In situ study of the effect of naringin, talinolol and protein-energy undernutrition on intestinal absorption of saquinavir in rats.

To study the potential interactions of naringin (NAR), talinolol (TAL) and protein-energy undernutrition (PEU) in the absorption process of saquinavir (SQV), perfusion experiments were performed in the small intestine of rats at different SQV concentrations. The results obtained demonstrated that SQV intestinal absorption was described by simultaneous passive diffusion (k(dif) = 3.44 hr) and saturable absorption (V(ma) = 127.31 µM/hr; K(ma) = 10.50 µM) together with a capacity-limited efflux (V(ms) = 270.53 µM/hr; K(ms) = 23.44 µM). The competitive inhibition constants of NAR on the SQV input and efflux processes were [IC50](a) = 3.98 µM and [IC50](s) = 5.00 µM, respectively. NAR significantly decreased (23-29%; p < 0.05) or kept unaltered the absorption rate constant (k(a) ) of SQV in function of the concentration of both compounds administered. Finally, SQV k(a) significantly increased in PEU status (around 1.8 times) when the drug was perfused either in the presence (p < 0.05) or in the absence (p < 0.01) of NAR. The variations of SQV k(a) when the antiretroviral drug is co-administered with NAR and/or TAL reinforce their interaction in the absorptive process. Malnutrition may result in altered SQV absorption, and further studies are strongly recommended to analyse the impact of this finding on the pharmacokinetic drug profile.

Dosage individualization of erythropoietin using a profile-dependent support vector regression.

The external administration of recombinant human erythropoietin is the chosen treatment for those patients with secondary anemia due to chronic renal failure in periodic hemodialysis. The objective of this paper is to carry out an individualized prediction of the EPO dosage to be administered to those patients. The high cost of this medication, its side-effects and the phenomenon of potential resistance which some individuals suffer all justify the need for a model which is capable of optimizing dosage individualization. A group of 110 patients and several patient factors were used to develop the models. The support vector regressor (SVR) is benchmarked with the classical multilayer perceptron (MLP) and the Autoregressive Conditional Heteroskedasticity (ARCH) model. We introduce a priori knowledge by relaxing or tightening the epsilon-insensitive region and the penalization parameter depending on the time period of the patients' follow-up. The so-called profile-dependent SVR (PD-SVR) improves results of the standard SVR method and the MLP. We perform sensitivity analysis on the MLP and inspect the distribution of the support vectors in the input and feature spaces in order to gain knowledge about the problem.

Prediction of cyclosporine dosage in patients after kidney transplantation using neural networks.

This paper proposes the use of neural networks for individualizing the dosage of cyclosporine A (CyA) in patients who have undergone kidney transplantation. Since the dosing of CyA usually requires intensive therapeutic drug monitoring, the accurate prediction of CyA blood concentrations would decrease the monitoring frequency and, thus, improve clinical outcomes. Thirty-two patients and different factors were studied to obtain the models. Three kinds of networks (multilayer perceptron, finite impulse response (FIR) network, and Elman recurrent network) and the formation of neural-network ensembles are used in a scheme of two chained models where the blood concentration predicted by the first model constitutes an input to the dosage prediction model. This approach is designed to aid in the process of clinical decision making. The FIR network, yielding root-mean-square errors (RMSEs) of 52.80 ng/mL and mean errors (MEs) of 0.18 ng/mL in validation (10 patients) showed the best blood concentration predictions and a committee of trained networks improved the results (RMSE = 46.97 ng/mL, ME = 0.091 ng/mL). The Elman network was the selected model for dosage prediction (RMSE = 0.27 mg/Kg/d, ME = 0.07 mg/Kg/d). However, in both cases, no statistical differences on the accuracy of neural methods were found. The models' robustness is also analyzed by evaluating their performance when noise is introduced at input nodes, and it results in a helpful test for models' selection. We conclude that neural networks can be used to predict both dose and blood concentrations of cyclosporine in steady-state. This novel approach has produced accurate and validated models to be used as decision-aid tools.