Our company is involved in developing and manufacturing APIs that can be utilized with drug-eluting stents (DES). Despite ensuring constancy in pharmaceutical composition, we are experiencing issues with variations in drug release during in vitro studies. We are working closely with a stent manufacturer to develop the system, but could surface analysis techniques investigate the problem further?

In short the answer is yes — particularly as the composition constancy is closely monitored. However, before going into the types of analysis that may shed light on your problem, it is useful to understand more about the development of DES.

Technology

Figure 1 A stent before expansion.

Stents are metal mesh tubes that have been used since the 1990s. They are inserted post-angioplasty to treat blocked coronary arteries. Initially, they were simply bare metal tubes that had been passivated and deemed fit to be implanted. At first, these metal scaffolds provided positive results with arteries remaining open after insertion, but, with time, it became apparent there was an issue with artery reclosure (restenosis). This issue led to the development of drug-coated stents that release specially designed drugs targeted at preventing restenosis. Figure 1 shows a stent approximately 3 cm in length prior to expansion.

Controlled release

The coating used for drug-eluting stents facilitates the controlled release of pharmaceuticals to ensure a specific dosage is released during a set period of time.

Controlling drug release in this way delivers drug therapy to the point at which it is most required, improving efficacy and avoiding the issue of 'human error' when it comes to self-medication. For such benefits to apply, DES coatings must release drugs in vivo according to a predictable, therapeutically rational programmed rate.

The release processes used to achieve this 'rational programmed rate' are varied; to name but a few:

• biodegradation
• diffusion (from matrix and membrane systems)
• elasticity
• conductivity
• osmosis
• pH sensitivity
• vapour pressure.

Increasingly important applications of controlled release technology also include patterned, targeted, triggered and closed-loop delivery. The question is: what can affect these systems and, subsequently, cause release variation problems? The following have all proven to be common culprits:

• variation in base material composition
• variation in base material roughness
• variation in oxide layer thickness
• variation in coating composition (i.e. drug distribution)
• variation in coating thickness.

It is essential to characterize and monitor each of these issues during initial studies to ensure the drug elution profile is correct before in vivo studies begin. This is where surface and interface analysis techniques can provide valuable analytical data. The most appropriate techniques are dynamic secondary ion mass spectrometry (DSIMS) and 3D non-contact surface profiling (3DP).

DSIMS is suitable for this particular issue because the depth profiling mode can closely monitor composition with depth. As well as identifying foreign contaminants that may be present at the very surface of a drug-eluting stent, this type of profiling also analyses the chemical composition of each layer (down to the metal substrate itself) once the stent is treated with its specific coating.

3DP complements data obtained using DSIMS and provides important information on surface parameters. These data can prove particularly useful in identifying drug release issues caused by base material roughness or coating thickness.

Common surface parameters used to analyse the surface roughness/coating thickness of drug-eluting stents are:

• S_a: arithmetic mean of the deviations from the mean. This parameter represents the mean surface roughness, measured over the whole analysed area.
• S_Z: height of the 10 points of the surface. Mean of distance between the five highest peaks and five deepest valleys. A matrix of 3×3 pixels is taken into account to identify the peaks and valleys.
• S_y: peak to peak. This parameter is a measure of the height difference between the highest pixel and the lowest pixel.

To illustrate the way these techniques and calculations are used more effectively, it is beneficial to look at a sample analysis process.