Use of QSAR Modeling to Predict the Carcinogenicity of Color Additives

Brown, Ronald P.; White, Shannon; Goode, Jennifer; Pradeep, Prachi; Merrill, Stephen J.

doi:10.1115/fmd2013-16161

Cited by 4 publications

(4 citation statements)

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“…In fact, QSAR models are claimed to be particularly appropriate for the safety assessment of medical devices. It has been shown for instance that QSAR modelling is effective in predicting the carcinogenicity of colorants contained in medical devices 194 . The ability to evaluate such adverse responses through modelling is relevant for the field since colorants may leach into the body during clinical use and thus pose a risk to the patient.…”

Section: Test Methods Validationmentioning

confidence: 99%

Medical Devices Biological Safety Assessment: Towards Animal-free Testing

Sachot¹,

Vitale

2019

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Animals have been used since a very long time as experimental subjects to support scientific progress and medical advances. Currently, various testing procedures implying animals are still being conducted within a broad range of scientific fields and applications, as for example the assessment of medical devices safety. The aim of this assessment is to investigate whether a medical device is safe for human use or poses a risk for patients' health. Animal testing for that purpose is especially encouraged by regulatory requirements, which prerequisite however that the use of such testing is deemed necessary and justified in the context of a risk management process. Thus, animal models are no longer systematically used as in the past, but they remain a cornerstone within the delicate decision-making process related to the marketing of medical devices. Though, animal testing has some critical limitations, such as ethical concerns and relevance of animal data, which challenge its further use to that objective. A particular attention is therefore currently given to the development of animal-free alternatives to better support the application of the 3Rs Principle (replacement, reduction, refining) within this field. Unfortunately, the highlyadvanced methods and technologies being developed face barriers (e.g. validation and standardization) which hinder and slow down their implementation as regulatory-accepted alternatives. To overcome these difficulties, the process of regulatory acceptance of these alternatives may be optimized. This may be achieved for example by improving the cooperation, coordination and communication between the different stakeholders, i.e. researchers, regulatory approval bodies and industries. Mostly, a special focus may be set on commitment and shared effort to enhance the efficiency of that process and to establish a regulatory testing framework that no longer relies on animal models.

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Section: Test Methods Validationmentioning

confidence: 99%

Medical Devices Biological Safety Assessment: Towards Animal-free Testing

Sachot¹,

Vitale

2019

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“…There is a broad range of modeling disciplines that OSEL scientists are using in their medical device-driven research, including photon transport, fluid dynamics, heat transfer, electromagnetism, solid mechanics, acoustics and optics, along with anatomical, physiological, and mechanistic modeling. Other include (Q)SAR models for assessing molecular carcinogenicity ( 11 ), deep learning methods and artificial intelligence for analyzing and synthesizing real-world data. Within this diverse range, OSEL has been advancing different areas of computational modeling for medical devices.…”

Section: Computational Modeling Researchmentioning

confidence: 99%

Advancing Regulatory Science With Computational Modeling for Medical Devices at the FDA's Office of Science and Engineering Laboratories

et al. 2018

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Protecting and promoting public health is the mission of the U.S. Food and Drug Administration (FDA). FDA's Center for Devices and Radiological Health (CDRH), which regulates medical devices marketed in the U.S., envisions itself as the world's leader in medical device innovation and regulatory science–the development of new methods, standards, and approaches to assess the safety, efficacy, quality, and performance of medical devices. Traditionally, bench testing, animal studies, and clinical trials have been the main sources of evidence for getting medical devices on the market in the U.S. In recent years, however, computational modeling has become an increasingly powerful tool for evaluating medical devices, complementing bench, animal and clinical methods. Moreover, computational modeling methods are increasingly being used within software platforms, serving as clinical decision support tools, and are being embedded in medical devices. Because of its reach and huge potential, computational modeling has been identified as a priority by CDRH, and indeed by FDA's leadership. Therefore, the Office of Science and Engineering Laboratories (OSEL)—the research arm of CDRH—has committed significant resources to transforming computational modeling from a valuable scientific tool to a valuable regulatory tool, and developing mechanisms to rely more on digital evidence in place of other evidence. This article introduces the role of computational modeling for medical devices, describes OSEL's ongoing research, and overviews how evidence from computational modeling (i.e., digital evidence) has been used in regulatory submissions by industry to CDRH in recent years. It concludes by discussing the potential future role for computational modeling and digital evidence in medical devices.

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“…The risk assessment process involves two elements for a given system: (1) quantification of exposure, that is, the extent to which the color additive is released into the body over time . and (2) estimation of the color additive toxicity, expressed as a tolerable intake (TI) value in units of additive mass/body weight/day (see Figure ) . However, toxicity data are often not available for compounds released from medical devices, and it is challenging to directly measure the amount of color additives released from a device in vivo .…”

Section: Introductionmentioning

confidence: 99%

“…[2][3][4][5][6] and (2) estimation of the color additive toxicity, expressed as a tolerable intake (TI) value in units of additive mass/body weight/day (see Figure 1). [7][8][9][10][11][12] However, toxicity data are often not available for compounds released from medical devices, and it is challenging to directly measure the amount of color additives released from a device in vivo. Therefore, in vitro extraction methods performed under physiologically representative conditions may be used to develop suitable exposure predictions.…”

Section: Introductionmentioning

confidence: 99%

Improving risk assessment of color additives in medical device polymers

Chandrasekar

Janes²,

Forrey

et al. 2017

J Biomed Mater Res

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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 5 4.8 3 10 210 cm 2 s 21 , and solubility S 5 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 mg/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.

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Use of QSAR Modeling to Predict the Carcinogenicity of Color Additives

Cited by 4 publications

References 0 publications

Medical Devices Biological Safety Assessment: Towards Animal-free Testing

Medical Devices Biological Safety Assessment: Towards Animal-free Testing

Advancing Regulatory Science With Computational Modeling for Medical Devices at the FDA's Office of Science and Engineering Laboratories

Improving risk assessment of color additives in medical device polymers

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