# Presentation: 651561

## DPQR: Advancing “Critical Path” Research

- _ACPS Meeting, October 19th, 2004_
- _Mansoor A. Khan, R.Ph., Ph.D._
- _Director, Division of Product Quality Research_

## Outline

- DPQR Mission/Vision
- Present teams and projects
- Current needs related to critical path and cGMP initiatives
- Future directions
- Examples for “design space”
- Questions

## Division of Product Quality Research 

**Mission**
* **	**Advance the scientific basis of regulatory policy with comprehensive research and collaboration;  focus/identify low and high-risk product development and manufacturing practices; share scientific knowledge with CDER review staff and management through laboratory support, training programs, seminars and consultations, and foster the utilization of innovative technology in the development, manufacture and regulatory assessment of product development – Stay aligned with OPS and CDER missions*
**Vision**
- 	Be recognized leaders in providing support for guidance based on science and peer-reviewed data; well trained staff in state-of-the-art product quality laboratories that is capable of providing any information sought by reviewers, industry, or the FDA leadership.
**Culture:**
- 	The way we live and act – cooperation, mutual respect, synergy, professional development with life-long learning opportunities

## Teams

- 19 scientists divided as follows:
- Pharm./Analytical Chemistry Team
- Physical Pharmacy Team
- Biopharmaceutics Team
- Novel Drug Delivery Systems Team (New)

## Pharm/Analytical Chemistry Projects (Team Leader: Dr. Patrick Faustino)

- Prussian Blue (safety, efficacy and product quality studies)
- Shelf-Life Extension Program (collaborative)
- Isotretinoin (bioanalytical and kinetic studies (collaborative)

## Safety and Efficacy of Prussian Blue

## Biopharmaceutics Team (Team Leader: Dr. Donna Volpe)

- Small team (Needs to grow)
- BCS guidance
- Levothyroxine Sodium (Stability and Bioequivalence issues-Collaborative)
- Effect of cyclodextrin on permeability
- Database on permeability of several drugs
- Variability of permeability in caco2 cells
- Liposome uptake studies

## Clinical BA Study-Excipient EffectBCS Class I-Drug    BCS Class III-Drug

## Physical Pharmacy Team(Deputy Director: Dr. Robbe Lyon; Team Leader: Everett Jefferson)

*Content Determination*
*Blend Uniformity*
*Moisture uptake*
*Polymorphic Form*
*Predicting Dissolution*
*Particle Sizing*
*Powder Flow*

**Some PAT related activities include:**

## Content determination with NIR

## Content Determination with Raman Spectra  

**Content Determination with Raman Spectra **

***785 nm Laser ***
***Excitation***

## Blend Uniformity: NIR PLS Score Images and Localized Spectra

**Blend C Tablet**

- API
- Excipient

## Final Dosage: Hydration

*Commercial Nitrofurantoin Capsules*
*Brand 1: Capsule contains 2 cores:*
*Core A: 25 mg nitrofurantoin anhydrous (9%)*
*Core B: 75 mg nitrofurantoin monohydrate (40%)*
*Brand 2: Capsule contains 3 cores:*
*Core A: 25 mg nitrofurantoin anhydrous (12.5%)*
*2 x Core B: each 40 mg nitrofurantoin *
*monohydrate (ea 20%)*
*Sensors: **	**NIR Spectroscopy/**	**NIR Imaging*

## API Hydration by Chemical Imaging: NIR PLS Concentration Maps of Brand 1 Capsule Cores

## PLS Model: NIR-Dissolution Correlation

*NIR Spectra and Dissolution Values of Furosemide Tablets*
*144 Tablets*
*Spectral Range: 1100-2300 nm*
*Dependent Variable: Dissolution Values at 15 min*
*Preprocessing*
*Savitzky Golay 2nd-Derivative*
*Validation Set (N = 72)0*
*Cross-Validation Model*
*3 samples from each formulation*
*Prediction Set (N = 72)*
*Remaining 3 samples from each formulation*

## Predicting Dissolution from NIR Spectra:Direct Compression (%Diss at 15 min)

## The DPQR Today....

**DS**

- Characterization

- Analytical Methods

**DP**

**Cell Culture**

- Slep

**Stability**

## Critical Path Science Base

**The science necessary to evaluate and predict safety and efficacy, and to enable manufacture is different from the science that generates the new idea for a drug, biologic, or device.**
**In general, NIH and academia do not perform research in this area**
-                            Dr. Woodcock, May 2004

## OPS programs and projects will support the achievement of the following attributes of drug products: 

***Drug quality and performance is achieved and assured through design of effective and efficient development and manufacturing processes ***
***Regulatory specifications are based on a mechanistic understanding of how product and process factors impact product performance ***
- 			Helen Winkle, ACPS, April 2004

## “THE DESIRED STATE”/Q8(as agreed by EWG)

- Product quality and performance _achieved and assured by design_ of effective and efficient manufacturing processes
- Product _specifications based on mechanistic_ _understanding_ of how formulation and process factors impact product performance
- An ability to effect Continuous Improvement and Continuous "real time" assurance of quality
- 			John Berridge, Q8 Rapporteur, FDA, July 2004

## DS

**DS**

**Characterization**

***Analytical****** Methods***

**DP**

**Cell Culture**

- Slep

**Stability**

** **Nanoparticles
-  Liposomes
-  SR/MR
-  TDDS
-  Nasal
-  Pulmonary
-  Fast disintegration
-  Solid dispersion

**PK/**
**Bioavailability**

**NDDS**

-  Excipients
-  Formulation variables
-  Process variables
-  Mechanistic evaluations
-  Optimization &
-     ANN procedures

- Mixing
- Milling
- Granulation
- Drying
- Compression
- Coating
- Packaging

**DPQR Vision for Tomorrow**..

- Physical

- Chemical

## New Projects?

- Novel Drug Delivery Systems including nanoparticulates; preparation, characterization, development of in-vitro procedures – in DPQR laboratories
- Science-based projects with mechanistic understanding
- Process engineering with real time monitoring and modeling – in-house and with collaborations
- SLEP/Stability and repackaging issues
- Generic Drugs; I*n vitro* methods for determining bioequivalence of locally acting GI drugs; Stability issues with split tablets; Stability issues with Repackaging
- Stents?
- New CRADAS
- Permeability of drugs from nanoparticles/bioavailability studies

- Near IR probe

## Box, Hunter and Hunter, 1978

## Box, Hunter, and Hunter, 1978

## Evolutionary Operation

- Box, Hunter, and Hunter, 1978

## Example of design space

_***Example of design space***_

- Osmotic push-pull system

- water

## Plackett-Burman Screening Design

- Independent factors				          Levels used
- X1 = orifice size (mm)					0.35	0.64
- X2 = coating level (%)					100 	200
- X3 =amount of NaCl in osmotic layer (mg)		1 	10
- X4 = amount of Polyox N10 (mg) in drug layer		40 	60
- X5 = amount of Polyox N80 (mg) in osmotic layer	60 	80
- X6 = amount of Carbopol 934P (mg) in drug layer	0 	3
- X7 = amount of Carbopol 974P (mg) in osmotic layer	0 	3
- Dependent variable
- Y1 = cumulative % sCT released up to 3 hr
- Constraints
- Y2 (> 5 %) =  % tOVM release at 1 hr
- Y3 (> 10 %) = % tOVM release at 2 hr
- Y4 (> 20 %) = % tOVM release at 3 hr

- 7-factor 2-level design

## Plackett-Burman Screening Design

_***Plackett-Burman Screening Design***_

- Dissolution profiles

**Y****1**** = 56.03+3.33X****1****+8.65X****2****–5.14X****3****–9.25X****4****–2.26X****5****–25.16X****6****- 2.60X****7**** **

- Rakhi Shah et al., *Clin. Res. & Reg. Affairs*, (In press) 2004 A

## Box-Behnken Optimization Design

_***Box-Behnken Optimization Design***_

- Independent factors			Levels used
- X1 = amount of NaCl (mg)			0.1	0.5	0.9
- X2 = coating level (%)			100	200	300
- X3 = amount of Polyox N10 (mg)		40	50	60
- Dependent variable
- Y5 = cumulative % sCT released up to 3 hr
- Constraints
- Y1 (16.65  10 %) =  cumulative % sCT released up to 0.5 hr
- Y2 (33.33  10 %) =  cumulative % sCT released up to 1 hr
- Y3 (49.95  10 %) =  cumulative % sCT released up to 1.5 hr
- Y4 (66.66  10 %) =  cumulative % sCT released up to 2 hr

- 3-factor 3-level design = 15 runs

- Drug layer: sCT+tOVM+glycyrrhetinic acid

## Box-Behnken Optimization Design

- Factors
- X1	0.2875
- X2	-0.9994
- X3	1
- Responses
- Y1	6.65
- Y2	31.8
- Y3	58.1
- Y4	76.6
- Y5	93.88

- R2 = 0.94

- Y5 = 89.35 - 2.78X1 - 1.66X2 + 1.38X3 –0.46X1X2 –0.41X2X3 –2.23X1X3 –6.21X21 –1.67 X22 + 2.23 X23

## Box-Behnken Optimization Design

**Effect of X****1****(NaCl), X****3**** (Polyox N10) on Y****5**** (sCT release)**

- Contour
-  plot

- Response-
- surface plot

## Examples of nanoparticles

-  **Excipient induced recrystallization** **(excipient selection)**
-  **Droplet size analysis**
** ****Thermal analysis**** ****(DSC)**
-  **Binary phase diagrams** **(formation of eutectic mixtures)**
-  **Pseudo ternary phase diagram** **(area of spontaneous            ****	**** ****	**** ****	****         emulsion formation)**
-  **FTIR analysis** **( for stability evaluation)**
-  **Liquid crystalline phase determination**
-  **Dissolution studies**
-  **Turbidimetry** **(Time-turbidity profile for emulsification rate)**

- Int. J. Pharm. 2002, 235, 247-265

_**Examples of nanoparticles**_

## Optimization by Box-Behnken Design

**Palamakula et al., AAPS Pharm. Sci. Tech., (2004, In press)**

## 	Palamakula et al., 2004, AAPS PharmSciTech

- 	Palamakula et al., 2004, AAPS PharmSciTech

## Questions to the advisory committee

- Do you think we are missing anything important that needs to be pursued at this time?
- Does a systematic study with a designed set of experiments provide opportunities for reduction of PAS documents?
- Do you agree that the information on “design space” with a designed set of experiments will reduce the OOS situations?
- Do you agree that the research with well-designed set of experiments on lab scale will create opportunities for continuous improvements and innovations in manufacturing?