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

Effect of co-loaded vitamin D3 on intravenous injectable raloxifene delivery system

Owing to its promising advantages, including improved drug bioavailability and therapeutic efficiency at low doses and frequency, increased patient convenience and compliance, and prolonged storage life, nanomedicine has received heightened attention over conventional pharmaceuticals. Human serum albumin (HSA)-based nanoparticles have been used as drug carriers in injectable formulations, with great success and versatility. In this study, raloxifene and vitamin D3 were co-encapsulated in HSA-based nanoparticles (Ral/VitaD/HSA/PSS NPs) as an intravenously injected pharmaceutical formulation in order to enhance their availability in the body. The lyophilization-hydration method was utilized to develop the Ral/VitaD/HSA/PSS NPs. In addition, the characteristics and stability of the NP and the effect of the co-loading of vitamin D3 on raloxifene release in vitro and in vivo were discussed. The raloxifene and vitamin D3 molecules were successfully encapsulated and well dispersed in an amorphous state within Ral/VitaD/HSA/PSS NPs. The prepared Ral/VitaD/HSA/PSS NPs were lyophilized for long-term storage and were both biocompatible and hemocompatible, enhancing alkaline phosphtase activity in osteoblasts. Delivered via intravenous injection, Ral/VitaD/HSA/PSS NPs addressed the low bioavailability of raloxifene and vitamin D3 caused by oral administration, and improved their compatibility and residence time in the body. Overall, the established raloxifene-vitamin D3-co-loaded NPs may be a potential nanomedicine contender for treating postmenopausal osteoporosis.

Application of the Population Pharmacokinetics Model-Based Approach to the Prediction of Drug-Drug Interaction between Rivaroxaban and Carbamazepine in Humans

Rivaroxaban (RIV) is one of the direct oral anticoagulants used to prevent and treat venous and arterial thromboembolic events. Considering the therapeutic indications, RIV is likely to be concomitantly administered with various other drugs. Among these is carbamazepine (CBZ), one of the recommended first-line options to control seizures and epilepsy. RIV is a strong substrate of cytochrome P450 (CYP) enzymes and Pgp/BCRP efflux transporters. Meanwhile, CBZ is well known as a strong inducer of these enzymes and transporters. Therefore, drug-drug interaction (DDI) between CBZ and RIV is expected. This study aimed to predict the DDI profile of CBZ and RIV in humans by using a population pharmacokinetics (PK) model-based approach. We previously investigated the population PK parameters of RIV administered alone or with CBZ in rats. In this study, those parameters were extrapolated from rats to humans by using simple allometry and liver blood flow scaling, and then applied to back-simulate the PK profiles of RIV in humans (20 mg RIV per day) used alone or with CBZ (900 mg CBZ per day). Results showed that CBZ significantly reduced RIV exposure. The AUC_inf and C_max of RIV decreased by 52.3% and 41.0%, respectively, following the first RIV dose, and by 68.5% and 49.8% at the steady state. Therefore, the co-administration of CBZ and RIV warrants caution. Further studies investigating the extent of DDIs between these drugs should be conducted in humans to fully understand their safety and effects.

Human serum albumin-based nanoparticles alter raloxifene administration and improve bioavailability

Osteoporosis is a disease that reduces bone mass and microarchitecture, which makes bones fragile. Postmenopausal osteoporosis occurs due to estrogen deficiency. Raloxifene is a selective estrogen receptor modulator used to treat postmenopausal osteoporosis. However, it has a low bioavailability, which requires long-term, high-dose raloxifene administration to be effective and causes several side effects. Herein, raloxifene was encapsulated in human serum albumin (HSA)-based nanoparticles (Ral/HSA/PSS NPs) as an intravenous-injection pharmaceutical formulation to increase its bioavailability and reduce the treatment dosage and time. In vitro results indicated that raloxifene molecules were well distributed in HSA-based nanoparticles as an amorphous state, and the resulting raloxifene formulation was stabile during long-term storage duration. The Ral/HSA/PSS NPs were both biocompatible and hemocompatible with a decreased cytotoxicity of high-dose raloxifene. Moreover, the intravenous administration of the prepared Ral/HSA/PSS NPs to rats improved raloxifene bioavailability and improved its half-life in plasma. These raloxifene-loaded nanoparticles may be a potential nanomedicine candidate for treating postmenopausal osteoporosis with lower raloxifene dosages.

Retention Time Extended by Nanoparticles Improves the Eradication of Highly Antibiotic-Resistant Helicobacter pylori

Helicobacter pylori infection usually causes gastrointestinal complications, including gastrointestinal bleeding or perforation, and serious infections may lead to gastric cancer. Amoxicillin is used to treat numerous bacterial infections but is easily decomposed in the gastric acid environment via the hydrolyzation of the β-lactam ring. In this study, we develop chitosan-based nanoparticles loaded with amoxicillin (CAANs) as an H. pylori eradication platform. The CAANs were biocompatible and could retain the antibiotic activity of amoxicillin against H. pylori growth. The mucoadhesive property of chitosan and alginate enabled the CAANs to adhere to the mucus layers and penetrate through these to release amoxicillin in the space between the layers and the gastric epithelium. The use of this nanoparticle could prolong the retention time and preserve the antibiotic activity of amoxicillin in the stomach and help enhance the eradication rate of H. pylori and reduce treatment time. These CAANs, therefore, show potential for the effective treatment of highly antibiotic-resistant H. pylori infection using amoxicillin.

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.

First dose in neonates: pharmacokinetic bridging study from juvenile mice to neonates for drugs metabolized by CYP3A

First dose prediction is challenging in neonates. Our objective in this proof-of-concept study was to perform a pharmacokinetic (PK) bridging study from juvenile mice to neonates for drugs metabolized by CYP3A. We selected midazolam and clindamycin as model drugs. We developed juvenile mice population PK models using NONMEM. The PK parameters of these two drugs in juvenile mice were used to bridge PK parameters in neonates using different correction methods. The bridging results were evaluated by the fold-error of 0.5- to 1.5-fold. Simple allometry with and without a correction factor for maximum lifespan potential could be used for a bridging of clearance (CL) and volume of distribution (V_d), respectively, from juvenile mice to neonates. Simulation results demonstrated that for midazolam, 100% of clinical studies for which both the predictive CL and V_d were within 0.5- to 1.5-fold of the observed. For clindamycin, 75% and 100% of clinical studies for which the predictive CL and V_d were within 0.5- to 1.5-fold of the observed. A PK bridging of drugs metabolized by CYP3A is feasible from juvenile mice to neonates. It could be a complement to the ADE and PBPK models to support the first dose in neonates.

First dose in neonates: are juvenile mice, adults and in vitro-in silico data predictive of neonatal pharmacokinetics of fluconazole

BACKGROUND AND OBJECTIVES

Selection of the first-dose-in-neonates is challenging. The objective of this proof-of-concept study was to evaluate a pharmacokinetic bridging approach to predict a neonatal dosing regimen.

METHODS

We selected fluconazole as a paradigm compound. We used data from studies in juvenile mice and adults to develop population pharmacokinetic models using NONMEM. We also develop a physiologically-based pharmacokinetic model from in vitro-in silico data using Simcyp. These three models were then used to predict neonatal pharmacokinetics and dosing regimens for fluconazole.

RESULTS

From juvenile mice to neonates, a correction factor of maximum lifespan potential should be used for extrapolation, while a "renal factor" taking into account renal maturation was required for successful bridging based on adult and in vitro-in silico data. Simulations results demonstrated that the predicted drug exposure based on bridging approach was comparable to the observed value in neonates. The prediction errors were -2.2, +10.1 and -4.6 % for juvenile mice, adults and in vitro-in silico data, respectively.

CONCLUSION

A model-based bridging approach provided consistent predictions of fluconazole pharmacokinetic parameters in neonates and demonstrated the feasibility of this approach to justify the first-dose-in-neonates, based on all data available from different sources (including physiological informations, preclinical studies and adult data), allowing evidence-based decisions of neonatal dose rather than empiricism.

Development and validation of a rapid HPLC method for quantitation of SP-141, a novel pyrido[b]indole anticancer agent, and an initial pharmacokinetic study in mice

There is an increasing interest in targeting the MDM2 oncogene for cancer therapy. SP-141, a novel designed small molecule MDM2 inhibitor, exerts excellent in vitro and in vivo anticancer activity. To facilitate the preclinical development of this candidate anticancer agent, we have developed an HPLC method for the quantitative analysis of SP-141. The method was validated to be precise, accurate, and specific, with a linear range of 16.2-32,400 ng/mL in plasma, 16.2-6480 ng/mL in homogenates of brain, heart, liver, kidneys, lungs, muscle and tumor, and 32.4-6480 ng/mL in spleen homogenates. The lower limit of quantification was 16.2 ng/mL in plasma and all the tissue homogenates, except for spleen homogenates, where it was 32.4 ng/mL. The intra- and inter-assay precisions (coefficient of variation) were between 0.86 and 13.39%, and accuracies (relative errors) ranged from -8.50 to 13.92%. The relative recoveries were 85.6-113.38%. SP-141 was stable in mouse plasma, modestly plasma bound and metabolized by S9 microsomal enzymes. We performed an initial pharmacokinetic study in tumor-bearing nude mice, demonstrating that SP-141 has a short half-life in plasma and wide tissue distribution. In summary, this HPLC method can be used in future preclinical and clinical investigations of SP-141.

Implementation of pharmacokinetic and pharmacodynamic strategies in early research phases of drug discovery and development at Novartis Institute of Biomedical Research

Characterizing the relationship between the pharmacokinetics (PK, concentration vs. time) and pharmacodynamics (PD, effect vs. time) is an important tool in the discovery and development of new drugs in the pharmaceutical industry. The purpose of this publication is to serve as a guide for drug discovery scientists toward optimal design and conduct of PK/PD studies in the research phase. This review is a result of the collaborative efforts of DMPK scientists from various Metabolism and Pharmacokinetic (MAP) departments of the global organization Novartis Institute of Biomedical Research (NIBR). We recommend that PK/PD strategies be implemented in early research phases of drug discovery projects to enable successful transition to drug development. Effective PK/PD study design, analysis, and interpretation can help scientists elucidate the relationship between PK and PD, understand the mechanism of drug action, and identify PK properties for further improvement and optimal compound design. Additionally, PK/PD modeling can help increase the translation of in vitro compound potency to the in vivo setting, reduce the number of in vivo animal studies, and improve translation of findings from preclinical species into the clinical setting. This review focuses on three important elements of successful PK/PD studies, namely partnership among key scientists involved in the study execution; parameters that influence study designs; and data analysis and interpretation. Specific examples and case studies are highlighted to help demonstrate key points for consideration. The intent is to provide a broad PK/PD foundation for colleagues in the pharmaceutical industry and serve as a tool to promote appropriate discussions on early research project teams with key scientists involved in PK/PD studies.

Applications of human pharmacokinetic prediction in first-in-human dose estimation

Quantitative estimations of first-in-human (FIH) doses are critical for phase I clinical trials in drug development. Human pharmacokinetic (PK) prediction methods have been developed to project the human clearance (CL) and bioavailability with reasonable accuracy, which facilitates estimation of a safe yet efficacious FIH dose. However, the FIH dose estimation is still very challenging and complex. The aim of this article is to review the common approaches for FIH dose estimation with an emphasis on PK-guided estimation. We discuss 5 methods for FIH dose estimation, 17 approaches for the prediction of human CL, 6 methods for the prediction of bioavailability, and 3 tools for the prediction of PK profiles. This review may serve as a practical protocol for PK- or pharmacokinetic/pharmacodynamic-guided estimation of the FIH dose.

Prediction of pharmacokinetic parameters

In silico tools specifically developed for prediction of pharmacokinetic parameters are of particular interest to pharmaceutical industry because of the high potential of discarding inappropriate molecules during an early stage of drug development itself with consequent saving of vital resources and valuable time. The ultimate goal of the in silico models of absorption, distribution, metabolism, and excretion (ADME) properties is the accurate prediction of the in vivo pharmacokinetics of a potential drug molecule in man, whilst it exists only as a virtual structure. Various types of in silico models developed for successful prediction of the ADME parameters like oral absorption, bioavailability, plasma protein binding, tissue distribution, clearance, half-life, etc. have been briefly described in this chapter.

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.

Towards a better prediction of peak concentration, volume of distribution and half-life after oral drug administration in man, using allometry

BACKGROUND

It is imperative that new drugs demonstrate adequate pharmacokinetic properties, allowing an optimal safety margin and convenient dosing regimens in clinical practice, which then lead to better patient compliance. Such pharmacokinetic properties include suitable peak (maximum) plasma drug concentration (C(max)), area under the plasma concentration-time curve (AUC) and a suitable half-life (t(½)). The C(max) and t(½) following oral drug administration are functions of the oral clearance (CL/F) and apparent volume of distribution during the terminal phase by the oral route (V(z)/F), each of which may be predicted and combined to estimate C(max) and t(½). Allometric scaling is a widely used methodology in the pharmaceutical industry to predict human pharmacokinetic parameters such as clearance and volume of distribution. In our previous published work, we have evaluated the use of allometry for prediction of CL/F and AUC. In this paper we describe the evaluation of different allometric scaling approaches for the prediction of C(max), V(z)/F and t(½) after oral drug administration in man.

METHODS

Twenty-nine compounds developed at Janssen Research and Development (a division of Janssen Pharmaceutica NV), covering a wide range of physicochemical and pharmacokinetic properties, were selected. The C(max) following oral dosing of a compound was predicted using (i) simple allometry alone; (ii) simple allometry along with correction factors such as plasma protein binding (PPB), maximum life-span potential or brain weight (reverse rule of exponents, unbound C(max) approach); and (iii) an indirect approach using allometrically predicted CL/F and V(z)/F and absorption rate constant (k(a)). The k(a) was estimated from (i) in vivo pharmacokinetic experiments in preclinical species; and (ii) predicted effective permeability in man (P(eff)), using a Caco-2 permeability assay. The V(z)/F was predicted using allometric scaling with or without PPB correction. The t(½) was estimated from the allometrically predicted parameters CL/F and V(z)/F. Predictions were deemed adequate when errors were within a 2-fold range.

RESULTS

C(max) and t(½) could be predicted within a 2-fold error range for 59% and 66% of the tested compounds, respectively, using allometrically predicted CL/F and V(z)/F. The best predictions for C(max) were obtained when k(a) values were calculated from the Caco-2 permeability assay. The V(z)/F was predicted within a 2-fold error range for 72% of compounds when PPB correction was applied as the correction factor for scaling.

CONCLUSIONS

We conclude that (i) C(max) and t(½) are best predicted by indirect scaling approaches (using allometrically predicted CL/F and V(z)/F and accounting for k(a) derived from permeability assay); and (ii) the PPB is an important correction factor for the prediction of V(z)/F by using allometric scaling. Furthermore, additional work is warranted to understand the mechanisms governing the processes underlying determination of C(max) so that the empirical approaches can be fine-tuned further.

Prediction of human oral plasma concentration-time profiles using preclinical data: comparative evaluation of prediction approaches in early pharmaceutical discovery

BACKGROUND AND OBJECTIVES

Empirically based methods remain one of our tools in human pharmacokinetic predictions. The Dedrick approach and the steady-state plasma drug concentration (C(ss))-mean residence time (MRT) approach are based on the assumption that concentration-time profiles are similar among species, including man, and that curves derived from a variety of animal species can be superimposed after mathematical transformation. In the Dedrick approach the transformation is based on the slope and intercept of the allometric relationship. The C(ss)-MRT approach is based on the implementation of measured animal and predicted human MRT and dose/volume of distribution at steady state (V(ss)). The aims of the present study were to compare the predictive performance of concentration-time profiles obtained by these approaches, to evaluate the prediction of individual pharmacokinetic parameters by these approaches and to further refine these approaches incorporating the experience from our previous work.

METHODS

A retrospective analysis using 35 proprietary compounds developed at Johnson & Johnson Pharmaceutical Research and Development was conducted to compare the accuracies of the Dedrick and C(ss)-MRT approaches for predicting oral concentration-time profiles and pharmacokinetic parameters in man. In the first step, input for the transformation was based on simple allometry. Then we assessed whether both methods could be fine-tuned by systematically incorporating correction factors (maximum life span potential, brain weight and plasma protein binding), depending on the interspecies relationship. In addition, for the C(ss)-MRT approach, we used formulas based on multivariate regression analysis as input for the transformation.

RESULTS

Inclusion of correction factors significantly improved the profile predictability for the Dedrick and C(ss)-MRT approaches. This was mainly linked to an improved prediction of terminal elimination half-life (t(½)), MRT and the ratio between the maximum plasma concentration and the concentration at the last observed time point (C(max)/C(last)). No significant differences were observed between the Dedrick approach with correction factors, the C(ss)-MRT approach with correction factors and the C(ss)-MRT approach, based on the regression equations.

CONCLUSIONS

Based on the dataset evaluated in this study, we demonstrated that human plasma concentration-time profiles and pharmacokinetic parameters could be predicted with the Dedrick and C(ss)-MRT approaches and that if correction factors were implemented, the predictions improved significantly. With the requirement of only a limited preclinical in vivo pharmacokinetic dataset, these empirical methods could offer potential in the early stages of drug discovery.

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.

Development and validation of a higher-throughput equilibrium dialysis assay for plasma protein binding

We describe the practical aspects of developing a semiautomated, higher-throughput plasma protein binding (PPB) assay. The assay has a capacity of 32 PPB measurements per screen using triplicate incubations per measurement, and it is flexible with respect to the number of compounds and the number of plasma types used. The described method is based on the 48-well format rapid equilibrium dialysis (RED) device in combination with a robotic liquid handling platform and quantitative bioanalysis. The RED device method was optimized with respect to equilibration time. Method validation was performed by comparison of results from the semiautomated RED PPB assay with both of those obtained using an alternative, manual equilibrium dialysis method and with literature values. Propranolol and warfarin were used as control compounds. We have modeled the effect of dialysis membrane leakage on the measured unbound fraction and implemented a test for measuring protein content in the buffer compartment to confirm the integrity of each insert of the RED device. With the described method, it is possible to screen a relatively large number of compounds for PPB in a drug discovery environment.

An interspecies extrapolation of the pharmacokinetics of telavancin, a rapidly bactericidal, concentration-dependent antibiotic

Telavancin is an intravenous lipoglycopeptide antibiotic active against many Gram-positive pathogens via inhibition of bacterial cell wall synthesis and disruption of bacterial membrane function. Non-compartmental pharmacokinetic parameters of telavancin (clearance [Cl], steady-state volume of distribution [Vss], area under the concentration curve [AUC], and elimination half-life [t(1/2)]) were determined for five preclinical species (mice, rats, rabbits, dogs, and monkeys). Interspecies scaling was applied to predict the corresponding parameters in humans and compare retrospectively with observed values. Plasma concentrations of single doses of telavancin declined monoexponentially in all species with half-lives between 1.2 and 2.4 h. The pharmacokinetics of telavancin was demonstrated to be dose-proportional in rabbits and gender-independent in monkeys. Application of the simple allometric equation (Y = aW(b)) resulted in a good correlation between predicted and observed values of Vss in humans. Application of a modified allometric equation that includes brain weight (Cl × BW = aW(b)) resulted in a good correlation between predicted and observed values of Cl, AUC, and t(1/2) in humans. These data suggest that interspecies scaling may be useful to predict pharmacokinetic parameters of telavancin in humans.

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.

Development and validation of an HPLC method for quantitation of BA-TPQ, a novel iminoquinone anticancer agent, and an initial pharmacokinetic study in mice

We herein describe the development and validation of an HPLC method for the quantitation of 7-(benzylamino)-1,3,4,8-tetrahydropyrrolo [4,3,2-de]quinolin-8(1H)-one (BA-TPQ), a newly synthesized iminoquinone anticancer agent. BA-TPQ was extracted from plasma and tissue samples by first precipitating proteins with acetonitrile followed by a liquid-liquid extraction with ethyl acetate. Chromatographic separation was carried out using a gradient flow rate on a Zorbax SB C(18) column, and the effluent was monitored by UV detection at 346 nm. The method was found to be precise, accurate, and specific, with a linear range of 3.91-1955.0  ng/mL in plasma, 19.55-1955.0  ng/mL in spleen, brain, and liver homogenates and 19.55-3910.0  ng/mL in heart, lung and kidney homogenates. The method was stable under all relevant conditions. Using this method, we also carried out an initial study determining plasma pharmacokinetics and tissue distribution of BA-TPQ in mice following intravenous administration. In summary, this simple and sensitive HPLC method can be used in future preclinical and clinical studies of BA-TPQ.

Comparative assessment of empirical and physiological approaches on predicting human clearances

The empirical and physiological predictive approaches to human clearance were evaluated using preclinical in vitro and in vivo data of various datasets to establish a methodology for the prediction of clearance. Among the examined empirical approaches, an allometric scaling method with the rule of exponent (ROE), based on the exponent in simple allometry, provided better prediction. The effect of lipophilicity (clog P) and clearance on the predictivity was investigated using the ROE method. High predictivity was found for a low lipophilic compound with clog P < 0 and for a compound with moderate or high clearance. As a physiological approach, the in vitro-in vivo scaling method using metabolic stability in liver microsomes and hepatocytes was evaluated, and the predictivity taking the plasma protein binding and the nonspecific binding in incubation into consideration was compared with the ROE method. The two methods appeared to show comparable predictivity, although the in vitro-in vivo scaling was conducted under limited conditions like the use of physiological scaling factor and lipophilicity-derived nonspecific binding data. The ROE method could be an alternative predictor of the human clearance of compounds to which a physiological approach cannot be applied, in addition to low lipophilic compounds, with acceptable accuracy.

A brief survey of first-in-human studies

First-in-human (FIH) studies are a critical step in the drug development process and typically aim to characterize a compound's pharmacokinetics, potential effective concentration or dose, and safety or tolerability margins. Although effort continues to enhance the predictive quality of the selection of FIH doses from preclinical data, and little consensus is available on the design and conduct of FIH studies, detailed surveys describing general approaches taken in FIH studies are useful in the optimization of early-phase clinical drug development. Although allometric scaling techniques continue to provide poor predictive estimates for human pharmacokinetic parameters, FIH starting doses are selected with substantial safety factors applied to human equivalent dose, often in excess of regulatory guidelines. Based on these examples, it appears that relatively conservative 2-fold dose escalations are the most common escalation approach within FIH single ascending dose studies. The combination of conservative dose escalations with low starting doses can result in large FIH trials, consuming both time and resources. Approaches that could enhance the predictive nature of a compound's disposition and adaptive nature of FIH studies could provide a tremendous benefit for drug development.

PhRMA survey on the conduct of first-in-human clinical trials under exploratory investigational new drug applications

The FDA guidance on exploratory IND studies is intended to enable sponsors to move ahead more efficiently with the development of promising candidates. A survey of PhRMA member companies was conducted in 2007 to obtain a cross-sectional industry perspective on the current and future utility of exploratory IND studies. About 56% of survey responders (9 companies of 16 survey responders) conducted or were planning to conduct clinical studies under exploratory INDs. The majority of microdosing studies are performed to characterize human pharmacokinetics or to examine target organ pharmacokinetics using PET imaging techniques. On the other hand, the majority of pharmacological end point studies conducted under exploratory IND are performed to determine whether the compound modulated its pharmacological target or to evaluate the degree of saturation of a target receptor. The present survey suggests that although the merits of exploratory INDs are still being debated, the diversity in the applications cited, the potential for early clinical guidance in decision making and the increasing pressure on containing drug development costs, suggest that the exploratory IND/CTA will be a valuable option with evolving and possibly more specific applications for the future.

Pharmacokinetic allometric scaling of antibodies: application to the first-in-human dose estimation

The objectives of this study are: (i) to evaluate the predictive performance of interspecies scaling for antibodies to predict clearance, volume of distribution at steady state, and half-life in humans from animal data and (ii) to estimate first-in-human dose based on the predicted human clearance. Four methods were used to predict clearance in humans: (1) clearance versus body weight (simple allometry), (2) the product of clearance and maximum life-span potential (MLP) versus body weight, (3) the product of clearance and brain weight versus body weight, and (4) the application of a fixed exponent of 0.75. Based on the predicted human clearance, six methods were used to estimate the first-in-human dose. The predicted pharmacokinetic parameters and the estimated first-in-human dose of antibodies were compared with the observed human values. The results of the study indicated that the clearance of antibodies can be predicted with reasonable accuracy in humans and a good estimate of first human dose can be obtained from the predicted human clearance.

Practical anticipation of human efficacious doses and pharmacokinetics using in vitro and preclinical in vivo data

Accurate predictions of human pharmacokinetic and pharmacodynamic (PK/PD) profiles are critical in early drug development, as safe, efficacious, and "developable" dosing regimens of promising compounds have to be identified. While advantages of successful integration of preclinical PK/PD data in the "anticipation" of human doses (AHD) have been recognized, pharmaceutical scientists have faced difficulties with practical implementation, especially for PK/PD profile projections of compounds with challenging absorption, distribution, metabolism, excretion and formulation properties. In this article, practical projection approaches for formulation-dependent human PK/PD parameters and profiles of Biopharmaceutics Classification System classes I-IV drugs based on preclinical data are described. Case examples for "AHD" demonstrate the utility of preclinical and clinical PK/PD modeling for formulation risk identification, lead candidate differentiation, and prediction of clinical outcome. The application of allometric scaling methods and physiologically based pharmacokinetic approaches for clearance or volume of distribution projections is described using GastroPlus. Methods to enhance prediction confidence such as in vitro-in vivo extrapolations in clearance predictions using in vitro microsomal data are discussed. Examples for integration of clinical PK/PD and formulation data from frontrunner compounds via "reverse pharmacology strategies" that minimize uncertainty with PK/PD predictions are included. The use of integrated softwares such as GastroPlus in combination with established PK projection methods allow the projection of formulation-dependent preclinical and human PK/PD profiles required for compound differentiation and development risk assessments.

To scale or not to scale: the principles of dose extrapolation

The principles of inter-species dose extrapolation are poorly understood and applied. We provide an overview of the principles underlying dose scaling for size and dose adjustment for size-independent differences. Scaling of a dose is required in three main situations: the anticipation of first-in-human doses for clinical trials, dose extrapolation in veterinary practice and dose extrapolation for experimental purposes. Each of these situations is discussed. Allometric scaling of drug doses is commonly used for practical reasons, but can be more accurate when one takes into account species differences in pharmacokinetic parameters (clearance, volume of distribution). Simple scaling of drug doses can be misleading for some drugs; correction for protein binding, physicochemical properties of the drug or species differences in physiological time can improve scaling. However, differences in drug transport and metabolism, and in the dose-response relationship, can override the effect of size alone. For this reason, a range of modelling approaches have been developed, which combine in silico simulations with data obtained in vitro and/or in vivo. Drugs that are unlikely to be amenable to simple allometric scaling of their clearance or dose include drugs that are highly protein-bound, drugs that undergo extensive metabolism and active transport, drugs that undergo significant biliary excretion (MW > 500, ampiphilic, conjugated), drugs whose targets are subject to inter-species differences in expression, affinity and distribution and drugs that undergo extensive renal secretion. In addition to inter-species dose extrapolation, we provide an overview of dose extrapolation within species, discussing drug dosing in paediatrics and in the elderly.

Modelling and PBPK simulation in drug discovery

Physiologically based pharmacokinetic (PBPK) models are composed of a series of differential equations and have been implemented in a number of commercial software packages. These models require species-specific and compound-specific input parameters and allow for the prediction of plasma and tissue concentration time profiles after intravenous and oral administration of compounds to animals and humans. PBPK models allow the early integration of a wide variety of preclinical data into a mechanistic quantitative framework. Use of PBPK models allows the experimenter to gain insights into the properties of a compound, helps to guide experimental efforts at the early stages of drug discovery, and enables the prediction of human plasma concentration time profiles with minimal (and in some cases no) animal data. In this review, the application and limitations of PBPK techniques in drug discovery are discussed. Specific reference is made to its utility (1) at the lead development stage for the prioritization of compounds for animal PK studies and (2) at the clinical candidate selection and "first in human" stages for the prediction of human PK.

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.