Perspectives from the Innovation and Quality Consortium Induction Working Group on Factors Impacting Clinical Drug-Drug Interactions Resulting from Induction: Focus on Cytochrome 3A Substrates.

A recent publication from the Innovation and Quality Consortium Induction Working Group collated a large clinical data set with the goal of evaluating the accuracy of drug-drug interaction (DDI) prediction from in vitro data. Somewhat surprisingly, comparison across studies of the mean- or median-reported area under the curve ratio showed appreciable variability in the magnitude of outcome. This commentary explores the possible drivers of this range of outcomes observed in clinical induction studies. While recommendations on clinical study design are not being proposed, some key observations were informative during the aggregate analysis of clinical data. Although DDI data are often presented using median data, individual data would enable evaluation of how differences in study design, baseline expression, and the number of subjects contribute. Since variability in perpetrator pharmacokinetics (PK) could impact the overall DDI interpretation, should this be routinely captured? Maximal induction was typically observed after 5-7 days of dosing. Thus, when the half-life of the inducer is less than 30 hours, are there benefits to a more standardized study design? A large proportion of CYP3A4 inducers were also CYP3A4 inhibitors and/or inactivators based on in vitro data. In these cases, using CYP3A selective substrates has limitations. More intensive monitoring of changes in area under the curve over time is warranted. With selective CYP3A substrates, the net effect was often inhibition, whereas less selective substrates could discern induction through mechanisms not susceptible to inhibition. The latter included oral contraceptives, which raise concerns of reduced efficacy following induction. Alternative approaches for modeling induction, such as applying biomarkers and physiologically based pharmacokinetic modeling (PBPK), are also considered. SIGNIFICANCE STATEMENT: The goal of this commentary is to stimulate discussion on whether there are opportunities to optimize clinical drug-drug interaction study design. The overall aim is to reduce, understand and contextualize the variability observed in the magnitude of induction across reported clinical studies. A large clinical CYP3A induction dataset was collected and further analyzed to identify trends and gaps. Reporting individual victim PK data, characterizing perpetrator PK and including additional PK assessments for mixed-mechanism perpetrators may provide insights into how these factors impact differences observed in clinical outcomes. The potential utility of biomarkers and PBPK modeling are discussed in considering future directions.

Simultaneous Assessment of Clearance, Metabolism, Induction, and Drug-Drug Interaction Potential Using a Long-Term In Vitro Liver Model for a Novel Hepatitis B Virus Inhibitor.

Long-term in vitro liver models are now widely explored for human hepatic metabolic clearance prediction, enzyme phenotyping, cross-species metabolism, comparison of low clearance drugs, and induction studies. Here, we present studies using a long-term liver model, which show how metabolism and active transport, drug-drug interactions, and enzyme induction in healthy and diseased states, such as hepatitis B virus (HBV) infection, may be assessed in a single test system to enable effective data integration for physiologically based pharmacokinetic (PBPK) modeling. The approach is exemplified in the case of (3S)-4-[[(4R)-4-(2-Chloro-4-fluorophenyl)-5-methoxycarbonyl-2-thiazol-2-yl-1,4-dihydropyrimidin-6-yl]methyl]morpholine-3-carboxylic acid RO6889678, a novel inhibitor of HBV with a complex absorption, distribution, metabolism, and excretion (ADME) profile. RO6889678 showed an intracellular enrichment of 78-fold in hepatocytes, with an apparent intrinsic clearance of 5.2 µl/min per mg protein and uptake and biliary clearances of 2.6 and 1.6 µl/min per mg protein, respectively. When apparent intrinsic clearance was incorporated into a PBPK model, the simulated oral human profiles were in good agreement with observed data at low doses but were underestimated at high doses due to unexpected overproportional increases in exposure with dose. In addition, the induction potential of RO6889678 on cytochrome P450 (P450) enzymes and transporters at steady state was assessed and cotreatment with ritonavir revealed a complex drug-drug interaction with concurrent P450 inhibition and moderate UDP-glucuronosyltransferase induction. Furthermore, we report on the first evaluation of in vitro pharmacokinetics studies using HBV-infected HepatoPac cocultures. Thus, long-term liver models have great potential as translational research tools exploring pharmacokinetics of novel drugs in vitro in health and disease.

Simulation-Based Evaluation of PK/PD Indices for Meropenem Across Patient Groups and Experimental Designs.

PURPOSE: Antibiotic dose predictions based on PK/PD indices rely on that the index type and magnitude is insensitive to the pharmacokinetics (PK), the dosing regimen, and bacterial susceptibility. In this work we perform simulations to challenge these assumptions for meropenem and Pseudomonas aeruginosa. METHODS: A published murine dose fractionation study was replicated in silico. The sensitivity of the PK/PD index towards experimental design, drug susceptibility, uncertainty in MIC and different PK profiles was evaluated. RESULTS: The previous murine study data were well replicated with fT > MIC selected as the best predictor. However, for increased dosing frequencies fAUC/MIC was found to be more predictive and the magnitude of the index was sensitive to drug susceptibility. With human PK fT > MIC and fAUC/MIC had similar predictive capacities with preference for fT > MIC when short t1/2 and fAUC/MIC when long t1/2. CONCLUSIONS: A longitudinal PKPD model based on in vitro data successfully predicted a previous in vivo study of meropenem. The type and magnitude of the PK/PD index were sensitive to the experimental design, the MIC and the PK. Therefore, it may be preferable to perform simulations for dose selection based on an integrated PK-PKPD model rather than using a fixed PK/PD index target.

Pharmacology of basimglurant (RO4917523, RG7090), a unique metabotropic glutamate receptor 5 negative allosteric modulator in clinical development for depression.

Major depressive disorder (MDD) is a serious public health burden and a leading cause of disability. Its pharmacotherapy is currently limited to modulators of monoamine neurotransmitters and second-generation antipsychotics. Recently, glutamatergic approaches for the treatment of MDD have increasingly received attention, and preclinical research suggests that metabotropic glutamate receptor 5 (mGlu5) inhibitors have antidepressant-like properties. Basimglurant (2-chloro-4-[1-(4-fluoro-phenyl)-2,5-dimethyl-1H-imidazol-4-ylethynyl]-pyridine) is a novel mGlu5 negative allosteric modulator currently in phase 2 clinical development for MDD and fragile X syndrome. Here, the comprehensive preclinical pharmacological profile of basimglurant is presented with a focus on its therapeutic potential for MDD and drug-like properties. Basimglurant is a potent, selective, and safe mGlu5 inhibitor with good oral bioavailability and long half-life supportive of once-daily administration, good brain penetration, and high in vivo potency. It has antidepressant properties that are corroborated by its functional magnetic imaging profile as well as anxiolytic-like and antinociceptive features. In electroencephalography recordings, basimglurant shows wake-promoting effects followed by increased delta power during subsequent non-rapid eye movement sleep. In microdialysis studies, basimglurant had no effect on monoamine transmitter levels in the frontal cortex or nucleus accumbens except for a moderate increase of accumbal dopamine, which is in line with its lack of pharmacological activity on monoamine reuptake transporters. These data taken together, basimglurant has favorable drug-like properties, a differentiated molecular mechanism of action, and antidepressant-like features that suggest the possibility of also addressing important comorbidities of MDD including anxiety and pain as well as daytime sleepiness and apathy or lethargy.

Physiologically-based pharmacokinetic modelling in sepsis: A tool to elucidate how pathophysiology affects meropenem pharmacokinetics.

OBJECTIVES: Applying physiologically-based pharmacokinetic (PBPK) modelling in sepsis could help to better understand how PK changes are influenced by drug- and patient-related factors. We aimed to elucidate the influence of sepsis pathophysiology on the PK of meropenem by applying PBPK modelling. METHODS: A whole-body meropenem PBPK model was developed and evaluated in healthy individuals, and renally impaired non-septic patients. Sepsis-induced physiological changes in body composition, organ blood flow, kidney function, albumin, and haematocrit were implemented according to a previously proposed PBPK sepsis model. Model performance was evaluated, and a local sensitivity analysis was conducted. RESULTS: The model-predicted PK metrics (AUC, Cmax, CL, Vss) were within 1.33-fold-error margin of published data for 87.5% of the simulated profiles in healthy individuals. In sepsis, the model provided good predictions for literature-digitised average plasma and tissue exposure data, where the model-predicted AUC was within 1.33-fold-error margin for 9 out 11 simulated study profiles. Furthermore, the model was applied to individual plasma concentration data from 52 septic patients, where the model-predicted AUC, Cmax, and CL had a fold-error ratio range of 0.98-1.12, with alignment of the predicted and observed variability. For Vss, the fold-error ratio was 0.81, and the model underpredicted the population variability. CL was sensitive to renal plasma clearance, and kidney volume, whereas Vss was sensitive to the unbound fraction, organ volume fraction of the interstitial compartment, and the organ volume. CONCLUSIONS: These findings may be extended to more diverse drug types and support a more mechanistic understanding of the effect of sepsis on drug exposure.

Expression profiles of metabolic enzymes and drug transporters in the liver and along the intestine of beagle dogs.

Beagle dogs are widely used in preclinical pharmacokinetic, safety, and formulation studies. However, little is known about intestinal and hepatic distribution of major enzymes and transporters involved in oral absorption and presystemic drug metabolism. We characterized mRNA levels of CYP3A12, CYP3A26, CYP2D15, UGT1A6, ABCB1 (MDR1), ABCC1 (MRP1), ABCG2 (BCRP), SLC15A1 (PEPT1), and SLC22A1 (OCT1) in dog liver and along the intestine by real-time quantitative reverse transcription-polymerase chain reaction. Tissue protein levels of CYP2D15, MDR1, and PEPT1 were obtained by Western blot. Gene distribution and expression variability was statistically described by a generalized additive mixed model smoothing function and correspondence analysis. Results were compared with the expression pattern known for the human orthologs. Hepatic mRNA levels for metabolic enzymes were generally higher than those for membrane transporters, whereas in the intestine the opposite was observed. Hepatic mRNA levels followed the order CYP2D15 > UGT1A6 ≈ CYP3A26 > ABCB1 ≈ SLC15A1 ≈ SLC22A1 > ABCG2 > ABCC1 ≈ CYP3A12. Along the gut, the genes were differentially distributed with greatest expression in duodenum/upper jejunum (ABCG2), middle jejunum (ABCB1 and SLC15A1), or in cecum/colon (ABCC1 and CYP2D15). CYP3A12, CYP3A26, SLC22A1, and UGT1A6 had a rather uniform expression. Intestinal mRNA profiles of CYP2D15, ABCB1, and SLC15A1 correlated with the respective protein levels. Canine CYP3A12/26, CYP2D15, and ABCB1 colonic distributions differed from those of human orthologs, whereas UGT1A6, ABCC1, ABCG2, SLC15A1, and SLC22A1 were comparable to those of humans in both small and large intestine. We aim to apply these data to better interpret pharmacokinetic studies in dogs with respect to their human relevance.

Mechanistic Population Pharmacokinetic Model of Oseltamivir and Oseltamivir Carboxylate Accounting for Physiological Changes to Predict Exposures in Neonates and Infants.

A mechanistic population-pharmacokinetic model was developed to predict oseltamivir exposures in neonates and infants accounting for physiological changes during the first 2 years of life. The model included data from 13 studies, comprising 436 subjects with normal renal function (317 pediatric subjects (≥ 38 weeks postmenstrual age (PMA), ≥ 13 days old) and 119 adult subjects < 40 years). Concentration-time profiles of oseltamivir and its active metabolite, oseltamivir carboxylate (OC), were characterized by a four-compartment model, with absorption described by three additional compartments. Renal maturational changes were implemented by description of OC clearance with allometric function of weight and Hill function of PMA. Clearance of OC increased with weight up to 43 kg (allometric coefficient 0.75). Half the adult OC clearance was reached at a PMA of 45.6 weeks (95% confidence interval (CI) 41.6-49.6) with a Hill coefficient of 2.35 (95% CI 1.67-3.04). The model supports the European Union/United States-approved 3 mg/kg twice-daily oseltamivir dose for infants < 1 year (PMA ≥ 38 weeks) and allows prediction of exposures in preterm neonates.

Importance of target-mediated drug disposition for small molecules.

Target concentration is typically not considered in drug discovery. However, if targets are expressed at relatively high concentrations and compounds have high affinity, such that most of the drug is bound to its target, in vitro screens can give unreliable information on compound affinity. In vivo, a similar situation will generate pharmacokinetic (PK) profiles that deviate greatly from those normally expected, owing to target binding affecting drug distribution and clearance. Such target-mediated drug disposition (TMDD) effects on small molecules have received little attention and might only become apparent during clinical trials, with the potential for data misinterpretation. TMDD also confounds human microdosing approaches by providing therapeutically unrepresentative PK profiles. Being aware of these phenomena will improve the likelihood of successful drug discovery and development.

Advancing Predictions of Tissue and Intracellular Drug Concentrations Using In Vitro, Imaging and Physiologically Based Pharmacokinetic Modeling Approaches.

This white paper examines recent progress, applications, and challenges in predicting unbound and total tissue and intra/subcellular drug concentrations using in vitro and preclinical models, imaging techniques, and physiologically based pharmacokinetic (PBPK) modeling. Published examples, regulatory submissions, and case studies illustrate the application of different types of data in drug development to support modeling and decision making for compounds with transporter-mediated disposition, and likely disconnects between tissue and systemic drug exposure. The goals of this article are to illustrate current best practices and outline practical strategies for selecting appropriate in vitro and in vivo experimental methods to estimate or predict tissue and plasma concentrations, and to use these data in the application of PBPK modeling for human pharmacokinetic (PK), efficacy, and safety assessment in drug development.

Characterising Drug Release from Immediate-Release Formulations of a Poorly Soluble Compound, Basmisanil, Through Absorption Modelling and Dissolution Testing.

The study aimed to characterise the mechanism of release and absorption of Basmisanil, a biopharmaceutics classification system (BCS) class 2 compound, from immediate-release formulations via mechanistic absorption modelling, dissolution testing, and Raman imaging. An oral absorption model was developed in GastroPlus® and verified with single-dose pharmacokinetic data in humans. The properties and drug release behaviour of different oral Basmisanil formulations were characterised via biorelevant dissolution and Raman imaging studies. Finally, an in vitro-in vivo correlation (IVIVC) model was developed using conventional and mechanistic deconvolution methods for comparison. The GastroPlus model accurately simulated oral Basmisanil exposure from tablets and granules formulations containing micronized drug. Absorption of oral doses below 200 mg was mostly dissolution rate-limited and thus particularly sensitive to formulation properties. Indeed, reduced exposure was observed for a 120-mg film-coated tablet and the slower dissolution rate measured in biorelevant media was attributed to differences in drug load. This hypothesis was confirmed when Raman imaging showed that the percolation threshold was exceeded in this formulation. This biorelevant dissolution method clearly differentiated between the formulations and was used to develop a robust IVIVC model. The study demonstrates the applicability and impact of mechanistic absorption modelling and biopharmaceutical in vitro tools for rational drug development.

Metabolic Profiling of Human Long-Term Liver Models and Hepatic Clearance Predictions from In Vitro Data Using Nonlinear Mixed-Effects Modeling.

Early prediction of human clearance is often challenging, in particular for the growing number of low-clearance compounds. Long-term in vitro models have been developed which enable sophisticated hepatic drug disposition studies and improved clearance predictions. Here, the cell line HepG2, iPSC-derived hepatocytes (iCell®), the hepatic stem cell line HepaRG™, and human hepatocyte co-cultures (HµREL™ and HepatoPac®) were compared to primary hepatocyte suspension cultures with respect to their key metabolic activities. Similar metabolic activities were found for the long-term models HepaRG™, HµREL™, and HepatoPac® and the short-term suspension cultures when averaged across all 11 enzyme markers, although differences were seen in the activities of CYP2D6 and non-CYP enzymes. For iCell® and HepG2, the metabolic activity was more than tenfold lower. The micropatterned HepatoPac® model was further evaluated with respect to clearance prediction. To assess the in vitro parameters, pharmacokinetic modeling was applied. The determination of intrinsic clearance by nonlinear mixed-effects modeling in a long-term model significantly increased the confidence in the parameter estimation and extended the sensitive range towards 3% of liver blood flow, i.e., >10-fold lower as compared to suspension cultures. For in vitro to in vivo extrapolation, the well-stirred model was used. The micropatterned model gave rise to clearance prediction in man within a twofold error for the majority of low-clearance compounds. Further research is needed to understand whether transporter activity and drug metabolism by non-CYP enzymes, such as UGTs, SULTs, AO, and FMO, is comparable to the in vivo situation in these long-term culture models.

Physiologically Based Absorption Modeling to Explore the Impact of Food and Gastric pH Changes on the Pharmacokinetics of Alectinib.

Alectinib, a lipophilic, basic, anaplastic lymphoma kinase (ALK) inhibitor with very low aqueous solubility, has received Food and Drug Administration-accelerated approval for the treatment of patients with ALK+ non-small-cell lung cancer. This paper describes the application of physiologically based absorption modeling during clinical development to predict and understand the impact of food and gastric pH changes on alectinib absorption. The GastroPlus™ software was used to develop an absorption model integrating in vitro and in silico data on drug substance properties. Oral pharmacokinetics was simulated by linking the absorption model to a disposition model fit to pharmacokinetic data obtained after an intravenous infusion. Simulations were compared to clinical data from a food effect study and a drug-drug interaction study with esomeprazole, a gastric acid-reducing agent. Prospective predictions of a positive food effect and negligible impact of gastric pH elevation were confirmed with clinical data, although the exact magnitude of the food effect could not be predicted with confidence. After optimization of the absorption model with clinical food effect data, a refined model was further applied to derive recommendations on the timing of dose administration with respect to a meal. The application of biopharmaceutical absorption modeling is an area with great potential to further streamline late stage drug development and with impact on regulatory questions.

Dissolution media simulating the proximal canine gastrointestinal tract in the fasted state.

Human biorelevant media have been shown to be a useful tool in pharmaceutical development and to provide input for in silico prediction of pharmacokinetic profiles after oral dosing. Dogs, in particular Beagles, are often used as animal models for preclinical studies. Key differences in the composition of human and canine gastric and intestinal fluids are described in the literature and underscore the need to develop a discrete set of biorelevant media, adapted to the conditions of the proximal canine gastrointestinal (GI) tract, to improve forecast and interpretation of preclinical results using in vitro dissolution studies. Canine biorelevant media can also be used in the development of oral dosage forms for companion animals, which is a rapidly growing market. The compositions of Fasted State Simulated Gastric Fluid canine (FaSSGFc) and Fasted State Simulated Intestinal Fluid canine (FaSSIFc) are adapted to the physiological composition of the corresponding gastrointestinal fluids in terms of pH, buffer capacity, osmolality, surface tension, as well as the bile salt, phospholipid, and free fatty acid content (in terms of concentration and reported subtypes). It was demonstrated that canine Fasted State Simulated Intestinal Fluid (FaSSIFc) is superior in predicting the solubility of model compounds in Canine Intestinal Fluid (CIF) compared to the human biorelevant media (FaSSIF and FaSSIF-V2). Two different versions of FaSSGFc, composed at pH 1.5 and pH 6.5, offer the possibility to design in vitro studies which correspond to the in vivo study design, depending on whether pentagastrin is used to decrease the gastric pH in the dogs or not. Canine biorelevant media can therefore be recommended to achieve more accurate forecasting and interpretation of pharmacokinetic studies of oral drug products in dogs.