Dissolution Process MOST CRITICAL method for End Process Testing of Solid Tablet Formulations.

Dissolution Process MOST CRITICAL method for End Process Testing of Solid Tablet Formulations

Measurement System for Dissolution Rate
Equipment Types are called “Apparatus” Types 1 through 7 (1 & 2 are most popular) Current State of Calibration and Validation Ensuring cross lab and equipment reliability Sources of Variability Troubleshooting, tuning, improving Risk Based Methods Development Strategies

USP Dissolution Apparatus
ID Common Name Temp Use 1 Basket 37 Most Common 2 Paddle 3 Reciprocating Cylinder 4 Flow Through 5 Paddle over disk 32 Transdermal Delivery System, use paddle and vessel from Apparatus 2 with a stainless steel disk assembly to hold the transdermal on the bottom of vessel. 6 Cylinder Transdermal Delivery System, use Apparatus 1 except replace the basket shaft with a stainless steel cylinder element. 7 Reciprocating Holder Reciprocating Holder, for transdermal delivery systems and also a variety of dosage forms

Apparatus 1 Basket Apparatus 2 Paddle

Apparatus 3 (shown) Glass reciprocating cylinder Apparatus 7 Holders a) Reciprocating disk sample holder b) Transdermal system holder - angled disk (32º) c) Transdermal system holder - cylinder (32º) d) Oral extended-release tablet holder—rod, pointed for gluing e) Oral extended-release tablet holder—spring holder

Current State for Dissolution Test Methods
Most use: Apparatus 1 (Basket) or Apparatus 2 (Paddle) USP Equipment Set-Up and Calibration Criteria One point acceptance criteria for Immediate Release A lot of work for only one point of measure

Current State for Dissolution Test Methods
Instrument Suitability Choice of instrument Mechanical Calibration (affecting hydrodynamics) Calibrator Tablets Method Development / Validation Target dissolution profile: A set of goals provided for all dissolution methods to achieve. This is about the only generic measure in the process. Concept screening evaluation: This work is done given the knowledge around the compound (solubility, stability, polymorph potential, dose ranges) and document such knowledge in the first step of the FMEA Dissolution optimization: Once a given formulation is chosen, a more in depth understaning into the dissolution method should be evaluated and ultimate dissolution method parameters justified. Validation: Ultimately done throughout the lifecycle of the product to ensure basic methods quality and presented last to help ensure the validation is based on basic previous steps of methods development. Robustness testing: Used to evaluate the proposed methods parameters sensitivity towards instrument to instrument and site to site transfer.

Current State: Instrument Suitability
USP Mechanical Calibration Parameters include: Basket/Shaft Wobble (No significant wobble) Vessel/Shaft Centering (2 mm from centerline) Height check/Basket or Paddle Depth as measured at basket bottom or Paddle bottom ( mm) No significant vibration Rotational speed (+ 4%) Vessel Temperature (37.0 C) Basket Wobble (bottom rim) (+ 1mm)

Calibrator Tablets 1970’s : USP Calibrator Tablets Introduced
Disintegrating – 50 mg Prednisone (Upjohn) Non Disintegrating – 300 mg Salicylic Acid (Hoffman LaRoche) 1997 : 50 mg Prednisone replaced with 10 mg Prednisone manufactured at University of Maryland 2004 : USP begins search for replacement for 10 mg Prednisone tablet USP: Both Calibrators on a given apparatus (i.e. 4 calibration tests if instrument is used for paddle and basket methods) JP, BP and EP: No calibrator tablets

Current State: Instrument Suitability
Calibrators Every 6 months 10 mg Prednisone Tablet (Lot O0C056) Basket: 53 – 77% (now 51-81%) (DPA /- 5.4, n=36) Paddle: 27 – 48% (now 26-47%) (DPA /- 2.0, n=24) Salicylic Acid Tablet (Lot O) Basket: 23 – 29% Paddle: 17 – 26% Action with Out of Specification value Gets in the way of continuous improvement

Current State: Method Development / Validation
Development –Discriminatory? Repeatable? Instrument – which one to use? Media Degassing Sinkers Validation: Determinative Step – Main Focus Linearity and Recovery Filtering Stability of solutions Interferences Even if we don’t understand development, we need to understand variability of our system (product, measurement, random)

Managing Variability Total variability σ2Total
Assuming independent variable (if not independent for example interaction between measurement and product a covariance term needs to be included) σ2Total = σ2Product + σ2Measurement σ2Measurement = σ2Repeatability + σ2Reprodicibility Common Cause Vs. Special Cause variability Process capability: Customer Needs Process Ability

Current State: Variability
Instrument Suitability Apparatus Variability Operator Set-up Calibrator Assignment Variability Manufacturing of Calibrator Tablet Stability Instrument Set-up Degassing Product Specific Media including degassing Manufacturing Dissolution equipment parameters (clips, sensitivity to set-up) Sinkers Determinative Step

Variability: Instrument Suitability
Set-up Parameters: USP DPA Shaft Wobble No significant wobble ≤ 0.5 mm total run out Vessel/Shaft Centering 2 mm from centerline 1 mm from centerline Height check/Basket or Paddle Depth as measured at Basket or Paddle bottom mm mm Vibration as measured at center of vessel support plate while operating at 100 rpm/head above plate, 900 ml medium in vessels No significant vibration ≤ 0.1 mil displacement Rotational speed + 4% + 1 rpm Basket Wobble (Bottom Rim) + 1mm ≤ 0.1mm total

Variability: Media and Degassing
Total Dissolved Gas and Oxygen Meter A B Used for determining water quality in agriculture, fisheries and industry

Total Gas Pressure and Oxygen Gas Pressure in Water Degassing by Various Methods

Product 1: paddle, 50 rpm, DI Water Product 2: basket, 100 rpm, pH 1.2 Product 3: paddle, 50 rpm, pH 7.4 buffer Variability: Media and Degassing

Variability: Media or Manufacturing?
pH 7.2 pH 6.8

What are the sources of variability at pH 6.8?
Variability: Media or Manufacturing? What are the sources of variability at pH 6.8? Product handling during testing? Tablet to tablet differences? Instrumentation variation? Vessel defects? Inconsistent Centering? RPM variations Etc.

Microscope Images of the Coating
Variability: Media or Manufacturing? Microscope Images of the Coating

Variability Sinkers Product 1: paddle, 50rpm, DI water, off-center 10mm Sinkers are sometimes needed to hold product at the bottom of the dissolution vessel.

Variability Sinkers Commercial Sinker 3 Wire Turns
60% - 72% Dissolved at 30 min. 66% ± 4% 89% – 99% Dissolved at 30 min. 95% ± 4%

Opportunities for Improvement
Alternative regulatory approach to dissolution calibration and validation Understand and control measurement system variability Understand the product specific sources of variability Understand relationship between physicochemical properties and dissolution results Understand the benefits and limitations of different dissolution apparatus – develop scientific criteria Investigate new approaches to assess product quality and availability Communication and training of FDA personnel

Alternative approach to dissolution calibration and validation
Stringent Mechanical Calibration to replace the need for a calibrator tablet ID and Control all sources of variability Apparatus Type including sinkers Set-up Parameters Media including degassing Understanding of interaction between instrument and product during pharmaceutical development If necessary, establish an internal calibrator (biobatch or clinical batch) for system suitability and stability Confirm suitability using Gauge R&R using pivotal clinical trial product or pivotal “bio-batch”

Gauge R&R Design Chance to characterize variability on an internal reference Make it representative Controlled manufacturing process For design include variables such as Instrument Personnel Media

Use design and development information to help choose an apparatus
Understand the benefits and limitations of different dissolution apparatus Use design and development information to help choose an apparatus Model dissolution environments and understand hydrodynamics from first principles Look beyond apparatus 1 and 2 to alternative systems that may be easier to model and test (i.e., flow through?)

Investigate new approaches to assess product quality and availability
New tools to assess product and media variability Spectroscopy? NIR, RAMAN, Terahertz PAT including feedback loops First principles and modeling

10-mg Prednisone Tablet DPA Distribution USP Limits Paddle Limits: % DPA: /- 2.0 n=24 Basket Limits: % DPA: /- 5.4 n=36 % Dissolved

Conclusions Dissolution is a key sensitive endpoint detection for solids tablets Attempts to model the process in the body Ensuring complete release Ultimate goal is to achieve dissolution profile that matches the clinical trial blood levels VERY DIFFICULT – BUT POSSIBLE

Process Analytical Technology (PAT)
Incorporating Process Analytical Technologies Framework into the plant to manage risk

Challenge Worlds most complex bioreactor: Homosapien
Small variations can have catastrophic effects Discovery Development Clinical Pharmaceutical Analysis Tools help maintain safety and quality throughout Pharmaceutical Analysis: Fall 2014

Pharmaceutical CGMPs for the 21st Century: A Risk-Based Approach.
An initiative started in August 2002, recognizing the need to eliminate the hesitancy to innovate, the Food and Drug Administration (FDA) launched a new initiative entitled CGMP’s = Current Good Manufacturing processes The most up-to-date concepts of risk management and quality systems approaches are incorporated into the manufacture of pharmaceuticals while maintaining product quality Manufacturers are encouraged to use the latest scientific advances in pharmaceutical manufacturing and technology The Agency's submission review and inspection programs operate in a coordinated and synergistic manner Regulations and manufacturing standards are applied consistently by the Agency and the manufacturer Management of the Agency's Risk-Based Approach encourages innovation in the pharmaceutical manufacturing sector Agency resources are used effectively and efficiently to address the most significant health risks

Pharmaceutical CGMPs for the 21st Century: FDA Guidance on PAT
Released in September 2004. Ajaz S. Hussain, Ph.D. Previously Deputy Directory Office of Pharmaceutical Science CDER, FDA Purpose: Help remove confusion in industry about the PAT initiative Historically called Process analytical chemistry (PAC), name change did not help implementation Companies were not willing to “risk” gathering more data for fear of what to do with the data on non-validated PAT technologies

The “Scope” The framework is founded on process understanding to facilitate innovation and risk-based regulatory decisions by industry and the Agency The framework has two components: a set of scientific principles and tools supporting innovation a strategy for regulatory implementation that will accommodate innovation The regulatory implementation strategy includes creation of a PAT Team approach to chemistry manufacturing and control (CMC) review and current good manufacturing practice (CGMP) inspections as well as joint training and certification of PAT review and inspection staff. Note: Team = Agency and corporations ensuring a common understanding Agency is consistently presenting partnerships in their efforts.

The “Background” Conventional pharmaceutical manufacturing is generally accomplished using batch processing with laboratory testing conducted on collected samples to evaluate quality. This conventional approach has been successful in providing quality pharmaceuticals to the public. However, today significant opportunities exist for improving pharmaceutical development, manufacturing, and quality assurance through innovation in product and process development, process analysis, and process control. Efficient pharmaceutical manufacturing is a critical part of an effective U.S. health care system. The health of our citizens (and animals in their care) depends on the availability of safe, effective, and affordable medicines. Unfortunately, the pharmaceutical industry generally has been hesitant to introduce innovative systems into the manufacturing sector for a number of reasons

PAT Framework The Agency considers PAT to be a system for designing, analyzing, and controlling manufacturing through timely measurements (i.e., during processing) of critical quality and performance attributes of raw and in-process materials and processes, with the goal of ensuring final product quality. The goal of PAT is to enhance understanding and control the manufacturing process, which is consistent with our current drug quality system: quality cannot be tested into products; it should be built-in or should be by design. Consequently, the tools and principles described in this guidance should be used for gaining process understanding and can also be used to meet the regulatory requirements for validating and controlling the manufacturing process

Building quality into products
The intended therapeutic objectives; patient population; route of administration; and pharmacological, toxicological, and pharmacokinetic characteristics of a drug The chemical, physical, and biopharmaceutic characteristics of a drug Design of a product and selection of product components and packaging based on drug attributes listed above The design of manufacturing processes using principles of engineering, material science, and quality assurance to ensure acceptable and reproducible product quality and performance throughout a product's shelf life First objective: Not much we can really do about that – it is inherent to the drug design – for the most part Second Objective: We can influence the chemical, physical and biopharmaceutical behaviors of a drug Thrid objective: Usually locked in place Forth objective: Most important and most complicanted Process Understanding

Process Understanding
A process is generally considered well understood when: All critical sources of variability are identified and explained Variability is managed by the process Product quality attributes can be accurately and reliably predicted over the design space established for: Materials used Process Parameters Manufacturing unit operations Environmental impacts “Other considerations” Although retrospective process capability data are indicative of a state of control, these alone may be insufficient to gauge or communicate process understanding.

Continuous Learning Structured product and process development on a small scale, using experimental design or in-line process analyzers to collect data in real time, can provide increased insight and understanding for: process development Optimization Scale-up Technology transfer Control Process understanding continues in the production phase when other variables may be encountered Supplier changes

Principles and Tools Pharmaceutical manufacturing processes often consist of a series of unit operations, each intended to modulate certain properties of the materials being processed. To ensure acceptable and reproducible modulation, consideration should be given to the quality attributes of incoming materials and their process-ability for each unit operation Independent Variables Independent Variables Independent Variables Unit Operation A Unit Operation B Unit Operation C Material Attributes Equipment Settings Dependent Variables (Material Attributes) Dependent Variables (Material Attributes) Dependent Variables (Material Attributes)

Process Understanding and Recipes
Process understanding leads to a set of instructions that if followed will provide materials of defined quality attributes These set of instructions are developed in the R&D phases and transferred to plant ideally as a recipe Recipe defines how resources come together in a sequence of steps to make a product or measurement Recipe provides a proven framework to facilitate technology transfers A recipe typically defines a unit operation and/or analytical quality testing process Both have material genealogy at it’s core to ensure process understanding is transferred into a control strategy for the plant

Principles and Tools Pharmaceutical manufacturing processes often consist of a series of recipes, each intended to modulate certain properties of the materials being processed. To ensure acceptable and reproducible modulation, consideration should be given to the quality attributes of incoming materials and their process-ability for each recipe Independent Variables Independent Variables Independent Variables Recipe A Recipe B Recipe C Material Attributes Equipment Settings Dependent Variables (Material Attributes) Dependent Variables (Material Attributes) Dependent Variables (Material Attributes)

PAT Tools There are many tools available that enable process understanding for scientific, risk-managed pharmaceutical development, manufacturing and quality assurance. These tools when used within a system can provide effective means for acquiring information to facilitate process understanding, continuous improvement and development of risk-mitigation strategies. In PAT framework, these tools can be categorized by: Multivariate tools for design, data acquisition and analysis Process analyzers Process control tools Continuous improvement and knowledge management tools An appropriate combination of some, or all, of these tools may be applicable to a single-unit operation, or to an entire manufacturing process and its quality assurance

Multivariate tools From a physical, chemical, or biological perspective, pharmaceutical products and processes are complex multi- factorial systems. Inputs to the process control the variability of the output Inputs Variables (X) y = f(x) People Environment y Equipment Materials Output Process Measurement Variability - source of the big risks to the product

Importance of Knowledge Management
The knowledge acquired in these development programs is the foundation for product and process design It is noted in other sections of the guidance that continuous learning and scale up scale down models can help facilitate the overall process knowledge. A knowledge base can be of most benefit when it consists of scientific understanding of the relevant multi-factorial relationships (e.g., between formulation, process, and quality attributes), as well as a means to evaluate the applicability of this knowledge in different scenarios This benefit can be achieved through the use of multivariate mathematical approaches, such as statistical design of experiments, response surface methodologies, process simulation, and pattern recognition tools, in conjunction with knowledge management systems. The applicability and reliability of knowledge in the form of mathematical relationships and models can be assessed by statistical evaluation of model predictions.

Building Knowledge based on Data
Capture data in a structured format: Recipe Aggregate that data in a common location: Data Warehouse Understand relationships: Multivariate Analysis Multivariate Analysis Recipe Framework Data Warehouse S88/S95 Ref:

Process Analyzers Process analyzers are embedded analytical technologies into the manufacturing process to : Monitor – collect data passively to aid in process understanding Control – collect and analyze data to control the outcome of the process Release product (“real time release”) – collect, analyze and use data to release the product in real time Optimization - variation of input parameters to achieve a desired outputs Typically require Multivariate tools to analyze Requires knowledge management system to manage quantity of data/information

How to deploy PAT Framework
Hardware: PAT probes are well developed and deployed in food and chemical production People In drug development technologists that are not afraid of learning curve both for the tools and the equipment Learning what a probe can measure may be product dependent and/or independent. pH, Temperature, Pressure, p02 Chemical composition – requires model building Production – must be practical and open minded Software Helps build complex models to aid scientists in correlating signals to process parameters

Traditional PAT Analyzers
Don’t forget the basics – you may already have the probes in place to measure your process! Temperature pH Pressure Humidity Dew Point Air flow Common to use more “advanced” PAT probes to help understand fundamentals of your process and leverage traditional analyzers Cost effective, helps minimize plant impacts

Advanced PAT Analyzers
Near Infrared Inexpensive, probes molecular absorption (chemical bonds) Raman Spectroscopy to observe vibrational, rotational, and other low-frequency modes in a system (molecular vibrations) Focused Beam Reflectance Measurements Measure particle size in system (API or formulation/suspension) Fluorescence Acoustic Others 

NIR/Raman Comparison Near Infrared Raman Absorption process promotes electron from ground state to higher energy level state Electromagnetic radiation is “perturbed” (i.e., shifted up or down) due to stokes or anti-stokes raman scattering A virtual state is an unnatural energy state that is in close proximity to a real energy state and will decay very rapidly because of its low stability

NIR/Raman Spectra Water insensitive High S/N Water absorbs
Broad spectra

NIR Advantages/Disadvantages
No dilution of samples No need of matrices such as KBr or mineral oil No sample preparation Eliminates sample preparation errors, analyst labor and sample destruction Excellent for on-line, in-line applications, wireless technology available Disadvantages Low structural selectivity (no structure elucidation) Not recommended for studying wetted samples unless interested in monitoring moisture (water bands are too strong)

Raman Advantages/Disadvantages
Rich information content (fundamental vibrational modes) Suitable for solids, liquids and gases Requires minimal if any sample preparation Excellent for aqueous systems (Water is not a strong Raman scatterer) Remote sampling capability Fiber optics Non-invasive Long working distance objectives Non destructive Disadvantages Small amount of fluorescence can swamp signal Leverage higher wavelength lasers to reduce chances (lower energy) but even 785nm lasers can have this effect on some samples

Tools used for PAT Framework
Standard mechanism to describe the manufacturing risks of a medicinal product Drives an overarching control strategy Defines a development strategy to reduce risk to patients Risk Management Planning, conducting, analyzing and interpreting controlled tests to evaluate the factors that control the value of a parameter or group of parameters. Factorial Designs Optimizations Design of Experiments The science of extracting information from chemical systems by data-driven means from controlled or uncontrolled tests Multivariate analysis and data mining Production of predictive mathematical models Chemometrics

Risk Management Approach
Failure Mode and Effects Analysis (FMEA) Developed by the US military back in the 1940’s Some Basic Steps Break down the product and process into its components or steps Identification and assessment of the following for every item listed: function(s), potential failure mode(s), failure mode effect(s), failure mode cause(s), and controls for detecting or preventing the failure mode(s); Evaluation of the risks associated with the failures modes and prioritizing them according to importance; Implementation of corrective actions to minimize the occurrence of the more significant failure modes; Risk Management is a methodology to derive a process and product independent measure of performance related to the clinic

FMEA Process in Laymen Terms
Tell me the manufacturing process to make the product Tell me how great the process is TELL ME HOW THE PROCESS MAY FAIL TELL ME HOW I CAN PULL THE PROCESS BACK INTO CONTROL AFTER “FAILURE” OBSERVED Dog and Pony Show How to Fix it?

Classically Risk in quantified in terms of:
Severity What will happen if this exposure occurs? Probability What is the likelihood of the exposure? Detectability Is the exposure easily detected?

Documentation of Knowledge in FMEA
Defining the rationale behind the decisions as a function of current knowledge enables risk management to steer the appropriate direction Transparent communication helps decision making

Classic Risk mitigation strategy
Quantify the risks How input parameters (independent variables) are influencing output parameters (dependent variables) Pareto the risks Ranks risks high to low to prioritize Establish counter measures and mitigation routes to: Minimise Severity Probability Maximise Detectability

Evolution of Knowledge leading to Reduced Risk
API (CMA's) RPN API Bulk Density 84 API bulk Morphology 210 API interfacial forces 140 API Moisture Content 72 API Particle Size 192 API Polymorphic form 200 API purity API rheological properties 75 API Surface Area API solubility Excipients & Media (CMA's) Binder Viscosity 150 Coating material identification 96 Packaging materials (CMA's) Process parameters (CPP's) Granulating solvent desired weight 280 FIller 2 desired weight 320 Filler desired weight "Inlet Air dew point-spraying" "Inlet Air Temperature profile-spraying" Blending time 105 Disintigrant desired weight Lubricant desired weight Binder desired weight Coating Nozzles cleanliness "Air Flow Rate-profile-spraying" 125 Campaign size Granulation Spray Rate profile 160 Phase 1 Development  Phase 2 Development

Design of experiments (DOE)
Design of Experiments (DOEs) refers to a structured, planned method, which is used to find the relationship between different factors (let's say, x variables) that affect a project and the different outcomes of a project (let's say, y variables). The method was coined by Sir Ronald A. Fisher in the 1920s and 1930. DOEs are mainly used in the research and development department of an organization where majority of resources goes towards optimization problems. In order to minimize optimization problems, it is important to keep costs low by conducting few experiments. Design of Experiments is useful in this case, as it only necessitates a small number of experiments, thereby helping to reduce costs.

Approaches Allows for optimization of several variables simultaneously
Can observe effects of individual factors as well as combinations Factorial Design (screening) An experiment with at least two factors, each with a high and low Experiment eventually takes on every possible combination of these factors Response Surface Design (optimization) An experiment that takes the significant factors and uses several intermediate volumes in its generated experiments

Process Step 1: Define risk of overall process to define what parameters should be prioritized and studied Risk assessment will have identified the input parameters that need to be studied Parameters should be measured in a quantitative method to obtain best results Step 2: Incorporate replication through your design to measure variability in design space. This will determine what is noise versus signal. Step 3: Randomize the Run Order: In order to evade uncontrollable influences such as changes in raw material and equipment variation, it is necessary to run experiments in a randomized order. Step 4: Block out Known Sources of Variation: Through blocking one can screen out the effects of known variables such as shift changes or machine differences. One can divide the experimental runs into homogenous blocks and then mathematically remove the differences. This increases the sensitivity of the design of experiment. However it is important to not block out anything one wants to study. Step 5: Know Which Effects (if any) Will be Aliased: An alias means that one has changed one or more things in the same way at the same time. Step 6: Always Confirm Critical Findings: At the end of a design of experiment it is easy to assume that the results are accurate. However it is important to confirm one's findings and to verify the results. This validation can be done using many other management tools available.

Reality: Minitab to the rescue!

Minitab to rescue Continued:

Minitab to rescue Continued:
Ask to “create design” Run the design in order prescribed Enter corresponding data Analyze data independent to dependent variables

DOE in Automated Sample Prep
Used to optimize automated derivatization/sample preparation method Responses Number of GC peaks Area of each peak/area of the external standard peak Factors: EtOH Pyridine ECF1 NaOH ECF2 Levels: Reagent volumes Cell Culture Media Characterization by GC-MS with Automated Sample Preparation EAS 2011: Ingersol et. al.

Design Process Gilson Software Experimental Design Minitab sample data
Factor (reagent) Level (amt mL) EtOH 25-333 Pyridine 25-85 ECF1 25-45 NaOH 25-80 ECF2 Experimental Design Minitab sample data GC-MS Minitab

Data Analysis Representation of the data Cell Culture Media Characterization by GC-MS with Automated Sample Preparation EAS 2011: Ingersol et. al.

Data Analysis Component 1 ECF2 Component 2 Component 3 Component 4
EtOH Cell Culture Media Characterization by GC-MS with Automated Sample Preparation EAS 2011: Ingersol et. al.

Chemometrics The science of extracting information from chemical systems by data-driven means. Highly interfacial discipline, using methods frequently employed in core data-analytic disciplines such as: Multivariate statistics Applied mathematics Computer science in order to address problems in Chemistry Biochemistry Medicine Biology and Chemical engineering

Multivariate Statistics
Principal component Analysis (PCA) PCA projects high dimensional data to a lower dimension (X’s only are classified) PCA projects the data in the least square sense– it captures big (principal) variability in the data and ignores small variability Conceptually how it works: Find a component (dimension vector) which explains as much x-variation as possible Find a second component which: is orthogonal to (uncorrelated with) the first explains as much as possible of the remaining x-variation Process continues until researcher satisfied or increase in explanation is judged minimal Projection to Latent Structures (PLS) a.k.a. “Partial Least Squares” PLS finds a set of orthogonal components that : maximize the level of explanation of both X and Y provide a predictive equation for Y in terms of the X’s This is done by: fitting a set of components to X (as in PCA) similarly fitting a set of components to Y reconciling the two sets of components so as to maximize explanation of X and Y

Correlation to Dissolution
PLS2 Model for Dissolution

How does all this come together?
Mitigation of risk through PAT framework

Today’s Validated Process

Tomorrow’s Controlled and Monitored Process

PAT and Process Monitoring and Control

Basic Tablet Manufacturing Process

Example 1: How to use NIR to help monitor qualitatively material identity

Risk – Raw Materials Correct Material? Correct Specification?
Purity Water content Etc. Manufacturability? Chemical Physical properties

Qualitative Analysis Application - Raw Material ID
Pharmaceutical users want to apply NIR to inspect incoming raw materials quickly at the loading dock. They often would like to equip the receiving area with an NIR to be used by technicians who are not trained as scientists. The instrument will be used to confirm the identity of each container of material that is received. This would otherwise have to be done in the QC lab by wet chemical techniques Bunch of X’s = PCA Analysis

Why test all these if they are not all in your formulation?
Library Samples One lot each of the following substances was provided for the construction of the raw material library D-Glucose D-Fructose Sucrose D-Mannitol D-Sorbitol D-Lactose Monohydrate Acetylsalicylic Acid Acetaminophen L-Ascorbic Acid Citric Acid Why test all these if they are not all in your formulation?

Data collected and library built
Library is a collection of spectra with correlating material IDs and Lots in this case Principal component analysis was done on the library to define a model for predicting each excipient It worked (Trust me ) Fructose Glucose

Example 2: Using FTIR to determine blend mixture time

Risk – Blended Materials
During formulation the API and excipients are dispensed then blended into a homogenous mixture.

Blend Uniformity in Tablets
Achieving proper blend uniformity for tablets prior to pressing is critical to achieve proper distribution of actives and excipients. Under-blending results in inhomogeneous distribution of active ingredients in tablet Over-blending leads to unrecoverable material Example shows how blend uniformity can be successfully monitored in seconds using FT-NIR technology

Region of Interest for Component #2
How long would you wait? Component #1 Component #2

Example 3: Using FT NIR to determine content uniformity and formulation composition

Show Concentration Differences in Tablets
Can use both Transmission and Reflection measurements to analyze tablets. Can be done without removing the tablets from the system Distinguish different clinical tablet formulations by amount of active ingredient Finished product is a tablet and manufacturing protocols need independent verification of amount of active ingredient. Distance Matching becomes more viable as the library for a given material becomes more mature. The Distance Matching technique works best when the product distribution is well represented.

Reflectance Spectra for Clinical Tablets
0.25 0.35 0.45 0.55 0.65 0.75 Log(1/R) 5000 6000 7000 8000 9000 -2.2 -1.8 -1.4 -1.0 -0.6 -0.2 0.2 0.6 1.0 1.4 5400 5500 5600 5700 5800 5900 Wavenumbers (cm-1) Expanded Second Derivative

Tablet Reflectance Results
10 20 30 Label Claim (Rel. Units) NIR Calculated (Rel. Units) Calibration Validation R = RMSEC = 0.606 There was a clear correlation in the data with the active ingredient but the error associated with this analysis is not adequate to be able to distinguish between the formulations containing the lower levels of active ingredient.

Tablet Transmittance Results
30 20 R = RMSEC = NIR Calculated (Rel. Units) 10 Calibration Validation The transmittance data correlated much better with the active ingredient compared to the reflectance data. The active contents of the validation samples were also predicted much more accurately by comparison. The use of transmittance FT-NIR appears to accomplish the desired distinction. 10 20 30 Label Claim (Rel. Units)

Tablet Transmission Data Classification Using Discriminant Analysis
PC1 PC3 1 2 4 12 16 24 The transmittance data may also be able to be used in a discriminant analysis method as a means of distinguishing the formulations. This is further evidence that the use of FT-NIR transmittance spectroscopy may allow the desired distinction.

Ideally now you know the defintion
PAT Framework PAT is a system for designing, analyzing, and controlling manufacturing through timely measurements (i.e., during processing) of critical quality and performance attributes of raw and in-process materials and processes with the goal of ensuring final product quality Process requires a set of tools greater than the physical analyzers Risk Based Analysis (FMEA) DOEs to help build models Chemometrics to help monitor and build complex relationships

What/When/Why PAT? What? When? Why?
PAT is the incorporation of process measurements within the manufacturing process Goals: Monitor, Control, Release Product, Optimize When? Should be part of the development process – helps optimize product and monitor as a function of scale up Trend to include them as part of equipment trains Deployment to the plant must always be kept at forefront of design. If it can not be deployed to the plant, must translate knowledge to tools that can be deployed Why? Ultimate goal is to gain enough process understanding to reduce overall risk of poor product quality

Dissolution Process MOST CRITICAL method for End Process Testing of Solid Tablet Formulations.

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