Biomedical subjects

Steven C Sutton

Publications and source records attributed to Steven C Sutton.

5 recordsLinked to original sources

PubMed2006

Dog colonoscopy model for predicting human colon absorption.

PURPOSE: This study was conducted to develop and validate a dog colon model that predicts colon permeability in humans. METHODS: The following compounds were studied: Class 1 highly soluble (HS)/highly permeable (HP): aminophylline, propranolol, CP-409092; Class 2 LS/HP: nifedipine; trovafloxacin, sertraline; Class 3 HS/LP: azithromycin, atenolol, CP-331684, CP-424391; Class 4 LS/LP: CJ-13610. Administration to dogs was made 30 cm cranial to the anal sphincter with a lubricated Schott Model VFS-5 flexible endoscope. The bioavailability of the compound following the colon administration in dogs, relative to the same formulation administered orally (relative bioavailability), was determined. RESULTS: Except for atenolol, a small hydrophillic molecule, the relative bioavailability from administration to the colon of the dog correlated well with the following compound properties: high solubility and high, passive permeability > high solubility, low permeability > low solubility, high, passive permeability approximately low solubility, low permeability. CONCLUSION: The dog colon model is proposed as a surrogate for human intubation studies when the controlled release candidate falls in BCS Classes 2 (LS/HP), 3 (HS/LP), and 4 (LS/LP). However, no human intubation or dog colon studies are required for Class 1 (HS/HP), as these compounds are likely to be well absorbed from the colon.

Administration, Oral↗

PubMed2006

An automated process for building reliable and optimal in vitro/in vivo correlation models based on Monte Carlo simulations.

Many mathematical models have been proposed for establishing an in vitro/in vivo correlation (IVIVC). The traditional IVIVC model building process consists of 5 steps: deconvolution, model fitting, convolution, prediction error evaluation, and cross-validation. This is a time-consuming process and typically a few models at most are tested for any given data set. The objectives of this work were to (1) propose a statistical tool to screen models for further development of an IVIVC, (2) evaluate the performance of each model under different circumstances, and (3) investigate the effectiveness of common statistical model selection criteria for choosing IVIVC models. A computer program was developed to explore which model(s) would be most likely to work well with a random variation from the original formulation. The process used Monte Carlo simulation techniques to build IVIVC models. Data-based model selection criteria (Akaike Information Criteria [AIC], R2) and the probability of passing the Food and Drug Administration "prediction error" requirement was calculated. To illustrate this approach, several real data sets representing a broad range of release profiles are used to illustrate the process and to demonstrate the advantages of this automated process over the traditional approach. The Hixson-Crowell and Weibull models were often preferred over the linear. When evaluating whether a Level A IVIVC model was possible, the model selection criteria AIC generally selected the best model. We believe that the approach we proposed may be a rapid tool to determine which IVIVC model (if any) is the most applicable.

Automation↗

PubMed2004

Companion animal physiology and dosage form performance.

Among the most critical parameters for any drug candidate are tolerability, dose, solubility and permeability. For controlled release formulations, gastrointestinal transit is an added hurdle. While we might assume that intestinal transit is independent of the drug candidate, the relative importance of gastrointestinal transit time (GITT) depends directly on the other parameters. For example, a formulation of a drug with low solubility (LS) and/or low permeability (LP) characteristics might provide the required systemic concentrations when administered with food, but not if administered on an empty stomach. In the LS case, the drug may require the solubilizing effects of increased fluid and bile salts that accompany the meal. Likewise, a controlled release formulation of a drug with a region of preferred absorption may empty from the fasted stomach and move beyond the region before drug release is complete. Companion animals (e.g. cats and dogs) differ from humans and each other with respect to GITT, food effects, eating habit influences, breed and size variability, gastric pH, intestinal enzymes, GI permeability and absorption regions. This review examines how the anatomy and physiology of companion animals relates to the performance of orally administered immediate and controlled release formulations. Examples are presented of techniques used to predict the dose and acceptable solubility of drug candidates, and the performance of formulations in companion animals.

Administration, Oral↗

PubMed2003

A computational model for particle size influence on drug absorption during controlled-release colonic delivery.

The effect of particle size on the percent drug absorbed is computationally modeled for controlled-release dosage forms that deliver drug particles to the colon. The relative benefit of reducing particle size is mapped on a diagram of the drug's absorption rate constant (estimated from rat intestinal perfusion, CACO-2 or human intubation permeation rates) versus the drug's solubility. Some drugs fall into a limit of high percentage absorption even with large particles such that particle size reduction has little impact. Another group of drugs is solubility limited such that even with small particles, absorption is negligible. Between the two regions, only drugs with sufficiently high absorption rates are influenced by the drug dissolution rate and thereby the particle size. The size of this region is a function of dosing rate. Comparisons between calculated particle size effects on colon absorption as a function of colon volume suggest caution when using animal models to predict bioavailability from colonic drug delivery. This volume dependence also suggests that the particle size influence will vary as a function of the digestive cycle.

Colon↗

PubMed2002

A statistical method for the determination of absorption rate constant estimated using the rat single pass intestinal perfusion model and multiple linear regression.

The guide "Waiver of In Vivo Bioavailability and Bioequivalence Studies for Immediate Release Solid Dosage Forms Containing Certain Active Moieties/Active Ingredients Based on a Biopharmaceutical Classification System" (Rockville, MD: CDER, 2000) outlined non-in vivo tests of permeability that may satisfy the classification of a compound in the biopharmaceutical classification system. However, absent from that document were specific statistical methods to legitimatize the non-in vivo tests. This report describes the appropriate statistical treatment of absorption data, and recommends its adoption in the estimation of absorption and/or permeability measurements. The calculation of the absorption rate constants (k(a)) of ten compounds by a new multiple linear regression (MLR) method was completed after the separate perfusion of each compound through the rat single pass intestinal perfusion model (n = 3 rats per compound). Studentized residuals were evaluated to determine whether any statistically significant outliers were present in the data. The standard error of k(a) was estimated using variance components from the random effects model. The results were compared with the "traditional method" for k(a) calculations. Although both methods produced similar values of k(a), the MLR method's error estimate included multiple components of variability, which was largely ignored by the traditional method. The MLR method provided objective tests for outliers and achievement of steady-state. A preferred method for the statistical analysis of absorption data was demonstrated. These methods should be applied to all forms of permeability measurements, especially the non-in vivo measurements that classify a compound in the biopharmaceutical classification system.

Absorption↗