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Biomedical Application, Patent Repository, Clinical Trial and Regulatory Updates on Hydrogel: An Extensive Review.

ABSTRACT: Hydrogels are known for their leading role in biomaterial systems involving pharmaceuticals that fascinate material scientists to work on the wide variety of biomedical applications. The physical and mechanical properties of hydrogels, along with their biodegradability and biocompatibility characteristics, have made them an attractive and flexible tool with various applications such as imaging, diagnosis and treatment. The water-cherishing nature of hydrogels and their capacity to swell-contingent upon a few ecological signals or the simple presence of water-is alluring for drug conveyance applications. Currently, there are several problems relating to drug delivery, to which hydrogel may provide a possible solution. Hence, it is pertinent to collate updates on hydrogels pertaining to biomedical applications. The primary objective of this review article is to garner information regarding classification, properties, methods of preparations, and of the polymers used with particular emphasis on injectable hydrogels. This review also covers the regulatory and other commerce specific information. Further, it enlists several patents and clinical trials of hydrogels with related indications and offers a consolidated resource for all facets associated with the biomedical hydrogels.

SUBMITTER: Mohapatra S

PROVIDER: S-EPMC8628667 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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Biomedical Application, Patent Repository, Clinical Trial and Regulatory Updates on Hydrogel: An Extensive Review.

Mohapatra Sradhanjali S Mirza Mohd Aamir MA Hilles Ayah Rebhi AR Zakir Foziyah F Gomes Andreia Castro AC Ansari Mohammad Javed MJ Iqbal Zeenat Z Mahmood Syed S

Gels (Basel, Switzerland) 20211112 4

Hydrogels are known for their leading role in biomaterial systems involving pharmaceuticals that fascinate material scientists to work on the wide variety of biomedical applications. The physical and mechanical properties of hydrogels, along with their biodegradability and biocompatibility characteristics, have made them an attractive and flexible tool with various applications such as imaging, diagnosis and treatment. The water-cherishing nature of hydrogels and their capacity to swell-continge ...[more]

PMID: 34842705

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Project description:ObjectiveMining disease-specific associations from existing knowledge resources can be useful for building disease-specific ontologies and supporting knowledge-based applications. Many association mining techniques have been exploited. However, the challenge remains when those extracted associations contained much noise. It is unreliable to determine the relevance of the association by simply setting up arbitrary cut-off points on multiple scores of relevance; and it would be expensive to ask human experts to manually review a large number of associations. We propose that machine-learning-based classification can be used to separate the signal from the noise, and to provide a feasible approach to create and maintain disease-specific vocabularies.MethodWe initially focused on disease-medication associations for the purpose of simplicity. For a disease of interest, we extracted potentially treatment-related drug concepts from biomedical literature citations and from a local clinical data repository. Each concept was associated with multiple measures of relevance (i.e., features) such as frequency of occurrence. For the machine purpose of learning, we formed nine datasets for three diseases with each disease having two single-source datasets and one from the combination of previous two datasets. All the datasets were labeled using existing reference standards. Thereafter, we conducted two experiments: (1) to test if adding features from the clinical data repository would improve the performance of classification achieved using features from the biomedical literature only, and (2) to determine if classifier(s) trained with known medication-disease data sets would be generalizable to new disease(s).ResultsSimple logistic regression and LogitBoost were two classifiers identified as the preferred models separately for the biomedical-literature datasets and combined datasets. The performance of the classification using combined features provided significant improvement beyond that using biomedical-literature features alone (p-value<0.001). The performance of the classifier built from known diseases to predict associated concepts for new diseases showed no significant difference from the performance of the classifier built and tested using the new disease's dataset.ConclusionIt is feasible to use classification approaches to automatically predict the relevance of a concept to a disease of interest. It is useful to combine features from disparate sources for the task of classification. Classifiers built from known diseases were generalizable to new diseases.

Project description:Background: Given the increasing number and heterogeneity of data repositories, an improvement and harmonisation of practice within repositories for clinical trial data is urgently needed. The objective of the study was to develop and evaluate a demonstrator repository, using a widely used repository system (DSpace), and then explore its suitability for providing access to individual participant data (IPD) from clinical research. Methods: After a study of the available options, DSpace (version 6.3) was selected as the software for developing a demonstrator implementation of a repository for clinical trial data. In total, 19 quality criteria were defined, using previous work assessing clinical data repositories as a guide, and the demonstrator implementation was then assessed with respect to those criteria. Results: Generally, the performance of the DSpace demonstrator repository in supporting sensitive personal data such as that from clinical trials was strong, with 14 requirements demonstrated (74%), including the necessary support for metadata and identifiers. Two requirements could not be demonstrated (inability to incorporate de-identification tools in the submission workflow, lack of a self-attestation system) and three requirements were only partially demonstrated (ability to provide links to de-identification tools and requirements, incorporation of a data transfer agreement in system workflow, and capability to offer managed access through application on a case by case basis). Conclusions: Technically, the system was able to support most of the pre-defined requirements, though there are areas where support could be improved. Of course, in a productive repository, appropriate policies and procedures would be needed to direct the use of the available technical features. A technical evaluation should therefore be seen as indicating a system's potential, rather than being a definite assessment of its suitability. DSpace clearly has considerable potential in this context and appears a suitable base for further exploration of the issues around storing sensitive data.

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Biomedical Application, Patent Repository, Clinical Trial and Regulatory Updates on Hydrogel: An Extensive Review.

Publications

Biomedical Application, Patent Repository, Clinical Trial and Regulatory Updates on Hydrogel: An Extensive Review.

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