In-silico approaches to assessing multiple high-level drug-drug and drug-disease adverse drug effects.

INTRODUCTION: Pharmacovigilance plays a pivotal role in monitoring adverse events (AEs) related to chemical substances in human/animal populations. With increasing spontaneous-reporting systems, researchers turned to in-silico approaches to efficiently analyze drug safety profiles. Here, we review in-silico methods employed for assessing multiple drug-drug/drug-disease AEs covered by comparative analyses and visualization strategies. AREAS COVERED: Disproportionality, involving multi-stage statistical methodologies and data processing, identifies safety signals among drug-AE pairs. By stratifying data based on disease indications/demographics, researchers address confounders and assess drug safety. Comparative analyses, including clustering techniques and visualization techniques, assess drug similarities, patterns, and trends, calculate correlations, and identify distinct toxicities. Furthermore, we conducted a thorough Scopus search on 'pharmacovigilance,' yielding 5,836 publications spanning 2003 to 2023. EXPERT OPINION: Pharmacovigilance relies on diverse data sources, presenting challenges in the integration of in-silico approaches and requiring compliance with regulations and AI adoption. Systematic use of statistical analyses enables identifications of potential risks with drugs. Frequentist and Bayesian methods are used in disproportionalities, each with its strengths and weaknesses. Integration of pharmacogenomics with pharmacovigilance enables personalized medicine, with AI further enhancing patient engagement. This multidisciplinary approach holds promise, improving drug efficacy and safety, and should be a core mission of One-Health studies.

Adult Ossabaw Pigs Prefer Fermented Sorghum Tea over Isocaloric Sweetened Water.

Ossabaw pigs (n = 11; 5-gilts, 6-barrows; age 15.6 ± 0.62 SD months) were exposed to a three-choice preference maze to evaluate preference for fermented sorghum teas (FSTs). After conditioning, pigs were exposed, in four sessions, to choices of white FST, sumac FST, and roasted sumac-FST. Then, pigs were exposed, in three sessions, to choices of deionized H2O (-control; avoidance), isocaloric control (+control; deionized H2O and sucrose), and blended FST (3Tea) (equal portions: white, sumac, and roasted sumac). When tea type was evaluated, no clear preference behaviors for tea type were observed (p > 0.10). When the 3Tea and controls were evaluated, pigs consumed minimal control (p < 0.01;18.0 ± 2.21% SEM), and they consumed great but similar volumes of +control and 3Tea (96.6 and 99.0 ± 2.21% SEM, respectively). Likewise, head-in-bowl duration was the least for -control, but 3Tea was the greatest (p < 0.01; 5.6 and 31.9 ± 1.87% SEM, respectively). Head-in-bowl duration for +control was less than 3Tea (p < 0.01; 27.6 vs. 31.9 ± 1.87% SEM). Exploration duration was the greatest in the area with the -control (p < 0.01; 7.1 ± 1.45% SEM), but 3Tea and +control exploration were not different from each other (1.4 and 3.0 ± 1.45% SEM, respectively). Regardless of tea type, adult pigs show preference for FST, even over +control. Adult pigs likely prefer the complexity of flavors, rather than the sweetness alone.

COVID-19 vaccine immune response in patients with plasma cell dyscrasia: a systematic review.

Background: Patients with plasma cell dyscrasia are at a higher risk of developing a severe Coronavirus-2019 (COVID-19) infection. Here we present a systematic review of clinical studies focusing on the immune response to the COVID-19 vaccination in patients with plasma cell dyscrasia. Objectives: This study aims to evaluate the immune response to COVID-19 vaccines in patients with plasma cell dyscrasia and to utilize the results to improve day-to-day practice. Design: Systematic Review. Methods: Online databases (PubMed, CINAHL, Ovid, and Cochrane) were searched following the preferred reporting items for systematic review and meta-analysis (PRISMA) guidelines. Only articles published in the English language were included. Out of 59 studies, nine articles (seven prospective and two retrospective studies) were included in this systematic review. Abstracts, case reports, and case series were excluded. Results: In all nine studies (N = 1429), seroconversion post-vaccination was the primary endpoint. Patients with plasma cell disorders had a lower seroconversion rate compared to healthy vaccinated individuals and the overall percentage of seroconversion ranged between 23% and 95.5%. Among patients on active therapy, lower seroconversion rates were seen on an anti-CD38 agent, ranging from 6.5 up to 100%. In addition, a significantly lower percentage was recorded in older patients, especially in those aged equal to or greater than 65 years and those who have been treated with multiple therapies previously. Only one study reported a statistically significant better humoral response rate with the mRNA vaccine compared to ADZ1222/Ad26.Cov.S. Conclusion: Variable seropositive rates are seen in patients with plasma cell dyscrasia. Lower rates are reported in patients on active therapy, anti-CD38 therapy, and elderly patients. Hence, we propose patients with plasma cell dyscrasias should receive periodic boosters to maintain clinically significant levels of antibodies against COVID-19. Registration: PROSPERO ID: CRD42023404989.

COVID-19 vaccine immune response in patients with plasma cell dyscrasia- a systematic review Background: Patients with plasma cell disorders are at a higher risk of developing a severe coronavirus-19 infection. Here we present a systematic review of clinical studies focusing on the immune response to the coronavirus-19 vaccination in patients with plasma cell dyscrasia. Objectives: This study aims to evaluate the immune response to COVID-19 vaccines in patients with plasma cell dyscrasia and to transcribe the results to day-to-day practice. Design: Systematic Review Data sources: PubMed, CINAHL, Ovid, and Cochrane. Methods: Online databases were searched following the preferred reporting items for systematic review and meta-analysis (PRISMA) guidelines. Only articles published in the English language were included. Abstracts, case reports, and case series were excluded. Out of 59 studies, nine articles were selected for a systematic review. Results: In all 9 studies (N = 1,429), seroconversion post-vaccination was the primary endpoint that our review assessed. Patients with plasma cell disorders had a lower seroconversion rate compared to healthy vaccinated individuals and the overall percentage of seroconversion ranged between 23 and 95.5%. Amongst patients on active therapy, lower seroconversion rates were seen in patients on an anti-CD38 agent, ranging from 6.5 up to 100%. In addition, a significantly lower percentage was recorded in older patients, especially those aged equal to or greater than 65 years and those who have been treated with multiple therapies previously. Only one study reported a statistically significant better humoral response rate with the mRNA vaccine compared to ADZ1222/Ad26.Cov.S. Conclusion: Variable seropositive rates are seen in patients with plasma cell dyscrasia. Lower rates are reported in patients on active therapy, anti-CD38 therapy, and elderly patients. Hence, we propose patients with plasma cell disorders should receive periodic boosters to maintain clinically significant levels of antibodies against COVID-19.

Development of machine learning algorithms to estimate maximum residue limits for veterinary medicines.

Establishing maximum-residue limits (MRLs) for veterinary medicine helps to protect the human food supply. Guidelines for establishing MRLs are outlined by regulatory authorities that drug sponsors follow in each country. During the drug approval process, residue limits are targeted for specific animal species and matrices. Therefore, MRLs are commonly not established for other species. This study demonstrates unestablished MRLs can be reliably predicted for under-represented food commodity groups using machine learning (ML). Classification methods with imbalanced data were used to analyze MRL data from multiple countries by implementing resampling techniques in different ML classifiers. Afterward, we developed and evaluated a data-mining method for predicting unestablished MRLs. Seven different ML classifiers such as support vector classifier, multi-layer perceptron (MLP), random forest, decision tree, k-neighbors, Gaussian NB, and AdaBoost have been selected in this baseline study. Among these, the neural network MLP classifier reliably scored the highest average-weighted F1 score (accuracy >99% with markers and ≈88% without markets) in predicting unestablished MRLs. This provides the first study to apply ML algorithms in regulatory food animal medicine. By predicting and estimating MRLs, we can potentially decrease the use and cost of live animals and the overall research burden of determining new MRLs.

The Potential for Cover Crops to Reduce the Load of Escherichia coli in Contaminated Agricultural Soil.

Cover crops are plants seeded before or after cash crops to improve soil health, reduce weed pressure, and prevent erosion. Cover crops also produce various antimicrobial secondary metabolites (i.e., glucosinolates, quercetin), yet the role of cover crops in moderating the population of human pathogens in the soil has rarely been investigated. This study aims to determine the antimicrobial capacity of three cover crop species to reduce the population of generic Escherichia coli (E. coli) in contaminated agricultural soil. Four-week-old mustard greens (Brassicajuncea), sunn hemp (Crotalaria juncea), and buckwheat (Fagopyrum esculentum) were mixed into autoclaved soil and inoculated with rifampicin-resistant generic E. coli to achieve a starting concentration of 5 log CFU/g. The surviving microbial populations on days 0, 4, 10, 15, 20, 30, and 40 were enumerated. All three cover crops significantly reduced the population of generic E. coli compared to the control (p < 0.0001), particularly between days 10 and to 30. Buckwheat resulted in the highest reduction (3.92 log CFU/g). An inhibitory effect (p < 0.0001) on microbial growth was also observed in soils containing mustard greens and sunn hemp. This study provides evidence for the bacteriostatic and bactericidal effect of particular cover crops. More research regarding the secondary metabolites produced by certain cover crops and their potential as a bio mitigation strategy to improve on-farm produce safety is warranted.

An Automated Customizable Live Web Crawler for Curation of Comparative Pharmacokinetic Data: An Intelligent Compilation of Research-Based Comprehensive Article Repository.

Data curation has significant research implications irrespective of application areas. As most curated studies rely on databases for data extraction, the availability of data resources is extremely important. Taking a perspective from pharmacology, extracted data contribute to improved drug treatment outcomes and well-being but with some challenges. Considering available pharmacology literature, it is necessary to review articles and other scientific documents carefully. A typical method of accessing articles on journal websites is through long-established searches. In addition to being labor-intensive, this conventional approach often leads to incomplete-content downloads. This paper presents a new methodology with user-friendly models to accept search keywords according to the investigators' research fields for metadata and full-text articles. To accomplish this, scientifically published records on the pharmacokinetics of drugs were extracted from several sources using our navigating tool called the Web Crawler for Pharmacokinetics (WCPK). The results of metadata extraction provided 74,867 publications for four drug classes. Full-text extractions performed with WCPK revealed that the system is highly competent, extracting over 97% of records. This model helps establish keyword-based article repositories, contributing to comprehensive databases for article curation projects. This paper also explains the procedures adopted to build the proposed customizable-live WCPK, from system design and development to deployment phases.

A retrospective study of adverse drug events in anticoagulant administration with relevance to COVID-19.

Initial studies in COVID-19 patients reported lower mortality rates associated with the use of the drug heparin, a widely used anticoagulant. The objective of this analysis was to determine whether there are adverse events associated with the administration of anticoagulants, and specifically how this might apply in patients known to have COVID-19. Data for this study were obtained from the Food and Drug Administration's Adverse Event Reporting System (FAERS) public database and from the NIH's clinical trials website. Proportional Reporting Ratios (PRR) with lower 95% confidence intervals (lower CI) and empirical Bayes geometric mean (EBGM) scores with lower 95% confidence limits were calculated for data from the FAERS database where the adverse events studied mimicked COVID-19 symptoms.

Evaluation of Precision Livestock Technology and Human Scoring of Nursery Pigs in a Controlled Immune Challenge Experiment.

The objectives were to determine the sensitivity, specificity, and cutoff values of a visual-based precision livestock technology (NUtrack), and determine the sensitivity and specificity of sickness score data collected with the live observation by trained human observers. At weaning, pigs (n = 192; gilts and barrows) were randomly assigned to one of twelve pens (16/pen) and treatments were randomly assigned to pens. Sham-pen pigs all received subcutaneous saline (3 mL). For LPS-pen pigs, all pigs received subcutaneous lipopolysaccharide (LPS; 300 µg/kg BW; E. coli O111:B4; in 3 mL of saline). For the last treatment, eight pigs were randomly assigned to receive LPS, and the other eight were sham (same methods as above; half-and-half pens). Human data from the day of the challenge presented high true positive and low false positive rates (88.5% sensitivity; 85.4% specificity; 0.871 Area Under Curve, AUC), however, these values declined when half-and-half pigs were scored (75% sensitivity; 65.5% specificity; 0.703 AUC). Precision technology measures had excellent AUC, sensitivity, and specificity for the first 72 h after treatment and AUC values were >0.970, regardless of pen treatment. These results indicate that precision technology has a greater potential for identifying pigs during a natural infectious disease event than trained professionals using timepoint sampling.

Predicting the Prognostic Value of POLI Expression in Different Cancers via a Machine Learning Approach.

Translesion synthesis (TLS) is a cell signaling pathway that facilitates the tolerance of replication stress. Increased TLS activity, the particularly elevated expression of TLS polymerases, has been linked to resistance to cancer chemotherapeutics and significantly altered patient outcomes. Building upon current knowledge, we found that the expression of one of these TLS polymerases (POLI) is associated with significant differences in cervical and pancreatic cancer survival. These data led us to hypothesize that POLI expression is associated with cancer survival more broadly. However, when cancers were grouped cancer type, POLI expression did not have a significant prognostic value. We presented a binary cancer random forest classifier using 396 genes that influence the prognostic characteristics of POLI in cervical and pancreatic cancer selected via graphical least absolute shrinkage and selection operator. The classifier was then used to cluster patients with bladder, breast, colorectal, head and neck, liver, lung, ovary, melanoma, stomach, and uterus cancer when high POLI expression was associated with worsened survival (Group I) or with improved survival (Group II). This approach allowed us to identify cancers where POLI expression is a significant prognostic factor for survival (p = 0.028 in Group I and p = 0.0059 in Group II). Multiple independent validation approaches, including the gene ontology enrichment analysis and visualization tool and network visualization support the classification scheme. The functions of the selected genes involving mitochondrial translational elongation, Wnt signaling pathway, and tumor necrosis factor-mediated signaling pathway support their association with TLS and replication stress. Our multidisciplinary approach provides a novel way of identifying tumors where increased TLS polymerase expression is associated with significant differences in cancer survival.

Data mining methodology for response to hypertension symptomology-application to COVID-19-related pharmacovigilance.

Background: Potential therapy and confounding factors including typical co-administered medications, patient's disease states, disease prevalence, patient demographics, medical histories, and reasons for prescribing a drug often are incomplete, conflicting, missing, or uncharacterized in spontaneous adverse drug event (ADE) reporting systems. These missing or incomplete features can affect and limit the application of quantitative methods in pharmacovigilance for meta-analyses of data during randomized clinical trials. Methods: Data from patients with hypertension were retrieved and integrated from the FDA Adverse Event Reporting System; 134 antihypertensive drugs out of 1131 drugs were filtered and then evaluated using the empirical Bayes geometric mean (EBGM) of the posterior distribution to build ADE-drug profiles with an emphasis on the pulmonary ADEs. Afterward, the graphical least absolute shrinkage and selection operator (GLASSO) captured drug associations based on pulmonary ADEs by correcting hidden factors and confounder misclassification. Selected drugs were then compared using the Friedman test in drug classes and clusters obtained from GLASSO. Results: Following multiple filtering stages to exclude insignificant and noise-driven reports, we found that drugs from antihypertensives agents, urologicals, and antithrombotic agents (macitentan, bosentan, epoprostenol, selexipag, sildenafil, tadalafil, and beraprost) form a similar class with a significantly higher incidence of pulmonary ADEs. Macitentan and bosentan were associated with 64% and 56% of pulmonary ADEs, respectively. Because these two medications are prescribed in diseases affecting pulmonary function and may be likely to emerge among the highest reported pulmonary ADEs, in fact, they serve to validate the methods utilized here. Conversely, doxazosin and rilmenidine were found to have the least pulmonary ADEs in selected drugs from hypertension patients. Nifedipine and candesartan were also found by signal detection methods to form a drug cluster, shown by several studies an effective combination of these drugs on lowering blood pressure and appeared an improved side effect profile in comparison with single-agent monotherapy. Conclusions: We consider pulmonary ADE profiles in multiple long-standing groups of therapeutics including antihypertensive agents, antithrombotic agents, beta-blocking agents, calcium channel blockers, or agents acting on the renin-angiotensin system, in patients with hypertension associated with high risk for coronavirus disease 2019 (COVID-19). We found that several individual drugs have significant differences between their drug classes and compared to other drug classes. For instance, macitentan and bosentan from endothelin receptor antagonists show major concern while doxazosin and rilmenidine exhibited the least pulmonary ADEs compared to the outcomes of other drugs. Using techniques in this study, we assessed and confirmed the hypothesis that drugs from the same drug class could have very different pulmonary ADE profiles affecting outcomes in acute respiratory illness. Funding: GJW and MJD accepted funding from BioNexus KC for funding on this project, but BioNexus KC had no direct role in this article.

Targeting SARS-CoV-2 Variants with Nucleic Acid Therapeutic Nanoparticle Conjugates.

The emergence of SARS-CoV-2 variants is cause for concern, because these may become resistant to current vaccines and antiviral drugs in development. Current drugs target viral proteins, resulting in a critical need for RNA-targeted nanomedicines. To address this, a comparative analysis of SARS-CoV-2 variants was performed. Several highly conserved sites were identified, of which the most noteworthy is a partial homopurine palindrome site with >99% conservation within the coding region. This sequence was compared among recently emerged, highly infectious SARS-CoV-2 variants. Conservation of the site was maintained among these emerging variants, further contributing to its potential as a regulatory target site for SARS-CoV-2. RNAfold was used to predict the structures of the highly conserved sites, with some resulting structures being common among coronaviridae. An RNA-level regulatory map of the conserved regions of SARS-CoV-2 was produced based on the predicted structures, with each representing potential target sites for antisense oligonucleotides, triplex-forming oligomers, and aptamers. Additionally, homopurine/homopyrimidine sequences within the viral genome were identified. These sequences also demonstrate appropriate target sites for antisense oligonucleotides and triplex-forming oligonucleotides. An experimental strategy to investigate these is summarized along with potential nanoparticle types for delivery, and the advantages and disadvantages of each are discussed.

Global Trends in Cancer Nanotechnology: A Qualitative Scientific Mapping Using Content-Based and Bibliometric Features for Machine Learning Text Classification.

This study presents a new way to investigate comprehensive trends in cancer nanotechnology research in different countries, institutions, and journals providing critical insights to prevention, diagnosis, and therapy. This paper applied the qualitative method of bibliometric analysis on cancer nanotechnology using the PubMed database during the years 2000-2021. Inspired by hybrid medical models and content-based and bibliometric features for machine learning models, our results show cancer nanotechnology studies have expanded exponentially since 2010. The highest production of articles in cancer nanotechnology is mainly from US institutions, with several countries, notably the USA, China, the UK, India, and Iran as concentrated focal points as centers of cancer nanotechnology research, especially in the last five years. The analysis shows the greatest overlap between nanotechnology and DNA, RNA, iron oxide or mesoporous silica, breast cancer, and cancer diagnosis and cancer treatment. Moreover, more than 50% of the information related to the keywords, authors, institutions, journals, and countries are considerably investigated in the form of publications from the top 100 journals. This study has the potential to provide past and current lines of research that can unmask comprehensive trends in cancer nanotechnology, key research topics, or the most productive countries and authors in the field.

Hybrid computational modeling demonstrates the utility of simulating complex cellular networks in type 1 diabetes.

Persistent destruction of pancreatic ß-cells in type 1 diabetes (T1D) results from multifaceted pancreatic cellular interactions in various phase progressions. Owing to the inherent heterogeneity of coupled nonlinear systems, computational modeling based on T1D etiology help achieve a systematic understanding of biological processes and T1D health outcomes. The main challenge is to design such a reliable framework to analyze the highly orchestrated biology of T1D based on the knowledge of cellular networks and biological parameters. We constructed a novel hybrid in-silico computational model to unravel T1D onset, progression, and prevention in a non-obese-diabetic mouse model. The computational approach that integrates mathematical modeling, agent-based modeling, and advanced statistical methods allows for modeling key biological parameters and time-dependent spatial networks of cell behaviors. By integrating interactions between multiple cell types, model results captured the individual-specific dynamics of T1D progression and were validated against experimental data for the number of infiltrating CD8+T-cells. Our simulation results uncovered the correlation between five auto-destructive mechanisms identifying a combination of potential therapeutic strategies: the average lifespan of cytotoxic CD8+T-cells in islets; the initial number of apoptotic ß-cells; recruitment rate of dendritic-cells (DCs); binding sites on DCs for naïve CD8+T-cells; and time required for DCs movement. Results from therapy-directed simulations further suggest the efficacy of proposed therapeutic strategies depends upon the type and time of administering therapy interventions and the administered amount of therapeutic dose. Our findings show modeling immunogenicity that underlies autoimmune T1D and identifying autoantigens that serve as potential biomarkers are two pressing parameters to predict disease onset and progression.

Large-Scale Data Mining of Rapid Residue Detection Assay Data From HTML and PDF Documents: Improving Data Access and Visualization for Veterinarians.

Extra-label drug use in food animal medicine is authorized by the US Animal Medicinal Drug Use Clarification Act (AMDUCA), and estimated withdrawal intervals are based on published scientific pharmacokinetic data. Occasionally there is a paucity of scientific data on which to base a withdrawal interval or a large number of animals being treated, driving the need to test for drug residues. Rapid assay commercial farm-side tests are essential for monitoring drug residues in animal products to protect human health. Active ingredients, sensitivity, matrices, and species that have been evaluated for commercial rapid assay tests are typically reported on manufacturers' websites or in PDF documents that are available to consumers but may require a special access request. Additionally, this information is not always correlated with FDA-approved tolerances. Furthermore, parameter changes for these tests can be very challenging to regularly identify, especially those listed on websites or in documents that are not publicly available. Therefore, artificial intelligence plays a critical role in efficiently extracting the data and ensure current information. Extracting tables from PDF and HTML documents has been investigated both by academia and commercial tool builders. Research in text mining of such documents has become a widespread yet challenging arena in implementing natural language programming. However, techniques of extracting tables are still in their infancy and being investigated and improved by researchers. In this study, we developed and evaluated a data-mining method for automatically extracting rapid assay data from electronic documents. Our automatic electronic data extraction method includes a software package module, a developed pattern recognition tool, and a data mining engine. Assay details were provided by several commercial entities that produce these rapid drug residue assay tests. During this study, we developed a real-time conversion system and method for reflowing contents in these files for accessibility practice and research data mining. Embedded information was extracted using an AI technology for text extraction and text mining to convert to structured formats. These data were then made available to veterinarians and producers via an online interface, allowing interactive searching and also presenting the commercial test assay parameters in reference to FDA-approved tolerances.

Zn-based physiometacomposite nanoparticles: distribution, tolerance, imaging, and antiviral and anticancer activity.

The aim of this study was to investigate the distribution, tolerance, and anticancer and antiviral activity of Zn-based physiometacomposites (PMCs). Manganese, iron, nickel and cobalt-doped ZnO, ZnS or ZnSe were synthesized. Cell uptake, distribution into 3D culture and mice, and biochemical and chemotherapeutic activity were studied by fluorescence/bioluminescence, confocal microscopy, flow cytometry, viability, antitumor and virus titer assays. Luminescence and inductively coupled plasma mass spectrometry analysis showed that nanoparticle distribution was liver >spleen >kidney >lung >brain, without tissue or blood pathology. Photophysical characterization as ex vivo tissue probes and LL37 peptide, antisense oligomer or aptamer delivery targeting RAS/Ras binding domain (RBD) was investigated. Treatment at 25 µg/ml for 48 h showed ≥98-99% cell viability, 3D organoid uptake, 3-log inhibition of ß-Galactosidase and porcine reproductive respiratory virus infection. Data support the preclinical development of PMCs for imaging and delivery targeting cancer and infectious disease.

Pulmonary adverse drug event data in hypertension with implications on COVID-19 morbidity.

Hypertension is a recognized comorbidity for COVID-19. The association of antihypertensive medications with outcomes in patients with hypertension is not fully described. However, angiotensin-converting enzyme 2 (ACE2), responsible for host entry of the novel coronavirus (SARS-CoV-2) leading to COVID-19, is postulated to be upregulated in patients taking angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin II receptor blockers (ARBs). Here, we evaluated the occurrence of pulmonary adverse drug events (ADEs) in patients with hypertension receiving ACEIs/ARBs to determine if disparities exist between individual drugs within the respective classes using data from the FDA Spontaneous Reporting Systems. For this purpose, we proposed the proportional reporting ratio to provide a statistical summary for the commonality of an ADE for a specific drug as compared to the entire database for drugs in the same or other classes. In addition, a statistical procedure, multiple logistic regression analysis, was employed to correct hidden confounders when causative covariates are underreported or untrusted to correct analyses of drug-ADE combinations. To date, analyses have been focused on drug classes rather than individual drugs which may have different ADE profiles depending on the underlying diseases present. A retrospective analysis of thirteen pulmonary ADEs showed significant differences associated with quinapril and trandolapril, compared to other ACEIs and ARBs. Specifically, quinapril and trandolapril were found to have a statistically significantly higher incidence of pulmonary ADEs compared with other ACEIs as well as ARBs (P < 0.0001) for group comparison (i.e., ACEIs vs. ARBs vs. quinapril vs. trandolapril) and (P ≤ 0.0007) for pairwise comparison (i.e., ACEIs vs. quinapril, ACEIs vs. trandolapril, ARBs vs. quinapril, or ARBs vs. trandolapril). This study suggests that specific members of the ACEI antihypertensive class (quinapril and trandolapril) have a significantly higher cluster of pulmonary ADEs.

A field trial comparing four oral nonsteroidal anti-inflammatory drugs on controlling cautery dehorning pain and stress in calves.

The purpose of this study was to compare the analgesic effect of four nonsteroidal anti-inflammatory drugs (NSAIDs) administered as a single, standardized, oral dose in dairy calves at the time of cautery dehorning. The NSAIDs investigated have pharmacokinetic properties in cattle that produce persistent plasma concentrations that may provide prolonged analgesia with the added practicality of a simple administration regimen. One hundred and eighty-five Holstein calves aged approximately 50 d old were either sham dehorned (n = 31) or cautery dehorned following oral administration of carprofen (n = 31), firocoxib (n = 31), flunixin meglumine (n = 30), meloxicam (n = 31) or placebo (n = 31) in a randomized, controlled trial. A standard dose of 2.0 mg/kg was administered to all calves receiving an oral NSAID. All calves received local anesthesia prior to actual or sham dehorning. Cortisol concentrations, heart rate, mechanical nociception thresholds, ocular and dehorning area temperatures, and average daily gains were evaluated. A linear mixed-effects model with repeated measures was used for statistical analysis. Administration of oral meloxicam, flunixin meglumine, and firocoxib at 2.0 mg/kg resulted in decreased cortisol concentrations compared to placebo-treated controls for the first 24 h postdehorning (AUEC0-24) (P = 0.03). Moreover, firocoxib, flunixin meglumine, and meloxicam attenuated the maximum cortisol concentrations compared to placebo-treated calves (P = 0.04, P= 0.02). In calves treated with flunixin meglumine, cortisol concentrations was reduced at 4 h (P = 0.04) and 8 h (P = 0.02). In addition, analgesic administration was associated with changes in ocular and dehorning area temperature differences (P = 0.09). Carprofen and meloxicam reduced heart rates during the entire study period (P = 0.003). Although a treatment effect (P < 0.0001) was observed in the determination of mechanical nociception threshold among all treatment groups, meloxicam expressed marginally significant effects (P = 0.09) among NSAID treated groups dehorned. A single dose of oral meloxicam, flunixin meglumine, or firocoxib administered at 2.0 mg/kg reduced the acute stress response associated with cautery dehorning. However, carprofen administration was associated with increased cortisol concentrations and dehorning area temperatures for the initial 24 h. Given the changes in pain and stress outcome variables assessed in this study, NSAIDs should be administered at the time of dehorning.