Minimum sample size for external validation of a clinical prediction model with a binary outcome

In prediction model research, external validation is needed to examine an existing model's performance using data independent to that for model development. Current external validation studies often suffer from small sample sizes and consequently imprecise predictive performance estimates. To address this, we propose how to determine the minimum sample size needed for a new external validation study of a prediction model for a binary outcome. Our calculations aim to precisely estimate calibration (Observed/Expected and calibration slope), discrimination (C-statistic), and clinical utility (net benefit). For each measure, we propose closed-form and iterative solutions for calculating the minimum sample size required. These require specifying: (i) target SEs (confidence interval widths) for each estimate of interest, (ii) the anticipated outcome event proportion in the validation population, (iii) the prediction model's anticipated (mis)calibration and variance of linear predictor values in the validation population, and (iv) potential risk thresholds for clinical decision-making. The calculations can also be used to inform whether the sample size of an existing (already collected) dataset is adequate for external validation. We illustrate our proposal for external validation of a prediction model for mechanical heart valve failure with an expected outcome event proportion of 0.018. Calculations suggest at least 9835 participants (177 events) are required to precisely estimate the calibration and discrimination measures, with this number driven by the calibration slope criterion, which we anticipate will often be the case. Also, 6443 participants (116 events) are required to precisely estimate net benefit at a risk threshold of 8%. Software code is provided.</p

Optimizing the Design of Oligonucleotides for Homology Directed Gene Targeting

BACKGROUND: Gene targeting depends on the ability of cells to use homologous recombination to integrate exogenous DNA into their own genome. A robust mechanistic model of homologous recombination is necessary to fully exploit gene targeting for therapeutic benefit. METHODOLOGY/PRINCIPAL FINDINGS: In this work, our recently developed numerical simulation model for homology search is employed to develop rules for the design of oligonucleotides used in gene targeting. A Metropolis Monte-Carlo algorithm is used to predict the pairing dynamics of an oligonucleotide with the target double-stranded DNA. The model calculates the base-alignment between a long, target double-stranded DNA and a probe nucleoprotein filament comprised of homologous recombination proteins (Rad51 or RecA) polymerized on a single strand DNA. In this study, we considered different sizes of oligonucleotides containing 1 or 3 base heterologies with the target; different positions on the probe were tested to investigate the effect of the mismatch position on the pairing dynamics and stability. We show that the optimal design is a compromise between the mean time to reach a perfect alignment between the two molecules and the stability of the complex. CONCLUSION AND SIGNIFICANCE: A single heterology can be placed anywhere without significantly affecting the stability of the triplex. In the case of three consecutive heterologies, our modeling recommends using long oligonucleotides (at least 35 bases) in which the heterologous sequences are positioned at an intermediate position. Oligonucleotides should not contain more than 10% consecutive heterologies to guarantee a stable pairing with the target dsDNA. Theoretical modeling cannot replace experiments, but we believe that our model can considerably accelerate optimization of oligonucleotides for gene therapy by predicting their pairing dynamics with the target dsDNA

The Nucleocapsid Region of HIV-1 Gag Cooperates with the PTAP and LYPXnL Late Domains to Recruit the Cellular Machinery Necessary for Viral Budding

HIV-1 release is mediated through two motifs in the p6 region of Gag, PTAP and LYPXnL, which recruit cellular proteins Tsg101 and Alix, respectively. The Nucleocapsid region of Gag (NC), which binds the Bro1 domain of Alix, also plays an important role in HIV-1 release, but the underlying mechanism remains unclear. Here we show that the first 202 residues of the Bro1 domain (Broi) are sufficient to bind Gag. Broi interferes with HIV-1 release in an NC–dependent manner and arrests viral budding at the plasma membrane. Similar interrupted budding structures are seen following over-expression of a fragment containing Bro1 with the adjacent V domain (Bro1-V). Although only Bro1-V contains binding determinants for CHMP4, both Broi and Bro1-V inhibited release via both the PTAP/Tsg101 and the LYPXnL/Alix pathways, suggesting that they interfere with a key step in HIV-1 release. Remarkably, we found that over-expression of Bro1 rescued the release of HIV-1 lacking both L domains. This rescue required the N-terminal region of the NC domain in Gag and the CHMP4 binding site in Bro1. Interestingly, release defects due to mutations in NC that prevented Bro1 mediated rescue of virus egress were rescued by providing a link to the ESCRT machinery via Nedd4.2s over-expression. Our data support a model in which NC cooperates with PTAP in the recruitment of cellular proteins necessary for its L domain activity and binds the Bro1–CHMP4 complex required for LYPXnL–mediated budding