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A Molecular Dynamics Simulation Approach

towards Designing of Drug Formulations:

Case study of Anti-cancer Drug (Taxol)

Apoorva Purohit, Ravi C Dutta, Beena Rai

Tata Consultancy Services, Pune, India

Email: beena.rai@tcs.com

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Anti-cancer Agent : Paclitaxel (Taxol)

Natural product from the Western yew tree (Taxus brevifolia)

Activity against a broad band of tumor types, including breast, ovarian,

lung, head and neck cancers

Also used for previously-treated lymphoma, small cell lung cancers,

oesophageal, gastric endometrial, bladder and germ cell tumors

Active against AIDS-associated Kaposi's sarcoma and restenosis

Difficulty in its oral absorption

• Low water solubility & membrane permeability

Yew treeTAXOL

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Commercial Formulations: Challenges

1:1 Cremophor EL (polyoxyethylated castor oil) and

ethanol

Serious side effects (immune system, kidneys and nervous

system)

– Premedication with corticosteroids and antihistamine

5 to 20-fold dilution of the product

– Stability of diluted Taxol is limited to 12-24

Need for a new formulation of Taxol

– More efficient

– Less toxic

Six 100-year old trees to provide enough Taxol to treat just

one patient

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Alternative Formulations

Ref.: A.K. Singla et al. / International Journal of Pharmaceutics

235 (2002) 179–192

in-silico approach

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in-silico Formulation Development

Design issues • Rheological parameters

– viscosity, yield stress

• Stability

– Thermal & Chemical

• Additional Functional Requirements

– Low friction

– Anti-stick

– Scratch resistance

– Seal compatibility

Solvent

(Water/Organic)

Drug/Pigment/Filler

Excipients/Binder/Resin

Typical

Formulation

Dispersant

Existing Methods: Trial & error

Largely experimental

Solution:

Atomistic Simulations to

delineate interactions amongst

different components and

design/optimize formulations

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Drug Formulations

Established means to improve the solubility, toxicity and/or

efficacy of a drug

The physico-chemical properties of drug-excipient blend

effect the performance of formulation

Drug loading and retention is largely influenced by the

specific interactions between drug and excipient

Molecular modeling provide an attractive alternative for

prediction of– Drug solubility

– Viscosity

– Water absorption

Benefits– Savings in cost and time

– Identification of early stage failures

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Rational Design Paradigm

•Techniques – Force field (Atomistic)

– Quantum Mechanics • Density Functional Theory (DFT)

– Molecular Dynamics

– Quantitative Structure Activity Relationship (QSAR)

•Tools – Cerius2, Material Studio (Accelrys Inc.)

– PWScf (http://www.quantum-espresso.org/

– LAMMPS (http://lammps.sandia.gov/

• Platform- Linux cluster

- EKA Super computer

Correlation of E with experimental

properties: screen/design for better

efficiency

O OH

R

r

Surface

Surfactant

Interaction energy (E) = TEcomplex - (TEsurface + TEsurfactant)

Ref.: Molecular Modeling for the Design of Novel Performance

Chemicals and Materials, (Ed.) Beena Rai, Ch. 2, 2012,CRC Press

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Earlier Simulation Studies*

Molecular Dynamics (MD) simulations

Solubility of docetaxel in five different excipients

Computation of solubility using semi-empirical and MD methods

MD with single solvent/excipient systems

Good agreement with the experimental data

MD method was found to be more accurate

* Allen et al., Pharm. Research, 25, 147 (2008)

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Present Study

Taxol in binary (1:1) and ternary solvent (1:1:1) mixtures

Solvent

– Ethanol, Water, PEG 400, Glycerol, Tween 80

Molecular dynamics simulations to compute solubility and

relative diffusivity of Taxol in the solvent combinations

Comparison of simulated solubility with the experimental

data

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System Details

Construction of molecules (solvent and drug) using Material Studio 4.1

3D periodic boxes of solvent system (all of fixed volume - approx.

42875 Å ³) was done using the Amorphous Cell Builder Module

(Material Studio) at 300K.

The constructed solvent boxes were then minimized

Water-water interactions:

– SPC/E model – hydrogen atoms are located at 1A0 from the

oxygen with an H-O-H angle of 109.47 deg.

In every optimized solvent box, Taxol molecules were added and the

system was again geometrically optimized and exported to LAMMPS

Cohesive energy density and Solubility parameter were computed

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Computation Details

Tools: Material Studio 4.1 (Accelrys Inc.) & Large-scale

Atomic/Molecular Massively Parallel Simulator (LAMMPS)

(Sandia National Lab)

Ensemble : NVT/NVE

Time : 20- 200 ns

Force field : CVFF

SPC/E model for water

Bond distances and bond angles were fixed throughout the

simulation with SHAKE algorithm

LJ 12-6 potential for short range interactions

Long range electrostatic interactions: Particle-Particle Mesh

Ewald method

EKA @ Computation Research Laboratory, Pune, India

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Solubility Parameter

Cohesive energy density is intermolecular non-bond energy per

unit volume

Solubility parameter is defined as the square root of cohesive

energy density

Energy of mixing

Flory-Huggins interaction parameter

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Approach

Calculation of CED for given volume fraction (φ)

Calculation of ΔE mix

Calculation of χ FH for given φ

Volume fraction of Taxol at χ FH = 0.5 is considered as maximum solubility

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Model Validation : Water

SPC/E model

5000 water molecules

Diffusion coefficient

Thermal Conductivity

Viscosity

RDF Analysis

Hydrogen bond analysis

0

1000

2000

3000

0 1 2

MS

D (

Ų

)

time (ns)

MSD vs time

Simulation Expt.*

D (X 10-5 cm2/sec) 2.75 2.49

K (W/m.K) 0.62 0.58

*Ref. D. R. Lide, CRC Handbook of Chemistry and Physics,

Boca Raton (FL), CRC Press, 1990

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Model Validation: Taxol Solubility

Excipients Experimental Solubility

(mg/ml)*

Simulated solubility

(mg/ml)

Tributyrin 108+1.8 112+0.6

Tricaporion 85.7+2.0 84+0.9

Vitamin E 75.0+1.8 76+0.5

Tricapryylin 55.6+2.2 63+0.8

*Ref. Huynh et al. Pharm. Research, 25(1), 2008, 147-157

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Taxol in Solvent Combinations

1440 Water + 1 Taxol 764 Water + 236 Ethanol + 1 Taxol

722 Water + 11 Tween 80 + 1 Taxol 481 Water + 148 Ethanol

+ 7 Tween 80 + 1 Taxol

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Contd.

668 Water + 165 Glycerol + 1 Taxol471 Water + 145 Ethanol +

23 PEG 400 + 1 Taxol

470 Water + 23 PEG 400

+ 7 Tween 80 + 1 Taxol 146 Ethanol + 116 Glycerol +

7 Tween 80 + 1 Taxol

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System CED

(Kcal/mole ų)

δ

(Kcal/mole ų)1/2

Diffusivity

(x10-9 m²/s)

Water + (T) 543.51 23.31 6.9

Water + ethanol + (T) 543.70 23.32 5.3

Water + tween80 + (T) 514.69 22.69 0.9

Water + glycol + (T) 544.42 23.33 2.73

Water + ethanol + tween80 + (T) 544.31 23.33 4.28

Water + PEG + tween80 + (T) 522.53 22.86 0.451

Ethanol + water + PEG + (T) 546.19 23.37 1.81

Ethanol + tween80 + glycol + (T) 530.52 23.03 0.52

Computed Properties of Taxol

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Experimental Validation

Difference in turbidity of the solvent system before and after

addition of the Taxol*

*Ref. SRI International, USA, Copyright © 2010

Frederick Furness Publishing www.ondrugdelivery.com

Solvent System

3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5T

urb

idity D

iffe

rence

1.8

1.9

2.0

2.1

2.2

2.3

2.4

2.5

Solu

bili

ty P

ara

mete

r

23.30

23.32

23.34

23.36

23.38

23.40

Turbidity Difference

Solubility Parameter

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Concluding Remarks

A molecular modleing protocol for predicting drug solubility in

the solvent mixtures

The roder of predicted solubility

eth+W+PEG ≈ W+gly ≈ eth+W+twe ≈ eth+W ≈ W>eth+tween+gly>

W+PEG+twe > W+tween

MD simulation results compare well with the experimental

results

MD simulations a complimentary tool for the design and

optimization of drug formulations

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Way Forward

Remaining 28 solvent combinations

Force field for propylene glycol

Bigger solvent box

DOE for new combinations

Experimental validation

Extend to other drugs

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Thank You!