Claire Mackie

In-Vivo, In-Vitro and In-Silico methods for Small Molecule transfer across the BBB

The inability of molecules to permeate the BBB is a significant source of attrition in Central Nervous System (CNS) drug discovery. Given the increasing medical drivers for new and improved CNS drugs, small molecule transfer across the BBB is attracting a heightened awareness within pharmaceutical industry and medical fields. In order to assess the potential for small CNS molecules to permeate the BBB, a variety of methods and models, from in silico to in vivo going through in vitro models are developed as predictive tools in drug discovery. This review gives a comprehensive overview of different approaches currently considered in drug discovery to circumvent the lack of small molecule transfer through the BBB, together with their inherent advantages and disadvantages. Particularly, special attention is drawn to in silico models, with a detailed and contemporary point of view on prediction tools and guidelines for rational design.

PNU-120596, a positive allosteric modulator of α7 nicotinic acetylcholine receptors, reverses a sub-chronic phencyclidine-induced cognitive deficit in the attentional set-shifting task in female rats

The α7 nicotinic acetylcholine receptors (nAChRs) have been highlighted as a target for cognitive enhancement in schizophrenia. Adult female hooded Lister rats received sub-chronic phencyclidine (PCP) (2 mg/kg) or vehicle i.p. twice daily for 7 days, followed by 7 days’ washout. PCP-treated rats then received PNU-120596 (10 mg/kg; s.c.) or saline and were tested in the attentional set-shifting task. Sub-chronic PCP produced a significant cognitive deficit in the extra-dimensional shift (EDS) phase of the task (p < 0.001, compared with vehicle). PNU-120596 significantly improved performance of PCP-treated rats in the EDS phase of the attentional set-shifting task (p < 0.001). In conclusion, these data demonstrate that PNU-120596 improves cognitive dysfunction in our animal model of cognitive dysfunction in schizophrenia, most likely via modulation of α7 nACh receptors.

Predicting oral clearance in humans: how close can we get with allometry?

BackgroundOral clearance (CL/F) is an important pharmacokinetic parameter and plays an important role in the selection of a safe and tolerable dose for first-in-human studies. Throughout the pharmaceutical industry, many drugs are administered via the oral route; however, there are only a handful of published scaling studies for the prediction of oral pharmacokinetic parameters.MethodsWe evaluated the predictive performances of four different allometric approaches–simple allometry (SA), the rule of exponents, the unbound CL/F approach, and the unbound fraction corrected intercept method (FCIM)–for the prediction of human CL/F and the oral area under the plasma concentration-time curve (AUC). Twenty-four compounds developed at Johnson and Johnson Pharmaceutical Research and Development, covering a wide range of physicochemical and pharmacokinetic properties, were selected. The CL/F was predicted using these approaches, and the oral AUC was then estimated using the predicted CL/F.ResultsThe results of this study indicated that the most successful predictions of CL/F and the oral AUC were obtained using the unbound CL/F approach in combination with the maximum lifespan potential or the brain weight as correction factors based on the rule of exponents. We also observed that the unbound CL/F approach gave better predictions when the exponent of SA was between 0.5 and 1.2. However, the FCIM seemed to be the method of choice when the exponent of SA was <0.50 or >1.2.ConclusionsOverall, we were able to predict CL/F and the oral AUC within 2-fold of the observed value for 79% and 83% of the compounds, respectively, by selecting the allometric approaches based on the exponents of SA.

Prediction of Human Oral Plasma Concentration-Time Profiles Using Preclinical Data

Background and Objectives: Empirically based methods remain one of our tools in human pharmacokinetic predictions. The Dedrick approach and the steady-state plasma drug concentration (Css)-mean residence time (MRT) approach are based on the assumption that concentration-time profiles are similar among species, including man, and that curves derived from a variety of animal species can be superimposed after mathematical transformation. In the Dedrick approach the transformation is based on the slope and intercept of the allometric relationship. The Css-MRT approach is based on the implementation of measured animal and predicted human MRT and dose/volume of distribution at steady state (Vss). The aims of the present study were to compare the predictive performance of concentration-time profiles obtained by these approaches, to evaluate the prediction of individual pharmacokinetic parameters by these approaches and to further refine these approaches incorporating the experience from our previous work.Methods: A retrospective analysis using 35 proprietary compounds developed at Johnson & Johnson Pharmaceutical Research and Development was conducted to compare the accuracies of the Dedrick and Css-MRT approaches for predicting oral concentration-time profiles and pharmacokinetic parameters in man. In the first step, input for the transformation was based on simple allometry. Then we assessed whether both methods could be fine-tuned by systematically incorporating correction factors (maximum life span potential, brain weight and plasma protein binding), depending on the interspecies relationship. In addition, for the Css-MRT approach, we used formulas based on multivariate regression analysis as input for the transformation.Results: Inclusion of correction factors significantly improved the profile predictability for the Dedrick and Css-MRT approaches. This was mainly linked to an improved prediction of terminal elimination half-life (t1/2), MRT and the ratio between the maximum plasma concentration and the concentration at the last observed time point (Cmax/Clast). No significant differences were observed between the Dedrick approach with correction factors, the Css-MRT approach with correction factors and the Css-MRT approach, based on the regression equations.Conclusions: Based on the dataset evaluated in this study, we demonstrated that human plasma concentration-time profiles and pharmacokinetic parameters could be predicted with the Dedrick and Css-MRT approaches and that if correction factors were implemented, the predictions improved significantly. With the requirement of only a limited preclinical in vivo pharmacokinetic dataset, these empirical methods could offer potential in the early stages of drug discovery.

Evaluation of Three Amorphous Drug Delivery Technologies to Improve the Oral Absorption of Flubendazole

This study investigates 3 amorphous technologies to improve the dissolution rate and oral bioavailability of flubendazole (FLU). The selected approaches are (1) a standard spray-dried dispersion with hydroxypropylmethylcellulose (HPMC) E5 or polyvinylpyrrolidone-vinyl acetate 64, both with Vitamin E d-α-tocopheryl polyethylene glycol succinate; (2) a modified process spray-dried dispersion (MPSDD) with either HPMC E3 or hydroxypropylmethylcellulose acetate succinate (HPMCAS-M); and (3) confining FLU in ordered mesoporous silica (OMS). The physicochemical stability and in vitro release of optimized formulations were evaluated following 2 weeks of open conditions at 25°C/60% relative humidity (RH) and 40°C/75% RH. All formulations remained amorphous at 25°C/60% RH. Only the MPSDD formulation containing HPMCAS-M and 3/7 (wt./wt.) FLU/OMS did not crystallize following 40°C/75% RH exposure. The OMS and MPSDD formulations contained the lowest and highest amount of hydrolyzed degradant, respectively. All formulations were dosed to rats at 20 mg/kg in suspension. One FLU/OMS formulation was also dosed as a capsule blend. Plasma concentration profiles were determined following a single dose. In vivo findings show that the OMS capsule and suspension resulted in the overall highest area under the curve and Cmax values, respectively. These results cross-evaluate various amorphous formulations and provide a link to enhanced biopharmaceutical performance.

Cardio-vascular safety beyond hERG: in silico modelling of a guinea pig right atrium assay

As chemists can easily produce large numbers of new potential drug candidates, there is growing demand for high capacity models that can help in driving the chemistry towards efficacious and safe candidates before progressing towards more complex models. Traditionally, the cardiovascular (CV) safety domain plays an important role in this process, as many preclinical CV biomarkers seem to have high prognostic value for the clinical outcome. Throughout the industry, traditional ion channel binding data are generated to drive the early selection process. Although this assay can generate data at high capacity, it has the disadvantage of producing high numbers of false negatives. Therefore, our company applies the isolated guinea pig right atrium (GPRA) assay early-on in discovery. This functional multi-channel/multi-receptor model seems much more predictive in identifying potential CV liabilities. Unfortunately however, its capacity is limited, and there is no room for full automation. We assessed the correlation between ion channel binding and the GPRA’s Rate of Contraction (RC), Contractile Force (CF), and effective refractory frequency (ERF) measures assay using over six thousand different data points. Furthermore, the existing experimental knowledge base was used to develop a set of in silico classification models attempting to mimic the GPRA inhibitory activity. The Naïve Bayesian classifier was used to built several models, using the ion channel binding data or in silico computed properties and structural fingerprints as descriptors. The models were validated on an independent and diverse test set of 200 reference compounds. Performances were assessed on the bases of their overall accuracy, sensitivity and specificity in detecting both active and inactive molecules. Our data show that all in silico models are highly predictive of actual GPRA data, at a level equivalent or superior to the ion channel binding assays. Furthermore, the models were interpreted in terms of the descriptors used to highlight the undesirable areas in the explored chemical space, specifically regions of low polarity, high lipophilicity and high molecular weight. In conclusion, we developed a predictive in silico model of a complex physiological assay based on a large and high quality set of experimental data. This model allows high throughput in silico safety screening based on chemical structure within a given chemical space.

P.1.g.052 Understanding blood-brain barrier penetration and link to target engagement

V. Sinha, I. Loryan, P. De Boer, X. Langlois, C. Mackie, A. Van Peer, W. Drinkenburg, A. Vermeulen, D. Heald, M. Hammarlund-Udenaes Janssen Research and Development, Clinical Pharmacology, Beerse, Belgium Uppsala University, Translational PKPD Group Department of Pharmaceutical Biosciences, Uppsala, Sweden Janssen Research and Development, Experimental Medicine, Beerse, Belgium Janssen Research and Development, Neurosciences, Beerse, Belgium Janssen Research and Development, Pharmaceutical Development and Manufacturing Sciences, Beerse, Belgium Janssen Research and Development, Model Based Drug Development, Beerse, Belgium Janssen Research and Development, Clinical Pharmacology, Titusville, USA