Should pharmacometrics be training the next R&D president?

We continue to train pharmacometric scientists primarily in methodology. The lack of training in business and drug development concepts, however, is preventing pharmacometricians from becoming high-level decision makers. The more recent growth of opportunities in pharmacometrics is propelled by applications within both companies and regulatory agencies. However, these applications themselves may not lead to sustained growth of opportunities. How can we prepare pharmacometricians to fundamentally re-engineer the drug development paradigm?

ASCPT Task Force for advancing pharmacometrics and integration into drug development.

Traditionally, medical and biostatistical experts have played a central role in ensuring validity of pharmaceutical testing. The science of pharmacometrics provides powerful approaches for supporting important drug development and regulatory decisions. Numerous case studies published by academic, industry, and US Food and Drug Administration scientists attest to the significant contribution of pharmacometrics to decision making. The economic and public health benefits of applying this discipline to clinical trials far outweigh the cost associated with its implementation. The purpose of the American Society for Clinical Pharmacology and Therapeutics (ASCPT) Task Force is to build on the momentum and accelerate dissemination of its impact and adoption into drug development. We describe briefly the contributions of pharmacometrics and the specific goals of the Task Force.

Exposure-response of posaconazole used for prophylaxis against invasive fungal infections: evaluating the need to adjust doses based on drug concentrations in plasma.

The purpose of this article is to report the exposure-response (E-R) relationship of posaconazole oral suspension (POS) for prophylaxis against invasive fungal infections (IFIs), on the basis of the US Food and Drug Administration (FDA) clinical pharmacology review of two randomized, active-controlled clinical studies. Posaconazole average steady state plasma concentrations (C(avg)) ranged from 22 to 3,650 ng/ml after administration of POS 200 mg three times daily (t.i.d.). In a double-blind, randomized clinical trial, the quartile ranges of C(avg) with midpoint values of 289, 736, 1,239, and 2,607 ng/ml had clinical failure rates of 44, 21, 18, and 18%, respectively, indicating an inverse association between C(avg) and clinical failure rate. There were no significant relationships between C(avg) and posaconazole-related major adverse events. Determining posaconazole concentrations in plasma will aid in assessing the need for either POS dose adjustment (e.g., increasing the POS dose) or switching to another systemic antifungal drug, thereby improving the effectiveness of prophylaxis against IFIs.

Biomarkers in clinical drug development.

The primary purpose of this Commentary is to complement the description of biomarkers given in this issue in the Pharmaceutical Research and Manufacturers of America report by Lathia et al. I offer several examples of the use of biomarkers that highlight their value in drug development and regulatory decision making.

Pharmacometrics at FDA: evolution and impact on decisions.

Drug development and regulatory decisions are driven by information that is compiled primarily from clinical trials and other supportive experiments, but also through clinical experience in the post-market period. The wisdom of these decisions determines the efficiency of drug development, the decision to approve the drug, and the resultant drug product quality including guidance on how to use the product known as the label. Although the decisions are usually simple in nature (e.g., trial design and project progression at the company, product and labeling approval at the Food and Drug Administration (FDA)), the information informing the decision is complex and diverse.

Impact of pharmacometric reviews on new drug approval and labeling decisions--a survey of 31 new drug applications submitted between 2005 and 2006.

Exploratory analyses of data pertaining to pharmacokinetic, pharmacodynamic, and disease progression are often referred to as the pharmacometrics (PM) analyses. The objective of the current report is to assess the role of PM, at the Food and Drug Administration (FDA), in drug approval and labeling decisions. We surveyed the impact of PM analyses on New Drug Applications (NDAs) reviewed over 15 months in 2005-2006. The survey focused on both the approval and labeling decisions through four perspectives: clinical pharmacology primary reviewer, their team leader, the clinical team member, and the PM reviewer. A total of 31 NDAs included a PM review component. Review of NDAs involved independent quantitative evaluation by FDA pharmacometricians. PM analyses were ranked as important in regulatory decision making in over 85% of the 31 NDAs. Case studies are presented to demonstrate the applications of PM analysis.

Application of modeling and simulation to integrate clinical pharmacology knowledge across a new drug application.

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.

Utilisation of pharmacokinetic-pharmacodynamic modelling and simulation in regulatory decision-making.

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.

Pharmacokinetic-pharmacodynamic modeling of rivastigmine, a cholinesterase inhibitor, in patients with Alzheimer's disease.

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.

Role of dosage regimen in controlling indirect pharmacodynamic responses.

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.

Carbamazepine level-A in vivo-in vitro correlation (IVIVC): a scaled convolution based predictive approach.

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.

Pharmacokinetic-pharmacodynamic modeling of ipamorelin, a growth hormone releasing peptide, in human volunteers.

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.

Influence of gender on prednisolone effects on whole blood T-cell deactivation and trafficking in rats.

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.

Pharmacokinetics/dynamics of 5c8, a monoclonal antibody to CD154 (CD40 ligand) suppression of an immune response in monkeys.

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.

Artificial neural networks as a novel approach to integrated pharmacokinetic-pharmacodynamic analysis.

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.

Quantitative structure-pharmacokinetic relationships (QSPR) of beta blockers derived using neural networks.

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.