QbD harsh new ggu central university .pptx

Qbd practical application

1. DOE Software for Quality by Design( Qbd ) & Process Optimization Submitted to Dr. Dinesh K. Mishra Submitted by Harsh anand sahu M.Pharm 2 nd sem

2. Content Introduction Design Of Experiments(DOE) Applications of qbd in pharmaceutical product development Tutorial for two-level factorial designs using DX 13 Conclusion

3. Introduction to Quality by Design ( QbD ) Quality by Design ( QbD ) is a systematic approach to pharmaceutical development that begins with predefined objectives and emphasizes product and process understanding and process control, based on sound science and quality risk management . It is a key quality enabler that accelerates development with minimal efforts to produce maximal performance

4. Design Of Experiments(DOE) DOE is a data analytics method that ensures product robustness, define an optimal process or formula, or determine the best way to make change or substitutions. It is a data analytics method that helps you plan, conduct, analyze and interpret controlled tests to determine which factors exert influence over your product quality, stability or other key process attributes. Rather than experimenting with one parameter at a time, DOE speeds up the process and helps you identify important interactions by manipulating multiple factors at the same time. There are various tools for DOE, Design Expert 13 is one of the majorly used software in pharmaceutical industries for DOE.

5. Applications of DOE For QbD in Pharmaceutical Product Development

6. Tutorial for two-level factorial designs using DX 13 STEP 1 : Start the program and click New Design. STEP 2 : Select the regular two level factorial under factorial design.

7. STEP 3 : Click the white square labelled 2 2 in column 4(number of factors) in the row labelled 16(number of runs) & click next button.

8. STEP 4 : Now enter the data such as names, units of measure, and levels for your experimental factors & click next button.

9. STEP 5 : Response dialogue box will appear, we can enter as many response we want upto 999 but as of now single response will be added ( filteration rate) & units (gallon/hour). Set signal to 10 ( which means average difference must be more than 10 gallons/hour as per management orders and set noise to 5 ( the process variability). Signal to noise ratio will automatically appear to be (10/5) = 2. Then click next button .

10. STEP 6 : Now the positive outcome (power that exceeds 80% probability of seeing the desired difference will appear). Click the finish button to accept these inputs and generate the design layout window.

11. STEP 7 : At this stage you normally would print the run sheet, perform the experiments , and record the responses . The software automatically lists the runs in randomized order. For this tutorial we will just load the data by clicking Help, Tutorial Data -> Filtration Rate.

12. STEP 8 : Your data should now match the screen shot shown below except for a different random run order.

13. STEP 9 : You can quickly sort columns by double-clicking on them to see how going from low to high temperature affects Filtration. Select graph coloumns to see scatter plot and correlation value.

14. STEP 10 : You will now see more clearly the impact of temperature on the response. Observe by looking at the graph how temperature makes a big impact on the response. This leads to the high correlation reported on the legend.

15. STEP 11 : Another indicator of the strong connection of temperature to filtration rate is the red color in the correlation grid at the intersection of these two variables. Note that you can also see the correlation number just above the grid next to a colored scale indicating correlation.

16. STEP 12 : Now for a really awesome scatterplot change the Y Axis to D :Stir Rate and the Z-Axis to Filtration Rate . This shifts graph columns to the third dimension .

17. STEP 13 : To begin analyzing the design, click the Filtration Rate response node on the left side of your screen. Leave the Configure analysis options at their default of linear regression with no transform, and press Start Analysis .

18. STEP 14 : Under the Effects tab, click (plotted as squares) on half-normal probability plot.

19. STEP 15 : On the right click the Pareto plot . Color-coding provides details whether the effects are positive or negative.

20. STEP 16 : I t is now time to look at the statistics in detail with the analysis of variance (ANOVA) table. Click the ANOVA tab to see the selected effects and their coefficients.

21. STEP 17 : Click the Diagnostics tab to generate a normal probability plot of the residuals.

22. STEP 18 STEP 19

23. STEP 20 STEP 21

24. Step 22 : Click to design(actual) to return to original data. Step 23 : And then right click and select export table to word and save it. This completes the basic tutorial on factorial design .

25. CONCLUSION The application of QbD in the pharmaceutical industry is multifaceted, ranging from product development to regulatory compliance and manufacturing processes. By embracing QbD , the pharmaceutical industry can ensure the production of safe, effective, and regulation-compliant products while simultaneously improving process efficiency and product quality The proactive and integrative approach of QbD , built on scientific and mathematical foundations, is essential for achieving high-quality manufacturing