computational model of drug disposition.

this presentation is about digitalization in pharmacy for prediction of parameter like pharmacokinetics and pharmacodynamics before drug dicovery process or formulation development process.

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COMPUTATIONAL MODEL OF
DRUG DISPOSITION
Guided by……..Guided by……..
Dr. Vaishali ThakkarDr. Vaishali Thakkar
Head of Department PharmaceuticsHead of Department Pharmaceutics
PG co-ordinatorPG co-ordinator
ANAND PHARMACY COLLEGE
DEPARTMENT OF PHARMACEUTICS
Presented by….Presented by….
Dalwadi SaloniDalwadi Saloni
M.Pharm (Pharmaceutics)M.Pharm (Pharmaceutics)

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Road map of presentationRoad map of presentation
Introduction of simulated models
Conventional drug discovery process
Computational simulated model of drug discovery process
ADMET process and it’s  relationship
PC parameters
ADME prediction model
Toxicological study model
Computational models used in the pharmacy drug development 
process

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What is simulation modeling? What is simulation modeling? 
•It is the imitation of the operation of real world process or system over 
time study.
•It involves generation of artificially history of system and drawing 
inference from it.
•It express the assumption of mathematical, logical and symbolic 
relationship between system.

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Significance of simulation modeling.Significance of simulation modeling.
•It enable the study of and experimentation with the internal interaction of a 
complex system, or of a subsystem within a complex system
•Informational , organizational and environmental changes can be simulated and 
the effect of those alternation on the model’s behavior can be observed.
•It is very important for the suggestion improvement in the system under 
investigation.
•By changing the simulation input and observing the resulting outputs, valuable 
insight may be obtained into which variables are most important and how 
variable interact.

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•It is also used as a device to reinforce analytical solution methodology.
•Also used to verify analytical solution.
•It can be used to experiment with new designs or policies prior to 
implementation, so as to prepare for what to happen.
•It is design for training, flow with learning without the cost and disruption of on 
the job learning.
•The morden system are so complicated so the interaction can be treated only 
through simulation.

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Application of simulated modelsApplication of simulated models
 in pharmacy in pharmacy

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Conventional drug Conventional drug 
discovery processdiscovery process

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Computational simulated modelComputational simulated model
of drug discovery processof drug discovery process

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ADMET process and it’s ADMET process and it’s 
relationshiprelationship
ADMET
Absorption

Solubility

Log p
Log g
Permiability
COCO-2
MDCK
PAMPA
Active
transport
PEPT-1
ASBT
NT
P-gp
BCRP
MRP
Distribution
PPB BBB
Passive
Log
BB
Log
PS
Active
VD
Metabolism Excretion
Hepatic
Passive Active
OATP NTCP
Renal
Passive Active
OAT OCP
Toxicity

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Types of model Types of model 

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Empirical classical Empirical classical 
compartmental model compartmental model 
•In the classical compartment model, a drug is inputted into the gut compartment, 
and absorption into the systemic circulation compartment is governed by the 
absorption rate constant (ka). Elimination is described by the elimination rate 
constant (ke).
Mathematical 
model depend 
on two 
properties
Permeability Solubility

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• Classical compartmental pharmacokinetic models  simply describe absorption as 
a single first-order process.
•Typical empirical absorption models generally assume zero order or first-order 
absorption kinetics, with or without a lag time, where the absorption rate 
constants can be easily obtained by simple compartmental modeling of the 
drug’s plasma concentration time profiles.
•All  assumption are  based on a conceptual but not on a physiological basis.

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Importance of model in Importance of model in 
drug dispositiondrug disposition

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AbsorptionAbsorption

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Metabolism processMetabolism process
Metabolism
Phase-1
•Oxidation
•Reduction
•Hydrolysis
•Cyclization
•Decyclization
Phase-2
•Methylation
•Sulphonation
•Acetylation
•Glucurodination
•Glutathione conjugation
•Glycine conjugation
Phase-3
•Further modification

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Drug ExcretionDrug Excretion

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Drug ToxicityDrug Toxicity

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General types of 
computational 
models
Molecular 
docking
Lock & key
Flexible Rigid
Pharmacophore
2D & 3D
Ligand based
Structural 
based

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Molecular dockingMolecular docking

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PharmacophorePharmacophore

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Pharmaceutical parameters Pharmaceutical parameters 
 Lipophilicity  Lipophilicity 
•It contributes to the solubility, permeability, potency and selectivity of a 
compound.
•lipophilicity of organic molecule is typically quantified as 
log Po/w
• where P is the ratio of the concentrations of a compound in a mixture of 
octanol and water phases at equilibrium.

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Calculation of 
log p value
Molecular 
simulation 
ab initio 
methods
Property-based 
methods
Empirical 
models
Linear solvation energy relationship; 
molecular size and H-bond strength; 
estimation of perturbed molecular 
orbitals
Statistical-
based models
Developed-based on various descriptors, 
such as topological indices, graph molecular 
connectivity, estate descriptors, and 
machine-learning methods
Substructure-
based methods

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ab initio methods
log p
o/w
 = ΔG
oct
solv− ΔG
w
solv/2.303RT
–ΔG
oct
solv  -
is the solvation Gibbs free 
energies of compounds in water-
saturated octanol, 
– ΔG
w
solv  
is the solvation Gibbs free 
energies of compounds in water, 
–R is the molar gas constant  
–T is the temperature
Substructure-based
models
log P =∑
n
i=1 
ai fi + ∑
m
i=1  
bj fj
–ai is the incidence of the 
fragment or atom fi 
–bj is the incidence of the 
correction factor Fj

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SolubilitySolubility  
•Aqueous solubility is one of the most important factors affecting drug bioavailability. To 
be absorbed, a drug must be soluble in water first and then have the opportunity to 
permeate across biological membranes
•ΔG
∗
sol
= ΔG
∗
sub
+ ΔG
∗
solv 
−RT ln S
0
V
m
,
•ΔG
∗
sol  
is the Gibbs free energy for solution,
• ΔG
∗
sub  
is the Gibbs free energy for sublimation
•ΔG
∗
sol  
is the Gibbs free energy for solvation, 
•R  is the molar gas constant
•T is the temperature
•Thermodynamic cycle for the transfer from crystal to vapour and then to solution

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Models of 
solubility
ab initio
 Hartree–Fock 
(HF) theory)
Depend on
molecular
orbital theory
Semi-empirical 
molecular 
orbital theory
 AustinModel 
1 (AM1)
It’s a
computational
chemistry
 Hybrid density 
functional 
theory 
Becke-3-Lee-
Yang-Parr 
(B3LYP)
HF based
Recover
electrone
Through semi-emparical method
Loocv=leave one out cv
(estimation of error)

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 Ionization constant  Ionization constant 
•Drug distribution and diffusion rely heavily on the ionized state of the drugs 
at a physiological pH because the neutral species of compounds are more 
lipophilic, whereas ionized ones are polar and water soluble. 
•Additionally, log D, which is an extension of log P by considering all forms of 
the compound (i.e. ionized and un-ionized), was introduced to consider 
the influences of ionization on the octanol-water partition coefficient

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Models of ionization 
constant
ab initio                   QM 
calculations 
statistical and 
machine-learning 
approaches
multi-linear 
regression (MLR)
 kernel-based 
machine learning
ANNs
Semiempirical 
approaches

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ADME Prediction Model (Human ADME Prediction Model (Human 
intestinal absorption)intestinal absorption)
•(https://www.youtube.com/watch?v=xETq-LvRlrM)
•(https://www.youtube.com/watch?v=xETq-LvRlrM)

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CLOE HIACLOE HIA
•   Predicted total absorption at several user-specified dose levels.
•   Identification of factors (solubility and/or permeability) limiting 
absorption at any poorly absorbed dose level(s).

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•Predicted dependence of the solubility on pH, over the range of the GI tract 
contents (pH 2-7.5) and the peak concentration of compound achieved in 
the different parts of the tract.
•Predicted absorption from the different segments of the GI tract at each 
dose level.
•Predicted time course data representing absorption over time.

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Plasma protein binding  Plasma protein binding  
•Drugs can bind to plasma proteins at constant rates, and this PPB may cause less 
bioavailability and undesirable drug–drug interactions (DDIs). it is critical to predict 
the binding rate and modify the problematic candidates.
•(HPLC) screening human serum albumin (HSA) binding affinity binding constant is given 
by                                          
                                                            logK
HSA
 = log((t − t
0
)/t
0
)
where t and t
0
 are the retention times of the drug and the dead time of the column 
respectively

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•But it has several deficiencies.
–more than one potential ligand binding site
–HSA-immobilized column cannot precisely represent the highly dynamic nature 
of HSA
Computational models can be predict 
structural characteristics.
binding affinity between the compound and 
docking site
computational docking
 molecular dynamic 
(MD) analysis 
HPLC data mining 
models

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Schematic Schematic 
presentation of the presentation of the 
workflow for the workflow for the 
ligand- and receptor-based 
in silico predicting binding 
affinity, site, and pose of 
any user provided small 
molecule with HSA.

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Blood-brain barrier (BBB)Blood-brain barrier (BBB)
•The BBB is the micro-vascular endothelial 
cell layer with the tight junction of the 
brain and plays a pivotal role in separating 
the brain from the blood.
•High penetration is needed for most of 
the drugs targeting the central nervous 
system (CNS), whereas BBB penetration 
should be minimized for non-CNS drugs 
to avoid undesired side-effects.

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•The rate and extent of brain permeation, which are expressed as log PS 
(logarithm of the permeable-surface area product) and log BB (logarithm of the 
brain/blood partitioning ratio at a steady-state).
•P-glycoprotein (P-gp) substrate probability of compounds was used to predict the 
log BB
•small molecules may be able to cross the BBB.

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Computer 
based model
QSAR model
based on log P, pKa, and fraction unbound on the 
plasma for log BB prediction, also considering the 
influence of brain tissue binding by estimating the 
negative logarithm of the fraction that is unbound in 
the brain (−log fu,br)
non-linear ionization-
specific model
based on log P and pKa.

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  MetabolismMetabolism  
•metabolism-related prediction models have mainly focused on the following studies: 
(1) The interaction models of enzymes with xenobiotics, which were often used to 
distinguish whether a xenobiotic is a substrate or inhibitor of Cytochrome P450 
monooxygenase system (CYP450s), and then to evaluate DDIs.
 (2) The clearance of the liver that could quantitatively predict the metabolic stability of 
xenobiotic models.
(3) The site of metabolism (SOM) that can be used to predict the ‘soft spots’ on xenobiotics
(4) The metabolite prediction models that could predict all of the potential metabolites for 
xenobiotics.

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 SOM prediction SOM prediction
Predictio
n model
Reactivity 
based
structure-
based
statistical 
learning 
models

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Computational model
semi-empirical 
QM calculations
Site with a hydrogen 
abstraction energy lower 
than 27 kcal mol
−1
 and 
solvent accessible surface 
area (SASA) greater than 8 Å 
would be more likely to be 
metabolized
docking 
methods
Catalytic binding 
mode of 
xenobiotics can 
be predicted
quantum chemistry 
calculations
To predict the 
hydrogen 
abstraction energy
SMART Cyp
Predict SOM directly from the 2D 
structure of a molecule without 3D 
structure generation based on 
atom reactivity and accessibility, 
where the reactivity can be rapidly 
retrieved from a pool of pre-
calculated energies of fragments by 
SMARTS matching, and the 
accessibility was evaluated using 
the relative location of an atom in a 
molecule

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Statistical learning 
model
Random
forest (RF) method
Descriptors included structural descriptors, 
SASA and topological descriptors to develop 
SOM prediction models for CYP3A4, 2D6, 
and 2C9
MetaPrint2D
Fast 
Metabolizer
Naive Bayesian 
(NB) models
 Identify 88% of the experimentally observed 
sites for CYP 3A4, 2D6, and 2D9.

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Other computationalOther computational
 models models

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Membrane transporters Membrane transporters 
•Membrane transporters are vital proteins for transmembrane processes that 
selectively transport endogenous substances and xenobiotics in the intracellular 
or extracellular directions.
Main 
transporters
ATP-binding 
cassette (ABC) 
transporters
Solute carrier 
(SLC) 
transporters

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Other 
transporter
P-gp 
(MDR1)
 Multidrug 
Resistance
-
Associated 
Proteins 
(MRPs)
Breast 
Cancer 
Resistan
ce 
Protein 
(BCRP) 
3D-QSAR CoMFA 
and CoMSIA 
Organic 
Anion 
Transporting 
Polypeptides 
(OATPs)
ABC family and 
Organic 
Cation/Anion 
Transporters 
(OCTs/OATs)
OCT-1 
inhibitor
SVM(support 
vector machine) 
model
OCT-2 
inhibitor
Combinatorial 
pharmacophore (CP) 
model
Multidrug and Toxin 
Extrusion Transporters 
(MATE)
MATE-1 
inhibitor
Random forest 
(RF) model 
(regression 
analysis)

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Software 
used
SVM (Support 
vector machine) 
average predicting 
accuracy of >90%
k-Nearest 
Neighbour (kNN) 
decision trees Bayesian classifiers

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Physiology
based method
Compartmental
absorption and
transit (CAT)
model
for drug
absorption
prediction that
assumes the
GIT as a series
of
compartments.
Well stirred
model
Steady-state model
for hepatic drug
clearance that
assumes that the liver
is a well-stirred
compartment and
that the drug is
distributed instantly
and homogenously
throughout the liver
and plasma in blood.
Advance compartmental
absorption and transit
(ACAT)
extension of CAT
and also considers
the influences of
first-pass
metabolism and
colon absorption
parallel
tube
model
assumes that the
liver is a series
of parallel
tubes and that
there is a
declining
hepatic drug
concentration
along the
length of the
tube
dispersion
model
assumption of
both the well-
stirred
model and
tube model.
whole-body
physiologically
based
pharmacokinetic
(WB-PBPK)
models
treated an
organism as a
closed circulatory
system consisting
of compartments
that are
important for
ADME

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Physiologically based pharmacokineticPhysiologically based pharmacokinetic
 (PBPK) models (PBPK) models
•Physiologically based pharmacokinetic (PBPK) modeling is  
mathematical modeling technique for predicting the absorption, distribution, 
metabolism and excretion (ADME) of synthetic or natural chemical substances in 
humans and other animal species.
•Physiologically-based pharmacokinetic (PBPK) models are describe biological 
processes in order to mimic biology. 
•They are dynamic in nature and are defined by series of differential equations
•PBPK models differ in that they are mechanistic in nature and incorporate 
physiological processes such as GI transit time and organ blood flows.

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ACATACAT
Uses the Advanced
Compartmental Absorption
and Transit (ACAT™) Model
from GastroPlus™, lumps
everything else other than first-
pass liver and kidney into a single
central compartment.

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•The use of PBPK modeling in pharmaceutical 
industry has rapidly expanded in recent times 
and has been used in sophisticated 
mechanistic applications such as the 
prediction of drug-drug interactions, the 
prediction of pharmacokinetic profiles in 
special populations, and the assessment of 
population variability. 
•https://www.youtube.com/watch?v=qvSPA
NtkptQ

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Scheme of PBPK modelingScheme of PBPK modeling
 and proposal and proposal
Identify and quantify the elimination pathways of a drug
Incorporate the drug-dependent parameters into the 
models
Compare the simulated profiles with the in vivo data
Refine the model with the results from step 3
Predict the unknown clinical settings.

53. This presentation uses a free template provided by FPPT.com
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Applications of Oral Applications of Oral 
PBPK ModelsPBPK Models
•PBPK models can have many different applications during the course of drug 
discovery and development.
•Oral PBPK models offer an ideal tool to explore complex dynamic phenomenon 
such as pH and food effects. 
•The applications of oral PBPK models to guide drug form selection and 
formulation optimization as well as to investigate pH and food effects are of high 
value. 

54. This presentation uses a free template provided by FPPT.com
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Drug Form Selection and Drug Form Selection and 
FormulationFormulation
•The oral absorption of drugs is highly dependent on various drug specific 
properties such as particle size and drug solubility of different drug forms.
•Oral PBPK models can be utilized to quantify how changes in these properties 
influence oral absorption through the use of sensitivity analysis. 
•Sensitivity analysis involves the systematic alteration of a specific drug 
parameter (e.g., particle size) in an oral PBPK model in order to identify the 
optimal values required for maximizing oral absorption.

55. This presentation uses a free template provided by FPPT.com
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Salt selectionSalt selection
•Salt selection for new molecular entities remains a challenge, due to the 
difficulties in translating in vitro solubility to in vivo bioavailability. 
•For most cases, salt forms with the highest solubility are selected with little regard 
for other factors (i.e., which salt form has the best solid-state properties for 
further drug development).
•The exercise identified a solubility of ~0.3 mg/mL that was required in order to 
produce the maximum bioavailability of phenytoin. 

56. This presentation uses a free template provided by FPPT.com
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•Any increase in solubility beyond 0.3 mg/mL was predicted to provide no 
additional increases in oral bioavailability, as drug solubility would no longer be 
rate limiting for oral absorption. 

57. This presentation uses a free template provided by FPPT.com
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Particle SizeParticle Size
•Particle Size A critical factor influencing the oral absorption of poorly soluble 
drugs is the drug dissolution rate that is dependent on the drug particle size. 
•The information from this analysis exemplifies how oral PBPK models can be used 
to provide guidance on optimal particle size ranges to produce the desired in vivo 
dissolution profile and resultant oral pharmacokinetic profile.

58. This presentation uses a free template provided by FPPT.com
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Food and pH effectFood and pH effect
•A second area of application in which oral PBPK models can serve as a useful tool 
is in the investigation of pH and food effects on oral bioavailability.
•Due to the many physiological changes that occur with food intake and the 
potential for direct food-drug interaction, PBPK modeling of food effects is more 
challenging. 
•However, as GI physiology can differ between animals and humans, human PBPK 
models combined with in vitro data may be the most appropriate means to 
capture and predict how drugs are absorbed in the human fasted and fed states.

59. This presentation uses a free template provided by FPPT.com
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Predicting Human OralPredicting Human Oral
 Pharmacokinetics Pharmacokinetics
•A third application of PBPK models of oral absorption includes early attempts to 
predict human pharmacokinetics following oral dosing of drug candidates. 
•These predictions often occur during the drug discovery phase, and with limited 
PBPK model verification in humans. 
•An example of this application was described by Liu et al., where the oral 
pharmacokinetic properties in humans of drug candidate YQA-14, a dopamine D3 
receptor antagonist, was successfully predicted

60. This presentation uses a free template provided by FPPT.com
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Toxicity prediction modelsToxicity prediction models
•Toxicity is the degree to which a substance can damage an organism or 
substructure of the organism, such as cells and organs, and remains one of the 
most significant reasons for late-stage drug development failure.

61. This presentation uses a free template provided by FPPT.com
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Software 
used
DEREK
It is a classic 
knowledge (rule)-
based expert system 
that is based on 
toxicologists’ 
experience and 
information from the 
literature
ToxAlerts
web server 
(http://ochem.eu/alerts) 
of structural alerts for 
toxic chemicals with 
potential adverse effects, 
whose database is open 
and expandable
TOPKAT employs 
cross-validated 
QSTR models
MCASE
uses a machine-
learning approach 
to identify 
molecular
fragments with a 
high probability of 
being associated 
with observed 
activity

62. This presentation uses a free template provided by FPPT.com
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Acute toxicityAcute toxicity
•The adverse effects of a substance that occur within a short period after dose or 
exposure and is an important indicator of the drug safety assessment. 
•Acute toxicity is typically the first step in toxicological investigations of unknown 
substances.
• A common criterion that measures the acute toxicity of a compound is the 
median lethal dose (LD
50
), a dose causing 50% death of the treated animals in a 
given period when administered in an acute toxicity test.

63. This presentation uses a free template provided by FPPT.com
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Computational 
model
global QSAR 
model
MLR 
method
non-
congeneric 
data sets
Sub-QSAR 
models
SVMOAO C4·5 NB kNN RF 
MACCS 
FP4 finger  
prints
MACCS-SVMOAO is the optimum combination, and the corresponding model 
yielded the highest predictive accuracy.

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web 
servers
Toxicophores
 protein-ligand-based pharmacophores .   It predict possible 
toxicity target and shed light on the mechanisms that are 
involved in toxicity development
ProTox
rodent oral 
toxicity 
prediction

65. This presentation uses a free template provided by FPPT.com
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GenotoxicityGenotoxicity  
•The mechanism of genotoxicity is complex, including inhibiting DNA synthesis by 
nucleotide analogues or base pair mismatch caused by macrocyclic organics 
embedding into the DNA helix.
•Mutagenicity should be tested in the early stages of pharmaceutical research. 
•The Ames test is widely use to test mutagenicity.
•the estimated inter-laboratory reproducibility is only 85% due to the limitation of 
the in vitro test itself.

66. This presentation uses a free template provided by FPPT.com
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•To address the complex situation and gain insight into the hidden mechanism of 
genotoxicity, additional descriptors  such as physical, quantum chemical, and 
molecular connectivity descriptors, must be explored, together with advanced 
statistical analysis tools (e.g. SVMs, k-means clustering, classifiers, and ANNs)
•comprehensive structural alert systems (for example, DEREK) and databases for 
predicting toxicity.

67. This presentation uses a free template provided by FPPT.com
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•One of these tools, the Leadscope system, has been used by FDA and US 
Environmental Protection Agency researchers for chemical and biological 
analyses to generate predictive models in the early stages of 
pharmaceutical development by read-across based on databases, as it 
provides data-mining and prediction methods considering both biological 
and chemical data.

68. This presentation uses a free template provided by FPPT.com
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Publicly available 
genetic toxicity and 
carcinogenicity 
datasets
Chemical 
Carcinogenesis 
Research 
Information 
System (CCRIS)
DEREK from 
Lhasa
ISSCAN
Carcinogenic 
Potency project 
(CPDB),
Sci-QSAR
EPA Gene-tox
the National 
Toxicology 
Program (NTP)
Leads cope FDA 
Model Applier

69. This presentation uses a free template provided by FPPT.com
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hERG toxicity hERG toxicity 
•Sudden death induced by a blockade of hERG K+ channels (encoded by the 
hERG) is widely regarded as the predominant cause of drug-induced QT interval 
prolongation. Because a diverse range of drug structures can cause hERG toxicity 
, the early regulatory detection of compounds with this undesirable side effect 
has become another important objective in the pharmaceutical industry.
•In vitro electrophysiology tests on primary cardiac tissues, such as Purkinje 
fibres, are performed using the voltage clamp technique (Nobel Prize 1991) and 
are considered as ‘gold standards’ in hERG toxicity prediction.

70. This presentation uses a free template provided by FPPT.com
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Software
PubChem 
BioAssay
WOMBAT-
PK 
Docking 
molecular 
dynamics 

71. This presentation uses a free template provided by FPPT.com
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Software used during drugSoftware used during drug
 discovery process discovery process

72. This presentation uses a free template provided by FPPT.com
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Software and computer based programs used Software and computer based programs used 
during new drug discovery and developmentduring new drug discovery and development
Sr no Software Name Major Use
1Pharmacokinetic Parameter
DDD plus Dissolution and disintigration study
Gastro plus IVIVC for various formulation
MapCheck Compare dose of fluency measurement
2Ligand interaction & molecular dynamics
AutoDock Evaluate the protein drug interaction
Schrodinger Ligand receptor Docking
GOLD Protein Ligand Docking
Biosuit Genome analysing and sequence analysing
3Molecular modeling & structural relation
Maestro Molecular modeling analysis
ArgusLab Molecular docking calculation and molecular modeling package
GRAMM Protein protein docking and protein ligand docking
SYBYL-X suite Molecular modeling and ligand based design
Sanjeevini Predict protein ligand binding affinity
PASS Creat and analysis of SAR model

73. This presentation uses a free template provided by FPPT.com
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DDDPlus (Dose Disintegration DDDPlus (Dose Disintegration
and Dissolution Plus)and Dissolution Plus)
•DDDPlus is used to study disintegration and dissolution pattern of dosage
form and active ingredients.
•It is an advanced computer program employed by formulation scientists to
simulate in vitro disintegration and dissolution of active pharmaceutical
ingredients (API) and excipient under different experimental conditions.
•This software provides precise information of dissolution and disintegration rate
so it is not necessary to rely on conventional ‘cut and try’ methods to finalize a
formulation design.

74. This presentation uses a free template provided by FPPT.com
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•DDDPlus allow selecting from one of 5 mathematical models and 5 dosage
forms employed to illustrate dissolution of a single ingredient.
•The mathematical models used for the in vitro dissolution simulation describes
the effect of following parameters on dissolution:
•Physicochemical properties of the formulation ingredients under study:
– pKa’s, solubility, diffusion coefficient, and density.
https://youtu.be/sTIo1HMYXQ8

75. This presentation uses a free template provided by FPPT.com
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Manufacturing properties for immediate release dosage forms.
Particle size distribution for each of the formulation ingredients.
Different flow patterns and fluid velocities for each experimental 
apparatus.
Interactions between the active ingredient and formulation excipients.
Microclimate pH-dependence of solubility and dissolution/precipitation.
Micelle-facilitated dissolution through the incorporation of surfactants in 
the media.
Use

76. This presentation uses a free template provided by FPPT.com
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GastroPlus (simulation software GastroPlus (simulation software 
for drug discovery and development)for drug discovery and development)
•GastroPlus is a mechanistically based simulation software package that simulates
intravenous, oral, oral cavity, ocular, intranasal and pulmonary absorption,
pharmacokinetics, and pharmacodynamics in human and animals.
•Model parameters can be fitted to data for a single record, or across multiple
records simultaneously. The program will run one simulation for each record
each time and it changes the values of one or more model parameters.
https://youtu.be/nzl1QBvF7h0

77. This presentation uses a free template provided by FPPT.com
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78. This presentation uses a free template provided by FPPT.com
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Transporter-based drug-drug interactions.
Metabolic and/or transporter induction.
Linked with the industry's 1-ranked dissolution/absorption (ACAT)
model.
Use with either compartmental or physiologically based
pharmacokinetics (PBPKPlus).
  Use

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Apply competitive and/or time-dependent inhibition kinetics by
parent and/or metabolite.
Simulate DDIs for any species (human, beagle, rat, mouse,
rhesus monkey, cynomolgous monkey, rabbit, or cat).
Account for enzyme expression level differences in various
populations.
Built-in tool to easily calculate the fraction metabolized from in
vitro assays.

80. This presentation uses a free template provided by FPPT.com
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MapCheckMapCheck
•The MapCheck system create verification plan for each field, export calculated
dose map (Frontal) to MapCheck for each field, for an example calibrated
diode array prior to collecting data. Standard deviation increases with plan
complexity.
•The average measured dose is independent of plan complexity.
•It is user friendly software for data analysis, easier commissioning process and
generates comprehensive report

81. This presentation uses a free template provided by FPPT.com
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Use
 IMRT verification. 
(medical imaging 
and radiation 
therapy)
Small 
detectors 
identify MLC.
Dose based EPID 
(enhanced privacy 
ID)IMRT QA done 
by using MapCheck

82. This presentation uses a free template provided by FPPT.com
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Ligand interactions and molecular Ligand interactions and molecular 
dynamic dynamic AutoDock AutoDock
•AutoDock is an automated program employed to predict ligand and protein
(bio-macromolecular targets) interactions.
•Continuous advancement in bimolecular X-ray crystallography helps to provide
structural information of complex biomolecules such as protein and nucleic acids.
•These structures could be employed as targets for new drug molecules in
controlling human, animal and plant diseases and disorders, and understanding of
fundamental aspects of biology.

83. This presentation uses a free template provided by FPPT.com
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Multiple steps are employed for AutoDock calculations:
Preparation of coordinate files using AutoDock tools.
Pre-calculation of atomic affinities using AutoGrid.
Docking of ligands using AutoDock.
Analysis of results using AutoDock Tools.

84. This presentation uses a free template provided by FPPT.com
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Use
Identification of aromatic rings.
Used to explore the conformational
states of a flexible ligand, using the
maps generated by AutoGrid to
evaluate the ligand-protein
interaction at each point in the
docking simulation.

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SchrodingerSchrodinger
•It can solve most of the challenges these bio-molecules will bring.
•It highlights particular advances in - molecular modeling,
- molecular dynamics,
- ligand- receptor docking,
- biologics that were designed to handle these
challenges.
•Structure based properties of molecule such as understanding of conformational
changes and hydrophobicity of structures can be analyzed by this software.

86. This presentation uses a free template provided by FPPT.com
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•Confirmation of macrocycles is performed by utilizing a high-performance
molecular dynamics simulation engine for bimolecular systems that combines
speed and accuracy.
•This intern provides information atomic movements of macrocycles that further
used to understand shape, stability, and energetics.
•Schrodinger provides powerful and intuitive graphical interfaces for system
setup, running simulations, and analyzing trajectories.

87. This presentation uses a free template provided by FPPT.com
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•The molecular dynamics simulations software is employed to study a series of
stabilized stapled α-helical peptides at different temperatures.
•These simulations explore new approaches for the α-helical stapled peptides
designing and development of potent inhibitors of α- helical protein–protein
interfaces
•Use
•Molecular dynamics simulation studies
•Quantum mechanics
•Prediction of binding affinity

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GOLD (Genetic Optimization forGOLD (Genetic Optimization for
 Ligand Docking) Ligand Docking)
•GOLD (Genetic Optimization for Ligand Docking) is a genetic algorithm to
provide docking of flexible ligand and a protein with flexible hydroxyl groups.
•It provide structural database and on empirical results on weak chemical
interactions.
• It gives reliable results and correct atom typing for both protein and ligand.
GOLD is a part of GOLD Suite software that also includes two additional
software components, Hermes and GoldMine.

89. This presentation uses a free template provided by FPPT.com
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•GOLD provides all the functionality
required for docking ligands into protein
binding sites from prepared input files.
•The Hermes visualize is used for the
preparation of input files for docking with
GOLD, visualization of docking results and
calculation of descriptors.
•Gold Mine is a tool for the analysis and post-
processing of docking results.
Use
Forbinding mode
predictions.
Protein-Ligand
Docking by using
Genetic Algorithm

90. This presentation uses a free template provided by FPPT.com
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BioSuiteBioSuite
•BioSuite together utilize the functions of macromolecular sequence and
structural analysis, chemo informatics and algorithms for aiding drug
discovery.
•The four major modules
–Genome and Proteome Sequence Analysis,
–3D Modeling and Structural Analysis,
–Molecular Dynamics Simulations
–Drug design,
They are made available
through a convenient
graphics-user interface
along with adequate
documentation and
tutorials.

91. This presentation uses a free template provided by FPPT.com
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•It is deals with the applications relating to the analysis of the nucleic acid and
protein sequences, not only of individual molecules, but also of complete genome
and proteome sequences.
•This module would enable to
– annotate genomes,
– predict protein secondary structures,
–derive a phylogenetic relationship among organisms and compare two
genomes for similarities at the gene or protein level.

92. This presentation uses a free template provided by FPPT.com
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Prediction of biological activities of unknown chemical 
entities using QSAR.
Identification of pharmacophores in biologically active 
molecules.
Superimposition of a set of molecules in 3D space by 
alignment.
Identification of the ligand poses in 3D space when it binds 
to a target using Docking.
The Drug Design module provides the following functionalities:

93. This presentation uses a free template provided by FPPT.com
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Molecular modeling and structural activity Molecular modeling and structural activity 
relationship (Maestro)relationship (Maestro)
•Maestro is a powerful tool for interpreting, managing,
and sharing the results of computational
experiments.
• It helps for building, visualizing, and sharing 3-
dimensional chemical models.
•Maestro's intuitive interface makes setting up
calculations easy and straight forward.
Use
Quantitative
structural analysis.
Visualization of
• vibrational modes,
• molecular orbital,
•electron density.
• molecular properties

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ArgusLabArgusLab
•ArgusLab is a molecular modeling, graphics, and drug design program for
Windows operating systems.
•ArgusLab calculate minimum potential energy using geometry convergence function
•This software works on the principle of quantum mechanics and helps to predict
–potential energies
–molecular structures
–geometry optimization of structure
–vibration frequencies of coordinates of atoms
–bond length, bond angle
–reactions pathway

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GRAMM GRAMM 
(global range molecular matching)(global range molecular matching)
•GRAMM software is used for protein docking.
•It predicts structure using atomic co-ordinates of the two molecules.
•This software performs 6-dimensional search through the relative translations
and rotations of the molecules.

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SYBYL–X SuiteSYBYL–X Suite
•SYBYL-X gives information to understand and balance the competing SAR’s
for each of the multiple criteria which successful drug candidate must meet.
• It visualizes and explores relationships between multiple properties with the
analysis tools in the new Molecular Data Explorer (MDE) in SYBYL-X, and
obtains insights into data in least time.
•SYBYL-X explore different insights of drug interaction mechanism with its
receptor to identify potential new binding interactions that will provide ‘step
jumps’ in potency, or to identify options for improving ADME or physical
properties without disrupting key receptor interactions.

97. This presentation uses a free template provided by FPPT.com
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•By using SYBYL-X, anyone can:
– Build a 3D structural model or homology model for the receptor of interest.
– Identify and visualize the cavities present on target protein and the 
properties of protein/ligand interaction surface.
– Predict and rationalize potential drug interactions with its receptor using 
Surflex-Dock, docking software.
–Identify promising lead candidates using Surflex Dock for virtual screening of 
databases of in-house or commercially available compounds.

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SanjeeviniSanjeevini
•This software is developed to provide a
computational pathway for automating lead
design by utilizing bimolecular (protein)
target and a candidate drug.
•Software is perform identification of
potential active sites, docking and scores the
candidate drug and returns four structures of
the candidate drug bound to protein target
together with binding free energies.
Use
drug
designing
predicts
binding
affinity
Prediction of
protein-ligand
binding affinity

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PASS (prediction of activity PASS (prediction of activity 
spectra of substances)spectra of substances)
•This software predicts possible biological activities of new pharmaceutical
substance of lead molecule based on comparison of library of existing
structures.
•PASS predicts 4366 kinds of biological activity with an average prediction
accuracy of about 95%.
•To know possible biological activities, the structure of new chemical compound is
converted in 2D structural formulae.
•The molecular structure is represented in PASS by the set of unique MNA
descriptors.

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Reveal new effects and mechanisms of action for 
known substances in corporate and personal databases.
Find new leads with given biological activity profiles 
among the compounds from in-house and commercial 
databases.
Select the most promising compounds from available 
samples for high throughput screening.
Use

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102. This presentation uses a free template provided by FPPT.com
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ReferanceReferance
1.In Silico, Experimental, Mechanistic Model for Extended-Release Felodipine Disposition 
Exhibiting Complex Absorption and a Highly Variable Food Interaction.Sean H. J. Kim1*, 
Andre J. Jackson2, C. Anthony Hunt. September 30, 2014.
2.In silico ADME/T modelling for rational drug design. Yulan Wang1, Jing Xing1, Yuan Xu1, 
Nannan Zhou2, Jianlong Peng1, Zhaoping Xiong3, Xian Liu1, Xiaomin Luo1, Cheng Luo1, 
Kaixian Chen1, Mingyue Zheng1* and Hualiang Jiang1,2,3. Quarterly Reviews of 
Biophysics (2015), 48(4), pages 488–515.
3.Modeling solubility in supercritical carbon dioxide using quantitative structure–property 
relationshipsLoreto M. Valenzuela∗, Andrea G. Reveco-Chilla, José M. del Valle. 6 July 
2014.

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4.Predicting apparent passive permeability of Caco-2 and MDCK cell-
monolayers: A mechanistic model Kai Bittermann1☯, Kai-Uwe Goss1,2☯*. 
December 27, 2017.

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THANK THANK 
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