Indu...impurity profiling of api’s using rp hplc as per

04/13/2023 Dept.of pharmaceutical analysis 1

SEMINAR ON IMPURITY PROFILING OF API’s USING RP-HPLC

For the partial fulfillment of…

MASTER DEGREE IN PHARMACY BY

J.N.V.INDIRA DEVI

REG NO:611235004011

YALAMARTY PHARMACY COLLEGE

UNDER GUIDANCE OF

Sri.J.V.L.N SESHAGIRI RAO, PROFESSOR Mr.N.K SAHOO, ASSISTANT PROFESSOR

ANDHRA UNIVERSITY

ContentsIntroductionObjectiveDescription:ICH Impurities guidelines

Classification of impurities

ICH limits for impurities

Impurity profiling by RP-HPLCApplicationsConclusionReference

Impurity as per International Conference on Harmonization…

According to the ICH of Technical Requirements for the Registration of Pharmaceuticals for Human Use (ICH) guideline Q3A(R2) on impurities in new drug substances,1 an impurity is defined as 'any component of the new drug substance that is not the chemical entity defined as the new drug substance.

Impurities can be formed during drug synthesis, manufacturing, and/or storage.1,2

Introduction


What is an impurity profile? ‘A description of the identified and

unidentified impurities present in a new drug substance'.1

Identified Impurity: An impurity for which a structural characterization has been achieved.

Unidentified Impurity: An impurity for which a structural characterization has not been achieved and that is defined solely by qualitative analytical properties .2

A general acceptance criteria of not more than 0.1 percent for any unspecified impurity should be included.3

Various regulatory authorities and Health Agency are emphasizing on the purity requirements and the identification of impurities in API.

Qualification of the impurities is the process of acquiring and evaluating data that establishes biological safety of an individual impurity; thus, revealing the need and scope of impurity profiling of drugs in pharmaceutical research2.


ICH impurities guidelines

Codes TitleQ 3 A (R2)

Dated 25th Oct 2006

Impurities in new drug substances

Q 3 B (R2)Dated 2nd June

2006

Impurities in new drug product

Q 3 C (R3)Dated 17th July

1997,revised Nov 2005

Impurities: Guidelines for residual solvents

Classification of impurities2

=>Organic(process and drug related)-includes starting materials, by-products, intermediates, degradation

products, reagents, ligands and catalysts=>Inorganic-includes reagents, ligands and catalysts, heavy metals or other

residual metals, inorganic salts, other materials (eg. filter aids, charcoal etc.)

=>Residual solvents-organic or inorganic

Specifically doesn’t cover:1) extraneous contaminants more appropriately addressed under GMP;2) polymorphic forms3)Enantiomeric impuritiesThese are addressed in other ICH guidelines eg. Q6A

1. Organic Impurity

Degradation product

The degradation of penicillins and cephalosporins is a well-known example of degradation products.

The presence of a ß-lactam ring as well as that of an amino group in the C6/C7 side chain plays a critical role in their degradation. 2.Inorganic impurity

Reagent , ligands, and catalysts :

• The chances of having this impurities are rare.

Heavy metal:

• The main sources of heavy metals are water used in process and the reactor, where acidification or acid hydrolysis takes place.

• These impurities can easily avoided using demineralised water.

3. Residual Solvents:

Residual solvents are defined as the organic volatile chemicals that are used or produced in the manufacture of drug substances or excipients or in the preparation of the drug products.

Classification of Residual solvent:

Solvents are evaluated for their possible risk to human health and placed in to one of three classes as follows:

Class 1 solvents : solvents to be avoided. Class 2 solvents : solvents to be limited. Class 3 solvents : solvents with low toxic potential. Other solvents (“class 4”) : no adequate toxicological data.

Class 1 Solvent: Solvent to be avoided• Solvents in class 1 should not be employed in the manufacturing of

drug substance because of their unacceptable toxicity or their deleterious environmental effect.

Solvent Concentration in ppm

concern

Benzene 2 Carcinogen

Carbontetrachloride 4 Toxic and environmental hazard

1,2 Dichloroethan 5 Toxic

Class 2 Solvent: Solvent To Be Limited

• Solvent in the following table should be limited in pharmaceutical product because of their inherent toxicity.

Solvent Permitted daily exposure (mg/day)

Concentration limit (ppm)

Acetonitrile 4.1 4100

Chlorobenzene 3.6 3600

Class 3 Solvent : Solvent with low toxic potential

-Regarded as less toxic and of lower risk to human health. -No long term toxicity or carcinogenicity studies. -Less toxic in acute or short term studies and negative in

Genotoxicity studies. Eg .- acetic acid - acetone - anisole - 2-propanol - methyl acetate - ethyl ether.

Other solvents (“class 4”): no adequate toxicological data

The following solvent may also be of interest to manufacture of Drug substances or drug product.

Eg.: - Isooctane - petroleum ether - methyl isopropyl ketone - trichloroacetic acid.

Maximum daily dose1

Reporting Threshold2,

3

Identification Threshold3

Qualification threshold3

≤2g/day 0.05% 0.10% or 1.0mg per day intake (whichever is lower)

0.15% or 1.0mg per day intake (whichever is lower)

>2g/day 0.03% 0.05% 0.05%

ICH Q3A(R2): LIMITS FOR IMPURITIES1

[1] The amount of drug substance administered per day[2] Higher reporting thresholds should be scientifically justified[3] Lower thresholds can be appropriate if the impurity is unusually toxic

Impurity profiling by rp-hplcChromatographic impurity profiles are most often developed using (RP-HPLC). The chromatographic impurity profile should allow detecting and separating all (un)identified impurities in each new active compound. 4.

Selection of dissimilar chromatographic columns

Optimisation of mobile phase pH and column selection

Optimisation of organic modifier composition

Optimisation of gradient slope and

temperature

Different steps when developing chromatographic drug impurity profiles

1.Selection of a Set of Dissimilar Chromatographic Columns

To reveal and/or separate all impurities in a new drug substance, several chromatographic profiles with different selectivities will be created.

In impurity profiling, the stationary phase largely influences the selectivity of the chromatographic system.5

Therefore, one possibility to obtain a set of profiles with different selectivities is to use a set of dissimilar (or orthogonal) RP-HPLC columns to screen the drug impurities mixture.

This selection can be based on several approaches,5-8 . It often is preferred to use only silica-based columns, which are widely available.

HPLC Columns:Orthogonal Screening – Columns10

Stationary Phasea

Column pH Rangeb Manufacturer Part Number

C18 – Twin Technology

Gemini C18, 5 μm, 110A, 4.6 x 150 mm

1-12 Phenomenex 00F-4435-E0

Phenyl with Hexyl (C6) linker, endcapped

Luna Phenyl-Hexyl, 3 μm, 4.6 x 150 mm

1.5-10 Phenomenex 00F-4256-E0

C18-20% C loading

Discovery HS-C18, 3μm, 4.6 x 150 mm

2-8 Supelco 569252-U

C18 – polar embedded, hybrid particle with Shield Technology

XTerra RP18, 3.5 μm, 4.6 x 150 mm

1-12 Waters 186000442

C18– silica Sunfire C18, 3.5 μm, 4.6 x 150 mm

2.8 Waters 186002554

Pentafluorophenyl Curosil PFP, 3 μm, 4.6 x 150 mm

2-7.5 Phenomenex 00F-4122-E0

a Other columns could be selected based on the compound properties. b Columns were screened only against mobiles phases within their compatible pH range.

2.Optimization of the Mobile Phase pH and Selection of a Suitable Column Fig.2 consists of first modelling the retention of the peaks as a function of

the pH and also the peak width in case of isocratic elution.

For gradient elution, the peak width can be considered constant and does not need to be modelled.

For a compound with acidic or basic properties, retention changes with pH, following a sigmoidal curve. However, depending on the pKa of the impurities and the examined pH range, the whole curve is not considered. This makes modelling tR as a function of pH challenging.9

Resolutions between consecutive peaks are then calculated and the minimal resolution, Rsmin, determined at each pH value on the given column.

04/13/2023 17

These Rsmin are plotted as a function of the pH values for each column (Figure 3).

The pH value with maximal Rsmin, the one with the best separation for the worst-separated peak pair, is considered the optimum on the considered column.

For example, on column1, the pH with maximal Rsmin is indicated on Figure 3. The same is done for all columns (Figure 3), and on each column the pH value with maximal Rsmin is determined.

Finally, the overall maximal Rsmin is determined. This overall maximal Rsmin then specifies the best pH and column. For example, on Figure 3, the pH with the overall maximal Rsmin is indicated.

3.Optimization of the Organic Modifier Composition

Modifier Mobile Phase Concentration

Approximate pH

Trifluoroacetic Acid (TFA)

0.05% 2

Formic Acid 0.1% 2.8

Ammonium Acetate + Acetic Acid

8 mM + 0.1% 4

Ammonium Acetate 8 mM 7

Ammonium Acetate + Ammonium Hydroxide

8 mM + 0.05% 10.2

Ammonium Hydroxide 0.05% 10.8

Mobile Phase Modifiers10:


In general, for a three-component mixture, for eg. with modifiers methanol (MeOH), acetonitrile (ACN) and tetrahydrofurane (THF), all possible compositions can be represented in a triangle (Figure 1).

Each vertex of the triangle represents a condition where the organic part of the mobile phase only consists of one modifier.

Each side represents binary mixtures of organic modifiers and inside the triangle ternary mixtures can be found.

Additionally, each condition contains a given amount of water to make all compositions isoeluotropic.

Any composition can be considered as a mixture with given fractions of x1, x2 and x3, each ranging between 0 and 1.

When applying a gradient elution, isoleluotropy of the beginning and end conditions often is ignored.

In fact in a gradient elution one moves from one triangle to another, from one with a larger water content to one with a smaller.

Fig.1 Solvents triangle, with x1, for instance, representing an ACN/H2O mixture, x2 MeOH/H2O, and x3 THF/H2O. Water content is determined by solvent strength. Numbers 1–10 (see Table 1).


Here only one side of the triangle is explored (e.g., side 2 to 3 in Figure 1). Two different retention models can be constructed depending on whether the model is based on two or three measurements (Figure 2).

log(k) = b1x + b0 [1]

log(k) = b'11x2 + b'1x + b'0 [2]

where k is the retention factor, x the fraction of one of both modifiers (between 0 and 100% or between 0 and 1) and b1, b0, b'11, b'1 and b'0 are the regression coefficients of the models.

To optimize a binary mixture of organic modifiers, the drug mixture is analysed at two or three conditions and the model (Equations 1 or 2) is built for each impurity.


In Figure 2, this is represented for four substances. For each intermediate mixture of organic modifiers, the retention is predicted for all impurities.

At each composition, the retentions are then sorted and the selectivity factor α or the resolution Rs is calculated for each pair of consecutive peaks.

To determine the optimal conditions, the minimal selectivity factor αmin or the minimal resolution Rsmin is plotted as a function of the composition of the mobile phase.

The plot does not have a smooth behavior because the plotted αmin or Rsmin values at different conditions may originate from different peak pairs.

That composition where αmin or Rsmin is maximal is then selected as the optimum, for example X = 40% x3 in Figure 2(a), meaning that 2/5 of composition x3 and 3/5 of composition x2 are mixed.


Figure 2: Optimization of a binary mixture of organic modifiers using (a) a linear model, and (b) a quadratic polynomial model to model retention. Si = substance i from mixture.

4.Optimization of the Gradient Slope and the Temperature

This step concerns factors with less influence on the selectivity.

This step often can be considered optional or rather as a fine-tuning of the method.

It can be done using response surface designs. In general, the optimized conditions for two examined

factors, x1 and x2(e.g., gradient slope and temperature), to separate a mixture of compounds, is derived from a plot of Rsmin values at different x1–x2grid conditions, as shown in Figure .

Similar plots are obtained for the triangle (mixture domain) when plotting Rsmin values as a function of the organic modifier composition


Impurity profiling of Famotidine in bulk drugs and pharmaceutical formulations by RP-HPLC method using ion pairing agent11

Column :C18(250 mm x 4.6 mm) Mobile phase :acetonitrile, methanol and 1-hexane sodium sulfonate. Flow rate : 1.5 ml/min. Detector photo diode array was operated at 266 nm. The degree of linearity of the calibration curves, the percent recoveries of

famotidine and impurities, the limit of detection and quantitation, for the hplc method were determined.

The method was found to be simple, specific, precise, accurate and reproducible.


Fig.The chromatogram of Famotidine and its related impurities

Numerous applications have been sought in the areas of drug designing and in monitoring quality, stability and safety of pharmaceutical compounds, whether produced synthetically, extracted from natural products or produced by recombinant methods.

Impurity profiling of API’s by RP-HPLC12

Drug Impurities Method

Cefdinir Related substances HPLC

Donepezil Process related impurities

HPLC

Linezolid Process related impurities

HPLC

Loratidine Process related impurities

HPLC

Repaglinide Process related impurities

HPLC

Rofecoxib Process related impurities

HPLC

Zaleplon Process related impurities

HPLC

Isolation and characterization of impurities is required for acquiring and evaluating data that establishes biological safety which reveals the need and scope of impurity profiling of drugs in pharmaceutical research.Orthogonal methods are necessary for ongoing assessment of method specificity

1. International Conference on Harmonisation of Technical Requirements for the Registration of Pharmaceuticals for Human Use (ICH) guideline Q3A(R2), Impurities in new drug substances, (2006). Pg no.1-11

2.Ahuja Satindar, Impurities evaluation of pharmaceuticals, Marcel & Dekker publication, Edition Second, New York :1998 PP1-17,95--111

3. http://www.locumusa.com ,International Journal of Generic Drugs pgno.370 ,ISSN 0793 7784 Euro

4. Method Development for Drug Impurity Profiling: Part 1,Apr 1, 2010,By: Bieke Dejaegher, Yvan Vander Heyden, Melanie Dumarey ,LCGC EUROPEVolume 23, Issue 4 ,chromatography.online.com

5. E. Van Gyseghem et al., J. Chrom. A, 988, 77–93 (2003).

6. E. Van Gyseghem et al., J. Pharm. Biomed. Anal., 41, 141–151 (2006).

7. M. Dumarey et al., Anal. Chim. Acta, 609, 223–234 (2008).

8. D. Visky et al., J. Chrom. A, 1101, 103–114 (2006).

9.M. Dumarey et al., Anal. Chim. Acta, 656, 85–92 (2009).

10.Development and Use of Orthogonal Methods for Impurity Profiling of Pharmaceuticals by HPLC by Henrik T. Rasmussen, Fengmei Zheng, Dora Visky, Rhonda Jackson, Analytical Development,slide 13-14

11. Rewiew article:Impurity profiling of Famotidine in bulk drugs and pharmaceutical formulations by RP-HPLC method using ion pairing agent M.Vamsi Krishna*, G. Madhavi*, L. A. Rama Prasad** and D. Gowri Sankar**, Der Pharmacia Lettre, 2010,1-11(http://scholarsresearchlibrary.com/archive.html )

12. Impurity profiling of pharmaceuticals, anita ayre et al:, ARPB, 2011; vol 1(2),Table:2,pg.No:86

http://www.chromatographyonline.com/lcgc/author/authorInfo.jsp?id=50439








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Indu...impurity profiling of api’s using rp hplc as per