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Multi-scale pharmaceutical process understanding: From particle

to powder to dosage form

Mazen L. Hamad a,, Keith Bowman b, Nathan Smith c, Xiaohong Sheng d, Kenneth R. Morris e

a Department of Chemistry, University of Hawai’i at Hilo, 200 W. Kawili ST. Hilo, HI 96720, USAb School of Materials Engineering, Purdue University, 701 West Stadium Ave, West Lafayette, IN 47907, USAc Harris Corporation, GSCD, P.O. Box 37 M.S. 1-11D Melbourne, FL 32902-0037, USAd Department of Industrial and Physical Pharmacy, Purdue University, 575 Stadium Mall Drive, West Lafayette, IN 47907-2091, USAe Department of Pharmaceutical Sciences, University of Hawai’i at Hilo, 200 W. Kawili ST. Hilo, HI 96720, USA

a r t i c l e i n f o

Article history:

Received 7 August 2009

Received in revised form

16 January 2010

Accepted 28 January 2010Available online 10 February 2010

Keywords:

Particle

Powders

Pharmaceuticals

Materials processing

Informatics

Systems engineering

a b s t r a c t

Understanding the properties and behavior of pharmaceutical materials is critical to the design of a safe

and effective dosage form. The desired performance of pharmaceutical products differs from other areas

of engineered material products. With pharmaceutical products, there is an increased level of

importance on solubility, dissolution and stability; while a secondary level of importance is given to

mechanical properties. The use of multi-scale process understanding suggests incorporating data from

the different scales (particle, powder, and dosage form) into a single informatics database. The

properties of the active pharmaceutical ingredient and the excipients must be interrogated at each

scale. At the particle level, the primary concerns are with solubility, dissolution rate, the anisotropic

properties of pharmaceutical crystals, polymorphism and the degree of crystallinity. At the level of the

powder scale, the primary concerns are powder flow and the ability of the bulk powder to be compacted

into a dosage form. Finally, at the dosage form level, critical issues include the effect of excipient

crystallinity on dosage form dissolution rate and the tensile strength of compacts made from milled,

roller compacted ribbons. Examples of existing and emerging approaches for understanding these

properties and behaviors at each scale are illustrated as key elements in developing a multi-scale

process understanding of a pharmaceutical process.& 2010 Elsevier Ltd. All rights reserved.

1. The science of pharmaceutical materials

The importance of the properties and behavior of pharmaceu-

tical materials continues to be critical to the economical design

and production of drug and other therapeutic products. However,

there are significant challenges in implementing best practices

and leveraging existing tools in addition to developing detailed

understanding necessary for control of pharmaceutical materials.

Given the compressed time-line and increased cost of pharma-ceutical development, materials considerations must occur very

early yet not add appreciably to the bottom line nor slow the

development process (i.e., it must stay off the ‘‘critical path’’). The

majority of the technical challenge is quite well recognized by

materials scientists and engineers. These can be factored into

issues of implementing known approaches for molecular solids

and natural products; and developing new approaches for issues

specific to the pharmaceutical materials domain.

Pharmaceutical materials of interest are typically solids at

some stage of development and comprise a range of molecular

types and sources. The drugs are still primarily small molecular

organics though large molecules are clearly on the rise, in addition

drugs from natural products may be more heterogeneous than

those synthetically derived both in their chemical and physical

properties. Excipients are components other than the drug that are

included to enhance or modify the stability and/or performance of

a dosage form (e.g., disintegration aids in tablets) and representperhaps the most important opportunity for materials sciences to

contribute to dosage form and process design. These materials are

typically derived from natural products (e.g., cellulose) and as

they are intended to modify the properties of the drug or dosage

form when present often determine the physical and mechanical

properties.

A unique aspect of pharmaceutical materials properties is that

unlike most other industries, the primary interest is on the

solubility and dissolution of the active pharmaceutical ingredient

(API). For solid oral dosage forms, it is based upon this and

secondary considerations of stability that the specific dosage form

(e.g., tablet, capsules, etc.) is selected. Next but of much less

Contents lists available at ScienceDirect

journal homepage: www.elsevier.com/locate/ces

Chemical Engineering Science

0009-2509/$- see front matter & 2010 Elsevier Ltd. All rights reserved.

doi:10.1016/j.ces.2010.01.037

Corresponding author. Tel.: + 1 808933 2194; fax: + 1 808974 7693.

E-mail address: [email protected] (M.L. Hamad).

Chemical Engineering Science 65 (2010) 5625–5638

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consideration are the powder and mechanical properties of the

API. Once selected the properties must be ‘‘matched’’ with the

selected processing train which typically requires the addition of

the excipients, which may also facilitate the manufacture of the

dosage form as well as the stability/performance. Here of course

the powder and/or mechanical properties are of primary

importance for the materials as they function to achieve the

goals of solubilization, stabilization, and reproducibility of the

behavior across all dosage forms produced. This is a true ‘‘multi-scale’’ process.

While many of the properties described are well understood or

predictable for materials ‘‘traditionally’’ the subject of materials,

mechanical, and chemical engineering, there has been a far less

concentrated effort to develop the same level of knowledge for

organic molecular materials with the notable exception of

synthetic polymers. This is partially due to the realities of the

relative importance of materials science to the pharmaceutical

industry compared to the semiconductor or aircraft industries, for

example, but also due to the complexity of the systems. The top

three reasons for drug product recalls are as follows:

(1) lack of content uniformity (meaning that each unit does not

meet the criteria for having the correct range of activepharmaceutical ingredient (API) amount),

(2) too fast or too slow dissolution (meaning that API within each

unit does not dissolve at the appropriate rate) and

(3) too high impurity concentration (meaning that the concen-

tration of an unwanted and potentially harmful compound is

higher than allowed)

As each of these problems are traceable directly to failures in

understanding of pharmaceutical materials and processing meth-

odology, it is clear that there is a need for support in the area.

When considering the major class of API’s, i.e., small organic

molecular crystals, we see a system that is stabilized in the lattice

by non-bonded interactions much weaker and less predictable

than covalent, metallic, or ionic bonding in traditional materials.

Also unlike the traditional structures, the molecules are flexible

and respond less predictably and more anisotropically to the

mechanical and other stresses experienced during processing.

While every effort is made to control the properties of the API the

observed variation in properties is still sufficient to cause manyproblems with homogeneity of powder blends and dosage forms,

dissolution, and impurities. So variability is the enemy (a familiar

concept in optimization and control) and this has given rise to a

minor revolution in both regulation and research in the area of

quality by design for pharmaceutical products. Unfortunately, this

came too late to be the platform to launch into biotechnology

derived pharmaceuticals but is gaining ground thanks in large

part to initiatives by the US FDA (Food and Drug Administration).

One such FDA initiative, referred to as the Process Analytical

Technology (PAT) initiative, provides guidance and encourages

the pharmaceutical industry to enhance control of manufacturing

processes in an effort to minimize the variability in the dosage

form (FDA, 2004).

Quality by design for pharmaceutical products is really the

recognition that pharmaceutical design and manufacturing is

another ‘‘engineering system’’ that requires all the usual

consideration of 1st principles, data driven, and heuristic model-

ing and control. Concepts of control strategies, fault analysis and

informatics are directly applicable but in many ways it is our lack

of understanding of the materials properties and behavior that

presents our largest challenge for true model predictive control.

The area that is growing in parallel in the engineering and

pharmaceutical domains is the informatics to leverage the data to

generate knowledge and understanding (Venkatasubramanian

et al., 2006; Venkatasubramanian and Morris, 2008). This

Fig. 1. Pharmaceutical material properties at multiple scales. The critical properties and behaviors at each material scale (i.e., particle, powder, and dosage form) are

illustrated with existing and emerging approaches to understand the phenomena. Informatics is used to interrelate data across scales and manage the developing

knowledge in the domain.

M.L. Hamad et al. / Chemical Engineering Science 65 (2010) 5625–56385626

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seemingly obvious need for ‘‘knowledge systems’’ may require a

more resource intensive effort for the pharmaceutical industry

than all of the other scientific and organizational changes

combined.

The purpose of this brief review is to highlight critical

properties and behaviors and capture the major gaps in our

understanding at each material scale, i.e., particle, powder, and

dosage form (Fig. 1). Examples focused on the still dominant

dosage forms, i.e., tablets and capsules, are included to illustrateexisting and emerging approaches to understanding the

phenomena and for managing the knowledge in the domain.

The challenges and principles discussed, however, span a wide

range of concerns across dosage forms.

2. The particle level

In the pursuit of quality by design, the particle represents the

smallest scale containing the complexity of the molecule coupled

with the defining state of aggregation, often the crystal structure,

which dictates the thermodynamic and mechanical properties.

Once a desired dosage form is identified, the design phase starts

with determining the properties at the particulate level and

seeing if there is a match between these properties and what is

required for the viable processing operations. If not, either

alternate processing trains must be identified or the particle

properties must be modified. Similarly for more disordered

systems, properties must be identified for their potential impact

on the same performance attributes.

2.1. Chemical stability

The first issue with particles is typically considerations of

chemical stability as solubility and other properties are incidental

if the compound does not survive to be delivered. Sodium

levothyroxine pentahydrate does not suffer from solubility

problems (the reason it is a salt) but is an example of the particle

properties’ influence on chemical stability and the choice of viable

processing operations. The crystal structure of levothyroxine is

shown in Fig. 2. It is clear that this is a channel hydrate ( Morris

and Rodriguez, 1993; Te et al., 2003) and as such would be at risk

of dehydration and subsequent phase and/or chemical changes.

PTBA (prednisalone tertiary butyl acetate) is an example of a

compound undergoing oxidative decomposition on dehydration

(Byrn et al., 1988) and levothyroxine has been reported to degrade

when exposed to certain processing stresses (Kannamkumarath

et al., 2004; Patel et al., 2003). Patel observed that while the bulk

material is quite chemically and physically stable even exposed

on the bench for months at a time, almost any stress induced

during processing, i.e., combining with excipients during dry or

wet granulation, exposure to heat, and mechanical stress during

compaction, compromises the chemical stability. The exact

mechanism was not reported, however, as with PTBA, exposure

to the atmosphere on mechanically induced dehydration may

cause degradation, or perhaps it is the intimate contact with

the excipients provided by the densification operations thatprovide the driving force for dehydration and degradation. In

either case, the knowledge of the liability offered by the crystal

structure should have been a much more active area of

investigation to avoid the hundreds of recalls, millions of

dollars, and many adverse events caused by the lack of design

(Hubbard, 1997).

2.2. Solubility and dissolution rate

Once stability is established, attention is immediately focused

on solubility and the dissolution of the API. Solubility is important

in pharmaceutics for a variety of reasons. Most important among

these is that the dissolution rate of a drug is in some way

proportional to its solubility and for compounds that are passivelyabsorbed so, therefore, is bioavailability. During the early stages

of drug development the salt and/or crystal form must be selected

and it must have sufficient solubility for further study. Similar

considerations are important during the production of the first

supplies for toxicological studies. As development proceeds to the

formulation stage, the physical stability of a solid phase to

manufacturing processes will be assessed. This is to determine the

suitability or liability of a unit operation that involves partial or

total solubilization of the active during wet granulation, lyophil-

lization, and other solvent based operations. The inter-conversion

of solid forms will often be proportional to the solubilities of the

forms in question.

The general relationship from basic thermodynamics found in

the Schroeder–Van Laar equation describes the ‘‘ideal’’ solubilityof a solid crystalline compound in any solvent:

ln 1

x ¼

DH f

R

1

T i

1

T m

DC P

R

1

T i

1

T m

þ

DC P

R ln

T mT i

ð1Þ

where w is the mole fraction of solute in solvent at temperature

T i; T m the melting point of solute; DH f the heat of fusion of solute

at its melting point T m; DC pCL p C s p (C p of liquid C p of solid);

R the universal gas constant.

This is a complete but ideal expression that may be simplified

using the following assumption. That is, the change in heat

capacity between the solid and the liquid is zero, i.e.,

DC solid-liq p 0. While this substitution is not exact, the difference

is often small for typical compounds. After substitution, Eq. (1)

yields the familiar relationship

ln 1

x ¼

DH f

R

1

T i

1

T m

ð2Þ

This applies to ideal solutions where it is assumed that the mixing

of the resulting liquids is ideal. Of course for ‘‘real’’ solutions,

particularly in aqueous media, this is seldom the case and the

activity coefficient (g) is included to factor in the resistance of the

liquid mixing. The simplified ‘‘real’’ relationship is

ln 1

x ¼

DH f

R

1

T i

1

T mp

lng ð3Þ

This deceptively simple relationship actually teaches an impor-

tant concept. When considering the compounds with exceedingly

low aqueous solubility (which is unfortunately the rule for new

‘‘small’’ drug molecules), designing strategies to increase theFig. 2. Crystal structure of levothyroxine.

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solubilization begin by considering the very balance between the

‘‘resistance’’ to solubilization contributed by the lattice energy

and the resistance for the two liquids to mix ideally. Overcoming

the lattice energy by disordering or creating an amorphous solid

in the limit of total disorder predicts that ln wElng. So if the

main resistance is contributed by the liquid mixing, no amount of

decrease in the lattice energy will be of much use (Pinal, 2004).

The rate of achieving the solubility may be improved but the

equilibrium value will still be low.So when examining the factors that impact dissolution, a form

of Fick’s 1st law is typically employed. This is called the Noyes–

Whitney equation and encompasses the solubility considerations

discussed above as well as other material properties, i.e., particle

size and surface area as well as morphology or shape:

dC

dt ¼

DA

Vh

ðC S C Þ ð4Þ

where dC/dt is the dissolution rate, D the diffusion coefficient of

drug in medium, A the surface area, h the apparent thickness of

diffusion layer (depends on stirring rate and temperature), C s the

solubility of drug in medium; V the volume of solution.

From the most general Noyes–Whitney equation the inte-

grated form (assuming a linear concentration gradient) is

lnðC S C Þ ¼ DA

Vh

t þln C S ð5Þ

Clearly the higher the surface area the faster the dissolution,

however, as discussed the maximum obtainable concentration

will be determined by the equilibrium solubility. In addition, most

small molecular organic crystals are of lower symmetry than

typical inorganic compounds which gives rise to anisotropy of a

variety of properties. These include mechanical, thermal, and

energetic properties (Duncan-Hewitt and Weatherly, 1990a,

1990b). So a single surface area number may not tell the whole

story for dissolution or a variety of other particle interaction

parameters.

2.3. The effect of anisotropy on physical properties

More specifically, the impact of the innate anisotropy of small

molecular organic crystals evident from even a casual inspection

of the crystal structures is that different faces of a given

morphology host different chemical moieties. This gives rise

to different interfacial energies and differential ‘‘wetting’’.

Aspirin for example often exhibits a habit with large (1 0 0) and/

or (0 0 1) faces. From the packing diagram and Bravais–Friedel–

Donnay–Harker simulated morphology it is clear that these

faces host moieties of differing polarity (Fig. 3). Furthermore,

measurement of the contact angle for water on these faces of

appropriately grown crystals shows a significant difference

(Fig. 4).

One could posit that a particle of the same crystal structure

and surface area but different crystal habit might exhibit a

different dissolution rate. This is undoubtedly a contributor to lot-

to-lot variation in drug product performance but is seldom if ever

dealt with quantitatively. Rather a trial and error approach is used

to fix such problems temporarily (every time it comes up). It has

been established that morphology can play a role in compaction

and flow of powders (Sun and Grant, 2001).

2.4. Understanding amorphous forms

Of course if the lattice energy does provide the primary

resistance to solubilization, then creating disorder or in the

extreme making the material amorphous should produce the

largest impact on dissolution. The problem is that some

compounds resist being driven to the amorphous form and/or

crystallize unpredictably to the more thermodynamically less

soluble crystalline form(s).

Preventing the latter problem is the subject of much interest

and research and many heuristics have been developed to at least

guide the development and assess the risks of using amorphous

materials. A rule of thumb has been reported (Hancock et al.,

1995) based on experience and later theory that teaches thatmaintaining a temperature 50 1C below the glass transition

temperature (T g ) will minimize the risk of crystallization of

amorphous materials (of course sorption of water will decrease

the T g to sometimes unusable levels (Hancock and Zografi, 1994).

This allows assessment of the risk and if not reduction perhaps

mitigation.

The former issue was addressed by Wildfong et al. (2006)

adapting the theory of Tromans and Meech (2001). It was

assumed that mechanical disordering (i.e., milling and/or grind-

ing) are the only large scale, economically feasible technique for

most drugs. The concept is that to mechanically disorder a crystal

completely a critical defect density (rcrit ) must be reached.

However, physically, a given crystal can only accommodate

a maximum density of disolcations ðrdÞ before their strain

Fig. 3. The Bravais–Friedel–Donnay–Harker algorithm is used to predict the shape

of an aspirin crystal, while elucidating the chemical moieties exposed on the

different crystal faces.

Fig. 4. Contact angle measurements made on difference faces of an aspirin crystal.


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tensile strength of compressed pharmaceutical powders. The

devised transverse compression technique requires platens that

have a contact surface that is 40% of the width of the square

compact. The tensile strength (sT ) is then equated as 0.16 times

the compressive stress (sC

) at failure or sT

=0.16sC

. The transverse

compression test described is incorporated into Hiestand’s

tableting indices used to provide measurement of properties of

tableting performance.

Fig. 9 shows the failure of such compacts of MCC with and

without a ‘‘defect’’ as prescribed by the BFI index. Given the high

ductility of the material it might be expected that the failure

mode would consist of significant plastic deformation followed by

fracture. However, Smith found that the compacts failed in mode I,

i.e., in a linear elastic failure mode.

Smith demonstrated that the cause of the apparently brittle

behavior of the otherwise ductile material was due to the fact that

the compacts failed at the inter-granule interface. So while the

individual particles are easily deformed to create the bonding and

a high BI, the compacts fail in the elastic region of the stress–

strain curve (Fig. 10). This has special significance for assessing

tablet ‘‘hardness’’ which is a term of art in the pharmaceutical

domain which really refers to tablet tensile strength for product

control.

3.3. Powder flow

This also highlights another aspect of practical formulation and

process design, i.e., at the bulk powder scale (alone and in

mixtures), the particulate properties and their response to

stresses may be amplified, attenuated, or changed as a result of

the increase in scale. This concept is nowhere better illustrated

than in the determination of powder flow. While there is a general

but difficult to predict correlation between particle size and flow(Pitkin and Carstensen, 1990), there is little else in the way of

even heuristic understanding of flow. Particle shape, charging

potential, and surface characteristics have all been demonstrated

to impact flow and methods for characterizing the properties have

been developed (Engers et al., 2007, 2006) but no 1st principles

methodologies have yet evolved.

Rather indices based on heuristics and logic are used (although

not on as clear a set of principles as Hiestand’s indices). Most

general are the Carr (1976) indices. To generate the indices,

individual experimental micromeritic techniques are used to

determine the relative ability (relative to the database created

by the inventor) of the material to flow during manufacturing

but also allows determination of material and environmental

conditions.

The flowability index is a ‘‘score’’ from 0 to 100, with 100

indicating excellent flowability, derived from measurements of a

powder’s poured angle of repose, angle of spatula, compressibility,

and either a measure of cohesion (for fine powders) or a coefficient

of uniformity (for coarse material). These are simple measure-ments and can be done on specific particles size ‘‘sieve fractions’’.

After making measurements the indices are found by tabulating

the points assigned in Carr’s empirically derived tables. Qualita-

tive descriptions of a powder’s flowability, ranging from excellent

to very, and very, very poor, are reported in Table 2.

Figs. 11 and 12 show the angles of repose and spatula for

cycloserine, respectively. This is in the category of terrible by

visual inspection and next to the worst group with a Carr

flowability index of approximately 36. The indices provide tools

for design and facilitate the identification of appropriate flow aids,

or glidants (e.g., talc), and the amount required to obtain

acceptable behavior. Cycloserine is in fact a commercial product

(seromycin) in a powder filled capsule with significant amounts of

talc required for the process. In mixture of course, the behavior

Fig. 9. Failure of compacts resulting from a transverse compressive stress. (a) compact with a defect and (b) compact without a defect.

Fig. 10. Stress–strain curve for MCC compact showing the failure mode occurs in

the linear elastic portion of the stress–strain curve. Significant plastic deformation

does not occur.


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is more complicated and less predictable. The situation is

exacerbated by the unavoidable variation in raw materials

(as discussed). This means that even if one were to assemble a

database of behaviors with the limited number of common

excipients typically used, the predictions would be suspect. Yet

considering mass flow data such as shown for mixtures of two

excipients, lactose and dicalcium phosphate (DiTab) in Fig. 13 and

mixtures of an API, APAP, and lactose in Fig. 14, provides some

measure of understanding of the behavior (Morris, unpublished

results).

3.4. Using informatics and chemometrics to understand powder

systems

It seems likely that for common systems a large enough

database of physical properties (size, shape, charging, structure,

etc.) could provide sufficient predictability for early decision

making and later control strategies. The key is capturing all of the

relevant data and behavior in a form that allows integration into

the decision making process. This is one of the issues addressed by

trying to employ recent advances in informatics to create

‘‘ontologies’’ to reconcile data from disparate sources and types

Fig. 11. Angle of repose for cycloserine.

Fig. 12. Angle of spatula for cycloserine.

Fig. 13. Mass flow data for a mixture of ditab and lactose.

Fig. 14. Mass flow data for a mixture of lactose and APAP.

Table 2

Flowability score based on a collection of powder measurements.

Flowability and performance

Carr paper Hosokawa manual Points

Excellent, aid not needed will not arch Very Good 90–100

Good, aid not needed will not arch Fairly Good 80–89

Fair, aid not needed but vibrate if necessary Good, sometimes vibrator is required 70–79

Passable, borderline, may hang up Normal, bridging will take place at marginal point 60–69Poor, must agitate, vibrate Not Good, required 40–59

Very poor, agitate more positively Bad, powerful measures should be provided 20–39

Very, very poor, special agitator, hopper or eng’r required Very bad, special apparatus and techniques required 0–19


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(heuristic, empirical, 1st principles or otherwise based) so that

they can be combined into relationships for model predictive

control (Venkatasubramanian et al., 2006). Clearly for a large

operation, it should not take long to capture sufficient data to

develop at least a reduced order data driven model, what is

lacking in the industry is the will to develop the economic

justification and rationale.

The informatics provides the opportunity to integrate systems

and build a unified model, which potentially takes the form of anontology, to control and manage multiple manufacturing pro-

cesses operating on different scales (from particle to powder to

dosage form). The informatics in Fig. 1 is illustrated to tie together

and communicate information from different scales. However, it

is often the case in pharmaceutics that models based on

1st principles have not been developed to sufficiently describe

certain complex processes. One such example is predicting the

behavior of mixtures of bulk powders. This leads to the conclusion

that estimating or anticipating such behaviors is really an exercise

in multi-variate analysis, i.e., chemometrics. Chemometrics is a

useful empirical modeling approach for information-mining

multi-variate data, where the multi-variate data represent

measurements made with multiple sensors on the sample (or

system being studied). A multiple sensor system, for example,

could be a near infrared (NIR) spectrum, with each sensor element

representing a value at a given wavelength.

Often the goal of chemometrics is to understand trends in the

measured multi-variate data (called the X-block data) and this

can be done using a pattern recognition technique called principal

component analysis (PCA). Many other chemometrics techniques

are related to or built upon PCA so PCA is a good place to begin

understanding how chemometrics techniques work. In the case of

powder measurements, the X-block data might be a collection of

NIR spectra made on a series of powder samples, it might be a set

of univariate measurements (e.g., true density, tapped density,

surface area, etc.), or it might be a combination of multi-variate

data and univariate data. When combining data from different

instruments into a single X-block matrix of data, the key step is to

appropriately preprocess (scale or normalize) the data prior to

running any chemometric algorithms. Some commonly used

preprocessing steps include mean-centering, autoscaling, stan-

dard normal variate (SNV), multiplicative scatter correction

(MSC), and Savitzky–Golay smoothing and derivative (1st and

2nd) functions. Chemometric techniques do not understand units

and weight importance based on variance, so the preprocessing

techniques are used to put the X-block data on a ‘‘level playing

field’’. The preprocessing step, therefore, allows NIR spectra, for

example, to be examined side-by-side with density measure-

ments. Once the X-block data are appropriately scaled, it becomes

useful in almost all cases of complex data evaluation to run a PCA.

For purposes of illustration here, it is worthwhile to examine

some simulated spectra. The mathematics behind PCA are

illustrated elsewhere ( Johnson and Wichern, 2007). Rather thanrepeat those mathematics, this review will provide an example of

PCA at work on a ‘‘realistic’’ and understandable dataset. Most

importantly, the developed chemometric model will be elucidated

and interpreted, even though chemometrics has the reputation of

yielding difficult-to-interpret models.

Imagine the spectra obtained from a three-component mixture

of powders. Raman spectra are simulated here, since they are

easier to simulate than NIR spectra; but a real-world NIR

chemometric model is demonstrated in the discussion that

follows. The simulated spectra are modeled on a Lorentzian

probability distribution; this distribution provides a good esti-

mate of the line-shape of Raman spectra (Singh et al., 1994). The

simulated spectra are shown in Fig. 15. Fig. 15A shows pure

component spectra for each compound. Compound #1 has two

peaks at values of 100 and 278; compound #2 has two peaks at

values of 200 and 300; and compound #3 has a single peak at 372.

A constrained mixture design was used to simulate 47 samples

containing varying concentrations of each compound, subject to

the constraint that

conc A þ concB þ concC ¼ 100% ð12Þ

and the concentration of A (conc A), concentration of B (concB), and

concentration of C (concC ) ranges from 0% to 100%. Fig. 15B shows

simulated spectra for each of the 47 samples. For each peak, the

larger the concentration, the taller the peak. A PCA was performed

on the sample set using the ‘‘PCA’’ command of the PLS_Toolbox

(Eigenvector, Inc.). To yield the simplest PCA model, the data were

preprocessed by mean-centering the data (subtracting the mean

spectrum from each of the spectra) without scaling the data

(i.e., dividing by the standard deviation of the spectra). The PCA

model determined that all of the variance in the spectra could be

measured with only two principal components (PCs). So the

original dataset has undergone a transformation allowing each

sample to be represented by only two score values on the newly

created PC axes. This is exactly what is expected from the spectra

of a three component mixture; although real spectra would

contain noise which would have been incorporated into the 3rd,

4th, etc. PCs. Thus, PCA is a data reduction technique, eliminating

all the unnecessary data which do not contain variance; and it is anoise-reducing technique since the signal can be focused in the

first few PCs while the noise is incorporated into higher order PCs.

The PCs, by the way, must be orthogonal to one another; and are,

by convention, organized such that the first PC contains

maximum variance, the 2nd PC contains the 2nd largest amount

of variance, and so on. Also, the PCA will capture and retain all of

the variance in the dataset. The resulting scores plot for the data is

shown in Fig. 16. A triangle-shaped pattern is formed, with each

vertex of the triangle representing one of the pure components, as

illustrated. Along line segment going from compound #1 to #2,

the concentration of compound #1 decreases while the

concentration of #2 increases and the concentration of #3

remains constant. Thus, the location of the sample (based on its

scores on PC1 and PC2) within the triangle (the PC space) becomes

Fig. 15. Simulated spectra for a three component mixture of powders. (A) Pure

component spectra of compound #1 (black), compound #2 (red), and compound

#3 (blue). (B) All 47 spectra of the mixture design, with concentrations of eachcomponent ranging from 0% to 100%. (For interpretation of the references to colour

in this figure legend, the reader is referred to the web version of this article.)


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the new representation of the concentration of compounds #1,

#2, and #3 for each sample.

The secret to interpreting the model is to understand the

loadings plots. Each principal component has a loading vector

associated with it. The sample is ‘‘scored’’ by how well it performs

on (or relates to) that loading vector. Thus, if there is a close

match between the sample spectrum and the loading vector for

PC 1, then that sample will be given a high score on PC 1. If the

sample spectrum and loading vector are opposite in sign, then a

negative score will be given. The loadings plots for PC 1 and PC 2

are given in Figs. 17A and B, respectively. It can be seen (by

comparing Figs. 16 and 17) why compound #3 has a high score

and compounds #1 and #2 have low scores (negative values) on

PC1; and why compounds #1 and #3 have high scores and

compound #2 has a low score on PC 2.

As the preceding argument describes, the PCA elucidates

trends in the data which result in quantitative score values that

may be correlated with variables of interest (called the y-block

data or Y-block data depending on whether single or multiple

variables are predicted, respectively). The goal of multi-variate

regression techniques it to correlate the results from techniques

like PCA with Y-block data with the goal of building a regression

model. The most widely used multi-variate regression chemo-metric technique is called partial least squares (PLS) regression.

The behavior of bulk powder mixtures is a classic case of a

multi-variate problem which has yet to be solved using a 1st

principles modeling approach. Thus, chemometrics tools can be

utilized to develop an understanding of the powder mixture

system. An example of the use of chemometrics for the under-

standing of mixtures of bulk powders was demonstrated by Gupta

et al. (2005). In this case, a roller compactor was used to compress

MCC powder into a ribbon and the variables of interest for the

ribbon (the Y-block) were the moisture content (represented by

the loss on drying or LOD), the relative density (RD), the tensile

strength (TS), and the Young’s modulus (YM). A calibration set of

simulated ribbon samples was developed by uni-axially compres-

sing MCC powder into the shape of a compacted bar using a

rectangular punch and die set (similar to that illustrated in Fig. 6).

The X-block data was obtained by analyzing the training sample

set with near infrared (NIR) spectroscopy. The NIR spectrometer

Fig. 16. PCA scores plot for spectra in Fig. 15B. All of the variance in the spectra is i ncorporated into two PCs. The scores values on PC1 and PC2 form a triangle. The samples

at each vertex of the triangle are the pure component samples. The location of each sample within the triangle corresponds to the concentration of compounds #1, #2, and

#3 in that sample. The original spectra for some of the samples are included to illustrate the point.

Fig. 17. PCA loadings plots for (A) PC1 and (B) PC2 are used to interpret

chemometric models. (A) The loadings plot of PC1 indicates that compound #3

should have a high score and compounds #2 and #3 should have low scores on

PC1 (refer to Fig. 16). (B) The loadings plot of PC2 indicates that compound #1 and

#3 should score high and compound #2 should score low on PC2.


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contained a 256 element detector which represented wavelengths

ranging from 1100 to 2200 nm. The variables within the NIR

spectra are considered to be highly correlated, as they represent

broad and overlapping peaks that arise from the overtones of

vibrations of bonds within the solid-state molecules. The vibra-

tions are a form of chemical information, giving rise to broad

peaks centered at specific wavelengths in the NIR spectra. The

moisture content in pharmaceutical samples is often measured

using this concept, since the OH stretch of water has a strong

absorption peak at 1930nm. The other three variables of interest,

on the other hand, are not related to intramolecular vibrations. In

fact, the RD, TS, and YM are physical parameters that result in a

baseline offset and/or slope change in the NIR spectra. Therefore,

changes in moisture concentration and changes in the physical

parameters lead to changes in multiple variables within the NIR

spectra. The chemometrics regression tool, PLS, was used to build

models that correlated NIR spectra with each of the 4 variables

(Fig. 18). Although the PLS model was based on the simulated

rectangular compacts, the models were able to satisfactorily

provide a real-time prediction of each of the 4 parameters when

they were applied to the roller compacted ribbons.

4. The dosage form

The dosage form scale is the most complex and the most

constrained. If the particles and powders have been properly

engineered and/or controlled, the solid dosage form will meet the

necessary yet opposing criteria of mechanical strength and

stability yet rapid and reproducible disintegration and/or dissolu-

tion upon administration. This must be true for the entire

population of product which means billions of units per year.

4.1. Dissolution of the dosage form

Returning to cornstarch as a capsule diluent, Sheng (2009)

found that translating the materials characterization from

laboratory designed samples to commercially viable and available

materials was a challenge due to the variability in the amylo-

pectin-amylose ratio and the limited range of crystallinityobserved. Fig. 19 shows the variation in dissolution from filled

capsules with nadolol API and various commercial corn starches.

It is clear that one could never count on reproducibility when

changing suppliers, or even lots within a supplier’s stocks,

without significant requalification. This process wastes time and

resources seldom practical during drug development.

Sheng was able to establish that the amylopectin fragmenta-

tion (AP/A ratio) was the dominant factor in dissolution variability

from the commercial sources (Fig. 20). Given the unlikely hood of

commercial starch manufacturers modifying their process for

such a small demand, it was advisable to try to estimate the

dissolution rate constant (from Noyes–Whitney equation;

Carstensen and Dali, 1999) from the two primary materials

characteristics.

Fig. 18. Comparing actual measured variables of interest with predicted values derived from a PLS model of NIR spectra of simulated ribbons. (A) moisture content.

(B) relative density. (C) tensile strength. (D) Young’s modulus. The root mean square error of estimation is for the training set (circles); the root mean square error of

prediction (RMSEP) 1 is for the first prediction set (squares); and RMSEP2 is for the second prediction set (triangles).

Fig. 19. Nadolol dissolution profiles for capsules filled with nadolol and corn

starch from various suppliers.


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Sheng reported an estimation correlation between the dis-

solution rate constant (Y ) and the crystallinity ( X 1) and theamylopectin fragmentation ( X 2), that allows the characterization

of different commercial materials prior to use to control the

ultimate behavior:

Y ¼ 0:58ð0:0061 X 10:0547Þ þ 2:37ð0:0007 X 2 þ0:428Þ ð13Þ

4.2. Heckel analysis

Tablets provide the richest area for leveraging mechanical

materials properties. As discussed, all of the particle and powder

properties must combine to produce tablets that are strong

enough to survive handling and use but fragile with respect to

certain stresses so they dissolve as required. In practice, if there is

a restrictive set of API properties (e.g., high dose and poor

compaction properties), directly compressing powders is often

preceded by a granulation step such as the dry granulation of

roller compaction discussed above in the multi-variate analysis

section. Wet granulation is an agglomeration technique involving

high shear forces during the addition of a liquid dispersed binder.

Although this is an area of active research (Iveson et al., 2001), the

materials properties considerations are really the same as

discussed in this review and will not be specifically addressed.

The most common relationship to describe the relationship

between the pressure applied to compact a tablet and the

resulting tablet density (and presumably the tensile strength) is

that of Heckel (1961):

ln 1

1D ¼ kP þln

1

1D0

where D is the relative compact density=rc /rt , rc is compact

density and rt is material true density; P the compaction

pressure; D0 the initial relative compact density, when no

pressure is applied; k the plastic region slope.

The relationship allows the identification of the phases of

consolidation, deformation and compaction. Others have modified

the essentially 1st order relationship and developed slightly

different versions in terms of the variables of interest (Fig. 21).

These are essentially phenomenologically based stress–strain

relationships developed for design purposes but typically used

when problems arise rather than for routine development. This is

in part due to the already introduced concept that there

are relatively few diluents used in tablets and given the

historically large experience base; the heuristics serve to direct

the formulation activities. However, the now lack of continuity in

companies and the introduction of less suitable APIs and alternateexcipients, one expects this to change.

The problem is that being based upon domain expertise and

model assumptions, the predictability will not be 1st principles

based. Multi-scale modeling using FEM for prediction has

succeeded in producing excellent preliminary results in predict-

ing pressure–density relationships and the variation in density

within compacts (Zhao et al., 2008). Zhao et al., have shown this

for pharmaceutically relevant systems and what remains is to

have more accurate descriptions of the particle level mechanical

properties of the materials for inclusion in the models.

Once created, the tablet must perform as required with respect

to some behavior deemed critical to its intended delivery

function. This is assessed using a ‘‘use test’’ which is a surrogate

for the desired behavior. Typical use tests are tablet dissolution

and disintegration which are supposed to reflect the reproduci-

bility of the process and ostensibly the in vivo behavior (although

this is a controversial topic). As real-time determination of

solution properties of tablets is unlikely, often surrogate proper-

ties are used for well characterized processes. In addition, non-

destructive surrogate properties (e.g., spectroscopic response as in

the chemometrics discussion) are the topic of much interest to

sample larger populations of product.

4.3. Relating hardness to dissolution

The tensile strength of tablets is of paramount importance to

both the survival during handling and control for reproducible

dissolution. The most common measure of tensile strength is theso called ‘‘Brazilian test’’ in which the tablet is placed between to

platens connected to a load-cell ram assembly and the tablet is

stressed to failure (Fig. 22). This is what is commonly called tablet

‘‘hardness’’ in the pharmaceutical domain.

Building on the roller compaction work discussed in the bulk

powder mixture, multi-variate analysis discussion, Gupta pro-

vides an example of the relationship between hardness and

performance: creating tablets from the granules generated from

the milled roller compacted powder. He used a design of

experiments to select the conditions and conducted the dissolu-

tion use test to assess the impact of the various conditions. The

results are shown in Fig. 23 and while not surprising in their

trends, the specific dependence is the key to controlling the

process.

Fig. 21. The Heckel relationship along with the slightly modified density–pressure

relationships of Panelli and Leuenberger.Fig. 20. Amylopectin fragmentation (AP/A ratio) was the dominant factor in the

variability of the dissolution rate constant of nadolol and starch filled capsules.


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5. Summary

This brings us to the point of forecasting the direction of

pharmaceutical materials science and engineering as a discipline.

One can only conclude that the future lies in the three main areas

of multi-scale modeling, advanced analytics, and informatics.

Several existing tools are useful in developing multi-scale

understanding but new technologies, especially those focused

on multi-scale modeling will be helpful in advancing the field. In

particular, at the particle level, further understanding of chemical

stability, solubility and dissolution rate, the effects of anisotropy,

and the understanding of amorphous and crystalline forms will be

critical. At the powder level, the use of Hiestand indices canbe used to predict tensile strength, and powder flow can be

empirically modeled with the help of chemometrics. Finally, at

the dosage form level, producing dosage form strong enough to

survive shipping and handling yet weak enough to disintegrate

and dissolve upon ingestion at a predictable rate continues to

be a challenge. In summary, the data gathered from multi-

scale modeling become one ‘‘domain’’ required to overcome the

artificial separation of API and excipient properties from the

manufacturing processes. A holistic view of this pharmaceutical

‘‘domain’’ is gaining momentum in the pharmaceutical industry.

Using informatics to manage data from advanced analytics across

multiple scales is a key aspect in leveraging the engineering and

science to improve pharmaceutical quality while bringing down

the cost of product development and manufacturing.

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