Automating Unpredictable Processes The key to the pharmaceutical industry’s R&D efficiency By Chuck Piccirillo, CEO and Founder, Zealic Solutions

While the $2.6 billion cost of bringing a new drug to market as found in a recent Tufts Center for the Study of Drug Development (CSDD) research report is jaw-dropping, of greater concern is the fact that this figure has increased 145% since the last time the study was conducted in 2003.

Despite a significant industry focus in recent years on improving drug development efficiencies and lowering development costs, R&D efficiency in the industry remains low, and costs continue to mushroom. Why are drug R&D costs rising so rapidly? The reasons are multifaceted, but they include the increased complexity of administering clinical trials, a greater focus on developing drugs for complicated diseases, increased regulatory burdens, and many other factors.

However, there is also strong evidence demonstrating that pharmaceutical management shares much of the blame. Questionable drug development projects are too often pushed forward when perhaps they should be discontinued early in the process, too many processes across all aspects of the life sciences industry are not automated, and data is often not being transformed to allow it to serve its proper role of enabling decision-making.


Drug development is an inherently unpredictable enterprise. Partially due to this unpredictability and partially due to compliance issues, a large portion of the pharmaceutical and biopharmaceutical industries still use fairly arcane process management strategies to manage a wide range of activities. The use of traditional paper-based approaches and the use of technology tools not fit for the purpose are still far more common than is ideal.

Planning for Inevitable Unpredictability & Needed Course Changes

In addition to unpredictability, another challenge in pharmaceutical R&D is automating uniqueness — seemingly a contradiction in terms. Drug development often consists of a series of established processes, but mid-process decision-making is always needed as the uniqueness of each project reveals itself. A great example is the design and execution of clinical trials. Every clinical trial undergoes the same series of basic steps for setup and execution. However, every trial quickly delivers a set of unique conditions that could not be definitively anticipated and characteristics that are different from other trials being conducted.

How Can Automated Decision-Making in Life Sciences Work? Let History Be Our Guide

Technology that supports automated decision-making is needed for unpredictable, complex, and rapidly moving scenarios. An example that we can all relate to is the evolution of transportation and navigation.

In the days of the horse and buggy, travel was slow so people tended not to travel very far from home. They were typically very familiar with the routes they needed to travel and when they did venture to unfamiliar areas, there were not all that many roads to choose from — remaining on course was fairly easy.

As automotive travel became more prevalent and people traveled more frequently to unfamiliar areas, more sophisticated navigational decision-making tools were needed, and the ubiquitous road map played a critical role. Road maps, despite the near-impossibility of refolding them, were extremely helpful decision-making tools. However, they could not provide directions based on changing factors: enter Mapquest.

Mapquest, and other digital tools of this nature, were revolutionary. After entering starting point and destination information, possible routes were calculated and offered for user selection. The driver selected the route that seemed to be the best, printed out turn-by-turn directions and carried these directions with him/her to help with accurately and efficiently reaching the destination. The primary drawback was that these directions could not respond to changing mid-journey conditions. While these directions were extremely helpful, when confronted with new information, changing conditions, or navigational error, their usefulness quickly dissipated.

Finally, we arrive at modern GPS navigation technology that calculates predictive routes based on recent historic information and in some cases, real-time information. These navigation technologies are robust decision-making support tools when course changes are made; users are notified immediately of navigational errors and provided with suggested course corrections. Finally, arrival time estimates and distances are continually updated in response to executed decisions.

How Does Travel Navigation Apply to Pharmaceutical R&D?

Drug development processes share common steps and stages, and the end goal for every drug is regulatory approval. Like the navigation analogy outlined above, there is a clear start and end point to pharmaceutical development, and many decisions need to be made along the drug development journey as new learning occurs, with inevitable course changes.

So how can drug development processes, with unanticipated and/or changing mid-process steps, be automated? The answers lie in first understanding the differences between flow-centric processes and decision-centric processes.

Flow-centric process designs assume that the order of actions is not variable. A workflow engine determines what happens next based on unchanging rules. Returning to our navigation analogy, a journey that never changes midway in response to traffic, road closures, error, or other factors, represents a flow-centric process. The primary advantage of flow-centric processes is that they are easy to understand and construct. However, they break down within workflows that do not progress in a serial or completely predictable fashion.

In contrast, decision-centric workflows are driven by the user and constrained by business rules that determine which activities are selectable and/or required. Based on conditions (e.g., the data captured, decisions made, the status of the workflow), the process workflow may present different options. Conditions may change in such a way that some of the planned activities are no longer appropriate, and other activities might become appropriate that were not available before.

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