Italo Poggesi

Physiologically based pharmacokinetics (PBPK).

Allometric scaling is widely used to predict human pharmacokinetic parameters from preclinical species, and many different approaches have been proposed over the years to improve its predictive performance. Nevertheless, prediction errors are commonly observed in the practical application of simple allometry, for example, in cases where the hepatic metabolic clearance is mainly determined by enzyme activities, which do not scale allometrically across species. Therefore, if good correlation was noted for some drugs, poor correlation was observed for others, highlighting the need for other conceptual approaches. Physiologically based pharmacokinetic (PBPK) models are now a well-established approach to conduct extrapolations across species and to generate simulations of pharmacokinetic profiles under various physiological conditions. While conventional pharmacokinetic models are defined by drug-related data themselves, PBPK models have richer information content and integrate information from various sources, including drug-dependent, physiological, and biological parameters as they vary in between species, subjects, or with age and disease state. Therefore, the biological and mechanistic bases of PBPK models allow the extrapolation of the kinetic behavior of drugs with regard to dose, route, and species. In addition, by providing a link between tissue concentrations and toxicological or pharmacological effects, PBPK modeling represents a framework for mechanistic pharmacokinetic-pharmacodynamic models.

Chapter 13 Enantioselective recognition of two anticonvulsants, FCE 26743 and FCE 28073, by MAO, and relationship between MAO-B inhibition and FCE 26743 concentrations in rat brain

Publisher Summary The two alanine derivatives—namely, FCE 26743 [(S)-244- (3-fluorobenzyloxy)-benzylamino)propionamide] and its enantiomeric counterpart FCE 28073, have been found to display similar, potent anticonvulsant activities in the animal models of epilepsy. This chapter reports on the in vitro and ex vivo monoamine oxidase (MAO) inhibitory properties of FCE 26743 and FCE 28073 in the rat and on the in vitro MAO inhibitory properties of aldehyde [4-(3-fluorobenzyloxy)benzaldehyde], which would be produced by MAO if FCE 26743 and/or FCE 28073 are the substrates of that enzyme. In addition, to examine whether products formed by MAO-independent oxidative metabolism of FCE 26743 could contribute to its MAO-B inhibitory properties, experiments have been carried out in rats pretreated with SKF-525A, an inhibitor of oxidative drug metabolism. The relationship between ex vivo MAO-B inhibition and FCE 26743 concentrations in the rat brain was investigated in the chapter by developing a pharmacokinetic-pharmacodynamic model. In the in vitro studies where the rat brain homogenates, FCE 26743 was found to be a potent inhibitor of MAO-B and a weak inhibitor of MAO-A, while FCE 28073 was approximately 10-times less potent.

First Cdc7 kinase inhibitors: pyrrolopyridinones as potent and orally active antitumor agents. 2. Lead discovery.

Cdc7 kinase is a key regulator of the S-phase of the cell cycle, known to promote the activation of DNA replication origins in eukaryotic organisms. Cdc7 inhibition can cause tumor-cell death in a p53-independent manner, supporting the rationale for developing Cdc7 inhibitors for the treatment of cancer. In this paper, we conclude the structure-activity relationships study of the 2-heteroaryl-pyrrolopyridinone class of compounds that display potent inhibitory activity against Cdc7 kinase. Furthermore, we also describe the discovery of 89S, [(S)-2-(2-aminopyrimidin-4-yl)-7-(2-fluoro-ethyl)-1,5,6,7-tetrahydropyrrolo[3,2-c]pyridin-4-one], as a potent ATP mimetic inhibitor of Cdc7. Compound 89S has a Ki value of 0.5 nM, inhibits cell proliferation of different tumor cell lines with an IC50 in the submicromolar range, and exhibits in vivo tumor growth inhibition of 68% in the A2780 xenograft model.

Pharmacokinetics of reboxetine enantiomers in the dog

Reboxetine, (RS)-2-[(RS)-alpha-(2-ethoxyphenoxy)benzyl]morpholine methanesulphonate, is a racemic compound and consists of a mixture of the (R,R)- and (S,S)-enantiomers. The pharmacokinetics of reboxetine enantiomers were determined in a crossover study in three male beagle dogs. Each animal received the following oral treatments, separated by 1-week washout period: 10 mg/kg reboxetine, 5 mg/kg (R,R)- and 5 mg/kg (S,S)-. Plasma and urinary levels of the reboxetine enantiomers were monitored up to 48 h post-dosing using an enantiospecific HPLC method with fluorimetric detection (LOQ: 1.1 ng/ml in plasma and 5 ng/ml in urine for each enantiomer). After reboxetine administration mean tmax was about 1 h for both enantiomers. Cmax and AUC were about 1.5 times higher for the (R,R)- than for the (S,S)-enantiomer, mean values +/- SD being 704 +/- 330 and 427 +/- 175 ng/ml for Cmax and 2,876 +/- 1,354 and 1,998 +/- 848 ng.h/ml for AUC, respectively. No differences between the (R,R)- and (S,S)-enantiomers were observed in t1/2 (3.9 h). Total recovery of the two enantiomers in urine was similar, the Ae (0-48 h) being 1.3 +/- 0.7 and 1.1 +/- 0.7% of the enantiomer dose for the (R,R)- and the (S,S)-enantiomers, respectively. No marked differences in the main plasma pharmacokinetic parameters were found for either enantiomer on administration of the single enantiomers or reboxetine. No chiral inversion was observed after administration of the separate enantiomers, as already observed in humans.

Testing additivity of anticancer agents in pre-clinical studies: A PK/PD modelling approach

In clinical oncology, combination regimens may result in a synergistic, additive or antagonistic interaction (i.e. the effect of the combination is greater, similar or smaller than the sum of the effects of the individual compounds). For this reason, during the drug development process, in vivo pre-clinical studies are performed to assess the interaction of anticancer agents given in combination. Starting from a widely used single compound PK/PD model, a new additivity model able to predict the tumour growth inhibition in xenografted mice after the administration of compounds in combination was developed, under the assumption of a pharmacodynamic null interaction. By comparing the predicted curves with actual tumour weight data, possible departures from additivity can be immediately ascertained by visual inspection; a statistical procedure based on a chi(2) test has also been developed for this aim. The advantages of the proposed approach in comparison to other modelling methodologies are discussed and its application to four combination studies is presented.

Modeling of human tumor xenografts and dose rationale in oncology

Xenograft models are commonly used in oncology drug development. Although there are discussions about their ability to generate meaningful data for the translation from animal to humans, it appears that better data quality and better design of the preclinical experiments, together with appropriate data analysis approaches could make these data more informative for clinical development. An approach based on mathematical modeling is necessary to derive experiment-independent parameters which can be linked with clinically relevant endpoints. Moreover, the inclusion of biomarkers as predictors of efficacy is a key step towards a more general mechanism-based strategy.

Mathematical modeling of tumor growth and tumor growth inhibition in oncology drug development

Introduction: Approaches aiming to model the time course of tumor growth and tumor growth inhibition following a therapeutic intervention have recently been proposed for supporting decision making in oncology drug development. When considered in a comprehensive model-based approach, tumor growth can be included in the cascade of quantitative and causally related markers that lead to the prediction of survival, the final clinical response. Areas covered: The authors examine articles dealing with the modeling of tumor growth and tumor growth inhibition in both preclinical and clinical settings. In addition, the authors review models describing how pharmacological markers can be used to predict tumor growth and models describing how tumor growth can be linked to survival endpoints. Expert opinion: Approaches and success stories of application of model-based drug development centered on tumor growth modeling are growing. It is also apparent that these approaches can answer practical questions on drug development more effectively than that in the past. For modeling purposes, some improvements are still needed related to study design and data quality. Further efforts are needed to encourage the mind shift from a simple description of data to the prediction of untested conditions that modeling approaches allow.

Model-based approaches to increase efficiency of drug development in schizophrenia: a can't miss opportunity

Background: Schizophrenia is a severe mental disorder characterized by definite specificities and complexities (heterogeneity of the disease symptoms, large between-subject variability in disease progression and response to therapeutic agents, placebo response, dropout, questionable preclinical models, importance of market differentiation, etc.) that make drug development in this field particularly difficult when compared to the other therapeutic areas. However, drug receptor binding (especially to D2, 5-HT2 and H1 receptors) can provide a useful quantitative framework that can be related to the downstream clinical (amelioration of disease-related scores) and unwanted (neurological effects and metabolic disregulation) effects. Objective: This paper reviews the pharmacokinetic, pharmacodynamic and disease progression approaches applied to the development of new drugs for the treatment of schizophrenia. Conclusions: Only model-based methodologies, able to integrate the diverse characteristics of a compound, can provide a rational approach to increase efficiency in drug development in this area, through the development of pharmacokinetic–pharmacodynamics models able to integrate quantitative descriptions of pharmacokinetics, desired and unwanted effects. These holistic approaches can be used in clinical trial simulations for reliably predicting the outcome of future trials. Meta-analyses of the competitor environment are also essential to position the new drug into a crowded competitive landscape.

Correlation between Prostate-Specific Antigen Kinetics and Overall Survival in Abiraterone Acetate–Treated Castration-Resistant Prostate Cancer Patients

Purpose: We constructed a biomarker-survival modeling framework to explore the relationship between prostate-specific antigen (PSA) kinetics and overall survival (OS) in metastatic castration-resistant prostate cancer (mCRPC) patients following oral administration of 1,000 mg/day of abiraterone acetate (AA). Experimental Design: The PSA-survival modeling framework was based on data from two phase III studies, COU-AA-301 (chemotherapy pretreated, n = 1,184) and COU-AA-302 (chemotherapy naïve, n = 1,081), and included a mixed-effects tumor growth inhibition model and a Cox proportional hazards survival model. Results: The effect of AA on PSA kinetics was significant (P < 0.0001) and comparable between the chemotherapy-naïve and -pretreated patients. PSA kinetics [e.g., PSA nadir, PSA response rate (≥30%, 50%, and 90%), time to PSA progression, PSA doubling time (PSADT)] were highly associated with OS in both populations. The model-based posttreatment PSADT had the strongest association with OS (HR ∼0.9 in both populations). The models could accurately predict survival outcomes. After adjusting for PSA kinetic endpoints, the treatment effect of AA on survival was no longer statistically significant in both studies, and the Prentice criteria of surrogacy were met for the PSA kinetic endpoints. A strong correlation was also observed between PSA and radiographic progression-free survival. Conclusions: The analysis revealed a consistent treatment effect of AA on PSA kinetics and strong associations between PSA kinetics and OS in chemotherapy-pretreated and -naïve patients, thereby providing a rationale to consider PSA kinetics as surrogacy endpoints to indicate clinical benefit in AA-treated patients with mCRPC regardless of chemotherapy treatment. Clin Cancer Res; 21(14); 3170–7. ©2015 AACR.

A Minimal Model of Tumor Growth Inhibition

The preclinical development of antitumor drugs greatly benefits from the availability of models capable of predicting tumor growth as a function of the drug administration schedule. For being of practical use, such models should be simple enough to be identifiable from standard experiments conducted on animals. In the present paper, a stochastic model is derived from a set of minimal assumptions formulated at cellular level. Tumor cells are divided in two groups: proliferating and nonproliferating. The probability that a proliferating cell generates a new cell is a function of the tumor weight. The probability that a proliferating cell becomes nonproliferating is a function of the plasma drug concentration. The time-to-death of a nonproliferating cell is a random variable whose distribution reflects the nondeterministic delay between drug action and cell death. The evolution of the expected value of tumor weight obeys two differential equations (an ordinary and a partial differential one), whereas the variance is negligible. Therefore, the tumor growth dynamics can be well approximated by the deterministic evolution of its expected value. The tumor growth inhibition model, which is a lumped parameter model that in the last few years has been successfully applied to several antitumor drugs, is shown to be a special case of the minimal model presented here.