Method Development and validation for Polymorphic purity ...shodhganga.inflibnet.ac.in/bitstream/10603/8657/11/11...In the solid state, Sertraline hydrochloride exists in various crystalline

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CHAPTER 6

Method Development and validation for Polymorphic purity

determination of Sertraline Hydrochloride by NIR

Near-infrared spectroscopy

The IR region is divided into three regions: the near IR, mid IR, and far IR. The mid

IR region is used mostly in chemical analysis. This is the region of wavelengths

between 3 x 10-4

and 3 x 10-3

cm (figure 1)1. Practically it becomes easy to work with

numbers which are easy to write; therefore IR spectra are sometimes reported in µm,

although another unit , (nu bar or wavenumber), is currently preferred. A

wavenumber is the inverse of the wavelength in cm. The mid IR range is 4000–400

cm–1

in wavenumbers. An increase in wavenumber corresponds to an increase in

energy.

Figure 6.1: Near infrared region

William Herschel discovered the near-IR region2 in the 19th century, but the first

industrial application began in the 1950s. In the first applications, NIR spectroscopy

was used only as an add-on unit to other optical devices that used other wavelengths

such as ultraviolet (UV), visible (Vis), or mid-infrared (MIR) spectrometers.

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Near-infrared spectroscopy is a spectroscopic method which uses the near-infrared

region of the electromagnetic spectrum. Near-infrared spectroscopy is based on

molecular overtone and combination vibrations3. This is energetic enough to excite

overtones and combinations of molecular vibrations to higher energy levels. NIR

spectroscopy is typically used for quantitative measurement of organic functional

groups, especially O-H, N-H, and C=O. Detection limits are typically 0.1% and

applications include pharmaceutical, agricultural, polymer, and clinical analysis. The

molecular overtone and combination bands seen in the near IR are typically very

broad, leading to complex spectra; it can be difficult to assign specific features to

specific chemical components. Multivariate (multiple variables) calibration

techniques (e.g., principal components analysis, partial least squares, or artificial

neural networks) are often employed to extract the desired chemical information.

Careful development of a set of calibration samples and application of multivariate

calibration techniques is essential for near-infrared analytical methods. In contrast to

sharp absorption peaks in the MIR region, NIR spectra show less intensity and broad

bands. An assignment of peaks to individual vibrations is thus not possible.

Near-infrared spectroscopy (NIRS) is a fast and nondestructive technique that

provides multi-constituent analysis in virtually any matrix. In recent years, NIR

spectroscopy has gained wide acceptance within the pharmaceutical industry for raw

material testing, product quality control and process monitoring.

The growing pharmaceutical interest in NIR spectroscopy is probably because it

meets the criteria of being accurate, reliable, rapid, non-destructive, and inexpensive

and its advantages over other analytical techniques, namely, an easy sample

preparation without any pretreatments, the possibility of separating the sample

measurement position and spectrometer by use of fiber optic probes, and the

prediction of chemical and physical sample parameters from one single spectrum.

NIR absorption bands are typically broad, overlapping and 10–100 times weaker than

their corresponding fundamental mid-IR absorption bands. These characteristics

severely restrict sensitivity in the classical spectroscopic sense and call for

chemometric data processing to relate spectral information to sample properties. The

low absorption coefficient, however, permits high penetration depth and, thus, an

adjustment of sample thickness. This aspect is actually an analytical advantage, since

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it allows direct analysis of strongly absorbing and even highly scattering samples,

such as turbid liquids or solids in either transmittance or reflectance mode without

further pretreatments.

The NIR spectrum of sucrose is given below as an example ( Figure 6.2).

Figure 6.2: Typical NIR spectrum

As seen from the above spectrum, unlike an IR spectrum, there are no characteristic

peaks representing the chemical bonds or representing the characteristic stretching

and bending thus making it difficult to interpret or draw any conclusion. In order to

make it applicable, there needs to be a strong data collection system coupled with a

software capable of multivariate analysis and chemometrics.

Instrumentation

NIR spectroscopy instrumentation is similar to those of other spectrophotometers such

as IR or UV. This typically contains a source, a detector, and a dispersive element

(such as a prism, or, more commonly, a diffraction grating) to allow the intensity at

different wavelengths to be recorded. Fourier transform NIR instruments using an

interferometer are also common, especially for wavelengths above ~1000 nm.

Depending on the sample, the spectrum can be measured in either reflection or

transmission.

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The radiation is generated usually by means of a quartz halogen or incandescent lamp

used as broadband sources of near-infrared radiation.

There are a variety of sample analysis units based on the type of application. The most

important aspect of this analysis is that there is no need of sample preparation, i.e., the

sample can be analysed as such.

Measurement

Some of the types of analysis types include diffuse reflectance, transmission and

transflection. Based on the sample, the measurement mode can either be absorbance

or reflectance. The sample for pharmaceutical application can range from powders i.e.

APIs or Excepient or liquids, semi finished inprocess sample or liquid samples or

finished products like tablets. Based on the type of sample, the analysis mode can be

selected, for e.g. for analysis of powders or liquids, either representative sample can

be analysed in a sample compartment or an optical fiber probe can be directly placed

in contact with the sample for analysis. In order to analyse the formulation e.g. tablets,

the same can be placed in tablet holders and analysed directly.

Pretreatment of spectra

Because the NIR spectral bands are broad, data pretreatment is typically necessary to

convert the raw data into useful spectral signature information. This can be performed

in several ways. The most common type is smoothening and derivative ( 1st order, 2

nd

order etc.)

Figure 6.3: Typical untreated NIR Spectrum

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The above figure is a representative NIR spectrum (Figure 6.3). As we can see, the

spectrum does not let us understand any details nor can it be used to differentiate

between similar spectra with different particle size or polymorphs. This happens in

most of the cases, thus in order to make it more elaborative and to bring out minute

details, the same needs to be treated, and the most common treatment is its derivative.

Given below is a sample picture ( Figure 6.4) of how the same would be changed if

derivitized to 1st order.

Figure 6.4: Example of Derivative NIR spectra

As we can see, the spectrum signals have become more predominant and thus now the

spectrum is in a position to differentiate itself from materials of similar chemical as

well as physical properties. There are other means of sample treatment depending

upon the instrument software capabilities e.g. Bruker Mpa NIR equipped with OPUS

software uses a calculation called a Conformity Index. In this calculation, the spectra

of the pure compound is considered as a base and the differentiation is calculated by a

specific number by virtue of its dissimilarity with that of the base spectra by means of

assigning a number i.e. the CI number.

Chemometrics

Chemometrics is the science of extracting information from chemical systems by

data-driven means. It is a highly interfacial discipline, using methods frequently

employed in core data-analytic disciplines such as multivariate statistics, applied

mathematics, and computer science, in order to address some complex issues. Such

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multivariate data has traditionally been analyzed using one or two variables at a time.

We must process all of the data simultaneously to understand the relations.

In the context of near infrared spectra, several data points are collected from the start

to the end and during the data processing, all the data points are considered. Minor

changes in spectrum is thus differentiated as numbers and interpreted as variations

both quantitatively as well as quantitatively.

Calibration curve

A calibration curve is a plot of increment of one variable relatively with that of

another variable. In this scenario this relationship curve is drawn between responses

with respect to concentration. A linear relationship is considered when a small

increment of concentration reflects in the increment of analytical signal of an

instrument i.e. response (absorbance or reflectance) obtained by a spectrophotometer.

The data - the concentrations of the analyte and the instrument response for each

standard - can be fit to a straight line, using linear regression analysis. This yields a

model described by the equation y = mx + C, where y is the instrument response, m

represents the sensitivity, and C is a constant that describes the background. The

analyte concentration (x) of unknown samples may be calculated from this equation.

Once the calibration curve is drawn, the concentration of an unknown sample can be

identified based on the response obtained from it by the above mentioned formula.

A calibration curve can also be drawn with concentration of an impure compound by

means of conformity index (CI) values also.

Validation:

Once the calibration curve is obtained, the same can be validated to ascertain the

reliability. Samples with known concentrations of samples are prepared and the

concentration is calculated by using the calibration curve. The true value is then

compared with that of the calculated value. The agreement or disagreement of both

these values determines the validity of the calibration curve.

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Sertraline Hydrochloride

Sertraline Hcl is chemically (1S,4S)-4-(3,4-dichlorophenyl)-N-methyl-1,2,3,4-

tetrahydronaphthalen-1-amine. This belongs to a class of drugs known as

antidepressants4. This can further be classified as selective serotonin reuptake

inhibitor (SSRI) . It was discovered by Pfizer Pharmaceuticals. The chemical details

are given below

CAS No. : 79559-97-0

Molecular Formula : C17H18Cl3N

Formula Weight : 342.69

Form : solid

Color : white crystalline powder

Sertraline is a widely used antidepressant belonging to the selective serotonin

reuptake inhibitor class; its efficacy has been demonstrated not only in the treatment

of major depression, obsessive compulsive and panic disorders, but also for eating,

premenstrual dysphoric and post-traumatic stress disorders. The antidepressant effect

of Sertraline is presumed to be linked to its ability to inhibit the neuronal reuptake of

serotonin5. It has only very weak effects on norepinephrine and dopamine neuronal

reuptake. At clinical doses, Sertraline blocks the uptake of serotonin into human

platelets6. Like most clinically effective antidepressants, Sertraline down regulates

brain norepinephrine and serotonin receptors in animals7.

In the solid state, Sertraline hydrochloride exists in various crystalline forms having

different physical properties8.Various claims have been made emphasizing the

differences in bioavailability between different polymorphic forms of Sertraline HCl.

Two different polymorphic forms were selected for this experimentation and have

been named as Form I and Form II for ease of differentiation between the forms.

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Several works have been published on polymorphic purity determinations by XRD9,

10. A few publications are also found for polymorphic purity determinations by NIR

11.

However no reference is available to the best of our knowledge which describes the

polymorphic determination of Sertraline HCl by NIR. This chapter describes this

novel work to determine the polymorphic impurity in Sertraline HCl.

Sertraline Polymorphic purity Method by NIR

Polymorphism is an important phenomenon in the drug development and

manufacturing process since different polymorphs of compound show variations in

physicochemical properties such as density, morphology, solubility, dissolution rate,

stability, and hygroscopicity. As a result, different polymorphs of the same drug

exhibit differences in bioavailability, efficacy, and drug product performance. In order

to control polymorphism in the drug development and manufacturing processes, it is

critical to identify, characterize, and quantitate the presence of the various

polymorphs of a pharmaceutical compound

There are various techniques to identify the polymorphic form in pharmaceutical drug

substances and drug products. The most common are the X-Ray powder diffraction

and Differential scanning calorimetry. The other techniques such as the Mid Infra Red

Spectroscopy, Near Infra Red Spectroscopy, Raman Spectroscopy and the optical

Microscopy are also of great importance and have proved to be of great importance in

this regard. The NIR and Raman spectroscopy have come in to picture in the recent

times. NIR rays have an ability to penetrate much deeper in to the compounds thus

can identify the physical variations in a compound. We have tried to use NIR

spectroscopy (Bruker MPA) to Identify (By means of conformity index and

quantitative estimation mode) and quantitatively estimate a polymorphic impurity in

the other pure polymorphic form.

The sample analysis of Sertraline form I( Figure 5) and Form II ( Figure 6)were

analyzed by using Bruker NIR and spectra were generated by using OPUS software.

The qualitative spectra are given below for reference

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Figure 6.5: NIR Spectrum of Sertraline Form I

Figure 6.6: NIR Spectrum of Sertraline Form II

For this experiment, Form II was considered as the API and Form I was considered

as polymorphic impurity. The Aim of the experiment is to develop a method to

determine the content of form I in form II by using NIR.

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Procedure:

To develop the method, along with pure samples of Form I and Form II, solid spiking

of Form I in Form II were performed with serial dilution method and scanned .By

this technique, individual samples were prepared containing 0.5%, 1.0%, 2.0%,

5.0%, 10.0% and 20% w/w of form I in Form II and analysed by means of NIR (Solid

probe) . The NIR spectra were recorded from 4000cm-1

to 12500 cm-1

at 8.0 cm-1

resolution. All the spectra which were obtained in this manner have been overlaid as a

single figure as given below for reference.

Figure 6.7: Overlaid NIR Spectrum of Sertraline Form I, II and spiked samples

As seen from the above figure, there are no major differences. On careful observation, however there

is slight variation in absorbance in the region between 8750 and 8800cm-1

(Figure 8).

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Figure 6.8: Overlaid NIR Spectrum of Sertraline Form I, II and spiked samples

depicting the visible variation

Figure 6.9: Zoomed section of the variation in absorbance within the sample

spectra

The selected elaboration in spectra gives a clear representation of this difference.

spectra showed minute differences in certain regions.

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Conformity Index method

One of a special feature of the OPUS software is called as conformity index. For this

type of analysis, pure Form II material was analysed six times and individual baseline

numerical values were generated to it by carefully selecting the regions of

differentiations in the spectra. In the similar fashion, spectra were generated in

triplicate for each of the spiked materials. The software generates average numerical

values to each of the levels. The conformity index values is plotted graphically by the

software as shown below( Figure 10).

Figure 6.10: Conformity index graph for spiked and pure sample

The spots on the conformity index plot have been indicated for pure form I ( green

spots) and 0.5% spike (blue spots). Similarly the other sets of the spots are

representations for 1.0%, 2.0%, 5.0%, 10.0% and 20%. The values obtained for these

values have been tabulated below.

0.5% spike

Pure Form II

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Table 6.1: Table for conformity index values

S.No %spike CI value

1 0.5 4.49

2 0.5 4.56

3 0.5 5.37

4 1.0 6.01

5 1.0 6.66

6 1.0 6.86

7 2.0 12.09

8 2.0 11.89

9 2.0 13.3

10 5.0 28.52

11 5.0 28.45

12 5.0 27.92

13 10.0 64.55

14 10.0 65.31

15 10.0 63.00

16 20.0 124.24

17 20.0 129.52

18 20.0 127.16

Correl coef (r) 0.999062 ~ 0.9991

The correlation coefficient value thus obtained is 0.999(Table 1) thus shows that there

is a linear relationship between both the variables. Thus this method of determining

the percentage of polymorphic impurity in form II is accurate and can be used to

determining the polymorphic impurity.

Quantitative model

The spectra generated earlier were calculated using the software and by means of

statistical analysis, numerous data points on the spectra are utilized to define the

spectrum value. This type of analysis is called as Quant model. A calibration curve

was drawn by the software with all the values against the true spiking percentage

values of each spiked value(Figure 11). The calibration curve is stored in the

software.

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Figure 6. 11: Quant Model development by means of calibration curve.

A random mixture of Form I in Form II is prepared and analysed by NIR. IN this case

the true value is determined by means of the software. The closeness of the values is

used to validate the utility of the calibration curve.

The table presented below shows the true values i.e. intentional amount of spiking

against the predicted values. As seen from this table, there is a close agreement

between the true and that of the predictions. The correlation coefficient obtained is

more than 0.9999, thus is an accurate method for determining the polymorphic

impurity. A graph has also been plotted with the true and predicted values. This data

has been tabulated containing both the pure forms I as well as that of pure form II

(Table2). The r2 value of above 0.999 shows that the method can be applied in the

total range and not restricted to the experimental conditions.

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Table 6.2: Table for validation of the calibration curve

S.No Sample name TRUE Prediction Difference

1 F2.3 0 0.09293 -0.0929

2 F2.4 0 0.08121 -0.0812

3 F2.5 0 0.1436 -0.144

4 0.5% F1 inF2.0 0.5 0.3714 0.129

5 0.5% F1 inF2.1 0.5 0.3593 0.141

6 0.5% F1 inF2.2 0.5 0.3602 0.14

7 1% F1 inF2.0 1 0.8608 0.139

8 1% F1 inF2.1 1 0.9692 0.0308

9 1% F1 inF2.2 1 0.9415 0.0585

10 2% F1 inF2.0 2 2.122 -0.122

11 2% F1 inF2.1 2 2.217 -0.217

12 2% F1 inF2.2 2 2.359 -0.359

13 5% F1 inF2.0 5 4.818 0.182

14 5% F1 inF2.1 5 4.642 0.358

15 5% F1 inF2.2 5 4.707 0.293

16 10% F1 inF2.0 10 10.27 -0.268

17 10% F1 inF2.1 10 10.34 -0.339

18 10% F1 inF2.2 10 10.06 -0.0592

19 20% F1 inF2.0 20 19.31 0.69

20 20% F1 inF2.1 20 20.17 -0.17

21 20% F1 inF2.2 20 20.21 -0.206

22 F1.0 100 99.94 0.0578

23 F1.1 100 99.98 0.017

24 F1.2 100 100.1 -0.0806

Correl Coef (r) 0.999973

Figure 6.12: Regression curve for the predicted and true values

y = 1x - 0.003R² = 0.999

-20

0

20

40

60

80

100

120

0 20 40 60 80 100 120

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All these spiked samples were also analysed by Differential scanning calorimeter

(DSC) and attempts were made to correlate the results with those obtained form NIR.

The thermograms are provided below( Figure 6.13 to 6.19).

Figure 6.13: DSC Thermogram for 0.5% spiked sample of form I in Form II

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Figure 6.19: DSC Thermogram for sample of form I

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Observation

The DSC spectra could not yield any significant differentiation between the samples

and thus is not found to be a suitable technique determination of polymorphic purity

for Sertraline HCL.

Conclusion

The polymorphic purity determination method by NIR spectroscopy is thus developed

as an accurate method to determine even very low levels of polymorphic impurity

i.e.as low as 0.5%

Both the Conformity index method as well as the quant model can be used as efficient

modes to determine the polymorphic purity for Sertraline Hcl by NIR.

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References

1. Online edition for students of organic chemistry lab courses at the University of

Colorado, Boulder, Dept of Chem and Biochem. (2002)

2. W. Fred McClure Anal. Chem., 66 (1), pp 42A–53A (1994)

3. F. Westad, A. Schimdt and M Kermit, J.Near Infrared Spectrosc. 16, 265-273 (2008)

4. W. M Welch, Discovery and preclinical development of the serotonin reuptake

inhibitor Sertraline, Advances in Medicinal Chemistry.3, 113-148, (1995).

5. D. Healy, The Antidepressant Era. Cambridge, Massachusetts: Harvard University

Press.168, (1999).

6. J. Couzin, The Brains Behind Blockbusters. Science. 309, 728, (2005).

7. R. Sarges.; JR Tretter.; SS Tenen, A. Weissman, Journal of Medicinal Chemistry, 16,

1003, (1973)

8. US patent No 6872853, Polymorphic forms of sertraline hydrochloride.

9. M. Varasteh.; Z. Deng.; H. Hwang.; Y.J. Kim.; B Geoffrey.; International Journal of

Pharmaceutics 366 74–81(2009)

10. Y Xie.; W Tao.; H Morrison.; R Chiu.; J Jona.; J Fang.; N Cauchonl.; International

Journal of Pharmaceutics 362 29–36, (2008)

11. M Blanco.; J Coello.; H. Iturriaga.; S Maspoch.; C. Pérez-Maseda.; Analytica Chimica Acta 407 247–254(2000)

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Method Development and validation for Polymorphic purity ...shodhganga.inflibnet.ac.in/bitstream/10603/8657/11/11...In the solid state, Sertraline hydrochloride exists in various crystalline