For Food And Drug Administration Research Articles

Abstract PS06-09: Fragmentomic analysis of a circulating tumor DNA targeted cancer gene panel discriminates ER status in metastatic breast cancer liquid biopsies

Abstract Background: Circulating tumor DNA (ctDNA) isolated from the peripheral blood of patients with cancer is now routinely used in the clinic to detect tumor somatic mutations, with multiple ctDNA-targeted sequencing panels commercially available for Food and Drug Administration (FDA)-approved biomarker indications to guide targeted therapy options. More recently, it has been appreciated that ctDNA fragmentation patterns are influenced by the chromatin structure of the cell of origin, with greater diversity in fragment size in areas with open chromatin and less diversity in fragment size in areas with closed chromatin. In consequence, multiple studies have found that ctDNA fragmentation patterns can be used to accurately infer epigenomic and transcriptomic phenotypes from their tumor cells of origin. However, these analyses have previously relied on whole-genome sequencing (WGS), and it is not feasible to identify FDA-approved biomarker indications in a cost-effective manner with WGS. Methods: Peripheral blood was collected from a cohort of 90 patients with metastatic HER2-negative breast cancer (ER-positive n=71, ER-negative n=19). Cell-free DNA was extracted from plasma and sequenced using a custom 822 gene pan-cancer targeted sequencing panel (IDT). Shannon entropy of first coding exon fragment sizes (E1SE) was used to quantify fragmentation patterns. Higher diversity of fragment sizes, leading to a higher E1SE, are expected in ctDNA derived from areas of the genome with more open chromatin. A machine learning model utilizing the E1SE metric across all genes in the targeted panel was developed to distinguish between ER-positive and ER-negative disease. Results: Training cross-validated accuracy of the machine learning model was 87.8% with AUC of 0.91 to distinguish between ER-positive and ER-negative disease, despite a median ctDNA fraction of only 0.02. E1SE metrics for ESR1 and CCND1 were significantly higher in ER-positive than ER-negative samples, while E1SE metrics for EGFR, c-Kit and members of the MAP kinase family were significantly higher in ER-negative than ER-positive samples, consistent with anticipated differences in gene expression between ER-positive and ER-negative triple negative breast cancers. In contrast, HER2 E1SE was similar between groups, as expected in this clinically HER2-negative cohort. Conclusions: We have shown for the first time that sequencing from standard targeted ctDNA panels can be utilized to infer phenotypic information through analysis of ctDNA fragmentation patterns in patients with metastatic breast cancer. This expands the depth of potential biologic data that can be extracted from targeted ctDNA sequencing panels in addition to DNA mutation status in a cost-effective manner. In the future, this approach can be exploited to expand ctDNA-based biomarker development beyond DNA mutation status. Citation Format: Kyle Helzer, Jamie Sperger, Yue Shi, Viridiana Carreno, Hannah Krause, Katherine Kaufmann, Leilani Mora-Rodriguez, Matthew Bootsma, Mark Burkard, Ruth O'Regan, Kari Wisinski, Joshua Lang, Shuang (George) Zhao, Malinda West, Marina Sharifi. Fragmentomic analysis of a circulating tumor DNA targeted cancer gene panel discriminates ER status in metastatic breast cancer liquid biopsies [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS06-09.

Animal Models in Regulatory Breakpoint Determination: Review of New Drug Applications of Approved Antibiotics from 2014-2022.

We sought to better understand the utility and role of animal models of infection for Food and Drug Administration (FDA)-approved antibiotics for the indications of community-, hospital-acquired-, and ventilator-associated bacterial pneumonia (CABP, HABP, VABP), complicated urinary tract infection (cUTI), complicated intra-abdominal infection (cIAI), and acute bacterial skin and structural infections (ABSSSIs). We reviewed relevant documents from new drug applications (NDA) of FDA-approved antibiotics from 2014-2019 for the above indications. Murine neutropenic thigh infection models supported the choice of a pharmacokinetic-pharmacodynamic (PKPD) target in 11/12 NDAs reviewed. PKPD targets associated with at least a 1-log bacterial decrease were commonly considered ideal (10/12 NDAs) to support breakpoints. Plasma PK, as opposed to organ specific PK, was generally considered most reliable for PKPD correlation. Breakpoint determination was multi-disciplinary, accounting at minimum for epidemiologic cutoffs, non-clinical PKPD, clinical exposure-response and clinical efficacy. Non-clinical PKPD targets in combination with probability of target attainment (PTA) analyses generated breakpoints that were consistent with epidemiologic cutoffs and clinically derived breakpoints. In 6/12 NDAs, there was limited data to support clinically derived breakpoints, and hence the non-clinical PKPD targets in combination with PTA analyses played a heightened role in the final breakpoint determination. Sponsor and FDA breakpoint decisions were in general agreement. Disagreement may have arisen from differences in the definition of the optimal PKPD index or the ability to extrapolate protein binding from animals to humans. Overall, murine neutropenic thigh infection models supported the reviewed NDAs by providing evidence of pre-clinical efficacy and PKPD target determination, and played, in combination with PTA analysis, a significant role in breakpoint determination for labeling purposes.

RxBERT: Enhancing drug labeling text mining and analysis with AI language modeling.

The US drug labeling document contains essential information on drug efficacy and safety, making it a crucial regulatory resource for Food and Drug Administration (FDA) drug reviewers. Due to its extensive volume and the presence of free-text, conventional text mining analysis have encountered challenges in processing these data. Recent advances in artificial intelligence (AI) for natural language processing (NLP) have provided an unprecedented opportunity to identify key information from drug labeling, thereby enhancing safety reviews and support for regulatory decisions. We developed RxBERT, a Bidirectional Encoder Representations from Transformers (BERT) model pretrained on FDA human prescription drug labeling documents for an enhanced application of drug labeling documents in both research and drug review. RxBERT was derived from BioBERT with further training on human prescription drug labeling documents. RxBERT was demonstrated in several tasks using regulatory datasets, including those involved in the National Institutes of Technology Text Analysis Challenge Dataset (NIST TAC dataset), the FDA Adverse Drug Event Evaluation Dataset (ADE Eval dataset), and the classification of texts from submission packages into labeling sections (US Drug Labeling dataset). For all these tasks, RxBERT reached 86.5 F1-scores in both TAC and ADE Eval classification, respectively, and prediction accuracy of 87% for the US Drug Labeling dataset. Overall, RxBERT was shown to be as competitive or have better performance compared to other NLP approaches such as BERT, BioBERT, etc. In summary, we developed RxBERT, a transformer-based model specific for drug labeling that outperformed the original BERT model. RxBERT has the potential to be used to assist research scientists and FDA reviewers to better process and utilize drug labeling information toward the advancement of drug effectiveness and safety for public health. This proof-of-concept study also demonstrated a potential pathway to customized large language models (LLMs) tailored to the sensitive regulatory documents for internal application.

Fragmentomic analysis of circulating tumor DNA-targeted cancer panels

The isolation of cell-free DNA (cfDNA) from the bloodstream can be used to detect and analyze somatic alterations in circulating tumor DNA (ctDNA), and multiple cfDNA-targeted sequencing panels are now commercially available for Food and Drug Administration (FDA)-approved biomarker indications to guide treatment. More recently, cfDNA fragmentation patterns have emerged as a tool to infer epigenomic and transcriptomic information. However, most of these analyses used whole-genome sequencing, which is insufficient to identify FDA-approved biomarker indications in a cost-effective manner. We used machine learning models of fragmentation patterns at the first coding exon in standard targeted cancer gene cfDNA sequencing panels to distinguish between cancer and non-cancer patients, as well as the specific tumor type and subtype. We assessed this approach in two independent cohorts: a published cohort from GRAIL (breast, lung, and prostate cancers, non-cancer, n = 198) and an institutional cohort from the University of Wisconsin (UW; breast, lung, prostate, bladder cancers, n= 320). Each cohort was split 70%/30% into training and validation sets. In the UW cohort, training cross-validated accuracy was 82.1%, and accuracy in the independent validation cohort was 86.6% despite a median ctDNA fraction of only 0.06. In the GRAIL cohort, to assess how this approach performs in very low ctDNA fractions, training and independent validation were split based on ctDNA fraction. Training cross-validated accuracy was 80.6%, and accuracy in the independent validation cohort was 76.3%. In the validation cohort where the ctDNA fractions were all <0.05 and as low as 0.0003, the cancer versus non-cancer area under the curve was 0.99. To our knowledge, this is the first study to demonstrate that sequencing from targeted cfDNA panels can be utilized to analyze fragmentation patterns to classify cancer types, dramatically expanding the potential capabilities of existing clinically used panels at minimal additional cost.

Clinical and economic outcomes associated with intravenous albumin fluid use in the intensive care unit: a retrospective cohort study

ABSTRACT Objectives This study was undertaken to evaluate the prescribing practice of albumin in the intensive care unit (ICU) and to compare the clinical and economic outcomes associated with intravenous (IV) albumin compared to crystalloids in the ICU. Methods This was a retrospective cohort study of ICU adult patients admitted to King Abdullah University Hospital during 2018–2019. Patient demographics, clinical characteristics, and admission charges were retrieved from medical records and billing system. Survival analysis, multivariable regression models, and propensity score matching estimator were performed to evaluate the impact of IV resuscitation fluid types on the clinical and economic outcomes. Results Albumin administration in the ICU was associated with significantly lower hazards of ICU death (HR = 0.57; P value <0.001), but without improving overall death probability compared to crystalloids. Albumin was associated with significant prolongation in the ICU length of stay (5.86 days; P value <0.001). Only 88 patients (24.3%) were prescribed albumin for Food and Drug Administration (FDA)-approved indications. Admission charges were significantly higher for patients treated with albumin (p value <0.001). Conclusions IV Albumin use in the ICU was not associated with significant improvement in clinical outcomes, but with a remarkable increase in economic burden. The majority of patients received albumin for non-FDA-approved indications.

Orthogonal Proteogenomic Approaches Identify the Druggable PA2G4-MYC Axis in 3q26 AML

Despite next-generation technologies improved our understanding of the complexity of high-risk acute myeloid leukemia (AML), effective targeted therapies are delayed due to the prevalence of major oncogenic disease drivers such as RAS, RUNX1, TP53. In some cases, the integration of orthogonal omics led to the identification or repurposing of drugs for "undruggable" transcription factors (TFs), prospecting a benefit for patients with the most aggressive leukemia subtypes. Here, we sought to discover how to target the Ecotropic Viral Integration Site-1 gene (EVI1/MECOM), the most lethal oncogenic TF in AML carrying chromosome 3q26 abnormalities. To this end, we intersected phenotypic, gene expression-based and proteomic high-throughput approaches for small molecule and target identification. We screened a collection of 5294 compounds enriched for Food and Drug Administration (FDA) approved or bioactive drugs at 1 µM and identified those that caused more than 95% reduction of cell viability compared to vehicle control in MOLM1 and UCSD/AML1 cell lines carrying 3q26 rearrangments (EVI1high). We then selected a set of sentinel genes that defined an EVI1 "on" and "off" transcriptional signature based on the TF1 EVI1high cell line treated with an EVI1-directed siRNA or control, and used the Connectivity Map to identify inducers of an EVI1 "off" state. Histone deacetylase inhibitors (HDACi) scored as the top class of compounds able to repress leukemia proliferation by suppressing the EVI1 signature. We focused on the compounds with pan (AR-42, belinostat) or selective (entinostat) HDAC inhibitory properties. These molecules induced apoptosis and decreased the levels of EVI1 in AML cell lines and in primary EVI1high blast cells. Interestingly, the expression of EVI1 sensitizes cells to HDAC inhibition both in an inducible EVI1 cell model and in EVI1high compared to EVI1low AML cell lines. Primary EVI1high blasts were also more sensitive to HDAC inhibition compared to those from other AML subtypes, while equally resistant to chemotherapy agents. We translated these results in a "N-of-1" clinical trial proposing entinostat in association with azacytidine to patients with relapse/refractory 3q26 AML based on a compassionate use program for this regimen. Pharmacodynamic modulation of EVI1 was assessed at 6 hours (T6) after entinostat administration in patients and in matched patient-derived AML xenografts (PDLX). EVI1 expression markedly decreased in circulating blasts collected at T6 compared to T0 (before treatment), and EVI1 depletion paralleled clinical and hematologic improvements at follow-up evaluations. This effect was not observed in patients treated with cytarabine. Longitudinal single-cell RNA sequencing (scRNASeq) of bone marrow AML cells collected before and after patients' treatment cycle revealed an inhibitory effect on Myc target genes in the leukemic clone. This effect was also observed in cell lines treated with AR-42, belinostat and entinostat suggesting that different HDACi regulates the Evi1-Myc axis in 3q26 AML. Not surprisingly, HDACi mediated or genetic silencing of EVI1 led to a decrement of MYC in EVI1high AML models. To dissect the EVI1 co-transcriptional complex and explain for the modulation of Myc signaling in EVI1high AML, we performed rapid immunoprecipitation mass spectrometry of endogenous proteins (RIME). Sixty-nine proteins co-immunoprecipitated with endogenous EVI1 both in cell lines and in 3q26 PDLX models. Eleven of these EVI1 interactors were targets of Myc signaling, including PA2G4, a RNA-binding protein that was markedly modulated by HDACi. Importantly, PA2G4 silencing or inhibition (WS6) depletes MYC at transcriptional and protein levels recapitulating the phenotypic effect seen with HDACi in EVI1high cell models. Consequently, WS6 treatment reduced the growth of AML cells in PDLX models in vivo by suppressing an EV1-MYC signature at single-cell leukemia level without inducing widespread toxicities. In contrast, PA2G4 overexpression rescued EVIhigh AML cell lines from the effects induced by HDACi inhibition. These data support the evidence that PA2G4 is a mediator of Evi1-Myc axis in AML. In conclusion, our work positions PA2G4 at the crosstalk of the Evi1-Myc leukemogenic signal for developing new therapeutics and, at this interesting time, urges upfront HDACi-based combination therapies in patients with 3q26 AML.

Quality of biomarker defined subgroups in FDA approvals of PD-1/PD-L1 inhibitors 2014 to 2020.

PD-L1 expression is associated with differential response in cancers treated with checkpoint inhibitors. Clinical trials for Food and Drug Administration (FDA) approvals of programmed death receptor-1 (PD-1)/programmed death ligand-1 (PD-L1) inhibitors include limited subgroup analyses based on PD-L1 expression. We aimed to define the characteristics of PD-L1 defined subgroups of clinical trials leading to FDA approvals for new indications of PD-1/PD-L1 inhibitors. FDA approvals for PD-1/PD-L1 inhibitors from January 2014 to December 2020 were identified and the clinical trials leading to each drug approval were reviewed. We collected key variables from publicly available information on FDA website and peer-reviewed publications of clinical trials. We assessed regulatory characteristics (approval date, approved drug[s], cancer type, line of therapy and biomarker-restricted approval criteria) of each approval. Clinical trials leading to approvals were reviewed for trial design (RCT vs single arm study, primary endpoint) and PD-L1 defined subgroup design (no subgroup analysis, single threshold 2-group analysis, nested subgroups and adjacent subgroups). We then compared regulatory and trials characteristics (trial design, primary endpoint and biomarker approval criteria) between studies with nested and adjacent subgroups. There were 60 approvals for PD-1/PD-L1 inhibitors between January 2014 and December 2020. Twelve of 60 (20%) did not include any PD-L1 subgroups. Twenty-five of 60 (42%) approvals reported only two subgroups, 14 (23%) included adjacent subgroups and 9 (15%) had nested subgroups. Twenty-five of 60 trials (42%) are single arm studies. Comparison of characteristics between trials with nested subgroup design and adjacent subgroup design did not show differences. We conclude that approvals for new indications of PD-1/PD-L1 inhibitors are based on studies that do not include comprehensive reporting of outcomes by PD-L1 biomarker subgroups.

Chemical Interrogation of Nuclear Size Identifies Compounds with Cancer Cell Line-Specific Effects on Migration and Invasion.

Background: Lower survival rates for many cancer types correlate with changes in nuclear size/scaling in a tumor-type/tissue-specific manner. Hypothesizing that such changes might confer an advantage to tumor cells, we aimed at the identification of commercially available compounds to guide further mechanistic studies. We therefore screened for Food and Drug Administration (FDA)/European Medicines Agency (EMA)-approved compounds that reverse the direction of characteristic tumor nuclear size changes in PC3, HCT116, and H1299 cell lines reflecting, respectively, prostate adenocarcinoma, colonic adenocarcinoma, and small-cell squamous lung cancer. Results: We found distinct, largely nonoverlapping sets of compounds that rectify nuclear size changes for each tumor cell line. Several classes of compounds including, e.g., serotonin uptake inhibitors, cyclo-oxygenase inhibitors, β-adrenergic receptor agonists, and Na+/K+ ATPase inhibitors, displayed coherent nuclear size phenotypes focused on a particular cell line or across cell lines and treatment conditions. Several compounds from classes far afield from current chemotherapy regimens were also identified. Seven nuclear size-rectifying compounds selected for further investigation all inhibited cell migration and/or invasion. Conclusions: Our study provides (a) proof of concept that nuclear size might be a valuable target to reduce cell migration/invasion in cancer treatment and (b) the most thorough collection of tool compounds to date reversing nuclear size changes specific to individual cancer-type cell lines. Although these compounds still need to be tested in primary cancer cells, the cell line-specific nuclear size and migration/invasion responses to particular drug classes suggest that cancer type-specific nuclear size rectifiers may help reduce metastatic spread.

Asthma biologic trial eligibility and real-world outcomes in the United States

Objective To compare the outcomes of real-world patients who would have been eligible for asthma biologics to those who would not have been eligible. Methods We used data from the OptumLabs Data Warehouse (OLDW) to categorize patients into eligible and ineligible groups based on clinical trials (n = 19 trials) used for Food and Drug Administration (FDA) approval. We then compared the change in the number of asthma exacerbations before and after biological initiation between the two groups. Results The percentage of people who would have been eligible for asthma biologic clinical trials ranged from 0–10.2%. The eligible group had a greater reduction in number of asthma exacerbations compared to the ineligible group based on eligibility criteria from 1 omalizumab trial (1.52, 95% CI 1.25, 1.8 in eligible vs. 0.47, 95% CI 0.43, 0.52 in ineligible) and from 1 dupilumab trial (1.6, 95% CI 0.92, 2.28 in eligible vs. 0.52, 95% CI 0.38, 0.65 ineligible). Notably, 15 of the 19 trials had fewer than 11 eligible people, limiting additional comparisons. Conclusions Fewer than 1 in 10 people in the United States treated with asthma biologics would have been eligible to participate in the trial for the biologic they used. Where comparisons could be made, trial eligible people have a greater reduction in exacerbations. Supplemental data for this article is available online at https://doi.org/10.1080/02770903.2021.2010749 .

Erectile dysfunction treatment with Phosphodiesterase-5 Inhibitors: Google trends analysis of last 10 years and COVID-19 pandemic.

We aimed to analyze the trend change of the most popular Phosphodiesterase-5 Inhibitors (PDE5i) over time and geography by using Google Trends (GT) data in 10 years period and COVID-19 pandemic. GT is able to generate a "line-graph", showing how interest has increased or decreased over a period within specific territories. The search values for specific terms are indexed as relative search volume (RSV), which is presented on a scale from 0-100. Avarage annual percentage change (AAPC) and RSV were analyzed to evaluate gain or loss of interest in trends. Search terms were generated for Food and Drug Administration (FDA)-approved PDE5i; tadalafil, sildenafil, vardenafil, avanafil, and their most-used brand names. The data was within "worldwide" from 1 January 2010, to 31 December 2020, using the ''global'' query category. The overall interest in PDE5i has doubled. Sildenafil has become the most trend PDE5i of today with a regular increase (AAPC: 0.016, p < 0.01). Although the search trend of tadalafil remained almost constant until 2014, the rate of increase in the last 6 years raised and tadalafil has become the 2nd most popular PDE5i recently (AAPC: 0.007, p < 0.01). For vardenafil there has been a decreased interest (AAPC: -0.009, p < 0.01). There is no significant change in avanafil trend (AAPC: 0.000, p: 0.5). All PDE5i interest on GT decreased notably from February to June 2020. But after June, search trends reached the level before the COVID-19 period in a month. These findings show us, with its increasing prevalence, erectile dysfunction (ED) has become a major public health problem. People from different geographies search the internet for ED treatment options. Patients should be informed that ED may be the first sign of many comorbid diseases, and patients with ED should be referred to a health institution for diagnosis and treatment.

Performance of popular pulse oximeters compared with simultaneous arterial oxygen saturation or clinical-grade pulse oximetry: a cross-sectional validation study in intensive care patients

ObjectivesTo evaluate the performance of direct-to-consumer pulse oximeters under clinical conditions, with arterial blood gas measurement (SaO2) as reference standard.DesignCross-sectional, validation study.SettingIntensive care.ParticipantsAdult patients requiring SaO2-monitoring.InterventionsThe studied oximeters are top-selling...

Creating a Pro-Regenerative Tissue Microenvironment: Local Control is the Key.

Creating a Pro-Regenerative Tissue Microenvironment: Local Control is the Key.

Role of CTSA institutes and academic medical centers in facilitating preapproval access to investigational agents and devices during the COVID-19 pandemic.

With no approved treatments for COVID-19 initially available, the Food and Drug Administration utilized multiple preapproval pathways to provide access to investigational agents and/or medical devices: Expanded Access, Emergency Use Authorizations, and Clinical Trials. Regulatory units within an Academic Medical Center (AMC), including those part of the Clinical and Translational Science Award (CTSA) consortium, have provided support for clinicians in navigating these options prior to the pandemic. As such, they were positioned to be a resource for accessing therapies during the COVID-19 public health emergency. A small survey and a follow-on poll of the national Investigational New Drug (IND)/Investigational Device Exemption (IDE) Workgroup were conducted in October and December 2020 to determine whether CTSA regulatory units assisted in facilitating access to COVID-19 therapies and the extent of pandemic-related challenges these units faced. Fifteen survey and 21 poll responses were received, which provided insights into the demands placed on these regulatory support units due to the pandemic and the changes required to provide critical support during this and future crises. Key changes and lessons learned included the importance of regulatory knowledge to support the institutional response, the critical need for electronic submission capacity for Food and Drug Administration (FDA) documents, and the nimble reallocation of regulatory and legal resources to support patient access to investigational agents and/or medical devices during the pandemic. AMC- and CTSA-based regulatory units played a meaningful role in the COVID-19 pandemic but further unit modifications are needed for enabling more robust regulatory support in the future.

Use of Real-World Evidence to Support FDA Approval of Oncology Drugs.

Real-world evidence (RWE) has gained increased attention in recent years as a complement to traditional clinical trials. The use of RWE to establish the efficacy of oncology drugs for Food and Drug Administration (FDA) approval has not been described. In this paper, we review 5 recent examples where RWE was submitted in support of the FDA approvals of original or supplementary indications for oncology drugs. To identify cases where RWE was used, we reviewed drug approval packages available at Drugs@FDA for oncology drugs approved between 2017 and 2019. Five cases were selected to present a broad overview of different types of RWE, different circumstances under which RWE has been used for regulatory approvals, and how FDA evaluated the data in each case. The type of RWE submitted, the indication, limitations identified by FDA reviewers, and the outcome of the submission are discussed. RWE, particularly historical controls for rare or orphan indications, has been used to support both original and supplementary oncology drug approvals. Types of RWE included data from electronic health records, claims, post-marketing safety reports, retrospective medical record reviews, and expanded access studies. Small sample sizes, data quality, and methodological issues were among concerns cited by FDA reviewers. By bridging the gap between the constraints of the trial setting and the realities of clinical practice, RWE can add value to a regulatory submission. These early examples provide insight into how regulators evaluated RWE submitted as evidence of efficacy for oncology drugs.

Repurposed agents in the Alzheimer\u2019s disease drug development pipeline

BackgroundTreatments are needed to address the growing prevalence of Alzheimer’s disease (AD). Clinical trials have failed to produce any AD drugs for Food and Drug Administration (FDA) approval since 2003, and the pharmaceutical development process is both time-consuming and costly. Drug repurposing provides an opportunity to accelerate this process by investigating the AD-related effects of agents approved for other indications. These drugs have known safety profiles, pharmacokinetic characterization, formulations, doses, and manufacturing processes.MethodsWe assessed repurposed AD therapies represented in Phase I, Phase II, and Phase III of the current AD pipeline as registered on ClinicalTrials.gov as of February 27, 2020.ResultsWe identified 53 clinical trials involving 58 FDA-approved agents. Seventy-eight percent of the agents in trials had putative disease-modifying mechanisms of action. Of the repurposed drugs in the pipeline 20% are hematologic-oncologic agents, 18% are drugs derived from cardiovascular indications, 14% are agents with psychiatric uses, 12% are drug used to treat diabetes, 10% are neurologic agents, and the remaining 26% of drugs fall under other conditions. Intellectual property strategies utilized in these programs included using the same drug but altering doses, routes of administration, or formulations. Most repurposing trials were supported by Academic Medical Centers and were not funded through the biopharmaceutical industry. We compared our results to a European trial registry and found results similar to those derived from ClinicalTrials.gov.ConclusionsDrug repurposing is a common approach to AD drug development and represents 39% of trials in the current AD pipeline. Therapies from many disease areas provide agents potentially useful in AD. Most of the repurposed agents are generic and a variety of intellectual property strategies have been adopted to enhance their economic value.

Adverse drug reactions associated with amitriptyline\xa0\u2014\xa0protocol for a systematic multiple-indication review and meta-analysis

BackgroundUnwanted anticholinergic effects are both underestimated and frequently overlooked. Failure to identify adverse drug reactions (ADRs) can lead to prescribing cascades and the unnecessary use of over-the-counter products. The objective of this systematic review and meta-analysis is to explore and quantify the frequency and severity of ADRs associated with amitriptyline vs. placebo in randomized controlled trials (RCTs) involving adults with any indication, as well as healthy individuals.MethodsA systematic search in six electronic databases, forward/backward searches, manual searches, and searches for Food and Drug Administration (FDA) and European Medicines Agency (EMA) approval studies, will be performed. Placebo-controlled RCTs evaluating amitriptyline in any dosage, regardless of indication and without restrictions on the time and language of publication, will be included, as will healthy individuals. Studies of topical amitriptyline, combination therapies, or including < 100 participants, will be excluded. Two investigators will screen the studies independently, assess methodological quality, and extract data on design, population, intervention, and outcomes ((non-)anticholinergic ADRs, e.g., symptoms, test results, and adverse drug events (ADEs) such as falls). The primary outcome will be the frequency of anticholinergic ADRs as a binary outcome (absolute number of patients with/without anticholinergic ADRs) in amitriptyline vs. placebo groups. Anticholinergic ADRs will be defined by an experienced clinical pharmacologist, based on literature and data from Martindale: The Complete Drug Reference. Secondary outcomes will be frequency and severity of (non-)anticholinergic ADRs and ADEs. The information will be synthesized in meta-analyses and narratives. We intend to assess heterogeneity using meta-regression (for indication, outcome, and time points) and I2 statistics. Binary outcomes will be expressed as odds ratios, and continuous outcomes as standardized mean differences. Effect measures will be provided using 95% confidence intervals. We plan sensitivity analyses to assess methodological quality, outcome reporting etc., and subgroup analyses on age, dosage, and duration of treatment.DiscussionWe will quantify the frequency of anticholinergic and other ADRs/ADEs in adults taking amitriptyline for any indication by comparing rates for amitriptyline vs. placebo, hence, preventing bias from disease symptoms and nocebo effects. As no standardized instrument exists to measure it, our overall estimate of anticholinergic ADRs may have limitations.Systematic review registrationSubmitted to PROSPERO; assignment is in progress.

Characteristics of Trials Associated With Drugs Approved by the Food and Drug Administration in 2015 and 2016.

New drug products are tested for safety and efficacy in clinical trials before being approved for use in medical practice. Clinical trial data are often misreported or underreported to ClinicalTrials.gov and in the medical literature. There is limited research on clinical trial characteristics for Food and Drug Administration (FDA) approved drugs, particularly examining differences in characteristics across different approval pathways or therapeutic indications. Data from the Aggregate Analysis of ClinicalTrials.gov (AACT) were used to compare the characteristics of completed clinical trials for drugs approved by the FDA in 2015 and 2016 across different approval pathways (expedited vs. nonexpedited) and therapeutic indications (oncology vs. nononcology). There were 59 novel therapeutic drugs approved by the FDA in 2015 and 2016. A search of the AACT database yielded 955 studies that were associated with these 59 drugs. Median Phase 2 trial enrollment was smaller for drugs granted expedited approval compared with drugs without expedited approval (60 vs. 94; P=0.0079) and for oncology drugs compared with nononcology drugs (53 vs. 92; P<0.001). In general, trials across all phases were less likely to be blinded for drugs that received expedited approval compared with drugs without expedited approval and for oncology drugs compared with nononcology drugs. The characteristics of clinical trials differ across different approval pathways and therapeutic indications. More research is needed to determine whether the information from clinical trials of approved drugs is sufficient to adequately inform the public regarding their potential benefits and harms.

GPCRs show widespread differential mRNA expression and frequent mutation and copy number variation in solid tumors.

G protein-coupled receptors (GPCRs) are the most widely targeted gene family for Food and Drug Administration (FDA)-approved drugs. To assess possible roles for GPCRs in cancer, we analyzed The Cancer Genome Atlas (TCGA) data for mRNA expression, mutations, and copy number variation (CNV) in 20 categories and 45 subtypes of solid tumors and quantified differential expression (DE) of GPCRs by comparing tumors against normal tissue from the Gene Tissue Expression Project (GTEx) database. GPCRs are overrepresented among coding genes with elevated expression in solid tumors. This analysis reveals that most tumor types differentially express >50 GPCRs, including many targets for approved drugs, hitherto largely unrecognized as targets of interest in cancer. GPCR mRNA signatures characterize specific tumor types and correlate with expression of cancer-related pathways. Tumor GPCR mRNA signatures have prognostic relevance for survival and correlate with expression of numerous cancer-related genes and pathways. GPCR expression in tumors is largely independent of staging, grading, metastasis, and/or driver mutations. GPCRs expressed in cancer cell lines largely parallel GPCR expression in tumors. Certain GPCRs are frequently mutated and appear to be hotspots, serving as bellwethers of accumulated genomic damage. CNV of GPCRs is common but does not generally correlate with mRNA expression. Our results suggest a previously underappreciated role for GPCRs in cancer, perhaps as functional oncogenes, biomarkers, surface antigens, and pharmacological targets.

Publication statuses of clinical trials supporting FDA-approved immune checkpoint inhibitors: a meta-epidemiological investigation

BackgroundThe low data publication rate for Food and Drug Administration (FDA)-approved drugs, and discrepancies between FDA-submitted versus published data, remain a concern. We investigated the publication statuses of sponsor-submitted clinical trials supporting recent anticancer drugs approved by the FDA, with a focus on immune checkpoint inhibitors (ICPis).MethodsWe identified all ICPis approved between 2011 and 2014, thereby obtaining 3 years of follow-up data. We assessed the clinical trials performed for each drug indication and matched each trial with publications in the literature. The primary benchmark was the publication status 2 years post-approval. We examined the association between time to publication and drug type using a multilevel Cox regression model that was adjusted for clustering within drug indications and individual covariates.ResultsBetween 2011 and 2014, 36 anticancer drugs including 3 ICPis were newly approved by the FDA. Of 19 trials investigating the 3 ICPis, 11 (58%) were published within 2 years post-approval. We randomly selected 10 of the 33 remaining anticancer drugs; 68 of 101 trials investigating these drugs (67%) were published. Overall, the publication rate was 66% at 2 years post-approval with a median time to publication of 2.3 years. There was no significant difference in the time to trial publication between ICPis and other anticancer drugs (adjusted hazard ratio [HR], 1.1; 95% confidence interval [CI], 0.8–1.7; P = 0.55). However, findings related to non-ICPis investigated specifically in randomized phase 2 or phase 3 trials were significantly more likely to be published earlier than those related to ICPis (adjusted HR, 7.4; 95% CI, 1.8–29.5; P = 0.005).ConclusionOne in 3 sponsor-submitted trials of the most recently approved anticancer drugs remained unpublished 2 years post-FDA approval. We found no evidence that the drug type was associated with the time to overall trial publication.

Short- and Long-Term Antidepressant Clinical Trials for Major Depressive Disorder in Youth: Findings and Concerns.

The diagnosis of major depressive disorder (MDD) in U.S. youth is increasing as is the rate of antidepressant medication (ADM) treatment for the disorder. Fluoxetine and escitalopram are FDA approved for the short term and maintenance treatment of MDD in youth. Placebo-controlled short-term ADM trials represent the basis for Food and Drug Administration (FDA) approval. Meta-analyses in 2007 and 2016 revealed that short-term ADM treatment of youth diagnosed with MDD resulted in no meaningful benefit for children and only marginal benefit for adolescents. Placebo substitution trials of ADM short-term responders represent the basis for FDA approval of ADM maintenance treatment. These ADM placebo substitution maintenance trials for youth with MDD are characterized by high dropout rates, a rapid withdrawal that often can follow the switch to placebo, and relapse rates that are not dissimilar from those in the natural course of the disorder. Without the evidence from problematic ADM placebo substitution trials, there is no acceptable support for the inclusion of ADM in maintenance treatment for MDD in youth.