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Biomedical subjects

Paolo Magni

Publications and source records attributed to Paolo Magni.

21 records · Page 2Linked to original sources

Plasma concentrations of the enantiomers of fluoxetine and norfluoxetine: sources of variability and preliminary observations on relations with clinical response.

Factors affecting the plasma concentrations of the R- and S-enantiomers of fluoxetine and norfluoxetine were investigated in 131 adult patients receiving long-term fluoxetine, of 10 to 60 mg/d (mean, 24 +/- 10 mg/d). Plasma concentration values (geometric means, CI 95%) in these patients were 186 (156, 223) nmol/L for S-fluoxetine, 67 (58, 77) nmol/L for R-fluoxetine, 247 (212, 287) nmol/L for S-norfluoxetine, and 118 (102, 137) nmol/L for R-norfluoxetine. The difference between the concentrations of the respective R- and S-enantiomers was statistically significant ( P< 0.0001) for both the parent drug and the demethylated metabolite. A significant correlation was found between the concentrations of each enantiomer and the prescribed daily dosage (r = 0.44, P< 0.0001 for S-fluoxetine; r = 0.48, P < 0.0001 for R-fluoxetine; r = 0.36, < 0.0001 for S-norfluoxetine; r = 0.32, P = 0.0003 for R-norfluoxetine), but the variability in concentration at any given dosage was considerable. When an iterative model based on multiple polynomial regressions was applied to determine the potential contributions of dosage, age, gender, body weight, and concomitant medication to the variability in the plasma concentration of the enantiomers, dosage was consistently found to provide the greatest predictive value. The predictive value of the model could be consistently improved when concentrations of other enantiomers were included as covariates. Of 58 patients with depressive symptoms for whom evaluation of clinical response (CGI scale) was available, 33 (57%) responded favorably to treatment. The plasma levels of individual enantiomers and of the active moiety (ActM, sum of the concentrations of R-fluoxetine, S-fluoxetine, and S-norfluoxetine) in these patients did not differ significantly from those found in patients with unsatisfactory therapeutic response. Likewise, the concentrations of individual enantiomers and of the ActM were similar in patients with or without adverse effects. Overall, these results demonstrate that the pharmacokinetics of fluoxetine and norfluoxetine exhibit marked stereoselectivity and considerable interpatient variability, which could not be explained by differences in gender, age, or comedication. In addition, a considerable variability was found in the enantiomers' concentrations associated with a favorable therapeutic response.

Adolescent↗

Minimal model S(I)=0 problem in NIDDM subjects: nonzero Bayesian estimates with credible confidence intervals.

The minimal model of glucose kinetics, in conjunction with an insulin-modified intravenous glucose tolerance test, is widely used to estimate insulin sensitivity (S(I)). Parameter estimation usually resorts to nonlinear least squares (NLS), which provides a point estimate, and its precision is expressed as a standard deviation. Applied to type 2 diabetic subjects, NLS implemented in MINMOD software often predicts S(I)=0 (the so-called "zero" S(I) problem), whereas general purpose modeling software systems, e.g., SAAM II, provide a very small S(I) but with a very large uncertainty, which produces unrealistic negative values in the confidence interval. To overcome these difficulties, in this article we resort to Bayesian parameter estimation implemented by a Markov chain Monte Carlo (MCMC) method. This approach provides in each individual the S(I) a posteriori probability density function, from which a point estimate and its confidence interval can be determined. Although NLS results are not acceptable in four out of the ten studied subjects, Bayes estimation implemented by MCMC is always able to determine a nonzero point estimate of S(I) together with a credible confidence interval. This Bayesian approach should prove useful in reanalyzing large databases of epidemiological studies.

Bayes Theorem↗

Integrating model-based decision support in a multi-modal reasoning system for managing type 1 diabetic patients.

We present a multi-modal reasoning (MMR) methodology that integrates case-based reasoning (CBR), rule-based reasoning (RBR) and model-based reasoning (MBR), meant to provide physicians with a reliable decision support tool in the context of type 1 diabetes mellitus management. In particular, we have implemented a decision support system that is able to jointly exploit a probabilistic model of the glucose-insulin system at the steady state, a RBR system for suggestion generation and a CBR system for patient's profiling. The integration of the CBR, RBR and MBR paradigms allows for an optimized exploitation of all the available information, and for the definition of a therapy properly tailored to the patient's needs, overcoming the single approaches limitations. The system has been tested both on simulated and on real patients' data.

Artificial Intelligence↗