SlideShare une entreprise Scribd logo
1  sur  27
Similarity and Difference
Factors in Dissolution Studies
By: Jessica Fernandes
Reg. No. 150617004
Department of Pharmaceutics
Guided By Mr. Lalit Kumar
Assistant Professor-senior scale
Department of Pharmaceutics
MCOPS, Manipal
2
Definition
It is graphical representation [in terms of concentration vs.
time] of complete release of drug from a dosage form in an
appropriate selected dissolution medium.
i.e. in short it is the measure of the release of A.P.I from a
dosage form with respect to time.
3
Importance of dissolution profile Comparison
 Dissolution profile of an A.P.I. reflects its release pattern under
the selected condition sets.
 FDA has placed more emphasis on dissolution profile
comparison in the field of post approval changes.
 The most important application of the dissolution profile is that
by knowing the dissolution profile of particular product of the
BRAND LEADER, we can make appropriate necessary
change in our formulation to achieve the same profile of the
brand leader.
4
Objective of dissolution profile Comparison
• To Develop in-vitro in-vivo correlation which can help
to reduce costs, speed-up product development and
reduce the need to perform costly bioavailability human
volunteer studies.
• Establish the similarity of pharmaceutical dosage
forms, for which composition, manufacture site, scale of
manufacture, manufacture process and/or equipment
may have changed within defined limits.
5
METHODS TO COMPARE DISSOLUTION PROFILE
Graphical
method
Statistical
method
t- Test ANOVA
Zero order First order Hixson-
crowell law
Higuchi
model
Korsemeyar and
peppas model
f1 and f2 comparison
Model independent method
(Pair Wise Procedure)
Model dependent methods
6
Graphical method
 In this method we plot graph of Time V/S concentration
of solute (drug) in the dissolution medium or biological
fluid.
 The shape of two curves is compared for comparison of
dissolution pattern and the concentration of drug at each
point is compared for extent of dissolution.
 If two or more curves are overlapping then the
dissolution profile is comparable.
 If difference is small then it is acceptable but higher
differences indicate that the dissolution profile is not
comparable.
7
Statistical Analysis
1. Student’s t-Test
Following tests are commonly used…
a) Paired t-test
b) Unpaired t-test
𝑡 =
𝐷
𝑆𝐸
Calculated ‘t’ value is compared with tabulated value of ‘t’ if
the calculated value exceeds the tabulated value, then the null
hypothesis should be rejected and vice versa.
8
2. ANOVA METHOD (Analysis of Variance)
 This test is generally applied to different groups of data.
Here we compare the variance of different groups of data
and predict whether the data are comparable or not.
 Minimum three sets of data are required. Here first we
have to find the variance within each individual group and
then compare them with each other.
9
Model dependent methods
 Zero order kinetics
Qt = Q0+K0t
Where,
Qt is the amount of drug dissolved in time t
Q0 is the initial amount of drug in the solution
K0 is the zero order release constant expressed in units of
concentration/time
.
Plot: Cumulative amount of drug released versus time.
Applications: Transdermal systems, as well as matrix tablets
with low solubility drugs in coated forms, osmotic systems, etc.
10
R² = 0.9652
0
10
20
30
40
50
60
70
80
90
100
0 5 10 15 20 25
cumulativepercentofdrugreleased
Time (h)
Zero order Plot:
11
First order model
log C = log C0 - Kt / 2.303
Where
C0 is the initial concentration of drug,
K is the first order rate constant, and
t is the time.
Plot: log concentration of drug remaining vs. time which would
yield a straight line with a slope of -K/2.303.
Application: This relationship can be used to describe the drug
dissolution in pharmaceutical dosage forms such as those
containing water soluble drugs in porous matrices.
12
R² = 0.9333
94
95
96
97
98
99
100
101
0 5 10 15 20 25 30
Logconcentration
Time (h)
First order plot
13
Higuchi model (Diffusion matrix formulation)
• This is given by Higuchi.
𝑄 = 𝐾 𝑇
Where Q is the amount of drug released in time‘t’ per unit
area,
K is higuchi constant
T is time in hr.
Plot: The data obtained is to be plotted as cumulative
percentage drug release versus Square root of time.
Application:
modified release pharmaceutical dosage forms, transdermal
systems and matrix tablets with water soluble drugs.
15
 Hixson-Crowell model
Hixson and Crowell described this
W0
1
3
− Wt
1
3
= Kt
Where W0 is the initial amount of drug
Wt is the remaining amount of drug at time t .
Plot: Data is to be plotted as cube root of drug percentage
remaining in matrix versus time.
Application: This expression applies to pharmaceutical dosage
form such as tablets, where the dissolution occurs in planes
that are parallel to the drug surface if the tablet dimensions
diminish proportionally in such a manner that the initial
geometrical form keeps constant all the time.
17
 Korsmeyer - Peppas model
The KORSEMEYER AND PEPPAS described this method..
It is given by the equation :
Mt /M∞ = Ktn
where Mt / M ∞ is fraction of drug released
t = time
K=constant includes structural and geometrical characteristics
of the dosage form
n= release component which is indicative of drug release
mechanism
where , n is diffusion exponent.
If n= 1 , the release is zero order .
n = 0.5 the release is best described by the Fickian diffusion
0.5 < n < 1 then release is through anomalous diffusion or case
two diffusion. In this model a plot of percent drug release
versus time is linear.
19
Model Independent Approach
Moore and Flanner proposed a model independent mathematical
approach to compare the dissolution profile using two factors, f1
and f2.
1. Difference Factor
The difference factor (f1 ) calculates the percent (%)
difference between the two curves at each time point and is a
measurement of the relative error between the two curves:
f1= {[Σ t=1
n |R-T|] / [Σ t=1
n R]} ×100
where n is the number of time points, R is the dissolution
value of the reference (prechange) batch at time t, and T is
the dissolution value of the test (postchange) batch at
time t.
Applications
• F1 is specially used to compare two dissolution profiles,
being necessary to consider one of them as the reference
standard product.
• It can measure the percent of error between two curves
over all time points.
21
The similarity factor (f2 ) is a logarithmic reciprocal square root
transformation of the sum of squared error and is a
measurement of the similarity in the percent (%) dissolution
between the two curves.
f2= 50×log {[1+ (1/n) Σ t=1
n (R-T) 2]-0.5 ×100
2. Similarity Factor
PARAMETERS VALUES
Difference Factor (f1) 0 – 15
Similarity Factor (f2) 50-100
Applications
• This method is more appropriate when more than three or
four dissolution time points are available.
• The f2 may become invariant with respect to the location
change and the consequence of failure to take into account
the shape of the curve and the unequal spacing between
sampling time points lead to errors.
• Nevertheless, with a slight modification in the statistical
analysis, similarity factor would definitely serves as an
efficient tool for reliable comparison of dissolution
profiles.
23
Advantages
1. They are easy to compute.
2. They provide a single number to describe the comparison
of dissolution profile data.
Disadvantages
1. The values of f1 and f2 are sensitive to the number of
dissolution time points used.
2. The basis of the criteria for deciding the difference or
similarity between dissolution profile is unclear.
24
Conclusion
 Graphical method is first step in comparing dissolution
profile and it is easy to implement but it is difficult to make
definitive conclusions.
 Various model dependent methods can be used to compare
the dissolution profile but selecting the model, interpretation
of model parameters and setting similarity limit is difficult.
 f1 and f2 comparison is easy and this is most widely used
method to compare dissolution profiles. This is also
recommended by FDA.
 By using all the above given models we can compare
dissolution profile of drug.
25
References
1. Hussain L, Ashwini D, Sirish D. Kinetic modelling and
dissolution profiles comparison: an overview. Int J Pharm
Bio Sci. 2013; 4(1): 728 - 737.
2. Thomas O’H, Adrian D, Jackie B and John D. A review of
methods used to compare dissolution profile
data. PSTT. 1998; 1(5): 214-223.
26
3. U.S. Department of Health and Human Services Food and
Drug Administration Centre for Drug Evaluation and
Research (CDER). Guidance for industry dissolution testing
of immediate release solid oral dosage forms.
http://www.fda.gov/downloads/Drugs/GuidanceComplianceR
egulatoryInformation/Guidances/ucm070237.pdf. Accessed
on 15 November 2015.
4. Jignesh A, Maulik P, Sachi P. Comparison of dissolution
profile by model independent & model dependent methods.
http://pharmaquest.weebly.com/uploads/9/9/4/2/9942916/com
parison_of_dissolution_profile.pdf. Accessed on 15
November 2015.
ThankYou

Contenu connexe

Tendances

DRUG PRODUCT PERFORMANCE, IN VITRO
DRUG PRODUCT PERFORMANCE, IN VITRODRUG PRODUCT PERFORMANCE, IN VITRO
DRUG PRODUCT PERFORMANCE, IN VITROAnkit Malik
 
Kinetics of Stability & Stability Testing
Kinetics of Stability & Stability Testing Kinetics of Stability & Stability Testing
Kinetics of Stability & Stability Testing Sidharth Mehta
 
Rate-Controlled Drug Delivery System
Rate-Controlled Drug Delivery SystemRate-Controlled Drug Delivery System
Rate-Controlled Drug Delivery SystemKailas Mali
 
Study of consolidation parameters
Study of consolidation parametersStudy of consolidation parameters
Study of consolidation parametersayesha samreen
 
Physics of tablet compression
Physics of tablet compressionPhysics of tablet compression
Physics of tablet compressionMahadev Birajdar
 
Self Micro Emulsifying Drug Delivery System
Self Micro Emulsifying Drug Delivery SystemSelf Micro Emulsifying Drug Delivery System
Self Micro Emulsifying Drug Delivery SystemSagar Savale
 
Preparation & stability of large & small volume parentrals
Preparation & stability of large & small volume parentralsPreparation & stability of large & small volume parentrals
Preparation & stability of large & small volume parentralsROHIT
 
Drug excipient interaction
Drug excipient interaction Drug excipient interaction
Drug excipient interaction DeeptiGupta154
 
Theories of dispersion
Theories of dispersionTheories of dispersion
Theories of dispersionRahul Krishnan
 
Compaction profiles
Compaction profilesCompaction profiles
Compaction profilesSiddu K M
 
Drug product performance , in vivo: bioavailability and bioequivalence
Drug product performance , in vivo: bioavailability and bioequivalenceDrug product performance , in vivo: bioavailability and bioequivalence
Drug product performance , in vivo: bioavailability and bioequivalenceDipakKumarGupta3
 
ICH & WHO GUIDELINES ON validation
ICH & WHO GUIDELINES ON validationICH & WHO GUIDELINES ON validation
ICH & WHO GUIDELINES ON validationSACHIN C P
 
Drug product performance in-vivo
Drug product performance in-vivoDrug product performance in-vivo
Drug product performance in-vivoSayaliDarekar
 
Protein and peptide delivery system
Protein and peptide delivery systemProtein and peptide delivery system
Protein and peptide delivery systemNikita Gangwani
 
Compression and compaction
Compression and compactionCompression and compaction
Compression and compactionMehak AggarwAl
 
alternative methods for dissolution.pptx
alternative methods for dissolution.pptxalternative methods for dissolution.pptx
alternative methods for dissolution.pptxanumalagundam sreekanth
 
Self micro-emulsifying drug delivery system (SMEDDS)
Self micro-emulsifying drug delivery system (SMEDDS)Self micro-emulsifying drug delivery system (SMEDDS)
Self micro-emulsifying drug delivery system (SMEDDS)Himal Barakoti
 
DIffusion, Dissolution and Pharmacokinetic Parameters.pptx
DIffusion, Dissolution and Pharmacokinetic Parameters.pptxDIffusion, Dissolution and Pharmacokinetic Parameters.pptx
DIffusion, Dissolution and Pharmacokinetic Parameters.pptxKailas Mali
 

Tendances (20)

DRUG PRODUCT PERFORMANCE, IN VITRO
DRUG PRODUCT PERFORMANCE, IN VITRODRUG PRODUCT PERFORMANCE, IN VITRO
DRUG PRODUCT PERFORMANCE, IN VITRO
 
Kinetics of Stability & Stability Testing
Kinetics of Stability & Stability Testing Kinetics of Stability & Stability Testing
Kinetics of Stability & Stability Testing
 
Rate-Controlled Drug Delivery System
Rate-Controlled Drug Delivery SystemRate-Controlled Drug Delivery System
Rate-Controlled Drug Delivery System
 
Study of consolidation parameters
Study of consolidation parametersStudy of consolidation parameters
Study of consolidation parameters
 
Physics of tablet compression
Physics of tablet compressionPhysics of tablet compression
Physics of tablet compression
 
Self Micro Emulsifying Drug Delivery System
Self Micro Emulsifying Drug Delivery SystemSelf Micro Emulsifying Drug Delivery System
Self Micro Emulsifying Drug Delivery System
 
Preparation & stability of large & small volume parentrals
Preparation & stability of large & small volume parentralsPreparation & stability of large & small volume parentrals
Preparation & stability of large & small volume parentrals
 
Drug excipient interaction
Drug excipient interaction Drug excipient interaction
Drug excipient interaction
 
Theories of dispersion
Theories of dispersionTheories of dispersion
Theories of dispersion
 
Compaction profiles
Compaction profilesCompaction profiles
Compaction profiles
 
Drug product performance , in vivo: bioavailability and bioequivalence
Drug product performance , in vivo: bioavailability and bioequivalenceDrug product performance , in vivo: bioavailability and bioequivalence
Drug product performance , in vivo: bioavailability and bioequivalence
 
ICH & WHO GUIDELINES ON validation
ICH & WHO GUIDELINES ON validationICH & WHO GUIDELINES ON validation
ICH & WHO GUIDELINES ON validation
 
Concepts of Similarity and Difference factors
Concepts of Similarity and Difference factorsConcepts of Similarity and Difference factors
Concepts of Similarity and Difference factors
 
Drug product performance in-vivo
Drug product performance in-vivoDrug product performance in-vivo
Drug product performance in-vivo
 
Protein and peptide delivery system
Protein and peptide delivery systemProtein and peptide delivery system
Protein and peptide delivery system
 
Compression and compaction
Compression and compactionCompression and compaction
Compression and compaction
 
Outsourcing BA and BE to CRO
Outsourcing BA and BE to CROOutsourcing BA and BE to CRO
Outsourcing BA and BE to CRO
 
alternative methods for dissolution.pptx
alternative methods for dissolution.pptxalternative methods for dissolution.pptx
alternative methods for dissolution.pptx
 
Self micro-emulsifying drug delivery system (SMEDDS)
Self micro-emulsifying drug delivery system (SMEDDS)Self micro-emulsifying drug delivery system (SMEDDS)
Self micro-emulsifying drug delivery system (SMEDDS)
 
DIffusion, Dissolution and Pharmacokinetic Parameters.pptx
DIffusion, Dissolution and Pharmacokinetic Parameters.pptxDIffusion, Dissolution and Pharmacokinetic Parameters.pptx
DIffusion, Dissolution and Pharmacokinetic Parameters.pptx
 

Similaire à Similarity and difference factors of dissolution

Comparision of dissolution profile
Comparision of dissolution profileComparision of dissolution profile
Comparision of dissolution profileRanjith Karanam
 
Dissolution profile comparisons
Dissolution profile comparisonsDissolution profile comparisons
Dissolution profile comparisonsNagaraju Ravouru
 
HECKEL PLOT CEUTICS .pptx
HECKEL PLOT CEUTICS .pptxHECKEL PLOT CEUTICS .pptx
HECKEL PLOT CEUTICS .pptxAbdulNaim14
 
Study of consolidation parameters
Study of consolidation parametersStudy of consolidation parameters
Study of consolidation parametersDurga Bhavani
 
Dissolution models (sem 1)
Dissolution models (sem 1)Dissolution models (sem 1)
Dissolution models (sem 1)HARSHALA DHENDE
 
Modeling and comparison of dissolution profiles - Review by Paulo Costa*, Jos...
Modeling and comparison of dissolution profiles - Review by Paulo Costa*, Jos...Modeling and comparison of dissolution profiles - Review by Paulo Costa*, Jos...
Modeling and comparison of dissolution profiles - Review by Paulo Costa*, Jos...MAHENDRA PRATAP SWAIN
 
In-Vitro-In Vivo (IVIVC).pdf
In-Vitro-In Vivo (IVIVC).pdfIn-Vitro-In Vivo (IVIVC).pdf
In-Vitro-In Vivo (IVIVC).pdfPrachi Pandey
 
IN-VITRO-IN VIVO CORRELATION (IVIVC).pptx
IN-VITRO-IN VIVO CORRELATION (IVIVC).pptxIN-VITRO-IN VIVO CORRELATION (IVIVC).pptx
IN-VITRO-IN VIVO CORRELATION (IVIVC).pptxRAHUL PAL
 
Dissolution model.pptx
Dissolution model.pptxDissolution model.pptx
Dissolution model.pptxRajdeepaKundu
 
Similarity factor, higuchi plot, peppas plot
Similarity factor, higuchi plot, peppas plotSimilarity factor, higuchi plot, peppas plot
Similarity factor, higuchi plot, peppas plotmaheshgarje3
 
Optimizationinpharmaceuticsprocessing SIDDANNA M BALAPGOL
Optimizationinpharmaceuticsprocessing SIDDANNA M BALAPGOLOptimizationinpharmaceuticsprocessing SIDDANNA M BALAPGOL
Optimizationinpharmaceuticsprocessing SIDDANNA M BALAPGOLSiddanna Balapgol
 
Consolidation parameters
Consolidation parametersConsolidation parameters
Consolidation parametersSurbhi Narang
 
STUDY OF CONSOLIDATION PARAMETERS
STUDY OF CONSOLIDATION PARAMETERSSTUDY OF CONSOLIDATION PARAMETERS
STUDY OF CONSOLIDATION PARAMETERSJayeshRajput7
 
Model dependent approach for drug release testing of controlled drug delivery
Model dependent approach for drug release testing of controlled drug deliveryModel dependent approach for drug release testing of controlled drug delivery
Model dependent approach for drug release testing of controlled drug deliveryNehaFernandes2
 
Consolidation parameters
Consolidation parametersConsolidation parameters
Consolidation parametersPawanYadav285
 

Similaire à Similarity and difference factors of dissolution (20)

Comparision of dissolution profile
Comparision of dissolution profileComparision of dissolution profile
Comparision of dissolution profile
 
Dissolution profile comparisons
Dissolution profile comparisonsDissolution profile comparisons
Dissolution profile comparisons
 
HECKEL PLOT CEUTICS .pptx
HECKEL PLOT CEUTICS .pptxHECKEL PLOT CEUTICS .pptx
HECKEL PLOT CEUTICS .pptx
 
disso models ppt
 disso models ppt disso models ppt
disso models ppt
 
Study of consolidation parameters
Study of consolidation parametersStudy of consolidation parameters
Study of consolidation parameters
 
Dissolution models (sem 1)
Dissolution models (sem 1)Dissolution models (sem 1)
Dissolution models (sem 1)
 
Modeling and comparison of dissolution profiles - Review by Paulo Costa*, Jos...
Modeling and comparison of dissolution profiles - Review by Paulo Costa*, Jos...Modeling and comparison of dissolution profiles - Review by Paulo Costa*, Jos...
Modeling and comparison of dissolution profiles - Review by Paulo Costa*, Jos...
 
In-Vitro-In Vivo (IVIVC).pdf
In-Vitro-In Vivo (IVIVC).pdfIn-Vitro-In Vivo (IVIVC).pdf
In-Vitro-In Vivo (IVIVC).pdf
 
IN-VITRO-IN VIVO CORRELATION (IVIVC).pptx
IN-VITRO-IN VIVO CORRELATION (IVIVC).pptxIN-VITRO-IN VIVO CORRELATION (IVIVC).pptx
IN-VITRO-IN VIVO CORRELATION (IVIVC).pptx
 
Dissolution model.pptx
Dissolution model.pptxDissolution model.pptx
Dissolution model.pptx
 
Similarity factor, higuchi plot, peppas plot
Similarity factor, higuchi plot, peppas plotSimilarity factor, higuchi plot, peppas plot
Similarity factor, higuchi plot, peppas plot
 
Optimizationinpharmaceuticsprocessing SIDDANNA M BALAPGOL
Optimizationinpharmaceuticsprocessing SIDDANNA M BALAPGOLOptimizationinpharmaceuticsprocessing SIDDANNA M BALAPGOL
Optimizationinpharmaceuticsprocessing SIDDANNA M BALAPGOL
 
Consolidation parameters
Consolidation parametersConsolidation parameters
Consolidation parameters
 
Madhu k s
Madhu k s Madhu k s
Madhu k s
 
Kinetic models
Kinetic modelsKinetic models
Kinetic models
 
STUDY OF CONSOLIDATION PARAMETERS
STUDY OF CONSOLIDATION PARAMETERSSTUDY OF CONSOLIDATION PARAMETERS
STUDY OF CONSOLIDATION PARAMETERS
 
Model dependent approach for drug release testing of controlled drug delivery
Model dependent approach for drug release testing of controlled drug deliveryModel dependent approach for drug release testing of controlled drug delivery
Model dependent approach for drug release testing of controlled drug delivery
 
concept of optimization
concept of optimizationconcept of optimization
concept of optimization
 
Dissolution models
Dissolution modelsDissolution models
Dissolution models
 
Consolidation parameters
Consolidation parametersConsolidation parameters
Consolidation parameters
 

Dernier

Paris 2024 Olympic Geographies - an activity
Paris 2024 Olympic Geographies - an activityParis 2024 Olympic Geographies - an activity
Paris 2024 Olympic Geographies - an activityGeoBlogs
 
IGNOU MSCCFT and PGDCFT Exam Question Pattern: MCFT003 Counselling and Family...
IGNOU MSCCFT and PGDCFT Exam Question Pattern: MCFT003 Counselling and Family...IGNOU MSCCFT and PGDCFT Exam Question Pattern: MCFT003 Counselling and Family...
IGNOU MSCCFT and PGDCFT Exam Question Pattern: MCFT003 Counselling and Family...PsychoTech Services
 
Unit-IV- Pharma. Marketing Channels.pptx
Unit-IV- Pharma. Marketing Channels.pptxUnit-IV- Pharma. Marketing Channels.pptx
Unit-IV- Pharma. Marketing Channels.pptxVishalSingh1417
 
BASLIQ CURRENT LOOKBOOK LOOKBOOK(1) (1).pdf
BASLIQ CURRENT LOOKBOOK  LOOKBOOK(1) (1).pdfBASLIQ CURRENT LOOKBOOK  LOOKBOOK(1) (1).pdf
BASLIQ CURRENT LOOKBOOK LOOKBOOK(1) (1).pdfSoniaTolstoy
 
1029-Danh muc Sach Giao Khoa khoi 6.pdf
1029-Danh muc Sach Giao Khoa khoi  6.pdf1029-Danh muc Sach Giao Khoa khoi  6.pdf
1029-Danh muc Sach Giao Khoa khoi 6.pdfQucHHunhnh
 
Sanyam Choudhary Chemistry practical.pdf
Sanyam Choudhary Chemistry practical.pdfSanyam Choudhary Chemistry practical.pdf
Sanyam Choudhary Chemistry practical.pdfsanyamsingh5019
 
social pharmacy d-pharm 1st year by Pragati K. Mahajan
social pharmacy d-pharm 1st year by Pragati K. Mahajansocial pharmacy d-pharm 1st year by Pragati K. Mahajan
social pharmacy d-pharm 1st year by Pragati K. Mahajanpragatimahajan3
 
The Most Excellent Way | 1 Corinthians 13
The Most Excellent Way | 1 Corinthians 13The Most Excellent Way | 1 Corinthians 13
The Most Excellent Way | 1 Corinthians 13Steve Thomason
 
Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...
Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...
Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...christianmathematics
 
Disha NEET Physics Guide for classes 11 and 12.pdf
Disha NEET Physics Guide for classes 11 and 12.pdfDisha NEET Physics Guide for classes 11 and 12.pdf
Disha NEET Physics Guide for classes 11 and 12.pdfchloefrazer622
 
Advanced Views - Calendar View in Odoo 17
Advanced Views - Calendar View in Odoo 17Advanced Views - Calendar View in Odoo 17
Advanced Views - Calendar View in Odoo 17Celine George
 
microwave assisted reaction. General introduction
microwave assisted reaction. General introductionmicrowave assisted reaction. General introduction
microwave assisted reaction. General introductionMaksud Ahmed
 
Q4-W6-Restating Informational Text Grade 3
Q4-W6-Restating Informational Text Grade 3Q4-W6-Restating Informational Text Grade 3
Q4-W6-Restating Informational Text Grade 3JemimahLaneBuaron
 
Interactive Powerpoint_How to Master effective communication
Interactive Powerpoint_How to Master effective communicationInteractive Powerpoint_How to Master effective communication
Interactive Powerpoint_How to Master effective communicationnomboosow
 
Measures of Dispersion and Variability: Range, QD, AD and SD
Measures of Dispersion and Variability: Range, QD, AD and SDMeasures of Dispersion and Variability: Range, QD, AD and SD
Measures of Dispersion and Variability: Range, QD, AD and SDThiyagu K
 
Key note speaker Neum_Admir Softic_ENG.pdf
Key note speaker Neum_Admir Softic_ENG.pdfKey note speaker Neum_Admir Softic_ENG.pdf
Key note speaker Neum_Admir Softic_ENG.pdfAdmir Softic
 
Beyond the EU: DORA and NIS 2 Directive's Global Impact
Beyond the EU: DORA and NIS 2 Directive's Global ImpactBeyond the EU: DORA and NIS 2 Directive's Global Impact
Beyond the EU: DORA and NIS 2 Directive's Global ImpactPECB
 
9548086042 for call girls in Indira Nagar with room service
9548086042  for call girls in Indira Nagar  with room service9548086042  for call girls in Indira Nagar  with room service
9548086042 for call girls in Indira Nagar with room servicediscovermytutordmt
 

Dernier (20)

Paris 2024 Olympic Geographies - an activity
Paris 2024 Olympic Geographies - an activityParis 2024 Olympic Geographies - an activity
Paris 2024 Olympic Geographies - an activity
 
IGNOU MSCCFT and PGDCFT Exam Question Pattern: MCFT003 Counselling and Family...
IGNOU MSCCFT and PGDCFT Exam Question Pattern: MCFT003 Counselling and Family...IGNOU MSCCFT and PGDCFT Exam Question Pattern: MCFT003 Counselling and Family...
IGNOU MSCCFT and PGDCFT Exam Question Pattern: MCFT003 Counselling and Family...
 
Unit-IV- Pharma. Marketing Channels.pptx
Unit-IV- Pharma. Marketing Channels.pptxUnit-IV- Pharma. Marketing Channels.pptx
Unit-IV- Pharma. Marketing Channels.pptx
 
BASLIQ CURRENT LOOKBOOK LOOKBOOK(1) (1).pdf
BASLIQ CURRENT LOOKBOOK  LOOKBOOK(1) (1).pdfBASLIQ CURRENT LOOKBOOK  LOOKBOOK(1) (1).pdf
BASLIQ CURRENT LOOKBOOK LOOKBOOK(1) (1).pdf
 
1029-Danh muc Sach Giao Khoa khoi 6.pdf
1029-Danh muc Sach Giao Khoa khoi  6.pdf1029-Danh muc Sach Giao Khoa khoi  6.pdf
1029-Danh muc Sach Giao Khoa khoi 6.pdf
 
Sanyam Choudhary Chemistry practical.pdf
Sanyam Choudhary Chemistry practical.pdfSanyam Choudhary Chemistry practical.pdf
Sanyam Choudhary Chemistry practical.pdf
 
social pharmacy d-pharm 1st year by Pragati K. Mahajan
social pharmacy d-pharm 1st year by Pragati K. Mahajansocial pharmacy d-pharm 1st year by Pragati K. Mahajan
social pharmacy d-pharm 1st year by Pragati K. Mahajan
 
The Most Excellent Way | 1 Corinthians 13
The Most Excellent Way | 1 Corinthians 13The Most Excellent Way | 1 Corinthians 13
The Most Excellent Way | 1 Corinthians 13
 
Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...
Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...
Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...
 
Disha NEET Physics Guide for classes 11 and 12.pdf
Disha NEET Physics Guide for classes 11 and 12.pdfDisha NEET Physics Guide for classes 11 and 12.pdf
Disha NEET Physics Guide for classes 11 and 12.pdf
 
Advanced Views - Calendar View in Odoo 17
Advanced Views - Calendar View in Odoo 17Advanced Views - Calendar View in Odoo 17
Advanced Views - Calendar View in Odoo 17
 
microwave assisted reaction. General introduction
microwave assisted reaction. General introductionmicrowave assisted reaction. General introduction
microwave assisted reaction. General introduction
 
Q4-W6-Restating Informational Text Grade 3
Q4-W6-Restating Informational Text Grade 3Q4-W6-Restating Informational Text Grade 3
Q4-W6-Restating Informational Text Grade 3
 
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptxINDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
 
Mattingly "AI & Prompt Design: The Basics of Prompt Design"
Mattingly "AI & Prompt Design: The Basics of Prompt Design"Mattingly "AI & Prompt Design: The Basics of Prompt Design"
Mattingly "AI & Prompt Design: The Basics of Prompt Design"
 
Interactive Powerpoint_How to Master effective communication
Interactive Powerpoint_How to Master effective communicationInteractive Powerpoint_How to Master effective communication
Interactive Powerpoint_How to Master effective communication
 
Measures of Dispersion and Variability: Range, QD, AD and SD
Measures of Dispersion and Variability: Range, QD, AD and SDMeasures of Dispersion and Variability: Range, QD, AD and SD
Measures of Dispersion and Variability: Range, QD, AD and SD
 
Key note speaker Neum_Admir Softic_ENG.pdf
Key note speaker Neum_Admir Softic_ENG.pdfKey note speaker Neum_Admir Softic_ENG.pdf
Key note speaker Neum_Admir Softic_ENG.pdf
 
Beyond the EU: DORA and NIS 2 Directive's Global Impact
Beyond the EU: DORA and NIS 2 Directive's Global ImpactBeyond the EU: DORA and NIS 2 Directive's Global Impact
Beyond the EU: DORA and NIS 2 Directive's Global Impact
 
9548086042 for call girls in Indira Nagar with room service
9548086042  for call girls in Indira Nagar  with room service9548086042  for call girls in Indira Nagar  with room service
9548086042 for call girls in Indira Nagar with room service
 

Similarity and difference factors of dissolution

  • 1. Similarity and Difference Factors in Dissolution Studies By: Jessica Fernandes Reg. No. 150617004 Department of Pharmaceutics Guided By Mr. Lalit Kumar Assistant Professor-senior scale Department of Pharmaceutics MCOPS, Manipal
  • 2. 2 Definition It is graphical representation [in terms of concentration vs. time] of complete release of drug from a dosage form in an appropriate selected dissolution medium. i.e. in short it is the measure of the release of A.P.I from a dosage form with respect to time.
  • 3. 3 Importance of dissolution profile Comparison  Dissolution profile of an A.P.I. reflects its release pattern under the selected condition sets.  FDA has placed more emphasis on dissolution profile comparison in the field of post approval changes.  The most important application of the dissolution profile is that by knowing the dissolution profile of particular product of the BRAND LEADER, we can make appropriate necessary change in our formulation to achieve the same profile of the brand leader.
  • 4. 4 Objective of dissolution profile Comparison • To Develop in-vitro in-vivo correlation which can help to reduce costs, speed-up product development and reduce the need to perform costly bioavailability human volunteer studies. • Establish the similarity of pharmaceutical dosage forms, for which composition, manufacture site, scale of manufacture, manufacture process and/or equipment may have changed within defined limits.
  • 5. 5 METHODS TO COMPARE DISSOLUTION PROFILE Graphical method Statistical method t- Test ANOVA Zero order First order Hixson- crowell law Higuchi model Korsemeyar and peppas model f1 and f2 comparison Model independent method (Pair Wise Procedure) Model dependent methods
  • 6. 6 Graphical method  In this method we plot graph of Time V/S concentration of solute (drug) in the dissolution medium or biological fluid.  The shape of two curves is compared for comparison of dissolution pattern and the concentration of drug at each point is compared for extent of dissolution.  If two or more curves are overlapping then the dissolution profile is comparable.  If difference is small then it is acceptable but higher differences indicate that the dissolution profile is not comparable.
  • 7. 7 Statistical Analysis 1. Student’s t-Test Following tests are commonly used… a) Paired t-test b) Unpaired t-test 𝑡 = 𝐷 𝑆𝐸 Calculated ‘t’ value is compared with tabulated value of ‘t’ if the calculated value exceeds the tabulated value, then the null hypothesis should be rejected and vice versa.
  • 8. 8 2. ANOVA METHOD (Analysis of Variance)  This test is generally applied to different groups of data. Here we compare the variance of different groups of data and predict whether the data are comparable or not.  Minimum three sets of data are required. Here first we have to find the variance within each individual group and then compare them with each other.
  • 9. 9 Model dependent methods  Zero order kinetics Qt = Q0+K0t Where, Qt is the amount of drug dissolved in time t Q0 is the initial amount of drug in the solution K0 is the zero order release constant expressed in units of concentration/time . Plot: Cumulative amount of drug released versus time. Applications: Transdermal systems, as well as matrix tablets with low solubility drugs in coated forms, osmotic systems, etc.
  • 10. 10 R² = 0.9652 0 10 20 30 40 50 60 70 80 90 100 0 5 10 15 20 25 cumulativepercentofdrugreleased Time (h) Zero order Plot:
  • 11. 11 First order model log C = log C0 - Kt / 2.303 Where C0 is the initial concentration of drug, K is the first order rate constant, and t is the time. Plot: log concentration of drug remaining vs. time which would yield a straight line with a slope of -K/2.303. Application: This relationship can be used to describe the drug dissolution in pharmaceutical dosage forms such as those containing water soluble drugs in porous matrices.
  • 12. 12 R² = 0.9333 94 95 96 97 98 99 100 101 0 5 10 15 20 25 30 Logconcentration Time (h) First order plot
  • 13. 13 Higuchi model (Diffusion matrix formulation) • This is given by Higuchi. 𝑄 = 𝐾 𝑇 Where Q is the amount of drug released in time‘t’ per unit area, K is higuchi constant T is time in hr. Plot: The data obtained is to be plotted as cumulative percentage drug release versus Square root of time. Application: modified release pharmaceutical dosage forms, transdermal systems and matrix tablets with water soluble drugs.
  • 14.
  • 15. 15  Hixson-Crowell model Hixson and Crowell described this W0 1 3 − Wt 1 3 = Kt Where W0 is the initial amount of drug Wt is the remaining amount of drug at time t . Plot: Data is to be plotted as cube root of drug percentage remaining in matrix versus time. Application: This expression applies to pharmaceutical dosage form such as tablets, where the dissolution occurs in planes that are parallel to the drug surface if the tablet dimensions diminish proportionally in such a manner that the initial geometrical form keeps constant all the time.
  • 16.
  • 17. 17  Korsmeyer - Peppas model The KORSEMEYER AND PEPPAS described this method.. It is given by the equation : Mt /M∞ = Ktn where Mt / M ∞ is fraction of drug released t = time K=constant includes structural and geometrical characteristics of the dosage form n= release component which is indicative of drug release mechanism where , n is diffusion exponent. If n= 1 , the release is zero order . n = 0.5 the release is best described by the Fickian diffusion 0.5 < n < 1 then release is through anomalous diffusion or case two diffusion. In this model a plot of percent drug release versus time is linear.
  • 18.
  • 19. 19 Model Independent Approach Moore and Flanner proposed a model independent mathematical approach to compare the dissolution profile using two factors, f1 and f2. 1. Difference Factor The difference factor (f1 ) calculates the percent (%) difference between the two curves at each time point and is a measurement of the relative error between the two curves: f1= {[Σ t=1 n |R-T|] / [Σ t=1 n R]} ×100 where n is the number of time points, R is the dissolution value of the reference (prechange) batch at time t, and T is the dissolution value of the test (postchange) batch at time t.
  • 20. Applications • F1 is specially used to compare two dissolution profiles, being necessary to consider one of them as the reference standard product. • It can measure the percent of error between two curves over all time points.
  • 21. 21 The similarity factor (f2 ) is a logarithmic reciprocal square root transformation of the sum of squared error and is a measurement of the similarity in the percent (%) dissolution between the two curves. f2= 50×log {[1+ (1/n) Σ t=1 n (R-T) 2]-0.5 ×100 2. Similarity Factor PARAMETERS VALUES Difference Factor (f1) 0 – 15 Similarity Factor (f2) 50-100
  • 22. Applications • This method is more appropriate when more than three or four dissolution time points are available. • The f2 may become invariant with respect to the location change and the consequence of failure to take into account the shape of the curve and the unequal spacing between sampling time points lead to errors. • Nevertheless, with a slight modification in the statistical analysis, similarity factor would definitely serves as an efficient tool for reliable comparison of dissolution profiles.
  • 23. 23 Advantages 1. They are easy to compute. 2. They provide a single number to describe the comparison of dissolution profile data. Disadvantages 1. The values of f1 and f2 are sensitive to the number of dissolution time points used. 2. The basis of the criteria for deciding the difference or similarity between dissolution profile is unclear.
  • 24. 24 Conclusion  Graphical method is first step in comparing dissolution profile and it is easy to implement but it is difficult to make definitive conclusions.  Various model dependent methods can be used to compare the dissolution profile but selecting the model, interpretation of model parameters and setting similarity limit is difficult.  f1 and f2 comparison is easy and this is most widely used method to compare dissolution profiles. This is also recommended by FDA.  By using all the above given models we can compare dissolution profile of drug.
  • 25. 25 References 1. Hussain L, Ashwini D, Sirish D. Kinetic modelling and dissolution profiles comparison: an overview. Int J Pharm Bio Sci. 2013; 4(1): 728 - 737. 2. Thomas O’H, Adrian D, Jackie B and John D. A review of methods used to compare dissolution profile data. PSTT. 1998; 1(5): 214-223.
  • 26. 26 3. U.S. Department of Health and Human Services Food and Drug Administration Centre for Drug Evaluation and Research (CDER). Guidance for industry dissolution testing of immediate release solid oral dosage forms. http://www.fda.gov/downloads/Drugs/GuidanceComplianceR egulatoryInformation/Guidances/ucm070237.pdf. Accessed on 15 November 2015. 4. Jignesh A, Maulik P, Sachi P. Comparison of dissolution profile by model independent & model dependent methods. http://pharmaquest.weebly.com/uploads/9/9/4/2/9942916/com parison_of_dissolution_profile.pdf. Accessed on 15 November 2015.