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

Modeling extraction of medical device polymers for biocompatibility evaluation

Extraction testing is critical for biocompatibility evaluation of medical devices, whether to generate samples for biological testing or form the basis for toxicological risk assessment. However, it is not always clear how to compare extraction testing between different extraction conditions and sample geometries. We employ a physics-based model to elucidate the theoretical impact of extraction conditions, sample geometry and material properties on extraction efficiency (M/M⁰) and extract concentration (C/C⁰) for single-step and iterative/exhaustive extraction test methods. The model is specified by three parameters: thermodynamic contributions (Ψ), kinetic contributions (τ), and number of extraction iterations (N). We find that over the range of typical parameters for single-step extractions, M/M⁰ only approaches one (complete exhaustion) for relatively large values of Ψ (≥10) and τ(≥1). Further, the model suggests that test article geometry and solvent volume can have a dramatic and sometimes opposing effect on M/M⁰ and C/C⁰. Our results imply that iterative extractions can be approximated as a single-step extraction with scaled parameters Ψ' = ΨN and τ' = τN. The model provides a framework to reduce the biocompatibility evaluation test burden by optimizing test article and extraction condition selection and guiding development of new test protocols.

Predicting Solute Diffusivity in Polymers Using Time-Temperature Superposition

We demonstrate a novel application of the time-temperature superposition (TTS) principle to predict solute diffusivity D in glassy polymers using atomistic molecular dynamics simulations. Our TTS approach incorporates the Debye-Waller factor ⟨u²⟩, a measure of solute caging, along with concepts from thermodynamic scaling methods, allowing us to balance contributions to the dynamics from temperature and ⟨u²⟩ using adjustable parameters. Our approach rescales the solute mean-squared displacement curves at several temperatures into a master curve that approximates the diffusive dynamics at a reference temperature, effectively extending the simulation time scale from nanoseconds to seconds and beyond. With a set of "universal" parameters, this TTS approach predicts D with reasonable accuracy in a broad range of polymer/solute systems. Using TTS greatly reduces the computational cost compared to standard MD simulations. Thus, our method offers a means to rapidly and routinely provide order-of-magnitude estimates of D using simulations.

Chemical Characterization and Non-targeted Analysis of Medical Device Extracts: A Review of Current Approaches, Gaps, and Emerging Practices

The developers of medical devices evaluate the biocompatibility of their device prior to FDA's review and subsequent introduction to the market. Chemical characterization, described in ISO 10993-18:2020, can generate information for toxicological risk assessment and is an alternative approach for addressing some biocompatibility end points (e.g., systemic toxicity, genotoxicity, carcinogenicity, reproductive/developmental toxicity) that can reduce the time and cost of testing and the need for animal testing. Additionally, chemical characterization can be used to determine whether modifications to the materials and manufacturing processes alter the chemistry of a patient-contacting device to an extent that could impact device safety. Extractables testing is one approach to chemical characterization that employs combinations of non-targeted analysis, non-targeted screening, and/or targeted analysis to establish the identities and quantities of the various chemical constituents that can be released from a device. Due to the difficulty in obtaining a priori information on all the constituents in finished devices, information generation strategies in the form of analytical chemistry testing are often used. Identified and quantified extractables are then assessed using toxicological risk assessment approaches to determine if reported quantities are sufficiently low to overcome the need for further chemical analysis, biological evaluation of select end points, or risk control. For extractables studies to be useful as a screening tool, comprehensive and reliable non-targeted methods are needed. Although non-targeted methods have been adopted by many laboratories, they are laboratory-specific and require expensive analytical instruments and advanced technical expertise to perform. In this Perspective, we describe the elements of extractables studies and provide an overview of the current practices, identified gaps, and emerging practices that may be adopted on a wider scale in the future. This Perspective is outlined according to the steps of an extractables study: information gathering, extraction, extract sample processing, system selection, qualification, quantification, and identification.

Linear Solvation Energy Relationships (LSERs) for Robust Prediction of Partition Coefficients between Low Density Polyethylene and Water Part I: Experimental Partition Coefficients and Model Calibration

When equilibrium of leaching is reached within a product's duty cycle, partition coefficients polymer/solution dictate the maximum accumulation of a leachable and thus, patient exposure by leachables. Yet, in the pharmaceutical and food industry, exposure estimates based on predictive modeling typically rely on coarse estimations of the partition coefficient, with accurate and robust models lacking. This first part of the study aimed to explore linear solvation energy relationships (LSERs) as high performing models for the prediction of partition coefficients polymer/water. For this, partition coefficients between low density polyethylene (LDPE) and aqueous buffers for 159 compounds spanning a wide range of chemical diversity, molecular weight, vapor pressure, aqueous solubility and polarity (hydrophobicity) were determined and complimentary data collected from the literature (n=159, MW: 32 to 722, logK_i,O/W: -0.72 to 8.61 and logK_i,LDPE/W: -3.35 up to 8.36). The chemical space represented by this compounds set is considered indicative for the universe of compounds potentially leaching from plastics. Based on the dataset for the LDPE material purified by solvent extraction, a LSER model for partitioning between LDPE and water was calibrated to give:logK_i,LDPE/W=-0.529+1.098E_i-1.557S_i-2.991A_i-4.617B_i+3.886V_i. The model was proven accurate and precise (n = 156, R² = 0.991, RMSE = 0.264). Further, it was demonstrated superior over a log-linear model fitted to the same data. Nonetheless, it could be shown that log-linear correlations against logK_i,O/W can be of value for the estimation of partition coefficients for nonpolar compounds exhibiting low hydrogen-bonding donor and/or acceptor propensity. For these nonpolar compounds, the log - linear model was found to be: logK_i,LDPE/W=1.18logK_i,O/W-1.33 (n = 115, R²=0.985, RMSE = 0.313). In contrast, with mono-/bipolar compounds included into the regression data set, an only weak correlation was observed (n = 156, R² = 0.930, RMSE = 0.742) rendering the log-linear model of more limited value for polar compounds. Notably, sorption of polar compounds into native (non-purified) LDPE was found to be up to 0.3 log units lower than into purified LDPE. To identify maximum (i. e. worst-case) levels of leaching in support of chemical safety risk assessments on systems attaining equilibrium before end of shelf-life, it appears adequate to utilize LSER - calculated partition coefficients (in combination with solubility data) by ignoring any kinetical information.

Linear Solvation Energy Relationships (LSERs) for Robust Prediction of Partition Coefficients between Low Desity Polyethylene and Water Part II: Model Evaluation and Benchmarking

By neglecting the kinetics of leaching, the accumulation of leachables in a clinically relevant medium in contact with plastics is principally driven by the equilibrium partition coefficient between the polymer and the medium phase. Based on experimental partition coefficients for a wide set of chemically diverse compounds between low density polyethylene (LDPE) and water, a linear solvation energy relationship (LSER) model was obtained in part I of this study, reading: logK_i,LDPE/W=-0.529+1.098E_i-1.557S_i-2.991A_i-4.617B_i+3.886V_i. The model was proven accurate and precise (n = 156, R2 = 0.991, RMSE = 0.264).) In this part II of the study, for further evaluation and benchmarking of the LSER model ∼ 33% (n = 52) of the total observations were ascribed to an independent validation set. Calculation of partition coefficients logK_i,LDPE/W for this validation set was based on experimental LSER solute descriptors. Linear regression against the corresponding experimental values yielded R2 = 0.985 and RMSE = 0.352. When using LSER solute descriptors predicted from the compound's chemical structure by means of a QSPR prediction tool, instead, R2 = 0.984 and RMSE = 0.511 were obtained. These statistics are considered indicative for extractables with no experimental LSER solute descriptors available. By comparison to LSER models from the literature, a strong correlation between the quality of experimental partition coefficients and the chemical diversity of the training set to the model's predictability was observed, the latter of particular relevance for the application domain of the model. Further, to tentatively match partitioning into LDPE to partitioning into a liquid phase, partition coefficients logK_i,LDPE/W were converted into logK_{i,LDPEamorph/W} by considering the amorphous fraction of the polymer as effective phase volume only. A LSER model now recalibrated based on the observations for logK_{i,LDPEamorph/W} exhibited the constant in the equation above to now read -0.079 instead of -0.529 which rendered the model more similar to a corresponding LSER-model for n-hexadencane/water. Based on LSER system parameters available, the sorption behavior of LDPE could be efficiently compared to the one of polydimethylsiloxane (PDMS), polyacrylate (PA) and polyoxymethylene (POM). The latter, by offering capabilities for polar interactions due to their heteroatomic building blocks, exhibit stronger sorption than LDPE to the more polar, non-hydrophobic domain of sorbates up to an logK_i,LDPE/W range of 3 to 4. Above that range, all four polymers exhibited a roughly similar sorption behavior. Overall, LSERs were found to represent an accurate and user-friendly approach for the estimation of equilibrium partition coefficients involving a polymeric phase. All intrinsic input parameters can be retrieved from a free, web-based and curated database along with the outright calculation of the partition coefficient for any given neutral compound with a known structure for a given two-phased system.

Relations Between Dynamic Localization and Solute Diffusion in Polymers

Various public health concerns can arise from the unintended leaching of additives and impurities from polymeric medical devices or food packaging, which is directly related to each solute's diffusivity D. Both experimental and simulation methods can be used to quantify D, but slow diffusion at physiologic temperature in glassy polymers can render these approaches impractical. Here, we investigate a simulation approach with the potential to more rapidly calculate D. Specifically, we examine links between dynamic localization, characterized by the Debye-Waller factor, ⟨u²⟩, and D in a variety of polymer/solute systems using atomistic molecular dynamics (MD) simulations. Using short, high-temperature MD simulations to estimate D at physiologic temperature, we find that the relation ln D ∝ 1/⟨u²⟩ quantitatively predicts D for small solutes and produces an upper-bound estimate of D for larger solutes. Upper-bound estimates are useful in certain contexts, and we compare our results with another approach for determining upper bounds, the Piringer model, to show where each method may be useful. Then, we examine a modified relation where the Debye-Waller factor is rescaled by the mode coupling temperature T_c, which can produce better estimates of D if T_c is carefully chosen. Last, we compare our approach with several other models that relate temperature or localized dynamics with diffusivity. Although each of these approaches can be used to model D across wide temperature ranges using one or more adjustable parameters, none of them are truly predictive in glassy polymers. Further developments are needed to predict the optimal values of the adjustable parameters a priori.

How sensitive does chemical characterization of medical devices need to be? Calibration of analytical evaluation thresholds with the carcinogenic potency database

Chemical characterization is a component of the safety evaluation of medical devices. An analytical evaluation threshold (AET) is recommended to calculate the required analytical sensitivity. There is a lack of consensus whether to use 1.5 or 120 μg/day in calculating the AET with the lower value often requiring sensitivities beyond analytical capabilities. The Carcinogenic Potency Database (CPDB) was reviewed to compare risks associated with using either value to calculate an AET. The TD50s for non-Cohort of Concern (non-COC) substances in the CPDB were used to extrapolate the doses to an excess cancer risk of 10^-5 and calculate the total doses. The number of non-COC substances that would exceed this risk using an AET calculated using 1.5 μg/day or 120 μg/day were then compared. From the 199 substances evaluated, only two posed an excess risk at an AET calculated with 1.5 μg/day and only seven more with 120 μg/day. Furthermore, over 95 percent of non-COC substances would not pose an excess cancer risk using an AET calculated with 120 μg/day. Based on our evaluation, an AET based on 120 μg/day is protective and practical for chemical characterization of short and long-term medical devices.

Leveraging Extraction Testing to Predict Patient Exposure to Polymeric Medical Device Leachables Using Physics-based Models

Toxicological risk assessment approaches are increasingly being used in lieu of animal testing to address toxicological concerns associated with release of chemical constituents from polymeric medical device components. These approaches currently rely on in vitro extraction testing in aggressive environments to estimate patient exposure to these constituents, but the clinical relevance of the test results is often ambiguous. Physics-based mass transport models can provide a framework to interpret extraction test results to provide more clinically relevant exposure estimates. However, the models require system-specific material properties, such as diffusion (D) and partition coefficients (K), to be established a priori for the extraction conditions. Using systems comprised high-density polyethylene and 4 different additives, we demonstrate that these properties can be quantified through standard extraction testing in hexane and isopropyl alcohol. The values of D and K derived in this manner were consistent with theoretical predictions for these quantities. Based on these results, we discuss both the challenges and benefits to leveraging extraction data to parameterize physics-based exposure models. Our observations suggest that clinically relevant, yet still conservative, exposure dose estimates provided by applying this approach to a single extraction measurement can be more than 100 times lower than would be measured under typical aggressive extraction conditions. However, to apply the framework on a routine basis, limiting values of D and K must be established for device-relevant systems either through the aggregation and analysis of more extensive extraction test data and/or advancements in theoretical and computational modeling efforts to predict these quantities.