Leveling the Operational Playing Field in Clinical R&D

Some interesting findings from an industry study on the operational performance drivers in clinical R&D were published earlier this month, many of them challenging established wisdom. In this article, I comment on these findings and look at their implications for both the big pharmas and especially the smaller biopharma SMEs.

In a Nature Reviews Drug Discovery article (Ringel et al. 2016) entitled “What drives operational performance in clinical R&D?”[1], the authors summarize the key findings of a comprehensive data-driven study. This study was based on 2013 and 2014 data from 14 biopharma companies, with combined R&D spend in excess of US$50 billion per annum, each company conducting on average 130 clinical trials every year.

The focus of the study was operational performance, assessed along three dimensions:

1. Efficiency, defined as the difference between observed versus expected spending.
2. Speed, based on cycle times across Phases I-III.
3. Quality, measured by number of quality findings in regulatory audits.

The authors note that operational performance is only one of the three major constituents of R&D productivity (the other two being (i) timing and rate of portfolio attrition, and (ii) value of the resulting products). Nevertheless their findings on the operational front are very interesting and challenge several tenets of established wisdom. These findings are summarized as 18 factors (which the authors refer to as “design parameters” for clinical R&D) that either have a positive, negative or essentially no effect on operational performance in clinical R&D, as listed below:

Parameters having positive effect on operational performance:

• Geographic centralization of clinical development functions—strong positive impact on all the three operational dimensions of efficiency, speed and quality.

Parameters having negative effect on operational performance:

• Organizing clinical development by business units—strong negative impact on both efficiency and speed.
• Higher proportion of R&D budget outsourced—strong negative impact on speed and to some extent also on quality, although no effect on efficiency.
• Higher proportion of rare disease trials—strong negative impact on efficiency and to some extent also on speed.
• Higher proportion of trials with personalized medicine focus—strong negative impact on efficiency and to some extent also on both speed and quality.
• Higher proportion of trials using adaptive designs—strong negative impact on efficiency and to some extent also on speed.
• Higher proportion of trials using risk-based monitoring—some negative impact on efficiency.

Parameters having essentially no impact on operational performance:

• Splitting the clinical development organization by early- versus late-stage trials.
• Number of different therapeutic areas handled by the clinical development organization.
• Whether trials were mainly with biologics or small molecules.
• Whether majority of subjects were in emerging versus developed economies.
• More trials having end points specifically for payers/market access.
• Use of outsourced preferred providers versus outsourced functional service providers.
• Staff continuity on projects across phases.
• Trial size.
• Number of subjects per site.
• Size of company R&D budget.
• Use of enterprise-level resource management tools.

What wasn’t surprising, what was … and what to be careful about

Over the past decade, there has been a big push by many big pharmas (and even many mid-sized ones) to outsource a high proportion of clinical development through preferred provider arrangements at major global CROs and conduct more studies in lower-cost emerging economies, with consequent decentralization of the internal clinical development organization. Now there is evidence suggesting there is little operational advantage to be gained here. R&D externalization incurs a “collaboration tax”—it takes more time and effort to collaborate with external parties, especially when conducting trials in multiple sites scattered across the globe with ensuing diversity of medical practices and regulatory regimes. This study seems to indicate that higher levels of outsourcing negatively affect speed in particular, and also quality to some extent.

At best, the main operational benefit of clinical outsourcing to emerging economy sites is resource flexibility and scalability. But this does not necessarily mean the approach hitherto adopted is a bad idea, provided one can achieve lower attrition rates across the portfolio and deliver more valuable products by selecting the right centers (wherever they might be in the world) and the right outsourcing partners for each specific clinical program. Having said that, the conclusion in the study that one should have a more centralized geographical footprint for the in-house clinical development organization is also supported from the value enhancement and attrition reduction standpoints—decision making quality and speed is improved through closer physical proximity of the cross-functional team.

The negative operational impact of rare disease, precision medicine and adaptive trials is no surprise. The primary drivers for many companies going in this direction are to improve attrition rates and develop more valuable products, the price of which is a more problematic operational profile. What did pleasantly surprise me though was the neutral operational effect of conducting trials aimed at generating data for market access purposes.

There has also been a move by some pharma companies in recent years to distribute and focus their clinical development resources, splitting them by early- versus late-stage trials, specializing them by therapeutic area and aligning them more closely with the commercial franchises. The primary rationale for this strategy has always been to improve the outcomes of the trials, as regards both the probabilities of success and the commercial value of the resulting products. The findings from the study do not change this logic, even though they indicate that there is an operational drawback to this strategy which may be higher than initially anticipated.

In general, as the authors of this study say themselves, you have to also consider the value enhancement and attrition reduction effects rather than making decisions based purely on operational grounds.

Implications for biopharma companies

For many of the big pharmas, these findings could well usher another major round of clinical development restructuring as they seek to capture the operational benefits of increased centralization and implement other mechanisms to mitigate the effect of the negative factors mentioned above. For sure, the large variance in operational performance delivered by the sample of companies studied indicates a significant opportunity for improvement.

For the smaller focused biopharma companies and mid-sized pharmaceutical multinationals however, there is much good news here. For most of them, their in-house clinical development functions are already concentrated geographically and centralized organizationally. Although their budgets are lower and they generally run smaller trials, in fewer therapeutic areas and across fewer sites in comparison to the big pharmas, this study indicates there is comparatively little operational downside to this lower scale. And in a similar vein, the increasing need to generate data for market access purposes does not confer an operational advantage to the larger big pharmas. Furthermore, it has historically been harder for the smaller biopharma companies and mid-sized pharmas to establish strong preferred provider relationships with large global CROs as they lacked sufficient bargaining power owing to their lower volume of trials and patients. Whereas this study indicates that use of a large preferred provider for an entire clinical program does not confer any operational advantage compared to working with a portfolio of functional providers specialized in the areas of most relevance to the specific program at hand. In conclusion, the key headline for the smaller and mid-sized players from this study is that they can compete operationally on a level playing field with the bigger players.

Reference