Implications of variability on medical device chemical equivalence assessment.

Chemical equivalence testing can be used to assess the biocompatibility implications of a materials or manufacturing change for a medical device. This testing can provide a relatively facile means to evaluate whether the change may result in additional or different toxicological concerns. However, one of the major challenges in the interpretation of chemical equivalence data is the lack established criteria for determining if two sets of extractables data are effectively equivalent. To address this gap, we propose a two-part approach based upon a relatively simple statistical model. First, the probability of a false positive conclusion, wherein there is an incorrectly perceived increase for a given analyte in the comparator relative to the baseline device, can be reduced to a prescribed level by establishing an appropriate acceptance criterion for the ratio of the observed means. Second, the probability of a false negative conclusion, where an actual increase in a given analyte cannot be discerned from the test results, can be minimized by specifying a limiting value of applicability based on the margin of safety (MoS) of the analyte. This approach provides a quantitative, statistically motivated method to interpret chemical equivalence data, despite the relatively high intrinsic variability and small number of replicates typically associated with a chemical characterization evaluation.

Strategies for Rapid Risk Assessment of Color Additives Used in Medical Devices.

Many polymeric medical devices contain color additives for differentiation or labeling. Although some additives can be toxic under certain conditions, the risk associated with the use of these additives in medical device applications is not well established, and evaluating their impact on device biocompatibility can be expensive and time consuming. Therefore, we have developed a framework to conduct screening-level risk assessments to aid in determining whether generating color additive exposure data and further risk evaluation are necessary. We first derive tolerable intake values that are protective for worst-case exposure to 8 commonly used color additives. Next, we establish a model to predict exposure limited only by the diffusive transport of the additive through the polymer matrix. The model is parameterized using a constitutive model for diffusion coefficient (D) as a function of molecular weight (Mw) of the color additive. After segmenting polymer matrices into 4 distinct categories, upper bounds on D(Mw) were determined based on available data for each category. The upper bounds and exposure predictions were validated independently to provide conservative estimates. Because both components (toxicity and exposure) are conservative, a ratio of tolerable intake to exposure in excess of one indicates acceptable risk. Application of this approach to typical colored polymeric materials used in medical devices suggests that additional color additive risk evaluation could be eliminated in a large percentage (≈90%) of scenarios.

Conservative Exposure Predictions for Rapid Risk Assessment of Phase-Separated Additives in Medical Device Polymers.

A novel approach for rapid risk assessment of targeted leachables in medical device polymers is proposed and validated. Risk evaluation involves understanding the potential of these additives to migrate out of the polymer, and comparing their exposure to a toxicological threshold value. In this study, we propose that a simple diffusive transport model can be used to provide conservative exposure estimates for phase separated color additives in device polymers. This model has been illustrated using a representative phthalocyanine color additive (manganese phthalocyanine, MnPC) and polymer (PEBAX 2533) system. Sorption experiments of MnPC into PEBAX were conducted in order to experimentally determine the diffusion coefficient, D = (1.6 ± 0.5) × 10-11 cm2/s, and matrix solubility limit, C s = 0.089 wt.%, and model predicted exposure values were validated by extraction experiments. Exposure values for the color additive were compared to a toxicological threshold for a sample risk assessment. Results from this study indicate that a diffusion model-based approach to predict exposure has considerable potential for use as a rapid, screening-level tool to assess the risk of color additives and other small molecule additives in medical device polymers.

Improving risk assessment of color additives in medical device polymers.

Many polymeric medical device materials contain color additives which could lead to adverse health effects. The potential health risk of color additives may be assessed by comparing the amount of color additive released over time to levels deemed to be safe based on available toxicity data. We propose a conservative model for exposure that requires only the diffusion coefficient of the additive in the polymer matrix, D, to be specified. The model is applied here using a model polymer (poly(ether-block-amide), PEBAX 2533) and color additive (quinizarin blue) system. Sorption experiments performed in an aqueous dispersion of quinizarin blue (QB) into neat PEBAX yielded a diffusivity D = 4.8 × 10-10 cm2  s-1 , and solubility S = 0.32 wt %. On the basis of these measurements, we validated the model by comparing predictions to the leaching profile of QB from a PEBAX matrix into physiologically representative media. Toxicity data are not available to estimate a safe level of exposure to QB, as a result, we used a Threshold of Toxicological Concern (TTC) value for QB of 90 µg/adult/day. Because only 30% of the QB is released in the first day of leaching for our film thickness and calculated D, we demonstrate that a device may contain significantly more color additive than the TTC value without giving rise to a toxicological concern. The findings suggest that an initial screening-level risk assessment of color additives and other potentially toxic compounds found in device polymers can be improved. © 2017 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 106B: 310-319, 2018.