apply multiple statistical methods
and compare the results to identify the best approach.

7. Fig: in silico modeling
targets of drug disposition

8. DRUG ABSORPTION
 Drug absorption and resultant bioavailability 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 and requires a large
amount of (expensive) compound at the milligram scale.
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.

9. Solubility….
 Even though the process is simplified, it still requires the
synthesis of the compound. 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-
One is based on the underlying physiological
processes,
and the other is an empirical approach.

10. Solubility….
 The dissolution process involves the breaking up of the solute from
its crystal lattice and the association of the solute with solvent
molecules.
 Obviously, weaker interactions within the crystal lattice (lower
melting point) and stronger interactions between solute and solvent
molecules will result in better solubility and vice versa.
 For drug 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).

11. Solubility….
 Empirical approaches, represented by QSPR, utilize
multivariate analyses to identify correlations between
molecular descriptors and solubility.
 The target property for most models is the logarithm
of solubility (logS), and many models are trained and
verified with the AQUASOL and PhysProp databases.

12. Intestinal Permeation
 Intestinal permeation describes the ability of drugs to cross the
intestinal mucosa.
 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 cell lines like Caco-2, MDCK or
PAMPA, which have been a useful indicator of in vivo drug
absorption.

13. Factors affecting solubility and permeability
 The ionization state will affect both solubility and permeability, and
hence absorption.
 Given the environmental pH, the charge of a molecule can be
determined using the compound’s ionization constant value (pKa).
 Several commercially and publicly available programs provide pKa
estimation based on the input structure, including
SCSpKa (ChemSilico, Tewksbury, MA),
Pallas/pKalc (CompuDrug, Sedona, AZ),
ACD/pKa (ACD, Toronto, ON, Canada),
SPARC online calculator.

14. Factors affecting solubility and permeability…
 Both influx and efflux transporters are located in intestinal epithelial
cells and can either increase or decrease oral absorption.
 Influx transporters such as human peptide transporter 1 (hPEPT1),
apical sodium bile acid transporter (ASBT), and nucleoside transporters
actively transport drugs that mimic their native substrates across the
epithelial cell.
 Whereas efflux transporters such as P-glycoprotein (P-gp), multidrug
resistance-associated protein (MRP), and breast cancer resistance
protein (BCRP) actively pump absorbed drugs back into the intestinal
lumen.
 Drug metabolism in intestinal epithelial cells by cytochrome P450
enzymes should also be considered.

15.  Commercial packages such as GastroPlus and iDEA are
available to predict oral absorption and other
pharmacokinetic properties.
 They are both based on the advanced compartmental
absorption and transit (CAT) model.
 CAT incorporates the effects of drug moving through
the gastrointestinal tract and its absorption into each
compartment at the same time.

16. Drug Distribution
 The structural and physiochemical properties of a drug determine the
extent of its distribution, which is mainly reflected by three parameters:
volume of distribution (VD),
plasma-protein binding (PPB),
blood-brain barrier (BBB) permeability.
 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.

17. Drug Distribution…
 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.
 the effect of PPB is an important consideration when evaluating the
effective (unbound)drug plasma concentration.
 A nonlinear regression analysis over 300 drugs with experimental human
PPB percent data revealed that-
# for neutral and basic drugs a sigmoidal correlation exist
between logD (distribution coefficient) and PPB,
# for acidic drugs the same sigmoidal correlation between logP and
PPB.

18. Drug Distribution
 The BBB maintains the restricted extracellular environment in
the central nerve system (CNS).
 The evaluation of drug penetration through the BBB is an
integral part of the drug discovery and development process.
 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.
 Models based on log blood/brain (logBB), which is a
measurement of the drug partitioning between blood and brain
tissue.

19. Drug excretion
 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.
 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.

20. Drug excretion…
 The hepatic and renal clearance process is also complicated
by the presence of active transporters.
 the effect of active transport is incorporated by measuring
in vitro data from MDCK cells that express organic anion
transporting polypeptide (OATP) 4 and MRP2.
 However, to predict clearance for a given structure,
knowledge of the structural requirements for these
transporters is required.

21. Conclusion
 Data quality is the most limiting factor in ADMET
modeling.
 The major recent advancement in ADMET modeling is in
elucidating the role and successful modeling of various
transporters.
 Some commercial programs have already implemented the
capability of modeling active transport, such as the recent
versions of GastroPlus, PK-Sim and ADME/Tox WEB.

22. Conclusion…
 Not all pharmaceutical companies or research
organization can afford the resources to generate their
own in-house modeling programs, so the
commercially available in silico modeling suites have
become an attractive option.

23. References
 Computer Applications in Pharmaceutical Research
and Development, Sean Ekins, 2006, John Wiley and
Sons (Page no. 495-502).
 https://hemonc.mhmedical.com/content.aspx?bookid
=1810&sectionid=124489864 (4th April, 2020).
 Computational modeling to predict the functions and
impact of drug transporters (Matsson and Bergstrom,
In silico pharmacology 2015; 3:8)