applications of solubility prediction models in ht solid

8/4/2019 Applications of Solubility Prediction Models in HT Solid

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Applications of Solubility PredictionModels in High-throughput Crystal

Form Screening

Stephen R. Carino and David Igo

Solid Forms Sciences, ChemDev/Strategic Technologies

Pharmaceutical Seminar on aspenONE For Process Development16 May 2007


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Drug Development

DrugDiscovery

Pharm Dev Manufacturing

Solid FormScience Group


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Crystal Forms

y Polymorph different

arrangements and/or

conformations of the molecules

in a crystal lattice

y Solvates crystal form having

solvent in the crystal lattice

y Hydrates crystal form with

water in the crystal lattice

G. Stephenson, The Rigaku Journal , 22 (2005) pp. 215.


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Solid Forms: Part of Risk Assessmentand Risk Mitigation in Development

ImpactingMacroscopic Properties that

Can Impact Manufacture

and Clinical Outcomes

Polymorphs/Solvates Have

Fundamental Thermodynamic

and Physical Differences

Solubility

Hygroscopicity

& wettability

Morphology

(shape, density)

Thermodynamic

stability

Bioavailability,

Cmax, AUC

Chemical and

Physical

Stability

Dosage form &

formulation

processing

Security of

Supply Chain

(Primary)


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Polymorphism Risks : The Case of Norvir

y Years after launch, batches were failing specs dueto a second form precipitating in the capsule

y Abbott was compelled to recall Norvir

y Forced to reformulate and market liquidsuspension, a product which is not very attractiveto consumers because of its bad taste

y Had to go through further FDA scrutiny again: The

FDA has to be satisfied that the liquid formulationis bioequivalent with the old capsule


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Form Screening

yAn empirical exercise

y Crystallize from various solvents to search for solvates andpolymorphs

y Crystallization at various degree and rates of super-

saturation (evaporation, precipitation, vapor diffusion, etc)y Stress sample under high/low humidity, heat

Every compound has different polymorphic forms, and that, in general, the

number of forms known for a given compound is proportional to the time and

money spent in research on that compound (McCrone, W.C. Polymorphism in Physics andChemistry of the Organic Solid State, 1965)

)(($) tffn !


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High-Throughput Form Screening

HardwareHardware

AutomationAutomation

Data AnalysisData Analysis DataData

ManagementManagement

ExperimentalExperimental

DesignDesign


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Screening Designs

y HT screening is the shotgun approach to experimentation

Traditional

Approach

Rational

Approach1. Fire

2. Evaluate shot

3. Repeat (1)

1. Aim

2. Fire

3. Evaluate


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y Traditional

Start with a general set

y Selection based on apparent diversity (i.e., functional class)

y May not be a systematic selection

Results for the different crystallization modes are highly influenced by the

properties of the molecule

y Rational

Tailored for each compound and crystallization mode Require more initial planning on the experimental approach

May require large amounts of starting materials

Traditional vs Rational Solvent Selection

Utilize standard set of solvents understanding that hits/yields may be

affected

Increase productivity (hits/yields) for each crystallization mode while

maintaining solvent diversity


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Solvent Selection Criteria

y Solvent Diversity functional class, polarity, H-bond

donor/acceptor

y Increase the productivity of screen (Hits and Yield)

Drug/Solvent Suspensions

y Solubility

y BP constraints minimize evap*

y Miscibility

Cooling

y Solubility

y FP constraints*

y Miscibility

Evaporation

y Solubility

y BP constraints promote evap

y Miscibility

Solvent/Antisolvent

y Solubility

y BP constraints

y Miscibility

y Density

Drug slurries

y Solubility

y BP constraints minimize evap*

y Miscibility

Cooling

y Solubility

y FP constraints*

y Miscibility

Evaporation

y Solubility

y BP constraints promote evap

y Miscibility

Solvent/Antisolvent

y Solubility

y BP constraints

y Miscibility

y Density


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Solvent Selection for HT

y Primarily based on solubility anduses NRTL-SAC model

y Direct derivative of AspensSolubilityCalc Excel worksheet

y Added new functionalities

Allows users to define solventproperties for eachcrystallization mode

Fine tuning and cross-referencing of selection

Generated list can be exportedas a recipe ready for executionin automated sample prep

Solubility Detn

Aspen/Excel Interface1. Regress data

2. Predict Solubility for Broad

Range of Solvents and Solvent

Mixtures

Assess Suitability of Solvents

for Experimental Modes


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Solvent Selection Workflow

New API

Generate solubility

Data in 4-6 solvents (add 10

more solvent systems for

validation)

Analyze solids for

solvates and polymorphic

changes(NRTL-SAC wont work for these cases)

Run calculation worksheet

Run regression worksheet(generate molecular descriptor for API

using solubility data)

Selected Solvents

Experimental Space


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Solvent Selection for HT Screening

y Start with the solvents of interest Add-in


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y Enter experimental solubility data


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y Perform regression to estimate molecular

descriptors


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y Calculate solubility for neat solvents


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y Create solubility matrix for binaries

Log of solubility


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y Create the miscibility matrix


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y Create the initial selection following the main

guideline on solvent selection


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y Finalize selection; priority towards neat solvents


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y Generate the recipe

y Recipe is exported to a

experiment design

software


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Example 1

yA salt that is relatively insoluble in most common

solvents

y Poses a challenge for solution crystallization since

yield would be an issuey Resorted to binary mixtures to increase diversity of

solvent systems


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Example 1: Evaporation Crystallization

y Example evaporation

plate as observed via

PLM


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Example 1: Results

Mode # Expts % Hits (solids) # Exclusive

forms

Slurry

(5C,25C, TC,

60C)

384 97% (372) 18

Evaporation

(slow and fast)

192 58% (111) 1

Cooling 96 1% (1) 0

Solvent /

Antisolvent &

VD

192 14% (27) 2


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Example 2

y Highly soluble in many common organic solvents

y Solvent selection limited due to its propensity for

hydrolysis/alcoholysis

y

Challenge was how to produce slurries without usinglarge amount of materials (since solubility is in the

range of 100mg/ml)

yAgain, resorted to binary mixtures produce a means

of reducing the solubility and provide solvent diversity


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Example 2: Results

y Initial model only yielded 60% success in

producing slurries

y Subsequent refinement of the model increased

the yield to over 80%y Several samples yielded oils & amorphous

solids


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Example 2: Results

y Produced all known forms

y 2 new forms were identified from several solvent

systems

Form 6 (obtained by slurry from carbondisulfide with small amount of water)

Form 7 (from evaporation in 1,2-

dimethoxyethane, 1,4-dioxane and their

mixtures with other solvents)


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Current Issues with Solvent SelectionApproach

y Reducing the number of solvent systems

Need to find a way of prioritizing the solvents yet obtaining optimal diversity

y Solvates

A real concern specially when the guest solvent is miscible (i.e., has loweractivity) with the crystallization solvent since desolvation can likely occur

y Amorphous input

How do we approach the problem? Is this possible in NRTL-SAC? How dowe obtain molecular descriptors?

y Oiling and amorphous solids

Nucleation is largely influenced by kinetics; difficult to control

y Highly-soluble compound

Is there any better way to approach? Is using binary mixtures to limit

solubility negatively biasing our design?


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Summary

y NRTL-SAC provides an adequate guide for solvent

selection

y Prediction largely depends on the quality of the

solubility datay Miscibility prediction using NRTL-SAC is quite

reliable

y Need more test cases to assess value and

effectiveness of the rational approach


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Acknowledgment

y Lee Katrincic

y Joanna Bis

y Glenn Collupy

applications of solubility prediction models in ht solid

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