Identify Quantify Pharmaceutical Ingredient Enalapril Maleate Products Countries Biology Essay

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Quantification of API in commercial tablets from the UK market by NIR spectroscopy using diffuse reflectance and imaging techniques.

Working principally with intact tablets.

Abstract

The aim of this project was to identify and quantify the active pharmaceutical ingredient Enalapril Maleate in given different tablet products from different countries by Near Infrared Spectroscopic method. There were number of steps involved in the development of experimental method for Qualitative and Quantitative analysis of pharmaceutical products which are based on the comparison of the NIR spectrum of a sample with the spectra of authentic drug. There were three types of sample preparation done for collecting spectra which included intact tablets, powdered tablets and KBR discs made with tablet powder. There were two type of reference used in the experiment for better results and select the better one for the model preparation. This analysis was done by powerful chemo metric methods such as multiple linear regression method, Partial least square regression method. In the beginning the spectra were processed and multivariate model constructed for data acquisition. The acquired data then processed from different models, which gave equation for prediction of the active constituent in the sample. The constructed model equations then tested with external validation which shown the best regression model for quantification. The results show NIR spectroscopic method is rapid, non-destructive and reliable method for qualitative and quantitative analysis of pharmaceutical products and could be helpful to identify the counterfeit drugs among products.

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Introduction

Identification of counterfeit medicines

Currently the pharmaceutical industry is facing biggest threat from counterfeit products. The definition of the counterfeit drug was given by WHO in 1982 is as follows: "A counterfeit medicine is one which is deliberately and fraudulently mislabeled with respect to identity and/or source. Counterfeiting can apply to both branded and generic products and counterfeit products may include products with correct ingredients or with the wrong ingredients, without active ingredients, with insufficient active ingredients or with fake packing". The definition was changed in the FIT conference in Sydney 2003 as "Counterfeiting in relation to medical products means the deliberate and fraudulent mislabeling with respect to the identity, composition and/or source of a finished medicinal product or ingredient for the preparation of medicinal product" . This also impacts on patient health because they receive improper dose or even without proper ingredients, resulting in increase of their disease condition and potential disability and death. The industry is threatened by damage of its reputation resulting from the counterfeit products from the market places of its brand name.

Nowadays numbers of fake products are coming in the market under the famous brand names which have already become well-known in the pharmaceutical industry. There are different tricks to copy and mimic the original genuine products which are available in the market. The counterfeit medicines are prepared in terms of different ways. The marked active ingredient may not present in the packed product or active ingredient is not present within labeled range. Sometimes the costlier active substance is substituted by cheaper or less active substances .

The counterfeiting is not only problem of the developing country but it is also a notable offence in developed countries like USA, UK etc. The possibilities of counterfeit medicines in wealthier countries are in the area of new expensive lifestyle medicines, such as hormones, steroids and anti-histamines while in developing countries like India, China, Brazil the most counterfeited medicines are those used to treat life threatening conditions such as Malaria, tuberculosis and HIV. Hence, the problem of counterfeited medicines is more serious in developing countries.

The conventional methods for identification of counterfeit medicines are titration, mass spectroscopy and high performance liquid chromatography (HPLC). The HPLC method is very accurate method but needed high cost of experiment with highly skilled personnel to perform the test. Hence it is difficult for developing countries to carry out any identification of counterfeited medicines because of lack of fund for proper laboratory .

Near Infrared Spectroscopy is a spectroscopic analytical method. The near Infrared region is situated between the red band of the visible light and the mid-infrared region . The spectral regions span the wavelength 750-2500 nm. It is widely used techniques for the analytical purposes in pharmaceutical and food industries due to its speed and precision .

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Its main applications are in medical diagnostics, pharmaceutical industries because of several advantages of NIR spectroscopy like low cost per sample analyzed no need of sample preparation, wide range of products and parameters . The NIR spectroscopy is nondestructive and highly reproducible method for analyses. NIR spectroscopy can be used in identification of drug substances, excipient in short time of period. Direct reading of spectra is possible in Near Infrared technique because many packing materials allow penetrating Infrared radiation to the product. The obtained spectra used to convert into electronic library for comparison of authentic and false product among the given sample products .

The NIR region was discovered by Herschel in 1800. Although, the actual practice of NIR experiment was started in early 1920s when the potential of this analytical technique was incorporated in newfangled manner by U.S. department of agriculture .

Figure 1 Wavelength range of NIR region

Recently, NIR has achieved significant importance in pharmaceutical industries for quality control testing and process monitoring mainly for raw materials.

Although this technique has many advantages in the pharmaceutical industries but some drawbacks also limits its use in pharmaceutical industry as it lacks the ability of mid infrared spectroscopy to identify samples by simple inspection of spectra. Furthermore, quantitative analyses needs complicated micro computing and chemo metrics like mathematical techniques for calibrations which recently developed in advent of computer science knowledge .

However, there was initial ignorance at the NIR spectroscopy but now it is gaining popularity as a results of improvement in instruments, software and chemo metrics. There are sophisticated soft wares available for the interpretation of the spectra and chemo metric calculations .

In this experiment tablets are measured intact with no sample preparation. The NIR spectra were recorded using a FOSS NIR System equipped with a Rapid Content Analyzer. The data are recorded and processed using Vision software version 2.51. Chemo metric method like Partial least square regression was used for data treatment using software. The method of taking spectra was simple and fast. The general idea of the authenticity of the spectra was obtained by processing the spectra.

Principles of NIRS:-

NIRS is based on the absorption of the light due to overtones and combination of fundamental vibrational bands. The NIR region lies between the visible and MIR regions of the electromagnetic spectrum and is defined by the American Society for Testing and Materials (ASTM) as the spectral region spanning the wavelength range 780-2526 nm. There is a condition of the radiation to be absorbed is the frequency of the light should be exactly the same as a fundamental vibration frequency for a specific molecule and a molecule should go under a change in its dipole moment by virtue of its fundamental vibration .

The vibrational frequency for a diatomic molecule can be determined on the assumption of the harmonic oscillation model, where an atom shifts from its equilibrium position with strength proportional to the shift (Hook's law) .

Where is the speed of the light, k the bonding force constant and m is the reduced mass.

Polyatomic molecules possess several fundamental frequencies so they may exhibit simultaneous changes in the energies of two or more vibrational mode; the frequency observed will be the sum of or difference between the individual fundamental energies. This results in very weak bands that are called combination and subtractions bands. Thus vibrations can also be observed at approximately the multiples, e.g. twice, 3 times, 4 times etc. of the fundamental vibrational frequencies. These vibrations are called overtones. These overtones and frequencies combinations are observed in the near infrared region of the electromagnetic spectrum and are characterized by a large number of band overlap due to broad bands. This gives a fingerprint characteristic to the NIRS which makes it very useful for identification purpose .

Figure 2 Finger print region of the important functional group in NIR region

Resonance overtones of C-H, N-H, O-H, and S-H and combination of the fundamental vibrational modes have important information when they processed with related chemometric algorithms. When fundamental vibration compare with NIR absorption band in corresponding to mid infrared region later one has been found weaker in frequency. Near Infrared Radiation has low molar absorption therefore it could penetrate up to several millimeters into solid material. Hence, the NIR region is less sensitive and it permits operation in the reflectance mode and recording the spectra of the solid samples.

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'Reflectance spectroscopy measures the light reflected by the sample surface which contains a specular component and a diffuse component'. Specular reflectance described by Frensel's law, contain little information about composition; consequently its contribution to measurements is minimized by adjusting the detector's position relative to the sample. On the other hand diffuse reflectance, which is described by the Kubelka-Munk theory, is the basis for the measurements by this technique .

One widely used practical alternative is a relationship between concentration and relative reflectance similar to Beer's law, namely: .

A= log 1/R = a´c

Where A is apparent absorbance, R relative reflectance, c concentration and a´ a proportionality constant. However, this relationship is different from Kubelka-Munk equation in terms of theoretical basis but it provides satisfactory results under typical conditions used in many diffuse reflectance spectroscopic applications.

Enalapril Maleate:-

Enalapril Maleate is the maleate salt of enalapril, an ethyl ester of a long-acting angiotensin converting enzyme inhibitor, enalaprilat. The chemical name of enalapril maleate is (s)-1-[N-[1-(ethoxycarbonyl)-3-phenylpropyl]-L-alanyl]-L-proline, (Z)-2-butenedioate salt (1:1). Its empirical formula is C20H28N2O5*C4H4O4 and its structural formula is:

Figure 3 Structural formula of Enalapril Maleate

Enalapril Maleate is available as off white crystalline powder. Its molecular weight is 492.53. It is sparingly soluble in water, soluble in ethanol, and freely soluble in methanol.

Enalapril is a prodrug which is activated by hydrolysis of the ethyl ester to enalaprilat, which is active angiotensin converting enzyme inhibitor .

It is available as tablet dosage form in different range of 2.5mg, 5 mg, 10 mg and 20 mg tablets for oral administration. The tablet also contains following excipients: anhydrous lactose and zinc stearate and iron oxide.

Clinical Pharmacology: Mechanism of Action:-

EM mainly inhibits angiotensin converting enzyme in human subjects and animals. ACE is a peptidyl dipeptidase that catalyzes the conversion of angiotensin 1 to the vasoconstrictor substance, angiotensin 2. Furthermore angiotensin 2 stimulates secretion of aldosterone from adrenal cortex. This inhibition of Renin Angiotensin aldosterone system is the beneficial pharmacological effect of the enalapril .

Pharmacokinetic and Metabolism:-

The maximum plasma concentration of enalapril maleate reaches within one hour of administration. It has maximum plasma concentration about 60%. Food administration does not affect its oral absorption. The only metabolite of the enalapril is enalaprilat observed in urine. The half-life of the drug is 11 hours.

Pharmacodynamics effect:-

After several clinical studies it was found that the oral administration of enalapril maleate causes reduction in both supine and standing blood pressure usually with no orthostatic component. The antihypertensive effect of EM is seen after 1 hour of oral administration. In the treatment of heart failure EM shows good results like decrease in the vascular resistance, blood pressure, pulmonary capillary wedge pressure and heart size, and increase in cardiac output and exercise tolerance .

Indication and Usage:-

Enalapril maleate tablets are indicated to use in the treatment of Hypertension effectively alone or in combination with other anti-hypertensive agents such as thiazide type diuretics. It is also used in the treatment of Asymptomatic Left ventricular dysfunction and symptomatic congestive heart failure.

Contraindication:-

The use of Enalapril Maleate is restricted in hypersensitive patient to this product having history of angioedema related to previous treatment with an angiotensin converting enzyme inhibitor and in patients with heredity or idiopathic angioedema.

Chemometrics:-

Chemometrics is the science of extracting information from chemical systems by data driven means. The spectra obtained are needed to be processed to achieve clear results of the collected data. This statistical treatment of the spectral data, modeling and classification were performed by Vision software. This package provides multiple linear regression, partial least square regression, correlation in wavelength space algorithms.

The obtained absorption bonds are severely overlapping and difficult to interpret because they are the result of combinations and overtones of the fundamental mid-IR bonds. The spectra are complicated due to overlapping by other peaks. There are different parameter which affects the spectra like particle size, crystalline forms and variation in optical path length. Therefore the well-defined knowledge of interpretation of the spectral data by chemometrics required for better results . The use of chemometrics in NIRS is essential in number the steps of the process. From the experimental design to quantification of the regression model it plays crucial role in the method in each step. It can be used for development of predictive models, mathematical pretreatments of the spectra and sample grouping .

The processed spectra are both advantageous and dis advantageous for the original spectra. Though use of chemometrics clears the spectra and enhances resolution and facilitates computational treatment but it causes the worsening of signal to noise ratio and increases the complexity of the spectra.

Standard Normal Variate (SNV)

SNV and SNV D2 (Second derivative of SNV) are light scattering correction method used to normalize spectra whereas mean centering remove offset from the data. The difference in the spectra was because of granular and powdery nature of the sample which effects on its particle size. The correction in the factors is done in different way .

Correlation in wavelength space (CWS)

Correlation method identifies the similarity between a reference spectrum and a sample spectrum of library. It is a cosine of an angle between the two spectra. The two spectra are indicated as identical when their correlation coefficient is equal to 1 but it was set to 0.975.

Mathematically the equation of the correlation coefficient between two spectra X and Y is defined as:

r = Σixiyii = wavelength 1110  2498

√ Σxi Σiyi

Partial Least Square Regression (PLSR)

It is a calibration set up method first introduced by the Swedish statistician Herman Wold. It is used for complex matrices or analytes variables for which Multiple Linear Regression is not suitable. This technique can construct a linear model by projecting the lab data vs computed data in the regression equation. These data X and Y are projected in the new spaces.

In this method, the whole infra-red region of electromagnetic spectrum is needed to scan because a single peak could not account the matrices and analytes variables. However, there must be large number of samples used for a precise calibration. This method is more suitable when the matrix of predictors has more variables than observation .

Experimental Method:-

Materials

Total 15 product of Enalapril Maleate tablet were used for constructing libraries and models for analysis purposes. These products came from different manufacturers, different batches and different countries. The tablets have two dose strengths one was 5 mg and the other was 20 mg. All of the tablets got over their expiry dates but all were stored in good packing condition so there was no chance of contamination. The active ingredient of the all tablet is Enalapril Maleate. The tablet also contain various excipients mainly lactose. The list of different sample products is given below with batch number, manufacturer name and dose strengths.

Table 1 List of the tablets used in the calibration model with details

Manufacture

Dose (mg)

Batch no.

Label

1.Merck sharp & Dohme de Espana

5

M41

MSDREM41

2. Merck sharp & Dohme de Espana

5

N19

MSDREN19

3. Merck sharp & Dohme de Espana

5

M29

MSDREM29

4. Merck sharp & Dohme de Espana

5

M40

MSDREM40

5. SP Rise pharmacy

5

N12

MERN12

6. Dohme ohibret, Rfance

5

456810

MSDRE810

7. Euro-Chem, Spain

5

N17

MSDREN17

8. Merck, USA

5

J27

MERJ27

9. Merck

5

4769

ENAL4769

10. Abello Farmacia, Spain

20

N41

RENN41

11. Abello Farmacia, Spain

20

N25

MAREN25

12.Abello Farmacia, Spain

20

M66

ENALM66

13. Merck, USA

20

N36

ENALN36

14. Neopharmed, Italy

20

U631

ENAP631

15. Merck

20

9518

MER9518

There were 6 external validation sample used in the experiment to check whether the equation is authentic or not. The 5 products among the six do not contain Enalapril maleate therefore it was referred as blank samples, too. Total 36 tablets from different 6 sample products are listed below.

Table 2 List of the tablets used as external validation in quantitative analysis

Manufacturer

Tablet

Dose(mg)

Batch no.

1. Merck sharpe & Dohme, Italy

Enalapril Maleate

5

U1801

2. Parke-Davis, Germany

Quinalapril

20

10027127

3. Astrazeneca, UK

Atenolol

25

FV612

4. Galpharm Healthcare, UK

Ibuprofen

200

71310

5. H N Nortan and Co LTD

Cimetidine

200

HA59M18K

6. Wise pharmaceutical, U.K.

Ciprofloxacin

500

KF5703

The percentage of active ingredient in sample tablets are listed below:-

Table 3 List of the tablets with their percentage weight

Batch no.

Tablet 1

Tablet 2

Tablet 3

Tablet 4

Tablet 5

Tablet 6

M 41/1

2.17

2.17

2.12

2.16

2.14

2.19

N 41

10.23

10.08

10.03

10.09

10.15

10.00

456810

2.15

2.19

2.14

2.15

2.19

2.16

U631/1

10.02

10.02

10.06

9.84

10.00

10.00

N 17

2.13

2.15

2.14

2.12

2.13

2.16

N 12

2.17

2.18

2.22

2.17

2.18

2.14

M40/1

2.15

2.15

2.18

2.16

2.20

2.18

4769

2.15

2.17

2.12

2.16

2.14

2.19

M66

10.24

10.09

10.03

10.03

10.17

10.06

N 36

10.11

10.02

9.94

10.01

10.06

9.96

9518/1

9.98

10.03

9.99

10.07

9.99

9.89

N 19

2.17

2.17

2.23

2.20

2.15

2.10

8J27/3

2.17

2.10

2.23

2.19

2.23

2.49

N 25

9.75

9.75

10.07

9.96

10.06

9.96

M 29

2.15

2.15

2.15

2.15

2.20

2.16

Instrumentation:-

The FOSS Rapid content Analyzer is used for rapid analysis of the samples which is non-destructive, cost effective instrument for NIR spectroscopy in pharmaceutical manufacturing process. This also does not require any sample preparation which can be very useful in hazardous substance analysis.

The FOSS Rapid content Analyzer can be used for intact tablets, powdered samples, liquids and finished dosage form without changing the instrument. The handling of the instrument is very convenient and easy to understand the procedure of placement and running the spectra which saves the experiment time. The instrument does not require so much of attention of human but it work easily and comfortably throughout the experiment which further enhance the capability of the experiment. The sample can be placed directly or in glass vials, beakers or bottles which save time and labor.

The FOSS Rapid content Analyzer 6500 works with Vision Spectral Analyses Software for windows, a validated data acquisition and chemometric package.

Microsoft Windows operating system was used for the data acquit ion and storage of the datas. It was equipped with Pentium 4 processor.

Advantages of the FOSS Rapid content Analyzer system with Vision Software:-

It provides rapid, non destructive analysis of Tablet samples in just 45 to 50 seconds.The sample could be place into glass bottel or vial for direct analysis.It requires no or less sample preparation need so hazardouz substances can be identified safely.It quantifies the amount of active ingredient at very accurate level.

Figure 4 FOSS NIR instrument

Precisa balance was used to weigh the all materials.

Methods:-

The NIR insrument required calibration prior to quantify the active ingredient from the given sample there for multivariate models were constructed by acquiring spectra and stored into appropreate labelled products.

Sample peparation:-

Initially different samples of tablet products were collected from different manufacturers. Enalapril Maleate tablets were available in two dosage strength 5mg and 20mg. Total 90 tablets were collected from 15 different batches, 6 from each batch and stored into air tight labelled glass bottels to prevent from the moisture. Similarly the external validation samples also stored into labelled glass bottels which were 36 in numbers.

The second model of powdered tablets which was made by trituring the tablets and stored into appropriate labelled waters glass vials.

The third model was constructed with graduaaly upturn concentration of the active ingredient by adding pure Enalapril maleate to the powdered tablet sample obtained from Batch number M41. Total 20 tablets were powdered to prepare 7 samples having concentration of 2, 4, 6, 8, 10, 12, 14 percentages repectively. These samples were putted into waters glass vials.

The fourth model was constructed by making KBR discs of powdered samples. One tablet from each batch was collected from the total batch of 15 sample products and 6 external validation produts. The graduated powdered sample were also used for making KBR discs.

KBR disc :-

The making of KBR disc involved following steps.

Weigh the 250 mg of KBR and placed into mortar.Mix KBR with powdered tablet sample or graduated concentration powder sample.Triturate the mixture to very fine powder.Place the fine powder into punching die and assembel the punch.Apply pressure on punch upto 10 tons and release the pressure to take out the Disc from the die.Store in appropriate labelled glass bottle.

Measurement of Spectra:-

First of all the performance test allow to check wether the instrument work properly or not.

After that referance spectra was taken before taking sample spectra by using ceramic tile or tablet as a referance.

NIR is used because of ease of handling and placement of the sample products into instrument. There was very simple technique to take spectra of single product of any form.

Measurement of the Tablet:-

Tablets were just placed into the sample holder and make sure it should be in center of the beam of radiation source for better measurement by screwing the holder.

Powdered tablets sample placement:-

The powdered tablet was puted into the waters glas vials which can be directly puted into sample holder. Here total four spectra were measured by shaking or tapping the bottle after each measurement.

Mesurement of The spectra of KBR disc:-

The method for recording spectra of KBR disc was same as the tablet. There were two spectra taken from each side of the disc.

All the data of spectra was stored into appropriate product which name was given as stated on the label of the sample bottel.

Treatment of the spectra:-

The Vision software and Microsoft Excel version 2007 were used for the spectral treatment. The spectra were processed to standard normal variate and Second derivative (SNV-D2). Both the Qualitative and Quantitative approaches were taken for the spectral treatment. The parameter used for the qualitative approach were correlation in wavelength and principal component analysis while for quantitative approach, partial least square regression was done.

Qualitative Analysis:-

Qualitative analysis means classification of samples according to their NIR spectra.control approach primarly gives the identity of the material at each stage of the manufacturing process. It can be used for both identification and qualification purposes which are based on pattern recognition method. The complexicity of the application could affect the selection of the sample and development of the library.

A method was developed for the identification of the tablets. The main identification substance was the enalapril maleate. This process involves two steps, recording of the spectra of the product and generates the appropriate library which involves relative product spectra for comparision among them. The spectra should contain every possible source of variability associated with the batches and the manufacturers. It is difficult to determine the number of spectra to be included in a library. The number will depend on the study.

Library development:-

A qualitative library contains training data set of for the each material and will depend on the intended use of the library. A typical qualitative library development will involve the following steps .

Selection of samples/spectra for calibration set,Data pre -processing,Libraryconstruction,Determination of thresholds.

Selection of the samples for calibration set:-

The acquisition of the spectral data were given in the sample selection section. Responsible factors for the variability among the samples are moisture, particle size,residual solvent, degradation products, compositional change of formulated product, other chemical/physical properties,time,alternative source of materials,retained samples,tempreture,operator,presentation,between instrument variation

Display data:-

Visual examination of the spectra helps to determine any abnormal spectra or the outliers. The exlusion of any outlier required documentation with valid reason.

Calibration set selection:-

The number of samples required from the each group depends on the complexicity of the application of the development method.

Data pre-processing:-

It is significant to treat the data mathemetically to reduce the complexicity of the spectra. The use of scattered mathemetical algorithms and derivatives implicated to reduce offsets due to physical characteristics. Cautiousness should be essential during mathemetical transformation because it errors can be introduced or essential information can be lost. The algorithms, chemometrics like vital throughout procedure and the documentation work was done systemetically.

Library construction:-

The libray was constructed on the basis of requirenments and performance of the software. Generally all the products are incorporated into one library. After performing several mathemetical treatment they were divided into sublibraries to match the requirenment of the method specificity.Sub library should include all mutually related substances which will have similar spectra and were identified as ambigious samples with general library. The same mathemetical treatment was given to all sample products to ensure the specificity of the library. For identification purpose where physical paramemters are not important , a match by wavelength correlation method using second derivative data should be sufficient. The full instrument wavelength range was used .

Determination of thresholds:-

Mostly the threshold value is provided by manufacturers or internal validation is performed using the software default values. Here in this qualitative analysis the threshold value was set to 0.975.

After performing identification process of the constructed library the result was obtained and all of the tablets are identified as enalapril maleate because all of them were contained enalapril maleate as active ingredient. The following table shows the list of the tablets with correlation in wavelength coefficient value related with identified tablets.

Most of tablets identified as of they were classified in respective batches. In the second library which inlude blank samples also showed almost similar results. In this library the all blank samples identified as of their respective batches because they contain different active ingredients from each other.The subsequent libraries were constructed on the basis of the result found from earlier library identification.

Quantitative analysis:-

NIR spectra typically contain broad, overlapping bands that cann not always be ascribed to an individual samle component. Thus a calibaration is requred for the multivariate procedure for quantification purpose using NIRS techniue which could not affect by the physical or chemical properties of the sample to be determined by multivariate procedure.

Here, following steps are involved in the quantitative analysis: .

Acquisition of spectra:-

Spectra were recorded as shown before and processed to SNV and SNV D2. The spectra were saved in appropriate product class.

Active constitute:-

The amount of active constitute in terms of percentage weight inserted into sample product and saved the data for further process.

Construction of model:-

Different models were constructed according to their sample nature like tablet model, powdered tablet model, KBR disc model. The second parameter for constructing model was the type of the standard referance which have been used for taking spectra. The spectra which have taken against ceramic standard referance seperated from the spectra which have taken against tablet as standard referance. The models were given proper titles by which they can identify easily.

Quantitative analysis mode:-

This mode is available in the vision software for quantitative analysis. The first step was to select the samples which were needed for making regression equation. The appropriate samples were selected and combined under one equation. Then partial least square regression method was edited by setting parameters SNV and 2nd derivative of SNV. The obtained regression equation then saved for prediction purpose.

Prediction:-

For this function the suitable regression equation was selected and intially the equation was checked by iserting internal validation samples in it. Afterward the equation cross checked by using external validation. The enalapril tablet fro batch no EU180 was used as external validation. Six different tablet batches which did not contain enalapril maleate also used as blank external validation. After running the prediction the best fitted models were identified and results were recorded.

Results and Discussion:-

Qualitative analysis:-

This mode of analysis determined the presence of the active constituents in the given set of the sample by matching the spectra of the sample against the spectral library. The parameter was selected correlation coefficient.

The constructed library processed to identification and gives the result of correlation coefficient in wavelength space between different batches. To, identify the tablets, the correlation coefficient between the SNV D2 of each batch was calculated. Total 15 batches were used in the construction of the library.

All the tablets were failed in the identification test because of the similarity of their formulation

Quantitative analysis:-

Sample selection

The sample product for quantitative analysis were put into Vision Software to create PLSR models. This models were divided into two sets. One calibration set and another validation set. The ratio between the calibration and validation set was set to 3:1 that is for 3 spectra from each sample for calibration set and 1 spectra from same samlpe for validation set.

Edit regression method:-

The regression method was based on PLSR technique in which number of factors were included to obtain equation of the model. The wavelength range was set between 1100-2500 nm. The number of factors were restricted to the maximum of six to give a good standard error of calibration. After running the equation it shows the following results.

Figure 5 Calibration model shows graph of calculated data vs Lab Data using ceramic reference

Here, two strength of doses were used therfore the points situated near 2% and 10%. No outliers have found in this model equation so it was good PLSR equation for prediction of the internal and external validation samples.

The other model of tablets which spectra were recorded against the tablet referance processed to make PLSR equation.The following chart shows the validation set results which consist of 25% of the model set. Here, the points are variable to the regression lline.

Figure 6 Validation set shows calculated data vs lab data using tablet as reference

Internal validation of the model equation:-

In order to quantify Enalapril maleate tablet , calibration and validation spectra are needed to be set up first. After recording the spectra the regression equationwas made by PLSR technique and saved it for internal as well as external validation. Internal validation is the set of the samples which were used in the regression equation having similar components as Enalapril Maleate. Here, total 15 no of batches were used to make calibration model. Internal validation gives information about the similar samples which were used in the equation.

The first model was constructed with all 15 intect tablets which shows linearity. Second model was made with powdered tablets. Third model was constructed with powdered tablet with standard addition of the pure Enalapril Maleate powder. Fourth model was made by KBR disc of powdered tablet samples.

Model name

Referance used

Product samples

Quant3

Ceramic tile

Intact tablets

Quant3A

Tablet

Intact tablets, blank sample

Quant4

Ceramic tile

Intact tablets of 5mg

Quant5

Tablet

Intact tablets of 5mg

Quant6

Ceramic tile

Intact tablet of 20mg

Quant7

Tablet

Intact tablets of 20mg

Quantpow

Ceramic tile

Powdered tablets

QuantpowB

Ceramic tile

Powdered tablets

Quantpwd

Tablet

Powdered tablets including blank

Quant EMPWD

Ceramic tile

Standard addition powder sample including blank

Quant EMPWDA

Tablet

Standard addition powdered sample including blank

Quant KBR1

Ceramic tile

KBR disc

Quant KBR2

Tablets

KBR disc

QuantKBR3

Ceramic

KBR disc including blank

QuantKBR4

Tablets

KBR disc including blank

QuantKBR5

Ceramic

Standard addition powder KBR disc including blank

QuantKBR6

Tablets

Standard addition powder KBR disc including blank

Hence, there were mainly four types of calibration models were prepared and processed to obtain PLSR equation for internal and external validation to check the authensity of the regression equation.

Regression equation and prediction

Model 1-Tablets:-

Under 1st model of intact tablets there were total six new sample products were made by collecting the desire sample spectra with information about their respective percentage of weight of active constituent. The model was processed to generate regression equation gives good result as points situated near the line. The model of intact tablets shows better results if sample ran against ceramic referance. In the case of tablet referance spectra were spotted distant of the line. Different new sample product were constructed to achive best fit calibration model. The cascading sample product no 4,5 made only of 5mg tablet and sample product no6,7 made only of 20mg product to achieve straighter line.The addition of the blank samples in the equation was done to find new results from the model.

Figure 7 Prediction of Ciprofloxacin batch into model equation Pls33

Figure 8 Internal validation of the model equation by batch no M66

The above chart shows result of Equation EMpls33 in which Tablet batch no.M66 was evaluated. The range of computed data lies between 9.8 to 10.3 which appears as good result. The equation was validated by evaluating the blank sample of Ciprofloxacin tablet which do not contain enalapril maleate. It shows range between 2.84 to 10.38.

Figure 9 External validation of the model equation by batch no EU1801

Figure 10 External validation by blank sample Ciprofloxacin

Model 2-Powdered tablets:-

In the second model, the powdered tablet samples were taken into account to make regression equation to check the physical properies of the sample will affect the data or not. The data shown similar kind of results on the regression graph.

Figure 11 Regression equation of the powdered tablet including blank samples

Here, different types of processes were done to make sub model of the main to achieve better results. However the the model I which spectra were recorded against tablet referance shows similar result as the points were spotted distant to the line.

Figure 12 External validation of the regression equation by blak sample

The batch EU1801 quantified well in in this model equation as data were spotted near the line.

Figure 13 Prediction of the EU1801 batch into Quantpow regression equation

Model 3 Powdered tablete with standard addition of enalapril maleate:-

This model provided the wide range of the percentage of the active constituent for calibration. This calibration model gives straight line of the pointed data. Although prediction of the model with external validation did not give satisfactory results because the data were found too distant from the line. Furthermore, no additional models were prepared to check the further usability of this type of model.EQPWDAA

Figure 14 Calibration model of the regression equation EQPWDAA

The prediction of batch EU1801 gave not good result as the large variation among them was obtained. Thus the theoretical range was from 2.17 to 2.20 where the calculated data has shown variation from 13.4 to 20.5. So the further regression equations were constructed in Model 4.

Figure 15 Prediction of the EU1801 batch in the EQPWDAA equation

Model 4:-

Figure 16 Regression equation KBR5

Figure 17 Prediction of Ciprofloxacin in KBR5 equation model

Figure 18 External validation of equation by N19 intact tablets

Figure 19 Internal validation of equation by N19 Kbr disc