Ben K Margetts (1,2,3), Judith Breuer (2,3), Nigel Klein (2,3), and Joseph F Standing (1,2,3)
(1) UCL CoMPLEX, London, UK, (2) UCL Institute of Child Health, London, UK, (3) Great Ormond Street Hospital NHS Foundation Trust, London, UK
Objectives: To develop a viral kinetic (VK) model of Cytomegalovirus (CMV) that predicts how viral load changes in an individual as the infection responds to antiviral therapy.
Methods: Complete clinical datasets were taken from 335 bone marrow transplant (BMT) patients between January 2010 and December 2014, of whom 86 exhibited a serious active CMV infection following their immunosuppressive treatment regime. A total of 1598 CMV viral load observations for all patients over the time period were included. For each of the patients studied, we had access to clinical history, treatment outcomes, and the results of all clinical tests undertaken. Alongside this rich clinical data, we have access to full drug administration datasets for each patient during their stay at the hospital.
Viral load qPCR data was fitted to a viral growth model using NONMEM V7 3.0 [1], a growth inhibition term related to antiviral treatment was incorporated into the VK model alongside a growth limiting V MAX term that scales the rate of viral growth against the maximum amount of virus that is possible within an individual. Viral load was used as the primary predictor of treatment outcome as high viral loads (>1 million copies/mL) are typically associated with higher levels of mortality.
Results: The viral kinetic model was shown to appropriately fit the data from the BMT patients, despite the significant variation in the population, providing reasonable estimates for the viral growth and decay during key moments in the patient’s disease progression cycle, but failing to capture sharp increases in viral load. Model parameters generated during the estimation were appropriate, given the biological context, with viral doubling times often slightly exceeding the ~1.4 day CMV doubling time previously shown in BMT patients with CMV infections [2]. Alongside this, antiviral efficacy was estimated to be highly variable within the patient population. The model can provide approximate disease trajectories that can be fitted to newly infected patients as they present.
Conclusion: A dynamic VK model has now been developed for CMV. Future work will include a dynamic immune component that scales to an individual’s lymphocyte count, and the inclusion of individual antiviral PK information. Following this, the use of rich CMV sequencing data currently being collected from BMT patients may allow us to monitor and predict the emergence of drug resistance, and model its impact on the antiviral efficacy.
References:
[1] Beal SL, Sheiner LB, Boeckmann AJ & Bauer RJ (Eds.) NONMEM Users Guides. 1989-2013. Icon Development Solutions, Ellicott City, Maryland, USA.
[2] Mueller, Nicolas J. “Cytomegalovirus: Why Viral Dynamics Matter.” EBioMedicine 2.7 (2015): 631.
Reference: PAGE 25 (2016) Abstr 5914 [www.page-meeting.org/?abstract=5914]
Poster: Drug/Disease modeling - Infection