new pr ofesy conference presentation

Generic Prices Forecasting and Mega Trends

How do companies How do companies forecast generic prices?forecast generic prices?

Some just react to Some just react to market changesmarket changes

Fitting trends to pricesFitting trends to prices

But reality was differentBut reality was different

Same Therapeutic CatSame Therapeutic CatSelective Serotonin Re-Uptake Inhibitors

Month after launch

Citalopram Tabs 20mg28Paroxetine Tabs 20mg30

Same TherapeuticCategory,

But DifferentDecay Rates

Selective Serotonin Re-Uptake Inhibitors

Month after launch

Citalopram Tabs 20mg 28

Paroxetine Tabs 20mg 30

Selective Serotonin Re-Uptake Inhibitors

Same Market ValueSame Market Value£450 - £550 000

Months after launch

Quinapril Tabs 10mg 28

Nicardipine Caps 20mg 56

Same Value,But DifferentDecay Rates

£450,000 - £550,000

£450 - £550 000

Months after launch

Quinapril Tabs 10mg 28

Nicardipine Caps 20mg 56

Same moleculeSame moleculeOne Molecule But NO Uniformity!

Fluconazole Caps 150mg 1

Fluconazole Caps 200mg 7

Fluconazole Caps 50mg 7

Same MoleculeBut

No Uniformity

One Molecule But NO Uniformity!

Fluconazole Caps 150mg 1

Fluconazole Caps 200mg 7

Fluconazole Caps 50mg 7

Fluconazole Caps

But actual patterns are But actual patterns are very complex very complex

• Sophisticated Wild Ass Guesses

SWAGsSWAGs

Data CollectionData Collection

• Wavedata founded in 2000

• 60,000 hours of data entry

• 160 wholesalers and suppliers

• Thousands of generic products

• 2 years of analysis

Trend in average generic Trend in average generic priceprice

Each product follows a Each product follows a patternpattern

PatternsPatterns & Relationships& Relationships

£ ?Generic Price

Volume

Market Share

Value

Brand Generic Spilt

No of Manufacturers

Reimbursement

Statisticians found each Statisticians found each product can be modelledproduct can be modelled

0.000

2.000

4.000

6.000

8.000

10.000

12.000

14.000

16.000

18.000

Sep-0

0

Mar

-01

Sep-0

1

Mar

-02

Sep-0

2

Mar

-03

Sep-0

3

Mar

-04

Sep-0

4

Mar

-05

Sep-0

5

Mar

-06

Simvastatin Tabs10mg 28

Predicted

0.000

2.000

4.000

6.000

8.000

10.000

12.000

14.000

May-0

2Sep

-02

Jan-

03May

-03

Sep-0

3Ja

n-04

May-0

4Sep

-04

Jan-

05May

-05

Sep-0

5Ja

n-06

Omeprazole Caps 40mg 7

Predicted

Multiple models were produced,Multiple models were produced,one for each productone for each product

0.000

5.000

10.000

15.000

20.000

25.000

Jun-

04

Aug-0

4

Oct-0

4

Dec

-04

Feb-05

Apr-0

5

Jun-

05

Aug-0

5

Oct-0

5

Dec

-05

Feb-06

Apr-0

6

A1002

Predicted

0.000

5.000

10.000

15.000

20.000

25.000

30.000

Mar

-02

Jul-0

2

Nov

-02

Mar

-03

Jul-0

3

Nov

-03

Mar-04

Jul-04

Nov

-04

Mar-05

Jul-05

Nov

-05

Mar

-06

A1021

Predicted

0

1

2

3

4

5

6

May

-04

Jul-0

4

Sep-0

4

Nov

-04

Jan-

05

Mar

-05

May

-05

Jul-0

5

Sep-0

5

Nov

-05

Jan-

06

Mar

-06

A1010

Predicted

0.000

1.000

2.000

3.000

4.000

5.000

6.000

7.000

Jan-

04

Mar

-04

May

-04

Jul-0

4

Sep

-04

Nov

-04

Jan-

05

Mar

-05

May

-05

Jul-0

5

Sep

-05

Nov

-05

Jan-

06

Mar

-06

A1011

Predicted

0

1

2

3

4

5

6

7

8

9A

ug-0

0

Dec-0

0

Apr-

01

Aug-0

1

Dec-0

1

Apr-

02

Aug-0

2

Dec-0

2

Apr-

03

Aug-0

3

Dec-0

3

Apr-

04

Aug-0

4

Dec-0

4

Apr-

05

Aug-0

5

Dec-0

5

Apr-

06

A1012

Predicted

0

0.5

1

1.5

2

2.5

3

Mar-02

Jul-02

Nov

-02

Mar-03

Jul-03

Nov

-03

Mar-04

Jul-04

Nov

-04

Mar-05

Jul-05

Nov

-05

Mar-06

A1013

Predicted

0

1

2

3

4

5

6

7

Ma

r-0

2

Ju

n-0

2

Se

p-0

2

De

c-0

2

Ma

r-0

3

Ju

n-0

3

Se

p-0

3

De

c-0

3

Ma

r-0

4

Ju

n-0

4

Se

p-0

4

De

c-0

4

Ma

r-0

5

Ju

n-0

5

Se

p-0

5

De

c-0

5

Ma

r-0

6

A1014

Predicted

0.000

2.000

4.000

6.000

8.000

10.000

12.000

14.000

Dec-0

0

Apr-

01

Aug-0

1

Dec-0

1

Apr-

02

Aug-0

2

Dec-0

2

Apr-

03

Aug-0

3

Dec-0

3

Apr-

04

Aug-0

4

Dec-0

4

Apr-

05

Aug-0

5

Dec-0

5

Apr-

06

A1015

Predicted

0.000

2.000

4.000

6.000

8.000

10.000

12.000

14.000

May-0

2Sep

-02

Jan-

03May

-03

Sep-0

3Ja

n-04

May-0

4Sep

-04

Jan-

05May

-05

Sep-0

5Ja

n-06

Omeprazole Caps 40mg 7

Predicted

Each with its own formulaEach with its own formula

0.000

5.000

10.000

15.000

20.000

25.000

Jun-

04

Aug-0

4

Oct-0

4

Dec

-04

Feb-05

Apr-0

5

Jun-

05

Aug-0

5

Oct-0

5

Dec

-05

Feb-06

Apr-0

6

A1002

Predicted

0.000

5.000

10.000

15.000

20.000

25.000

30.000

Mar

-02

Jul-0

2

Nov

-02

Mar

-03

Jul-0

3

Nov

-03

Mar-04

Jul-04

Nov

-04

Mar-05

Jul-05

Nov

-05

Mar

-06

A1021

Predicted

0

1

2

3

4

5

6

May

-04

Jul-0

4

Sep-0

4

Nov

-04

Jan-

05

Mar

-05

May

-05

Jul-0

5

Sep-0

5

Nov

-05

Jan-

06

Mar

-06

A1010

Predicted

0.000

1.000

2.000

3.000

4.000

5.000

6.000

7.000

Jan-

04

Mar

-04

May

-04

Jul-0

4

Sep

-04

Nov

-04

Jan-

05

Mar

-05

May

-05

Jul-0

5

Sep

-05

Nov

-05

Jan-

06

Mar

-06

A1011

Predicted

0

1

2

3

4

5

6

7

8

9A

ug-0

0

Dec-0

0

Apr-

01

Aug-0

1

Dec-0

1

Apr-

02

Aug-0

2

Dec-0

2

Apr-

03

Aug-0

3

Dec-0

3

Apr-

04

Aug-0

4

Dec-0

4

Apr-

05

Aug-0

5

Dec-0

5

Apr-

06

A1012

Predicted

0

0.5

1

1.5

2

2.5

3

Mar-02

Jul-02

Nov

-02

Mar-03

Jul-03

Nov

-03

Mar-04

Jul-04

Nov

-04

Mar-05

Jul-05

Nov

-05

Mar-06

A1013

Predicted

0

1

2

3

4

5

6

7

Ma

r-0

2

Ju

n-0

2

Se

p-0

2

De

c-0

2

Ma

r-0

3

Ju

n-0

3

Se

p-0

3

De

c-0

3

Ma

r-0

4

Ju

n-0

4

Se

p-0

4

De

c-0

4

Ma

r-0

5

Ju

n-0

5

Se

p-0

5

De

c-0

5

Ma

r-0

6

A1014

Predicted

0.000

2.000

4.000

6.000

8.000

10.000

12.000

14.000

Dec-0

0

Apr-

01

Aug-0

1

Dec-0

1

Apr-

02

Aug-0

2

Dec-0

2

Apr-

03

Aug-0

3

Dec-0

3

Apr-

04

Aug-0

4

Dec-0

4

Apr-

05

Aug-0

5

Dec-0

5

Apr-

06

A1015

Predicted

First Model (2005)First Model (2005)

• 80 products analysed

• Linear dynamic model • 3 forecast models

• A, B and C

• Based on statistical coefficients

Further development (2006)Further development (2006)

• Another year of modelling

• 120 products analysed

• Non-linear polynomial model

• Adding Reimbursement arguments

• Including Tariff M

• Works for 99% of products

• No therapeutic adjustment needed

• No strength adjustment needed

• Integrated into a web site

Current model completed (2007)Current model completed (2007)

• Can generic prices really be forecast? • Before

• During

• Actuality

Does it work?Does it work?

• Model can be adapted for new markets

• Different coefficients for each market

• USA

• EU States

Other MarketsOther Markets

USAUSA

USA vs UKUSA vs UK

Other ProductsOther Products

The ‘Dead Cat Bounce’The ‘Dead Cat Bounce’

• Cost of goods

• Manufacturer withdrawal

• Short or long residual life

• Holiday link?

• Bounces are visible side of seasonality?

• Disease timings – ie hay fever

Key Bounce factorsKey Bounce factors

• 282 products analysed

• 42 products bounced once

• 6 products bounced twice

• 4 products bounced three times

• ∴ 18% of products bounced

How often bounces happenHow often bounces happen

Bounces after generic launchBounces after generic launch

Bounces

0%10%20%30%40%50%60%70%80%90%

100%

0 10 20 30 40 50 60 70 80

Months after launch

% o

f sta

rt p

rice

Bounces – the real pictureBounces – the real picture

Bounces

0%10%20%30%40%50%60%70%80%90%

100%

0 10 20 30 40 50 60 70 80

Months after launch

% o

f sta

rt p

rice

Bounce frequencyBounce frequency

Monthly Average PercentAmoxicillin Caps 250mg 21

0%

20%

40%

60%

80%

100%

120%

140%

160%

Aug

-00

Dec

-00

Apr

-01

Aug

-01

Dec

-01

Apr

-02

Aug

-02

Dec

-02

Apr

-03

Aug

-03

Dec

-03

Apr

-04

Aug

-04

Dec

-04

Apr

-05

Aug

-05

Dec

-05

Apr

-06

Aug

-06

Dec

-06

Month

%

Seasonality – OmeprazoleSeasonality – Omeprazole

Omeprazole Caps 40mg 7

0.80000.82000.84000.86000.88000.90000.92000.94000.96000.98001.00001.02001.04001.06001.08001.10001.12001.14001.1600

Janua

ry

Feb

ruary

Marc

h

April

May

Jun

e

July

August

Sep

tem

ber

Octo

ber

Nove

mber

De

cem

ber

Apr 02- Mar 06

Seasonality - OmeprazoleSeasonality - Omeprazole

Oct 02- Sep 06

Seasonality - CiprofloxacinSeasonality - Ciprofloxacin

Jan 03 – Dec 05

Seasonality - LevothyroxineSeasonality - Levothyroxine

Levothyroxine Tabs 100mcg 28

0.9000

0.9200

0.9400

0.9600

0.9800

1.0000

1.0200

January

Febru

ary

Marc

h

April

May

June

July

August

Septe

mber

Octo

ber

Novem

ber

Decem

ber

Oct 00-Sep 06

Seasonality - AtenololSeasonality - Atenolol

Atenolol Tabs 50mg 28

0.90000.92000.94000.96000.98001.00001.02001.04001.06001.08001.1000

January

Febru

ary

Marc

h

April

May

June

July

August

Septe

mber

Octo

ber

Novem

ber

Decem

ber

Oct 00-Sep 06

Jan 01- Dec 05

Seasonality - SimvastatinSeasonality - Simvastatin

Simvastatin Tabs 80mg 28

0.9600

0.9800

1.0000

1.0200

1.0400

January

Febru

ary

Marc

h

April

May

June

July

August

Septe

mber

Octo

ber

Novem

ber

Decem

ber

Oct 03-Sep 06

Seasonality - LisinoprilSeasonality - Lisinopril

Lisinopril Tabs 5mg 28

0.9000

0.9200

0.9400

0.9600

0.9800

1.0000

1.0200

1.0400

1.0600

January

Febru

ary

Marc

h

April

May

June

July

August

Septe

mber

Octo

ber

Novem

ber

Decem

ber

Jan 01 – Dec 05

ClustersClusters

• 8 Clusters seen so far

• Some product specific

• Some not

Clusters 1 - 4Clusters 1 - 4Cluster 1

0.6

0.7

0.8

0.9

1

1.1

1.2

1.3

Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec

Cluster 2

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1

Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec

Cluster 3

0.6

0.650.7

0.75

0.80.85

0.9

0.95

11.051.1

Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec

Cluster 4

0.6

0.7

0.8

0.9

1

1.1

1.2

1.3

Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec

Clusters 5 - 8Clusters 5 - 8Cluster 5

0.6

0.7

0.8

0.9

1

1.1

1.2

1.3

Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec

Cluster 6

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1

1.05

Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec

Cluster 7

0.6

0.7

0.8

0.9

1

1.1

1.2

Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec

Cluster 8

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1

1.05

Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec

• Low price point▪ February▪ June▪ November

• High price points▪ April▪ August▪ December

Highs and LowsHighs and Lows

• Highs▪ When commercial people are on holiday

• Lows▪ When commercial people are all working

• But what about the others?

Possible reasonsPossible reasons

• Natural decline √

• Reimbursement √

• Seasonality √

• UK √

• Other Markets ?

SummarySummary

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