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ABSTRACT: Dipeptidyl peptidase-4 (DPP-4) inhibition is a well- characterized treatment for type 2 diabetes mellitus (T2DM). The objective of this model-based meta-analysis was to describe the time course of HbA1c response after dosing with alogliptin (ALOG), saxagliptin (SAXA), sitagliptin (SITA), or vildagliptin (VILD). Publicly available data involving late-stage or marketed DPP-4 inhibitors were leveraged for the analysis. Nonlinear mixed-effects modeling was performed to describe the relationship between DPP-4 inhibition and mean response over time. Plots of the relationship between metrics of DPP-4 inhibition (ie, weighted average inhibition [WAI], time above 80% inhibition, and trough inhibition) and response after 12 weeks of daily dosing were evaluated. The WAI was most closely related to outcome, although other metrics performed well. A model was constructed that included fixed effects for placebo and drug and random effects for intertrial variability and residual error. The relationship between WAI and outcome was nonlinear, with an increasing response up to 98% WAI. Response to DPP-4 inhibitors could be described with a single drug effect. The WAI appears to be a useful index of DPP-4 inhibition related to HbA1c. Biomarker to response relationships informed by model-based meta-analysis can be leveraged to support study designs including optimization of dose, duration of therapy, and patient population.
The Journal of Clinical Pharmacology 12/2011; 52(10):1494-505. · 2.91 Impact Factor
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ABSTRACT: Pharmacokinetic and metabolism aspects of AMG 222 interaction with target enzyme, dipeptidylpeptidase IV (DPPIV) were investigated. Inhibition of recombinant human DPPIV by AMG 222 was measured. IC(50) decreased as preincubation time increased. k(off), k(on) and K(d) were measured. Dilution assay indicated a long dissociation half-life (730 min) relative to DPPIV inhibitor vildagliptin. AMG 222 is a slow-on, tight-binding, slowly reversible inhibitor of DPPIV. Amide and acid metabolites arising from hydrolysis of AMG 222's cyano group were formed slowly by rhDPPIV, but not by microsomes or S9. The amide metabolite was converted to the acid metabolite by rhDPPIV, but not by an active site mutant. These metabolites of AMG 222 are formed by target-mediated metabolism of the cyano group, similar to vildagliptin. Human plasma protein binding of [(14)C]AMG 222 was saturable and concentration-dependent. After 30 min, [(14)C]AMG 222 was 80.8% bound at 1 nM and binding decreased to 29.4% above 100 nM. The plasma DPPIV concentration (4.1 nM) and human plasma AMG 222 concentrations that inhibit DPPIV, occurred in the range of concentration-dependent binding. Target-mediated drug disposition influences AMG 222 pharmacokinetics, similar to DPPIV inhibitor, linagliptin.
Xenobiotica 08/2011; 41(11):945-57. · 1.79 Impact Factor
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John P Gibbs,
Maurice G Emery,
Ian McCaffery,
Brian Smith,
Megan A Gibbs,
Anna Akrami,
John Rossi,
Katherine Paweletz,
Marc R Gastonguay,
Edgar Bautista,
Minghan Wang,
Riccardo Perfetti,
Oranee Daniels
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ABSTRACT: Inhibition of 11β-HSD1 is hypothesized to improve measures of insulin sensitivity and hepatic glucose output in patients with type II diabetes. AMG 221 is a potent, small molecule inhibitor of 11β-HSD1. The objective of this analysis is to describe the pharmacokinetic/pharmacodynamic (PK/PD) relationship between AMG 221 and 11β-HSD1 inhibition in ex vivo adipose tissue samples. Healthy, obese subjects were administered a single dose of 3, 30, or 100 mg of oral AMG 221 (n = 44) or placebo (n = 11). Serial blood samples were collected over 24 hours. Subcutaneous adipose tissue samples were collected by open biopsy. Population PK/PD analysis was conducted using NONMEM. The inhibitory effects (mean ± standard error of the estimate) of AMG 221 on 11β-HSD1 activity were directly related to adipose concentrations with I(max) (the maximal inhibition of 11β-HSD1 activity) and IC₅₀ (the plasma AMG 221 concentration associated with 50% inhibition of enzyme activity) of 0.975 ± 0.003 and 1.19 ± 0.12 ng/mL, respectively. The estimated baseline 11β-HSD1 enzyme activity was 755 ± 61 pmol/mg. An equilibration rate constant (k(eo)) of 0.220 ± 0.021 h⁻¹ described the delay between plasma and adipose tissue AMG 221 concentrations. AMG 221 potently blocked 11β-HSD1 activity, producing sustained inhibition for the 24-hour study duration as measured in ex vivo adipose samples. Early characterization of concentration-response relationships can support rational selection of dose and regimen for future studies.
The Journal of Clinical Pharmacology 06/2011; 51(6):830-41. · 2.91 Impact Factor
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ABSTRACT: Prediction of human pharmacokinetics for monoclonal antibodies (mAbs) plays an important role for first-in-human (FIH) dose selection. This retrospective analysis compares observed FIH pharmacokinetic data for 16 mAbs to those predicted in humans based on allometric scaling of Cynomolgus monkey pharmacokinetic data.
Ten mAbs exhibited linear pharmacokinetics in monkeys based on non-compartmental analysis. For these, simple allometric scaling based on bodyweight was applied to predict human clearance (CL) and volume of distribution (V(d)) from those obtained in monkeys. Six mAbs exhibited nonlinear pharmacokinetics in monkeys based on population modelling. For these, a population modelling approach using nonlinear mixed-effects modelling software, NONMEM, was applied to describe monkey data by a two-compartment pharmacokinetic model with parallel linear and nonlinear elimination from the central compartment. The pharmacokinetic parameters in monkeys were then scaled to humans based on simple allometry. Human concentration-time profiles of these mAbs were then simulated and compared with those observed in the FIH studies.
Antibodies with linear elimination in monkeys also exhibited linear elimination in humans. For these, observed CL and V(d) were predicted within 2.3-fold by allometry. The predictability of human peak serum concentration (C(max)) and area under the serum concentration-time curve (AUC) for mAbs with nonlinear pharmacokinetics in monkeys was, however, concentration dependent. C(max) was consistently overestimated (up to 5.3-fold higher) when below the predicted Michaelis-Menten constant (Km; range 0.3-4 μg/mL). The prediction of human C(max) was within 2.3-fold when concentrations greatly exceeded Km. Similarly, differences between predicted human AUCs and those observed in the FIH studies were much greater at low doses/concentrations. Consequently, predicted drug exposure in humans at low starting doses (range 0.01-0.3 mg/kg) in FIH studies was poorly estimated for three of six mAbs with nonlinear pharmacokinetics.
Allometric prediction of human pharmacokinetics may be sufficient for mAbs that exhibit linear pharmacokinetics. For mAbs that exhibited nonlinear pharmacokinetics, the best predictive performance was obtained after doses that achieved target-saturating concentrations.
Clinical Pharmacokinetics 02/2011; 50(2):131-42. · 5.40 Impact Factor
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John P Gibbs
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ABSTRACT: In drug development, phase 1 first-in-human studies represent a major milestone as the drug moves from preclinical discovery to clinical development activities. The safety of human subjects is paramount to the conduct of these studies and regulatory considerations guide activities. Forces of evolution on the pharmaceutical industry are re-shaping the first-in-human dose selection strategy. Namely, high attrition rates in part due to lack of efficacy have led to the re-organization of research and development organizations around the umbrella of translational research. Translational research strives to bring basic research advances into the clinic and support the reverse transfer of information to enhance compound selection strategies. Pharmacokinetic/pharmacodynamic (PK/PD) modeling holds a unique position in translational research by attempting to integrate diverse sets of information. PK/PD modeling has demonstrated utility in dose selection and trial design for later stages of drug development and is now being employed with greater prevalence in the translational research setting to manage risk (i.e., oncology and inflammation/immunology). Moving from empirical E (max) models to more mechanistic representations of the biological system, a higher fidelity of human predictions is expected. Strategies that have proven useful for PK predictions are being applied to PK/PD predictions. This review article examines examples of the application of PK/PD modeling in establishing target concentrations for supporting first-in-human study design.
The AAPS Journal 10/2010; 12(4):750-8. · 5.09 Impact Factor
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ABSTRACT: A goal of preclinical discovery is the identification of drug candidates suitable for clinical testing. Successful integration of in vitro and in vivo experimental data sets can afford projections of human dose regimens anticipated to be safe and therapeutically beneficial. While in vitro experiments guide new chemical syntheses and are essential to understanding drug action and disposition, in vivo characterizations provide unique insight into complex biological systems that control concentrations at the site of action and pharmacologic response. Pharmacokinetic and pharmacodynamic (PK/PD) concepts underlying drug disposition and response provide a quantitative framework with which to identify potential clinical candidates. To improve throughput in earlier stages of drug discovery, in vivo pharmacokinetic study designs such as cassette dosing and sparse sampling schemes have been utilized. In later stages of discovery, pharmacokinetic studies using chemical inhibitors or surgical and genetic animal models are used to characterize the underlying determinants of drug disposition. In a complimentary fashion, modeling of in vivo pharmacodynamic effects may quantitatively link biomarkers to pharmacological response, validate in vitro to in vivo correlations and underwrite predictions of efficacious exposure targets. When applied to in vivo discovery data, PK/PD models have aided in understanding mechanisms of pharmacological response such as receptor theory in the central nervous system and cell turnover concepts in infectious disease and oncology. This review considers the role of in vivo testing toward understanding the pharmacokinetic and pharmacodynamic attributes of lead candidates in drug discovery.
Combinatorial chemistry & high throughput screening 02/2010; 13(2):207-18. · 2.46 Impact Factor
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ABSTRACT: Computer-aided pharmacokinetic, pharmacodynamic, and pharmacokinetic/pharmacodynamic methods are commonly applied to quantify the disposition and the pharmacological effects of the drug, to explore exposure-response relationships, and to predict safety and efficacy outcomes. Use of modeling and simulation throughout the drug development continuum can support more efficient preclinical and clinical study design and interpretation. Mechanism-based approaches where sound biological understanding exists provide meaningful quantitative comparisons between candidates and are sought to support science-based decisions. Simulations from these models allow for scientists to investigate a variety of trial designs where assumptions are clearly stated. The objectives of this review article are to describe commercially available PK/PD software packages and present examples of their application in drug discovery and development. With industry and regulatory support, use of exposure response information may optimize the path to delivery of new medicines to patients. This review is focused on the most common computer software applications in discovery through early development (i.e., GastroPlus, Simcyp Population-based ADME simulator, SAAM II, and WinNonlin), in development (i.e., NONMEM, ADAPT II, MATLAB, WinBUGS, Trial Simulator, and Drug Model Explorer), and across the continuum for data management (i.e., SAS, S-PLUS, and R).
Current Computer - Aided Drug Design 02/2008; 4(1):54-66. · 1.76 Impact Factor
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Xingrong Liu,
Bill J Smith,
Cuiping Chen,
Ernesto Callegari,
Stacey L Becker,
Xi Chen,
Julie Cianfrogna,
Angela C Doran,
Shawn D Doran, John P Gibbs,
Natilie Hosea,
Jianhua Liu,
Frederick R Nelson,
Mark A Szewc,
Jeffrey Van Deusen
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ABSTRACT: This study was designed to evaluate the use of cerebrospinal fluid (CSF) drug concentration and plasma unbound concentration (C(u,plasma)) to predict brain unbound concentration (C(u,brain)). The concentration-time profiles in CSF, plasma, and brain of seven model compounds were determined after subcutaneous administration in rats. The C(u,brain) was estimated from the product of total brain concentrations and unbound fractions, which were determined using brain tissue slice and brain homogenate methods. For theobromine, theophylline, caffeine, fluoxetine, and propranolol, which represent rapid brain penetration compounds with a simple diffusion mechanism, the ratios of the area under the curve of C(u,brain)/C(CSF) and C(u,brain)/C(u,plasma) were 0.27 to 1.5 and 0.29 to 2.1, respectively, using the brain slice method, and were 0.27 to 2.9 and 0.36 to 3.9, respectively, using the brain homogenate method. A P-glycoprotein substrate, CP-141938 (methoxy-3-[(2-phenyl-piperadinyl-3-amino)-methyl]-phenyl-N-methyl-methane-sulfonamide), had C(u,brain)/C(CSF) and C(u,brain)/C(u,plasma) ratios of 0.57 and 0.066, using the brain slice method, and 1.1 and 0.13, using the brain homogenate method, respectively. The slow brain-penetrating compound, N[3-(4'-fluorophenyl)-3-(4'-phenylphenoxy)propyl-]sarcosine, had C(u,brain)/C(CSF) and C(u,brain)/C(u,plasma) ratios of 0.94 and 0.12 using the brain slice method and 0.15 and 0.018 using the brain homogenate method, respectively. Therefore, for quick brain penetration with simple diffusion mechanism compounds, C(CSF) and C(u,plasma) represent C(u,brain) equally well; for efflux substrates or slow brain penetration compounds, C(CSF) appears to be equivalent to or more accurate than C(u,plasma) to represent C(u,brain). Thus, we hypothesize that C(CSF) is equivalent to or better than C(u,plasma) to predict C(u,brain). This hypothesis is supported by the literature data.
Drug Metabolism and Disposition 10/2006; 34(9):1443-7. · 3.73 Impact Factor
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ABSTRACT: Minimizing interindividual variability in drug exposure is an important goal for drug discovery. The reliability of the selective CYP2D6 inhibitor quinidine was evaluated in a retrospective analysis using a standardized approach that avoids laboratory-to-laboratory variation. The goal was to evaluate the reliability of in vitro metabolism studies for predicting extensive metabolizer (EM)/poor metabolizer (PM) exposure differences. Using available literature, 18 CYP2D6 substrates were selected for further analysis. In vitro microsomal studies were conducted at 1 microM substrate and 0.5 microM P450 to monitor substrate depletion. An estimate of the fraction metabolized by CYP2D6 in microsomes was derived from the rate constant determined with and without 1 microM quinidine for 11 substrates. Clearance in EM and PM subjects and fractional recovery of metabolites were taken from the literature. A nonlinear relationship between the contribution of CYP2D6 and decreased oral clearance for PMs relative to EMs was evident. For drugs having <60% CYP2D6 involvement in vivo, a modest difference between EM and PM exposure was observed (<2.5-fold). For major CYP2D6 substrates (>60%), more dramatic exposure differences were observed (3.5- to 53-fold). For compounds primarily eliminated by hepatic P450 and with sufficient turnover to be evaluated in vitro, the fraction metabolized by CYP2D6 in vitro compared favorably with the in vivo data. The in vitro estimation of fraction metabolized using quinidine as a specific inhibitor provided an excellent predictive tool. Results from microsomal substrate depletion experiments can be used with confidence to select compounds in drug discovery using a cutoff of >60% metabolism by CYP2D6.
Drug Metabolism and Disposition 10/2006; 34(9):1516-22. · 3.73 Impact Factor
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Xingrong Liu,
Bill J Smith,
Cuiping Chen,
Ernesto Callegari,
Stacey L Becker,
Xi Chen,
Julie Cianfrogna,
Angela C Doran,
Shawn D Doran, John P Gibbs,
Natilie Hosea,
Jianhua Liu,
Frederick R Nelson,
Mark A Szewc,
Jeffery Van Deusen
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ABSTRACT: This study was designed 1) to examine the effects of blood-brain barrier (BBB) permeability [quantified as permeability-surface area product (PS)], unbound fraction in plasma (f(u,plasma)), and brain tissue (f(u,brain)) on the time to reach equilibrium between brain and plasma and 2) to investigate the drug discovery strategies to design and select compounds that can rapidly penetrate the BBB and distribute to the site of action. The pharmacokinetics of seven model compounds: caffeine, CP-141938 [methoxy-3-[(2-phenyl-piperadinyl-3-amino)-methyl]-phenyl-N-methyl-methane-sulfonamide], fluoxetine, NFPS [N[3-(4'-fluorophenyl)-3-(4'-phenylphenoxy)propyl]sarcosine], propranolol, theobromine, and theophylline in rat brain and plasma after subcutaneous administration were studied. The in vivo log PS and log f(u,brain) calculated using a physiologically based pharmacokinetic model correlates with in situ log PS (R(2) = 0.83) and in vitro log f(u,brain) (R(2) = 0.69), where the in situ PS and in vitro f(u,brain) was determined using in situ brain perfusion and equilibrium dialysis using brain homogenate, respectively. The time to achieve brain equilibrium can be quantitated with a proposed parameter, intrinsic brain equilibrium half-life [t(1/2eq,in) = V(b)ln2/(PS . f(u,brain))], where V(b) is the physiological volume of brain. The in vivo log t(1/2eq,in) does not correlate with in situ log PS (R(2) < 0.01) but correlates inversely with log(PS . f(u,brain)) (R(2) = 0.85). The present study demonstrates that rapid brain equilibration requires a combination of high BBB permeability and low brain tissue binding. A high BBB permeability alone cannot guarantee a rapid equilibration. The strategy to select compounds with rapid brain equilibration in drug discovery should identify compounds with high BBB permeability and low nonspecific binding in brain tissue.
Journal of Pharmacology and Experimental Therapeutics 07/2005; 313(3):1254-62. · 3.83 Impact Factor
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Angela Doran,
R Scott Obach,
Bill J Smith,
Natilie A Hosea,
Stacey Becker,
Ernesto Callegari,
Cuiping Chen,
Xi Chen,
Edna Choo,
Julie Cianfrogna, [......],
Matthew Troutman,
Elaine Tseng,
Meihua Tu,
Jeffrey W Van Deusen,
Karthik Venkatakrishnan,
Gary Walens,
Ellen Q Wang,
Diane Wong,
Adam S Yasgar,
Chenghong Zhang
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ABSTRACT: Thirty-two structurally diverse drugs used for the treatment of various conditions of the central nervous system (CNS), along with two active metabolites, and eight non-CNS drugs were measured in brain, plasma, and cerebrospinal fluid in the P-glycoprotein (P-gp) knockout mouse model after subcutaneous administration, and the data were compared with corresponding data obtained in wild-type mice. Total brain-to-plasma (B/P) ratios for the CNS agents ranged from 0.060 to 24. Of the 34 CNS-active agents, only 7 demonstrated B/P area under the plasma concentration curve ratios between P-gp knockout and wild-type mice that did not differ significantly from unity. Most of the remaining drugs demonstrated 1.1- to 2.6-fold greater B/P ratios in P-gp knockout mice versus wild-type mice. Three, risperidone, its active metabolite 9-hydroxyrisperidone, and metoclopramide, showed marked differences in B/P ratios between knockout and wild-type mice (6.6- to 17-fold). Differences in B/P ratios and cerebrospinal fluid/plasma ratios between wild-type and knockout animals were correlated. Through the use of this model, it appears that most CNS-active agents demonstrate at least some P-gp-mediated transport that can affect brain concentrations. However, the impact for the majority of agents is probably minor. The example of risperidone illustrates that even good P-gp substrates can still be clinically useful CNS-active agents. However, for such agents, unbound plasma concentrations may need to be greater than values projected using receptor affinity data to achieve adequate receptor occupancy for effect.
Drug Metabolism and Disposition 02/2005; 33(1):165-74. · 3.73 Impact Factor
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ABSTRACT: The basis for low brain permeability of valproic acid (VPA) appears to be the result of efflux transport at the blood-brain barrier (BBB); however, the identity of the putative efflux transporter has not been investigated. The objective of our studies was to determine whether the multidrug resistance-associated protein (MRP) might be involved in efflux transport of VPA. Brain microvessel endothelial cells (BMEC) were isolated from cow brains and grown to confluence. MRP messenger RNA (mRNA) in BMEC were verified by reverse transcriptase-polymerase chain reaction (RT-PCR). Functional activity was demonstrated using the steady-state retention of calcein and MRP inhibitors, indomethacin (IND) and probenecid (PRB). Probenecid (0.50 mM) and indomethacin (10 microM) produced a 26 and 13% ( P<0.05 ) elevation in steady-state cellular VPA uptake following a 30-min-incubation with tracer 3H-VPA and 30 microM cold VPA. In contrast, at higher concentrations of probenecid (2 mM) and indomethacin (500 microM), an 11 and 31% reduction in VPA uptake was observed. The biphasic pattern of VPA uptake suggested concurrent inhibition of uptake and efflux transporters by the inhibitor with differing sensitivities, i.e. the efflux transporter being more susceptible to inhibition than the influx transporter. Similar results were obtained in the MRP overexpressing cell line A549. Overall, the results suggest that MRP(s) is(are) involved in the efflux transport of VPA, but do not preclude the possible contribution(s) of other organic anion transporters. The findings also adds to the growing evidence that up-regulation of active drug efflux transporters at the BBB may contribute to the development of drug resistance to antiepileptic drug therapy.
Epilepsy Research 01/2004; 58(1):53-66. · 2.29 Impact Factor
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ABSTRACT: For the pharmaceutical industry, one of the challenges in evaluating the risk of future compound attrition at the discovery stage is the successful prediction of the major routes of clearance in humans. For compounds cleared by metabolism, such information will help to avoid the development of compounds that will exhibit large interpatient differences in pharmacokinetics via 1). routes of metabolism catalyzed by functionally polymorphic enzymes and/or 2). clinically significant metabolic drug-drug interactions, in the later stages of development. The degree of intersubject variability that is acceptable for a drug candidate is uncertain in the discovery stage where knowledge of other important factors is limited or unavailable (i.e. therapeutic index, pharmacodynamic variability, etc). Reaction phenotyping is the semi-quantitative in vitro estimation of the relative contributions of specific drug-metabolizing enzymes to the metabolism of a test compound. However, reaction phenotyping in the discovery stage of drug development is complicated by the absence of radiolabelled parent compound or metabolite bioanalytical standards relative to later stages of development. In this commentary, some of the approaches, based on published data, which can be taken to overcome these challenges are discussed. In addition, knowledge of the molecular structure (i.e. specific chemical substituents), physicochemical properties, and routes of clearance in animals can all help in making a successful prediction for the routes of clearance in humans. In combination, the objective of these studies should be to reduce to a minimum the risk of finding significant inter-patient differences in pharmacokinetics at a later stage in development due to significant metabolism by polymorphic enzymes or drug-drug interactions. Consequently, this data should be used to avoid costly late stage attrition.
Current Drug Metabolism 01/2004; 4(6):527-34. · 5.11 Impact Factor
