The document summarizes the optimization of baicalin-loaded solid lipid nanoparticles using response surface methodology. A central composite design was employed to optimize the formulation using two factors - the amount of stearic acid and the amount of polyoxyl 40 hydrogenated castor oil. The design consisted of 20 experimental runs to determine the effects of the factors on the dependent variables of encapsulation efficiency and particle size. Optimization of the formulation led to improved drug loading and release characteristics.
Optimization techniques in formulation Development Response surface methodol...D.R. Chandravanshi
The term “optimize” is “to make as perfect”. It is defined as follows: choosing the best element from some set of variable alternatives.
An art ,process ,or methodology of making something (a design system or decision ) as perfect ,as functional, as effective as possible .
you can know about the central composite design, historical design, optimisation techniques and also about the TYPES OF CENTRAL COMPOSITE DESIGN, BOX-BEHNKEN DESIGN, DATA COLLECTION, CRITICISM OF DATA, PRESENTATION OF FACTS, PURPOSE, OPTIMISATION PROCESS, DIFFERENT TYPES PRESENT IN IT AND THEIR CLASSIFICATION AND EXPLANATION.
Introduction & Basics of DoE
Terminologies
Key steps in DOE
Softwares used for DOE
Factorial Designs ( Full and Fractional)
Mixture Designs
Response Surface Methodology
Central Composite Design
Box -Behnken Design
Conclusion
References
Optimization techniques in formulation Development Response surface methodol...D.R. Chandravanshi
The term “optimize” is “to make as perfect”. It is defined as follows: choosing the best element from some set of variable alternatives.
An art ,process ,or methodology of making something (a design system or decision ) as perfect ,as functional, as effective as possible .
you can know about the central composite design, historical design, optimisation techniques and also about the TYPES OF CENTRAL COMPOSITE DESIGN, BOX-BEHNKEN DESIGN, DATA COLLECTION, CRITICISM OF DATA, PRESENTATION OF FACTS, PURPOSE, OPTIMISATION PROCESS, DIFFERENT TYPES PRESENT IN IT AND THEIR CLASSIFICATION AND EXPLANATION.
Introduction & Basics of DoE
Terminologies
Key steps in DOE
Softwares used for DOE
Factorial Designs ( Full and Fractional)
Mixture Designs
Response Surface Methodology
Central Composite Design
Box -Behnken Design
Conclusion
References
DESIGN OF EXPERIMENTS (DOE)
DOE is invented by Sir Ronald Fisher in 1920’s and 1930’s.
The following designs of experiments will be usually followed:
Completely randomised design(CRD)
Randomised complete block design(RCBD)
Latin square design(LSD)
Factorial design or experiment
Confounding
Split and strip plot design
FACTORIAL DESIGN
When a several factors are investigated simultaneously in a single experiment such experiments are known as factorial experiments. Though it is not an experimental design, indeed any of the designs may be used for factorial experiments.
For example, the yield of a product depends on the particular type of synthetic substance used and also on the type of chemical used.
ADVANTAGES OF FACTORIAL DESIGN.
Factorial experiments are advantageous to study the combined effect of two or more factors simultaneously and analyze their interrelationships. Such factorial experiments are economic in nature and provide a lot of relevant information about the phenomenon under study. It also increases the efficiency of the experiment.
It is an advantageous because a wide range of factor combination are used. This will give us an idea to predict about what will happen when two or more factors are used in combination.
DISADVANTAGES
It is disadvantageous because the execution of the experiment and the statistical analysis becomes more complex when several treatments combinations or factors are involved simultaneously.
It is also disadvantageous in cases where may not be interested in certain treatment combinations but we are forced to include them in the experiment. This will lead to wastage of time and also the experimental material.
2(square) FACTORIAL EXPERIMENT
A special set of factorial experiment consist of experiments in which all factors have 2 levels such experiments are referred to generally as 2n factorials.
If there are four factors each at two levels the experiment is known as 2x2x2x2 or 24 factorial experiment. On the other hand if there are 2 factors each with 3 levels the experiment is known as 3x3 or 32 factorial experiment. In general if there are n factors each with p levels then it is known as pn factorial experiment.
The calculation of the sum of squares is as follows:
Correction factor (CF) = (𝐺𝑇)2/𝑛
GT = grand total
n = total no of observations
Total sum of squares = ∑▒〖𝑥2−𝐶𝐹〗
Replication sum of squares (RSS) = ((𝑅1)2+(𝑅2)2+…+(𝑅𝑛)2)/𝑛 - CF
Or
1/𝑛 ∑▒𝑅2−𝐶𝐹
2(Cube) FACTORIAL DESIGN
In this type of design, one independent variable has 2 levels, and the other independent variable has 3 levels.
Estimating the effect:
In a factorial design the main effect of an independent variable is its overall effect averaged across all other independent variable.
Effect of a factor A is the average of the runs where A is at the high level minus the average of the runs
DESIGN OF EXPERIMENTS (DOE)
DOE is invented by Sir Ronald Fisher in 1920’s and 1930’s.
The following designs of experiments will be usually followed:
Completely randomised design(CRD)
Randomised complete block design(RCBD)
Latin square design(LSD)
Factorial design or experiment
Confounding
Split and strip plot design
FACTORIAL DESIGN
When a several factors are investigated simultaneously in a single experiment such experiments are known as factorial experiments. Though it is not an experimental design, indeed any of the designs may be used for factorial experiments.
For example, the yield of a product depends on the particular type of synthetic substance used and also on the type of chemical used.
ADVANTAGES OF FACTORIAL DESIGN.
Factorial experiments are advantageous to study the combined effect of two or more factors simultaneously and analyze their interrelationships. Such factorial experiments are economic in nature and provide a lot of relevant information about the phenomenon under study. It also increases the efficiency of the experiment.
It is an advantageous because a wide range of factor combination are used. This will give us an idea to predict about what will happen when two or more factors are used in combination.
DISADVANTAGES
It is disadvantageous because the execution of the experiment and the statistical analysis becomes more complex when several treatments combinations or factors are involved simultaneously.
It is also disadvantageous in cases where may not be interested in certain treatment combinations but we are forced to include them in the experiment. This will lead to wastage of time and also the experimental material.
2(square) FACTORIAL EXPERIMENT
A special set of factorial experiment consist of experiments in which all factors have 2 levels such experiments are referred to generally as 2n factorials.
If there are four factors each at two levels the experiment is known as 2x2x2x2 or 24 factorial experiment. On the other hand if there are 2 factors each with 3 levels the experiment is known as 3x3 or 32 factorial experiment. In general if there are n factors each with p levels then it is known as pn factorial experiment.
The calculation of the sum of squares is as follows:
Correction factor (CF) = (𝐺𝑇)2/𝑛
GT = grand total
n = total no of observations
Total sum of squares = ∑▒〖𝑥2−𝐶𝐹〗
Replication sum of squares (RSS) = ((𝑅1)2+(𝑅2)2+…+(𝑅𝑛)2)/𝑛 - CF
Or
1/𝑛 ∑▒𝑅2−𝐶𝐹
2(Cube) FACTORIAL DESIGN
In this type of design, one independent variable has 2 levels, and the other independent variable has 3 levels.
Estimating the effect:
In a factorial design the main effect of an independent variable is its overall effect averaged across all other independent variable.
Effect of a factor A is the average of the runs where A is at the high level minus the average of the runs
Formulation and development is a process of selection of component and processing.
Now days computer tools used in the formulation and development of pharmaceutical product.
Various technique, such as design of experiment are implemented for optimization of formulation and processing parameter.
Many times finding the correct answer is not simple and straight forward in such cases use of computer tools (optimization procedure) for best compromise is the smarter way to solve problem.
Here is a piece of detailed information about the experimental design used in the field of statistics. This also features some information on the three most widely accepted and most widely used designs.
Similar to Optimization through statistical response surface methods (20)
263778731218 Abortion Clinic /Pills In Harare ,sisternakatoto
263778731218 Abortion Clinic /Pills In Harare ,ABORTION WOMEN’S CLINIC +27730423979 IN women clinic we believe that every woman should be able to make choices in her pregnancy. Our job is to provide compassionate care, safety,affordable and confidential services. That’s why we have won the trust from all generations of women all over the world. we use non surgical method(Abortion pills) to terminate…Dr.LISA +27730423979women Clinic is committed to providing the highest quality of obstetrical and gynecological care to women of all ages. Our dedicated staff aim to treat each patient and her health concerns with compassion and respect.Our dedicated group ABORTION WOMEN’S CLINIC +27730423979 IN women clinic we believe that every woman should be able to make choices in her pregnancy. Our job is to provide compassionate care, safety,affordable and confidential services. That’s why we have won the trust from all generations of women all over the world. we use non surgical method(Abortion pills) to terminate…Dr.LISA +27730423979women Clinic is committed to providing the highest quality of obstetrical and gynecological care to women of all ages. Our dedicated staff aim to treat each patient and her health concerns with compassion and respect.Our dedicated group of receptionists, nurses, and physicians have worked together as a teamof receptionists, nurses, and physicians have worked together as a team wwww.lisywomensclinic.co.za/
Prix Galien International 2024 Forum ProgramLevi Shapiro
June 20, 2024, Prix Galien International and Jerusalem Ethics Forum in ROME. Detailed agenda including panels:
- ADVANCES IN CARDIOLOGY: A NEW PARADIGM IS COMING
- WOMEN’S HEALTH: FERTILITY PRESERVATION
- WHAT’S NEW IN THE TREATMENT OF INFECTIOUS,
ONCOLOGICAL AND INFLAMMATORY SKIN DISEASES?
- ARTIFICIAL INTELLIGENCE AND ETHICS
- GENE THERAPY
- BEYOND BORDERS: GLOBAL INITIATIVES FOR DEMOCRATIZING LIFE SCIENCE TECHNOLOGIES AND PROMOTING ACCESS TO HEALTHCARE
- ETHICAL CHALLENGES IN LIFE SCIENCES
- Prix Galien International Awards Ceremony
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
Follow us on: Pinterest
Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
HOT NEW PRODUCT! BIG SALES FAST SHIPPING NOW FROM CHINA!! EU KU DB BK substit...GL Anaacs
Contact us if you are interested:
Email / Skype : kefaya1771@gmail.com
Threema: PXHY5PDH
New BATCH Ku !!! MUCH IN DEMAND FAST SALE EVERY BATCH HAPPY GOOD EFFECT BIG BATCH !
Contact me on Threema or skype to start big business!!
Hot-sale products:
NEW HOT EUTYLONE WHITE CRYSTAL!!
5cl-adba precursor (semi finished )
5cl-adba raw materials
ADBB precursor (semi finished )
ADBB raw materials
APVP powder
5fadb/4f-adb
Jwh018 / Jwh210
Eutylone crystal
Protonitazene (hydrochloride) CAS: 119276-01-6
Flubrotizolam CAS: 57801-95-3
Metonitazene CAS: 14680-51-4
Payment terms: Western Union,MoneyGram,Bitcoin or USDT.
Deliver Time: Usually 7-15days
Shipping method: FedEx, TNT, DHL,UPS etc.Our deliveries are 100% safe, fast, reliable and discreet.
Samples will be sent for your evaluation!If you are interested in, please contact me, let's talk details.
We specializes in exporting high quality Research chemical, medical intermediate, Pharmaceutical chemicals and so on. Products are exported to USA, Canada, France, Korea, Japan,Russia, Southeast Asia and other countries.
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
Acute scrotum is a general term referring to an emergency condition affecting the contents or the wall of the scrotum.
There are a number of conditions that present acutely, predominantly with pain and/or swelling
A careful and detailed history and examination, and in some cases, investigations allow differentiation between these diagnoses. A prompt diagnosis is essential as the patient may require urgent surgical intervention
Testicular torsion refers to twisting of the spermatic cord, causing ischaemia of the testicle.
Testicular torsion results from inadequate fixation of the testis to the tunica vaginalis producing ischemia from reduced arterial inflow and venous outflow obstruction.
The prevalence of testicular torsion in adult patients hospitalized with acute scrotal pain is approximately 25 to 50 percent
These simplified slides by Dr. Sidra Arshad present an overview of the non-respiratory functions of the respiratory tract.
Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
Study Resources:
1. Chapter 39, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 17, Human Physiology by Lauralee Sherwood, 9th edition
4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
The prostate is an exocrine gland of the male mammalian reproductive system
It is a walnut-sized gland that forms part of the male reproductive system and is located in front of the rectum and just below the urinary bladder
Function is to store and secrete a clear, slightly alkaline fluid that constitutes 10-30% of the volume of the seminal fluid that along with the spermatozoa, constitutes semen
A healthy human prostate measures (4cm-vertical, by 3cm-horizontal, 2cm ant-post ).
It surrounds the urethra just below the urinary bladder. It has anterior, median, posterior and two lateral lobes
It’s work is regulated by androgens which are responsible for male sex characteristics
Generalised disease of the prostate due to hormonal derangement which leads to non malignant enlargement of the gland (increase in the number of epithelial cells and stromal tissue)to cause compression of the urethra leading to symptoms (LUTS
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
Couples presenting to the infertility clinic- Do they really have infertility...Sujoy Dasgupta
Dr Sujoy Dasgupta presented the study on "Couples presenting to the infertility clinic- Do they really have infertility? – The unexplored stories of non-consummation" in the 13th Congress of the Asia Pacific Initiative on Reproduction (ASPIRE 2024) at Manila on 24 May, 2024.
New Drug Discovery and Development .....NEHA GUPTA
The "New Drug Discovery and Development" process involves the identification, design, testing, and manufacturing of novel pharmaceutical compounds with the aim of introducing new and improved treatments for various medical conditions. This comprehensive endeavor encompasses various stages, including target identification, preclinical studies, clinical trials, regulatory approval, and post-market surveillance. It involves multidisciplinary collaboration among scientists, researchers, clinicians, regulatory experts, and pharmaceutical companies to bring innovative therapies to market and address unmet medical needs.
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
ASA GUIDELINE
NYSORA Guideline
2 Case Reports of Gastric Ultrasound
These lecture slides, by Dr Sidra Arshad, offer a quick overview of physiological basis of a normal electrocardiogram.
Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar leads (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
Study Resources:
1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
3. INTRODUCTION
“ RESPONSE SURFACE METHODOLOGY (RSM) IS A COLLECTION OF STATISTICAL AND
MATHEMATICAL TECHNIQUES USEFUL FOR DEVELOPING, IMPROVING, AND
OPTIMIZING PROCESSES.” It is used in the design, development, and formulation of
new products, as well as in the improvement of existing product designs.
What is optimization ?
Merriam Webster dictionary defines optimization as
“ an act, process, or methodology of making something (as a design, system, or decision)
as fully perfect, functional, or effective as possible; specifically : the mathematical
procedures (as finding the maximum of a function) involved in this”
Optimization characterizes the activities involved to find “the best”.
In pharmaceutics optimization is relative to processing and formulation of drug
products
account all the factors that influences decisions in any experiment.
of
CRITERIA
In pharmaceutics optimization is relative to processing and formulation of drug
products in various forms.
process of finding the best way of using the existing resources while taking into
account all the factors that influences decisions in any experiment.
Pharmaceutical formulator aims to produce product not only meets the requirements
of BIO-AVAILABILITY but also from the PRACTICAL MASS PRODUCTION
CRITERIA 3
5. • Pharmaceutical formulating in research and industrial level involves large number
•
• Pharmaceutical formulating in research and industrial level involves large number
of attributes. Any process or product under study may have various critical factors
or attributes defining it. Optimization studies are aiming at identifying and
quantifying these factors to obtain a design space where an optimized result is
expected.
• For addressing these critical factors and its effects different terms are coined in
experimental designs
BACKGROUND
Independent variables
Independent or input variables are those
factors which are at control for the
experimenter. These factors are varied in
an accepted limits to identify the response
it makes on the dependant variable
Independent variables
Independent or input variables are those
factors which are at control for the
experimenter. These factors are varied in
an accepted limits to identify the response
it makes on the dependant variable
Dependant variable
Dependant variables are those
factors which are studied in an
experiment. Factors are
described to define this
independent variable
Dependant variable
Dependant variables are those
factors which are studied in an
experiment. Factors are
described to define this
independent variable
While defining the independent variables a confidence level must be
specified. This is obtained by the extensive literature survey and analysis of
the data
In pharmaceutics there are two type of variables PROCESS AND
FORMULATION VARIABLES
5
6. • Consider any dependent variable “y”and there is a set of input variables
(dependant variable) x1, x2, …, xk( For e.g. y might be the viscosity of a
polymer and x1, x2, and x3 might be the reaction time, the reactor
temperature, and the catalyst feed rate in the process).
• When the underlying mechanism is not fully understood, and the
experimenter must approximate the unknown function f with an appropriate
empirical model (ε is error factor),
y = f(x , x , …, x ) + εy = f(x1, x2, …, xk) + ε
HOW TO GENERATE A MODEL ?
DESIGN OF EXPERIMENTS(DOE)
MULTIPLE LINEAR REGRESSION
TECHNIQUE
SURFACE RESPONSE PLOTS
This
empirical
model is
called
RESPONSE
SURFACE
MODEL
This
empirical
model is
called
RESPONSE
SURFACE
MODEL
6
7. Y= β +β X +β X +β X X2Y= β0+β1X1+β2X2+β12X1X2
B0 – CONSTANT
B1, B2, B12
Y
X1, X2
X1X2
B0 – CONSTANT
B1, B2, B12 – REGRESSION COEFFICIENT
Y- DEPENDANT VARIABLE
X1, X2- INDEPENDENT VARIABLES
X1X2-INTERACTION
+ SIGN INDICATES
INCREASE IN Y
-
DECREASE IN Y
+ SIGN INDICATES
INCREASE IN Y
- SIGN INDICATES
DECREASE IN Y
IT CAN BE 1
ORDER OR 2
ORDER
POLYNOMIALS
IT CAN BE 1ST
ORDER OR 2ND
ORDER
POLYNOMIALS
BIVARIATE - MULTIVARIATE
7
8. SURFACE RESPONSE PLOTS
• Graphical perspective of the information is plotted in 3 dimensional
•
• Graphical perspective of the information is plotted in 3 dimensional
response surface plots.
• It is also convenient to view the response surface in the two-
dimensional. This type of plot is called contour plot
Design
space
8
9. TYPES OF SECOND ORDER RESPONSE
SURFACES AND THEIR CONTOUR PLOTS.
TYPES OF SECOND ORDER RESPONSE
SURFACES AND THEIR CONTOUR PLOTS.
(a) shows the surface with a maximum point,
(b) shows the surface with a minimum point
(c) shows the surface with a saddle point.
9
10. HOW TO
KNOW
THE
DATA
MODEL
EXACTLY
FITS THE
DATA?
STATISTICAL METHODS
P VALUE - The p-value for each
term tests the null hypothesis that
the coefficient is equal to zero (no
effect). A low p-value (< 0.05)
indicates that the independent
variable has significant effect on
response variable.
R SQUARE VALUE- Accounts for
the variation of the data. Higher the
R square the curve fits
better(max=1).
F- VALUE- The F Value or F ratio is
the test statistic used to decide
whether the model as a whole has
statistically significant predictive
capability. The null hypothesis is
rejected if the F ratio is large. If the
F ratio is high model is therefore
accepted.
EXPERIMENTAL
METHOD
Validation of the
model can be done
by the
experimental
method. Testing of
the sample in the
design space
matching the
observed value
and the predicted
value
10
12. WHY TO DESIGN EXPERIMENT?
TO OBTAIN MAXIMUM INFORMATION
FROM MINIMUM NUMBER OF
EXPERIMENTS.
TO SCREEN FACTORS WHEN LARGE
NUMBER OF FACTORS ARE PRESENT
TO GET A CURVE THAT EXACTLY FITS
THE ACTUAL MODEL. EXPERIMENTS HAS
TO BE DESIGNED IN SUCH A WAY THAT
OBTAINED DATA EXACTLY SIMULATES
ORIGINAL MODEL
HOW TO CONDUCT EXPERIMENTS?
DIFFERENT SOFTWARES ARE AVAILABE
Design expert
Statistica
MODDE
DOE++
StatSoft
Experstat
12
13. EXPERIMENTAL DESIGNS FOR OPTIMIZATION
SCREENING DESIGNS
• TWO LEVEL FACTORIAL
DESIGN
• FRACTIONAL FACTORIAL
DESIGNS
• PLACKETT BURMAN DESIGN
• MIXED LEVEL SCREENING
DESIGN
• D OPTIMAL SCREENING
DESIGN
RESPONSE SURFACE
DESIGN
•THREE-LEVEL FULL
FACTORIAL
• CENTRAL COMPOSITE
•BOX–BEHNKEN
• DOEHLERT DESIGNS.
13
15. PLACKETT BURMAN DESIGN
• in 1946 by statisticians Robin L. Plackett and J.P. Burman, it is
an efficient screening method to identify the active factors using as few
•
• Only main effects are analyzed in this design. Interaction effects are not
•
• Developed in 1946 by statisticians Robin L. Plackett and J.P. Burman, it is
an efficient screening method to identify the active factors using as few
experimental runs as possible.
• Placket and Burman showed how full factorial design can be
fractionalized in a different manner, to yield saturated designs where
the number of runs is a multiple of 4, rather than a power of 2.
• Only main effects are analyzed in this design. Interaction effects are not
included
• General polynomial obtained plackett burmen screening is
Y = b0 + b1x1 + b2x2 + ...................... + bkxk
b(0-k) is the regression coefficients
Y is the dependant variable
K number of independent variable
X (1-k) independent variables
15
16. Possible to neglect higher order
When to use plackett burman?
• Carried out at the early stages
for screening
• Possible to neglect higher order
interactions
• 2-level multi-factor
experiments.
• When number of factors
involved is higher.
• To economically detect large
main effects.
• Particularly useful for number
of runs(N) = 12, 20, 24, 28 and
36
ASSUMPTIONS
•Fractional factorial designs for studying k
= N – 1 variables in N runs, where N is a
multiple of 4.
• Only main effects are of interest.
• No defining relation since interactions
are not identically equal to main effects.
16
17. APPLICATION OF PLACKETT–BURMAN SCREENING DESIGN FOR PREPARING
GLIBENCLAMIDE NANOPARTICLES FOR DISSOLUTION ENHANCEMENT
Sunny R. Shah, Rajesh H. Parikh, Jayant R. Chavda, Navin R. Sheth; Bhagvanlal
Kapoorchand Mody Government Pharmacy College, Rajkot, India, Ramanbhai
Patel College of Pharmacy, CHARUSAT, Changa, India; Department of
Pharmaceutical Sciences, Saurashtra University, Rajkot, India
OBJECTIVE : - To improve the dissolution characteristics of a poorly water-soluble drug
glibenclamide (GLB), by preparing nano particles through liquid anti solvent precipitation.
A Plackett
and process variables.
OBJECTIVE : - To improve the dissolution characteristics of a poorly water-soluble drug
glibenclamide (GLB), by preparing nano particles through liquid anti solvent precipitation.
A Plackett–Burman screening design was employed to screen the significant formulation
and process variables.
INDEPENDENT
VARIABLES
DEPENDANT
VARIABLES
amount of
poloxamer 188 (X1),
amount of PVP S
630 D (X2),
solvent to anti
solvent volume ratio
(S/AS) (X3),
amount of GLB(X4)
speed of mixing (X5).
Mean particle size
(Y1),
Saturation solubility
(Y2)
% DE 5min
dissolution efficiency
after 5 min (Y3)
GLB was dissolved in acetone at definite
concentration and sonicated for 20 s. The
solution was filtrated through a 0.22 μ
Whatman filter paper. The prepared GLB
solution was injected by syringe onto the
tip of the anti solvent water containing
each specific concentration of polymer
and/or surfactant with stirring.
Precipitation took place immediately
upon mixing and formed a suspension
with bluish appearance. Then centrifuged
and dried to get nano particles 17
18. A total of 12 experimental trials involving 5 independent variables were generated by
Minitab® 16 (USA).
DISSOLUTION EFFICIENCY AFTER 5 MINUTUS(% DE5min )=13.4 +0.156 PX−0.578
PD+0.513S/AS−0.0721 Drug concentration +0.0387 Speed
SATURATION SOLUABILITY(SS) = 9.87 + 0.0893 PX−0.193 PD+ 0.253S/AS −0.0352 Drug
concentration + 0.0151 Speed
MEAN PARTICLE SIZE(PS) = 830−8.14 PX + 12.8 PD−11.1S/AS+1.42 Drug
concentration−0.676 Speed
POLYNOMIAL EQUATIONS DERIVED
18
19. All the predetermined independent variables except drug concentration were
found to affect the dependent variables. The optimized formulation maintained
the crystallinity of GLB and released almost 80% drug as compared to pure GLB
which showed 8.5% dissolution within 5 min. The improved formulation could
offer an improved drug delivery strategy, which still needs to be correlated with
in vivo studies.
All the predetermined independent variables except drug concentration were
found to affect the dependent variables. The optimized formulation maintained
the crystallinity of GLB and released almost 80% drug as compared to pure GLB
which showed 8.5% dissolution within 5 min. The improved formulation could
offer an improved drug delivery strategy, which still needs to be correlated with
in vivo studies.
RESULTS AND CONCLUSIONS
19
20. CENTRAL COMPOSITE DESIGN
A Box-Wilson Central Composite Design, commonly called `a central composite design is
an important experimental design which gives a response surface curve that suites a 2nd
order polynomial(quadratic) empirical model.
A second-order model can be constructed efficiently with central composite designs
(CCD). It is a combination of
It contains an imbedded factorial or fractional factorial design with center points that is
enlarged with a group of `star points' that allow estimation of curvature and formation of
second order model
FIRST-ORDER (2 ) DESIGNSFIRST-ORDER (2N) DESIGNS
(FACTORIAL DESIGNS)
+
CENTRE AND AXIAL POINTS
20
21. SALIENT FEATURES OF CENTRAL COMPOSITE DESIGNS
The design consists of three distinct sets
of experimental runs:
A factorial(perhaps fractional)
design in the factors studied, each
having two levels;
A set of center points, experimental
runs whose values of each factor are
the medians of the values used in
the factorial portion.
A set of axial points (star point),
experimental runs identical to the
centre points except for one factor,
which will take on values both
below and above the median of the
two factorial levels, and typically
both outside their range.
If the distance from the center of the
design space to a factorial point is ±1
unit for each factor, the distance from
the center of the design space to a star
point is |α| > 1. The precise value
of α depends on certain properties
desired for the design and on the
number of factors involved.
The value of α is given by the following
equations
First equation is for full factorial cases and
second one for fractional factorial
α=*2k]1/4
α = *number of factorial runs+1/4
21
22. The star points represent new extreme value (low & high) for each
factor in the design
To picture central composite design, it must imagined that there are
several factors that can vary between low and high values.
Central composite designs are of three types
CIRCUMSCRIBED(CCC)
DESIGNS-Cube points at
the corners of the unit cube
,star points along the axes
at or outside the cube and
centre point at origin
INSCRIBED (CCI)
DESIGNS-Star points take
the value of +1 & -1 and
cube points lie in the
interior of the cube
FACED(CCI) –star points
on the faces of the cube.
22
23. Development and optimization of baicalin-loaded solid lipid nanoparticles
prepared by coacervation method using central composite design
Jifu Hao , Fugang Wang, Xiaodan Wang; Department of Pharmaceutics, College
of Pharmacy, Shandong University, 44 Wenhua Xilu, China
OBJECTIVE : - To design and optimize a novel baicalin-loaded solid lipid nano particles (SLNs)
carrier system composed of a stearic acid alkaline salt as lipid matrix and prepared as per the
coacervation method in which fatty acids precipitated from their sodium salt micelles in the
presence of polymeric nonionic surfactants. A two-factor five-level central composite design
(CCD) was introduced to perform the experiments
ENCAPSULATION EFFICIENCY,
PARTICLE SIZE AND
POLYDISPERSITY INDEX (PDI) ARE
THE DEPENDANT VARIABLES
WHICH ARE EVALUATED
DRUG/LIPID RATIO AS
INDEPENDENT VARIABLE
SELECTED AS A LIPID MATRIX
ARE SELECTED
ENCAPSULATION EFFICIENCY,
PARTICLE SIZE AND
POLYDISPERSITY INDEX (PDI) ARE
THE DEPENDANT VARIABLES
WHICH ARE EVALUATED
AMOUNT OF LIPID AND THE
DRUG/LIPID RATIO AS
INDEPENDENT VARIABLE
STEARATE SODIUM WAS
SELECTED AS A LIPID MATRIX
AXIAL AND CENTRAL POINTS
ARE SELECTED
23
24. CCD matrix was generated by Design-Expert software.
A total of 13 experiments, including four factorial points, four axial points and five
replicated center points were selected.
A quadratic polynomial model was generated to predict and evaluate the
independent variables with respect to the dependent variables.
Yi = A0 + A1X1 + A2X2 + A3X1X2 + A4X21 + A5X22Yi = A0 + A1X1 + A2X2 + A3X1X2 + A4X21 + A5X22
24
25. Response surface analyses were plotted
in three-dimensional model graphs for
optimization of nano particles with
suitable and satisfied physicochemical
properties.
It describe the interaction and quadratic
effects of two independent variables on
the responses or dependent variables.
25
26. RESULTS
Optimization of an SLN formulation is a complex process, which requires to consider a
large number of variables and their interactions with each other.
This study conclusively demonstrates that the optimal formulations may be successfully
obtained using the central composite design.
The derived polynomial equations and response surface plots aid in predicting the values
of selected independent variables for preparation of optimum formulations with desired
properties
The composition of optimum formulation was determined as 0.69% (w/v) lipid and
26.64% (w/w) drug/lipid ratio, which fulfilled the requirements of optimization. At
these levels, the predicted values of Y1 (EE), Y2 (particle size), and Y3 (PDI) were
84.13%, 356.6 nm, and 0.178, respectively.
formulation factors levels was prepared. The observed optimized formulation had EE of
(86.29
were in good agreement with the predicted values.
The composition of optimum formulation was determined as 0.69% (w/v) lipid and
26.64% (w/w) drug/lipid ratio, which fulfilled the requirements of optimization. At
these levels, the predicted values of Y1 (EE), Y2 (particle size), and Y3 (PDI) were
84.13%, 356.6 nm, and 0.178, respectively.
To confirm the predicted model, a new batch of SLN according to the optimal
formulation factors levels was prepared. The observed optimized formulation had EE of
(86.29 ± 1.43)%, particle size of (343.7 ± 7.07) nm, and PDI of (0.169 ± 0.0036), which
were in good agreement with the predicted values.
CONCLUSION
26
27. BOX- BEHNKEN DESIGN
INTRODUCTION
Behnken designs have treatment combinations that are at the midpoints of
INTRODUCTION
This is a second order design introduced by Box and Behnken in 1960.They
do not contain embedded factorial or fractional factorial design. Box-
Behnken designs have treatment combinations that are at the midpoints of
the edges of the experimental space and require at least three continuous
factors. The following figure shows a three-factor Box-Behnken design.
Points on the diagram represent the experimental runs that are done:
Yo = bo + b1X1 + b2X2 + b3X3 + b12X1X2 + b13X1X3 +
b23X2X3 + b11X2
1 + b22X2
2 + b33X2
3
The general quadratic equation obtained. It gives
three type of information
(1) main effects for factors x1 , ..., xk,
(2) their interactions (x1*x2, x1*x3, ... ,xk-1*xk),
(3) quadratic components (x1**2, ..., xk**2).
27
28. Number of
experiments in a
Box Behnken design
is determined by the
following equation
N =(2 f ( f − 1)) + c 0
F is the number factors
C0 is the central point
Levels are usually
expressed as (coded –
1, 0, +1) i.e. lower
higher and middle
The third level for a
continuous factor
facilitates investigation
of a quadratic
relationship between
the response and each
of the factors
28
29. WHY BBD IS USED?
It is rotatable or nearly rotatable second-order
It is rotatable or nearly rotatable second-order
designs based on three-level.
More efficient compared to CCD and other
designs.(number of coefficients in the model
divided by the number of experiments). More
data in less number of runs.
BBD does not contain combinations for which all
factors are simultaneously at their highest or
lowest levels. So these designs are useful in
avoiding experiments performed under extreme
conditions.
29
30. STATISTICAL OPTIMIZATION AND CHARACTERIZATION OF PH-INDEPENDENT
EXTENDED-RELEASE DRUG DELIVERY OF CEFPODOXIME PROXETIL USING
BOX–BEHNKEN DESIGN
Ali Mujtaba, Mushir Ali, Kanchan Kohli. Department of Pharmaceutics,
Faculty of Pharmacy, Hamdard University, New Delhi 110062, India
OBJECTIVE : - To develop and optimize the pH-independent extended-release (ER)
formulations of cef
by employing a 3
OBJECTIVE : - To develop and optimize the pH-independent extended-release (ER)
formulations of cef-podoxime proxetil (CP) using response surface methodology
by employing a 3-factor, 3-level Box–Behnken statistical design.
INDEPENDENT
VARIABLES
DEPENDANT
VARIABLES
AMOUNT OF RELEASE
RETARDANT POLYMERS
HPMC K4M
(X1),
sodium
alginate (X2)
MCC (X3).
Cumulative
percentage
release of drug at
2, 4, 8, 14 and 24
hours to detect
the burst effect
and to ensure
complete drug
release.
30
31. A total of 15 experimental runs are conducted to obtain results. Statistical analysis
were done for the fitting of the curve.
31
32. Evaluation of main effects, interaction effects and quadratic effects of the
formulation ingredients on the in vitro release of CP extended-release formulations.
Second order nonlinear polynomial models with Design Expert®(Version 8.0.7.1, Stat-
Ease Inc.)
Yo = bo + b1X1 + b2X2 + b3X3 + b12X1X2 + b13X1X3 + b23X2X3
+ b11X21 + b22X22 + b33X23
Yo is the dependent variable
bo is an intercept
b1–b33 are regression
coefficients computed from the
observed experimental values of Y
X1–X3are the coded levels of
independent variables.
X1X2and Xi(i = 1, 2 or 3)
represent the interaction and
quadratic terms
32
33. RESULTS AND CONCLUSIONS
Two-dimensional contour plot and three-
dimensional response surface plot are
presented.
was kept at a 20% (w/w) level.
nearly linear relationship of factor X1with
factors X2 on the response Y2 h.
relationship on the responses Y4 h, Y8 h, Y14
hand Y24 h.
Two-dimensional contour plot and three-
dimensional response surface plot are
presented.
In all the presented figures, the third factor
was kept at a 20% (w/w) level.
The almost straight lines in plots predicted
nearly linear relationship of factor X1with
factors X2 on the response Y2 h.
However, factors X1and X2 have non-linear
relationship on the responses Y4 h, Y8 h, Y14
hand Y24 h.
The optimum values of the variables were obtained by
graphical and numerical analyses using theDesign-
Expert®software. The optimum response was found with
Y2 h(19.89%), Y4 h(42.15%), Y8 h(68.32%), Y14
h(83.02%) and Y24 h(98.4%) at X1, X2and X3values of
25, 15.14,20.14% respectively. To verify these values, the
optimum formulation was prepared according the above
values of the factors at X1, X2and X3and subjected to the
dissolution test. 33
34. REFERENCE
• Bieke Dejaegher, Yvan Vander Heyden; Experimental designs and their recent advances in set-up, data
interpretation,and analytical applications; Journal of Pharmaceutical and Biomedical Analysis 56 (2011)
141– 158
• Raymond H. Myers, Douglas C. Montgomery, Christine M. Anderson-Cook;3RD Edition; Response surface
methodology : process and product optimization using designed experiments.
• Pharmaceutical statistics; practical and clinical application 5th edition. Sanford Bolton; Charles Bon
34