RESUMEN
On 11th March 2020, the UK government announced plans for the scaling of COVID-19 testing, and on 27th March 2020 it was announced that a new alliance of private sector and academic collaborative laboratories were being created to generate the testing capacity required. The Cambridge COVID-19 Testing Centre (CCTC) was established during April 2020 through collaboration between AstraZeneca, GlaxoSmithKline, and the University of Cambridge, with Charles River Laboratories joining the collaboration at the end of July 2020. The CCTC lab operation focussed on the optimised use of automation, introduction of novel technologies and process modelling to enable a testing capacity of 22,000 tests per day. Here we describe the optimisation of the laboratory process through the continued exploitation of internal performance metrics, while introducing new technologies including the Heat Inactivation of clinical samples upon receipt into the laboratory and a Direct to PCR protocol that removed the requirement for the RNA extraction step. We anticipate that these methods will have value in driving continued efficiency and effectiveness within all large scale viral diagnostic testing laboratories.
Asunto(s)
SARS-CoV-2RESUMEN
The domain of applicability is an important concept in quantitative structure activity relationships (QSAR) that allows one to estimate the uncertainty in the prediction of a particular molecule based on how similar it is to the compounds used to build the model. In this paper we discuss this important concept, providing details of the development and application of a method to compute the domain of applicability within model descriptor space and structural space as defined by daylight fingerprints. The importance of the domain of applicability is illustrated using five QSAR models generated on plasma protein binding and P450 inhibition datasets. Such methodologies will be shown to offer us a means to monitor the performance of QSARs over time, providing us both with a way to estimate the accuracy of a given prediction and identify when a model needs to be rebuilt.
