Biology subjects

Mistry, H.

Publications and source records attributed to Mistry, H..

10 recordsLinked to original sources

bioRxiv2024

Individual Patient Data Analysis of Liver Lesion Dynamics: A Pan Cancer Analysis

There are now several databases available that enable the exploration of individual patient data from past Oncology clinical trials. These databases provide researchers with the opportunity to investigate the dynamics of individual lesions across various tumour sites within a patient and compare them across different patients. In this report, we specifically focus on liver metastases and examine the dynamics of individual lesions across different treatment and tumour types. Our findings reveal that when a lesion progresses early on, the rate of growth is significantly higher for liver lesions compared to other types of lesions, regardless of the treatment modality (chemotherapy, targeted therapy, or combination of both). These results indicate the need for further basic research on tumours and their microenvironment specifically within liver metastases. This analysis demonstrates how analysing open trial data can uncover new research prospects at the fundamental science level, thereby bridging the gap between laboratory research and clinical applications.

cancer biology↗

bioRxiv2020

The search for surrogacy in patient derived xenograft mouse trials: glass is less than half full

Despite the efforts of many within the drug development and clinical community surrogate biomarkers for patient survival have remained elusive in Oncology. This failure in part is attributable to there being a paucity of clinical trials showing a treatment effect on patient survival. Given this issue an alternative system to explore the surrogacy potential of biomarkers are large preclinical xenograft studies i.e. panel of patient derived xenografts or mouse clinical trials. In this study we explored the surrogacy potential of tumour burden biomarkers, current size of tumour and how its changed, preclinically in a large patient derived xenograft database which contains a diverse number of drugs/treatments (n=61) and xenografts (n=245). We found that of the possible 1830 two-arm mouse trials, 1103 showed a treatment effect on the preclinical end-point, disease progression, (p<0.05). Of these only in 30% did tumour burden markers fully capture the treatment effect on disease progression times i.e. satisfied a key condition for surrogacy. These results highlight that preclinically it is very challenging to find a surrogate marker based purely on measures of tumour burden.

cancer biology↗

bioRxiv2020

Evolutionary Based Adaptive Dosing Algorithms: Beware the Cost of Cumulative Risk

Application of theories from ecology to cancer is growing. One such idea involves using drug sensitive cells to control drug resistant cells by cycling treatment on and off based on a marker of tumour burden. Many literature studies have highlighted the benefit of this approach when using time till reaching a certain level of burden as an end-point. These studies though have not considered that patients need to survive up until a certain time-point with a higher level of tumour burden compared to standard dosing to gain this benefit. Within this study once this cumulative cost is accounted for it can be seen that adaptive dosing, counter-intuitively, is likely to lead to poorer prognosis than continuous dosing. This study highlights that evolutionary based adaptive dosing algorithms may not be the “parachute” its protagonists believe it to be.Competing Interest StatementThe authors have declared no competing interest.View Full Text

cancer biology↗

bioRxiv2020

Cardiac toxicity predictions: Safety pharmacologists correlate with the CiPA model

There has been a lot of interest and publicity regarding the use of a complex biophysical model within drug development for predicting the TdeP risk of new compounds. Throughout the development of the complex model numerous groups have shown that a simple linear mechanistic model explains the predictive behaviour of complex mechanistic models. That is the input-output relationship is almost linear even when complex kinetic assays are used. We hypothesized that given this linear relationship that scientist would be able to predict the outcome of the biophysical model. The objective of this pilot study was to assess the feasibility of such an analysis but also assess the initial degree of correlation. A set of 15 compounds with diverse ion-channel blocking against 4 ion-channel currents, IKr, ICaL, INa and INaL, was generated. Safety pharmacologists across numerous companies were approached and asked to categorize the TdeP risk of these compounds using only the % block depicted via a bar chart into one of 3 categories: Risk, No-risk or Unsure. 12 scientists participated in the study, of which 11 correlated strongly with the model (11 person ROC AUC range: 0.86-1, 7 scientists had a value >0.9). The combined prediction of all scientists also correlated strongly with the model. These results highlight that the linear input-output relationship can indeed be predicted by the scientist. A future study exploring the degree of correlation with a wider group of scientists and wider set of compounds would be required to get a more precise estimate of the correlation. We hope this initial exploratory study will encourage the community to pursue this idea.Competing Interest StatementThe authors have declared no competing interest.View Full Text

pharmacology and toxicology↗

bioRxiv2019

Pathway Commons: 2019 Update

Pathway Commons (https://www.pathwaycommons.org) is an integrated resource of publicly available information about biological pathways including biochemical reactions, assembly of biomolecular complexes, transport and catalysis events and physical interactions involving proteins, DNA, RNA, and small molecules (e.g., metabolites and drug compounds). Data is collected from multiple providers in standard formats, including the Biological Pathway Exchange (BioPAX) language and the Proteomics Standards Initiative Molecular Interactions format, and then integrated. Pathway Commons provides biologists with (1) tools to search this comprehensive resource, (2) a download site offering integrated bulk sets of pathway data (e.g., tables of interactions and gene sets), (3) reusable software libraries for working with pathway information in several programming languages (Java, R, Python, and Javascript), and (4) a web service for programmatically querying the entire dataset. Visualization of pathways is supported using the Systems Biological Graphical Notation (SBGN). Pathway Commons currently contains data from 22 databases with 4,794 detailed human biochemical processes (i.e., pathways) and [~]2.3 million interactions. To enhance the usability of this large resource for end-users, we develop and maintain interactive web applications and training materials that enable pathway exploration and advanced analysis.

bioinformatics↗

bioRxiv2019

Analysis of patient derived xenograft studies in Oncology drug development: impact on design and interpretation of future studies

BackgroundPreclinical Oncology drug development is heavily reliant on xenograft studies to assess the anti-tumour effect of new compounds. Patient derived xenograft (PDX) have become popular as they may better represent the clinical disease, however variability is greater than in cell-line derived xenografts. The typical approach of analysing these studies involves performing an un-paired t-test on the mean tumour volumes between the treated and control group at the end of the study. This approach ignores the time-series and may result in false conclusions, especially when considering the increased variability of PDX studies.\n\nAimTo test the hypothesis that a model-based analysis provides increased power than analysis of final day volumes and to provide insights into more efficient PDX study designs.\n\nMethodsData was extracted from tumour xenograft time-series data from a large publicly available PDX drug treatment database released by Novartis. For all 2-arm studies the percent tumour growth inhibition (TGI) at two time-points, day 10 and day 14 was calculated. For each study, the effect of treatment was calculated using an un-paired t-test and also a model-based analysis using the likelihood ratio-test. In addition a simulation study was also performed to assess the difference in power between the two data-analysis approaches for different levels of TGI for PDX or standard cell-line derived xenografts (CDX).\n\nResultsThe model-based analysis had greater statistical power than the un-paired t-test approach within the PDX data-set. The model-based approach was able to detect TGI values as low as 25 percent whereas the un-paired t-test approach required at least 50 percent TGI. These findings were confirmed within the simulation study performed which also highlighted that CDX studies require less animals than PDX studies which show the equivalent level of TGI.\n\nConclusionThe analysis of 59 2-arm PDX studies highlighted that taking a model-based approach gave increased statistical power over simply performing an un-paired t-test on the final study day. Importantly the model-based approach was able to detect smaller size of effect compared to the un-paired t-test approach is which maybe common of such studies. These findings were confirmed within simulated studies which also highlighted the same sample size used for CDX studies would lead to inadequately powered PDX studies. Application of a model-based analysis should allow studies to use less animals and run experiments for a shorter period thus providing effective insight into compound anti-tumour activity

cancer biology↗

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↗