Investigation and Prediction of Small Intestinal Precipitation of Poorly Soluble Drugs : a Study Involving in silico, in vitro and in vivo Assessment

The main objectives of the present project were to increase the understanding of small intestinal precipitation of poorly soluble pharmaceutical drugs, investigate occurrence of crystalline small intestinal precipitation and effects of precipitation on absorption. The aim was to create and evaluate methods of predicting crystalline small intestinal drug precipitation using in vivo, in vitro and in silico models. In vivo small intestinal precipitation from highly supersaturated solutions of two weakly basic model drugs, AZD0865 and mebendazole, was investigated in humans and canine models. Potential precipitation of AZD0865 was investigated by examining dose dependent increases in human maximum plasma concentration and total exposure, which turned out to be dose linear over the range investigated, indicating no significant in vivo precipitation. The small intestinal precipitation of mebendazole was investigated from drug concentrations and amount of solid drug present in dog jejunum as well as through the bioavailability after direct duodenal administration in dogs. It was concluded that mebendazole small intestinal precipitation was limited, and that intestinal supersaturation was measurable for up to 90 minutes. In vitro precipitation methods utilizing simulated or real fasted gastric and intestinal fluids were developed in order to simulate the in vivo precipitation rate. The methods overpredicted in vivo precipitation when absorption of drug was not simulated. An in vitro-in silico approach was therefore developed, where the in vitro method was used for determining the interfacial tension (γ), necessary for describing crystallization in Classical Nucleation Theory (CNT). CNT was evaluated using a third model drug, bicalutamide, and could successfully describe different parts of the crystallization process of the drug. CNT was then integrated into an in silico absorption model. The in vivo precipitation results of AZD0865 and mebendazole were well predicted by the model, but only by allowing the fundamental constant γ to vary with concentration. Thus, the in vitro-in silico approach could be used for small intestinal precipitation prediction if the in vitro concentration closely matched in vivo small intestinal concentrations

Investigation of the Intra- and Interlaboratory Reproducibility of a Small Scale Standardized Supersaturation and Precipitation Method

The high number of poorly water-soluble compounds in drug development has increased the need for enabling formulations to improve oral bioavailability. One frequently applied approach is to induce supersaturation at the absorptive site, e.g., the small intestine, increasing the amount of dissolved compound available for absorption. However, due to the stochastic nature of nucleation, supersaturating drug delivery systems may lead to inter- and intrapersonal variability. The ability to define a feasible range with respect to the supersaturation level is a crucial factor for a successful formulation. Therefore, an <i>in vitro</i> method is needed, from where the ability of a compound to supersaturate can be defined in a reproducible way. Hence, this study investigates the reproducibility of an <i>in vitro</i> small scale standardized supersaturation and precipitation method (SSPM). First an intralaboratory reproducibility study of felodipine was conducted, after which seven partners contributed with data for three model compounds; aprepitant, felodipine, and fenofibrate, to determine the interlaboratory reproducibility of the SSPM. The first part of the SSPM determines the apparent degrees of supersaturation (aDS) to investigate for each compound. Each partner independently determined the maximum possible aDS and induced 100, 87.5, 75, and 50% of their determined maximum possible aDS in the SSPM. The concentration–time profile of the supersaturation and following precipitation was obtained in order to determine the induction time (<i>t</i>_ind) for detectable precipitation. The data showed that the absolute values of <i>t</i>_ind and aDS were not directly comparable between partners, however, upon linearization of the data a reproducible rank ordering of the three model compounds was obtained based on the β-value, which was defined as the slope of the ln­(<i>t</i>_ind) versus ln­(aDS)⁻² plot. Linear regression of this plot showed that aprepitant had the highest β-value, 15.1, while felodipine and fenofibrate had comparable β-values, 4.0 and 4.3, respectively. Of the five partners contributing with full data sets, 80% could obtain the same rank order for the three model compounds using the SSPM (aprepitant > felodipine ≈ fenofibrate). The α-value is dependent on the experimental setup and can be used as a parameter to evaluate the uniformity of the data set. This study indicated that the SSPM was able to obtain the same rank order of the β-value between partners and, thus, that the SSPM may be used to classify compounds depending on their supersaturation propensity

IMI – Oral biopharmaceutics tools project – Evaluation of bottom-up PBPK prediction success part 4: Prediction accuracy and software comparisons with improved data and modelling strategies

Oral drug absorption is a complex process depending on many factors, including the physicochemical properties of the drug, formulation characteristics and their interplay with gastrointestinal physiology and biology. Physiological-based pharmacokinetic (PBPK) models integrate all available information on gastro-intestinal system with drug and formulation data to predict oral drug absorption. The latter together with in vitro-in vivo extrapolation and other preclinical data on drug disposition can be used to predict plasma concentration-time profiles in silico. Despite recent successes of PBPK in many areas of drug development, an improvement in their utility for evaluating oral absorption is much needed. Current status of predictive performance, within the confinement of commonly available in vitro data on drugs and formulations alongside systems information, were tested using 3 PBPK software packages (GI-Sim (ver.4.1), Simcyp® Simulator (ver.15.0.86.0), and GastroPlusTM (ver.9.0.00xx)). This was part of the Innovative Medicines Initiative (IMI) Oral Biopharmaceutics Tools (OrBiTo) project. Fifty eight active pharmaceutical ingredients (APIs) were qualified from the OrBiTo database to be part of the investigation based on a priori set criteria on availability of minimum necessary information to allow modelling exercise. The set entailed over 200 human clinical studies with over 700 study arms. These were simulated using input parameters which had been harmonised by a panel of experts across different software packages prior to conduct of any simulation. Overall prediction performance and software packages comparison were evaluated based on performance indicators (Fold error (FE), Average fold error (AFE) and absolute average fold error (AAFE)) of pharmacokinetic (PK) parameters. On average, PK parameters (Area Under the Concentration-time curve (AUC0-tlast), Maximal concentration (Cmax), half-life (t1/2)) were predicted with AFE values between 1.11 and 1.97. Variability in FEs of these PK parameters was relatively high with AAFE values ranging from 2.08 to 2.74. Around half of the simulations were within the 2-fold error for AUC0-tlast and around 90% of the simulations were within 10-fold error for AUC0-tlast. Oral bioavailability (Foral) predictions, which were limited to 19 APIs having intravenous (i.v.) human data, showed AFE and AAFE of values 1.37 and 1.75 respectively. Across different APIs, AFE of AUC0-tlast predictions were between 0.22 and 22.76 with 70% of the APIs showing an AFE &gt; 1. When compared across different formulations and routes of administration, AUC0-tlast for oral controlled release and i.v. administration were better predicted than that for oral immediate release formulations. Average predictive performance did not clearly differ between software packages but some APIs showed a high level of variability in predictive performance across different software packages. This variability could be related to several factors such as compound specific properties, the quality and availability of information, and errors in scaling from in vitro and preclinical in vivo data to human in vivo behaviour which will be explored further. Results were compared with previous similar exercise when the input data selection was carried by the modeller rather than a panel of experts on each in vitro test. Overall, average predictive performance was increased as reflected in smaller AAFE value of 2.8 as compared to AAFE value of 3.8 in case of previous exercise.QC 20200930</p