Repurposing N-acetylcysteine for management of non-acetaminophen induced acute liver failure: an evidence scan from a global health perspective.

Background: The World Health Organization (WHO)'s Essential Medicines List (EML) plays an important role in advocating for access to key treatments for conditions affecting people in all geographic settings. We applied our established drug repurposing methods to one EML agent, N-acetylcysteine (NAC), to identify additional uses of relevance to the global health community beyond its existing EML indication (acetaminophen toxicity). Methods: We undertook a phenome-wide association study (PheWAS) of a variant in the glutathione synthetase (GSS) gene in approximately 35,000 patients to explore novel indications for use of NAC, which targets glutathione. We then evaluated the evidence regarding biologic plausibility, efficacy, and safety of NAC use in the new phenotype candidates. Results: PheWAS of GSS variant R418Q revealed increased risk of several phenotypes related to non-acetaminophen induced acute liver failure (ALF), indicating that NAC may represent a therapeutic option for treating this condition. Evidence review identified practice guidelines, systematic reviews, clinical trials, retrospective cohorts and case series, and case reports. This evidence suggesting benefit of NAC use in this subset of ALF patients. The safety profile of NAC in this literature was also concordant with existing evidence on safety of this agent in acetaminophen-induced ALF. Conclusions: This body of literature indicates efficacy and safety of NAC in non-acetaminophen induced ALF. Given the presence of NAC on the EML, this medication is likely to be available across a range of resource settings; promulgating its use in this novel subset of ALF can provide healthcare professionals and patients with a valuable and safe complement to supportive care for this disease.

Interactive network-based clustering and investigation of multimorbidity association matrices with associationSubgraphs.

MOTIVATION: Making sense of networked multivariate association patterns is vitally important to many areas of high-dimensional analysis. Unfortunately, as the data-space dimensions grow, the number of association pairs increases in O(n2); this means that traditional visualizations such as heatmaps quickly become too complicated to parse effectively. RESULTS: Here, we present associationSubgraphs: a new interactive visualization method to quickly and intuitively explore high-dimensional association datasets using network percolation and clustering. The goal is to provide an efficient investigation of association subgraphs, each containing a subset of variables with stronger and more frequent associations among themselves than the remaining variables outside the subset, by showing the entire clustering dynamics and providing subgraphs under all possible cutoff values at once. Particularly, we apply associationSubgraphs to a phenome-wide multimorbidity association matrix generated from an electronic health record and provide an online, interactive demonstration for exploring multimorbidity subgraphs. AVAILABILITY AND IMPLEMENTATION: An R package implementing both the algorithm and visualization components of associationSubgraphs is available at https://github.com/tbilab/associationsubgraphs. Online documentation is available at https://prod.tbilab.org/associationsubgraphs_info/. A demo using a multimorbidity association matrix is available at https://prod.tbilab.org/associationsubgraphs-example/.

Human and Machine Intelligence Together Drive Drug Repurposing in Rare Diseases.

Repurposing is an increasingly attractive method within the field of drug development for its efficiency at identifying new therapeutic opportunities among approved drugs at greatly reduced cost and time of more traditional methods. Repurposing has generated significant interest in the realm of rare disease treatment as an innovative strategy for finding ways to manage these complex conditions. The selection of which agents should be tested in which conditions is currently informed by both human and machine discovery, yet the appropriate balance between these approaches, including the role of artificial intelligence (AI), remains a significant topic of discussion in drug discovery for rare diseases and other conditions. Our drug repurposing team at Vanderbilt University Medical Center synergizes machine learning techniques like phenome-wide association study-a powerful regression method for generating hypotheses about new indications for an approved drug-with the knowledge and creativity of scientific, legal, and clinical domain experts. While our computational approaches generate drug repurposing hits with a high probability of success in a clinical trial, human knowledge remains essential for the hypothesis creation, interpretation, "go-no go" decisions with which machines continue to struggle. Here, we reflect on our experience synergizing AI and human knowledge toward realizable patient outcomes, providing case studies from our portfolio that inform how we balance human knowledge and machine intelligence for drug repurposing in rare disease.

PheWAS-ME: a web-app for interactive exploration of multimorbidity patterns in PheWAS.

SUMMARY: Electronic health records (EHRs) linked with a DNA biobank provide unprecedented opportunities for biomedical research in precision medicine. The Phenome-wide association study (PheWAS) is a widely used technique for the evaluation of relationships between genetic variants and a large collection of clinical phenotypes recorded in EHRs. PheWAS analyses are typically presented as static tables and charts of summary statistics obtained from statistical tests of association between a genetic variant and individual phenotypes. Comorbidities are common and typically lead to complex, multivariate gene-disease association signals that are challenging to interpret. Discovering and interrogating multimorbidity patterns and their influence in PheWAS is difficult and time-consuming. We present PheWAS-ME: an interactive dashboard to visualize individual-level genotype and phenotype data side-by-side with PheWAS analysis results, allowing researchers to explore multimorbidity patterns and their associations with a genetic variant of interest. We expect this application to enrich PheWAS analyses by illuminating clinical multimorbidity patterns present in the data. AVAILABILITY AND IMPLEMENTATION: A demo PheWAS-ME application is publicly available at https://prod.tbilab.org/phewas_me/. Sample datasets are provided for exploration with the option to upload custom PheWAS results and corresponding individual-level data. Online versions of the appendices are available at https://prod.tbilab.org/phewas_me_info/. The source code is available as an R package on GitHub (https://github.com/tbilab/multimorbidity_explorer). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The Astounding Breadth of Health Disparity: Phenome-Wide Effects of Race on Disease Risk.

OBJECTIVE: We conducted a phenotype-wide association study (PheWAS) to compare diagnoses among Blacks with those of Whites in one health center in Tennessee using data from 1,883,369 patients. METHODS: We used our deidentified EHR, the Synthetic Derivative, to assess risk of diagnoses associated with Black as compared with White race using Firth logistic regression with covariates including age, sex, and density of clinical encounters. RESULTS: There were anchoring associations in both directions, including the highest increased risk for Blacks of having sickle cell anemia, and strongest decreased risk of basal cell carcinoma. Results included established areas of disparity and many novel associations. CONCLUSIONS: PheWAS is a viable tool for calculating risk associated with any biomarker. The current analysis provide a new approach to generating hypotheses and understanding the breadth of health disparities. Future analyses will further explore causality, risk factors, and potential confounders not accounted for here.

Leveraging Human Genetics to Identify Safety Signals Prior to Drug Marketing Approval and Clinical Use.

INTRODUCTION: When a new drug or biologic product enters the market, its full spectrum of side effects is not yet fully understood, as use in the real world often uncovers nuances not suggested within the relatively narrow confines of preapproval preclinical and trial work. OBJECTIVE: We describe a new, phenome-wide association study (PheWAS)- and evidence-based approach for detection of potential adverse drug effects. METHODS: We leveraged our established platform, which integrates human genetic data with associated phenotypes in electronic health records from 29,722 patients of European ancestry, to identify gene-phenotype associations that may represent known safety issues. We examined PheWAS data and the published literature for 16 genes, each of which encodes a protein targeted by at least one drug or biologic product. RESULTS: Initial data demonstrated that our novel approach (safety ascertainment using PheWAS [SA-PheWAS]) can replicate published safety information across multiple drug classes, with validated findings for 13 of 16 gene-drug class pairs. CONCLUSIONS: By connecting and integrating in vivo and in silico data, SA-PheWAS offers an opportunity to supplement current methods for predicting or confirming safety signals associated with therapeutic agents.

Using What We Already Have: Uncovering New Drug Repurposing Strategies in Existing Omics Data.

The promise of drug repurposing is to accelerate the translation of knowledge to treatment of human disease, bypassing common challenges associated with drug development to be more time- and cost-efficient. Repurposing has an increased chance of success due to the previous validation of drug safety and allows for the incorporation of omics. Hypothesis-generating omics processes inform drug repurposing decision-making methods on drug efficacy and toxicity. This review summarizes drug repurposing strategies and methodologies in the context of the following omics fields: genomics, epigenomics, transcriptomics, proteomics, metabolomics, microbiomics, phenomics, pregomics, and personomics. While each omics field has specific strengths and limitations, incorporating omics into the drug repurposing landscape is integral to its success.

Systematically Prioritizing Candidates in Genome-Based Drug Repurposing.

Drug repurposing is the application of approved drugs to treat diseases separate and distinct from their original indications. Herein, we define the scope of all practical precision drug repurposing using DrugBank, a publicly available database of pharmacological agents, and BioVU, a large, de-identified DNA repository linked to longitudinal electronic health records at Vanderbilt University Medical Center. We present a method of repurposing candidate prioritization through integration of pharmacodynamic and marketing variables from DrugBank with quality control thresholds for genomic data derived from the DNA samples within BioVU. Through the synergy of delineated "target-action pairs," along with target genomics, we identify ∼230 "pairs" that represent all practical opportunities for genomic drug repurposing. From this analysis, we present a pipeline of 14 repurposing candidates across 7 disease areas that link to our repurposability platform and present high potential for randomized controlled trial startup in upcoming months.

A Phenome-Wide Association Study Uncovers a Pathological Role of Coagulation Factor X during Acinetobacter baumannii Infection.

Coagulation and inflammation are interconnected, suggesting that coagulation plays a key role in the inflammatory response to pathogens. A phenome-wide association study (PheWAS) was used to identify clinical phenotypes of patients with a polymorphism in coagulation factor X. Patients with this single nucleotide polymorphism (SNP) were more likely to be hospitalized with hemostatic and infection-related disorders, suggesting that factor X contributes to the immune response to infection. To investigate this, we modeled infections by human pathogens in a mouse model of factor X deficiency. Factor X-deficient mice were protected from systemic Acinetobacter baumannii infection, suggesting that factor X plays a role in the immune response to A. baumannii Factor X deficiency was associated with reduced cytokine and chemokine production and alterations in immune cell population during infection: factor X-deficient mice demonstrated increased abundance of neutrophils, macrophages, and effector T cells. Together, these results suggest that factor X activity is associated with an inefficient immune response and contributes to the pathology of A. baumannii infection.

Mining 100 million notes to find homelessness and adverse childhood experiences: 2 case studies of rare and severe social determinants of health in electronic health records.

Objective: Understanding how to identify the social determinants of health from electronic health records (EHRs) could provide important insights to understand health or disease outcomes. We developed a methodology to capture 2 rare and severe social determinants of health, homelessness and adverse childhood experiences (ACEs), from a large EHR repository. Materials and Methods: We first constructed lexicons to capture homelessness and ACE phenotypic profiles. We employed word2vec and lexical associations to mine homelessness-related words. Next, using relevance feedback, we refined the 2 profiles with iterative searches over 100 million notes from the Vanderbilt EHR. Seven assessors manually reviewed the top-ranked results of 2544 patient visits relevant for homelessness and 1000 patients relevant for ACE. Results: word2vec yielded better performance (area under the precision-recall curve [AUPRC] of 0.94) than lexical associations (AUPRC = 0.83) for extracting homelessness-related words. A comparative study of searches for the 2 phenotypes revealed a higher performance achieved for homelessness (AUPRC = 0.95) than ACE (AUPRC = 0.79). A temporal analysis of the homeless population showed that the majority experienced chronic homelessness. Most ACE patients suffered sexual (70%) and/or physical (50.6%) abuse, with the top-ranked abuser keywords being "father" (21.8%) and "mother" (15.4%). Top prevalent associated conditions for homeless patients were lack of housing (62.8%) and tobacco use disorder (61.5%), while for ACE patients it was mental disorders (36.6%-47.6%). Conclusion: We provide an efficient solution for mining homelessness and ACE information from EHRs, which can facilitate large clinical and genetic studies of these social determinants of health.

Motivation for Launching a Cancer Metastasis Inhibition (CMI) Program.

Metastatic cancers impose significant burdens on patients, affecting quality of life, morbidity, and mortality. Even during remission, microscopic metastases can lurk, but few therapies directly target tumor cell metastasis. Agents that interfere with this process would represent a new paradigm in cancer management, changing the 'waiting game' into a time of active prevention. These therapies could take multiple forms based on the pathways involved in the metastatic process. For example, a phenome-wide association study showed that a single nucleotide polymorphism in the gene TBXA2R is associated with increased metastasis in multiple primary cancers (P = 0.003), suggesting clinical applicability of TBXA2R antagonists. Emerging data related to the role of platelets in metastasis are concordant with our sense that these pathways present significant opportunities for therapeutic development. However, before real progress can be made toward clinical targeting of the metastatic process, foundational work is needed to define informative measures of critical elements such as circulating tumor cells and tumor DNA, and circulatory vs. lymphatic spread. These challenges require an expansion of team science and composition to obtain competitive funding. At our academic medical center, we have implemented a Cancer Metastasis Inhibition (CMI) program investigating this approach across multiple cancers.

Using Human 'Experiments of Nature' to Predict Drug Safety Issues: An Example with PCSK9 Inhibitors.

INTRODUCTION: When a new drug enters the market, its full array of side effects remains to be defined. Current surveillance approaches targeting these effects remain largely reactive. There is a need for development of methods to predict specific safety events that should be sought for a given new drug during development and postmarketing activities. OBJECTIVE: We present here a safety signal identification approach applied to a new set of drug entities, inhibitors of the serine protease proprotein convertase subtilisin/kexin type 9 (PCSK9). METHODS: Using phenome-wide association study (PheWAS) methods, we analyzed available genotype and clinical data from 29,722 patients, leveraging the known effects of changes in PCSK9 to identify novel phenotypes in which this protein and its inhibitors may have impact. RESULTS: PheWAS revealed a significantly reduced risk of hypercholesterolemia (odds ratio [OR] 0.68, p = 7.6 × 10-4) in association with a known loss-of-function variant in PCSK9, R46L. Similarly, laboratory data indicated significantly reduced beta mean low-density lipoprotein cholesterol (- 14.47 mg/dL, p = 2.58 × 10-23) in individuals carrying the R46L variant. The R46L variant was also associated with an increased risk of spina bifida (OR 5.90, p = 2.7 × 10-4), suggesting that further investigation of potential connections between inhibition of PCSK9 and neural tube defects may be warranted. CONCLUSION: This novel methodology provides an opportunity to put in place new mechanisms to assess the safety and long-term tolerability of PCSK9 inhibitors specifically, and other new agents in general, as they move into human testing and expanded clinical use.

When Enough Is Enough: Decision Criteria for Moving a Known Drug into Clinical Testing for a New Indication in the Absence of Preclinical Efficacy Data.

Many animal models of disease are suboptimal in their representation of human diseases and lack of predictive power in the success of pivotal human trials. In the context of repurposing drugs with known human safety, it is sometimes appropriate to conduct the "last experiment first," that is, progressing directly to human investigations. However, there are not accepted criteria for when to proceed straight to humans to test a new indication. We propose a specific set of criteria to guide the decision-making around when to initiate human proof of principle without preclinical efficacy studies in animal models. This approach could accelerate the transition of novel therapeutic approaches to human applications.

Accelerating Precision Drug Development and Drug Repurposing by Leveraging Human Genetics.

The potential impact of using human genetic data linked to longitudinal electronic medical records on drug development is extraordinary; however, the practical application of these data necessitates some organizational innovations. Vanderbilt has created resources such as an easily queried database of >2.6 million de-identified electronic health records linked to BioVU, which is a DNA biobank with more than 230,000 unique samples. To ensure these data are used to maximally benefit and accelerate both de novo drug discovery and drug repurposing efforts, we created the Accelerating Drug Development and Repurposing Incubator, a multidisciplinary think tank of experts in various therapeutic areas within both basic and clinical science as well as experts in legal, business, and other operational domains. The Incubator supports a diverse pipeline of drug indication finding projects, leveraging the natural experiment of human genetics.

Norepinephrine transporter variant A457P knock-in mice display key features of human postural orthostatic tachycardia syndrome.

Postural orthostatic tachycardia syndrome (POTS) is a common autonomic disorder of largely unknown etiology that presents with sustained tachycardia on standing, syncope and elevated norepinephrine spillover. Some individuals with POTS experience anxiety, depression and cognitive dysfunction. Previously, we identified a mutation, A457P, in the norepinephrine (NE; also known as noradrenaline) transporter (NET; encoded by SLC6A2) in POTS patients. NET is expressed at presynaptic sites in NE neurons and plays a crucial role in regulating NE signaling and homeostasis through NE reuptake into noradrenergic nerve terminals. Our in vitro studies demonstrate that A457P reduces both NET surface trafficking and NE transport and exerts a dominant-negative impact on wild-type NET proteins. Here we report the generation and characterization of NET A457P mice, demonstrating the ability of A457P to drive the POTS phenotype and behaviors that are consistent with reported comorbidities. Mice carrying one A457P allele (NET(+/P)) exhibited reduced brain and sympathetic NE transport levels compared with wild-type (NET(+/+)) mice, whereas transport activity in mice carrying two A457P alleles (NET(P/P)) was nearly abolished. NET(+/P) and NET(P/P) mice exhibited elevations in plasma and urine NE levels, reduced 3,4-dihydroxyphenylglycol (DHPG), and reduced DHPG:NE ratios, consistent with a decrease in sympathetic nerve terminal NE reuptake. Radiotelemetry in unanesthetized mice revealed tachycardia in NET(+/P) mice without a change in blood pressure or baroreceptor sensitivity, consistent with studies of human NET A457P carriers. NET(+/P) mice also demonstrated behavioral changes consistent with CNS NET dysfunction. Our findings support that NET dysfunction is sufficient to produce a POTS phenotype and introduces the first genetic model suitable for more detailed mechanistic studies of the disorder and its comorbidities.