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Recommendations for Benchmarking Preclinical Studies of Nanomedicines.

ABSTRACT: Nanoparticle-based delivery systems provide new opportunities to overcome the limitations associated with traditional small-molecule drug therapy for cancer and to achieve both therapeutic and diagnostic functions in the same platform. Preclinical trials are generally designed to assess therapeutic potential and not to optimize the design of the delivery platform. Consequently, progress in developing design rules for cancer nanomedicines has been slow, hindering progress in the field. Despite the large number of preclinical trials, several factors restrict comparison and benchmarking of different platforms, including variability in experimental design, reporting of results, and the lack of quantitative data. To solve this problem, we review the variables involved in the design of preclinical trials and propose a protocol for benchmarking that we recommend be included in in vivo preclinical studies of drug-delivery platforms for cancer therapy. This strategy will contribute to building the scientific knowledge base that enables development of design rules and accelerates the translation of new technologies.

SUBMITTER: Dawidczyk CM

PROVIDER: S-EPMC4592474 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature

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Recommendations for Benchmarking Preclinical Studies of Nanomedicines.

Dawidczyk Charlene M CM Russell Luisa M LM Searson Peter C PC

Cancer research 20150806 19

Nanoparticle-based delivery systems provide new opportunities to overcome the limitations associated with traditional small-molecule drug therapy for cancer and to achieve both therapeutic and diagnostic functions in the same platform. Preclinical trials are generally designed to assess therapeutic potential and not to optimize the design of the delivery platform. Consequently, progress in developing design rules for cancer nanomedicines has been slow, hindering progress in the field. Despite th ...[more]

PMID: 26249177

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Project description:IntroductionOver the last decade, the field of systems vaccinology has emerged, in which high throughput transcriptomics and other omics assays are used to probe changes of the innate and adaptive immune system in response to vaccination. The goal of this study was to benchmark key technical and analytical parameters of RNA sequencing (RNA-seq) in the context of a multi-site, double-blind randomized vaccine clinical trial.MethodsWe collected longitudinal peripheral blood mononuclear cell (PBMC) samples from 10 subjects before and after vaccination with a live attenuated Francisella tularensis vaccine and performed RNA-Seq at two different sites using aliquots from the same sample to generate two replicate datasets (5 time points for 50 samples each). We evaluated the impact of (i) filtering lowly-expressed genes, (ii) using external RNA controls, (iii) fold change and false discovery rate (FDR) filtering, (iv) read length, and (v) sequencing depth on differential expressed genes (DEGs) concordance between replicate datasets. Using synthetic mRNA spike-ins, we developed a method for empirically establishing minimal read-count thresholds for maintaining fold change accuracy on a per-experiment basis. We defined a reference PBMC transcriptome by pooling sequence data and established the impact of sequencing depth and gene filtering on transcriptome representation. Lastly, we modeled statistical power to detect DEGs for a range of sample sizes, effect sizes, and sequencing depths.Results and discussionOur results showed that (i) filtering lowly-expressed genes is recommended to improve fold-change accuracy and inter-site agreement, if possible guided by mRNA spike-ins (ii) read length did not have a major impact on DEG detection, (iii) applying fold-change cutoffs for DEG detection reduced inter-set agreement and should be used with caution, if at all, (iv) reduction in sequencing depth had a minimal impact on statistical power but reduced the identifiable fraction of the PBMC transcriptome, (v) after sample size, effect size (i.e. the magnitude of fold change) was the most important driver of statistical power to detect DEG. The results from this study provide RNA sequencing benchmarks and guidelines for planning future similar vaccine studies.

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Recommendations for Benchmarking Preclinical Studies of Nanomedicines.

Publications

Recommendations for Benchmarking Preclinical Studies of Nanomedicines.

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