Biology subjects

Mistry, H.

Publications and source records attributed to Mistry, H..

4 recordsLinked to original sources

bioRxiv2017

Complex versus simple models: ion-channel cardiac toxicity prediction

There is growing interest in applying detailed mathematical models of the heart for ion-channel related cardiac toxicity prediction. However, a debate as to whether such complex models are required exists. Here an assessment in the predictive performance between two established cardiac models, gold-standard and cardiac safety simulator, and a simple linear model Bnet was conducted. Three ion-channel data-sets were extracted from literature. Each compound was designated a cardiac risk category based on information within CredibleMeds. The predictive performance of each model within each data-set was assessed via a leave-one-out cross validation. In two of the data-sets Bnet performed equally as well as the leading cardiac model, cardiac safety simulator, both of these outperformed the gold-standard model. In the 3rd data-set, which contained the most detailed ion-channel pharmacology, Bnet outperformed both cardiac models. These results highlight the importance of benchmarking models but also encourage the development of simple models.

pharmacology and toxicology↗

bioRxiv2017

Simulation of cancer cell line pharmacogenomics data to optimise experimental design and analysis strategy

BackgroundExplaining the variability in drug sensitivity across a panel of cell lines using genomic information is a key aspect of cancer drug discovery. The results of such analyses may ultimately determine which patients are likely to benefit from a new treatment. There are numerous experimental factors that can influence the outcomes of cell line screening panels such as the number of replicates, number of doses explored etc. Simulation studies can aid in understanding how variability in these experimental factors can affect the statistical power of a given analysis method. In this study dose response data was simulated for a variety of experimental designs and the ability of different methods to retrieve the original simulation parameters was compared. The analysis methods under consideration were a combination of non-linear least squares and ANOVA, conventional approach, versus non-linear mixed effects.\n\nResultsAcross the simulation studies explored the mixed-effects approach gave similar and in some situations greater statistical power than the conventional approach. In particular the mixed-effects approach gave significantly greater power when there was less information per dose response curve, and when more cell lines screened. More generally the best way to improve statistical power was to screen more cell lines.\n\nConclusionsThis study demonstrates the value of simulating data to understand design and analysis choices in the context of cancer drug sensitivity screening. By illustrating the performance of different methods in different situations these results will help researchers in the field generate and analyse data on future preclinical compounds. Ultimately this will benefit patients by ensuring that biomarkers of drug sensitivity have an increased chance of being identified at the preclinical stage.

bioinformatics↗

bioRxiv2017

A cautionary tale on using tumour growth rate to predict survival

A recurrent question within oncology drug development is predicting phase III outcome for a new treatment using early clinical data. One approach to tackle this problem has been to derive metrics from mathematical models that describe tumour size dynamics termed re-growth rate and time to tumour re-growth. They have shown to be strong predictors of overall survival in numerous studies but there is debate about how these metrics are derived and if they are more predictive than empirical end-points. This work explores the issues raised in using model-derived metric as predictors for survival analyses. Re-growth rate and time to tumour re-growth were calculated for three large clinical studies by forward and reverse alignment. The latter involves re-aligning patients to their time of progression. Hence it accounts for the time taken to estimate re-growth rate and time to tumour re-growth but also assesses if these predictors correlate to survival from the time of progression. We found that neither re-growth rate nor time to tumour re-growth correlated to survival using reverse alignment. This suggests that the dynamics of tumours up until disease progression has no relationship to survival post progression. For prediction of a phase III trial we found the metrics performed no better than empirical end-points. These results highlight that care must be taken when relating dynamics of tumour imaging to survival and that bench-marking new approaches to existing ones is essential.

cancer biology↗

bioRxiv2017

Heterogeneity in the tumour size dynamics differentiates Vemurafenib, Dabrafenib and Trametinib in metastatic melanoma

Molecular heterogeneity in tumours leads to variability in drug response both between patients and across lesions within a patient. These sources of variability could be explored through analysis of routinely collected clinical trial imaging data. We applied a mathematical model of tumour growth to analyse both within and between patient variability in tumour size dynamics to clinical data from three drugs, Vemurafenib, Dabrafenib and Trametinib, used in the treatment of metastatic melanoma. The analysis revealed: 1) existence of homogeneity in drug response and resistance development within a patient; 2) tumour shrinkage rate does not relate to rate of resistance development; 3) Vemurafenib and Dabrafenib, two BRAF inhibitors, have different variability in tumour shrinkage rates. Overall these results show how analysis of the dynamics of individual lesions can shed light on the within and between patient differences in tumour shrinkage and resistance rates, which could be used to gain a macroscopic understanding of tumour heterogeneity.

cancer biology↗