James Smith
Postdoctoral Scientist
James is a postdoctoral scientist interested in meta-research, with ongoing projects relating to medical devices, prediction of drug approval, and development of automated tools for analysis of preclinical data.
His MRC funded DPhil was titled “Patent Landscape Reporting Quality and Predicting Drug Approval”, in which he found evidence for poor reporting quality in patent landscapes and developed the reporting items for patent landscapes (RIPL) statement, a reporting guideline. Hoping to reduce the extremely high costs of drug development, he also developed a predictive model which aimed to predict market approval of small molecule compounds from the pre-clinical stage.
He was previously a Research Associate of the CASMI Translational Stem Cell Consortium, where worked on a variety of projects relating to advanced therapeutics, and was a SENS Research Foundation Summer Scholar at the Harvard Stem Cell Institute.
James has a first class undergraduate degree in Biological Sciences from the University of Oxford, where he was an academic scholar at Worcester College.
Key publications
-
The Reporting Items for Patent Landscapes statement.
Journal article
Smith JA. et al, (2018), Nat biotechnol, 36, 1043 - 1047
-
Evidence of insufficient quality of reporting in patent landscapes in the life sciences.
Journal article
Smith JA. et al, (2017), Nat biotechnol, 35, 210 - 214
Recent publications
-
Proliferation of mesenchymal stem cell trials for COVID-19: risks and recommendations
Journal article
Smith JA. et al, (2020), Open science framework
-
Industry ties and evidence in public comments on the FDA framework for modifications to artificial intelligence/machine learning-based medical devices: a cross sectional study.
Journal article
Smith JA. et al, (2020), Bmj open, 10
-
Improving transparency and reproducibility of patent landscapes: the Reporting Items for Patent Landscapes (RIPL) statement and other considerations
Journal article
Smith JA., (2020), World patent information, 62
-
Industry ties and evidence in public comments on the FDA framework for modifications to artificial intelligence/machine learning-based medical devices: a cross sectional study
Journal article
Smith JA. et al, (2019)