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Launch control

Realistic values are needed to predict product success

An astronaut looking up to the stars Leaders in the pharma sector are well aware that tough investment decisions have to be made, but also recognise the foundations for these decisions must be solid. Investments are usually supported by sophisticated sales forecast models using well defined and rigorously challenged assumptions about the potential market.

The impact of the probability of launch (PoL) on achieving the sales forecast is crucial because it is the single most important factor in the calculation of the risk-adjusted net present value (rNPV). The rNPV is a critical component when comparing products to make strategic portfolio decisions, yet companies commonly use template 'default' values for PoL based on overall industry experience.

Our investigation defined the top 10 pharma companies by market capitalisation as at April 10, 2009 and used the MedTrack biomedical corporate intelligence database to identify all drugs in phase III industry-sponsored studies that were initiated or already ongoing between January 1, 1999 and April 10, 2009.

Fixed-drug combinations, such as Avandia and Avandamet, were treated as separate products. We noted products launched in the US or the EU top five markets and products not approved, pending approval, or still in phase III development.

Life cycle management is vital for maximising the value of a brand, so we also tracked outcomes of phase III trials for new additional indications in the same way over the same time period. In this second analysis, drugs can appear in more than one category, perhaps where they were launched for one indication but failed to get approval for another.

This analysis showed an average phase III product launch success rate of 59 per cent, consistent with previous studies, but also highlighted a very large range (30–90 per cent) in success rates across companies (Table 1).


Table 1: Product phase III success rates

Company Number of products Success rate
Launched Failed Pending approval Still in phase III
Roche 11 2 1 6 85%
Pfizer 8 11 2 8 42%
Novartis 14 13 6 18 52%
GlaxoSmithKline 19 12 6 17 61%
Sanofi-aventis 6 9 2 30 40%
Wyeth 9 1 1 6 90%
Merck 11 5 3 6 69%
Amgen 5 3 1 2 63%
AstraZeneca 3 7 3 15 30%
Bristol-Myers Squibb 9 4 1 9 69%
AVERAGE 10 7 3 12 59%


Once a product is approved for a first indication, the common view is that there is a higher probability of success in additional indications. However, as shown in Table 2, the success rates of trials to obtain additional new indications for products also ranged from 32–87 per cent, with an average of 56 per cent (Table 2).


Table 2: New indication phase III success rates 

Company Number of products Success rate
Launched Failed Pending approval Still in phase III
Roche 20 3 3 25 87%
Pfizer 7 15 6 21  32%
Novartis 22 22 6 32 50%
GlaxoSmithKline 27 22 8 31 55%
Sanofi-aventis 12 17 2 41 41%
Wyeth 10 3 3 16 77%
Merck 14 6 4 15 70%
Amgen 7 3 2 11 70%
AstraZeneca 13 13 6 21 50%
Bristol-Myers Squibb 12 9 1 18 57%
AVERAGE 14 11 4 23 56%


A further analysis of phase III outcomes revealed an overall PoL of 74 per cent for biologics compared with only 54 per cent for small-molecule drugs. This confirms the widely held belief that biologics, with their highly targeted modes of action, have a higher chance of success than small molecules. Comparison of the product and indication success rates with the percentage mix of biologics and novel modes of action (MOA) at each company does not suggest, however, a simple and direct relationship with PoL (Table 3).


Table 3: Top 10 pharma - phase III portfolio mix

Company Phase III products (%)
Biologic Novel MOA
Roche 54 38
Pfizer 9 54
Novartis 20 22
GlaxoSmithKline 28 20
Sanofi-aventis 40 23
Wyeth 25 25
Merck 21 17
Amgen 75 67
AstraZeneca 11 40
Bristol-Myers Squibb 23 50


Other factors that may impact on PoL include:
• Company experience in a disease area
• A proven regulatory pathway with established end points or surrogates
• First-in-class versus follow-on candidate
• First versus supplementary indications.

Other tools, such as sensitivity analysis, could be applied to test the impact of different ideas and increase confidence around rNPVs. Senior managers with cross-portfolio responsibility should challenge the product team to ensure that relative values for PoL between different brands are reasonable. This should lead to more realistic values being used to calculate rNPVs.

Defining consistent criteria for PoL assumptions and applying these across brands, could improve the ability to compare business options accurately and support better business investment decisions.

The Authors
Mark Williams is lead business analyst, Andrew Millest is medical affairs analyst and Su Webber is a director at Cence Consulting

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22nd December 2009


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