Author Correction: Design of amidobenzimidazole STING receptor agonists with systemic activity.

Change history: In this Letter, author Ana Puhl was inadvertently omitted; this error has been corrected online.An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Design of amidobenzimidazole STING receptor agonists with systemic activity.

Stimulator of interferon genes (STING) is a receptor in the endoplasmic reticulum that propagates innate immune sensing of cytosolic pathogen-derived and self DNA1. The development of compounds that modulate STING has recently been the focus of intense research for the treatment of cancer and infectious diseases and as vaccine adjuvants2. To our knowledge, current efforts are focused on the development of modified cyclic dinucleotides that mimic the endogenous STING ligand cGAMP; these have progressed into clinical trials in patients with solid accessible tumours amenable to intratumoral delivery3. Here we report the discovery of a small molecule STING agonist that is not a cyclic dinucleotide and is systemically efficacious for treating tumours in mice. We developed a linking strategy to synergize the effect of two symmetry-related amidobenzimidazole (ABZI)-based compounds to create linked ABZIs (diABZIs) with enhanced binding to STING and cellular function. Intravenous administration of a diABZI STING agonist to immunocompetent mice with established syngeneic colon tumours elicited strong anti-tumour activity, with complete and lasting regression of tumours. Our findings represent a milestone in the rapidly growing field of immune-modifying cancer therapies.

The Alzheimer's Knowledge Base: A Knowledge Graph for Alzheimer Disease Research.

BACKGROUND: As global populations age and become susceptible to neurodegenerative illnesses, new therapies for Alzheimer disease (AD) are urgently needed. Existing data resources for drug discovery and repurposing fail to capture relationships central to the disease's etiology and response to drugs. OBJECTIVE: We designed the Alzheimer's Knowledge Base (AlzKB) to alleviate this need by providing a comprehensive knowledge representation of AD etiology and candidate therapeutics. METHODS: We designed the AlzKB as a large, heterogeneous graph knowledge base assembled using 22 diverse external data sources describing biological and pharmaceutical entities at different levels of organization (eg, chemicals, genes, anatomy, and diseases). AlzKB uses a Web Ontology Language 2 ontology to enforce semantic consistency and allow for ontological inference. We provide a public version of AlzKB and allow users to run and modify local versions of the knowledge base. RESULTS: AlzKB is freely available on the web and currently contains 118,902 entities with 1,309,527 relationships between those entities. To demonstrate its value, we used graph data science and machine learning to (1) propose new therapeutic targets based on similarities of AD to Parkinson disease and (2) repurpose existing drugs that may treat AD. For each use case, AlzKB recovers known therapeutic associations while proposing biologically plausible new ones. CONCLUSIONS: AlzKB is a new, publicly available knowledge resource that enables researchers to discover complex translational associations for AD drug discovery. Through 2 use cases, we show that it is a valuable tool for proposing novel therapeutic hypotheses based on public biomedical knowledge.

PMLB v1.0: an open-source dataset collection for benchmarking machine learning methods.

MOTIVATION: Novel machine learning and statistical modeling studies rely on standardized comparisons to existing methods using well-studied benchmark datasets. Few tools exist that provide rapid access to many of these datasets through a standardized, user-friendly interface that integrates well with popular data science workflows. RESULTS: This release of PMLB (Penn Machine Learning Benchmarks) provides the largest collection of diverse, public benchmark datasets for evaluating new machine learning and data science methods aggregated in one location. v1.0 introduces a number of critical improvements developed following discussions with the open-source community. AVAILABILITY AND IMPLEMENTATION: PMLB is available at https://github.com/EpistasisLab/pmlb. Python and R interfaces for PMLB can be installed through the Python Package Index and Comprehensive R Archive Network, respectively.

A clinical model to predict postoperative improvement in sub-domains of the modified Japanese Orthopedic Association score for degenerative cervical myelopathy.

PURPOSE: The modified Japanese Orthopedic Association (mJOA) score consists of six sub-domains and is used to quantify the severity of cervical myelopathy. The current study aimed to assess for predictors of postoperative mJOA sub-domains scores following elective surgical management for patients with cervical myelopathy and develop the first clinical prediction model for 12-month mJOA sub-domain scores.Please confirm if the author names are presented accurately and in the correct sequence (given name, middle name/initial, family name). Author 1 Given name: [Byron F.] Last name [Stephens], Author 2 Given name: [Lydia J.] Last name [McKeithan], Author 3 Given name: [W. Hunter] Last name [Waddell], Author 4 Given name: [Anthony M.] Last name [Steinle], Author 5 Given name: [Wilson E.] Last name [Vaughan], Author 6 Given name: [Jacquelyn S.] Last name [Pennings], Author 7 Given name: [Jacquelyn S.] Last name [Pennings], Author 8 Given name: [Scott L.] Last name [Zuckerman], Author 9 Given name: [Kristin R.] Last name [Archer], Author 10 Given name: [Amir M.] Last name [Abtahi] Also, kindly confirm the details in the metadata are correct.Last Author listed should be Kristin R. Archer METHODS: A multivariable proportional odds ordinal regression model was developed for patients with cervical myelopathy. The model included patient demographic, clinical, and surgery covariates along with baseline sub-domain scores. The model was internally validated using bootstrap resampling to estimate the likely performance on a new sample of patients. RESULTS: The model identified mJOA baseline sub-domains to be the strongest predictors of 12-month scores, with numbness in legs and ability to walk predicting five of the six mJOA items. Additional covariates predicting three or more items included age, preoperative anxiety/depression, gender, race, employment status, duration of symptoms, smoking status, and radiographic presence of listhesis. Surgical approach, presence of motor deficits, number of surgical levels involved, history of diabetes mellitus, workers' compensation claim, and patient insurance had no impact on 12-month mJOA scores. CONCLUSION: Our study developed and validated a clinical prediction model for improvement in mJOA scores at 12 months following surgery. The results highlight the importance of assessing preoperative numbness, walking ability, modifiable variables of anxiety/depression, and smoking status. This model has the potential to assist surgeons, patients, and families when considering surgery for cervical myelopathy. LEVEL OF EVIDENCE: Level III.

The promise of automated machine learning for the genetic analysis of complex traits.

The genetic analysis of complex traits has been dominated by parametric statistical methods due to their theoretical properties, ease of use, computational efficiency, and intuitive interpretation. However, there are likely to be patterns arising from complex genetic architectures which are more easily detected and modeled using machine learning methods. Unfortunately, selecting the right machine learning algorithm and tuning its hyperparameters can be daunting for experts and non-experts alike. The goal of automated machine learning (AutoML) is to let a computer algorithm identify the right algorithms and hyperparameters thus taking the guesswork out of the optimization process. We review the promises and challenges of AutoML for the genetic analysis of complex traits and give an overview of several approaches and some example applications to omics data. It is our hope that this review will motivate studies to develop and evaluate novel AutoML methods and software in the genetics and genomics space. The promise of AutoML is to enable anyone, regardless of training or expertise, to apply machine learning as part of their genetic analysis strategy.

Automating Predictive Toxicology Using ComptoxAI.

ComptoxAI is a new data infrastructure for computational and artificial intelligence research in predictive toxicology. Here, we describe and showcase ComptoxAI's graph-structured knowledge base in the context of three real-world use-cases, demonstrating that it can rapidly answer complex questions about toxicology that are infeasible using previous technologies and data resources. These use-cases each demonstrate a tool for information retrieval from the knowledge base being used to solve a specific task: The "shortest path" module is used to identify mechanistic links between perfluorooctanoic acid (PFOA) exposure and nonalcoholic fatty liver disease; the "expand network" module identifies communities that are linked to dioxin toxicity; and the quantitative structure-activity relationship (QSAR) dataset generator predicts pregnane X receptor agonism in a set of 4,021 pesticide ingredients. The contents of ComptoxAI's source data are rigorously aggregated from a diverse array of public third-party databases, and ComptoxAI is designed as a free, public, and open-source toolkit to enable diverse classes of users including biomedical researchers, public health and regulatory officials, and the general public to predict toxicology of unknowns and modes of action.

Ten simple rules for writing a paper about scientific software.

Papers describing software are an important part of computational fields of scientific research. These "software papers" are unique in a number of ways, and they require special consideration to improve their impact on the scientific community and their efficacy at conveying important information. Here, we discuss 10 specific rules for writing software papers, covering some of the different scenarios and publication types that might be encountered, and important questions from which all computational researchers would benefit by asking along the way.

Embedding covariate adjustments in tree-based automated machine learning for biomedical big data analyses.

BACKGROUND: A typical task in bioinformatics consists of identifying which features are associated with a target outcome of interest and building a predictive model. Automated machine learning (AutoML) systems such as the Tree-based Pipeline Optimization Tool (TPOT) constitute an appealing approach to this end. However, in biomedical data, there are often baseline characteristics of the subjects in a study or batch effects that need to be adjusted for in order to better isolate the effects of the features of interest on the target. Thus, the ability to perform covariate adjustments becomes particularly important for applications of AutoML to biomedical big data analysis. RESULTS: We developed an approach to adjust for covariates affecting features and/or target in TPOT. Our approach is based on regressing out the covariates in a manner that avoids 'leakage' during the cross-validation training procedure. We describe applications of this approach to toxicogenomics and schizophrenia gene expression data sets. The TPOT extensions discussed in this work are available at https://github.com/EpistasisLab/tpot/tree/v0.11.1-resAdj . CONCLUSIONS: In this work, we address an important need in the context of AutoML, which is particularly crucial for applications to bioinformatics and medical informatics, namely covariate adjustments. To this end we present a substantial extension of TPOT, a genetic programming based AutoML approach. We show the utility of this extension by applications to large toxicogenomics and differential gene expression data. The method is generally applicable in many other scenarios from the biomedical field.

A strategic action framework for multipurpose prevention technologies combining contraceptive hormones and antiretroviral drugs to prevent pregnancy and HIV.

OBJECTIVE: Multipurpose prevention technologies (MPTs) are an innovative class of products that deliver varied combinations of human immunodeficiency virus (HIV) prevention, other sexually transmitted infection (STI) prevention, and contraception. Combining separate strategies for different indications into singular prevention products can reduce the stigma around HIV and STI prevention, improve acceptability of and adherence to more convenient products, and be more cost-effective by addressing overlapping risks. METHODS: This article outlines a strategic action framework developed as an outcome of a series of expert meetings held between 2014 and 2016. The meetings focused on identifying opportunities and challenges for MPTs that combine hormonal contraception (HC) with antiretroviral drugs into single products. The framework aims to present an actionable strategy, by addressing key research gaps and outlining the key areas for progress, to guide current and future HC MPT development. RESULTS: We identified eight primary action areas for the development of impactful HC MPTs, and includes aspects from epidemiology, pharmacology, clinical trial design, regulatory requirements, manufacturing and commercialisation, behavioural science, and investment needs for research and development. CONCLUSION: Overall, the challenges involved with reconciling the critical social-behavioural context that will drive MPT product use and uptake with the complexities of research and development and regulatory approval are of paramount importance. To realise the potential of MPTs given their complexity and finite resources, researchers in the MPT field must be strategic about the way forward; increased support among policy-makers, advocates, funders and the pharmaceutical industry is critical.

Detection methods for stochastic gravitational-wave backgrounds: a unified treatment.

We review detection methods that are currently in use or have been proposed to search for a stochastic background of gravitational radiation. We consider both Bayesian and frequentist searches using ground-based and space-based laser interferometers, spacecraft Doppler tracking, and pulsar timing arrays; and we allow for anisotropy, non-Gaussianity, and non-standard polarization states. Our focus is on relevant data analysis issues, and not on the particular astrophysical or early Universe sources that might give rise to such backgrounds. We provide a unified treatment of these searches at the level of detector response functions, detection sensitivity curves, and, more generally, at the level of the likelihood function, since the choice of signal and noise models and prior probability distributions are actually what define the search. Pedagogical examples are given whenever possible to compare and contrast different approaches. We have tried to make the article as self-contained and comprehensive as possible, targeting graduate students and new researchers looking to enter this field.

Adapting simultaneous analysis phylogenomic techniques to study complex disease gene relationships.

The characterization of complex diseases remains a great challenge for biomedical researchers due to the myriad interactions of genetic and environmental factors. Network medicine approaches strive to accommodate these factors holistically. Phylogenomic techniques that can leverage available genomic data may provide an evolutionary perspective that may elucidate knowledge for gene networks of complex diseases and provide another source of information for network medicine approaches. Here, an automated method is presented that leverages publicly available genomic data and phylogenomic techniques, resulting in a gene network. The potential of approach is demonstrated based on a case study of nine genes associated with Alzheimer Disease, a complex neurodegenerative syndrome. The developed technique, which is incorporated into an update to a previously described Perl script called "ASAP," was implemented through a suite of Ruby scripts entitled "ASAP2," first compiles a list of sequence-similarity based orthologues using PSI-BLAST and a recursive NCBI BLAST+ search strategy, then constructs maximum parsimony phylogenetic trees for each set of nucleotide and protein sequences, and calculates phylogenetic metrics (Incongruence Length Difference between orthologue sets, partitioned Bremer support values, combined branch scores, and Robinson-Foulds distance) to provide an empirical assessment of evolutionary conservation within a given genetic network. In addition to the individual phylogenetic metrics, ASAP2 provides results in a way that can be used to generate a gene network that represents evolutionary similarity based on topological similarity (the Robinson-Foulds distance). The results of this study demonstrate the potential for using phylogenomic approaches that enable the study of multiple genes simultaneously to provide insights about potential gene relationships that can be studied within a network medicine framework that may not have been apparent using traditional, single-gene methods. Furthermore, the results provide an initial integrated evolutionary history of an Alzheimer Disease gene network and identify potentially important co-evolutionary clustering that may warrant further investigation.

Centralized and Federated Models for the Analysis of Clinical Data.

The progress of precision medicine research hinges on the gathering and analysis of extensive and diverse clinical datasets. With the continued expansion of modalities, scales, and sources of clinical datasets, it becomes imperative to devise methods for aggregating information from these varied sources to achieve a comprehensive understanding of diseases. In this review, we describe two important approaches for the analysis of diverse clinical datasets, namely the centralized model and federated model. We compare and contrast the strengths and weaknesses inherent in each model and present recent progress in methodologies and their associated challenges. Finally, we present an outlook on the opportunities that both models hold for the future analysis of clinical data.

The choice-wide behavioral association study: data-driven identification of interpretable behavioral components.

Behavior contains rich structure across many timescales, but there is a dearth of methods to identify relevant components, especially over the longer periods required for learning and decision-making. Inspired by the goals and techniques of genome-wide association studies, we present a data-driven method-the choice-wide behavioral association study: CBAS-that systematically identifies such behavioral features. CBAS uses a powerful, resampling-based, method of multiple comparisons correction to identify sequences of actions or choices that either differ significantly between groups or significantly correlate with a covariate of interest. We apply CBAS to different tasks and species (flies, rats, and humans) and find, in all instances, that it provides interpretable information about each behavioral task.

Knowledge graph aids comprehensive explanation of drug and chemical toxicity.

In computational toxicology, prediction of complex endpoints has always been challenging, as they often involve multiple distinct mechanisms. State-of-the-art models are either limited by low accuracy, or lack of interpretability due to their black-box nature. Here, we introduce AIDTox, an interpretable deep learning model which incorporates curated knowledge of chemical-gene connections, gene-pathway annotations, and pathway hierarchy. AIDTox accurately predicts cytotoxicity outcomes in HepG2 and HEK293 cells. It also provides comprehensive explanations of cytotoxicity covering multiple aspects of drug activity, including target interaction, metabolism, and elimination. In summary, AIDTox provides a computational framework for unveiling cellular mechanisms for complex toxicity endpoints.

Discovering Venom-Derived Drug Candidates Using Differential Gene Expression.

Venoms are a diverse and complex group of natural toxins that have been adapted to treat many types of human disease, but rigorous computational approaches for discovering new therapeutic activities are scarce. We have designed and validated a new platform-named VenomSeq-to systematically identify putative associations between venoms and drugs/diseases via high-throughput transcriptomics and perturbational differential gene expression analysis. In this study, we describe the architecture of VenomSeq and its evaluation using the crude venoms from 25 diverse animal species and 9 purified teretoxin peptides. By integrating comparisons to public repositories of differential expression, associations between regulatory networks and disease, and existing knowledge of venom activity, we provide a number of new therapeutic hypotheses linking venoms to human diseases supported by multiple layers of preliminary evidence.

Exploring genetic influences on adverse outcome pathways using heuristic simulation and graph data science.

Adverse outcome pathways provide a powerful tool for understanding the biological signaling cascades that lead to disease outcomes following toxicity. The framework outlines downstream responses known as key events, culminating in a clinically significant adverse outcome as a final result of the toxic exposure. Here we use the AOP framework combined with artificial intelligence methods to gain novel insights into genetic mechanisms that underlie toxicity-mediated adverse health outcomes. Specifically, we focus on liver cancer as a case study with diverse underlying mechanisms that are clinically significant. Our approach uses two complementary AI techniques: Generative modeling via automated machine learning and genetic algorithms, and graph machine learning. We used data from the US Environmental Protection Agency's Adverse Outcome Pathway Database (AOP-DB; aopdb.epa.gov) and the UK Biobank's genetic data repository. We use the AOP-DB to extract disease-specific AOPs and build graph neural networks used in our final analyses. We use the UK Biobank to retrieve real-world genotype and phenotype data, where genotypes are based on single nucleotide polymorphism data extracted from the AOP-DB, and phenotypes are case/control cohorts for the disease of interest (liver cancer) corresponding to those adverse outcome pathways. We also use propensity score matching to appropriately sample based on important covariates (demographics, comorbidities, and social deprivation indices) and to balance the case and control populations in our machine language training/testing datasets. Finally, we describe a novel putative risk factor for LC that depends on genetic variation in both the aryl-hydrocarbon receptor (AHR) and ATP binding cassette subfamily B member 11 (ABCB11) genes.

Strategic actions to advance multipurpose prevention technologies in low- and middle-income countries.

Background: HIV, other sexually transmitted infections (STIs) and unintended pregnancies are critical and interlinked health risks for millions of women of reproductive age worldwide. Multipurpose prevention technologies (MPTs) offer an innovative approach for expanding combined pregnancy and/or disease prevention. So far, MPT development efforts have focused mostly on HIV prevention, but about half of product candidates comprise compounds active against non-HIV STIs as well. This review aims to provide a framework that promotes the efficient advancement of the most promising preclinical products through the development pathway and into the hands of end-users, with a focus on women in low- and middle-income countries (L/MICs). Methods: This mini review provides a summary of the current landscape of the MPT field. It comprises a landscape assessment of MPTs in development, complemented by a series of 28 in-depth, semi-structured key informant interviews (KIIs) with experts representing different L/MIC perspectives. Main results: We identified six primary action strategies to advance MPTs for L/MICs, including identification of key research gaps and priorities. For each action strategy, progress to date and key recommendations are included. Conclusions: To realize the life-saving potential of MPTs and maximize the momentum made to date, a strategic, collaborative and well-funded response to the gaps and next steps outlined in this paper is critical. A coordinated response can add rigor and efficiency to the development process, to successfully advance the most promising MPT products to the hands of end-users.

Pharmacokinetics of tenofovir following intravaginal and intrarectal administration of tenofovir gel to rhesus macaques.

Tenofovir gel (1%) is being developed as a microbicide for the prevention of human immunodeficiency virus (HIV) infection and has been shown to reduce transmission to women by 39%. The gel also prevents infection in macaques when applied intravaginally or intrarectally prior to challenge with simian-human immunodeficiency virus (SHIV), but very little pharmacokinetic information for macaques is available to help extrapolate the data to humans and thus inform future development activities. We have determined the pharmacokinetics of tenofovir in macaques following intravaginal and intrarectal administration of 0.2, 1, and 5% gels. Plasma and vaginal and rectal fluid samples were collected up to 24 h after dosing, and at 24 h postdosing biopsy specimens were taken from the vaginal wall, cervix, and rectum. Following vaginal and rectal administration, tenofovir rapidly distributed to the matrices distal to the site of administration. In all matrices, exposure increased with increasing dose, and with the 1% and 5% formulations, concentrations remained detectable in most animals 24 h after dosing. At all doses, concentrations at the dosing site were typically 1 to 2 log units higher than those in the opposite compartment and 4 to 5 log units higher than those in plasma. Exposure in vaginal fluid after vaginal dosing was 58 to 82% lower than that in rectal fluid after rectal dosing, but plasma exposure was 1- to 2-fold greater after vaginal dosing than after rectal dosing. These data suggest that a tenofovir-based microbicide may have the potential to protect when exposure is via vaginal or anal intercourse, regardless of whether the microbicide is applied vaginally or rectally.

Development of macrocyclic inhibitors of HCV NS3/4A protease with cyclic constrained P2-P4 linkers.

A series of macrocyclic compounds containing a cyclic constraint in the P2-P4 linker region have been discovered and shown to exhibit excellent HCV NS3/4a genotype 3a and genotype 1b R155K, A156T, A156V, and D168V mutant activity while maintaining high rat liver exposure. The effect of the constraint is most dramatic against gt 1b A156 mutants where ~20-fold improvements in potency are achieved by introduction of a variety of ring systems into the P2-P4 linker.