Development models always start with addressing clinical needs, but scientific discoveries and technological advances typically happen independently from solving clinical problems. This article will explore ways you can ensure that you are connecting the promising technology you are developing, to addressing viable clinical needs.
Some visionary tech developers would have you believe that they envisioned a brave new world where their product created or filled a need that people didn’t know they had. In the reality of medical product development, this is rarely the case. The medical literature is replete with introductory paragraphs that explicitly identify medical needs, as well as others that describe how current products only partially address medical needs. The latter may be deficient in efficacy, adverse effect profile, convenience, cost and/or other characteristics.
Opposite these medical needs is the long list of existing and emerging technologies. The key strategic insight in medical product development is the decision to orient the use of one or more technologies to the medical need(s) where they can have the highest medical impact, and as a consequence, be most likely to be commercially successful.
In device development, new sensor and measurement technologies are frequently developed by researchers. Since the technologies are typically based on low-cost silicon processes, and are small and low power, the researches often believe the technology can revolutionize care by providing direct-to-consumer devices that can provide information to the consumer’s physicians. In reality, physicians have been skeptical of data from consumer devices and are unable to handle the volume of data provided. Applying new sensor and measurement technologies to delivering more cost-effective, rapid, and convenient measurements in the clinical setting has been much more successful.
In diagnostic development, new biomarkers may be identified by researchers working in a specific disease area. They may attempt to demonstrate an association or causal relationship between the biomarker and particular conditions. However, apart from genetic markers of select diseases, such biomarkers may well find utility in related disease entities or entirely different specialties, sometimes more significant than in the initial indication. This may occur if the drivers of the biomarker turn out to be different from those initially expected by the researchers working in a given disease area. An example is a C-reactive protein, first identified and widely used as a marker of inflammation useful in rheumatology, but later crossing over as a diagnostic for risk of cardiac events.
In pharmaceutical discovery and development, when a new molecular drug target is identified, researchers often hypothesize that a drug interacting with that target will have a beneficial effect in a particular therapeutic indication. However, identifying the diseases in which a targeted therapy will be useful is often impaired at this early time by incomplete knowledge of the physiology of the target. A well-known example is the phosphodiesterase-5 inhibitors that were initially studied as anti-angina drugs but were fortuitously discovered to have the potential to treat erectile dysfunction. There are many examples of targets expected to be useful in inflammatory and autoimmune diseases, as well as in oncology, which has undergone clinical testing in many indications before finding a home in one or more of these. The trick in these disease areas has been to determine in which diseases, and even in which patients, the target is a key driver of the disease and, thus, a useful drug target.
The unifying theme of all these examples is that researchers and product developers focused on technologies should keep open minds about how their technology can best be married to a medical need for which it can be most impactful. Conversely, researchers focused on specific medical needs to do well to scan emerging technologies to identify technologies potentially useful in their area. This is only initially an intellectual exercise. Instead, it usually plays out in the product development process, starting at the pilot/preclinical phase and ultimately requiring clinical testing to confirm the best fit between technology and medical applications.
Article written by John Randle, PhD