New Pr Ofesy Conference Presentation

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Background to Wavedata\'s approaches to forecasting generic pharma prices after loss of exclusivity / patent expiry and generic launch

Generic Prices Forecasting and Mega Trends

How do companies forecast generic prices? Time Price Generic Launch

Same Therapeutic Cat Same Therapeutic Category, But Different Decay Rates Selective Serotonin Re-Uptake Inhibitors

Same Market Value Same Value, But Different Decay Rates £450,000 - £550,000

Same molecule Same Molecule But No Uniformity Fluconazole Caps

Data Collection Wavedata founded in 2000 60,000 hours of data entry 160 wholesalers and suppliers Thousands of generic products 2 years of analysis

Statisticians found each product can be modelled

Multiple models were produced, one for each product

First Model (2005) 80 products analysed Linear dynamic model 3 forecast models A, B and C Based on statistical coefficients

Further development (2006) Another year of modelling 120 products analysed N on-linear polynomial model Adding Reimbursement arguments Including Tariff M

Current model completed (2007) Works for 99% of products No therapeutic adjustment needed No strength adjustment needed Integrated into a web site

Does it work? Can generic prices really be forecast? Before During Actuality

Other Markets Model can be adapted for new markets Different coefficients for each market USA EU States

Key Bounce factors Cost of goods Manufacturer withdrawal Short or long residual life Holiday link? Bounces are visible side of seasonality? Disease timings – ie hay fever

How often bounces happen 282 products analysed 42 products bounced once 6 products bounced twice 4 products bounced three times  18% of products bounced

Seasonality - Ciprofloxacin Jan 03 – Dec 05

Seasonality - Levothyroxine Oct 00-Sep 06

Seasonality - Atenolol Oct 00-Sep 06 Jan 01- Dec 05

Seasonality - Lisinopril Jan 01 – Dec 05

Clusters 8 Clusters seen so far Some product specific Some not

Highs and Lows Low price point February June November High price points April August December

Possible reasons Highs When commercial people are on holiday Lows When commercial people are all working But what about the others?

Summary Natural decline √ Reimbursement √ Seasonality √ UK √ Other Markets ?

This document discusses various topics related to generics and generic drug pricing, including data sources, price decay over time, seasonal fluctuations, generic company strategies, exchange rates, and challenges with forecasting generic prices. It notes that each generic drug's price appears to have its own unique decay pattern and can be modeled with statistical formulas specific to that product. Forecasting generic prices is challenging due to different rates of price decay even within the same therapeutic categories or market values.

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New Pr Ofesy Conference Presentation

1. Generic Prices Forecasting and Mega Trends

2. ��

3. How do companies forecast generic prices? Time Price Generic Launch

4. Some just react to market changes

5. Fitting trends to prices

6. But reality was different

7. Same Therapeutic Cat Same Therapeutic Category, But Different Decay Rates Selective Serotonin Re-Uptake Inhibitors

8. Same Market Value Same Value, But Different Decay Rates £450,000 - £550,000

9. Same molecule Same Molecule But No Uniformity Fluconazole Caps

10. But actual patterns are very complex

11. SWAGs Sophisticated Wild Ass Guesses

12. ��

13. Data Collection Wavedata founded in 2000 60,000 hours of data entry 160 wholesalers and suppliers Thousands of generic products 2 years of analysis

14. Trend in average generic price

15. Each product follows a pattern

16. Patterns & Relationships £ ?  Generic Price Volume Market Share Value Brand Generic Spilt No of Manufacturers Reimbursement 

17. Statisticians found each product can be modelled

18. Multiple models were produced, one for each product

19. Each with its own formula

20. First Model (2005) 80 products analysed Linear dynamic model 3 forecast models A, B and C Based on statistical coefficients

21. Further development (2006) Another year of modelling 120 products analysed N on-linear polynomial model Adding Reimbursement arguments Including Tariff M

22. Current model completed (2007) Works for 99% of products No therapeutic adjustment needed No strength adjustment needed Integrated into a web site

23. ��

24. Does it work? Can generic prices really be forecast? Before During Actuality

25. ��

26. ��

27. ��

28. ��

29. ��

30. ��

31. ��

32. Other Markets Model can be adapted for new markets Different coefficients for each market USA EU States

33. USA

34. USA vs UK

35. Other Products

36. ��

37. The ‘Dead Cat ��ǳܲԳ��’

38. Key Bounce factors Cost of goods Manufacturer withdrawal Short or long residual life Holiday link? Bounces are visible side of seasonality? Disease timings – ie hay fever

39. How often bounces happen 282 products analysed 42 products bounced once 6 products bounced twice 4 products bounced three times  18% of products bounced

40. Bounces after generic launch

41. Bounces – the real picture

42. Bounce frequency

43. Seasonality – Omeprazole Apr 02- Mar 06

44. Seasonality - Omeprazole Oct 02- Sep 06

45. Seasonality - Ciprofloxacin Jan 03 – Dec 05

46. Seasonality - Levothyroxine Oct 00-Sep 06

47. Seasonality - Atenolol Oct 00-Sep 06 Jan 01- Dec 05

48. Seasonality - Simvastatin Oct 03-Sep 06

49. Seasonality - Lisinopril Jan 01 – Dec 05

50. Clusters 8 Clusters seen so far Some product specific Some not

51. Clusters 1 - 4

52. Clusters 5 - 8

53. Highs and Lows Low price point February June November High price points April August December

54. Possible reasons Highs When commercial people are on holiday Lows When commercial people are all working But what about the others?

55. Summary Natural decline √ Reimbursement √ Seasonality √ UK √ Other Markets ?

56. Many Thanks www.wavedata.biz

Editor's Notes

#3: Research project Trying to find the hidden patterns behind Long term Points – interesting / surprising / unexpected
#4: How do companies set or predict prices. What would you do?
#5: A guess could be based on a particular competitor, or a key supplier, or an average, but it is still at heart a guess
#6: Trend lines can be added to what data you have, but often more than one line can be fitted to the same trend, and if you only have a few months of post patent generic prices, many trends could be fitted to the same data – each one a guess.
#7: The price went up, as it was following a hidden pattern, not immediately obvious to the uninformed
#8: Trend lines can be added to what data you have, but often more than one line can be fitted to the same trend, and if you only have a few months of post patent generic prices, many trends could be fitted to the same data – each one a guess.
#9: Trend lines can be added to what data you have, but often more than one line can be fitted to the same trend, and if you only have a few months of post patent generic prices, many trends could be fitted to the same data – each one a guess.
#10: But sometimes they don't
#11: The range of decline curves possible is very wide
#13: Information revolution Bill gates Email Design around the planet Ebay Lack of science in one area of pharma – pricing Aunt story
#14: The range of decline curves possible is very wide
#15: Each of the 100+ decline curves we looked at was based on a pattern
#16: Each could be modelled
#18: Each could be connected with the underlying factors
#19: Even the rises in price due to availability and price could be predicted
#20: Even the rises in price due to availability and price could be predicted
#21: The range of decline curves possible is very wide
#22: The range of decline curves possible is very wide
#24: Patterns are complex but predictable Not as complicated as shares But pharmaceuticals is an island – doesn't pick up techniques from other industries easily Why has no one done it before – not enough pricing history Norton healthcare SPSS experience
#32: So the method works Will get better as more examples are analysed Sugar traders / stocks and shares But bounces still an issue ….. But what about other markets
#37: Nothing is new But pharma is still left with vulnerability in those companies that don’t take this on board No reason why techniques won’t work in other states
#41: Pebble in a pond

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Editor's Notes