• View in gallery
    Figure 1

    Mean plasma misoprostol free acid (MFA) concentration-versus-time curve for 6 healthy, adult horses that received misoprostol (5 µg/kg, q 8 h for 10 doses) after dose 1 and dose 10 ORAL administration (solid circles) as well as dose 1 and dose 10 RECTAL administration (cross-hatched boxes). Error bars represent standard deviation.

  • View in gallery
    Figure 2

    Principal coordinate analysis based on Bray-Curtis similarities with results of 2-way permutational multivariate analysis of variance shown at top left. Samples cluster according to horse, with minimal to no separation of experimental conditions within horse (legend at right), showing clear interhorse differences in β-diversity.

  • View in gallery
    Figure 3

    Intrahorse similarity to baseline at each subsequent time point, according to treatment group. Tukey box plots showing intrahorse similarity to their baseline composition at each time point during ORAL (A) or RECTAL (B) administration of misoprostol, or during the CONTROL period (C). Different letters indicate significant differences in pairwise comparisons within each panel. *Significant difference between the CONTROL and ORAL conditions at the End Tx time point.

  • View in gallery
    Figure 4

    Interhorse and time-dependent effects on richness and α-diversity. Box plot and line graph showing the number of detected amplicon sequence variants (ASVs) as a measure of richness, grouped by horse (A) or treatment and time point (B). Main effects associated with horse and time point (3-way ANOVA) are included in A and B, respectively. Box plot and line graph representing Shannon α-diversity in the same fashion (C and D). Different letters indicate significant differences in pairwise comparisons within each panel.

  • 1.

    Dollery C. Misoprostol. Therapeutic Drugs. Churchill Livingstone; 1999;193197.

  • 2.

    Sangiah S, MacAllister C, Amouzadeh H. Effects of misoprostol and omeprazole on basal gastric pH and free acid content in horses. Res Vet Sci. 1989;47(3):350354. doi:10.1016/S0034-5288(18)31260-8

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 3.

    Varley G, Bowen I, Habershon-Butcher J, Nicholls V, Hallowell GD. Misoprostol is superior to combined omeprazole-sucralfate for the treatment of equine gastric glandular disease. Equine Vet J. 2019;51(5):575580. doi:10.1111/evj.13087

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 4.

    Martin E, Schirmer J, Jones SL, Davis JL. Pharmacokinetics and ex vivo anti-inflammatory effects of oral misoprostol in horses. Equine Vet J. 2019;51(3):415421. doi:10.1111/evj.13024

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 5.

    Martin EM, Messenger KM, Sheats MK, Jones SL. Misoprostol inhibits lipopolysaccharide-induced pro-inflammatory cytokine production by equine leukocytes. Front Vet Sci. 2017;4:160. doi:10.3389/fvets.2017.00160

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 6.

    Martin EM, Till RL, Sheats MK, Jones SL. Misoprostol inhibits equine neutrophil adhesion, migration, and respiratory burst in an in vitro model of inflammation. Front Vet Sci. 2017;4:159. doi:10.3389/fvets.2017.00159

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 7.

    Gobejishvili L, Ghare S, Khan R, et al. Misoprostol modulates cytokine expression through a cAMP pathway: Potential therapeutic implication for liver disease. Clin Immunol. 2015;161(2):291299. doi:10.1016/j.clim.2015.09.008

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 8.

    Chilcoat CD, Rowlingson KA, Jones SL. The effects of cAMP modulation upon the adhesion and respiratory burst activity of immune complex-stimulated equine neutrophils. Vet Immunol Immunopathol. 2002;88(1–2):6577. doi:10.1016/S0165-2427(02)00137-X

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 9.

    Kimura S, McCoy AM, Boothe DM, et al. Effects of a single dose of orally and rectally administered misoprostol in an in vivo endotoxemia model in healthy adult horses. Am J Vet Res. 2022;83(8):ajvr.21.12.0206.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 10.

    Lopp CT, McCoy AM, Boothe D, Schaeffer DJ, Lascola K. Single-dose pharmacokinetics of orally and rectally administered misoprostol in adult horses. Am J Vet Res. 2019;80(11):10261033. doi:10.2460/ajvr.80.11.1026

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 11.

    Davis J. Nonsteroidal anti-inflammatory drug associated right dorsal colitis in the horse. Equine Vet Educ. 2017;29:104113. doi:10.1111/eve.12454

  • 12.

    Tomlinson J, Blikslager A. Effects of cyclooxygenase inhibitors flunixin and deracoxib on permeability of ischaemic-injured equine jejunum. Equine Vet J. 2005;37(1):7580. doi:10.2746/0425164054406865

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 13.

    Ratnaike RN, Jones TE. Mechanisms of drug-induced diarrhoea in the elderly. Drugs Aging. 1998;13(3):245253. doi:10.2165/00002512-199813030-00007

  • 14.

    Zackular JP, Kirk L, Trindade BC, Skaar EP, Aronoff DM. Misoprostol protects mice against severe Clostridium difficile infection and promotes recovery of the gut microbiota after antibiotic perturbation. Anaerobe. 2019;58:8994. doi:10.1016/j.anaerobe.2019.06.006

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 15.

    Costa MC, Arroyo LG, Allen-Vercoe E, et al. Comparison of the fecal microbiota of healthy horses and horses with colitis by high throughput sequencing of the V3-V5 region of the 16S rRNA gene. PLoS One. 2012;7(7):e41484. doi:10.1371/journal.pone.0041484

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 16.

    Weese JS, Holcombe S, Embertson R, et al. Changes in the faecal microbiota of mares precede the development of post partum colic. Equine Vet J. 2015;47(6):641649. doi:10.1111/evj.12361

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 17.

    Stewart H, Southwood L, Indugu N, Vecchiarelli B, Engiles JB, Pitta D. Differences in the equine faecal microbiota between horses presenting to a tertiary referral hospital for colic compared with an elective surgical procedure. Equine Vet J. 2019;51(3):336342. doi:10.1111/evj.13010

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 18.

    Stewart HL, Pitta D, Indugu N, et al. Changes in the faecal bacterial microbiota during hospitalisation of horses with colic and the effect of different causes of colic. Equine Vet J. 2021;53(6):11191131. doi:10.1111/evj.13389

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 19.

    Arnold CE, Pilla R, Chaffin MK, et al. The effects of signalment, diet, geographic location, season, and colitis associated with antimicrobial use or Salmonella infection on the fecal microbiome of horses. J Vet Intern Med. 2021;35(5):24372448. doi:10.1111/jvim.16206

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 20.

    Venable E, Kerley M, Raub R. Assessment of equine fecal microbial profiles during and after a colic episode using pyrosequencing. J Equine Vet Sci. 2013;33(5):347348. doi:10.1016/j.jevs.2013.03.066

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 21.

    Costa MC, Stämpfli HR, Arroyo LG, et al. Changes in the equine fecal microbiota associated with the use of systemic antimicrobial drugs. BMC Vet Res. 2015;11:112. doi:10.1186/s12917-014-0312-6

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 22.

    Arnold CE, Isaiah A, Pilla R, et al. The cecal and fecal microbiomes and metabolomes of horses before and after metronidazole administration. PLoS One. 2020;15(5):e0232905. doi:10.1371/journal.pone.0232905

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 23.

    Whitfield-Cargile CM, Chamoun-Emanuelli AM, Cohen ND, Richardson LM, Ajami NJ, Dockery HJ. Differential effects of selective and non-selective cyclooxygenase inhibitors on fecal microbiota in adult horses. PLoS One. 2018;13(8):e0202527. doi:10.1371/journal.pone.0202527

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 24.

    Harlow BE, Lawrence LM, Flythe MD. Diarrhea-associated pathogens, lactobacilli and cellulolytic bacteria in equine feces: Responses to antibiotic challenge. Vet Microbiol. 2013;166(1–2):225232. doi:10.1016/j.vetmic.2013.05.003

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 25.

    Ericsson AC, Johnson PJ, Gieche LM, et al. The influence of diet change and oral metformin on blood glucose regulation and the fecal microbiota of healthy horses. Animals. 2021;11(4):976. doi:10.3390/ani11040976

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 26.

    Walters WA, Caporaso JG, Lauber CL, Berg-Lyons D, Fierer N, Knight R. PrimerProspector: de novo design and taxonomic analysis of barcoded polymerase chain reaction primers. Bioinformatics. 2011;27(8):11591161. doi:10.1093/bioinformatics/btr087

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 27.

    Caporaso JG, Lauber CL, Walters WA, et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc Natl Acad Sci. 2011;108(suppl 1):45164522. doi:10.1073/pnas.1000080107

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 28.

    Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17:1012. doi:10.14806/ej.17.1.200

  • 29.

    Bolyen E, Rideout JR, Dillon MR, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37(8):852857. doi:10.1038/s41587-019-0209-9

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 30.

    Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13(7):581583. doi:10.1038/nmeth.3869

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 31.

    Pruesse E, Quast C, Knittel K, et al. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res. 2007;35(21):71887196. doi:10.1093/nar/gkm864

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 32.

    Baggot JD, ed. Physiologic Basis of Veterinary Clinical Pharmacology. Blackwell Science Ltd, 2001:5591.

  • 33.

    Costa MC, Weese JS. Understanding the intestinal microbiome in health and disease. Vet Clin North Am Equine Pract. 2018;34(1):112. doi:10.1016/j.cveq.2017.11.005

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 34.

    Garber A, Hastie P, Murray JA. Factors influencing equine gut microbiota: current knowledge. J Equine Vet Sci. 2020;88:102943. doi:10.1016/j.jevs.2020.102943

  • 35.

    Daly K, Proudman CJ, Duncan SH, Flint HJ, Dyer J, Shirazi-Beechey SP. Alterations in microbiota and fermentation products in equine large intestine in response to dietary variation and intestinal disease. Br J Nutr. 2012;107(7):989995. doi:10.1017/S0007114511003825

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 36.

    Willing B, Vörös A, Roos S, Jones C, Jansson A, Lindberg JE. Changes in faecal bacteria associated with concentrate and forage-only diets fed to horses in training. Equine Vet J. 2009;41(9):908914. doi:10.2746/042516409X447806

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 37.

    Hansen NC, Avershina E, Mydland LT, et al. High nutrient availability reduces the diversity and stability of the equine caecal microbiota. Microb Ecol Health Dis. 2015;26:27216.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 38.

    Dougal K, de la Fuente G, Harris PA, et al. Characterisation of the faecal bacterial community in adult and elderly horses fed a high fibre, high oil or high starch diet using 454 pyrosequencing. PloS One. 2014;9(2):e87424. doi:10.1371/journal.pone.0087424

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 39.

    Massacci FR, Clark A, Ruet A, Lansade L, Costa M, Mach N. Inter-breed diversity and temporal dynamics of the faecal microbiota in healthy horses. J Anim Breed Genet. 2020;137:(1):103120. doi:10.1111/jbg.12441

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 40.

    Mshelia ES, Adamu L, Wakil Y, et al. The association between gut microbiome, sex, age and body condition scores of horses in Maiduguri and its environs. Microb Pathog. 2018;118:8186. doi:10.1016/j.micpath.2018.03.018

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 41.

    Warzecha C, Coverdale J, Janecka J, et al. Influence of short-term dietary starch inclusion on the equine cecal microbiome. J Anim Sci. 2017;95(11):50775090. doi:10.2527/jas2017.1754

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 42.

    Janabi AHD, Biddle AS, Klein DJ, McKeever KH. The effects of acute strenuous exercise on the faecal microbiota in Standardbred racehorses. Comp Exer Physiol. 2017;13(1):1324. doi:10.3920/CEP160030

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 43.

    Plancade S, Clark A, Philippe C, et al. Unraveling the effects of the gut microbiota composition and function on horse endurance physiology. Sci Rep. 2019;9(1):9620. doi:10.1038/s41598-019-46118-7

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 44.

    Faubladier C, Chaucheyras-Durand F, Da Veiga L, Julliand V. Effect of transportation on fecal bacterial communities and fermentative activities in horses: impact of Saccharomyces cerevisiae CNCM I-1077 supplementation. J Anim Sci. 2013;91:(4):17361744. doi:10.2527/jas.2012-5720

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 45.

    Schoster A, Mosing M, Jalali M, Staempfli HR, Weese JS. Effects of transport, fasting and anaesthesia on the faecal microbiota of healthy adult horses. Equine Vet J. 2016;48(5):595602. doi:10.1111/evj.12479

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 46.

    Salem SE, Maddox TW, Berg A, et al. Variation in faecal microbiota in a group of horses managed at pasture over a 12-month period. Sci Rep. 2018;8(1):110. doi:10.1038/s41598-018-26930-3

    • Crossref
    • Search Google Scholar
    • Export Citation

Advertisement

Multidose misoprostol pharmacokinetics and its effect on the fecal microbiome in healthy, adult horses

Rachel L. PfeifleDepartment of Clinical Sciences, College of Veterinary Medicine, Auburn University, Auburn, AL

Search for other papers by Rachel L. Pfeifle in
Current site
Google Scholar
PubMed
Close
 DVM
,
Aaron C. EricssonDepartment of Veterinary Pathobiology, College of Veterinary Medicine, University of Missouri Colombia, MO

Search for other papers by Aaron C. Ericsson in
Current site
Google Scholar
PubMed
Close
 DVM, PhD
,
Annette M. McCoyDepartment of Veterinary Clinical Medicine, College of Veterinary Medicine, University of Illinois Urbana, IL

Search for other papers by Annette M. McCoy in
Current site
Google Scholar
PubMed
Close
 DVM, PhD, DACVS
,
Dawn M. BootheDepartment of Anatomy, Physiology, and Pharmacology, College of Veterinary Medicine, Auburn University, Auburn, AL

Search for other papers by Dawn M. Boothe in
Current site
Google Scholar
PubMed
Close
 DVM, PhD, DACVIM, DACVCP
,
Anne A. WooldridgeDepartment of Clinical Sciences, College of Veterinary Medicine, Auburn University, Auburn, AL

Search for other papers by Anne A. Wooldridge in
Current site
Google Scholar
PubMed
Close
 DVM, PhD, DACVIM
,
Erin S. GrooverDepartment of Clinical Sciences, College of Veterinary Medicine, Auburn University, Auburn, AL

Search for other papers by Erin S. Groover in
Current site
Google Scholar
PubMed
Close
 DVM, DACVIM
,
Tamara Sierra-RodriguezDepartment of Clinical Sciences, College of Veterinary Medicine, Auburn University, Auburn, AL

Search for other papers by Tamara Sierra-Rodriguez in
Current site
Google Scholar
PubMed
Close
 DVM, MS, DACVIM
, and
Kara M. LascolaDepartment of Clinical Sciences, College of Veterinary Medicine, Auburn University, Auburn, AL

Search for other papers by Kara M. Lascola in
Current site
Google Scholar
PubMed
Close
 DVM, MS, DACVIM

Abstract

OBJECTIVE

To compare the pharmacokinetics between repeated doses and to characterize changes in the fecal microbiome after oral and rectal multidose misoprostol administration.

ANIMALS

6 healthy university-owned geldings.

PROCEDURES

In a randomized, crossover study, misoprostol (5 μg/kg) was administered orally or rectally every 8 hours for 10 doses, or not administered (control), with a 21-day washout between treatments. Concentration-versus-time data for dose 1 and dose 10 were subject to noncompartmental analysis. For microbiota analysis using 16S rRNA amplicon sequencing, manure was collected 7 days before study onset, immediately before dose 1, and 6 hours, 7 days, and 14 days after dose 10, with time-matched points in controls.

RESULTS

Repeated dosing-related differences in pharmacokinetic parameters were not detected for either administration route. The area under the concentration-versus-time curve was greater (P < .04) after oral versus rectal administration. The relative bioavailability of rectal administration was 4 to 86% of that of oral administration. Microbial composition, richness, and β-diversity differed among subjects (P < .001 all) while only composition differed between treatments (P ≤ .01). Richness was decreased 6 hours after dose 10 and at the control-matched time point (P = .0109) in all subjects. No other differences for time points, treatments, or their interactions were observed.

CLINICAL RELEVANCE

Differences in systemic exposure were associated with the route of administration but were not detected after repeated administration of misoprostol. Differences in microbiota parameters were primarily associated with interindividual variation and management rather than misoprostol administration.

Abstract

OBJECTIVE

To compare the pharmacokinetics between repeated doses and to characterize changes in the fecal microbiome after oral and rectal multidose misoprostol administration.

ANIMALS

6 healthy university-owned geldings.

PROCEDURES

In a randomized, crossover study, misoprostol (5 μg/kg) was administered orally or rectally every 8 hours for 10 doses, or not administered (control), with a 21-day washout between treatments. Concentration-versus-time data for dose 1 and dose 10 were subject to noncompartmental analysis. For microbiota analysis using 16S rRNA amplicon sequencing, manure was collected 7 days before study onset, immediately before dose 1, and 6 hours, 7 days, and 14 days after dose 10, with time-matched points in controls.

RESULTS

Repeated dosing-related differences in pharmacokinetic parameters were not detected for either administration route. The area under the concentration-versus-time curve was greater (P < .04) after oral versus rectal administration. The relative bioavailability of rectal administration was 4 to 86% of that of oral administration. Microbial composition, richness, and β-diversity differed among subjects (P < .001 all) while only composition differed between treatments (P ≤ .01). Richness was decreased 6 hours after dose 10 and at the control-matched time point (P = .0109) in all subjects. No other differences for time points, treatments, or their interactions were observed.

CLINICAL RELEVANCE

Differences in systemic exposure were associated with the route of administration but were not detected after repeated administration of misoprostol. Differences in microbiota parameters were primarily associated with interindividual variation and management rather than misoprostol administration.

Misoprostol, a synthetic prostaglandin E1 (PGE1) analog and E2, E3, and E4 prostanoid receptor subtype agonist, is approved in humans for the prevention of gastric and duodenal injury related to nonsteroidal anti-inflammatory drug (NSAID) use1 and is recommended in horses for treatment of equine gastric glandular disease (EGGD) and NSAID-induced colitis.2,3 Misoprostol may also hold promise as a potential anti-inflammatory medication. While numerous in vitro and ex vivo studies48 have described the anti-inflammatory effects of misoprostol through cytokine and cyclic adenosine monophosphate (c-AMP)-mediated pathways, its anti-inflammatory potential in horses is less clear. A recent study9 investigating the administration of single-dose misoprostol orally or rectally in healthy adult horses challenged with lipopolysaccharide (LPS) identified appreciable changes in select inflammatory cytokines.

Single-dose pharmacokinetics of misoprostol have been described after rectal and oral administration in fed and fasted healthy horses. While pharmacokinetic profiles vary between routes of administration, reported plasma concentrations for both routes were comparable or superior to those reported in humans.10 Interestingly, plasma concentrations after oral administration in horses experimentally challenged with LPS were increased relative to plasma concentrations after oral administration in healthy horses, suggesting that inflammation may influence oral drug absorption.9,10 In equine practice, misoprostol is typically administered for several days to weeks at a suggested dose of 5 μg/kg orally or rectally every 8 to 12 hours.3 To date, the pharmacokinetics of multidose misoprostol has yet to be described in the horse. Since the pharmacokinetic behavior of a drug can change with repeated administration, this is an essential step for optimizing misoprostol administration protocols.

The administration of misoprostol has the potential to alter gastrointestinal physiology through interactions with prostanoid receptors throughout the gastrointestinal tract. These effects can be beneficial with respect to misoprostol’s role in mucosal protection and repair48,11,12 but can also result in adverse events including alterations in smooth muscle contractility, colonic transit time, or intestinal fluid movement leading to reported side effects of abdominal cramping and diarrhea.13 To date, there is limited information available regarding whether misoprostol-mediated effects on gastrointestinal physiology have the potential to cause alterations in the gastrointestinal microbiome. In mice, misoprostol improved colonic barrier function and promoted the recovery of microbiome homeostasis after disruption with antibiotics, although the exact mechanism of these benefits remains unknown.14 This could be of therapeutic significance in horses where gastrointestinal disease1520 and administration of antibiotics or NSAIDs2124 have the potential to create dysbiosis, if misoprostol could be prescribed to help restore the microbiome population. In contrast, it is possible that the administration of misoprostol and the subsequent changes in gastrointestinal physiology previously described could result in enough changes to the local intestinal environment to cause dysbiosis itself. Given the essential role of the gastrointestinal microbiome in the health and disease of horses, as well as the increased frequency with which misoprostol is used to treat horses with gastrointestinal disease, investigation into its potential to alter the gastrointestinal microbiome in horses is warranted.

The objectives of this study were to determine the pharmacokinetics of misoprostol and to describe changes in the fecal microbiome after oral and rectal repeated-dose misoprostol administration to healthy, adult horses. We hypothesized that pharmacokinetic parameters would differ by route of administration but not after repeated-dose administration. Furthermore, we hypothesized that changes in the composition, richness, or α-diversity of the fecal microbiome would not be observed after oral or rectal repeated-dose misoprostol administration.

Materials and Methods

Animals

Six university-owned healthy adult (13 to 18 years old) mixed-breed geldings, ranging in body weight from 468 to 609 kg, were used for the study. All horses had no history of illness or antimicrobial administration within the previous 6 months and were deemed healthy on the basis of physical examination findings and biochemistry panel screenings. Horses were housed individually in stalls for a minimum of 12 hours before and for the duration of each experimental condition. Horses were maintained on a group-specific pasture before study onset, during all washout periods, and after study completion. Fresh clean water was provided ad libitum. Horses were offered grass hay (3 flakes twice daily) and a senior concentrate mash (3 pounds once daily) leading up to and throughout the entire study, including washout periods. Physical examinations were performed before and then every 12 hours for the duration of each experimental condition. Horses were monitored hourly during each experimental condition and daily during each washout period. All procedures were reviewed and approved by the Auburn University Institutional Care and Use Committee (protocol No. 2020-3736).

Experimental design

A prospective, 3 treatment randomized crossover study design was used. The treatment order was assigned by simple randomization using a random number generator (random.org). Horses were administered a 5 µg/kg dose of misoprostol orally (ORAL) or rectally (RECTAL) every 8 hours for a total of 10 doses, or no medication (CONTROL) for the same duration, with a minimum 21-day washout period between each experimental condition. For the ORAL and RECTAL conditions, horses were instrumented with a 14-gauge over-the-needle catheter in the left or right jugular vein to facilitate repeated blood sample collection for the measurement of plasma misoprostol free acid (MFA) concentrations. For all conditions, manure samples were collected by manual evacuation of the rectum for fecal microbiome analysis. Horses were maintained on a standard feeding schedule throughout the study with feed provided a minimum of 5 hours prior to and 1 hour following drug administration for the ORAL and RECTAL conditions and at time-matched points for the CONTROL condition.

Misoprostol administration

For oral administration (ORAL), misoprostol hydrochloride tablets (Greenstone; 100 µg) were dissolved in 30 mL water, administered via oral syringe, and followed immediately by administration of 30 mL water through the same syringe to ensure delivery of the total dose. For rectal administration (RECTAL), manure was manually evacuated prior to drug administration. Misoprostol tablets were dissolved in 30 mL water, administered via a syringe attached to a 40-cm, 18-French red rubber catheter advanced approximately 30 cm into the rectum, and followed immediately by infusion of 30 mL water through the same syringe and catheter to ensure delivery of the total dose.

Blood sample collection

For measurement of plasma MFA concentrations for the ORAL condition, blood samples were collected immediately prior to (time 0) and at 10, 15, 20, 30, 60, 90, and 120 minutes following drug administration for doses 1 (first) and 10 (last), as well as at time 0 and at 15 and 30 minutes for doses 2 and 8. For the RECTAL condition, blood samples were collected at time 0 and at 3, 5, 10, 15, 30, 60, and 90 minutes after drug administration for doses 1 and 10, as well as at time 0 and at 3 and 5 minutes after drug administration for doses 2 and 8. The timing of sample collection was based on previously reported pharmacokinetics for single-dose misoprostol administered by the oral and rectal routes.9,10 Samples were immediately placed in sodium heparin tubes, placed on ice, and then centrifuged (400 X g for 10 minutes at 4 °C) within 15 minutes of collection. Plasma was separated into 2-mL aliquots and stored at −80 °C until analysis.

Fecal sample collection

For all horses, a manure sample was collected 7 days prior to study onset (Baseline). Samples were then collected immediately prior to dose 1 (Start Tx) and then 6 hours (End Tx), 7 days (D7), and 14 days (D14) after dose 10 for the ORAL and RECTAL conditions and at time-matched points for the CONTROL condition. Samples were refrigerated within 10 minutes of collection. Aliquots of feces from the center of each fecal ball were then transferred to 2-mL Eppendorf tubes within 30 minutes of collection and stored at −80 °C until analysis.

Measurement of plasma MFA concentration

Plasma MFA concentrations were analyzed by liquid-chromatography-tandem mass spectrometry (LC-MS/MS) with a triple quadrupole system (Thermo Altis; Thermo Fisher Scientific) and software (TraceFinder 4.1) designed for data acquisition and analysis. The LC-MS/MS protocol and data acquisition parameters have been previously described for equine plasma.9,10 Briefly, 500-μL plasma sample was mixed with 1 mL acetonitrile spiked with 5 μL D5-misoprostol acid (100 ng/mL) and centrifuged, and the supernatant was dried and then reconstituted into 100 μL solvent prior to LC-MS/MS instrument injection. LC separation was performed on a C18+ column (2.1 X 100 mm, 1.5 μm; Thermo Accucore Vanquish; Thermo Scientific) with a mobile phase A (0.1% formic acid in water) and mobile phase B (0.1% formic acid in acetonitrile). For method validation, the calibrated concentration range was 2 pg/ml to 5,000 pg/ml with a lower limit of quantification (LLOQ) of 2 pg/mL and standard curve correlation coefficient values (R2) > 0.993. Percent MFA recovery and within and between-run accuracy and precision data are presented (Supplemental Table S1).

Pharmacokinetic analysis

Plasma MFA concentration-versus-time data were subjected to noncompartmental pharmacokinetic modeling using Phoenix WinNonlin, v8.1 (Certara) software. Maximum plasma concentration (Cmax) and its respective time (tmax) are reported. The area under the MFA concentration-versus-time curve to the last sample time point (AUC0→last), to infinity (AUC0→∞), and to the dosing interval (AUCtau) after dose 1 (first) and dose 10 (last) were calculated using the log-linear trapezoidal method. For AUC0→∞ and AUCtau, the percentage extrapolated from the terminal component of the curve was calculated and reported as AUCextrap and AUCtauextrap, respectively. Nonlinear regression was used to determine the slope of the terminal component of the drug-elimination time curve. Because intravenous drug administration was not performed, both the elimination rate constant (1/λ) and half-life (t1/2dis) are reported as disappearance values. Additional reported parameters include mean residence time (MRT), relative bioavailability (F) of rectal compared to oral administration (F = AUCRECTAL/AUCORAL), and the accumulation index. The coefficient of variation (CV) for selected values was calculated as the SD divided by the mean.

Fecal DNA extraction

DNA was extracted from fecal samples using QIAamp PowerFecal kits (Qiagen) according to the manufacturer’s instructions, with minor adaptations in the sample and lysis buffer homogenization as previously described.25 DNA, eluted in 100 µL of buffer, was quantified via fluorometry (Qubit 2.0; Invitrogen) using quant-iT BR dsDNA reagent kits (Invitrogen) and normalized to a uniform concentration and volume.

16S rRNA amplicon library preparation and sequencing

Construction and sequencing of bacterial 16S rRNA amplicon libraries were performed at the University of Missouri (MU) DNA Core Facility according to previously described methodology.25 Briefly, 16S rRNA amplicons were generated via amplification of the V4 hypervariable region of the 16S rRNA gene using universal primers (U515F/806R) flanked by Illumina standard adapter sequences26,27 and the following amplification parameters: 98 °C(3 min) + [98 °C(15 sec) + 50 °C(30 sec) + 72 °C(30 sec)] X 25 cycles + 72 °C(7 min). Amplicons were pooled, purified, and then washed. The final amplicon pool was quantified (quant-iT HS dsDNA reagent kit) and sequenced using a standard 2 X 250-bp paired-end reads protocol for sequencing on the Illumina MiSeq instrument.

Bioinformatics analysis

Bioinformatics on DNA sequences was performed at the MU Informatics Research Core Facility. Primers designed to match the 5' ends of forward and reverse reads were removed from the 5' end of the forward read using Cutadapt28 (version 2.6; https://github.com/marcelm/cutadapt). When identified, reverse complements of the primer and all bases downstream were then removed from the forward read. For the reverse reads the approach was similar but opposite. Two passes were made over each read with an allowable error rate of 0.1. Read pairs were rejected if both did not match a 5' primer. The QIIME229 DADA230 plugin (version 1.10.0) with R version 3.5.1 and Biom version 2.1.7 was used to denoise, dereplicate, and count amplicon sequence variants (ASVs). Finally, taxonomies were assigned using the Silva.v13231 database.

Statistical analysis

Analysis of pharmacokinetic data was performed using Graphpad Prism 9. Distribution of data was evaluated for normality using Shapiro-Wilk and Kolmogorov-Smirnov methods. Data are reported as mean ± standard deviation apart from tmax and t1/2dis, which are reported as median (range) and harmonic mean ± pseudostandard deviation, respectively. Comparisons between the first and last (10th) doses for each route of administration were evaluated using Wilcoxon signed rank test for tmax and paired t tests for all other parameters. Comparisons between routes of administration (ORAL versus RECTAL) were evaluated using mixed-effects analysis with Sidák’s post hoc multiple comparisons for tmax and repeated measures 2-way ANOVA with Tukey’s post hoc multiple comparisons test for all other parameters.

Analysis of fecal microbiota data was performed using SigmaPlot version 14.0. Differences in overall microbiota composition (β-diversity) between horses and treatments were tested via 2-way permutation multivariate analysis of variance (PERMANOVA) using Bray-Curtis and Jaccard similarities. Principal coordinate analysis (PCoA) was performed on ¼ root-transformed data. Effects of horse and treatment on change in intrasubject β-diversity over time were assessed for normality and equal variance using the Shapiro-Wilk and Brown-Forsythe methods, respectively, and then tested using a 2-way ANOVA with Holm-Sidak post hoc multiple comparisons. Univariate data including detected and predicted richness of ASVs (Taxa_S and Chao-1, respectively) and α-diversity (Shannon index and Simpson index) were assessed for normality and equal variance using the Shapiro-Wilk and Brown-Forsythe methods, respectively, and then tested via 3-way ANOVA with Holm-Sidak post hoc multiple comparisons. Significance for all statistical comparisons was defined as P < .05.

Results

Physical examination parameters remained normal, and no clinical evidence of abdominal discomfort or diarrhea, or changes in appetite, fecal consistency, or fecal output was noted for any horse during the study. One horse developed mild cellulitis of 1 distal hindlimb just prior to the third experimental condition necessitating the administration of a single dose each of flunixin meglumine (1.1 mg/kg, IV) and ceftiofur crystalline free acid (6.6 mg/kg, IM). The horse was maintained on a group-specific pasture until after resolution of the cellulitis. Since dysbiosis following NSAID and antibiotic administration has been demonstrated in horses,21,23,24 an additional 4-week washout period (from the time of medication administration) was instituted prior to the horse completing the third experimental condition. The fecal microbiome data for the horse’s third experimental condition was ultimately included in the final analysis for this study because significant differences were not identified between the horse’s fecal samples at baseline and the Start Tx time point of the third experimental condition.

Pharmacokinetic results

Plasma MFA concentrations remained above the LLOQ for all horses at 120 minutes after doses 1 and 10 in the ORAL condition and at 90 minutes in the RECTAL condition for 5 of 6 horses after dose 1 and 4 of 6 horses after dose 10. Plasma MFA concentrations fell below the lower limit of detection (LLOD) in all horses at time 0 for doses 1, 2, 8, and 10 in both ORAL and RECTAL conditions. Differences in time-matched plasma MFA concentrations were not observed across doses within both the ORAL and RECTAL conditions.

Plasma concentration-versus-time curves generated for dose 1 and dose 10 of the ORAL and RECTAL conditions are displayed (Figure 1), and pharmacokinetic parameters are summarized (Table 1). The mean ± standard deviation percentage of the AUC0→∞ that was extrapolated after dose 1 and 10 for the ORAL condition was 17 ± 10% and 18 ± 12% and for the RECTAL condition was 14 ± 15% and 15 ± 11%, respectively. For this reason, both AUC0→last and AUC0→∞ are reported and included in statistical comparisons. The mean percentage of AUCtau extrapolated was < 5% for all measurements. Within the ORAL and RECTAL conditions, differences between AUC0→last, AUC0→∞, and AUCtau were not detected for either dose 1 or dose 10 (P > .5 all). Differences in pharmacokinetic parameters were not detected between dose 1 and dose 10 for either the ORAL or RECTAL conditions.

Figure 1
Figure 1

Mean plasma misoprostol free acid (MFA) concentration-versus-time curve for 6 healthy, adult horses that received misoprostol (5 µg/kg, q 8 h for 10 doses) after dose 1 and dose 10 ORAL administration (solid circles) as well as dose 1 and dose 10 RECTAL administration (cross-hatched boxes). Error bars represent standard deviation.

Citation: American Journal of Veterinary Research 2023; 10.2460/ajvr.22.09.0161

Table 1

Plasma pharmacokinetic parameters for misoprostol free acid (MFA) following dose 1 and dose 10 for ORAL and RECTAL administration in six healthy, adult horses.

ORAL RECTAL
Parameter Dose 1 Dose 10 Dose 1 Dose 10
Cmax (pg/mL) (CV%) 1,648 ± 1,084 (66%) 1,138 ± 324 (29%) 957 ± 225 (24%) 1,117 ± 402 (36)
tmax (min) 20 (10–60)a 12.5 (10–30) 3 (3–5)a 3 (3–5)
AUC0→last (pg·min/mL) (CV%) 75,720 ± 29,060a (38%) 54,040 ± 15,060 (29%) 17,630 ± 16,160a (92%) 16,080 ± 11,600 (72%)
AUC0→∞ (pg·min/mL) (CV%) 90,860 ± 32,620a (36%) 66,400 ± 17,010b (26%) 23,960 ± 28,310a (118%) 19,290 ± 13,990b (73%)
AUCtau (pg·min/mL) 77,820 ± 29,490a 55,610 ± 15,180 17,930 ± 16,560a 16,460 ± 11,520
MRTinf (min) 61.92 ± 19.76 65.36 ± 24.20 38.81 ± 30.64 36.36 ± 21.08
t1/2dis (min) 38 ± 12a 41 ± 16b 28 ± 16a 30 ± 20b
λz (min−1) 0.02 ± 0.01 0.02 ± 0.01 0.03 ± 0.02 0.04 ± 0.03
Accumulation index 1.14 ± 0.10 1.17 ± 0.13 1.15 ± 0.17 1.19 ± 0.22
F (%) NA NA 28 ± 30 32 ± 27

Values are reported as mean ± SD for all parameters except tmax, which is reported as median (range), and t½dis, which is reported as harmonic mean ± pseudoSD.

λz = Disappearance rate constant. AUC0→∞ = Area under the concentration-versus-time curve from time 0 extrapolated to infinity. AUC0→last = Area under the concentration-versus-time curve from time 0 to the last measured concentration. AUCtau = Area under to concentration-versus-time curve from time 0 to the dosing interval (8 hours). Cmax = Maximum observed plasma concentration. CV = coefficient of variation presented for select variables. F = relative bioavailability of rectal compared to oral administration. MRTinf = mean residence time extrapolated to infinity. NA = not applicable. t1/2dis = Disappearance half-life. tmax = Time to Cmax.

a

Significant differences (P < .05) between ORAL and RECTAL for dose 1.

b

Significant differences (P < .05) between ORAL and RECTAL for dose 10.

Several differences in pharmacokinetic parameters were identified between the ORAL and RECTAL conditions. The tmax (P < .03) was significantly longer, and both AUC0→last (P = .01) and AUCtau (P = .01) were significantly greater for the ORAL condition after dose 1 but not after dose 10 (P > .06 all). AUC0→∞ (P = .01 both) was significantly greater, and t1/2dis (P ≤ .02 both) was significantly longer for the ORAL condition after both doses 1 and 10. No other differences in pharmacokinetic parameters were detected between routes of administration.

Assessment of microbial community

All samples yielded acceptable coverage with a mean read count of 87,662 per sample (range, 46,652 to 106,767). A total of 31 phyla, 249 families, 411 genera, and 15,647 species were detected. Major phyla and families identified and their associated mean relative abundances (%) included Firmicutes (55%), Bacteroidota (28%), and Verrucomicrobiota (5%) for phyla and Clostridia (11%), Lachnospiraceae (9%), and p-251-o5 (8%) for families. Significant differences in relative abundance of major identified taxa were not detected among experimental conditions or time points at the phylum, family, genus, or species level (Supplemental Figure S1).

β-Diversity

Principal coordinate analysis comparing composition of fecal microbiota between all 6 study horses and conditions (ORAL, RECTAL, and CONTROL) is depicted for the Bray-Curtis and Jaccard similarity indices (Figure 2 and Supplemental Figure S2), respectively. A strong significant effect of horse on β-diversity was detected by 2-way PERMANOVA (Bray-Curtis P < .001, F = 10.8; Jaccard P < .001, F = 5.7) with samples clearly clustering according to horse. A weak significant effect of experimental condition on β-diversity was also detected (Bray-Curtis P = .002, F = 1.8; Jaccard P = .01, F = 1.4), with minimal separation of samples identified according to condition.

Figure 2
Figure 2

Principal coordinate analysis based on Bray-Curtis similarities with results of 2-way permutational multivariate analysis of variance shown at top left. Samples cluster according to horse, with minimal to no separation of experimental conditions within horse (legend at right), showing clear interhorse differences in β-diversity.

Citation: American Journal of Veterinary Research 2023; 10.2460/ajvr.22.09.0161

To further evaluate the effect of experimental condition over time, samples within each condition and for each time point were compared to the baseline samples collected 7 days prior to study onset. Two-way ANOVA to assess the effect of experimental condition and time point on intrahorse similarity to baseline found that, when controlling for the individual horse, there were significant effects of condition (P < .001, F = 8.5) and time point (P < .001, F = 7.8) on fecal β-diversity, and no significant interaction between factors (P = .778, F = 0.5; Figure 3). Specifically, intrasubject similarity to baseline was greater during the CONTROL condition than during either the RECTAL (P = .009) or ORAL (P < .001) conditions and was lower at the End Tx time point compared to other time points (P < .019 all). When experimental condition within each time point was considered, significance was only identified between the CONTROL and ORAL conditions at the End Tx time point (P = .016).

Figure 3
Figure 3

Intrahorse similarity to baseline at each subsequent time point, according to treatment group. Tukey box plots showing intrahorse similarity to their baseline composition at each time point during ORAL (A) or RECTAL (B) administration of misoprostol, or during the CONTROL period (C). Different letters indicate significant differences in pairwise comparisons within each panel. *Significant difference between the CONTROL and ORAL conditions at the End Tx time point.

Citation: American Journal of Veterinary Research 2023; 10.2460/ajvr.22.09.0161

Richness and α-diversity

Univariate data including interhorse and time-dependent effects on observed ASV richness and alpha-diversity are presented (Figure 4). Results of the 3-way ANOVA identified differences in observed richness (Taxa_S) by horse (P < .001, F = 12.2) and time point (P = .002, F = 5.6) particularly when the End Tx time point was compared to the Start Tx (P = .001) and D14 (P = .037) time points. Differences were not detected among experimental conditions (P = .181, F = 1.7). Similarly, differences were detected in predicted richness (Chao-1) by horse (P < .001, F = 3.9) and time point (P = .014, F = 4.9) but not by experimental condition (P = .689, F = 0.4). Evaluation of diversity using the Shannon Index detected differences by horse (P < .001, F = 6.8) but not according to time point (P = .130, F = 2.0) or experimental condition (P = .077, F = 2.7). Evaluation using the Simpson index resulted in similar findings for diversity by horse (P < .001, F = 4.4), time point (P = .339, F = 1.2), and experimental condition (P = .5, F = 0.7).

Figure 4
Figure 4

Interhorse and time-dependent effects on richness and α-diversity. Box plot and line graph showing the number of detected amplicon sequence variants (ASVs) as a measure of richness, grouped by horse (A) or treatment and time point (B). Main effects associated with horse and time point (3-way ANOVA) are included in A and B, respectively. Box plot and line graph representing Shannon α-diversity in the same fashion (C and D). Different letters indicate significant differences in pairwise comparisons within each panel.

Citation: American Journal of Veterinary Research 2023; 10.2460/ajvr.22.09.0161

Discussion

To our knowledge, the present study is the first to describe the pharmacokinetics and detail the fecal microbiome in horses following repeated-dose oral and rectal administration of misoprostol. As predicted, differences in pharmacokinetic parameters were noted between routes of administration but not in response to repeated drug administration. With respect to fecal microbiome assessment, changes in the microbiome composition were observed over the course of the study. While associations with treatment and time point were identified, these were far outweighed by observed interhorse differences in fecal microbiome composition.

At the dose and dose interval described in this study, misoprostol did not appear to accumulate with repeated-dose administration or reach steady-state plasma concentrations. The mean disappearance t1/2 was well below 1 hour for both oral and rectal administration, and MFA plasma concentrations were below the LLOD when measured immediately prior to drug administration. Additionally, repeated-dose administration did not appear to alter misoprostol absorption or metabolism as suggested by the lack of detectable differences in pharmacokinetic parameters between the first and last dose for either route of administration. Considerable variability in misoprostol absorption (Cmax) and systemic exposure (AUC) has been recognized in horses4,9,10 and was also noted among horses in the present study. This is evident by the large CV observed for these parameters for both routes of administration (Table 1). Notable variability between the first and last dose was also observed when individual horses were considered, with differences for Cmax and AUC between the first and last dose exceeding 50% for some horses. Thus it is possible that both between and within-horse variability could have precluded the detection of small differences in pharmacokinetic parameters associated with repeated-dose administration.

To date, the pharmacokinetics of misoprostol in horses are described after single-dose administration by the oral or rectal route in fasted and fed horses and in horses challenged with LPS.4,9,10 Rapid absorption of misoprostol is suggested for this and all previous studies with reported values for tmax of 3 to 5 minutes after rectal and 10 to 45 minutes for oral administration.4,9,10 Additional similarities are particularly apparent when considering rectal administration of misoprostol. The mean Cmax and AUC values reported in the present repeated-dose study demonstrate notable overlap with what has been reported for these parameters in single-dose studies both with and without LPS challenge.9,10 The relative differences between oral and rectal pharmacokinetics observed in this study are also similar to those previously reported where the larger AUC and longer disappearance t1/2 observed after oral administration corresponds to greater systemic drug exposure with oral administration and decreased relative bioavailability for rectal administration.9,10 Thus while parenteral administration can provide an opportunity to improve relative bioavailability by bypassing hepatic first-pass metabolism, it is less predictable in horses32 and does not appear to have improved the bioavailability of misoprostol in this or previous studies.9,10

The variability in drug pharmacokinetics noted across studies with oral drug administration most likely reflects the impact that differences in experimental protocols such as fasting times or endotoxin challenge and inherent physiologic differences among horses may have on drug absorption and systemic exposure after oral administration. The Cmax and AUC values reported in this study are most similar to those reported by Lopp et al10 for fasted versus fed horses after single-dose oral misoprostol administration. While horses in this study were not specifically fasted, their similarity to the fasted horses in the Lopp et al study most likely reflects the feeding schedule relative to the timing of drug administration used in this repeated-dose study. Similar to what has been reported in comparisons to previous single-dose studies, the mean Cmax and AUC values reported in the present study were approximately 4 to 5 fold lower than those reported by Kimura et al in LPS-challenged horses.9 The study population in this repeated-dose study is identical to that used by Kimura et al9 with a 12-month gap between studies. However, comparisons between separate studies must be made with caution, and therefore, the influence of LPS or inflammation on misoprostol pharmacokinetics in horses remains to be determined.

With respect to the fecal microbiome, the overall composition and major taxa present at the phylum, family, and genus levels in the horses in this study were similar to what has been reported previously in healthy adult horses.33 When comparing microbiome composition using β-diversity indices, although significant differences were detected among individual horses and experimental conditions, marked clustering was only noted between individual horses and not between experimental conditions (Figure 2). Furthermore, intrahorse similarity to baseline decreased at the End Tx time point, particularly in the ORAL condition (Figure 3). Taken altogether, these results suggest that while interhorse differences outweigh other factors, there are nonetheless subtle effects of treatment and time point on fecal microbiome composition. Although the exact mechanism(s) underlying these effects remains unknown, it is proposed that prostanoid receptor interactions leading to alterations in intestinal smooth muscle contractility, ingesta transit time, and intestinal fluid movement may alter the local intestinal environment enough to impact the survival and composition of the microbiome. Since changes in the relative abundance of specific taxa were not identified, it is impossible to predict whether these differences in microbiome composition represent clinically or biologically significant changes.

When considering the univariate data, the decrease in observed (Taxa_S) and predicted (Chao-1) richness at the End Tx time point (Figure 4) might be considered an undesirable change.34 However, since the decline in richness occurred equally across all experimental conditions, including the CONTROL, this decline is likely in response to management changes during each experimental condition. All horses were fed the same hay and concentrate throughout the entire study and were housed on a group-specific grass pasture prior to, in between, and after all experimental periods. However, they were brought into individual stalls with pine shavings during each experimental condition. Similar changes in diet and environment have been associated with a decline in the richness of the microbiome in previous studies.3537 Given that the decline in richness affected all experimental conditions similarly, it is likely that study-specific management changes, rather than misoprostol administration, resulted in the decreased microbial richness observed at the End TX time point.

Finally, comparison of the Shannon and Simpson indices (Figure 4) suggests no significant impact of treatment or time point on fecal microbiome diversity. It is important to note that a considerable amount of variability was observed in these indices at the End Tx time point. This variability between horses, combined with the small number of horses included in the study, could have limited our ability to detect differences in these α-diversity indices. Regardless, the overall impact of misoprostol on the microbiome composition was minor, and the specific effects of misoprostol on richness or α-diversity were not appreciated in the current study.

There are limitations to this study. The small sample size, particularly in consideration of the observed variability in several measured parameters, may have precluded the ability to detect subtle differences in pharmacokinetic or microbiome parameters associated with repeated-dose misoprostol administration. Little information exists regarding therapeutic plasma concentrations for misoprostol. Current dosing recommendations in horses are described for the treatment of EGGD and are largely extrapolated from human literature3 and single-dose pharmacokinetic studies4,10 in healthy horses. To date, evaluation of a lower drug dose or shorter dosing interval for misoprostol has not been investigated in horses but is described in humans for the prevention of NSAID-induced gastric ulcers. While the 10-dose course of drug administration used in this study may be similar to that used for the treatment of acute colitis, it is shorter than what is recommended for the treatment of EGGD3 and may have been of insufficient duration to detect changes in drug pharmacokinetic behavior or alterations in microbiome composition associated with a longer course of treatment. Furthermore, only healthy horses were included in this study, and thus the potential impact that systemic illness or inflammation could have on misoprostol pharmacokinetics or the fecal microbiome, or the interaction of the 2, remains unknown at this time.

Finally, unavoidable changes in management between the experimental and washout periods were a limitation of the study. Multiple inherent factors such as age,38 breed,19,39 and sex or pregnancy status,16,40 as well as external factors such as diet,19,36,38,41 exercise,42,43 transport,44,45 geographic location,19 and season19,46 can all affect the fecal microbiome. While a study designed to avoid any management or environmental changes would have been preferred, this was not possible given the limitations of the facility. The CONTROL group was included to limit the overinterpretation of these confounding factors’ effects on the fecal microbiome.

In conclusion, this study did not identify differences in systemic exposure after repeated-dose administration of misoprostol, although increased systemic exposure was observed after oral as compared to rectal administration. Additionally, the observed differences in the fecal microbiome’s composition, richness, and α-diversity appeared to be primarily related to large interindividual variation and changes in management, with only minor effects on composition associated with misoprostol administration. Further evaluation of misoprostol after long-term administration to horses with gastrointestinal disease or systemic inflammation and with or without concurrent administration of anti-inflammatory or antimicrobial medications is warranted, as this may provide clinically relevant information regarding changes in drug pharmacokinetics and pharmacodynamics or the potential for misoprostol to impact the gastrointestinal microbiome.

Supplementary Materials

Supplementary materials are posted online at the journal website: avmajournals.avma.org

Acknowledgments

The authors thank Dr. Jennifer Reinhart for review and discussion of the pharmacokinetic data.

Funding was provided by the 2019 Animal Health and Disease Research Grant and the 2020 American College of Veterinary Internal Medicine (ACVIM) Resident Research Grant. The authors have no conflicts of interest to report.

References

  • 1.

    Dollery C. Misoprostol. Therapeutic Drugs. Churchill Livingstone; 1999;193197.

  • 2.

    Sangiah S, MacAllister C, Amouzadeh H. Effects of misoprostol and omeprazole on basal gastric pH and free acid content in horses. Res Vet Sci. 1989;47(3):350354. doi:10.1016/S0034-5288(18)31260-8

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 3.

    Varley G, Bowen I, Habershon-Butcher J, Nicholls V, Hallowell GD. Misoprostol is superior to combined omeprazole-sucralfate for the treatment of equine gastric glandular disease. Equine Vet J. 2019;51(5):575580. doi:10.1111/evj.13087

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 4.

    Martin E, Schirmer J, Jones SL, Davis JL. Pharmacokinetics and ex vivo anti-inflammatory effects of oral misoprostol in horses. Equine Vet J. 2019;51(3):415421. doi:10.1111/evj.13024

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 5.

    Martin EM, Messenger KM, Sheats MK, Jones SL. Misoprostol inhibits lipopolysaccharide-induced pro-inflammatory cytokine production by equine leukocytes. Front Vet Sci. 2017;4:160. doi:10.3389/fvets.2017.00160

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 6.

    Martin EM, Till RL, Sheats MK, Jones SL. Misoprostol inhibits equine neutrophil adhesion, migration, and respiratory burst in an in vitro model of inflammation. Front Vet Sci. 2017;4:159. doi:10.3389/fvets.2017.00159

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 7.

    Gobejishvili L, Ghare S, Khan R, et al. Misoprostol modulates cytokine expression through a cAMP pathway: Potential therapeutic implication for liver disease. Clin Immunol. 2015;161(2):291299. doi:10.1016/j.clim.2015.09.008

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 8.

    Chilcoat CD, Rowlingson KA, Jones SL. The effects of cAMP modulation upon the adhesion and respiratory burst activity of immune complex-stimulated equine neutrophils. Vet Immunol Immunopathol. 2002;88(1–2):6577. doi:10.1016/S0165-2427(02)00137-X

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 9.

    Kimura S, McCoy AM, Boothe DM, et al. Effects of a single dose of orally and rectally administered misoprostol in an in vivo endotoxemia model in healthy adult horses. Am J Vet Res. 2022;83(8):ajvr.21.12.0206.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 10.

    Lopp CT, McCoy AM, Boothe D, Schaeffer DJ, Lascola K. Single-dose pharmacokinetics of orally and rectally administered misoprostol in adult horses. Am J Vet Res. 2019;80(11):10261033. doi:10.2460/ajvr.80.11.1026

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 11.

    Davis J. Nonsteroidal anti-inflammatory drug associated right dorsal colitis in the horse. Equine Vet Educ. 2017;29:104113. doi:10.1111/eve.12454

  • 12.

    Tomlinson J, Blikslager A. Effects of cyclooxygenase inhibitors flunixin and deracoxib on permeability of ischaemic-injured equine jejunum. Equine Vet J. 2005;37(1):7580. doi:10.2746/0425164054406865

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 13.

    Ratnaike RN, Jones TE. Mechanisms of drug-induced diarrhoea in the elderly. Drugs Aging. 1998;13(3):245253. doi:10.2165/00002512-199813030-00007

  • 14.

    Zackular JP, Kirk L, Trindade BC, Skaar EP, Aronoff DM. Misoprostol protects mice against severe Clostridium difficile infection and promotes recovery of the gut microbiota after antibiotic perturbation. Anaerobe. 2019;58:8994. doi:10.1016/j.anaerobe.2019.06.006

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 15.

    Costa MC, Arroyo LG, Allen-Vercoe E, et al. Comparison of the fecal microbiota of healthy horses and horses with colitis by high throughput sequencing of the V3-V5 region of the 16S rRNA gene. PLoS One. 2012;7(7):e41484. doi:10.1371/journal.pone.0041484

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 16.

    Weese JS, Holcombe S, Embertson R, et al. Changes in the faecal microbiota of mares precede the development of post partum colic. Equine Vet J. 2015;47(6):641649. doi:10.1111/evj.12361

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 17.

    Stewart H, Southwood L, Indugu N, Vecchiarelli B, Engiles JB, Pitta D. Differences in the equine faecal microbiota between horses presenting to a tertiary referral hospital for colic compared with an elective surgical procedure. Equine Vet J. 2019;51(3):336342. doi:10.1111/evj.13010

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 18.

    Stewart HL, Pitta D, Indugu N, et al. Changes in the faecal bacterial microbiota during hospitalisation of horses with colic and the effect of different causes of colic. Equine Vet J. 2021;53(6):11191131. doi:10.1111/evj.13389

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 19.

    Arnold CE, Pilla R, Chaffin MK, et al. The effects of signalment, diet, geographic location, season, and colitis associated with antimicrobial use or Salmonella infection on the fecal microbiome of horses. J Vet Intern Med. 2021;35(5):24372448. doi:10.1111/jvim.16206

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 20.

    Venable E, Kerley M, Raub R. Assessment of equine fecal microbial profiles during and after a colic episode using pyrosequencing. J Equine Vet Sci. 2013;33(5):347348. doi:10.1016/j.jevs.2013.03.066

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 21.

    Costa MC, Stämpfli HR, Arroyo LG, et al. Changes in the equine fecal microbiota associated with the use of systemic antimicrobial drugs. BMC Vet Res. 2015;11:112. doi:10.1186/s12917-014-0312-6

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 22.

    Arnold CE, Isaiah A, Pilla R, et al. The cecal and fecal microbiomes and metabolomes of horses before and after metronidazole administration. PLoS One. 2020;15(5):e0232905. doi:10.1371/journal.pone.0232905

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 23.

    Whitfield-Cargile CM, Chamoun-Emanuelli AM, Cohen ND, Richardson LM, Ajami NJ, Dockery HJ. Differential effects of selective and non-selective cyclooxygenase inhibitors on fecal microbiota in adult horses. PLoS One. 2018;13(8):e0202527. doi:10.1371/journal.pone.0202527

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 24.

    Harlow BE, Lawrence LM, Flythe MD. Diarrhea-associated pathogens, lactobacilli and cellulolytic bacteria in equine feces: Responses to antibiotic challenge. Vet Microbiol. 2013;166(1–2):225232. doi:10.1016/j.vetmic.2013.05.003

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 25.

    Ericsson AC, Johnson PJ, Gieche LM, et al. The influence of diet change and oral metformin on blood glucose regulation and the fecal microbiota of healthy horses. Animals. 2021;11(4):976. doi:10.3390/ani11040976

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 26.

    Walters WA, Caporaso JG, Lauber CL, Berg-Lyons D, Fierer N, Knight R. PrimerProspector: de novo design and taxonomic analysis of barcoded polymerase chain reaction primers. Bioinformatics. 2011;27(8):11591161. doi:10.1093/bioinformatics/btr087

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 27.

    Caporaso JG, Lauber CL, Walters WA, et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc Natl Acad Sci. 2011;108(suppl 1):45164522. doi:10.1073/pnas.1000080107

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 28.

    Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17:1012. doi:10.14806/ej.17.1.200

  • 29.

    Bolyen E, Rideout JR, Dillon MR, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37(8):852857. doi:10.1038/s41587-019-0209-9

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 30.

    Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13(7):581583. doi:10.1038/nmeth.3869

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 31.

    Pruesse E, Quast C, Knittel K, et al. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res. 2007;35(21):71887196. doi:10.1093/nar/gkm864

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 32.

    Baggot JD, ed. Physiologic Basis of Veterinary Clinical Pharmacology. Blackwell Science Ltd, 2001:5591.

  • 33.

    Costa MC, Weese JS. Understanding the intestinal microbiome in health and disease. Vet Clin North Am Equine Pract. 2018;34(1):112. doi:10.1016/j.cveq.2017.11.005

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 34.

    Garber A, Hastie P, Murray JA. Factors influencing equine gut microbiota: current knowledge. J Equine Vet Sci. 2020;88:102943. doi:10.1016/j.jevs.2020.102943

  • 35.

    Daly K, Proudman CJ, Duncan SH, Flint HJ, Dyer J, Shirazi-Beechey SP. Alterations in microbiota and fermentation products in equine large intestine in response to dietary variation and intestinal disease. Br J Nutr. 2012;107(7):989995. doi:10.1017/S0007114511003825

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 36.

    Willing B, Vörös A, Roos S, Jones C, Jansson A, Lindberg JE. Changes in faecal bacteria associated with concentrate and forage-only diets fed to horses in training. Equine Vet J. 2009;41(9):908914. doi:10.2746/042516409X447806

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 37.

    Hansen NC, Avershina E, Mydland LT, et al. High nutrient availability reduces the diversity and stability of the equine caecal microbiota. Microb Ecol Health Dis. 2015;26:27216.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 38.

    Dougal K, de la Fuente G, Harris PA, et al. Characterisation of the faecal bacterial community in adult and elderly horses fed a high fibre, high oil or high starch diet using 454 pyrosequencing. PloS One. 2014;9(2):e87424. doi:10.1371/journal.pone.0087424

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 39.

    Massacci FR, Clark A, Ruet A, Lansade L, Costa M, Mach N. Inter-breed diversity and temporal dynamics of the faecal microbiota in healthy horses. J Anim Breed Genet. 2020;137:(1):103120. doi:10.1111/jbg.12441

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 40.

    Mshelia ES, Adamu L, Wakil Y, et al. The association between gut microbiome, sex, age and body condition scores of horses in Maiduguri and its environs. Microb Pathog. 2018;118:8186. doi:10.1016/j.micpath.2018.03.018

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 41.

    Warzecha C, Coverdale J, Janecka J, et al. Influence of short-term dietary starch inclusion on the equine cecal microbiome. J Anim Sci. 2017;95(11):50775090. doi:10.2527/jas2017.1754

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 42.

    Janabi AHD, Biddle AS, Klein DJ, McKeever KH. The effects of acute strenuous exercise on the faecal microbiota in Standardbred racehorses. Comp Exer Physiol. 2017;13(1):1324. doi:10.3920/CEP160030

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 43.

    Plancade S, Clark A, Philippe C, et al. Unraveling the effects of the gut microbiota composition and function on horse endurance physiology. Sci Rep. 2019;9(1):9620. doi:10.1038/s41598-019-46118-7

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 44.

    Faubladier C, Chaucheyras-Durand F, Da Veiga L, Julliand V. Effect of transportation on fecal bacterial communities and fermentative activities in horses: impact of Saccharomyces cerevisiae CNCM I-1077 supplementation. J Anim Sci. 2013;91:(4):17361744. doi:10.2527/jas.2012-5720

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 45.

    Schoster A, Mosing M, Jalali M, Staempfli HR, Weese JS. Effects of transport, fasting and anaesthesia on the faecal microbiota of healthy adult horses. Equine Vet J. 2016;48(5):595602. doi:10.1111/evj.12479

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 46.

    Salem SE, Maddox TW, Berg A, et al. Variation in faecal microbiota in a group of horses managed at pasture over a 12-month period. Sci Rep. 2018;8(1):110. doi:10.1038/s41598-018-26930-3

    • Crossref
    • Search Google Scholar
    • Export Citation

Contributor Notes

Corresponding author: Dr. Lascola (kml0068@auburn.edu)