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Italo Poggesi

Publications and source records attributed to Italo Poggesi.

11 recordsLinked to original sources
PubMed2026

Developing low-carbon metered-dose inhalers: effects of propellant HFA-152a on mucociliary clearance and bronchoconstriction in two Phase 1 randomised trials.

BACKGROUND: To reduce the impact of respiratory care on climate change, metered-dose inhalers (MDIs) are being reformulated with low-global warming potential (GWP) propellants. Next-generation propellant hydrofluoroalkane (HFA)-152a has >90% lower GWP than HFA-134a. As part of the safety evaluation for HFA-152a, mucociliary clearance (MCC), bronchoconstriction and safety were compared with HFA-134a. METHODS: Two Phase 1, randomised, two-way crossover studies (NCT06506266/NCT06702462) were conducted. MCC study: healthy participants inhaled HFA-152a and HFA-134a in two 7-day sequences. MCC was quantified as area under radiolabelled particle retention time curve over 4 h (AUC0-4h) after nebulised 99mTc sulphur colloid, following each propellant. Bronchoconstriction study: patients with mild asthma inhaled single doses of HFA-152a and HFA-134a. Non-inferiority of HFA-152a versus HFA-134a was defined as percent change in FEV1 (litres), 15 min post dose (95% confidence intervals [CI]: lower limit >-10%, upper limit >0%). Both studies assessed safety. RESULTS: In 22 healthy participants, the impact on MCC did not differ between HFA-134a and HFA-152a (AUC0-4h geometric mean ratio [90% CI]: 1.00 [0.99, 1.01]). In 19 patients with mild asthma, neither HFA-152a nor HFA-134a induced bronchoconstriction (percent change in FEV1 at 15 min: -0.37% [HFA-152a] vs -0.60% [HFA-134a]); HFA-152a was non-inferior to HFA-134a (mean difference [95% CI]: 0.23% [-3.61, 4.07]). Adverse event (AE) rates were low and similar for both propellants in both studies; all AEs were mild, with no serious AEs or deaths. CONCLUSION: HFA-152a and HFA-134a had almost identical effects on MCC, neither induced bronchoconstriction, supporting MDI reformulation with the low-GWP propellant HFA-152a.

Humans↗
PubMed2005

A predictive model for exemestane pharmacokinetics/pharmacodynamics incorporating the effect of food and formulation.

AIMS: Exemestane (Aromasin) is an irreversible aromatase inactivator used for the treatment of postmenopausal women with advanced breast cancer. The objective of this study was to evaluate the effect of formulation comparing a sugar-coated tablet (SCT) with a suspension and food on the pharmacokinetics (PK) and pharmacodynamics (PD) with respect to plasma estrone sulphate (E1S) concentrations of exemestane, using a PK/PD approach. METHODS: This was an open, three-period, randomized, crossover study. Twelve healthy postmenopausal women received single oral doses of 25 mg exemestane as a SCT after fasting or food and as a suspension after fasting. Exemestane and E1S concentrations were determined before and up to 14 days after drug administration. Population analysis was performed in two steps: (i) a compartmental PK model was selected incorporating the effect of food and formulation; (ii) conditional on the PK model, a PD model was developed employing indirect response models. Model selection was performed using standard statistical tests. Validation and assessment of the predictive capability of the selected model was performed using real test data sets obtained from the literature. RESULTS: A three-compartment model with first-order elimination rate best described exemestane disposition (k12 0.454, k21 0.158, k13 0.174, k31 0.016 and k 0.738 h(-1)). Absorption was described by a mono-exponential function [ka 2.3 (SCT after fasting), 1.1 (SCT after food) and 7.6 h(-1) (suspension); lag time 0.2 h]. The PD model assumed that E1S plasma concentrations are determined by a zero-order synthesis rate (6.5 pg ml(-1) h(-1)) and a first-order elimination constant (0.032 h(-1)). Exemestane inhibited E1S synthesis with a C50 value of 22.1 pg ml(-1). The mean population estimates were used to simulate the administration of different doses of the drug (0.5, 1, 2.5, 5 and 25 mg day(-1)). The model predictions were in agreement with historical data. CONCLUSIONS: Exemestane absorption is influenced by the formulation of the drug and by food, but its disposition is independent of both. PK differences do no translate into clinically important differences in the PD. The PK/PD model developed was able to predict successfully the response to different doses and administration schedules with respect to oestrogen suppression.

Administration, Oral↗
PubMed2004

The effects of degree of hepatic or renal impairment on the pharmacokinetics of exemestane in postmenopausal women.

PURPOSE: Two studies were conducted to compare the pharmacokinetics and tolerability of exemestane in postmenopausal subjects with various degrees of impairment of hepatic or renal function with those in healthy postmenopausal subjects. METHODS: All subjects were postmenopausal females. In study 1, nine subjects had normal hepatic function (Child-Pugh grade A), and nine had moderately (grade B) and eight severely (grade C) impaired hepatic function. In study 2, six subjects had normal renal function, and six moderately (creatinine clearance, CrCL, 30-60 ml/min per 1.73 m(2)) and seven severely (CrCL <29 ml/min per 1.73 m(2)) impaired renal function. Each subject took a single oral dose of 25 mg exemestane. Samples of plasma (to 168 h after dosing) and urine (to 72 h in study 1, or 96 h in study 2) were taken for pharmacokinetic analysis. Safety and tolerability were assessed by monitoring vital signs, laboratory safety tests, ECG and adverse events. RESULTS: Exposure to exemestane was increased two- to threefold in patients with hepatic impairment. Thus, the geometric mean AUC(0- infinity ) values were 41.71 (90% CI 32.2 to 54.0), 99.02 (76.5 to 128.2) and 118.59 ng.h/ml (90.2 to 156.0) in healthy subjects, and in patients with moderate and severe hepatic impairment, respectively ( P<0.01). C(max) also increased twofold. Compared with healthy subjects, patients with hepatic impairment had lower apparent oral clearance and apparent volume of distribution of exemestane. Renal impairment was also associated with two- to threefold increases in AUC(0- infinity ): 34.64 (90% CI 23.9 to 50.2), 94.10 (64.9 to 136.4) and 65.52 ng.h/ml (46.5 to 92.4) in healthy subjects, and in patients with moderate and severe hepatic impairment, respectively ( P<0.05). C(max) did not change significantly. Apparent oral clearance was directly correlated with CrCL ( r(2)=0.43). Exemestane was tolerated well, with no safety concerns. CONCLUSIONS: Oral clearance of exemestane was reduced in the presence of significant hepatic or renal disease. However, in view of the relatively large safety margin and the mild nature of the side effects of exemestane, the therapeutic implications of these changes in pharmacokinetics are considered minor and of no clinical significance.

Administration, Oral↗
PubMed2004

Predictive pharmacokinetic-pharmacodynamic modeling of tumor growth kinetics in xenograft models after administration of anticancer agents.

The available mathematical models describing tumor growth and the effect of anticancer treatments on tumors in animals are of limited use within the drug industry. A simple and effective model would allow applying quantitative thinking to the preclinical development of oncology drugs. In this article, a minimal pharmacokinetic-pharmacodynamic model is presented, based on a system of ordinary differential equations that link the dosing regimen of a compound to the tumor growth in animal models. The growth of tumors in nontreated animals is described by an exponential growth followed by a linear growth. In treated animals, the tumor growth rate is decreased by a factor proportional to both drug concentration and number of proliferating tumor cells. A transit compartmental system is used to model the process of cell death, which occurs at later times. The parameters of the pharmacodynamic model are related to the growth characteristics of the tumor, to the drug potency, and to the kinetics of the tumor cell death. Therefore, such parameters can be used for ranking compounds based on their potency and for evaluating potential differences in the tumor cell death process. The model was extensively tested on discovery candidates and known anticancer drugs. It fitted well the experimental data, providing reliable parameter estimates. On the basis of the parameters estimated in a first experiment, the model successfully predicted the response of tumors exposed to drugs given at different dose levels and/or schedules. It is, thus, possible to use the model prospectively, optimizing the design of new experiments.

Antineoplastic Agents↗
PubMed2004

Model based on GRID-derived descriptors for estimating CYP3A4 enzyme stability of potential drug candidates.

A number of computational approaches are being proposed for an early optimization of ADME (absorption, distribution, metabolism and excretion) properties to increase the success rate in drug discovery. The present study describes the development of an in silico model able to estimate, from the three-dimensional structure of a molecule, the stability of a compound with respect to the human cytochrome P450 (CYP) 3A4 enzyme activity. Stability data were obtained by measuring the amount of unchanged compound remaining after a standardized incubation with human cDNA-expressed CYP3A4. The computational method transforms the three-dimensional molecular interaction fields (MIFs) generated from the molecular structure into descriptors (VolSurf and Almond procedures). The descriptors were correlated to the experimental metabolic stability classes by a partial least squares discriminant procedure. The model was trained using a set of 1800 compounds from the Pharmacia collection and was validated using two test sets: the first one including 825 compounds from the Pharmacia collection and the second one consisting of 20 known drugs. This model correctly predicted 75% of the first and 85% of the second test set and showed a precision above 86% to correctly select metabolically stable compounds. The model appears a valuable tool in the design of virtual libraries to bias the selection toward more stable compounds.

Cytochrome P-450 CYP3A↗
PubMed2004

Predicting human pharmacokinetics from preclinical data.

Approaches used for the prediction of pharmacokinetics in relevant populations of human patients mostly rely on in vivo data from animals, using allometric scaling or time-invariant methods. The growth of in vitro and, more recently, in silico screens for evaluating pharmaceutical, pharmacokinetic and toxicity properties can also be used to predict complex in vivo behavior in humans. In most cases, careful and educated application of available approaches provides predictions of pharmacokinetic parameters within 2- or 3-fold of that observed. Attention should now be directed toward integrating information from different sources to increase the precision and accuracy of these pharmacokinetic predictions and to enable a better understanding of the processes underlying ADME behavior in humans.

Algorithms↗
PubMed2003

In vitro cell growth pharmacodynamic studies: a new nonparametric approach to determining the relative importance of drug concentration and treatment time.

PURPOSE: The effect of an anticancer treatment on tumor cell proliferation in vitro can be described as a three-dimensional surface where the inhibitory effect is related to drug concentration and treatment time. The analysis of this kind of response surface could provide critical information: for example, it could indicate whether a prolonged exposure to a low concentration of an anticancer agent will produce a different effect from exposure to higher concentrations for a shorter period of time. The parametric approach available in the literature was not flexible enough to accommodate the behavior of the response surface in some of the data sets collected as part of our research programs. Therefore, a new, general, nonparametric approach was developed. METHODS: The response surface of the inhibition of cell-based tumor growth was described using a radial basis function neural network (RBF-NN). The RBF-NN was trained using regularization theory, which provided the initialization of a constrained quadratic optimization algorithm that imposes monotonicity of the surface with respect to both concentration and exposure time. RESULTS: In the two analyzed cases (doxorubicin and flavopiridol), the proposed method was accurate and reliable in describing the inhibition surface of tumor cell growth as a function of drug concentration and exposure time. Residuals were small and unbiased. The new method improved on the parametric approach when the relative importance of drug concentration and exposure time in determining the overall effect was not constant across the experimental data. CONCLUSIONS: The proposed RBF-NN can be reliably applied for the analysis in cell-based tumor growth inhibition studies. This approach can be used for optimizing the administration regimens to be adopted in vivo. The use of this methodology can be easily extended to any cell-based experiment, in which the outcome can be seen as a function of two experimental variables.

Antineoplastic Agents↗
PubMedDate not supplied

Computational models for identifying potential P-glycoprotein substrates and inhibitors.

Multidrug resistance mediated by ATP binding cassette (ABC) transporters such as P-glycoprotein (P-gp) represents a serious problem for the development of effective anticancer drugs. In addition, P-gp has been shown to reduce oral absorption, modulate hepatic, renal, or intestinal elimination, and restrict blood-brain barrier penetration of several drugs. Consequently, there is a great interest in anticipating whether drug candidates are P-gp substrates or inhibitors. In this respect, two different computational models have been developed. A method for discriminating P-gp substrates and nonsubstrates has been set up based on calculated molecular descriptors and multivariate analysis using a training set of 53 diverse drugs. These compounds were previously classified as P-gp substrates or nonsubstrates on the basis of the efflux ratio from Caco-2 permeability measurements. The program Volsurf was used to compute the compounds' molecular descriptors. The descriptors were correlated to the experimental classes using partial least squares discriminant analysis (PLSD). The model was able to predict correctly the behavior of 72% of an external set of 272 proprietary compounds. Thirty of the 53 previously mentioned drugs were also evaluated for P-gp inhibition using a calcein-AM (CAM) assay. On the basis of these additional P-gp functional data, a PLSD analysis using GRIND-pharmacophore-based descriptors was performed to model P-gp substrates having poor or no inhibitory activity versus inhibitors having no evidence of significant transport. The model was able to discriminate between 69 substrates and 56 inhibitors taken from the literature with an average accuracy of 82%. The model allowed also the identification of some key molecular features that differentiate a substrate from an inhibitor, which should be taken into consideration in the design of new candidate drugs. These two models can be implemented in a virtual screening funnel.

ATP Binding Cassette Transporter, Subfamily B, Mem↗