Internal achievements and external benchmarks The macro-cycle times of the past decade might look discouraging, if not analyzed for confounding factors. From 1994 to 1999 there appeared to be a decrease in drug development from more than 12 to a little less than 10 years,^5,11 driven by decreases from first submission to first launch and first patient dose to first pivotal dose.⁵ This trend was, however, of a temporary nature. The latest analyses⁴ refer to a 12-year development time again.

These data do not account for the time spent for go/no-go decision-making, which is outside of any operational productivity impact. Neither do these evaluations account for the extent of regulatory requirements and related documentation workload, which have dramatically increased from the early nineties. The shortening of the authority review cycle time is probably a product of both strengthened productivity at the regulators level and also a reflection of higher dossier standards based on studies conforming fully to ICH GCP and other applicable regulations.

From 1996 to 1999, the cycle times from protocol approved to first patient first visit (FPFV) increased by 15%.⁵ This may be a result of inefficient start-up processes, a slow take-off at the study sites, or a more thorough and hence longer review process of clinical trial applications at the authority and/or IEC level. The cycle times of last patient last visit (LPLV) to database lock (DB lock) and from DB lock to key statistical analysis decreased to 110 and 50 days, respectively. They can probably be improved further. The entire subject enrollment period (300 days) and the time from key statistical analysis to final study report (150 days) remained essentially unchanged.

There were further achievements due to an internal performance metrics system applied via a three-year goal-setting program.¹⁰ They included cutting enrollment time into half, greatly increasing planning accuracy with respect to time to FPFV and enrollment time, and increasing the proportion of active sites from 65% to 88% of all initiated sites. Patient enrollment rates (patients/site/month) also matched or exceeded industry standards in the majority of countries.

Reaching specific metrics targets may be at the cost of other related aspects, and will require corrective action. For example, a monitoring team has managed to carry out an average of 10 site visits per month. But the median time to complete a monitoring visit report now exceeds 10 days and the internal audits provide evidence of decreasing quality. Given these circumstances, this should be the time to think of more training or system improvements and also the creation of more feasible metrics targets. There seems to be a maximum number of CRF pages and, hence, number of subjects that can be monitored by one CRA per year, as illustrated in Figure 2. For obvious reasons, this metric depends on the clinical trial indication and its pertinent treatment duration.¹⁰

The value of metrics for creating new study plans The collection of metrics in the same indication and in a comparable geography over three years allows construction of three proposed scenarios. The hypothetical multinational trial was for 800 asthma patients (Table 5) with a significant variance in enrollment duration and total monitoring visit costs (Figures 3A and 3B). Note how the key drivers, enrollment rate per site and total number of subjects per site, allow for lower total site number with comparable enrollment times, but considerably lower monitoring visit needs or greatly decreased enrollment time at an equal site basis. If only economics is superimposed and no strategic regulatory reasons, the likely choices are evident.

This example demonstrates one of the key values of such data. A huge amount of money and time could be saved if we utilize the corporate wisdom derived from properly maintained performance metrics systems for the sake of more effective future developments.