Renin-Angiotensin System Modulation With Synthetic Angiotensin (1-7) and Angiotensin II Type 1 Receptor-Biased Ligand in Adults With COVID-19: Two Randomized Clinical Trials.

Importance: Preclinical models suggest dysregulation of the renin-angiotensin system (RAS) caused by SARS-CoV-2 infection may increase the relative activity of angiotensin II compared with angiotensin (1-7) and may be an important contributor to COVID-19 pathophysiology. Objective: To evaluate the efficacy and safety of RAS modulation using 2 investigational RAS agents, TXA-127 (synthetic angiotensin [1-7]) and TRV-027 (an angiotensin II type 1 receptor-biased ligand), that are hypothesized to potentiate the action of angiotensin (1-7) and mitigate the action of the angiotensin II. Design, Setting, and Participants: Two randomized clinical trials including adults hospitalized with acute COVID-19 and new-onset hypoxemia were conducted at 35 sites in the US between July 22, 2021, and April 20, 2022; last follow-up visit: July 26, 2022. Interventions: A 0.5-mg/kg intravenous infusion of TXA-127 once daily for 5 days or placebo. A 12-mg/h continuous intravenous infusion of TRV-027 for 5 days or placebo. Main Outcomes and Measures: The primary outcome was oxygen-free days, an ordinal outcome that classifies a patient's status at day 28 based on mortality and duration of supplemental oxygen use; an adjusted odds ratio (OR) greater than 1.0 indicated superiority of the RAS agent vs placebo. A key secondary outcome was 28-day all-cause mortality. Safety outcomes included allergic reaction, new kidney replacement therapy, and hypotension. Results: Both trials met prespecified early stopping criteria for a low probability of efficacy. Of 343 patients in the TXA-127 trial (226 [65.9%] aged 31-64 years, 200 [58.3%] men, 225 [65.6%] White, and 274 [79.9%] not Hispanic), 170 received TXA-127 and 173 received placebo. Of 290 patients in the TRV-027 trial (199 [68.6%] aged 31-64 years, 168 [57.9%] men, 195 [67.2%] White, and 225 [77.6%] not Hispanic), 145 received TRV-027 and 145 received placebo. Compared with placebo, both TXA-127 (unadjusted mean difference, -2.3 [95% CrI, -4.8 to 0.2]; adjusted OR, 0.88 [95% CrI, 0.59 to 1.30]) and TRV-027 (unadjusted mean difference, -2.4 [95% CrI, -5.1 to 0.3]; adjusted OR, 0.74 [95% CrI, 0.48 to 1.13]) resulted in no difference in oxygen-free days. In the TXA-127 trial, 28-day all-cause mortality occurred in 22 of 163 patients (13.5%) in the TXA-127 group vs 22 of 166 patients (13.3%) in the placebo group (adjusted OR, 0.83 [95% CrI, 0.41 to 1.66]). In the TRV-027 trial, 28-day all-cause mortality occurred in 29 of 141 patients (20.6%) in the TRV-027 group vs 18 of 140 patients (12.9%) in the placebo group (adjusted OR, 1.52 [95% CrI, 0.75 to 3.08]). The frequency of the safety outcomes was similar with either TXA-127 or TRV-027 vs placebo. Conclusions and Relevance: In adults with severe COVID-19, RAS modulation (TXA-127 or TRV-027) did not improve oxygen-free days vs placebo. These results do not support the hypotheses that pharmacological interventions that selectively block the angiotensin II type 1 receptor or increase angiotensin (1-7) improve outcomes for patients with severe COVID-19. Trial Registration: ClinicalTrials.gov Identifier: NCT04924660.

Walk before you run: Feasibility challenges and lessons learned from the PROCLAIM study, a multicenter randomized controlled trial of misoprostol for prevention of recurrent Clostridioides difficile during COVID-19.

We analyzed our challenging experience with a randomized controlled trial of misoprostol for prevention of recurrent C. difficile. Despite careful prescreening and thoughtful protocol modifications to facilitate enrollment, we closed the study early after enrolling just 7 participants over 3 years. We share lessons learned, noting the importance of feasibility studies, inclusion of biomarker outcomes, and dissemination of such findings to inform future research design and implementation successes.

Medication history-wide association studies for pharmacovigilance of pregnant patients.

Background: Systematic exclusion of pregnant people from interventional clinical trials has created a public health emergency for millions of patients through a dearth of robust safety data for common drugs. Methods: We harnessed an enterprise collection of 2.8 M electronic health records (EHRs) from routine care, leveraging data linkages between mothers and their babies to detect drug safety signals in this population at full scale. Our mixed-methods signal detection approach stimulates new hypotheses for post-marketing surveillance agnostically of both drugs and diseases-by identifying 1,054 drugs historically prescribed to pregnant patients; developing a quantitative, medication history-wide association study; and integrating a qualitative evidence synthesis platform using expert clinician review for integration of biomedical specificity-to test the effects of maternal exposure to diverse drugs on the incidence of neurodevelopmental defects in their children. Results: We replicated known teratogenic risks and existing knowledge on drug structure-related teratogenicity; we also highlight 5 common drug classes for which we believe this work warrants updated assessment of their safety. Conclusion: Here, we present roots of an agile framework to guide enhanced medication regulations, as well as the ontological and analytical limitations that currently restrict the integration of real-world data into drug safety management during pregnancy. This research is not a replacement for inclusion of pregnant people in prospective clinical studies, but it presents a tractable team science approach to evaluating the utility of EHRs for new regulatory review programs-towards improving the delicate equipoise of accuracy and ethics in assessing drug safety in pregnancy.

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.

Repurposing of a Thromboxane Receptor Inhibitor Based on a Novel Role in Metastasis Identified by Phenome-Wide Association Study.

Although new drug discoveries are revolutionizing cancer treatments, repurposing existing drugs would accelerate the timeline and lower the cost for bringing treatments to cancer patients. Our goal was to repurpose CPI211, a potent and selective antagonist of the thromboxane A2-prostanoid receptor (TPr), a G-protein-coupled receptor that regulates coagulation, blood pressure, and cardiovascular homeostasis. To identify potential new clinical indications for CPI211, we performed a phenome-wide association study (PheWAS) of the gene encoding TPr, TBXA2R, using robust deidentified health records and matched genomic data from more than 29,000 patients. Specifically, PheWAS was used to identify clinical manifestations correlating with a TBXA2R single-nucleotide polymorphism (rs200445019), which generates a T399A substitution within TPr that enhances TPr signaling. Previous studies have correlated 200445019 with chronic venous hypertension, which was recapitulated by this PheWAS analysis. Unexpectedly, PheWAS uncovered an rs200445019 correlation with cancer metastasis across several cancer types. When tested in several mouse models of metastasis, TPr inhibition using CPI211 potently blocked spontaneous metastasis from primary tumors, without affecting tumor cell proliferation, motility, or tumor growth. Further, metastasis following intravenous tumor cell delivery was blocked in mice treated with CPI211. Interestingly, TPr signaling in vascular endothelial cells induced VE-cadherin internalization, diminished endothelial barrier function, and enhanced transendothelial migration by tumor cells, phenotypes that were decreased by CPI211. These studies provide evidence that TPr signaling promotes cancer metastasis, supporting the study of TPr inhibitors as antimetastatic agents and highlighting the use of PheWAS as an approach to accelerate drug repurposing.

Machine learning on drug-specific data to predict small molecule teratogenicity.

Pregnant women are an especially vulnerable population, given the sensitivity of a developing fetus to chemical exposures. However, prescribing behavior for the gravid patient is guided on limited human data and conflicting cases of adverse outcomes due to the exclusion of pregnant populations from randomized, controlled trials. These factors increase risk for adverse drug outcomes and reduce quality of care for pregnant populations. Herein, we propose the application of artificial intelligence to systematically predict the teratogenicity of a prescriptible small molecule from information inherent to the drug. Using unsupervised and supervised machine learning, our model probes all small molecules with known structure and teratogenicity data published in research-amenable formats to identify patterns among structural, meta-structural, and in vitro bioactivity data for each drug and its teratogenicity score. With this workflow, we discovered three chemical functionalities that predispose a drug towards increased teratogenicity and two moieties with potentially protective effects. Our models predict three clinically-relevant classes of teratogenicity with AUC = 0.8 and nearly double the predictive accuracy of a blind control for the same task, suggesting successful modeling. We also present extensive barriers to translational research that restrict data-driven studies in pregnancy and therapeutically "orphan" pregnant populations. Collectively, this work represents a first-in-kind platform for the application of computing to study and predict teratogenicity.

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.

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.

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.

Discovery of desketoraloxifene analogues as inhibitors of mammalian, Pseudomonas aeruginosa, and NAPE phospholipase D enzymes.

Phospholipase D (PLD) hydrolyses cellular lipids to produce the important lipid second messenger phosphatidic acid. A PLD enzyme expressed by Pseudomonas aeruginosa (PldA) has been shown to be important in bacterial infection, and NAPE-PLD has emerged as being key in the synthesis of endocannabinoids. In order to better understand the biology and therapeutic potential of these less explored PLD enzymes, small molecule tools are required. Selective estrogen receptor modulators (SERMs) have been previously shown to inhibit mammalian PLD (PLD1 and PLD2). By targeted screening of a library of SERM analogues, additional parallel synthesis, and evaluation in multiple PLD assays, we discovered a novel desketoraloxifene-based scaffold that inhibited not only the two mammalian PLDs but also structurally divergent PldA and NAPE-PLD. This finding represents an important first step toward the development of small molecules possessing universal inhibition of divergent PLD enzymes to advance the field.

Design, synthesis, and biological evaluation of halogenated N-(2-(4-oxo-1-phenyl-1,3,8-triazaspiro[4.5]decan-8-yl)ethyl)benzamides: discovery of an isoform-selective small molecule phospholipase D2 inhibitor.

Phospholipase D (PLD) catalyzes the conversion of phosphatidylcholine to the lipid second messenger phosphatidic acid. Two mammalian isoforms of PLD have been identified, PLD1 and PLD2, which share 53% sequence identity and are subject to different regulatory mechanisms. Inhibition of PLD enzymatic activity leads to increased cancer cell apoptosis, decreased cancer cell invasion, and decreased metastasis of cancer cells; therefore, the development of isoform-specific, PLD inhibitors is a novel approach for the treatment of cancer. Previously, we developed potent dual PLD1/PLD2, PLD1-specific (>1700-fold selective), and moderately PLD2-preferring (>10-fold preferring) inhibitors. Here, we describe a matrix library strategy that afforded the most potent (PLD2 IC(50) = 20 nM) and selective (75-fold selective versus PLD1) PLD2 inhibitor to date, N-(2-(1-(3-fluorophenyl)-4-oxo-1,3,8-triazaspiro[4.5]decan-8-yl)ethyl)-2-naphthamide (22a), with an acceptable DMPK profile. Thus, these new isoform-selective PLD inhibitors will enable researchers to dissect the signaling roles and therapeutic potential of individual PLD isoforms to an unprecedented degree.

Design and synthesis of isoform-selective phospholipase D (PLD) inhibitors. Part II. Identification of the 1,3,8-triazaspiro[4,5]decan-4-one privileged structure that engenders PLD2 selectivity.

This Letter describes the synthesis and structure-activity relationships (SAR) of isoform-selective PLD inhibitors. By virtue of the installation of a 1,3,8-triazaspiro[4,5]decan-4-one privileged structure, PLD inhibitors with nanomolar potency and an unprecedented 40-fold selectivity for PLD2 over PLD1 were developed. Interestingly, SAR for this diverged from our earlier efforts, and dual PLD1/2 inhibitors were also discovered within this series.

Design and synthesis of isoform-selective phospholipase D (PLD) inhibitors. Part I: Impact of alternative halogenated privileged structures for PLD1 specificity.

This Letter describes the synthesis and structure-activity-relationships (SAR) of isoform-selective PLD inhibitors. By virtue of the installation of alternative halogenated piperidinyl benzimidazolone privileged structures, in combination with a key (S)-methyl group, novel PLD inhibitors with low nM potency and unprecedented levels of PLD1 isoform selectivity (approximately 1700-fold) over PLD2 were developed.