PET-CR as a potential surrogate endpoint in untreated DLBCL: meta-analysis and implications for clinical trial design.

This study's focus is the association of end-of-therapy (EOT) PET results with progression-free (PFS) and overall survival (OS) in patients with diffuse large B-cell lymphoma receiving first-line chemoimmunotherapy. We develop a Bayesian hierarchical model for predicting PFS and OS from EOT PET-complete response (PET-CR) using a literature-based meta-analysis of 20 treatment arms and a substudy of 4 treatment arms in 3 clinical trials for which we have patient-level data. The PET-CR rate in our substudy was 72%. The modeled estimates for hazard ratio (PET-CR/non-PET-CR) were 0.13 for PFS (95% CI 0.10, 0.16) and 0.10 for OS (CI 0.07, 0.12). Hazard ratios varied little by patient subtype and were confirmed by the overall meta-analysis. We link these findings to designing future clinical trials and show how our model can be used in adapting the sample size of a trial to accumulating results regarding treatment benefits on PET-CR and a survival endpoint.

Steric Manipulation as a Mechanism for Tuning the Reduction and Oxidation Potentials of Phenothiazines.

Synthetic chemists customarily tune the redox characteristics of π-conjugated molecules by introducing electron-donating or electron-withdrawing substituents onto the molecular core, or by modifying the length of the π-conjugated pathway. Any steric effects of such efforts on molecular geometry typically affect both the neutral and charged (oxidized or reduced) states indiscriminately. However, in electroactive systems that undergo significant conformational changes upon oxidation or reduction, we can leverage the steric and inductive effects of substitution to attain considerable control over individual redox potentials. Here, we make use of density functional theory to elucidate the interplay between electronic and geometric effects of peripheral substitution on the model system of phenothiazine. For instance, we introduce substituents at positions ortho to the nitrogen atom (positions 1 and 9) to induce steric strain in the radical-cation state without significant effect on the neutral molecule, thereby augmenting the overall ionization potential. Notably, this steric effect persists for electron-donating substituents; the resulting ionization potentials therefore deviate from outcomes foretold by Hammett constants. Moreover, the same procedure has limited effect on electron affinities because of differences in phenothiazines' relaxation process upon reduction compared to oxidation. Our results promote molecular design guidelines for manipulating redox potentials in classes of electroactive compounds that experience dramatic changes in geometry upon ionization.

Feasibility as a mechanism for model identification and validation.

As new technologies permit the generation of hitherto unprecedented volumes of data (e.g. genome-wide association study data), researchers struggle to keep up with the added complexity and time commitment required for its analysis. For this reason, model selection commonly relies on machine learning and data-reduction techniques, which tend to afford models with obscure interpretations. Even in cases with straightforward explanatory variables, the so-called 'best' model produced by a given model-selection technique may fail to capture information of vital importance to the domain-specific questions at hand. Herein we propose a new concept for model selection, feasibility, for use in identifying multiple models that are in some sense optimal and may unite to provide a wider range of information relevant to the topic of interest, including (but not limited to) interaction terms. We further provide an R package and associated Shiny Applications for use in identifying or validating feasible models, the performance of which we demonstrate on both simulated and real-life data.

Derivation, Validation, and Potential Treatment Implications of Novel Clinical Phenotypes for Sepsis.

Importance: Sepsis is a heterogeneous syndrome. Identification of distinct clinical phenotypes may allow more precise therapy and improve care. Objective: To derive sepsis phenotypes from clinical data, determine their reproducibility and correlation with host-response biomarkers and clinical outcomes, and assess the potential causal relationship with results from randomized clinical trials (RCTs). Design, Settings, and Participants: Retrospective analysis of data sets using statistical, machine learning, and simulation tools. Phenotypes were derived among 20 189 total patients (16 552 unique patients) who met Sepsis-3 criteria within 6 hours of hospital presentation at 12 Pennsylvania hospitals (2010-2012) using consensus k means clustering applied to 29 variables. Reproducibility and correlation with biological parameters and clinical outcomes were assessed in a second database (2013-2014; n = 43 086 total patients and n = 31 160 unique patients), in a prospective cohort study of sepsis due to pneumonia (n = 583), and in 3 sepsis RCTs (n = 4737). Exposures: All clinical and laboratory variables in the electronic health record. Main Outcomes and Measures: Derived phenotype (α, ß, γ, and δ) frequency, host-response biomarkers, 28-day and 365-day mortality, and RCT simulation outputs. Results: The derivation cohort included 20 189 patients with sepsis (mean age, 64 [SD, 17] years; 10 022 [50%] male; mean maximum 24-hour Sequential Organ Failure Assessment [SOFA] score, 3.9 [SD, 2.4]). The validation cohort included 43 086 patients (mean age, 67 [SD, 17] years; 21 993 [51%] male; mean maximum 24-hour SOFA score, 3.6 [SD, 2.0]). Of the 4 derived phenotypes, the α phenotype was the most common (n = 6625; 33%) and included patients with the lowest administration of a vasopressor; in the ß phenotype (n = 5512; 27%), patients were older and had more chronic illness and renal dysfunction; in the γ phenotype (n = 5385; 27%), patients had more inflammation and pulmonary dysfunction; and in the δ phenotype (n = 2667; 13%), patients had more liver dysfunction and septic shock. Phenotype distributions were similar in the validation cohort. There were consistent differences in biomarker patterns by phenotype. In the derivation cohort, cumulative 28-day mortality was 287 deaths of 5691 unique patients (5%) for the α phenotype; 561 of 4420 (13%) for the ß phenotype; 1031 of 4318 (24%) for the γ phenotype; and 897 of 2223 (40%) for the δ phenotype. Across all cohorts and trials, 28-day and 365-day mortality were highest among the δ phenotype vs the other 3 phenotypes (P < .001). In simulation models, the proportion of RCTs reporting benefit, harm, or no effect changed considerably (eg, varying the phenotype frequencies within an RCT of early goal-directed therapy changed the results from >33% chance of benefit to >60% chance of harm). Conclusions and Relevance: In this retrospective analysis of data sets from patients with sepsis, 4 clinical phenotypes were identified that correlated with host-response patterns and clinical outcomes, and simulations suggested these phenotypes may help in understanding heterogeneity of treatment effects. Further research is needed to determine the utility of these phenotypes in clinical care and for informing trial design and interpretation.

A novel data structure to support ultra-fast taxonomic classification of metagenomic sequences with k-mer signatures.

Motivation: Metagenomic read classification is a critical step in the identification and quantification of microbial species sampled by high-throughput sequencing. Although many algorithms have been developed to date, they suffer significant memory and/or computational costs. Due to the growing popularity of metagenomic data in both basic science and clinical applications, as well as the increasing volume of data being generated, efficient and accurate algorithms are in high demand. Results: We introduce MetaOthello, a probabilistic hashing classifier for metagenomic sequencing reads. The algorithm employs a novel data structure, called l-Othello, to support efficient querying of a taxon using its k-mer signatures. MetaOthello is an order-of-magnitude faster than the current state-of-the-art algorithms Kraken and Clark, and requires only one-third of the RAM. In comparison to Kaiju, a metagenomic classification tool using protein sequences instead of genomic sequences, MetaOthello is three times faster and exhibits 20-30% higher classification sensitivity. We report comparative analyses of both scalability and accuracy using a number of simulated and empirical datasets. Availability and implementation: MetaOthello is a stand-alone program implemented in C ++. The current version (1.0) is accessible via https://doi.org/10.5281/zenodo.808941. Contact: liuj@cs.uky.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

rFSA: An R Package for Finding Best Subsets and Interactions.

Herein we present the R package rFSA, which implements an algorithm for improved variable selection. The algorithm searches a data space for models of a user-specified form that are statistically optimal under a measure of model quality. Many iterations afford a set of feasible solutions (or candidate models) that the researcher can evaluate for relevance to his or her questions of interest. The algorithm can be used to formulate new or to improve upon existing models in bioinformatics, health care, and myriad other fields in which the volume of available data has outstripped researchers' practical and computational ability to explore larger subsets or higher-order interaction terms. The package accommodates linear and generalized linear models, as well as a variety of criterion functions such as Allen's PRESS and AIC. New modeling strategies and criterion functions can be adapted easily to work with rFSA.

Beyond the Hammett Effect: Using Strain to Alter the Landscape of Electrochemical Potentials.

The substitution of sterically bulky groups at precise locations along the periphery of fused-ring aromatic systems is demonstrated to increase electrochemical oxidation potentials by preventing relaxation events in the oxidized state. Phenothiazines, which undergo significant geometric relaxation upon oxidation, are used as fused-ring models to showcase that electron-donating methyl groups, which would generally be expected to lower oxidation potential, can lead to increased oxidation potentials when used as the steric drivers. Reduction events remain inaccessible through this molecular design route, a critical characteristic for electrochemical systems where high oxidation potentials are required and in which reductive decomposition must be prevented, as in high-voltage lithium-ion batteries. This study reveals a new avenue to alter the redox characteristics of fused-ring systems that find wide use as electroactive elements across a number of developing technologies.

The fate of phenothiazine-based redox shuttles in lithium-ion batteries.

The stability and reactivity of the multiple oxidation states of aromatic compounds are critical to the performance of these species as additives and electrolytes in energy-storage applications. Both for the overcharge mitigation in ion-intercalation batteries and as electroactive species in redox flow batteries, neutral, radical-cation, and radical-anion species may be present during charging and discharging processes. Despite the wide range of compounds evaluated for both applications, the progress identifying stable materials has been slow, limited perhaps by the overall lack of analysis of the failure mechanism when a material is utilized in an energy-storage device. In this study, we examined the reactivity of phenothiazine derivatives, which have found interest as redox shuttles in lithium-ion battery applications. We explored the products of the reactions of neutral compounds in battery electrolytes and the products of radical cation formation using bulk electrolysis and coin cell cycling. Following the failure of each cell, the electrolytes were characterized to identify redox shuttle decomposition products. Based on these results, a set of decomposition mechanisms is proposed and further explored using experimental and theoretical approaches. The results highlight the necessity to fully characterize and understand the chemical degradation mechanisms of the redox species in order to develop new generations of electroactive materials.

N-substituted phenothiazine derivatives: how the stability of the neutral and radical cation forms affects overcharge performance in lithium-ion batteries.

Phenothiazine and five N-substituted derivatives were evaluated as electrolyte additives for overcharge protection in LiFePO4 /synthetic graphite lithium-ion batteries. We report on the stability and reactivity of both the neutral and radical-cation forms of these six compounds. While three of the compounds show extensive overcharge protection, the remaining three last for only one to a few cycles. UV/Vis studies of redox shuttle stability in the radical cation form are consistent with the overcharge performance: redox shuttles with spectra that show little change over time exhibit extensive overcharge performance, whereas those with changing spectra have limited overcharge protection. In one case, we determined that a C-N bond cleaves upon oxidation, forming the phenothiazine radical cation and leading to premature overcharge protection failure; in another case, poor solubility appears to limit protection.

Overcharge performance of 3,7-disubstituted N-ethylphenothiazine derivatives in lithium-ion batteries.

3,7-Disubstituted N-ethylphenothiazine derivatives were synthesized as redox shuttle candidates for lithium-ion batteries. Battery cycling results show that three derivatives prevent overcharge.