Optimization techniques are important in pharmaceutical formulation and processing to make products as effective as possible. There are two main types of optimization problems - unconstrained and constrained. Variables can be independent factors under the formulator's control, or dependent response variables. Classical optimization uses calculus to find the maximum or minimum of a function. Statistical experimental designs are also used, involving factors and responses in regression models. Several techniques are commonly used, including sequential hill-climbing methods, simultaneous methods using experimental designs, and combinations. Lagrangian and simplex methods are classical techniques to optimize multiple factors and responses. Statistical search methods incorporate experimental design and computer-assisted multivariate analysis to optimize complex systems with many variables.
This presentation includes the detail information about the physics of tablet compression and compaction, Compression, Effect of friction, distribution of forces, compaction profiles,solubility.
it provide a brief note on the drug excipient interaction and various technique to find it which is a part of preformulation studies. it gives help to mpharm(pharmaceutics) students. i.
This presentation includes the detail information about the physics of tablet compression and compaction, Compression, Effect of friction, distribution of forces, compaction profiles,solubility.
it provide a brief note on the drug excipient interaction and various technique to find it which is a part of preformulation studies. it gives help to mpharm(pharmaceutics) students. i.
Optimization techniques and various method of optimization
Full Factorial Design
Introduction to Contour Plots
Introduction of Response Surface Design
Classification
Characteristics of Design
Matrix and analysis of design with case study
Equation of Full and Reduced mathematical models in experimental designs
Central Composite designs
Taguchi and mixtures designs
Application of experimental designs in pharmacology for reduction of animal
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.
Title: Sense of Taste
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 structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
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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
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
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.
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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.
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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.
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Are you curious about what’s new in cervical cancer research or unsure what the findings mean? Join Dr. Emily Ko, a gynecologic oncologist at Penn Medicine, to learn about the latest updates from the Society of Gynecologic Oncology (SGO) 2024 Annual Meeting on Women’s Cancer. Dr. Ko will discuss what the research presented at the conference means for you and answer your questions about the new developments.
2. 2
I.INTRODUCTION
OPTIMIZATION
To make as perfect ,effective or functional as possible.
choosing the best element from some set of available
alternatives.
In Pharmacy word “optimization” is mainly applied to the
formulation and processing field.
3. 3
II. OPTIMIZATION PARAMETERS
There are two optimization parameters
1.Problem Types
2.Variables
• PROBLEM TYPES - two general types of optimization
problems:
1. Unconstrained
2. Constrained
5. 5
In unconstrained optimization problems there are no restrictions.
For a given pharmaceutical system one might wish to make the
hardest tablet possible.
This making of the hardest tablet is the unconstrained
optimization problem.
The constrained problem involved in it is to make the hardest
tablet possible, but it must disintegrate in less than 15 minutes.
6. 6
• VARIABLES –
Two types
1.Independent variables
2.Dependent variables
The independent variables are under the control of the formulator.
These might include the compression force or the die cavity
filling or the mixing time.
The dependent variables are the responses or the characteristics
that are developed due to the independent variables.
The more the variables that are present in the system the more the
complications that are involved in the optimization.
8. 8
Once the relationship between the
variable and the response is known,
it gives the response surface as
represented in the Fig. 1. Surface is
to be evaluated to get the
independent variables, X1 and X2,
which gave the response, Y.
Any number of variables can be
considered, it is impossible to
represent graphically, but
mathematically it can be evaluated.
9. 9
III. CLASSICAL OPTIMIZATION
•Classical optimization is done by using the calculus to basic
problem to find the maximum and the minimum of a function.
•The curve in the Fig. 2. represents the relationship between the
response Y and the single independent variable X and we can
obtain the maximum and the minimum.
• By using the calculus the data or equations are plotted
•the equation for Y as a function of X, is available [Eq. (1)]:
Y = f(X)
Figure 2. Graphic location of optimum (maximum or minimum)
10. 10
• When the relationship for the response Y is given as the function
of two independent variables, X1 and X2 ,
Y = f(X1, X2)
• Graphically, there are contour plots (Fig. 3.) on which the axes
represents the two independent variables, X1 and X2, and contours
represents the response Y.
Figure 3. Contour plot. Contour represents values of
the dependent variable Y
11. 11
STATISTICAL DESIGN
Forms of Optimization techniques:
There are three forms of systematic optimization techniques:
1. Sequential Optimization techniques.
2. Simultaneous Optimization techniques.
3. Combination of both .
12. 12
Sequential Methods:
This method is also referred to as the “Hill climbing method”.
As first of all a small number of experiments are done
further research will be done by using the increase or decrease
of response.
In this way a maximum or minimum will be reached i.e. an
optimum solution
13. 13
Simultaneous Methods:
This method involves the use of full range of
experiments by an experimental design .
Results are then used to fit in the mathematical model.
And maximum or minimum response will then be
found through this fitted model.
14. 14
Relationship between dependent and independent variable
should be known.
One variable – simple regression analysis or by least square
method
More than one - statistical design ,multiple regression analysis.
Widely used is – factorial design.
16. 16
EVOLUTIONARY OPERATIONS (EVOP)
• One of the most widely used methods – not in pharmaceutical
technology
• This technique is especially well suited to a production situation.
• The basic philosophy is that the production procedure
(formulation and process) - optimum by careful planning and
constant repetition.
• The process is run in a way such that it both produces a
product - meets all specifications and (at the same time) generates
information on product improvement.
17. 17
(EVOP)
Experimenter makes small changes-many times and
statistically determines the improvement
But in pharm.system – insufficient latitude in formula
Subjected to regulatory constraints since validated product
18. 18
• The simplex approach to the optimum is also an experimental
method and has been applied more widely to pharmaceutical
systems.
• A simplex is a geometric figure that has one more point than the
number of factors.
• for two factors or independent variables, the simplex is represented
by a triangle.
THE SIMPLEX METHOD
19. 19
• Once the shape of a simplex has been determined, the method
can employ a simplex of fixed size or of variable sizes that are
determined by comparing the magnitudes of the responses after
each successive calculation.
•The initial simplex is represented by the lowest triangle.
• the vertices represent the spectrophotometric response.
• The strategy is to move toward a better response by moving
away from the worst response.
Used in the development of an analytical method
- a continuous flow analyzer
20. 20
the worst response is
0.25,conditions are
selected at the vertex, 0.6,
and, indeed, improvement
is obtained.
One can follow the
experimental path to the
optimum, 0.721.
Figure 5 The simplex approach to optimization. Response is
spectorphotometric reading at a given wavelength.
The simplex algorithm begins at a starting vertex and moves
along the edges of the polytope until it reaches the vertex of
the optimum solution.
21. 21
The several steps in the Lagrangian method can be summarized as follows:
1. Determine objective function
2 .Determine constraints
3. Change inequality constraints to equality constraints.
4. Form the Lagrange function, F:
a. One Lagrange multiplier λ for each constraint
b. One slack variable q for each inequality constraint
5. Partially differentiate the Lagrange function for each variable and Set
derivatives equal to zero.
6. Solve the set of simultaneous equations.
7. Substitute the resulting values into the objective functions.
THE LAGRANGIAN METHOD
22. 22
The red line shows the constraint. The blue lines are contours of f(x,y).
The point where the red line tangentially touches a blue contour is our
solution.
23. 23
•The experimentation be completed before optimization - mathematical
models can be generated.
•Tablet formulation – phenylpropanolamine - constant
•Independent variable – disintegrant and lubricant(x1, x2)
•Experimental design – full 32 factorial
24. 24
• Polynomial models relating the response variables - generated by
backward stepwise regression analysis program.
•The analyses were performed on polynomial and the terms were
retained or eliminated according to standard stepwise regression
techniques.
y = B0+B1X1+B2X2+B3X1
2+B4X2
2+B5X1X2 +B6X1X2
2+B7X1
2X2+B8X1
2X2
2
y - any given response
Bi - regression coefficient for the various terms containing levels of
the independent variable.
25. 25
The dependent variables - tablet hardness, friability, volume, in
vitro release rate, and urinary excretion rate in human subject.
25
Figure 6. Contour plots for the Lagrangian method:
(a) tablet hardness;
27. 27
Figure 6. Contour plots for the Lagrangian method:
c) feasible solution space indicated by crosshatched area
•If the requirements on the final tablet are that hardness be 8-
10 kg and t50% be 20-33 min, the feasible solution space is
indicated in Fig. 6c.
•This has been obtained by superimposing Fig. 6a and b, and
several different combinations of X1 and X2 will suffice.
28. 28
Buck et al incorporated statistical design into above product design
It consist of following strategies
a. A preliminary planning phase
b. An experimental phase
c. An analytical phase
d. A verification phase
They included case studies of a tablet design and suspension
design to illustrate
• Efficient and effective procedures that might be applied
29. 29
Lagrangian method - handle several responses or dependent
variable, it limited to two independent variables.
Response surface explained by eqs. are searched by various methods
to find out the optimum combination of dependent variables.
It takes five independent variables into account and is computer-
assisted.
persons unfamiliar with the mathematics of optimization and with no
previous computer experience - carry out an optimization study.
THE SEARCH METHOD
30. 30
o The system selected here was also a tablet formulation .
The five independent variables or formulation factors selected for this
study are shown in Table 2.
32. 32
• The experimental design used was a modified factorial.
•five independent variable dictates that a total of 27 experiments or
formulations be prepared.
•This design is known as a five-factor, orthogonal, central, composite,
second-order design .
•The first 16 formulations represent a half-factorial design for five factors
at two levels, resulting in 25 − 2 design = 16 trials.
•The two levels are represented by +1 and -1, analogous to the high and
low values in any two level factorial design.
33. 33
For the remaining trials, three levels were selected: zero represents a
base level midway between the aforementioned levels, and the levels
noted as 1.547 represent extreme (or axial) values.
34. 34
• The translation of the statistical design into physical units is shown
in Table 5.
• Again the formulations were prepared and the responses measured.
The data were subject to statistical analysis, followed by multiple
regression analysis.
•This is an important step. One is not looking for the best of the 27
formulations, but the “global best.”
35. The type of predictor equation used with this type of design is
a second-order polynomial of the following form:
Y = a 0+a1X1+…..+a5X5+a11X12+…+a55X52+a12X1X2+a13X1X3+…+a45X4X5
Y = level of a given response,
X1 = level of the independent variable.
The full equation has 21 terms, and one such equation is generated
for each response variable .
The usefulness of equation is verified by R2 value
All pharma. responses will not fit 2nd order regression method
36. 36
1. Select a system
2. Select variables:
a. Independent
b. Dependent
3. Perform experiments and test product.
4. Submit data for statistical and regression analysis
5. Set specifications for feasibility program
6. Select constraints for grid search
7. Evaluate grid search printout
8. Request and evaluate:.
a. “Partial derivative” plots, single or composite
b. Contour plots
THE SEARCH METHODS
37. 37
For the optimization itself, two major steps were used:
1. The feasibility search
2. The grid search.
The feasibility program is used to locate a set of response
constraints that are just at the limit of possibility.
For example, the constraints in Table 6 were fed into the
computer and were relaxed one at a time until a solution was
found.
38. 38
This program is designed so that it stops after the first possibility, it is not a full
search.
The formulation obtained may be one of many possibilities satisfying the
constraints.
39. 39
•The grid search or exhaustive grid search, is essentially a brute
force method in which the experimental range is divided into a grid
of specific size and methodically searched.
• From an input of the desired criteria, the program prints out all
points (formulations) that satisfy the constraints.
• From a set of feasible constraints – educated guess based on
experience with the system.
•A multivariate statistical technique called principal component
analysis (PCA) can be effectively used .
40. 40
•The output includes plots of a given responses as a function of a single variable
(fig.11).
The abscissa for both types is produced in experimental units, rather than physical
units, so that it extends from -1.547 to + 1.547.
Graphic approaches are also available and graphic output is provided by a plotter
from computer tapes.
41. 41
The output includes plots of a given responses as a function of all
five variable (Fig 12).
42. 42
Contour plots (Fig.13) are also generated in the same manner. The specific response is
noted on the graph, and again, the three fixed variables must be held at some desired
level. For the contour plots shown, both axes are in experimental unit (eu) .
43. 43
Canonical analysis, or canonical reduction, is a technique
used to reduce a second-order regression equation, to an
equation consisting of a constant and squared terms, as
follows:
CANONICAL ANALYSIS
Y = Y0+λ1W1
2+λ2W2
2+…….
44. 44
In canonical analysis or canonical
reduction, second-order regression
equations are reduced to a simpler
form by
• rigid rotation and translation
of the response surface axes in
multidimensional space, as shown in
Fig.14 for a two dimension system.
45. 45
Formulation and Processing
Clinical Chemistry
Medicinal Chemistry
High Performance Liquid Chromatographic
Analysis
Formulation of Culture Medium in Virological
Studies.
Study of Pharmacokinetic Parameters.
VI. OTHER APPLICATIONS
46. 46
IX. REFERENCES
1. Webster's Marriam Dictionary, G & C Marriam.
2. L. Cooper and N. Steinberg, Introduction to Methods of Optimization, W.B.
Sunder.
3. O.L.Davis , The Design and Analysis of the Indusrial Experimentation,
Macmillan.
4. Gilbert S. Banker, Modern Pharmaceutics, Marcel Dekker Inc.
5. http://en.wikipedia.org/wiki/Optimization_(mathematics)
6. http://mat.gsia.cmu.edu/classes/QUANT/NOTES/chap4/node6.html