Antonello Caruso

Combined Treatment with a BACE Inhibitor and Anti-Aβ Antibody Gantenerumab Enhances Amyloid Reduction in APPLondon Mice

Therapeutic approaches for prevention or reduction of amyloidosis are currently a main objective in basic and clinical research on Alzheimer‘s disease. Among the agents explored in clinical trials are anti-Aβ peptide antibodies and secretase inhibitors. Most anti-Aβ antibodies are considered to act via inhibition of amyloidosis and enhanced clearance of existing amyloid, although secretase inhibitors reduce the de novo production of Aβ. Limited information is currently available on the efficacy and potential advantages of combinatorial antiamyloid treatment. We performed a chronic study in APPLondon transgenic mice that received treatment with anti-Aβ antibody gantenerumab and BACE inhibitor RO5508887, either as mono- or combination treatment. Treatment aimed to evaluate efficacy on amyloid progression, similar to preexisting amyloidosis as present in Alzheimers disease patients. Mono-treatments with either compound caused a dose-dependent reduction of total brain Aβ and amyloid burden. Combination treatment with both compounds significantly enhanced the antiamyloid effect. The observed combination effect was most pronounced for lowering of amyloid plaque load and plaque number, which suggests effective inhibition of de novo plaque formation. Moreover, significantly enhanced clearance of pre-existing amyloid plaques was observed when gantenerumab was coadministered with RO5508887. BACE inhibition led to a significant time- and dose-dependent decrease in CSF Aβ, which was not observed for gantenerumab treatment. Our results demonstrate that combining these two antiamyloid agents enhances overall efficacy and suggests that combination treatments may be of clinical relevance.

Application of PBPK modeling to predict human intestinal metabolism of CYP3A substrates – An evaluation and case study using GastroPlus™

First pass metabolism in the intestinal mucosa is a determinant of oral bioavailability of CYP3A substrates and so the prediction of intestinal availability (Fg) of potential drug candidates is important. Although intestinal metabolism can be modeled in commercial physiologically based pharmacokinetic (PBPK) software tools, a thorough evaluation of prediction performance is lacking. The current study evaluates the accuracy and precision of GastroPlus Fg predictions for 20 CYP3A substrates using in vitro and in silico input data for metabolic clearance and membrane permeation, and illustrates a potential impact of intestinal metabolism modeling on decision making in a drug Research and Development project. This analysis supports that CYP3A mediated metabolic clearance measured in human liver microsomes can be used to predict gut wall metabolism. Using values scaled from in vitro cell permeability as input for effective jejunal permeability resulted in good Fg prediction accuracy (no significant bias and ∼95% of predictions within 2 fold from in vivo estimated Fg), whereas simulations with in silico predicted permeability tended to overestimate gut metabolism (40% of Fg predictions under predicted more than 2 fold) ±2 fold range as an estimate of imprecision in metabolic clearance and permeability inputs propagated to >5 and <2 fold ranges of predicted Fg for compounds with <30% and >75% in vivo Fg, respectively, suggesting lower precision of predictions for high extraction compounds. Furthermore, parameter sensitivity analysis suggests that limitations in solubility or dissolution may either decrease Fg by preventing saturation of metabolism or increase Fg by shifting the site of absorption towards the colon where expression of CYP3A is low. The case example illustrates how, when accounting for the associated uncertainty in predicted pharmacokinetics and linking to predictive models for efficacy, PBPK modeling of intestinally metabolized compounds can support decision making in drug Research and Development.

Recent developments in using mechanistic cardiac modelling for drug safety evaluation

Highlights • Modelling and simulation can streamline decision making in drug safety testing.• Computational cardiac electrophysiology is a mature technology with a long heritage.• There are many challenges and opportunities in using in silico techniques in future.• We discuss how models can be used at different stages of drug discovery.• CiPA will combine screening platforms, human cell assays and in silico predictions.

PK/PD assessment in CNS drug discovery: Prediction of CSF concentration in rodents for P-glycoprotein substrates and application to in vivo potency estimation

The unbound drug concentration in brain parenchyma is considered to be the relevant driver for interaction with central nervous system (CNS) biological targets. Drug levels in cerebrospinal fluid (C_CSF) are frequently used surrogates for the unbound concentrations in brain. For drugs actively transported across the blood-brain barrier (BBB), C_CSF differs from unbound plasma concentration (Cu_p) to an extent that is commonly unknown. In this study, the relationship between CSF-to-unbound plasma drug partitioning in rats and the mouse Pgp (Mdr1a) efflux ratio (ER) obtained from in vitro transcellular studies has been investigated for a set of 61 CNS compounds exhibiting substantial diversity in chemical structure and physico-chemical properties. In order to understand the in vitro-in vivo extrapolation of Pgp efflux, a mechanistic model was derived relating in vivo CNS distribution kinetics to in vitro active transport. The model was applied to predict C_CSF from Cu_p and ER data for 19 proprietary Roche CNS drug candidates. The calculated CSF concentrations were correlated with CNS pharmacodynamic responses observed in rodent models. The correlation between in vitro and in vivo potency for different pharmacological endpoints indicated that the predicted C_CSF is a valuable surrogate of the concentration at the target site. Overall, C_CSF proved superior description of PK/PD data than unbound plasma or total brain concentration for Mdr1a substrates. Predicted C_CSF can be used as a default approach to understand the PK/PD relationships in CNS efficacy models and can support the extrapolation of efficacious brain exposure for new drug candidates from rodent to man.

Translational PK/PD modeling for cardiovascular safety assessment of drug candidates: Methods and examples in drug development

INTRODUCTION Cardiovascular toxicity is a significant cause of candidate failure in drug development. Pharmacokinetic/pharmacodynamic (PK/PD) modeling may reduce attrition by improving the understanding of the relationship between drug exposure and changes in cardiovascular endpoints. Diverse examples are discussed that elucidate how modeling can facilitate the interpretation of cardiovascular safety data in animals and enable quantitative translation of preclinical findings to man. METHODS Twelve compounds under development in diverse therapeutic areas were tested in cardiovascular safety studies in the telemetered beagle dog and cynomolgus monkey. Drug-induced changes observed in different cardiovascular endpoints (QRS complex and QTc interval of the ECG, heart rate, blood pressure, and myocardial contractility) were described by means of PK/PD modeling. A range of direct and indirect effect models were employed to characterize the plasma concentration-cardiovascular effect relationship for each compound. RESULTS For every drug candidate the proposed PK/PD models appropriately described the cardiovascular effects observed in dog and monkey. Two of the compounds subsequently reached clinical development and cardiovascular data were generated in first-in-human clinical trials. For one drug candidate, a threshold model was used to describe QTc prolongation in the monkey and man. Blood pressure changes induced by the second compound were linked to plasma exposure in dog and human via an indirect response model. In both cases it was found that translational modeling accurately predicted the human response observed during clinical development. DISCUSSION In this article, a range of PK/PD models are discussed that successfully described cardiovascular safety findings in the preclinical setting. Where clinical data were available, it was found that translational modeling enabled the accurate prediction of outcomes in man and facilitated the description of the therapeutic index. PK/PD modeling is thus demonstrated as a powerful tool to aid in the quantitative cardiovascular safety assessment of drug candidates and the optimization of early clinical study protocols.

The dynamics of Aβ distribution after γ-secretase inhibitor treatment, as determined by experimental and modelling approaches in a wild type rat.

Inhibition of the enzyme(s) that produce the Amyloid beta (Aβ) peptide, namely BACE and γ-secretase, is considered an attractive target for Alzheimer’s disease therapy. However, the optimal pharmacokinetic–pharmacodynamic modelling method to describe the changes in Aβ levels after drug treatment is unclear. In this study, turnover models were employed to describe Aβ levels following treatment with the γ-secretase inhibitor RO5036450, in the wild type rat. Initially, Aβ level changes in the brain, cerebral spinal fluid (CSF) and plasma were modeled as separate biological compartments, which allowed the estimation of a compound IC50 and Aβ turnover. While the data were well described, the model did not take into consideration that the CSF pool of Aβ most likely originates from the brain via the CSF drainage pathway. Therefore, a separate model was carried out, with the assumption that CSF Aβ levels originated from the brain. The optimal model that described the data involved two brain Aβ 40 sub-compartments, one with a rapid turnover, from which CSF Aβ 40 is derived, and a second quasi-static pool of ~20%. Importantly, the estimated in vivo brain IC50 was in a good range of the in vitro IC50 (ratio, 1.4). In conclusion, the PK/PD models presented here are well suited for describing the temporal changes in Aβ levels that occur after treatment with an Aβ lowering drug, and identifying physiological parameters.

Translational PK/PD modeling to increase probability of success in drug discovery and early development

In this review we present ways in which translational PK/PD modeling can address opportunities to enhance probability of success in drug discovery and early development. This is achieved by impacting efficacy and safety-driven attrition rates, through increased focus on the quantitative understanding and modeling of translational PK/PD. Application of the proposed principles early in the discovery and development phases is anticipated to bolster confidence of successfully evaluating proof of mechanism in humans and ultimately improve Phase II success. The present review is centered on the application of predictive modeling and simulation approaches during drug discovery and early development, and more specifically of mechanism-based PK/PD modeling. Case studies are presented, focused on the relevance of M&S contributions to real-world questions and the impact on decision making.