Search

Typical drug development includes few studies to find the right dose/dosing regimen and several other bridging studies evaluating various prognostic factors (e.g.: co-administration of other drugs, organ failure). The drug sponsors and the regulators use this information to formulate labeling instructions for safe and effective use of the drug. In the current article, modeling and simulation are proposed as tools to integrate the knowledge from the effectiveness/safety studies and the bridging studies. Simulations allow exploring the impact of various prognostic factors on the effectiveness and safety. The concept is exemplified using the new drug application of an anti-migraine drug. The exercise aids in integrating all the knowledge across the drug development to suggest rationale dosing strategies; effectively communicating the impact of the prognostic factors to the clinicians/regulators; and protect against any intellectual losses due to development team changes.

Purpose: One of the main objectives of the nonlinear mixed effects modeling is to provide rational individualized dosing strategies by explaining the interindividual variability using intrinsic and/or extrinsic factors (covariates). The aim of the current study was to evaluate, using computer simulations and real data, methods for estimating the exact significance level for including or excluding a covariate during model building., Methods: Original data were simulated using a simple one-compartment pharmacokinetic model with (full model) or without (null model) covariates (one or two). The covariate values in the original data were resampled (using either permutations or parametric bootstrap methods) to generate data under the null hypothesis that there is no covariate effect. The original and permuted data were fitted to null and full models, using first-order and first-order condition estimation (with or without interaction) methods in NONMEM, to compare the asymptotic and conditional p-value. Target log-likelihood ratio cutoffs for assessing covariate effects were derived., Results: The simulations showed that for sparse as well as dense data, the first-order condition estimation methods yielded the best results while the first-order method performs somewhat better for sparse data. Depending on the modeling objective, the appropriate asymptotic p-value can be substituted for the conditional significance level. Target log-likelihood ratio cutoffs should be determined separately for each covariate when exact p-values are important., Conclusions: Resampling methods can be employed to estimate the exact significance level for including a covariate during nonlinear mixed effects model building. Some reasonable inferences can be drawn for potential application to design future population analyses.

Rivastigmine is a cholinersterase inhibitor approved recently for the treatment of Alzheimer's disease (AD). The objective of this study is to characterize the pharmacokinetics-pharmacodynamics of rivastigmine in patients with AD. Eighteen AD patients received doses ranging from 1 to 6 mg bid for about 11 weeks. Rivastigmine and its active (major) metabolite (ZNS 114-666, also called NAP 226-90), plasma, and cerebrospinal fluid (CSF) concentrations were determined together with the AChE activity and computerized neuropsychological test battery (CNTB) scores. Nonlinear mixed-effects modeling of pharmacokinetic and pharmacodynamic data was conducted using NONMEM. Rivastigmine and its metabolite exhibited dose-disproportional pharmacokinetics. The apparent clearance and volume of distribution (plasma) of rivastigmine were estimated to be 120 L/h and 236 L, respectively. The relative bioavailability at the 6 mg dose was about 140%. The metabolite had a clearance of about 100 L/h and a volume of distribution of 256 L. The kinetics of the parent and metabolite in CSF showed an equilibration half-life of about 0.2 and 0.5 hours, respectively. The metabolite levels in CSF correlated very well with the acetylcholinesterase inhibition, with a ZNS 114-666 concentration of about 5.4 microg/L required for half-maximal inhibition of acetylcholinesterase activity. No statistically significant correlation of the CNTB scores with enzyme inhibition, parent or metabolite concentration (plasma/CSF), or rivastigmine dose could be established. The PK-PD model presented in this study can provide valuable information to optimize the drug development of rivastigmine and other related compounds and also in rationalizing dosing recommendations.

The role of drug delivery in controlling indirect pharmacodynamic responses was assessed via computer simulations and literature review. Simulations of responses related to basic indirect response mechanisms were performed for various drug input rates which allowed the importance of drug delivery rate on the overall pharmacodynamic response to be evaluated. Response versus time profiles of integrated or net responses and efficiency were examined. Rate of drug input has the greatest influence on the area under the effect curve when doses are larger and target drug concentrations are above the IC(50)/SC(50). The pharmacodynamics of drugs which elicit indirect pharmacologic responses such as corticosteroids, diuretics, growth hormone, erythropoietin and insulin indicate that sustained drug delivery enhances the therapeutic efficiency and pharmacodynamic availability.

Modelling and simulation (M&S) play an important role in regulatory decision-making that affects both the public and industry. Technological advances in various fields related to drug development call for more focus on ways to optimise current drug development practices. Recognition of the potential of M&S by regulatory agencies inevitably has a substantial impact on drug development. The objective of the current review is to present the various regulatory initiatives for application of M&S to clinical drug development. The relevant parts of the various recommendations issued by the US Food and Drug Administration (FDA), via guidance documents and advisory committee meeting proceedings, are highlighted. Application of M&S to a variety of activities, such as integrating pharmacokinetic-pharmacodynamic knowledge across a new drug application and designing efficient trials, is discussed. Some of the challenges that pharmaceutical institutions currently face when implementing M&S projects, such as team structure, communication with regulators, training and time constraints, are also presented, and solutions are proposed.

A method is presented for prediction of the systemic drug concentration profile from in vitro release/dissolution data for a drug formulation. The method is demonstrated using four different tablet formulations containing 200 mg carbamazepine (CZM), each administered in a four way cross-over manner to 20 human subjects, with 15 blood samples drawn to determine the resulting concentration profile. Amount versus time dissolution data were obtained by a 75 rpm paddle method for each formulation. Differentiation, with respect to time, of a monotonic quadratic spline fitted to the dissolution data provided the dissolution rate curve. The dissolution curve was through time and magnitude scaling mapped into a drug concentration curve via a convolution by a single exponential, and the estimated unit impulse response function. The method was tested by cross-validation, where the in vivo concentration profiles for each formulation were predicted based on correlation parameters determined from in vivo-in vitro data from the remaining three formulations. The mean prediction error (MPE), defined as the mean value of 100% x(observed-predicted)/observed was calculated for all 240 cross-validation predictions. The mean values of MPE were in the range of 10-36% (average 22%) with standard deviations (S.D.s) in the range of 9-33% (average 13%), indicating a good prediction performance of the proposed in vivo-in vitro correlation (IVIVC) method., (Copyright 2000 John Wiley & Sons, Ltd.)

Purpose: To examine the pharmacokinetics (PK) and pharmacodynamics (PD) of ipamorelin, a growth hormone (GH) releasing peptide, in healthy volunteers., Methods: A trial was conducted with a dose escalation design comprising 5 different infusion rates (4.21, 14.02, 42.13, 84.27 and 140.45 nmol/kg over 15 minutes) with eight healthy male subjects at each dose level. Concentrations of ipamorelin and growth hormone were measured., Results: The PK parameters showed dose-proportionality, with a short terminal half-life of 2 hours, a clearance of 0.078 L/h/kg and a volume of distribution at steady-state of 0.22 L/kg. The time course of GH stimulation by ipamorelin showed a single episode of GH release with a peak at 0.67 hours and an exponential decline to negligible GH concentration at all doses. The ipamorelin-GH concentration relationship was characterized using an indirect response model and population fitting. The model employed a zero-order GH release rate over a finite duration of time to describe the episodic release of GH. Ipamorelin induces the release of GH at all dose levels with the concentration (SC50) required for half-maximal GH stimulation of 214 nmol/L and a maximal GH production rate of 694 mIU/L/h. The inter-individual variability of the PD parameters was larger than that of the PK parameters., Conclusions: The proposed PK/PD model provides a useful characterization of ipamorelin disposition and GH responses across a range of doses.

Prednisolone (5 mg/kg intravenous) was administered to adrenalectomized male and female Sprague-Dawley rats (250-350 g) to assess the effects of gender on disposition and pharmacoimmunodynamics. Plasma concentrations of prednisolone were determined by high-performance liquid chromatography. Incorporation of [3H]thymidine (3H-TDR) was used to determine whole blood T-cell (WBTC) trafficking and deactivation following stimulation with Concanavalin-A. Whole blood T-cell trafficking was determined indirectly by using the glucocorticoid receptor antagonist RU-40555 (250 ng/mL) added to ex vivo cultures of whole blood from animals dosed with prednisolone. Mean (+/- SD) prednisolone clearance values were 3.22 +/- 0.88 and 3.46 +/- 0.96 L/h/kg in males and females, respectively. After administration of prednisolone, relative T-cell counts decreased slowly with time to reach a nadir at 3-5 h and returned to baseline levels by 8 h. Fitting data using an indirect response model yielded mean prednisolone 50% inhibitory concentration for inhibition of WBTC trafficking (IC50T) that was lower in males compared with females (0.14 +/- 0.16 versus 1.03 +/- 0.06 ng/mL; p < 0.05). In the absence of RU-40555, an immediate and complete inhibition of 3H-TDR incorporation into WBTC was observed (deactivation) and baseline levels were recovered slowly as prednisolone was cleared from blood. The mean 50% inhibitory concentration for inhibition of WBTC deactivation (IC50D) based on an inhibitory Imax model was similar in males and females (0.20 +/- 0.24 versus 0.18 +/- 0.12 ng/mL). Although male and female rats have similar exposure to prednisolone after 5-mg/kg doses, males are more sensitive to the inhibition of WBTC trafficking, whereas no gender effects on deactivation of WBTC exist.

The role of drug delivery in controlling indirect pharmacodynamic responses was assessed via computer simulations and literature review. Simulations of responses related to basic indirect response mechanisms were performed for various drug input rates which allowed the importance of drug delivery rate on the overall pharmacodynamic response to be evaluated. Response versus time profiles of integrated or net responses and efficiency were examined. Rate of drug input has the greatest influence on the area under the effect curve when doses are larger and target drug concentrations are above the IC(50)/SC(50). The pharmacodynamics of drugs which elicit indirect pharmacologic responses such as corticosteroids, diuretics, growth hormone, erythropoietin and insulin indicate that sustained drug delivery enhances the therapeutic efficiency and pharmacodynamic availability.

The pharmacokinetics and pharmacodynamics (PK/PD) of chimeric (Ch5c8) and humanized (Hu5c8) 5c8, a monoclonal antibody that binds CD154 (CD40 ligand), thus blocking the interaction between CD40 and CD154, were investigated in cynomolgus monkeys. Single-dose groups (n = 3 animals per dose) received saline, 0.2, 1, 5 or 20 mg/kg i.v. doses of Hu5c8. The repeat-dose groups (n = 4 animals) received 0 or 5 mg/kg i.v. doses of Ch5c8 or Hu5c8 on days 1, 2, 3, 5, 7 and 9. The single-dose PK parameters showed dose proportionality, with a terminal half-life of 300 h, a volume of distribution at steady state of 73 ml/kg and clearance of 0.2 ml.h-1.kg-1. The repeat-dose regimen produced a longer terminal half-life (500 h) and lower clearance (0.13 ml.h-1.kg-1) than in the single-dose groups. The antibody titer to tetanus toxoid (ATT) challenge served as the immunodynamic marker. The primary ATT response consisted of a latent phase of approximately 10 days, during which the immune system was processing antigen but not yet producing antibody, a rise to an antibody maximum titer at approximately 18 days and a decline toward baseline by approximately 40 days in controls. The 5c8 produced a log(dose)-proportional reduction in the area under the curve of ATT. An indirect PK/PD model based on the kinetics of tetanus toxoid exposure and inhibition of ATT production in relation to 5c8 concentrations was developed. A median inhibitory concentration of 0.84 microg/ml and a efficacy of 0.84 reflected marked inhibition of ATT response by 5c8. The model provides quantitation of reduced ATT responses after 5c8 and was applicable to primary and secondary immune responses and to both single-dose and multiple-dose treatments. The monoclonal antibody 5c8 blocks the CD40 and CD154 interaction, producing consistent and substantive reduction in antibody formation after administration of tetanus toxoid, which can be characterized with PK/PD modeling. It is anticipated that 5c8 may have utility in the treatment of antibody-mediated autoimmune disease.

A novel model-independent approach to analyze pharmacokinetic (PK)-pharmacodynamic (PD) data using artificial neural networks (ANNs) is presented. ANNs are versatile computational tools that possess the attributes of adaptive learning and self-organization. The emulative ability of neural networks is evaluated with simulated PK-PD data, and the power of ANNs to extrapolate the acquired knowledge is investigated. ANNs of one architecture are shown to be flexible enough to accurately predict PD profiles for a wide variety of PK-PD relationships (e.g., effect compartment linked to the central or peripheral compartment and indirect response models). Also, an example is given of the ability of ANNs to accurately predict PD profiles without requiring any information regarding the active metabolite. Because structural details are not required, ANNs exhibit a clear advantage over conventional model-dependent methods. ANNs are proved to be robust toward error in the data and perturbations in the initial estimates. Moreover, ANNs were shown to handle sparse data well. Neural networks are emerging as promising tools in the field of drug discovery and development.

This study demonstrates the application of neural networks to predict the pharmacokinetic properties of beta-adrenoreceptor antagonists in humans. A congeneric series of 10 beta-blockers, whose critical pharmacokinetic parameters are well established, was selected for the study. An appropriate neural network system was constructed and tested for its ability to predict the pharmacokinetic parameters from the octanol/water partition coefficient (shake flask method), the pKa, or the fraction bound to plasma proteins. Neural networks successfully trained and the predicted pharmacokinetic values agreed well with the experimental values (average difference = 8%). The neural network-predicted values showed better agreement with the experimental values than those predicted by multiple regression techniques (average difference = 47%). Because the neural networks had a large number of connections, two tests were conducted to determine if the networks were memorizing rather than generalizing. The "leave-one-out" method verified the generalization of the networks by demonstrating that any of the compounds could be deleted from the training set and its value correctly predicted by the new network (average error = 19%). The second test involved the prediction of pharmacokinetic properties of compounds never seen by the network, and reasonable results were obtained for three out of four compounds tested. The results indicate neural networks can be a powerful tool in exploration of quantitative structure-pharmacokinetic relationships.