Interspecies scaling: predicting oral clearance in humans

Model-informed drug development for antimicrobials: translational pharmacokinetic-pharmacodynamic modelling of apramycin to facilitate prediction of efficacious dose in complicated urinary tract infections

OBJECTIVES

The use of mouse models of complicated urinary tract infection (cUTI) has usually been limited to a single timepoint assessment of bacterial burden. Based on longitudinal in vitro and in vivo data, we developed a pharmacokinetic-pharmacodynamic (PKPD) model to assess the efficacy of apramycin, a broad-spectrum aminoglycoside antibiotic, in mouse models of cUTI.

METHODS

Two Escherichia coli strains were studied (EN591 and ATCC 700336). Apramycin exposure-effect relationships were established with in vitro time-kill data at pH 6 and pH 7.4 and in mice with cUTI. Immunocompetent mice were treated with apramycin (1.5-30 mg/kg) starting 24 h post-infection. Kidney and bladder tissue were collected 6-96 h post-infection for cfu determination. A PKPD model integrating all data was developed and simulations were performed to predict bacterial burden in humans.

RESULTS

Treatment with apramycin reduced the bacterial load in kidneys and bladder tissue up to 4.3-log compared with vehicle control. In vitro and in vivo tissue time-course efficacy data were integrated into the PKPD model, showing 76%-98% reduction of bacterial net growth and 3- to 145-fold increase in apramycin potency in vivo compared with in vitro. Simulations suggested that an 11 mg/kg daily dose would be sufficient to achieve bacterial stasis in kidneys and bladder in humans.

CONCLUSIONS

PKPD modelling with in vitro and in vivo PK and PD data enabled simultaneous evaluation of the different components that influence drug effect, an approach that had not yet been evaluated for antibiotics in the cUTI model and that has potential to enhance model-informed drug development of antibiotics.

Prediction of Oral Pharmacokinetics Using a Combination of In Silico Descriptors and In Vitro ADME Properties

Accurate prediction of oral pharmacokinetics remains challenging. This study investigated quantitative approaches for the prediction of the area under the plasma concentration-time curve after oral administration (AUC_p,oral) to rats using the in vitro-in vivo extrapolation (IVIVE), in silico model using machine learning approaches and the combination of the in silico model and in vitro data. A set of 595 structurally diverse compounds with determined AUC_p,oral at 1 mg/kg, in vitro intrinsic clearance (CL_int), an unbound fraction in plasma (f_u,p) in rats, and kinetic solubility at pH 6.8 was used for this assessment. Prediction models developed by two different types of machine learning techniques (i.e., random forest regression and Gaussian processes) were evaluated using three validation methods implementing the time and cluster-split training and test set and fivefold cross-validation. The developed machine learning models have a square of correlation coefficient (R²) in the range of 0.381-0.685 with 33-45% of the compounds being predicted within 2-fold of the observed AUC_p,oral value. The predictivity was improved by incorporating CL_int, f_u,p, and solubility as explanatory variables with R² = 0.554-0.743. In cases where extraction by the liver is the main elimination pathway and intestinal extraction is negligible, AUC_p,oral can be expressed by dose, CL_int, and f_u,p based on a well-stirred model. By using this conventional IVIVE approach, only 1.7-5.0% of compounds were predicted within the 2-fold error with R² = 0.354-0.487. Two empirical scaling factors (ESFs) determined by linear regression analysis and machine learning approaches improved the predictivity of AUC_p,oral with 33-44% predicted within twofold variability. The IVIVE using ESF predicted by random forest regression showed better predictivity of AUC_p,oral with R² = 0.471-0.618, while it still showed lower predictivity than machine learning approaches applied directly to AUC_p,oral prediction. This study demonstrated that the combination of in silico and in vitro parameters is useful to improve the predictivity of the machine learning model for rat AUC_p,oral and supports consideration for predicting AUC_p,oral for human and other non-clinical species in a similar manner.

Interpretation of Non-Clinical Data for Prediction of Human Pharmacokinetic Parameters: In Vitro-In Vivo Extrapolation and Allometric Scaling

Extrapolation of pharmacokinetic (PK) parameters from in vitro or in vivo animal to human is one of the main tasks in the drug development process. Translational approaches provide evidence for go or no-go decision-making during drug discovery and the development process, and the prediction of human PKs prior to the first-in-human clinical trials. In vitro-in vivo extrapolation and allometric scaling are the choice of method for projection to human situations. Although these methods are useful tools for the estimation of PK parameters, it is a challenge to apply these methods since underlying biochemical, mathematical, physiological, and background knowledge of PKs are required. In addition, it is difficult to select an appropriate methodology depending on the data available. Therefore, this review covers the principles of PK parameters pertaining to the clearance, volume of distribution, elimination half-life, absorption rate constant, and prediction method from the original idea to recently developed models in order to introduce optimal models for the prediction of PK parameters.

Polypharmacy in Zoological Medicine

Polypharmacy is a term that describes the inappropriate, concurrent use of multiple drugs in an individual patient. Zoological medicine practitioners must take approved agents (veterinary or human) and extrapolate their use to non-approved species often with little species-specific pharmacological evidence to support their decisions. When considering polypharmacy, even less information exists concerning multi-drug pharmacokinetics, pharmacodynamics, or potential drug-drug interactions in non-domestic species. Unfortunately, captive, zoological species are susceptible, just like their domestic counterparts, to chronic diseases and co-morbidities that may lead to the usage of multiple drugs. Polypharmacy is a recognized and important issue in human medicine, as well as an emerging issue for veterinarians; thus, this paper will discuss the novel, potential risks of polypharmacy in zoological medicine. Hopefully, this discussion will help bring the attention of veterinarians to this issue and serve as an interesting discussion topic for pharmacologists in general.

Approaches for predicting human pharmacokinetics using interspecies pharmacokinetic scaling

Reliably predicting pharmacokinetic behavior in humans from preclinical data is an important aspect of drug development. The most widely used technique in this regard is allometric scaling. In this review, various approaches developed for predicting pharmacokinetic parameters in humans using interspecies scaling are introduced and discussed. Methods to predict plasma concentration-time profiles in humans after intravenous and oral administration are also reviewed. The reliable prediction of human pharmacokinetics with regard to investigational drugs is aimed, ultimately, at selecting the first in-human dose with which to begin clinical studies. Approaches for the selection of the first in-human dose are also reviewed. Although there have been many trials to compare and optimize interspecies scaling methods, no firm conclusions have been reached. Because interspecies scaling methods are still highly empirical, further effort is needed to improve the reliability of predicting human pharmacokinetics by interspecies scaling.

PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 1: goals, properties of the PhRMA dataset, and comparison with literature datasets

This study is part of the Pharmaceutical Research and Manufacturers of America (PhRMA) initiative on predictive models of efficacy, safety, and compound properties. The overall goal of this part was to assess the predictability of human pharmacokinetics (PK) from preclinical data and to provide comparisons of available prediction methods from the literature, as appropriate, using a representative blinded dataset of drug candidates. The key objectives were to (i) appropriately assemble and blind a diverse dataset of in vitro, preclinical in vivo, and clinical data for multiple drug candidates, (ii) evaluate the dataset with empirical and physiological methodologies from the literature used to predict human PK properties and plasma concentration-time profiles, (iii) compare the predicted properties with the observed clinical data to assess the prediction accuracy using routine statistical techniques and to evaluate prediction method(s) based on the degree of accuracy of each prediction method, and (iv) compile and summarize results for publication. Another objective was to provide a mechanistic understanding as to why one methodology provided better predictions than another, after analyzing the poor predictions. A total of 108 clinical lead compounds were collected from 12 PhRMA member companies. This dataset contains intravenous (n = 19) and oral pharmacokinetic data (n = 107) in humans as well as the corresponding preclinical in vitro, in vivo, and physicochemical data. All data were blinded to protect the anonymity of both the data and the company submitting the data. This manuscript, which is the first of a series of manuscripts, summarizes the PhRMA initiative and the 108 compound dataset. More details on the predictability of each method are reported in companion manuscripts.

Comparative evaluation of US Food and Drug Administration and pharmacologically guided approaches to determine the maximum recommended starting dose for first-in-human clinical trials in adult healthy men

The authors compared US Food and Drug Administration (FDA) and 9 pharmacologically guided approaches (PGAs; simple allometry, maximum life span potential [MLP], brain weight, rule of exponent [ROE], two 2-sp methods and 3 one-sp methods) to determine the maximum recommended starting dose (MRSD) for first-in-human clinical trials in adult healthy men using 10 drugs. The ROE method as suggested by Mahmood and Balian1 gave the best prediction accuracy for a pharmacokinetic (PK) parameter. Values derived from clearance were consistently better than volume of distribution (Vd)-based methods and had lower root mean square error (RMSE) values. A pictorial method evaluation chart was developed based on fold errors for simultaneous evaluation of various methods. The one-sp method (rat) and the US FDA methods gave the highest prediction accuracy and low RMSE values, and the 2-sp methods gave the least prediction accuracy with high RMSE values. The ROE method gave more consistent predictions for PK parameters than other allometric methods. Despite this, the MRSD predictions were not better than US FDA methods, probably indicating that across-species variation in clearance may be higher than variation in no observed adverse effect level (NOAEL) and that PGA methods may not be consistently better than the NOAEL based methods.

Controversy in the allometric application of fixed- versus varying-exponent models: a statistical and mathematical perspective

This commentary is a reply to a recent article by Mahmood commenting on the authors' article on the use of fixed-exponent allometry in predicting human clearance. The commentary discusses eight issues that are related to criticisms made in Mahmood's article and examines the controversies (fixed-exponent vs. varying-exponent allometry) from the perspective of statistics and mathematics. The key conclusion is that any allometric method, which is to establish a power function based on a limited number of animal species and to extrapolate the resulting power function to human values (varying-exponent allometry), is infused with fundamental statistical errors.

Prediction of human pharmacokinetics—evaluation of methods for prediction of hepatic metabolic clearance

Methods for prediction of hepatic clearance (CLH) in man have been evaluated. A physiologically-based in-vitro to in-vivo (PB-IVIV) method with human unbound fraction in blood (fu,bl) and hepatocyte intrinsic clearance (CLint)-data has a good rationale and appears to give the best predictions (maximum ∼2-fold errors; < 25% errors for half of CL-predictions; appropriate ranking). Inclusion of an empirical scaling factor is, however, needed, and reasons include the use of cryopreserved hepatocytes with low activity, and inappropriate CLint- and fu,bl-estimation methods. Thus, an improvement of this methodology is possible and required. Neglect of fu,bl or incorporation of incubation binding does not seem appropriate. When microsome CLint-data are used with this approach, the CLH is underpredicted by 5- to 9-fold on average, and a 106-fold underprediction (attrition potential) has been observed. The poor performance could probably be related to permeation, binding and low metabolic activity. Inclusion of scaling factors and neglect of fu,bl for basic and neutral compounds improve microsome predictions. The performance is, however, still not satisfactory. Allometry incorrectly assumes that the determinants for CLH relate to body weight and overpredicts human liver blood flow rate. Consequently, allometric methods have poor predictability. Simple allometry has an average overprediction potential, > 2-fold errors for ∼1/3 of predictions, and 140-fold underprediction to 5800-fold overprediction (potential safety risk) range. In-silico methodologies are available, but these need further development. Acceptable prediction errors for compounds with low and high CLH should be ∼50 and ∼10%, respectively. In conclusion, it is recommended that PB-IVIV with human hepatocyte CLint and fu,bl is applied and improved, limits for acceptable errors are decreased, and that animal CLH-studies and allometry are avoided.

Metabotropic glutamate receptor modulation, translational methods, and biomarkers: relationships with anxiety

RATIONALE

The increasing awareness of the need to align clinical and preclinical research to facilitate rapid development of new drug therapies is reflected in the recent introduction of the term "translational medicine". This review examines the implications of translational medicine for psychiatric disorders, focusing on metabotropic glutamate (mGlu) receptor biology in anxiety disorders and on anxiety-related biomarkers.

OBJECTIVES

This review aims to (1) examine recent progress in translational medicine, emphasizing the role that translational research has played in understanding of the potential of mGlu receptor agonists and antagonists as anxiolytics, (2) identify lacunas where animal and human research have yet to be connected, and (3) suggest areas where translational research can be further developed.

RESULTS

Current data show that animal and human mGlu(5) binding can be directly compared in experiments using the PET ligand (11)C-ABP688. Testing of the mGlu(2/3) receptor agonist LY354740 in the fear-potentiated startle paradigm allows direct functional comparisons between animals and humans. LY354740 has been tested in panic models, but in different models in rats and humans, hindering efforts at translation. Other potentially translatable methods, such as stress-induced hyperthermia and HPA-axis measures, either have been underexploited or are associated with technical difficulties. New techniques such as quantitative trait loci (QTL) analysis may be useful for generating novel biomarkers of anxiety.

CONCLUSIONS

Translational medicine approaches can be valuable to the development of anxiolytics, but the amount of cross-fertilization between clinical and pre-clinical departments will need to be expanded to realize the full potential of these approaches.

Predicting oral clearance in humans: how close can we get with allometry?

BACKGROUND

Oral clearance (CL/F) is an important pharmacokinetic parameter and plays an important role in the selection of a safe and tolerable dose for first-in-human studies. Throughout the pharmaceutical industry, many drugs are administered via the oral route; however, there are only a handful of published scaling studies for the prediction of oral pharmacokinetic parameters.

METHODS

We evaluated the predictive performances of four different allometric approaches -- simple allometry (SA), the rule of exponents, the unbound CL/F approach, and the unbound fraction corrected intercept method (FCIM) -- for the prediction of human CL/F and the oral area under the plasma concentration-time curve (AUC). Twenty-four compounds developed at Johnson and Johnson Pharmaceutical Research and Development, covering a wide range of physicochemical and pharmacokinetic properties, were selected. The CL/F was predicted using these approaches, and the oral AUC was then estimated using the predicted CL/F.

RESULTS

The results of this study indicated that the most successful predictions of CL/F and the oral AUC were obtained using the unbound CL/F approach in combination with the maximum lifespan potential or the brain weight as correction factors based on the rule of exponents. We also observed that the unbound CL/F approach gave better predictions when the exponent of SA was between 0.5 and 1.2. However, the FCIM seemed to be the method of choice when the exponent of SA was <0.50 or >1.2.

CONCLUSIONS

Overall, we were able to predict CL/F and the oral AUC within 2-fold of the observed value for 79% and 83% of the compounds, respectively, by selecting the allometric approaches based on the exponents of SA.

Prediction of metabolic drug clearance in humans: in vitro-in vivo extrapolation vs allometric scaling

Previously in vitro-in vivo extrapolation (IVIVE) with the Simcyp Clearance and Interaction Simulator has been used to predict the clearance of 15 clinically used drugs in humans. The criteria for the selection of the drugs were that they are used as probes for the activity of specific cytochromes P450 (CYPs) or have a single CYP isoform as the major or sole contributor to their metabolism and that they do not exhibit non-linear kinetics in vivo. Where data were available for the clearance of the drugs in at least three animal species, the predictions from IVIVE have now been compared with those based on allometric scaling (AS). Adequate data were available for estimating oral clearance (CLp.o.) in 9 cases (alprazolam, sildenafil, caffeine, clozapine, cyclosporine, dextromethorphan, midazolam, omeprazole and tolbutamide) and intravenous clearance in 6 cases (CLi.v.) (cyclosporine, diclofenac, midazolam, omeprazole, theophylline and tolterodine). AS predictions were based on five different methods: (1) simple allometry (clearance versus body weight); (2) correction for maximum life-span potential (CL x MLP); (3) correction for brain weight (CL x BrW); (4) the use of body surface area; and (5) the rule of exponents. A prediction accuracy was indicated by mean-fold error and the Pearson product moment correlation coefficient. Predictions were considered successful if the mean-fold error was <or=2. IVIVE predictions were accurate in 14 of 15 cases (mean-fold error range: 1.02-4.00). All five AS methods were accurate in 13, 11, 10, 10 and 14 cases, respectively. However, in some cases the error of AS exceeded fivefold. On the basis of the current results, IVIVE is more reliable than AS in predicting human clearance values for drugs mainly metabolized by CYP450 enzymes. This suggests that the place of AS methods in pre-clinical drug development warrants further scrutiny.

Pharmacokinetic profile of single and repeated oral doses of MDMA in squirrel monkeys: relationship to lasting effects on brain serotonin neurons

A large body of data indicates that (+/-)3,4-methylenedioxymethamphetamine (MDMA, 'ecstasy') can damage brain serotonin neurons in animals. However, the relevance of these preclinical data to humans is uncertain, because doses and routes of administration used in animals have generally differed from those used by humans. Here, we examined the pharmacokinetic profile of MDMA in squirrel monkeys after different routes of administration, and explored the relationship between acute plasma MDMA concentrations after repeated oral dosing and subsequent brain serotonin deficits. Oral MDMA administration engendered a plasma profile of MDMA in squirrel monkeys resembling that seen in humans, although the half-life of MDMA in monkeys is shorter (3 vs 6-9 h). MDMA was biotransformed into MDA, and the plasma ratio of MDA to MDMA was 3-5 / 100, similar to that in humans. MDMA accumulation in squirrel monkeys was nonlinear, and plasma levels were highly correlated with regional brain serotonin deficits observed 2 weeks later. The present results indicate that plasma concentrations of MDMA shown here to produce lasting serotonergic deficits in squirrel monkeys overlap those reported by other laboratories in some recreational 'ecstasy' consumers, and are two to three times higher than those found in humans administered a single 100-150 mg dose of MDMA in a controlled setting. Additional studies are needed on the relative sensitivity of brain serotonin neurons to MDMA toxicity in humans and non-human primates, the pharmacokinetic parameter(s) of MDMA most closely linked to the neurotoxic process, and metabolites other than MDA that may play a role.

Population pharmacokinetic studies in pediatrics: issues in design and analysis

The current review addresses the following 3 frequently encountered challenges in the design and analysis of population pharmacokinetic studies in pediatrics: (1) body size adjustments during the development of pharmacostatistical models, (2) design and validation of limited sampling strategies, and (3) the integration of historical priors in data analysis and trial simulation. Size adjustments with empiric approaches based on body weight or body surface area have frequently proven as a pragmatic tool to overcome large size differences in a pediatric study population. Allometric size adjustments, however, provide a more mechanistic, physiologically based approach that, if used a priori, allows delineation of the effect of size from that of other covariates that show a high degree of collinearity. The frequent lack of dense data sets in pediatric clinical pharmacology because of ethical and logistic constraints in study design can be overcome with the application of D-optimality-based limited sampling schemes in combination with Bayesian and nonlinear mixed-effects modeling approaches. Empirically based dose selection and clinical trial designs for pediatric clinical pharmacology studies can be improved by applying clinical trial simulation techniques, especially if they integrate adult and pediatric in vitro and/or in vivo data as historic priors. Although integration of these concepts and techniques in population pharmacokinetic analyses is not only limited to pediatric research, their application allows researchers to overcome some major hurdles frequently encountered in pharmacokinetic studies in pediatrics and, thus, provides the basis for additional clinical pharmacology research in this previously insufficiently studied fraction of the general population.

Prediction of human clearance (CL) and volume of distribution (VD)

The crucial pharmacokinetic parameters 'volume of distribution' and 'human clearance' determine the extent and duration a compound remains in an organism. Potential drug candidates will fail to become successful drugs on the market without favorable values for these parameters, even if they are most efficacious at the target in vitro.The prediction of volume of distribution and human clearance in drug research and development is a key technology to assess possible drug candidates.:

Allometric principles for interspecies extrapolation in toxicological risk assessment--empirical investigations

Four types of data (toxicokinetic data of pharmaceuticals from six species including humans, LD(50) values from eight animal species, long-term NOAEL values of pesticides from mice, rats, and dogs, and toxicity data on anti-neoplastic agents from six species including humans) were used for interspecies comparisons. Species differences with regard to kinetic parameters and toxicity were evaluated and the concordance with predictions by allometric scaling according to caloric demand (allometric exponent 0.75) or to body weight (allometric exponent 1) was checked. For LD(50) values, agreement was poor for both allometric concepts. Recently reported concordance of LD(50) species differences with body weight scaling could be traced back to biased data selection. The other three datasets are clearly in agreement with the allometric scaling according to caloric demand. Caloric demand scaling is thus proposed as a generic interspecies extrapolation method in the absence of substance-specific data. Moreover, the evaluated data make it possible to describe uncertainty associated with the process of interspecies extrapolation by allometric rules.

Pharmacokinetic aspects of biotechnology products

In recent years, biotechnologically derived peptide and protein-based drugs have developed into mainstream therapeutic agents. Peptide and protein drugs now constitute a substantial portion of the compounds under preclinical and clinical development in the global pharmaceutical industry. Pharmacokinetic and exposure/response evaluations for peptide and protein therapeutics are frequently complicated by their similarity to endogenous peptides and proteins as well as protein nutrients. The first challenge frequently comes from a lack of sophistication in various analytical techniques for the quantification of peptide and protein drugs in biological matrices. However, advancements in bioassays and immunoassays--along with a newer generation of mass spectrometry-based techniques--can often provide capabilities for both efficient and reliable detection. Selection of the most appropriate route of administration for biotech drugs requires comprehensive knowledge of their absorption characteristics beyond physicochemical properties, including chemical and metabolic stability at the absorption site, immunoreactivity, passage through biomembranes, and active uptake and exsorption processes. Various distribution properties dictate whether peptide and protein therapeutics can reach optimum target site exposure to exert the intended pharmacological response. This poses a potential problem, especially for large protein drugs, with their typically limited distribution space. Binding phenomena and receptor-mediated cellular uptake may further complicate this issue. Elimination processes--a critical determinant for the drug's systemic exposure--may follow a combination of numerous pathways, including renal and hepatic metabolism routes as well as generalized proteolysis and receptor-mediated endocytosis. Pharmacokinetic/pharmacodynamic (PK/PD) correlations for peptide and protein-based drugs are frequently convoluted by their close interaction with endogenous substances and physiologic regulatory feedback mechanisms. Extensive use of pharmacokinetic and exposure/response concepts in all phases of drug development has in the past been identified as a crucial factor for the success of a scientifically driven, evidence-based, and thus accelerated drug development process. Thus, PK/PD concepts are likely to continue and expand their role as a fundamental factor in the successful development of biotechnologically derived drug products in the future.

Prediction of human pharmacokinetics from animal data and molecular structural parameters using multivariate regression analysis: oral clearance

The aim of the study reported here was to develop a regression equation for predicting oral clearance of various kinds of drugs in humans using experimental data from rats and dogs and molecular structural parameters. The data concerning the oral clearance of 87 drugs from rats, dogs, and humans were obtained from literature. The compounds have various structures, pharmacological activities, and pharmacokinetic characteristics. In addition, the molecular weight, calculated partition coefficient (c log P), and the number of hydrogen bond acceptors were used as possible descriptors related to oral clearance in human. Multivariate regression analyses, multiple linear regression analysis, and the partial least squares (PLS) method were used to predict oral clearance in human, and the predictive performances of these techniques were compared by allometric approaches, which have been used in interspecies scaling. Interaction terms were also introduced into the regression analysis to evaluate the nonlinear relationship. For the data set used in this study, the PLS model with the tertiary term descriptors gave the best predictive performance, and the value of the squared cross-validated correlation coefficient (q(2)) was 0.694. This PLS model, using animal oral clearance data for only two species and easily calculated molecular structural parameters, can generally predict oral clearance in human better than the allometric approaches. In addition, the molecular structural parameters and the interaction term descriptors were useful for predicting oral clearance in human by PLS. Another advantage of this PLS model is that it can be applied to drugs with various characteristics.