Greg Orzeck Explains the Keys to Making a Clinical Trial Successful
How adopting more flexible and adaptive models can reduce costs, expand the collection of useful data, and expedite results
Clinical trials lie at the heart of pharmaceutical development. The process of testing new treatments on human volunteers in controlled conditions and recording the results is imperative in ascertaining the safety and effectiveness of any new drug. Clinical trials are necessary and contribute immeasurably to the overall field of medical research. Most importantly, they light the way for additional pharmaceutical development, and in so doing, make more people healthy.
On the other side of things, clinical trials are incredibly time-consuming and expensive, especially considering that the ratio of rejection to approval of new medicines rests somewhere between nine-to-one and eight-to-one. They can be extremely inefficient, as well. In the past, pharmaceutical companies have adhered to rigid procedures when conducting clinical trials, not allowing for surprise findings, or results deviating from those spelled out in proposals. This has had the practical effect of handicapping researchers before even the first round of testing has begun.
So, in light of these nearly unquantifiable positive aspects (public safety, and the furtherance of medical knowledge), and these easily quantifiable negative aspects (high failure rates, high financial costs), on what criteria should the testing of new drugs be judged? Aside from the obvious — government approval for sale to the general pubic — what makes a clinical trial successful? According to Greg Orzeck of AI Research Inc., the key to a successful trial is reducing costs, increasing the amount of useful data, and expediting results when possible. Greg Orzeck graciously took the time to explain each of these keys in more detail.
It is estimated that the average cost of developing a new medicine for public consumption is in excess of $2.5 billion dollars — closer to $3 billion, if the mandatory post-approval studies are included in the equation. Greg Orzeck explains that of that money, a vast proportion is allocated to conducting clinical trials, whether or not the drug in question is deemed fit for public consumption. Historically, the reason for this gigantic price tag has been the 90–85 percent failure rate of new drugs. It therefore stands to reason that lowering the percentage of failed clinical trials will ultimately reduce these costs.
Recent thinking on the matter contradicts the old conventional wisdom of how clinical trials ought to operate. For example, allowing clinicians to foresee and establish criteria to pursue productive group sample results and disregard unproductive group sample results; whereas, under the old conventional wisdom, both would have been given the same amount of attention. In so doing, attention shifts from testing that yields meagre or spotty results to testing that yields primarily useful results. By building more flexibility into trial proposals it is theorized that the overall failure rate can be decreased, and the costs of testing new medicines can be significantly reduced. This will always remain a major factor in making a clinical trial successful.
Increasing the Amount of Useful Data Gathered
Along the same lines, in espousing more adaptive models of operation, many clinicians believe that a larger amount of useful data will be gathered, both in individual trials, and throughout the pharmaceutical industry, on the whole. This is simply by virtue of spending less time and fewer resources studying and documenting less promising leads, or outright dead ends. By focusing on the utility of data, and de-emphasizing the pursuit unfavorable results, Greg Orzeck states clinical trials will be acting in the interests of furthering medical knowledge and working more effectively toward improving the health of the general public. Again, this is a major factor in making a clinical trial successful.
One of the major drawbacks with clinical trials is how much time they consume. Here, again, fostering a culture of adaptability in clinical medical trials is a potential panacea. In much the same way that extending clinicians more flexibility in trials will likely decrease failure rates and increase the amount of useful data gathered, it will also likely decrease the amount of time needed to obtain and analyze results. Improving the speed at which conclusions are reached is a major factor in making a clinical trial successful.
Increasingly, pharmaceutical companies are adopting a more flexible and adaptive model in clinical trials, in the hopes of fostering speedier and more informative results for a lower operating cost. It is the considered opinion of Greg Orzeck, that any clinical trial taking steps in this direction ought to be viewed as making positive progress, and any clinical trial accomplishing this goal ought to be deemed successful.
Greg Orzeck is a co-founder and managing partner of AI Research Inc., and leads the company’s business development efforts. With over two decades of experience in the medical industry, and ten years as an owner/manager of a clinical research organization (CRO), he has established himself as an expert in the field of clinical research. Born in Malvern, Pennsylvania and a graduate from Temple University, Greg Orzeck currently resides in Exton, Pennsylvania, where he enjoys playing golf, and spending time with his wife of 18 years and their two dogs.