The Quick-Kill™ Model
The Quick-Kill™
model assumes drug discovery is a stochastic process. Here we consider the simplest case where the development
process is divided into just two phases.
The simplest form of the model assumes drug discovery to be a binomial process with probability of discovery p such that the expected number of failures before the first success has a geometric distribution with a mean of 1/p.
The simplest form of the model assumes drug discovery to be a binomial process with probability of discovery p such that the expected number of failures before the first success has a geometric distribution with a mean of 1/p.
If the costs of killing a drug candidate are $Ckill and the development cost for a successful product is $Csuccess then the expected costs for each successful market launch are:
EC = (1/p) Ckill + Csuccess
Clearly the expected costs are a function of both the cost of developing a successful compound and the costs of killing unsuccessful compounds.
Similarly, if the time to kill a compound is tkill and the development time for a successful drug is tsuccess and then the expected time to marketing authorisation approval:
EMAA = (1/p) tkill + tsuccess
This
latter finding means that the EMAA is a function not just of the
development time for successful compounds but the amount of time
unsuccessful compounds spend in the development pipeline.
Elsewhere in this blog we've considered the expected costs and expected time to market for two competing strategies - the Right First Time and the Fast-Fail strategies. The Right First Time approach has a two year discovery period leading to the first test in man followed by an eight year development time for successful molecules. In contrast the Fast-Fail strategy involves an early test in man after just one year but there is a re-work cost of an additional two years added to the development time before the product is delivered to the market place. In addition, on the face of it, the Fast-Fail strategy appears to be significantly more expensive with a re-work financial penalty of an additional $5m added to the development cost of successful molecules.
The financial costs and time penalties incurred by the Fast-Fail strategy are summarized in Table 1.
Elsewhere in this blog we've considered the expected costs and expected time to market for two competing strategies - the Right First Time and the Fast-Fail strategies. The Right First Time approach has a two year discovery period leading to the first test in man followed by an eight year development time for successful molecules. In contrast the Fast-Fail strategy involves an early test in man after just one year but there is a re-work cost of an additional two years added to the development time before the product is delivered to the market place. In addition, on the face of it, the Fast-Fail strategy appears to be significantly more expensive with a re-work financial penalty of an additional $5m added to the development cost of successful molecules.
The financial costs and time penalties incurred by the Fast-Fail strategy are summarized in Table 1.
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| Table 1: Development Times and Financial Costs for the Right First Time and Fast-Fail Strategies. Note that, on initial inspection, the Fast-Fail strategy appears to take longer and incur higher costs than the Right First Time strategy. However, this ignores the stochastic nature of drug development - see below. |
Armed
with the expected cost and expected time to market we can estimate the
R&D productivity rate as the number of new product launches per $bn
R&D spend (ie. 109/EC). Better still, we can estimate R&D Productivity Rates (RDP) as the number of new product launches per $bn per annum (ie. EMAA.109/EC).
These R&D Productivity Rates (RDP) are presented in Figure 1 for a modest company with development capacity of around 10 independent channels in the development pipeline.
Conclusion?
For high risk business processes, we should consider a strategy that quickly kills failing products even if means additional time for subsequent re-work and additional expense.
These R&D Productivity Rates (RDP) are presented in Figure 1 for a modest company with development capacity of around 10 independent channels in the development pipeline.
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| Figure 1: R&D Productivity Rates (RDP) for a range of probabilities of success from 0.10 to 0.50 for the Right First Time and Fast-Fail strategies. Though the Fast-Fail strategy appeared to take longer and cost more than the Right First Time strategy, the Fast-Fail strategy outperforms the Right First Time strategy until the probability of a success is greater hits 50% or more. |
Conclusion?
For high risk business processes, we should consider a strategy that quickly kills failing products even if means additional time for subsequent re-work and additional expense.
