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Purpose More than 15 years have passed since the first description of the unbound brain-to-plasma partition coefficient (Kp,uu,brain) by Prof. Margareta Hammarlund-Udenaes, which was enabled by advancements in experimental methodologies including cerebral microdialysis. Since then, growing knowledge and data continue to support the notion that the unbound (free) concentration of a drug at the site of action, such as the brain, is the driving force for pharmacological responses. Towards this end, Kp,uu,brain is the key parameter to obtain unbound brain concentrations from unbound plasma concentrations. Methods To understand the importance and impact of the Kp,uu,brain concept in contemporary drug discovery and development, a survey has been conducted amongst major pharmaceutical companies based in Europe and the USA. Here, we present the results from this survey which consisted of 47 questions addressing: 1) Background information of the companies, 2) Implementation, 3) Application areas, 4) Methodology, 5) Impact and 6) Future perspectives. Results and conclusions From the responses, it is clear that the majority of the companies (93%) has established a common understanding across disciplines of the concept and utility of Kp,uu,brain as compared to other parameters related to brain exposure. Adoption of the Kp,uu,brain concept has been mainly driven by individual scientists advocating its application in the various companies rather than by a top-down approach. Remarkably, 79% of all responders describe the portfolio impact of Kp,uu,brain implementation in their companies as ‘game-changing’. Although most companies (74%) consider the current toolbox for Kp,uu,brain assessment and its validation satisfactory for drug discovery and early development, areas of improvement and future research to better understand human brain pharmacokinetics/pharmacodynamics translation have been identified.

Purpose The current study aimed to illustrate how a non-linear mixed effect (NLME) model-based analysis may improve confidence in a Phase III trial through more precise estimates of the drug effect. Methods The FULFIL clinical trial was a Phase III study that compared 24 weeks of once daily inhaled triple therapy with twice daily inhaled dual therapy in patients with chronic obstructive pulmonary disease (COPD). Patient reported outcome data, obtained by using The Evaluating Respiratory Symptoms in COPD (E-RS:COPD) questionnaire, from the FULFIL study were analyzed using an NLME item-based response theory model (IRT). The change from baseline (CFB) in E-RS:COPD total score over 4-week intervals for each treatment arm was obtained using the IRT and compared with published results obtained with a mixed model repeated measures (MMRM) analysis. Results The IRT included a graded response model characterizing item parameters and a Weibull function combined with an offset function to describe the COPD symptoms-time course in patients receiving either triple therapy (n = 907) or dual therapy (n = 894). The IRT improved precision of the estimated drug effect compared to MMRM, resulting in a sample size of at least 3.64 times larger for the MMRM analysis to achieve the IRT precision in the CFB estimate. Conclusion This study shows the advantage of IRT over MMRM with a direct comparison of the same primary endpoint for the two analyses using the same observed clinical trial data, resulting in an increased confidence in Phase III.

Introduction The organic cation transporter 3 (OCT3, SLC22A3) is ubiquitously expressed and interacts with a wide array of compounds including endogenous molecules, environmental toxins and prescription drugs. Understudied as a determinant of pharmacokinetics and pharmacodynamics, OCT3 has the potential to be a major determinant of drug absorption and disposition and to be a target for drug-drug interactions (DDIs). Goal The goal of the current study was to identify prescription drug inhibitors of OCT3. Methods We screened a compound library consisting of 2556 prescription drugs, bioactive molecules, and natural products using a high throughput assay in HEK-293 cells stably expressing OCT3. Results We identified 210 compounds that at 20 μM inhibit 50% or more of OCT3-mediated uptake of 4-Di-1-ASP (2 μM). Of these, nine were predicted to inhibit the transporter at clinically relevant unbound plasma concentrations. A Structure-Activity Relationship (SAR) model included molecular descriptors that could discriminate between inhibitors and non-inhibitors of OCT3 and was used to identify additional OCT3 inhibitors. Proteomics of human brain microvessels (BMVs) indicated that OCT3 is the highest expressed OCT in the human blood-brain barrier (BBB). Conclusions This study represents the largest screen to identify prescription drug inhibitors of OCT3. Several are sufficiently potent to inhibit the transporter at therapeutic unbound plasma levels, potentially leading to DDIs or off-target pharmacologic effects.

Purpose Pharmacometric models provide useful tools to aid the rational design of clinical trials. This study evaluates study design-, drug-, and patient-related features as well as analysis methods for their influence on the power to demonstrate a benefit of pharmacogenomics (PGx)-based dosing regarding myelotoxicity. Methods Two pharmacokinetic and one myelosuppression model were assembled to predict concentrations of irinotecan and its metabolite SN-38 given different UGT1A1 genotypes (poor metabolizers: CLSN-38: -36%) and neutropenia following conventional versus PGx-based dosing (350 versus 245 mg/m2 (-30%)). Study power was assessed given diverse scenarios (n = 50–400 patients/arm, parallel/crossover, varying magnitude of CLSN-38, exposure-response relationship, inter-individual variability) and using model-based data analysis versus conventional statistical testing. Results The magnitude of CLSN-38 reduction in poor metabolizers and the myelosuppressive potency of SN-38 markedly influenced the power to show a difference in grade 4 neutropenia (9 cells/L) after PGx-based versus standard dosing. To achieve >80% power with traditional statistical analysis (χ2/McNemar’s test, α = 0.05), 220/100 patients per treatment arm/sequence (parallel/crossover study) were required. The model-based analysis resulted in considerably smaller total sample sizes (n = 100/15 given parallel/crossover design) to obtain the same statistical power. Conclusions The presented findings may help to avoid unfeasible trials and to rationalize the design of pharmacogenetic studies.

PurposeFor some biological systems, there exist several models with somewhat different features and perspectives. We propose an evaluation method for NLME models by analyzing real and simulated data from the model of main interest using a structurally different, but similar, NLME model. We showcase this method using the Integrated Glucose Insulin (IGI) model and the Integrated Minimal Model (IMM). Additionally, we try to map parameters carrying similar information between the two models.MethodsA bootstrap of real data and simulated datasets from both the IMM and IGI models were analyzed with the two models. Important parameters of the IMM were mapped to IGI parameters using a large IMM simulated dataset analyzed under the IGI model.ResultsComparison of the parameters estimated from real data and data simulated with the IMM and analyzed with the IGI model demonstrated differences between real and IMM-simulated data. Comparison of the parameters estimated from real data and data simulated with the IGI model and analyzed with the IMM also demonstrated differences but to a lower extent. The strongest parameter correlations were found for: insulin-dependent glucose clearance (IGI) similar to insulin sensitivity (IMM); insulin-independent glucose clearance (IGI) similar to glucose effectiveness (IMM); and insulin effect parameter (IGI) similar to insulin action (IMM).ConclusionsWe demonstrated a new approach to investigate models' ability to simulate real-life-like data, and the information captured in each model in comparison to real data, and the IMM clinically used parameters were successfully mapped to their corresponding IGI parameters.

Purpose Changes in drug absorption and first-pass metabolism have been reported throughout the pediatric age range. Our aim is to characterize both intestinal and hepatic CYP3A-mediated metabolism of midazolam in children in order to predict first-pass and systemic metabolism of CYP3A substrates. Methods Pharmacokinetic (PK) data of midazolam and 1-OH-midazolam from 264 post-operative children 1–18 years of age after oral administration were analyzed using a physiological population PK modelling approach. In the model, consisting of physiological compartments representing the gastro-intestinal tract and liver,intrinsic intestinal and hepatic clearances were estimated to derive values for bioavailability and plasma clearance. Results The whole-organ intrinsic clearance in the gut wall and liver were found to increase with body weight, with a 105 (95% confidence interval (CI): 5–405) times lower intrinsic gut wall clearance than the intrinsic hepatic clearance (i.e. 5.08 L/h (relative standard error (RSE) 10%) versus 527 L/h (RSE 7%) for a 16 kg individual, respectively). When expressed per gram of organ, intrinsic clearance increases with increasing body weight in the gut wall, but decreases in the liver, indicating that CYP3A-mediated intrinsic clearance and local bioavailability in the gut wall and liver do not change with age in parallel. The resulting total bioavailability was found to be age-independent with a median of 20.8% in children (95%CI: 3.8–50.0%). Conclusion In conclusion, the intrinsic CYP3A-mediated gut wall clearance is substantially lower than the intrinsic hepatic CYP3A-mediated clearance in children from 1 to 18 years of age, and contributes less to the overall first-pass metabolism compared to adults. Electronic supplementary material The online version of this article (10.1007/s11095-018-2458-6) contains, which is available to authorized users.

Purpose To identify conditions allowing the use of cell-based models for studies of drug absorption during in vitro lipolysis of lipid-based formulations (LBFs). Methods Caco-2 was selected as the cell-based model system. Monolayer integrity was evaluated by measuring mannitol permeability after incubating Caco-2 cells in the presence of components available during lipolysis. Pure excipients and formulations representing the lipid formulation classification system (LFCS) were evaluated before and after digestion. Porcine mucin was evaluated for its capacity to protect the cell monolayer. Results Most undigested formulations were compatible with the cells (II-LC, IIIB-LC, and IV) although some needed mucin to protect against damaging effects (II-MC, IIIB-MC, I-LC, and IIIA-LC). The pancreatic extract commonly used in digestion studies was incompatible with the cells but the Caco-2 monolayers could withstand immobilized recombinant lipase. Upon digestion, long chain formulations caused more damage to Caco-2 cells than their undigested counterparts whereas medium chain formulations showed better tolerability after digestion. Conclusions Most LBFs and components thereof (undigested and digested) are compatible with Caco-2 cells. Pancreatic enzyme is not tolerated by the cells but immobilized lipase can be used in combination with the cell monolayer. Mucin is beneficial for critical formulations and digestion products. Electronic supplementary material The online version of this article (10.1007/s11095-017-2327-8) contains supplementary material, which is available to authorized users.

Purpose This work investigates improved utilization of ADAS-cog data (the primary outcome in Alzheimer’s disease (AD) trials of mild and moderate AD) by combining pharmacometric modeling and item response theory (IRT). Methods A baseline IRT model characterizing the ADAS-cog was built based on data from 2,744 individuals. Pharmacometric methods were used to extend the baseline IRT model to describe longitudinal ADAS-cog scores from an 18-month clinical study with 322 patients. Sensitivity of the ADAS-cog items in different patient populations as well as the power to detect a drug effect in relation to total score based methods were assessed with the IRT based model. Results IRT analysis was able to describe both total and item level baseline ADAS-cog data. Longitudinal data were also well described. Differences in the information content of the item level components could be quantitatively characterized and ranked for mild cognitively impairment and mild AD populations. Based on clinical trial simulations with a theoretical drug effect, the IRT method demonstrated a significantly higher power to detect drug effect compared to the traditional method of analysis. Conclusion A combined framework of IRT and pharmacometric modeling permits a more effective and precise analysis than total score based methods and therefore increases the value of ADAS-cog data. Electronic supplementary material The online version of this article (doi:10.1007/s11095-014-1315-5) contains supplementary material, which is available to authorized users.