Human clearance prediction: shifting the paradigm

Real-Time Monitoring Platform for Ocular Drug Delivery

Real-time measurement is important in modern dissolution testing to aid in parallel drug characterisation and quality control (QC). The development of a real-time monitoring platform (microfluidic system, a novel eye movement platform with temperature sensors and accelerometers and a concentration probe setup) in conjunction with an in vitro model of the human eye (PK-Eye™) is reported. The importance of surface membrane permeability when modelling the PK-Eye™ was determined with a "pursing model" (a simplified setup of the hyaloid membrane). Parallel microfluidic control of PK-Eye™ models from a single source of pressure was performed with a ratio of 1:6 (pressure source:models) demonstrating scalability and reproducibility of pressure-flow data. Pore size and exposed surface area helped obtain a physiological range of intraocular pressure (IOP) within the models, demonstrating the need to reproduce in vitro dimensions as closely as possible to the real eye. Variation of aqueous humour flow rate throughout the day was demonstrated with a developed circadian rhythm program. Capabilities of different eye movements were programmed and achieved with an in-house eye movement platform. A concentration probe recorded the real-time concentration monitoring of injected albumin-conjugated Alexa Fluor 488 (Alexa albumin), which displayed constant release profiles. These results demonstrate the possibility of real-time monitoring of a pharmaceutical model for preclinical testing of ocular formulations.

Improving the Accuracy of Predicted Human Pharmacokinetics: Lessons Learned from the AstraZeneca Drug Pipeline Over Two Decades

During drug discovery and prior to the first human dose of a novel candidate drug, the pharmacokinetic (PK) behavior of the drug in humans is predicted from preclinical data. This helps to inform the likelihood of achieving therapeutic exposures in early clinical development. Once clinical data are available, the observed human PK are compared with predictions, providing an opportunity to assess and refine prediction methods. Application of best practice in experimental data generation and predictive methodologies, and a focus on robust mechanistic understanding of the candidate drug disposition properties before nomination to clinical development, have led to maximizing the probability of successful PK predictions so that 83% of AstraZeneca drug development projects progress in the clinic with no PK issues; and 71% of key PK parameter predictions [64% of area under the curve (AUC) predictions; 78% of maximum concentration (C_max) predictions; and 70% of half-life predictions] are accurate to within twofold. Here, we discuss methods to predict human PK used by AstraZeneca, how these predictions are assessed and what can be learned from evaluating the predictions for 116 candidate drugs.

Model-Informed Drug Discovery and Development Strategy for the Rapid Development of Anti-Tuberculosis Drug Combinations

The increasing emergence of drug-resistant tuberculosis requires new effective and safe drug regimens. However, drug discovery and development are challenging, lengthy and costly. The framework of model-informed drug discovery and development (MID3) is proposed to be applied throughout the preclinical to clinical phases to provide an informative prediction of drug exposure and efficacy in humans in order to select novel anti-tuberculosis drug combinations. The MID3 includes pharmacokinetic-pharmacodynamic and quantitative systems pharmacology models, machine learning and artificial intelligence, which integrates all the available knowledge related to disease and the compounds. A translational in vitro-in vivo link throughout modeling and simulation is crucial to optimize the selection of regimens with the highest probability of receiving approval from regulatory authorities. In vitro-in vivo correlation (IVIVC) and physiologically-based pharmacokinetic modeling provide powerful tools to predict pharmacokinetic drug-drug interactions based on preclinical information. Mechanistic or semi-mechanistic pharmacokinetic-pharmacodynamic models have been successfully applied to predict the clinical exposure-response profile for anti-tuberculosis drugs using preclinical data. Potential pharmacodynamic drug-drug interactions can be predicted from in vitro data through IVIVC and pharmacokinetic-pharmacodynamic modeling accounting for translational factors. It is essential for academic and industrial drug developers to collaborate across disciplines to realize the huge potential of MID3.

Systems Biology, Systems Medicine, Systems Pharmacology: The What and The Why

Systems biology is today such a widespread discipline that it becomes difficult to propose a clear definition of what it really is. For some, it remains restricted to the genomic field. For many, it designates the integrated approach or the corpus of computational methods employed to handle the vast amount of biological or medical data and investigate the complexity of the living. Although defining systems biology might be difficult, on the other hand its purpose is clear: systems biology, with its emerging subfields systems medicine and systems pharmacology, clearly aims at making sense of complex observations/experimental and clinical datasets to improve our understanding of diseases and their treatments without putting aside the context in which they appear and develop. In this short review, we aim to specifically focus on these new subfields with the new theoretical tools and approaches that were developed in the context of cancer. Systems pharmacology and medicine now give hope for major improvements in cancer therapy, making personalized medicine closer to reality. As we will see, the current challenge is to be able to improve the clinical practice according to the paradigm shift of systems sciences.

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.

Upcyte Human Hepatocytes: a Potent In Vitro Tool for the Prediction of Hepatic Clearance of Metabolically Stable Compounds

In vitro models based on primary human hepatocytes (PHH) have been advanced for clearance (CL) prediction of metabolically stable compounds, representing state-of-the-art assay systems for drug discovery and development. Yet, limited cell availability and large interindividual variability of metabolic profiles remain shortcomings of PHH. Upcyte human hepatocytes (UHH) represent a novel hepatic cell system derived from PHH, exhibiting proliferative capacity for approximately 35 population doublings. UHH from three donors were evaluated during culture for up to 18 days, investigating relative mRNA expression and in situ enzyme activity of cytochrome P450s (P450s), UDP-glucuronosyltransferases, and sulfotransferases. Furthermore, UHH were used for predicting hepatic CL of 21 marketed low to intermediate CL drugs. In a typical experiment, expansion from 3.9 × 10(6) up to 8.5 × 10(7) cells was achieved during subculture. When maintained at confluence, transcripts of major P450s were expressed at donor-specific levels with sustained activities for the majority of isoforms, showing generally low CYP1A2 and high CYP2B6 activity levels. For donor 151-03, CL prediction based on depletion experiments resulted in an average fold error of 2.0, and 80% of compounds being predicted within twofold to in vivo CL for a subset of 10 low CL drugs. UHH showed sustained and consistent activity of drug-metabolizing enzymes (DME), resulting in highly reproducible CL prediction performance. In conclusion, UHH show promising potential as alternative to PHH for standardized in vitro applications in discovery research based on their stable, hepatocyte-like DME phenotype and virtually unlimited cell availability.

The PK-Eye: A Novel In Vitro Ocular Flow Model for Use in Preclinical Drug Development

A 2-compartment in vitro eye flow model has been developed to estimate ocular drug clearance by the anterior aqueous outflow pathway. The model is designed to accelerate the development of longer-acting ophthalmic therapeutics. Dye studies show aqueous flow is necessary for a molecule injected into the vitreous cavity to clear from the model. The clearance times of proteins can be estimated by collecting the aqueous outflow, which was first conducted with bevacizumab using phosphate-buffered saline in the vitreous cavity. A simulated vitreous solution was then used and ranibizumab (0.5 mg) displayed a clearance time of 8.1 ± 3.1 days, which is comparable to that observed in humans. The model can estimate drug release from implants or the dissolution of suspensions as a first step in their clearance mechanism, which will be the rate-limiting step for the overall resident time of a candidate dosage form in the vitreous. A suspension of triamcinolone acetonide (Kenalog®) (4.0 mg) displayed clearance times spanning 26-28 days. These results indicate that the model can be used to determine in vitro-in vivo correlations in preclinical studies to develop long-lasting therapeutics to treat blinding diseases at the back of the eye.

Rationalizing underprediction of drug clearance from enzyme and transporter kinetic data: from in vitro tools to mechanistic modeling

Over the years, there has been an increase in the number and quality of available in vitro tools for the assessment of clearance. Complexity of data analysis and modelling of corresponding in vitro data has increased in an analogous manner, in particular for the simultaneous characterization of transporter and metabolism kinetics, together with intracellular binding and passive diffusion. In the current chapter, the impact of different factors on the in vitro-in vivo extrapolation of clearance will be addressed in a stepwise manner, from the selection of the most adequate in vitro system and experimental design/condition to the corresponding modelling of data generated. The application of static or physiologically based pharmacokinetic models in the prediction of clearance will be discussed, highlighting limitations and current challenges of some of the approaches. Particular focus will be on the ability of in vitro and in silico predictive tools to overcome the trend of clearance underprediction. Improvements made as a result of inclusion of extrahepatic metabolism and consideration of transporter-metabolism interplay across different organs will be discussed.

Prediction of drug disposition on the basis of its chemical structure

The chemical structure of any drug determines its pharmacokinetics and pharmacodynamics. Detailed understanding of relationships between the drug chemical structure and individual disposition pathways (i.e., distribution and elimination) is required for efficient use of existing drugs and effective development of new drugs. Different approaches have been developed for this purpose, ranging from statistics-based quantitative structure-property (or structure-pharmacokinetic) relationships (QSPR) analysis to physiologically based pharmacokinetic (PBPK) models. This review critically analyzes currently available approaches for analysis and prediction of drug disposition on the basis of chemical structure. Models that can be used to predict different aspects of disposition are presented, including: (a) value of the individual pharmacokinetic parameter (e.g., clearance or volume of distribution), (b) efficiency of the specific disposition pathway (e.g., biliary drug excretion or cytochrome P450 3A4 metabolism), (c) accumulation in a specific organ or tissue (e.g., permeability of the placenta or accumulation in the brain), and (d) the whole-body disposition in the individual patients. Examples of presented pharmacological agents include "classical" low-molecular-weight compounds, biopharmaceuticals, and drugs encapsulated in specialized drug-delivery systems. The clinical efficiency of agents from all these groups can be suboptimal, because of inefficient permeability of the drug to the site of action and/or excessive accumulation in other organs and tissues. Therefore, robust and reliable approaches for chemical structure-based prediction of drug disposition are required to overcome these limitations. PBPK models are increasingly being used for prediction of drug disposition. These models can reflect the complex interplay of factors that determine drug disposition in a mechanistically correct fashion and can be combined with other approaches, for example QSPR-based prediction of drug permeability and metabolism, pharmacogenomic data and tools, pharmacokinetic-pharmacodynamic modeling approaches, etc. Moreover, the PBPK models enable detailed analysis of clinically relevant scenarios, for example the effect of the specific conditions on the time course of the analyzed drug in the individual organs and tissues, including the site of action. It is expected that further development of such combined approaches will increase their precision, enhance the effectiveness of drugs, and lead to individualized drug therapy for different patient populations (geriatric, pediatric, specific diseases, etc.).

Dogs and monkeys in preclinical drug development: the challenge of reducing and replacing

INTRODUCTION

Animal experimentation is a very contentious issue affecting reputation of drug industry. There are several reasons to forecast an increase in the number of dogs and monkeys used in safety and pharmacokinetic studies. This increase may trigger a strong reaction of the public opinion. There have been many proposals and initiatives to change the present approach to safety and metabolic studies. Tests based on new technologies, in vitro cell assays, stem cells, imaging, and computational systems, have the potential to anticipate effects in humans. Unfortunately, all these efforts and ideas have not changed standard approaches and regulatory expectations.

AREAS COVERED

This review looks at opportunities to reduce the number of dogs and monkeys currently used in pharmaceutical research. It also discusses present efforts and approaches, their strengths and potentials and the reasons why they may not fulfill expectations.

EXPERT OPINION

Unless the pharmaceutical industry gets more involved, an alternative paradigm of preclinical drug development is unlikely to be established in the foreseeable future. One can imagine a scenario where the political pressure against the use of dogs and monkeys in biomedical research becomes irresistible while alternative methods are not yet established. To avoid this situation, the pharmaceutical industry should take the lead and preclinical scientists at all levels need to influence decision makers and help develop new innovative approaches in drug safety evaluation.

Prediction of hepatic plasma clearance in vivo from gel-entrapped rat and human hepatocytes

This paper aimed to evaluate the applicability of gel-entrapped rat and human hepatocytes in the prediction of hepatic plasma clearance (CLh,plasma) in vivo. The in vitro intrinsic clearances (CLint,in vitro) for the selected compounds were determined from the substrate disappearance rate, and further used to predict CLh,plasma using 3 classical mathematical models (well-stirred, parallel-tube, and dispersion) and disregarding drug binding. As a result, the predicted values from gel-entrapped rat hepatocytes were mostly within 2 SE of the literature data with a high correlation coefficient (R2) of 0.88–0.91. The predicted data with human hepatocytes also fitted well with the clinical data, indicating a high accuracy in prediction of in-vivo clearance. With respect to the mathematical model for predicting CLh,plasma, the parallel-tube and dispersion models produced a better prediction than the well-stirred model, and we suggest using the parallel-tube model because it is less complex mathematically. In conclusion, gel-entrapped hepatocytes predicted the drug clearance well and seemed to be a useful tool in the process of drug discovery.

Physiologically-based pharmacokinetics in drug development and regulatory science

The application of physiologically-based pharmacokinetic (PBPK) modeling is coming of age in drug development and regulation, reflecting significant advances over the past 10 years in the predictability of key pharmacokinetic (PK) parameters from human in vitro data and in the availability of dedicated software platforms and associated databases. Specific advances and contemporary challenges with respect to predicting the processes of drug clearance, distribution, and absorption are reviewed, together with the ability to anticipate the quantitative extent of PK-based drug-drug interactions and the impact of age, genetics, disease, and formulation. The value of this capability in selecting and designing appropriate clinical studies, its implications for resource-sparing techniques, and a more holistic view of the application of PK across the preclinical/clinical divide are considered. Finally, some attention is given to the positioning of PBPK within the drug development and approval paradigm and its future application in truly personalized medicine.

Drug structure-transport relationships

Malcolm Rowland has greatly facilitated an understanding of drug structure–pharmacokinetic relationships using a physiological perspective. His view points, covering a wide range of activities, have impacted on my own work and on my appreciation and understanding of our science. This overview summarises some of our parallel activities, beginning with Malcolm’s work on the pH control of amphetamine excretion, his work on the disposition of aspirin and on the application of clearance concepts in describing the disposition of lidocaine. Malcolm also spent a considerable amount of time developing principles that define solute structure and transport/pharmacokinetic relationships using in situ organ studies, which he then extended to involve the whole body. Together, we developed a physiological approach to studying hepatic clearance, introducing the convection–dispersion model in which there was a spread in blood transit times through the liver accompanied by permeation into hepatocytes and removal by metabolism or excretion into the bile. With a range of colleagues, we then further developed the model and applied it to various organs in the body. One of Malcolm’s special interests was in being able to apply this knowledge, together with an understanding of physiological differences in scaling up pharmacokinetics from animals to man. The description of his many other activities, such as the development of clearance concepts, application of pharmacokinetics to the clinical situation and using pharmacokinetics to develop new compounds and delivery systems, has been left to others.

Prediction of human blood-to-plasma drug concentration ratio

The objective of this study was to predict R(b) (blood/plasma ratio) in humans using a simple method. Human and rat R(b) and free fraction in plasma (f(p)) values were obtained from the literature. The ratio of total red blood cell concentration to the free concentration in plasma (K(b)) was calculated using f(p) and R(b). Four methods were used for the prediction of R(b): (A) use of rat R(b); (B) use of R(b) calculated from rat K(b) and human f(p); (C) correlation of human log ((1-f(p))/f(p)) and human log K(b); and (D) correlation of log D with human log K(b). The R(b) of 96 compounds in humans ranged from 0.52 to 2.00, with an average of 0.89. A significant correlation was observed among human log K(b), human log ((1-f(p))/f(p)), and log D; however, no obvious correlation was observed among human R(b), human log ((1-f(p))/f(p)), and log D. The errors within 1.25-fold for methods A-D were 68.3%, 77.6%, 61.5% and 64.8%, respectively. All predictive methods considered here were superior to the use of the average value of human R(b) or R(b)=1. Rat R(b) corrected by human f(p) improved the accuracy of the prediction. Method B was the most accurate of the four methods.