## 4 Conclusions  
This study focused on identifying and analysing previously published research on DOX-loaded 2D graphene nanohybrids for the treatment of ovarian and breast cancers with the aim of then understanding the relationships between different nanocarriers and in vitro drug release parameters. Although there are a host of different proof-of-concept publications of graphene-based nanocarriers, there have been no studies that focus on the systematic evaluation of graphene nanocarriers as a whole and their drug release efficiencies. Seventeen publications were identified, which utilised either pH or NIR/ultrasound to stimulate DOX release; both qualitative and quantitative data were analysed. By use of the programmes GetData and GraphPad Prism, the drug release profiles were extracted and fitted to four known kinetic models as drug release kinetics offers information that can be used to optimise nano delivery systems. It was found that most of the publications best fit the Weibull model, highlighting both the flexibility of the model to describe a range of different systems and the agreement between previous publications. This provides a predictable estimation of DOX release from graphene-based nanocarriers that can aid in the future development of drug delivery systems. The qualitative data extraction of this work unearthed inconsistencies of parameters reported between publications; a standardisation of information reporting could overcome this limitation and make further analysis more accurate. Future work should also focus on the development of in vitro/in vivo methods that can more accurately replicate physiological conditions; the use of computational modelling prior to pre-clinical experiments may aid in this translation. Future methods should take into consideration clinical relevance and translatability, cytotoxicity, cellular uptake, intracellular localisation, and the breakdown and clearance pathways of the nanocarriers.  
The following are available online at [[https://www.mdpi.com/article/10](https://www.mdpi.com/article/10)]([https://www.mdpi.com/article/10](https://www.mdpi.com/article/10)) 3390/app112311151/s1, Figures S1-S17, the linear regression graphs of the zero-order, first-order, Higuchi, and Weibull models for each of the seventeen publications analysed. The equation of the linear regression is displayed as well as the R\({}^{2}\) value for each model under each condition, Table S1: Table displaying the title, first author, journal, publication year and DOI for the seventeen publications analysed, Table S2: Table displaying the nanocarrier characteristics of each of the seventeen publications analysed, Table S3: Table displaying the payload characteristics and drug loading information, including both the drug loading capacity and drug loading/encapsulation efficiency, for the seventeen publications, Table S4: A table displaying the experimental conditions under which the drug release was recorded, including the temperature and pH. The maximum cumulative release and the timeframe of the measurement were recorded. Information on the biological assays undertaken by each publication is also recorded, Table S5: This table displays the numerical results of the statistical analysis. The \(R^{2}\) value, _Sy.x_ value, gradient, and \(p\) value are recorded for each of the four kinetic models, for each nanohybrid under the different conditions recorded by the publications. The results are given to 4 decimal places.  
Conceptualization, T.A.T., K.T. and R.J.N.; methodology, K.T.; validation, K.T., T.A.T. and R.J.N.; formal analysis, K.T.; investigation, K.T.; writing--original draft preparation, K.T.; writing--review and editing, K.T., T.A.T. and R.J.N.; supervision, T.A.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.