First time in human for GV196771: interspecies scaling applied on dose selection

Designing and conducting proof-of-concept chronic pain analgesic clinical trials

Introduction:

The evolution of pain treatment is dependent on successful development and testing of interventions. Proof-of-concept (POC) studies bridge the gap between identification of a novel target and evaluation of the candidate intervention's efficacy within a pain model or the intended clinical pain population.

Methods:

This narrative review describes and evaluates clinical trial phases, specific POC pain trials, and approaches to patient profiling.

Results:

We describe common POC trial designs and their value and challenges, a mechanism-based approach, and statistical issues for consideration.

Conclusion:

Proof-of-concept trials provide initial evidence for target use in a specific population, the most appropriate dosing strategy, and duration of treatment. A significant goal in designing an informative and efficient POC study is to ensure that the study is safe and sufficiently sensitive to detect a preliminary efficacy signal (ie, a potentially valuable therapy). Proof-of-concept studies help avoid resources wasted on targets/molecules that are not likely to succeed. As such, the design of a successful POC trial requires careful consideration of the research objective, patient population, the particular intervention, and outcome(s) of interest. These trials provide the basis for future, larger-scale studies confirming efficacy, tolerability, side effects, and other associated risks.

Prediction of Deoxypodophyllotoxin Disposition in Mouse, Rat, Monkey, and Dog by Physiologically Based Pharmacokinetic Model and the Extrapolation to Human

Deoxypodophyllotoxin (DPT) is a potential anti-tumor candidate prior to its clinical phase. The aim of the study was to develop a physiologically based pharmacokinetic (PBPK) model consisting of 13 tissue compartments to predict DPT disposition in mouse, rat, monkey, and dog based on in vitro and in silico inputs. Since large interspecies difference was found in unbound fraction of DPT in plasma, we assumed that K_t:pl,u (unbound tissue-to-plasma concentration ratio) was identical across species. The predictions of our model were then validated by in vivo data of corresponding preclinical species, along with visual predictive checks. Reasonable matches were found between observed and predicted plasma concentrations and pharmacokinetic parameters in all four animal species. The prediction in the related seven tissues of mouse was also desirable. We also attempted to predict human pharmacokinetic profile by both the developed PBPK model and interspecies allometric scaling across mouse, rat and monkey, while dog was excluded from the scaling. The two approaches reached similar results. We hope the study will help in the efficacy and safety assessment of DPT in future clinical studies and provide a reference to the preclinical screening of similar compounds by PBPK model.

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.

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.

Interspecies scaling and prediction of human clearance: comparison of small- and macro-molecule drugs

Human clearance prediction for small- and macro-molecule drugs was evaluated and compared using various scaling methods and statistical analysis. Human clearance is generally well predicted using single or multiple species simple allometry for macro- and small-molecule drugs excreted renally. The prediction error is higher for hepatically eliminated small-molecules using single or multiple species simple allometry scaling, and it appears that the prediction error is mainly associated with drugs with low hepatic extraction ratio (Eh). The error in human clearance prediction for hepatically eliminated small-molecules was reduced using scaling methods with a correction of maximum life span (MLP) or brain weight (BRW). Human clearance of both small- and macro-molecule drugs is well predicted using the monkey liver blood flow method. Predictions using liver blood flow from other species did not work as well, especially for the small-molecule drugs.

A global examination of allometric scaling for predicting human drug clearance and the prediction of large vertical allometry**This work was presented at the American Association of Pharmaceutical Scientists Annual meeting, Salt Lake City, USA, Oct. 26, 2003

Allometrically scaled data sets (138 compounds) used for predicting human clearance were obtained from the literature. Our analyses of these data have led to four observations. (1) The current data do not provide strong evidence that systemic clearance (CL(s); n = 102) is more predictable than apparent oral clearance (CL(po); n = 24), but caution needs to be applied because of potential CL(po) prediction error caused by differences in bioavailability across species. (2) CL(s) of proteins (n = 10) can be more accurately predicted than that of non-protein chemicals (n = 102). (3) CL(s) is more predictable for compounds eliminated by renal or biliary excretion (n = 33) than by metabolism (n = 57). (4) CL(s) predictability for hepatically eliminated compounds followed the order: high CL (n = 11) > intermediate CL (n = 17) > low CL (n = 29). All examples of large vertical allometry (% error of prediction greater than 1000%) occurred only when predicting human CL(s) of drugs having very low CL(s). A qualitative analysis revealed the application of two potential rules for predicting the occurrence of large vertical allometry: (1) ratio of unbound fraction of drug in plasma (f(u)) between rats and humans greater than 5; (2) C logP greater than 2. Metabolic elimination could also serve as an additional indicator for expecting large vertical allometry.

A novel model for prediction of human drug clearance by allometric scaling

Sixty-one sets of clearance (CL) values in animal species were allometrically scaled for predicting human clearance. Unbound fractions (f(u)) of drug in plasma in rats and humans were obtained from the literature. A model was developed to predict human CL: CL=33.35 ml/min x (a/Rf(u))(0.770), where Rf(u) is the f(u) ratio between rats and humans and a is the coefficient obtained from allometric scaling. The new model was compared with simple allometric scaling and the "rule of exponents" (ROE). Results indicated that the new model provided better predictability for human values of CL than did ROE. It is especially significant that for the first time the proposed model improves the prediction of CL for drugs illustrating large vertical allometry.

Allometric scaling of pharmacokinetic parameters in drug discovery: can human CL, Vss and t1/2 be predicted from in-vivo rat data?

In a drug discovery environment, reasonable go/no-go human in-vivo pharmacokinetic (PK) decisions must be made in a timely manner with a minimum amount of animal in-vivo or in-vitro data. We have investigated the accuracy of the in-vivo correlation between rat and human for the prediction of the total systemic clearance (CL), the volume of distribution at steady state (Vss), and the half-life (t1/2) using simple allometric scaling techniques. We have shown, using a large diverse set of drugs, that a fixed exponent allometric scaling approach can be used to predict human in-vivo PK parameters CL, Vss and t(1/2) solely from rat in-vivo PK data with acceptable accuracy for making go/no-go decisions in drug discovery. Human in-vivo PK predictions can be obtained using the simple allometric scaling relationships CL(Human) approximately = 40 CL(Rat) (L/hr), Vss(Human) approximately = 200 Vss(Rat) (L), and t1/2(Human) approximately = 4 t1/2(Rat) (hr). The average fold error for human CL predictions for N = 176 drugs was 2.25 with 79% of the drugs having a fold error less than 3. The average fold error for human Vss predictions for N = 144 drugs was 1.85 with 84% of the drugs having a fold error less than 3. The average fold error for human t1/2 predictions for N = 145 drugs was 2.05 with 76% of the drugs having a fold error less than 3. Using these simple allometric relationships, the sorting of drug candidates into a low/medium/high/very high human classification scheme was also possible from rat data. Since these simple allometric relationships between rat and human CL, Vss, and t1/2 are reasonably accurate, easy to remember and simple to calculate, these equations should be useful for making early go/no-go in-vivo human PK decisions for drug discovery candidates.

The systemic exposure of an N-methyl-D-aspartate receptor antagonist is limited in mice by the P-glycoprotein and breast cancer resistance protein efflux transporters

GV196771 [E-4,6-dichloro-3-(2-oxo-1-phenyl-pyrrolidin-3-glydenemethyl)-1H-indole-2 carboxylic acid] is a potent antagonist of the modulatory glycine site of the N-methyl-d-aspartate receptor. GV196771 has low oral bioavailability (<10%) and plasma clearance ( approximately 2 ml/min/kg) in rats. P-Glycoprotein (Pgp) and breast cancer resistance protein (Bcrp) are ATP-binding cassette (ABC) transporters that limit the oral absorption of drugs and dietary constituents. The objective of this work was to assess the involvement of Pgp and/or Bcrp on the systemic exposure of GV196771 in mice. In vitro, GV196771 was a Bcrp substrate [basolateral-to-apical/apical-to-basolateral (B-->A/A-->B) ratio = 5.1] with high passive membrane permeability (P(app) = 64-170 nm/s); however, GV196771 was not an in vitro Mdr1a substrate (B-->A/A-->B ratio = 1.9; no effect of GF120918 on efflux ratio). The role of Pgp and Bcrp on the systemic exposure of GV196771 was assessed by pretreatment of wild-type and Pgp-deficient mdr1a/1b(-/-) mice with a single oral dose of GF120918 (50 mg/kg; a dual Pgp and Bcrp inhibitor) or vehicle (0.5% hydroxypropylmethylcellulose and 1% Tween 80) 2 h before administration of a single oral dose of GV196771 (2 mg/kg). Compared with wild-type animals, the GV196771 area under the plasma concentration-time curve [AUC((0-->6 h))] increased 6.2-fold in Pgp-deficient mice, 10.3-fold in GF120918-pretreated wild-type mice, and 16.4-fold in GF120918-pretreated Pgp-deficient mice. C(max) values changed in parallel with the AUC((0-->6 h)) values; however, t(max) remained relatively unchanged. This study supports a role for Pgp and Bcrp in attenuating the systemic exposure of GV196771 in mice and demonstrates that two ABC efflux transporters can have nonredundant roles in attenuating the disposition of a compound.

Agents in Development for the Management of Cocaine Abuse

Cocaine abuse is a serious health problem in many areas of the world, yet there are no proven effective medications for the treatment of cocaine dependence. Preclinical studies suggest that the reinforcing effect of cocaine that promotes its abuse is mediated by blockade of the presynaptic dopamine transporter. This results in increased dopamine activity in the mesolimbic or meso-accumbens dopamine reward system of brain. Development of new medications to treat cocaine dependence has focused on manipulation of this dopamine system, either by direct action on dopamine binding sites (transporter or receptors) or indirectly by affecting other neurotransmitter systems that modulate the dopamine system. In principle, a medication could act via one of three mechanisms: (i) as a substitute for cocaine by producing similar dopamine effects; (ii) as a cocaine antagonist by blocking the binding of cocaine to the dopamine transporter; or (iii) as a modulator of cocaine effects by acting at other than the cocaine binding site. The US National Institute on Drug Abuse has a Clinical Research Efficacy Screening Trial (CREST) programme to rapidly screen existing medications. CREST identified four medications warranting phase II controlled clinical trials: cabergoline, reserpine, sertraline and tiagabine. In addition, disulfiram and selegiline (deprenyl) have been effective and well tolerated in phase II trials. However, selegiline was found ineffective in a recent phase III trial. Promising existing medications probably act via the first or third aforementioned mechanisms. Sustained-release formulations of stimulants such as methylphenidate and amfetamine (amphetamine) have shown promise in a stimulant substitution approach. Disulfiram and selegiline increase brain dopamine concentrations by inhibition of dopamine-catabolising enzymes (dopamine-beta-hydroxylase and monoamine oxidase B, respectively). Cabergoline is a direct dopamine receptor agonist, while reserpine depletes presynaptic stores of dopamine (as well as norepinephrine and serotonin). Sertraline, baclofen and vigabatrin indirectly reduce dopamine activity by increasing activity of neurotransmitters (serotonin and GABA) that inhibit dopamine activity. Promising new medications act via the second or third aforementioned mechanisms. Vanoxerine is a long-acting inhibitor of the dopamine transporter which blocks cocaine binding and reduces cocaine self-administration in animals. Two dopamine receptor ligands that reduce cocaine self-administration in animals are also undergoing phase I human safety trials. Adrogolide is a selective dopamine D(1) receptor agonist; BP 897 is a D(3) receptor partial agonist.A pharmacokinetic approach to treatment would block the entry of cocaine into the brain or enhance its catabolism so that less cocaine reached its site of action. This is being explored in animals using the natural cocaine-metabolising enzyme butyrylcholinesterase (or recombinant versions with enhanced capabilities), catalytic antibodies, and passive or active immunisation to produce anti-cocaine binding antibodies. A recent phase I trial of a "cocaine vaccine" found it to be well tolerated and producing detectable levels of anti-cocaine antibodies for up to 9 months after immunisation.

Prediction of human pharmacokinetics from animal data and molecular structural parameters using multivariate regression analysis: volume of distribution at steady state

The aim of this study was to develop a regression equation for predicting volume of distribution at steady state (Vd(ss)) in humans to enable application to various types of drugs using animal experimental data for rats and dogs and some molecular structural parameters. The Vd(ss) data for rats, dogs and humans of 64 drugs were obtained from literature. The compounds have various structures, pharmacological activities and pharmacokinetic characteristics. In addition, the molecular weight, calculated partition coefficient (clogP), and the number of hydrogen bond acceptors were used as possible descriptors related to the Vd(ss) in humans. Multivariate regression analyses, multiple linear regression analysis and the partial least squares (PLS) method were used to predict Vd(ss) in humans. Interaction terms were also introduced into the regression analysis to evaluate the non-linear relationship. For the data set used in the present study, PLS with quadratic term descriptors gave the best predictive performance. The PLS model using Vd(ss) data for only two animal species and using easily calculated structural parameters could generally predict Vd(ss) in humans better than an allometric method. In addition, the PLS model with only animal data gave almost the same predictive performance as the PLS model with quadratic term descriptors. This model may be easier to use and be practical in a realistic situation, and could predict Vd(ss) in humans better than the allometric method.

Pharmacokinetics, metabolism and excretion of the glycine antagonist GV150526A in rat and dog

1. The pharmacokinetics, metabolic fate and excretion of 3-[-2(phenylcarbamoyl) ethenyl-4,6-dichloroindole-2-carboxylic acid (GV150526), a novel glycine antagonist for stroke, in rat and dog following intravenous administration of [C14]-GV150526A were investigated. 2. Studies were also performed in bile duct-cannulated animals to confirm the route of elimination and to obtain more information on metabolite identity. 3. Metabolites in plasma, urine and bile were identified by HPLC-MS/MS and NMR spectroscopy. 4. GV150526A was predominantly excreted in the faeces via the bile, with only trace metabolites of radioactivity in urine (< 5%). Radioactivity in rat bile was predominantly due to metabolites, whereas approximately 50% of the radioactivity in dog bile was due to parent GV150526. 5. The principal metabolites in bile were identified as glucuronide conjugates of the carboxylic acid, whereas in rat urine the main metabolite was a sulphate conjugate of an aromatic oxidation metabolite. Multiple glucuronide peaks were observed and identified as isomeric glucuronides and their anomers arising from acyl migration and muta-rotation.

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

The aim of the study reported here was to develop a method for predicting human clearance that can be applied to various kinds of drugs using clearance values for rats and dogs and some molecular structural parameters. The clearance data for rats, dogs, and humans of 68 drugs were obtained from literature. The compounds have various structures, pharmacological activities, and pharmacokinetic characteristics. In addition, molecular weight, c log P, and the number of hydrogen bond acceptors were used as possible descriptors related to the human clearance value for each drug. Three types of regression methods, multiple linear regression (MLR) analysis, partial least squares (PLS) method, and artificial neural network (ANN), were used to predict human clearance, and their predictive performances were compared with allometric approaches, which have been widely used in interspecies scaling. In MLR and PLS analyses, interaction terms were introduced to evaluate the nonlinear relationships. For the data sets used in the present study, MLR and PLS with quadratic terms gave the same equation and the best predictive performance. The value of the squared cross-validated correlation coefficient (q(2)) was 0.682. In conclusion, the MLR method using animal clearance data from only two species and using easily calculated structural parameters can generally predict human clearance better than allometric methods. This approach can be applied to drugs with various characteristics.

Allometric issues in drug development

The concept of correlating pharmacokinetic parameters with body weight from different animal species has become a useful tool in drug development. The allometric approach is based on the power function, where the body weight of the species is plotted against the pharmacokinetic parameter(s) of interest. Clearance, volume of distribution, and elimination half-life are the three most frequently extrapolated pharmacokinetic parameters. Over the years, many approaches have been suggested to improve the prediction of these pharmacokinetic parameters in humans from animal data. A literature review indicates that there are different degrees of success with different methods for different drugs. Overall, though interspecies scaling requires refinement and better understanding, the approach has lot of potential during the drug development process.