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.

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

BACKGROUND: Oral 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. METHODS: We 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. RESULTS: The 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. CONCLUSIONS: Overall, 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.

Quantifying the impact of transporters on cellular drug permeability.

The conventional model of drug permeability has recently been challenged. An alternative model proposes that transporter-mediated flux is the sole mechanism of cellular drug permeation, instead of existing in parallel with passive transmembrane diffusion. We examined a central assumption of this alternative hypothesis; namely, that transporters can give rise to experimental observations that would typically be explained with passive transmembrane diffusion. Using systems-biology simulations based on available transporter kinetics and proteomic expression data, we found that such observations are possible in the absence of transmembrane diffusion, but only under very specific conditions that rarely or never occur for known human drug transporters.

Towards a better prediction of peak concentration, volume of distribution and half-life after oral drug administration in man, using allometry.

BACKGROUND: It is imperative that new drugs demonstrate adequate pharmacokinetic properties, allowing an optimal safety margin and convenient dosing regimens in clinical practice, which then lead to better patient compliance. Such pharmacokinetic properties include suitable peak (maximum) plasma drug concentration (C(max)), area under the plasma concentration-time curve (AUC) and a suitable half-life (t(½)). The C(max) and t(½) following oral drug administration are functions of the oral clearance (CL/F) and apparent volume of distribution during the terminal phase by the oral route (V(z)/F), each of which may be predicted and combined to estimate C(max) and t(½). Allometric scaling is a widely used methodology in the pharmaceutical industry to predict human pharmacokinetic parameters such as clearance and volume of distribution. In our previous published work, we have evaluated the use of allometry for prediction of CL/F and AUC. In this paper we describe the evaluation of different allometric scaling approaches for the prediction of C(max), V(z)/F and t(½) after oral drug administration in man. METHODS: Twenty-nine compounds developed at Janssen Research and Development (a division of Janssen Pharmaceutica NV), covering a wide range of physicochemical and pharmacokinetic properties, were selected. The C(max) following oral dosing of a compound was predicted using (i) simple allometry alone; (ii) simple allometry along with correction factors such as plasma protein binding (PPB), maximum life-span potential or brain weight (reverse rule of exponents, unbound C(max) approach); and (iii) an indirect approach using allometrically predicted CL/F and V(z)/F and absorption rate constant (k(a)). The k(a) was estimated from (i) in vivo pharmacokinetic experiments in preclinical species; and (ii) predicted effective permeability in man (P(eff)), using a Caco-2 permeability assay. The V(z)/F was predicted using allometric scaling with or without PPB correction. The t(½) was estimated from the allometrically predicted parameters CL/F and V(z)/F. Predictions were deemed adequate when errors were within a 2-fold range. RESULTS: C(max) and t(½) could be predicted within a 2-fold error range for 59% and 66% of the tested compounds, respectively, using allometrically predicted CL/F and V(z)/F. The best predictions for C(max) were obtained when k(a) values were calculated from the Caco-2 permeability assay. The V(z)/F was predicted within a 2-fold error range for 72% of compounds when PPB correction was applied as the correction factor for scaling. CONCLUSIONS: We conclude that (i) C(max) and t(½) are best predicted by indirect scaling approaches (using allometrically predicted CL/F and V(z)/F and accounting for k(a) derived from permeability assay); and (ii) the PPB is an important correction factor for the prediction of V(z)/F by using allometric scaling. Furthermore, additional work is warranted to understand the mechanisms governing the processes underlying determination of C(max) so that the empirical approaches can be fine-tuned further.

Prediction of human pharmacokinetics using physiologically based modeling: a retrospective analysis of 26 clinically tested drugs.

The aim of this study was to evaluate different physiologically based modeling strategies for the prediction of human pharmacokinetics. Plasma profiles after intravenous and oral dosing were simulated for 26 clinically tested drugs. Two mechanism-based predictions of human tissue-to-plasma partitioning (P(tp)) from physicochemical input (method Vd1) were evaluated for their ability to describe human volume of distribution at steady state (V(ss)). This method was compared with a strategy that combined predicted and experimentally determined in vivo rat P(tp) data (method Vd2). Best V(ss) predictions were obtained using method Vd2, providing that rat P(tp) input was corrected for interspecies differences in plasma protein binding (84% within 2-fold). V(ss) predictions from physicochemical input alone were poor (32% within 2-fold). Total body clearance (CL) was predicted as the sum of scaled rat renal clearance and hepatic clearance projected from in vitro metabolism data. Best CL predictions were obtained by disregarding both blood and microsomal or hepatocyte binding (method CL2, 74% within 2-fold), whereas strong bias was seen using both blood and microsomal or hepatocyte binding (method CL1, 53% within 2-fold). The physiologically based pharmacokinetics (PBPK) model, which combined methods Vd2 and CL2 yielded the most accurate predictions of in vivo terminal half-life (69% within 2-fold). The Gastroplus advanced compartmental absorption and transit model was used to construct an absorption-disposition model and provided accurate predictions of area under the plasma concentration-time profile, oral apparent volume of distribution, and maximum plasma concentration after oral dosing, with 74%, 70%, and 65% within 2-fold, respectively. This evaluation demonstrates that PBPK models can lead to reasonable predictions of human pharmacokinetics.

The prediction of drug metabolism, tissue distribution, and bioavailability of 50 structurally diverse compounds in rat using mechanism-based absorption, distribution, and metabolism prediction tools.

The aim of this study was to assess a physiologically based modeling approach for predicting drug metabolism, tissue distribution, and bioavailability in rat for a structurally diverse set of neutral and moderate-to-strong basic compounds (n = 50). Hepatic blood clearance (CL(h)) was projected using microsomal data and shown to be well predicted, irrespective of the type of hepatic extraction model (80% within 2-fold). Best predictions of CL(h) were obtained disregarding both plasma and microsomal protein binding, whereas strong bias was seen using either blood binding only or both plasma and microsomal protein binding. Two mechanistic tissue composition-based equations were evaluated for predicting volume of distribution (V(dss)) and tissue-to-plasma partitioning (P(tp)). A first approach, which accounted for ionic interactions with acidic phospholipids, resulted in accurate predictions of V(dss) (80% within 2-fold). In contrast, a second approach, which disregarded ionic interactions, was a poor predictor of V(dss) (60% within 2-fold). The first approach also yielded accurate predictions of P(tp) in muscle, heart, and kidney (80% within 3-fold), whereas in lung, liver, and brain, predictions ranged from 47% to 62% within 3-fold. Using the second approach, P(tp) prediction accuracy in muscle, heart, and kidney was on average 70% within 3-fold, and ranged from 24% to 54% in all other tissues. Combining all methods for predicting V(dss) and CL(h) resulted in accurate predictions of the in vivo half-life (70% within 2-fold). Oral bioavailability was well predicted using CL(h) data and Gastroplus Software (80% within 2-fold). These results illustrate that physiologically based prediction tools can provide accurate predictions of rat pharmacokinetics.