Utility of physiologically based pharmacokinetic models to drug development and rational drug discovery candidate selection

Data set of fraction unbound values in the in vitro incubations for metabolic studies for better prediction of human clearance

In vitro-in vivo extrapolation is a commonly applied technique for liver clearance prediction. Various in vitro models are available such as hepatocytes, human liver microsomes, or recombinant cytochromes P450. According to the free drug theory, only the unbound fraction (fu) of a chemical can undergo metabolic changes. Therefore, to ensure the reliability of predictions, both specific and nonspecific binding in the model should be accounted. However, the fraction unbound in the experiment is often not reported. The study aimed to provide a detailed repository of the literature data on the compound's fu value in various in vitro systems used for drug metabolism evaluation and corresponding human plasma binding levels. Data on the free fraction in plasma and different in vitro models were supplemented with the following information: the experimental method used for the assessment of the degree of drug binding, protein or cell concentration in the incubation, and other experimental conditions, if different from the standard ones, species, reference to the source publication, and the author's name and date of publication. In total, we collected 129 literature studies on 1425 different compounds. The provided data set can be used as a reference for scientists involved in pharmacokinetic/physiologically based pharmacokinetic modelling as well as researchers interested in Quantitative Structure-Activity Relationship models for the prediction of fraction unbound based on compound structure. Database URL: https://data.mendeley.com/datasets/3bs5526htd/1.

Mathematical Modeling of Vedolizumab Treatment's Effect on Microbiota and Intestinal Permeability in Inflammatory Bowel Disease Patients

Growing evidence suggests that impaired gut permeability and gut microbiota alterations are involved in the pathogenesis of Inflammatory Bowel Diseases (IBDs), which include Ulcerative Colitis (UC) and Crohn's Disease (CD). Vedolizumab is an anti-α4β7 antibody approved for IBD treatment, used as the first treatment or second-line therapy when the first line results in inadequate effectiveness. The aim of this study is to develop a mathematical model capable of describing the pathophysiological mechanisms of Vedolizumab treatment in IBD patients. In particular, the relationship between drug concentration in the blood, colonic mucosal permeability and fecal microbiota composition was investigated and modeled to detect and predict trends in order to support and tailor Vedolizumab therapies. To pursue this aim, clinical data from a pilot study on a cluster of 11 IBD patients were analyzed. Enrolled patients underwent colonoscopy in three phases (before (t0), after 24 weeks of (t1) and after 52 weeks of (t2 ) Vedolizumab treatment) to collect mucosal biopsies for transepithelial electrical resistance (TEER) evaluation (permeability to ions), intestinal permeability measurement and histological analysis. Moreover, fecal samples were collected for the intestinal microbiota analysis at the three time points. The collected data were compared to those of 11 healthy subjects at t0, who underwent colonoscopy for screening surveillance, and used to implement a three-compartmental mathematical model (comprising central blood, peripheral blood and the intestine). The latter extends previous evidence from the literature, based on the regression of experimental data, to link drug concentration in the peripheral blood compartment with Roseburia abundance and intestinal permeability. The clinical data showed that Vedolizumab treatment leads to an increase in TEER and a reduction in intestinal permeability to a paracellular probe, improving tissue inflammation status. Microbiota analysis showed increasing values of Roseburia, albeit not statistically significant. This trend was adequately reproduced by the mathematical model, which offers a useful tool to describe the pathophysiological effects of Vedolizumab therapy on colonic mucosal permeability and fecal microbiota composition. The model's satisfactory predictive capabilities and simplicity shed light on the relationship between the drug, the microbiota and permeability and allow for its straightforward extension to diverse therapeutic conditions.

Prediction of pharmacokinetics of an anaplastic lymphoma kinase inhibitor in rat and monkey: application of physiologically based pharmacokinetic model as an alternative tool to minimise animal studies

The pharmacokinetic (PK) and toxicokinetic profile of a drug from its preclinical evaluation helps the researcher determine whether the drug should be tested in humans based on its safety and toxicity.Preclinical studies require time and resources and are prone to error. Moreover, according to the United States Food and Drug Administration Modernisation Act 2, animal testing is no longer mandatory for new drug development, and an animal-free alternative, such as cell-based assay and computer models, can be used.Different physiologically based PK models were developed for an anaplastic lymphoma kinase inhibitor in rats and monkeys after intravenous and oral administration using its physicochemical properties and in vitro characterisation data.The developed model was validated against the in vivo data available in the literature, and the validation results were found within the acceptable limit. A parameter sensitivity analysis was performed to identify the properties of the compound influencing the PK profile.This work demonstrates the application of the physiologically based PK model to predict the PKs of a drug, which will eventually assist in reducing the number of animal studies and save time and cost of drug discovery and development.

A PBPK Model of Ternary Cyclodextrin Complex of ST-246 Was Built to Achieve a Reasonable IV Infusion Regimen for the Treatment of Human Severe Smallpox

ST-246 is an oral drug against pathogenic orthopoxvirus infections. An intravenous formulation is required for some critical patients. A ternary complex of ST-246/meglumine/hydroxypropyl-β-cyclodextrin with well-improved solubility was successfully developed in our institute. The aim of this study was to achieve a reasonable intravenous infusion regimen of this novel formulation by a robust PBPK model based on preclinical pharmacokinetic studies. The pharmacokinetics of ST-246 after intravenous injection at different doses in rats, dogs, and monkeys were conducted to obtain clearances. The clearance of humans was generated by using the allometric scaling approach. Tissue distribution of ST-246 was conducted in rats to obtain tissue partition coefficients (K_p). The PBPK model of the rat was first built using in vivo clearance and K_p combined with in vitro physicochemical properties, unbound fraction, and cyclodextrin effect parameters of ST-246. Then the PBPK model was transferred to a dog and monkey and validated simultaneously. Finally, pharmacokinetic profiles after IV infusion at different dosages utilizing the human PBPK model were compared to the observed oral PK profile of ST-246 at therapeutic dosage (600 mg). The mechanistic PBPK model described the animal PK behaviors of ST-246 via intravenous injection and infusion with fold errors within 1.2. It appeared that 6h-IV infusion at 5 mg/kg BID produced similar C_max and AUC as oral administration at 600 mg. A PBPK model of ST-246 was built to achieve a reasonable regimen of IV infusion for the treatment of severe smallpox, which will facilitate the clinical translation of this novel formulation.

Predicting Antimicrobial Activity at the Target Site: Pharmacokinetic/Pharmacodynamic Indices versus Time-Kill Approaches

Antibiotic dosing strategies are generally based on systemic drug concentrations. However, drug concentrations at the infection site drive antimicrobial effect, and efficacy predictions and dosing strategies should be based on these concentrations. We set out to review different translational pharmacokinetic-pharmacodynamic (PK/PD) approaches from a target site perspective. The most common approach involves calculating the probability of attaining animal-derived PK/PD index targets, which link PK parameters to antimicrobial susceptibility measures. This approach is time efficient but ignores some aspects of the shape of the PK profile and inter-species differences in drug clearance and distribution, and provides no information on the PD time-course. Time–kill curves, in contrast, depict bacterial response over time. In vitro dynamic time–kill setups allow for the evaluation of bacterial response to clinical PK profiles, but are not representative of the infection site environment. The translational value of in vivo time–kill experiments, conversely, is limited from a PK perspective. Computational PK/PD models, especially when developed using both in vitro and in vivo data and coupled to target site PK models, can bridge translational gaps in both PK and PD. Ultimately, clinical PK and experimental and computational tools should be combined to tailor antibiotic treatment strategies to the site of infection.

A Physiologically Based Pharmacokinetic Model for Predicting Diazepam Pharmacokinetics after Intravenous, Oral, Intranasal, and Rectal Applications

Diazepam is one of the most prescribed anxiolytic and anticonvulsant that is administered through intravenous (IV), oral, intramuscular, intranasal, and rectal routes. To facilitate the clinical use of diazepam, there is a need to develop formulations that are convenient to administer in ambulatory settings. The present study aimed to develop and evaluate a physiologically based pharmacokinetic (PBPK) model for diazepam that is capable of predicting its pharmacokinetics (PK) after IV, oral, intranasal, and rectal applications using a whole-body population-based PBPK simulator, Simcyp^®. The model evaluation was carried out using visual predictive checks, observed/predicted ratios (R_obs/pred), and the average fold error (AFE) of PK parameters. The Diazepam PBPK model successfully predicted diazepam PK in an adult population after doses were administered through IV, oral, intranasal, and rectal routes, as the R_obs/pred of all PK parameters were within a two-fold error range. The developed model can be used for the development and optimization of novel diazepam dosage forms, and it can be extended to simulate drug response in situations where no clinical data are available (healthy and disease).

The emerging role of physiologically-based pharmacokinetic/biopharmaceutics modeling in formulation development

Computer-based (in silico) modeling & simulation tools have been embraced in different fields of pharmaceutics for a variety of applications. Among these, physiologically-based pharmacokinetic/biopharmaceutics modeling (PBPK/PBBM) emerged as a particularly useful tool in formulation development. PBPK/PBBM facilitated strategies have been increasingly evaluated over the past few years, as demonstrated by several reports from the pharmaceutical industry, and a number of research and review papers on this subject. Also, the leading regulatory authorities have recently issued guidance on the use of PBPK modeling in formulation design. In silico PBPK models can comprise different dosing routes (oral, intraoral, parenteral, inhalation, ocular, dermal etc.), although the majority of published examples refer to modeling of oral drugs performance. In order to facilitate the use of PBPK modeling tools, a couple of companies have launched commercially available software such as GastroPlus™, Simcyp™ PBPK Simulator and PK-Sim®. This paper highlights various application fields of PBPK/PBBM modeling, along with the basic principles, advantages and limitations of this approach, and provides relevant examples to demonstrate the practical utility of modeling & simulation tools in different stages of formulation development.

Physiologically based pharmacokinetic-pharmacodynamic evaluation of meropenem plus fosfomycin in paediatrics

AIMS

The objective of the current study was to evaluate paediatric dosing regimens for meropenem plus fosfomycin that generate sufficient coverage against multidrug-resistant bacteria.

METHODS

The physiologically based pharmacokinetic (PBPK) models of meropenem and fosfomycin were developed from previously published pharmacokinetic studies in five populations: healthy subjects of Japanese origin, and healthy adults, geriatric, paediatric and renally impaired of primarily Caucasian origins. Pharmacodynamic (PD) analyses were carried out by evaluating dosing regimens that achieved a ≥90% joint probability of target attainment (PTA), which was defined as the minimum of the marginal probabilities to achieve the target PD index of each antibiotic. For meropenem, the percentage of time over a 24-hour period wherein the free drug concentration was above the minimum inhibitory concentration (fT > MIC) of at least 40% was its PD target. The fosfomycin PD index was described by fAUC/MIC of at least 40.8.

RESULTS

For coadministration consisting of 20 mg/kg meropenem q8h as a 3-hour infusion and 35 mg/kg fosfomycin q8h also as a 3-hour infusion in a virtual paediatric population between 1 month and 12 years of age with normal renal function and a corresponding body weight between 3 and 50 kg, a joint PTA ≥ 90% is achieved at MICs of 16 and 64 mg/L for meropenem and fosfomycin coadministration, respectively, against Klebsiella pneumoniae and Pseudomonas aeruginosa.

CONCLUSION

The current study identified potentially effective paediatric dosing regimens for meropenem plus fosfomycin coadministration against multidrug-resistant bacteria.

Ethambutol disposition in humans: Challenges and limitations of whole-body physiologically-based pharmacokinetic modelling in early drug development

Whole-body physiologically based pharmacokinetic (WB-PBPK) models have become an important tool in drug development, as they enable characterization of pharmacokinetic profiles across different organs based on physiological (systems-specific) and physicochemical (drug-specific) properties. However, it remains unclear which data are needed for accurate predictions when applying the approach to novel candidate molecules progressing into the clinic. In this work, as case study, we investigated the predictive performance of WB-PBPK models both for prospective and retrospective evaluation of the pharmacokinetics of ethambutol, considering scenarios that reflect different stages of development, including settings in which the data are limited to in vitro experiments, in vivo preclinical data, and when some clinical data are available. Overall, the accuracy of PBPK model-predicted systemic and tissue exposure was heavily dependant on prior knowledge about the eliminating organs. Whilst these findings may be specific to ethambutol, the challenges and potential limitations identified here may be relevant to a variety of drugs, raising questions about (1) the minimum requirements for prospective use of WB-PBPK models during the characterization of drug disposition and (2) implication of uncertainty for dose selection in humans.

Pharmacodynamics and pharmacokinetics of the novel synthetic opioid, U-47700, in male rats

Novel synthetic opioids are appearing in recreational drug markets worldwide as adulterants in heroin or ingredients in counterfeit analgesic medications. Trans-3,4-dichloro-N-[2-(dimethylamino)cyclohexyl]-N-methyl-benzamide (U-47700) is an example of a non-fentanyl synthetic opioid linked to overdose deaths. Here, we examined the pharmacodynamics and pharmacokinetics of U-47700 in rats. Male Sprague-Dawley rats were fitted with intravenous (i.v.) catheters and subcutaneous (s.c.) temperature transponders under ketamine/xylazine anesthesia. One week later, rats received s.c. injections of U-47700 HCl (0.3, 1.0 or 3.0 mg/kg) or saline, and blood samples (0.3 mL) were withdrawn via i.v. catheters at 15, 30, 60, 120, 240, 480 min post-injection. Pharmacodynamic effects were assessed at each blood withdrawal, and plasma was assayed for U-47700 and its metabolites by liquid chromatography tandem mass spectrometry. U-47700 induced dose-related increases in hot plate latency (ED₅₀ = 0.5 mg/kg) and catalepsy (ED₅₀ = 1.7 mg/kg), while the 3.0 mg/kg dose also caused hypothermia. Plasma levels of U-47700 rose linearly as dose increased, with maximal concentration (C_max) achieved by 15-38 min. C_max values for N-desmethyl-U-47700 and N,N-didesmethyl-U-47700 were delayed but reached levels in the same range as the parent compound. Pharmacodynamic effects were correlated with plasma U-47700 and its N-desmethyl metabolite. Using radioligand binding assays, U-47700 displayed high affinity for μ-opioid receptors (Ki = 11.1 nM) whereas metabolites were more than 18-fold weaker. Our data reveal that U-47700 induces typical μ-opioid effects which are related to plasma concentrations of the parent compound. Given its high potency, U-47700 poses substantial risk to humans who are inadvertently exposed to the drug.

Application and Impact of Human Dose Projection from Discovery to Early Drug Development

The application and impact of human dose projection (HDP) has been well recognized in the late drug development phase, with increasing appreciation earlier during discovery and early development. This commentary describes the perspective of pharmaceutical scientists on the evolving application and impact of HDP at various phases from discovery to early development, including lead generation, lead optimization, lead up to candidate nomination, and early drug development. The underlying fundamental concepts and key input parameters for HDP are briefly discussed. A broad overview of phase-specific tools and approaches commonly utilized for human dose projection in the pharmaceutical industry is provided. A discussion of phase-appropriate implementation strategies, associated limitations/assumptions and continuous refinement for HDP from discovery to early development is presented. The authors describe the phase-specific applications of human dose projection to facilitate key assessments and relative impact on decision points.

A Physiologically Based Pharmacokinetic Model for Pregnant Women to Predict the Pharmacokinetics of Drugs Metabolized Via Several Enzymatic Pathways

BACKGROUND

Physiologically based pharmacokinetic modeling is considered a valuable tool for predicting pharmacokinetic changes in pregnancy to subsequently guide in-vivo pharmacokinetic trials in pregnant women. The objective of this study was to extend and verify a previously developed physiologically based pharmacokinetic model for pregnant women for the prediction of pharmacokinetics of drugs metabolized via several cytochrome P450 enzymes.

METHODS

Quantitative information on gestation-specific changes in enzyme activity available in the literature was incorporated in a pregnancy physiologically based pharmacokinetic model and the pharmacokinetics of eight drugs metabolized via one or multiple cytochrome P450 enzymes was predicted. The tested drugs were caffeine, midazolam, nifedipine, metoprolol, ondansetron, granisetron, diazepam, and metronidazole. Pharmacokinetic predictions were evaluated by comparison with in-vivo pharmacokinetic data obtained from the literature.

RESULTS

The pregnancy physiologically based pharmacokinetic model successfully predicted the pharmacokinetics of all tested drugs. The observed pregnancy-induced pharmacokinetic changes were qualitatively and quantitatively reasonably well predicted for all drugs. Ninety-seven percent of the mean plasma concentrations predicted in pregnant women fell within a twofold error range and 63% within a 1.25-fold error range. For all drugs, the predicted area under the concentration-time curve was within a 1.25-fold error range.

CONCLUSION

The presented pregnancy physiologically based pharmacokinetic model can quantitatively predict the pharmacokinetics of drugs that are metabolized via one or multiple cytochrome P450 enzymes by integrating prior knowledge of the pregnancy-related effect on these enzymes. This pregnancy physiologically based pharmacokinetic model may thus be used to identify potential exposure changes in pregnant women a priori and to eventually support informed decision making when clinical trials are designed in this special population.

Utility of physiologically based pharmacokinetic (PBPK) modeling in oncology drug development and its accuracy: a systematic review

PURPOSE

Physiologically based pharmacokinetic (PBPK) modeling, a mathematical modeling approach which uses a pharmacokinetic model to mimick human physiology to predict drug concentration-time profiles, has been used for the discover and development of drugs in various fields, including oncology, since 2000. There have been a few general review articles on the utilization of PBPK in the development of oncology drugs, but these do not include an evaluation of model prediction accuracy. We therefore conducted a systematic review to define the accuracy of PBPK model prediction and its utility throughout all the developmental phases of oncology drugs.

METHODS

A systematic search was performed in the PubMed, PubMed Central and Cochrane Library databases from 1980 to February 2017 for articles (1) written in English, (2) focused on oncology or antineoplastic or anticancer drugs, tumor or cancer or anticancer drugs listed in the U.S. National Institutes of Health and (3) involving a PBPK model. The absolute-average-folding-errors (AAFEs) of the area under the curve (AUC) between predicted and observed values in each article were calculated to assess model prediction accuracy.

RESULTS

Of the 2341 articles initially identified by our search of the databases, 40 were included in the review analysis. These articles reported on six types of studies, i.e. in vivo (n = 4), first-in-human (n = 5), phase II/III clinical trials (n = 9), organ impairment (n = 3), pediatrics (n = 4) and drug-drug interactions (n = 15). AAFEs of the predicted AUC for all groups of studies were within 1.3-fold of each other despite variations in experimental methodologies.

CONCLUSION

PBPK modeling is a potential tool which can be effectively applied throughout all phases of oncology drug development. The number of experimental animals and human participants enrolled in the studies can be reduced using PBPK modeling and PBPK-population-PK modeling. The limited number of publications of unsuccessful model application to date may contribute to bias toward the usefulness of modeling.

Computational modeling of human oral bioavailability: what will be next?

INTRODUCTION

The oral route is the most convenient way of administrating drugs. Therefore, accurate determination of oral bioavailability is paramount during drug discovery and development. Quantitative structure-property relationship (QSPR), rule-of-thumb (RoT) and physiologically based-pharmacokinetic (PBPK) approaches are promising alternatives to the early oral bioavailability prediction. Areas covered: The authors give insight into the factors affecting bioavailability, the fundamental theoretical framework and the practical aspects of computational methods for predicting this property. They also give their perspectives on future computational models for estimating oral bioavailability. Expert opinion: Oral bioavailability is a multi-factorial pharmacokinetic property with its accurate prediction challenging. For RoT and QSPR modeling, the reliability of datasets, the significance of molecular descriptor families and the diversity of chemometric tools used are important factors that define model predictability and interpretability. Likewise, for PBPK modeling the integrity of the pharmacokinetic data, the number of input parameters, the complexity of statistical analysis and the software packages used are relevant factors in bioavailability prediction. Although these approaches have been utilized independently, the tendency to use hybrid QSPR-PBPK approaches together with the exploration of ensemble and deep-learning systems for QSPR modeling of oral bioavailability has opened new avenues for development promising tools for oral bioavailability prediction.

Whole body physiologically based modelling of β-blockers in the rat: events in tissues and plasma following an i.v. bolus dose

BACKGROUND AND PURPOSE

Whole body physiologically based pharmacokinetic (PBPK) models have been increasingly applied in drug development to describe kinetic events of therapeutic agents in animals and humans. The advantage of such modelling is the ability to incorporate vast amounts of physiological information, such as organ blood flow and volume, to ensure that the model is as close to reality as possible.

EXPERIMENTAL APPROACH

Previous PBPK model development of enantiomers of a series of seven racemic β-blockers, namely, acebutolol, betaxolol, bisoprolol, metoprolol, oxprenolol, pindolol and propranolol, together with S-timolol in rat was based on tissue and blood concentration data at steady state. Compounds were administered in several cassettes with the composition mix and blood and tissue sampling times determined using a D-optimal design.

KEY RESULTS

Closed-loop PBPK models were developed initially based on the application of open loop forcing function models to individual tissues and compounds. For the majority of compounds and tissues, distribution kinetics was adequately characterized by perfusion rate-limited models. For some compounds in the testes and gut, a permeability rate-limited distribution model was required to best fit the data. Parameter estimates of the tissue-to-blood partition coefficient through fitting of individual enantiomers and of racemic pair were generally in agreement and also concur with those from previous steady-state experiments.

CONCLUSIONS AND IMPLICATIONS

PBPK modelling is a very powerful tool to aid drug discovery and development of therapeutic agents in animals and humans. However, careful consideration of the assumptions made during the modelling exercise is essential.

A physiologically-based model to predict individual pharmacokinetics and pharmacodynamics of remifentanil

Remifentanil based anesthesia is nowadays spread worldwide. This drug is characterized by a rapid onset of the analgesic effects, but also by a rapid onset of the side effects. For this reason, the knowledge of the remifentanil concentration in the human body is a key topic in anesthesiology. The aims of this work are to propose and validate a physiologically based pharmacokinetic model capable to predict both the pharmacokinetics and pharmacodynamics of remifentanil, and to take into account the inter-individual differences among the patients (such as height and body mass). The blood concentration of remifentanil has been successfully simulated and compared with experimental literature data. The pharmacodynamics, in terms of effect of remifentanil on minute ventilation and electroencephalogram, has been implemented in this model. Moreover, the remifentanil concentration in various organs and tissues is predicted, which is a significant improvement with respect to the traditional compartmental models. The availability of the model makes possible the prediction of the effects of remifentanil administration, also accounting for individual parameters.

Cell cultures in drug discovery and development: The need of reliable in vitro-in vivo extrapolation for pharmacodynamics and pharmacokinetics assessment

For ethical and cost-related reasons, use of animals for the assessment of mode of action, metabolism and/or toxicity of new drug candidates has been increasingly scrutinized in research and industrial applications. Implementation of the 3 "Rs"¹; rule (Reduction, Replacement, Refinement) through development of in silico or in vitro assays has become an essential element of risk assessment. Physiologically based pharmacokinetic (PBPK²) modeling is the most potent in silico tool used for extrapolation of pharmacokinetic parameters to animal or human models from results obtained in vitro. Although, many types of in vitro assays are conducted during drug development, use of cell cultures is the most reliable one. Two-dimensional (2D) cell cultures have been a part of drug development for many years. Nowadays, their role is decreasing in favor of three-dimensional (3D) cell cultures and co-cultures. 3D cultures exhibit protein expression patterns and intercellular junctions that are closer to in vivo states in comparison to classical monolayer cultures. Co-cultures allow for examinations of the mutual influence of different cell lines. However, the complexity and high costs of co-cultures and 3D equipment exclude such methods from high-throughput screening (HTS).³In vitro absorption, distribution, metabolism, and excretion assessment, as well as drug-drug interaction (DDI), are usually performed with the use of various cell culture based assays. Progress in in silico and in vitro methods can lead to better in vitro-in vivo extrapolation (IVIVE⁴) outcomes and have a potential to contribute towards a significant reduction in the number of laboratory animals needed for drug research. As such, concentrated efforts need to be spent towards the development of an HTS in vitro platform with satisfactory IVIVE features.

A whole-body physiologically based pharmacokinetic (WB-PBPK) model of ciprofloxacin: a step towards predicting bacterial killing at sites of infection

The purpose of this study was to develop a whole-body physiologically based pharmacokinetic (WB-PBPK) model for ciprofloxacin for ICU patients, based on only plasma concentration data. In a next step, tissue and organ concentration time profiles in patients were predicted using the developed model. The WB-PBPK model was built using a non-linear mixed effects approach based on data from 102 adult intensive care unit patients. Tissue to plasma distribution coefficients (Kp) were available from the literature and used as informative priors. The developed WB-PBPK model successfully characterized both the typical trends and variability of the available ciprofloxacin plasma concentration data. The WB-PBPK model was thereafter combined with a pharmacokinetic-pharmacodynamic (PKPD) model, developed based on in vitro time-kill data of ciprofloxacin and Escherichia coli to illustrate the potential of this type of approach to predict the time-course of bacterial killing at different sites of infection. The predicted unbound concentration-time profile in extracellular tissue was driving the bacterial killing in the PKPD model and the rate and extent of take-over of mutant bacteria in different tissues were explored. The bacterial killing was predicted to be most efficient in lung and kidney, which correspond well to ciprofloxacin's indications pneumonia and urinary tract infections. Furthermore, a function based on available information on bacterial killing by the immune system in vivo was incorporated. This work demonstrates the development and application of a WB-PBPK-PD model to compare killing of bacteria with different antibiotic susceptibility, of value for drug development and the optimal use of antibiotics .

Physiological fidelity or model parsimony? The relative performance of reverse-toxicokinetic modeling approaches

Background

Physiologically-based toxicokinetic (PBTK) models are often developed to facilitate in vitro to in vivo extrapolation (IVIVE) using a top-down, compartmental approach, favoring architectural simplicity over physiological fidelity despite the lack of general guidelines relating model design to dynamical predictions. Here we explore the impact of design choice (high vs. low fidelity) on chemical distribution throughout an animal’s organ system.

Results

We contrast transient dynamics and steady states of three previously proposed PBTK models of varying complexity in response to chemical exposure. The steady states for each model were determined analytically to predict exposure conditions from tissue measurements. Steady state whole-body concentrations differ between models, despite identical environmental conditions, which originates from varying levels of physiological fidelity captured by the models. These differences affect the relative predictive accuracy of the inverted models used in exposure reconstruction to link effects-based exposure data with whole-organism response thresholds obtained from in vitro assay measurements.

Conclusions

Our results demonstrate how disregarding physiological fideltiy in favor of simpler models affects the internal dynamics and steady state estimates for chemical accumulation within tissues, which, in turn, poses significant challenges for the exposure reconstruction efforts that underlie many IVIVE methods. Developing standardized systems-level models for ecological organisms would not only ensure predictive consistency among future modeling studies, but also ensure pragmatic extrapolation of in vivo effects from in vitro data or modeling exposure-response relationships.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-017-0407-3) contains supplementary material, which is available to authorized users.

Physiologically Based Pharmacokinetic (PBPK) Model for Biodistribution of Radiolabeled Peptides in Patients with Neuroendocrine Tumours

Objective(s):

The objectives of this work was to assess the benefits of the application of Physiologically Based Pharmacokinetic (PBPK) models in patients with different neuroendocrine tumours (NET) who were treated with Lu-177 DOTATATE. The model utilises clinical data on biodistribution of radiolabeled peptides (RLPs) obtained by whole body scintigraphy (WBS) of the patients.

Methods:

The blood flow restricted (perfusion rate limited) type of the PBPK model for biodistribution of radiolabeled peptides (RLPs) in individual human organs is based on the multi-compartment approach, which takes into account the main physiological processes in the organism: absorption, distribution, metabolism and excretion (ADME). The approach calibrates the PBPK model for each patient in order to increase the accuracy of the dose estimation. Datasets obtained using WBS in four patients have been used to obtain the unknown model parameters. The scintigraphic data were acquired using a double head gamma camera in patients with different neuroendocrine tumours who were treated with Lu-177 DOTATATE. The activity administered to each patient was 7400 MBq.

Results:

Satisfactory agreement of the model predictions with the data obtained from the WBS for each patient has been achieved.

Conclusion:

The study indicates that the PBPK model can be used for more accurate calculation of biodistribution and absorbed doses in patients. This approach is the first attempt of utilizing scintigraphic data in PBPK models, which was obtained during Lu-177 peptide therapy of patients with NET.

Lipophilicity and biomimetic properties measured by HPLC to support drug discovery

HPLC methods that use chromatographic retention times for gaining information about the properties of compounds for the purpose of designing drug molecules are reviewed. Properties, such as lipophilicity, protein binding, phospholipid binding, and acid/base character can be incorporated in the design of molecules with the right biological distribution and pharmacokinetic profile to become an effective drug. Standardization of various methodologies is suggested in order to obtain data suitable for inter-laboratory comparison. The published HPLC methods for lipophilicity, acid/base character, protein and phospholipid binding are critically reviewed and compared with each other using the solvation equation approach. One of the most important discussion points is how these data can be used in models and how they can influence the drug discovery process. Therefore, the published models for volume of distribution, unbound volume of distribution and drug efficiency are also discussed. The general relationships between the chemical structure and biomimetic HPLC properties are described in view of ranking and selecting putative drug molecules.

Virtual population pharmacokinetic using physiologically based pharmacokinetic model for evaluating bioequivalence of oral lacidipine formulations in dogs

The aim of the present study was to investigate virtual population pharmacokinetic using physiologically based pharmacokinetic (PBPK) model for evaluating bioequivalence of oral lacidipine formulations in dogs. The dissolution behaviors of three lacidipine formulations including one commercial product and two self-made amorphous solid dispersions (ASDs) capsules were determined in 0.07% Tween 80 media. A randomized 3-period crossover design in 6 healthy beagle dogs after oral administration of the three formulations at a single dose of 4 mg was conducted. The PBPK modeling was utilized for the virtual bioequivalence study. In vitro dissolution experiment showed that the dissolution behaviors of lacidipine amorphous solid dispersions (ASDs) capsules, which was respectively prepared by HPMC-E5 or Soluplus, as polymer displayed similar curves compared with the reference formulation in 0.07% Tween 80 media. In vivo pharmacokinetics experiments showed that three formulations had comparable maximum plasma drug concentration (C_max), and the time (T_max) to reach C_max of lacidipine tablet, which was prepared by Soluplus, as polymer was slower than other two formulations in consistency with the in vitro dissolution rate. The 90% confidence interval (CI) for the C_max, AUC_0–24 h and AUC_0–∞ of the ratio of the test drug to the referencedrug exceeded the acceptable bioequivalence (BE) limits (0.80–1.25). However, the 90% CI of the AUC_0–24 h, AUC_0–∞ and C_max of the ratio of test to reference drug were within the BE limit, calculated using PBPK modeling when the virtual subjects reached 24 dogs. The results all demonstrated that virtual bioequivalence study can overcome the inequivalence caused by inter-subject variability of the 6 beagle dogs involved in in vivo experiments.

Heteroleptic arene Ru(ii) dipyrrinato complexes: DNA, protein binding and anti-cancer activity against the ACHN cancer cell line

The in vitro cytotoxicities of heteroleptic Ru(ii) complexes were studied in ACHN cell lines by MTT assay, DNA cleavage, (AO/EtBr) and FACS analysis.

In silico optimization of pharmacokinetic properties and receptor binding affinity simultaneously: a 'parallel progression approach to drug design' applied to β-blockers

The present work exploits the potential of in silico approaches for minimizing attrition of leads in the later stages of drug development. We propose a theoretical approach, wherein 'parallel' information is generated to simultaneously optimize the pharmacokinetics (PK) and pharmacodynamics (PD) of lead candidates. β-blockers, though in use for many years, have suboptimal PKs; hence are an ideal test series for the 'parallel progression approach'. This approach utilizes molecular modeling tools viz. hologram quantitative structure activity relationships, homology modeling, docking, predictive metabolism, and toxicity models. Validated models have been developed for PK parameters such as volume of distribution (log Vd) and clearance (log Cl), which together influence the half-life (t1/2) of a drug. Simultaneously, models for PD in terms of inhibition constant pKi have been developed. Thus, PK and PD properties of β-blockers were concurrently analyzed and after iterative cycling, modifications were proposed that lead to compounds with optimized PK and PD. We report some of the resultant re-engineered β-blockers with improved half-lives and pKi values comparable with marketed β-blockers. These were further analyzed by the docking studies to evaluate their binding poses. Finally, metabolic and toxicological assessment of these molecules was done through in silico methods. The strategy proposed herein has potential universal applicability, and can be used in any drug discovery scenario; provided that the data used is consistent in terms of experimental conditions, endpoints, and methods employed. Thus the 'parallel progression approach' helps to simultaneously fine-tune various properties of the drug and would be an invaluable tool during the drug development process.

Physiology-based pharmacokinetic modeling: ready to be used

Physiology-based pharmacokinetic (PBPK) modeling is well recognized as a technology for mechanistically simulating and predicting the fate of substances in a mammalian body. Today, the demand for this methodology is higher than ever. The pharma industry and regulatory agencies are looking for new methods, which help to speed up and increase the efficiency of the development process for new drugs. Implementing PBPK modeling in the drug research and development workflow contributes significantly to reach this goal.:

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.).

Computational approaches to analyse and predict small molecule transport and distribution at cellular and subcellular levels

Quantitative structure-activity relationship (QSAR) studies and mechanistic mathematical modeling approaches have been independently employed for analysing and predicting the transport and distribution of small molecule chemical agents in living organisms. Both of these computational approaches have been useful for interpreting experiments measuring the transport properties of small molecule chemical agents, in vitro and in vivo. Nevertheless, mechanistic cell-based pharmacokinetic models have been especially useful to guide the design of experiments probing the molecular pathways underlying small molecule transport phenomena. Unlike QSAR models, mechanistic models can be integrated from microscopic to macroscopic levels, to analyse the spatiotemporal dynamics of small molecule chemical agents from intracellular organelles to whole organs, well beyond the experiments and training data sets upon which the models are based. Based on differential equations, mechanistic models can also be integrated with other differential equations-based systems biology models of biochemical networks or signaling pathways. Although the origin and evolution of mathematical modeling approaches aimed at predicting drug transport and distribution has occurred independently from systems biology, we propose that the incorporation of mechanistic cell-based computational models of drug transport and distribution into a systems biology modeling framework is a logical next step for the advancement of systems pharmacology research.

PBTK modelling platforms and parameter estimation tools to enable animal-free risk assessment: recommendations from a joint EPAA--EURL ECVAM ADME workshop

Information on toxicokinetics is critical for animal-free human risk assessment. Human external exposure must be translated into human tissue doses and compared with in vitro actual cell exposure associated to effects (in vitro-in vivo comparison). Data on absorption, distribution, metabolism and excretion in humans (ADME) could be generated using in vitro and QSAR tools. Physiologically-based toxicokinetic (PBTK) computer modelling could serve to integrate disparate in vitro and in silico findings. However, there are only few freely-available PBTK platforms currently available. And although some ADME parameters can be reasonably estimated in vitro or in silico, important gaps exist. Examples include unknown or limited applicability domains and lack of (high-throughput) tools to measure penetration of barriers, partitioning between blood and tissues and metabolic clearance. This paper is based on a joint EPAA--EURL ECVAM expert meeting. It provides a state-of-the-art overview of the availability of PBTK platforms as well as the in vitro and in silico methods to parameterise basic (Tier 1) PBTK models. Five high-priority issues are presented that provide the prerequisites for wider use of non-animal based PBTK modelling for animal-free chemical risk assessment.

Impact of physiologically based pharmacokinetic modeling and simulation in drug development

Physiologically based pharmacokinetic modeling and simulation can be used to predict the pharmacokinetics of drugs in human populations and to explore the effects of varying physiologic parameters that result from aging, ethnicity, or disease. In addition, the effects of concomitant medications on drug exposure can be investigated; prediction of the magnitude of drug interactions can impact regulatory communications or internal decision-making regarding the requirement for a clinical drug interaction study. Modeling and simulation can also help to inform the design and timings of clinical drug interaction studies, resulting in more efficient use of limited resources and improved planning in addition to promoting mechanistic understanding of observed drug interactions. These approaches have been used in GlaxoSmithKline from drug discovery to registration and have been applied to 41 drugs from a number of therapeutic areas. This report highlights the variety of questions that can be addressed by prospective or retrospective application of modeling and simulation and the impact this can have on clinical drug development (from candidate selection through clinical development to regulatory submissions).

Incorporation of ABCB1-mediated transport into a physiologically-based pharmacokinetic model of docetaxel in mice

Docetaxel is one of the most widely used anticancer agents. While this taxane has proven to be an effective chemotherapeutic drug, noteworthy challenges exist in relation to docetaxel administration due to the considerable interindividual variability in efficacy and toxicity associated with the use of this compound, largely attributable to differences between individuals in their ability to metabolize and eliminate docetaxel. Regarding the latter, the ATP-binding cassette transporter B1 (ABCB1, PGP, MDR1) is primarily responsible for docetaxel elimination. To further understand the role of ABCB1 in the biodistribution of docetaxel in mice, we utilized physiologically-based pharmacokinetic (PBPK) modeling that included ABCB1-mediated transport in relevant tissues. Transporter function was evaluated by studying docetaxel pharmacokinetics in wild-type FVB and Mdr1a/b constitutive knockout (KO) mice and incorporating this concentration-time data into a PBPK model comprised of eight tissue compartments (plasma, brain, heart, lung, kidney, intestine, liver and slowly perfused tissues) and, in addition to ABCB1-mediated transport, included intravenous drug administration, specific binding to intracellular tubulin, intestinal and hepatic metabolism, glomerular filtration and tubular reabsorption. For all tissues in both the FVB and KO cohorts, the PBPK model simulations closely mirrored the observed data. Furthermore, both models predicted AUC values that were with 15 % of the observed AUC values, indicating that our model-simulated drug exposures accurately reflected the observed tissue exposures. Overall, our PBPK model furthers the understanding of the role of ABCB1 in the biodistribution of docetaxel. Additionally, this exemplary model structure can be applied to investigate the pharmacokinetics of other ABCB1 transporter substrates.

Dose selection based on physiologically based pharmacokinetic (PBPK) approaches

Physiologically based pharmacokinetic (PBPK) models are built using differential equations to describe the physiology/anatomy of different biological systems. Readily available in vitro and in vivo preclinical data can be incorporated into these models to not only estimate pharmacokinetic (PK) parameters and plasma concentration-time profiles, but also to gain mechanistic insight into compound properties. They provide a mechanistic framework to understand and extrapolate PK and dose across in vitro and in vivo systems and across different species, populations and disease states. Using small molecule and large molecule examples from the literature and our own company, we have shown how PBPK techniques can be utilised for human PK and dose prediction. Such approaches have the potential to increase efficiency, reduce the need for animal studies, replace clinical trials and increase PK understanding. Given the mechanistic nature of these models, the future use of PBPK modelling in drug discovery and development is promising, however some limitations need to be addressed to realise its application and utility more broadly.

Pharmacokinetic drivers of toxicity for basic molecules: strategy to lower pKa results in decreased tissue exposure and toxicity for a small molecule Met inhibitor

Several toxicities are clearly driven by free drug concentrations in plasma, such as toxicities related to on-target exaggerated pharmacology or off-target pharmacological activity associated with receptors, enzymes or ion channels. However, there are examples in which organ toxicities appear to correlate better with total drug concentrations in the target tissues, rather than with free drug concentrations in plasma. Here we present a case study in which a small molecule Met inhibitor, GEN-203, with significant liver and bone marrow toxicity in preclinical species was modified with the intention of increasing the safety margin. GEN-203 is a lipophilic weak base as demonstrated by its physicochemical and structural properties: high LogD (distribution coefficient) (4.3) and high measured pKa (7.45) due to the basic amine (N-ethyl-3-fluoro-4-aminopiperidine). The physicochemical properties of GEN-203 were hypothesized to drive the high distribution of this compound to tissues as evidenced by a moderately-high volume of distribution (Vd>3l/kg) in mouse and subsequent toxicities of the compound. Specifically, the basicity of GEN-203 was decreased through addition of a second fluorine in the 3-position of the aminopiperidine to yield GEN-890 (N-ethyl-3,3-difluoro-4-aminopiperidine), which decreased the volume of distribution of the compound in mouse (Vd=1.0l/kg), decreased its tissue drug concentrations and led to decreased toxicity in mice. This strategy suggests that when toxicity is driven by tissue drug concentrations, optimization of the physicochemical parameters that drive tissue distribution can result in decreased drug concentrations in tissues, resulting in lower toxicity and improved safety margins.