Leon Aarons

Molecular analysis of the subgingival microbiota in health and disease

ABSTRACT This investigation provides molecular analyses of the periodontal microbiota in health and disease. Subgingival samples from 47 volunteers with healthy gingivae or clinically diagnosed chronic periodontitis were characterized by PCR-denaturing gradient gel electrophoresis (DGGE) with primers specific for the V2-V3 region of the eubacterial 16S rRNA gene. A hierarchical dendrogram was constructed from band patterns. All unique PCR amplicons (DGGE bands) were sequenced for identity. Samples were also analyzed for the presence of Actinobacillus actinomycetemcomitans, Porphyromonas gingivalis, and Tannerella forsythensis by multiplex PCR. Associations of patient age, gender, and smoking status together with the presence of each unique band and putative periodontal pathogens with disease were assessed by logistic regression. Periodontal pockets were colonized by complex eubacterial communities (10 to 40 distinct DGGE bands) with substantial individual variation in the community profile. Species diversity in health and disease was determined by the Shannon-Weaver index of diversity and compared by the Mann-Whitney U test. Sequence analyses of DGGE amplicons indicated the occurrence of many nontypical oral species and eubacteria previously associated with this environment. With the exception of T. forsythensis, the putative pathogens were not detected by DGGE. Multiplex PCR, however, detected T. forsythensis, A. actinomycetemcomitans, and P. gingivalis in 9% 16%, and 29% of the patients with disease, respectively. The presence of A. actinomycetemcomitans was significantly associated with disease (P < 0.01). Statistical analyses indicated that the presence of Treponema socranskii and Pseudomonas sp. was a significant predictor of disease (P < 0.05) and that there was no significant difference (P > 0.05) in terms of eubacterial species diversity between health and disease.

The Use of Population Pharmacokinetics in Drug Development

SummaryCurrently, there is an increasing focus on the implementation of pharmacokinetic-pharmacodynamic (PK-PD) studies and modelling as essential tools for drug development. Strategies involving specifically the population approach, which are based on relatively recent statistical methodology (e.g. nonlinear mixed effects modelling, NONMEM) have been advocated for investigating pharmacokinetic and pharmacodynamic variability as well as dose-concentration-effect relationships. The present article outlines this approach, and discusses how it can be implemented within the framework of the studies currently performed as part of the clinical phases of new drug development. It also considers study design and performance, based on real-life experiences.Population approaches, if designed carefully and early, as part of the planning of the drug development programme, are expected to play a significant role at every phase of the programme and to contribute to providing information that is valuable for registration purposes. Statistical methodology and software are now widely available. However, practical issues such as integration of the population approach within existing protocols, quality control of the data, timing of laboratory and statistical analyses, as well as resource allocation, remain legitimate concerns to be considered in prospective studies.

Mefloquine Pharmacokinetic-Pharmacodynamic Models: Implications for Dosing and Resistance

ABSTRACT Antimalarial resistance develops and spreads when spontaneously occurring mutant malaria parasites are selected by concentrations of antimalarial drug which are sufficient to eradicate the more sensitive parasites but not those with the resistance mutation(s). Mefloquine, a slowly eliminated quinoline-methanol compound, is the most widely used drug for the treatment of multidrug-resistant falciparum malaria. It has been used at doses ranging between 15 and 25 mg of base/kg of body weight. Resistance to mefloquine has developed rapidly on the borders of Thailand, where the drug has been deployed since 1984. Mathematical modeling with population pharmacokinetic and in vivo and in vitro pharmacodynamic data from this region confirms that, early in the evolution of resistance, conventional assessments of the therapeutic response ≤28 days after treatment underestimate considerably the level of resistance. Longer follow-up is required. The model indicates that initial deployment of a lower (15-mg/kg) dose of mefloquine provides a greater opportunity for the selection of resistant mutants and would be expected to lead more rapidly to resistance than de novo use of the higher (25-mg/kg) dose.

Enoxacin‐warfarin interaction: Pharmacokinetic and stereochemical aspects

The interaction between the new quinoline‐azaquinoline antibiotic enoxacin and the oral anticoagulant warfarin was investigated in six healthy male volunteers. Enoxacin was found not to affect the hypoprothrombinemic response produced by warfarin but did produce a decrease in the clearance of the less pharmacologically potent enantiomer of warfarin, (R)‐warfarin. The decreased clearance of (R)‐warfarin produced by concomitant enoxacin administration was found to be a consequence of inhibition by enoxacin of the (R)‐6‐hydroxywarfarin metabolic pathway.

Lumping of whole-body physiologically based pharmacokinetic models

Lumping is a common pragmatic approach aimed at the reduction of whole-body physiologically based pharmacokinetic (PBPK) model dimensionality and complexity. Incorrect lumping is equivalent to model misspecification with all the negative consequences to the subsequent model implementation. Proper lumping should guarantee that no useful information about the kinetics of the underlying processes is lost. To enforce this guarantee, formal standard lumping procedures and techniques need to be defined and implemented. This study examines the lumping process from a system theory point of view, which provides a formal basis for the derivation of principles and standard procedures of lumping. The lumping principle in PBPK modeling is defined as follows: Only tissues with identical model specification, and occupying identical positions in the system structure should be lumped together at each lumping iteration. In order to lump together parallel tissues, they should have similar or close time constants. In order to lump together serial tissues, they should equilibrate very rapidly with one another. The lumping procedure should include the following stages: (i) tissue specification conversion (when tissues with different model specifications are to be lumped together); (ii) classification of the tissues into classes with significantly different kinetics, according to the basic principle of lumping above; (iii) calculation of the parameters of the lumped compartments; (iv) simulation of the lumped system; (v) lumping of the experimental data; and (vi) verification of the lumped model. The use of the lumping principles and procedures to be adopted is illustrated with an example of a commonly implemented whole-body physiologically based pharmacokinetic model structure to characterize the pharmacokinetics of a homologous series of barbiturates in the rat.

Role of modelling and simulation in Phase I drug development

Although the use of pharmacokinetic/pharmacodynamic modelling and simulation (M&S) in drug development has increased during the last decade, this has most notably occurred in patient studies using the population approach. The role of M&S in Phase I, although of longer history, does not presently have the same impact on drug development. However, trends such as the increased use of biomarkers and clinical trial simulation as well as adoption of the learn/confirm concept can be expected to increase the importance of modelling in Phase I. To help identify the role of M&S, its main advantages and the obstacles to its rational use, an expert meeting was organised by COST B15 in Brussels, January 10-11, 2000. This article presents the views expressed at that meeting. Although it is clear that M&S occurs in only a minority of Phase I clinical trials, it is used for a large number of different purposes. In particular, M&S is considered valuable in the following situations: censoring because of assay limitation, characterisation of non-linearity, estimating exposure-response relationship, combined analyses, sparse sampling studies, special population studies, integrating PK/PD knowledge for decision making, simulation of Phase II trials, predicting multiple dose profile from single dose, bridging studies and formulation development. One or more of the following characteristics of M&S activities are often present and severely impede its successful integration into clinical drug development: lack of trained personnel, lack of protocol and/or analysis plan, absence of pre-specified objectives, no timelines or budget, low priority, inadequate reporting, no quality assurance of the modelling process and no evaluation of cost-benefit. The early clinical drug development phase is changing and if these implementation aspects can be appropriately addressed, M&S can fulfill an important role in reshaping the early trials by more effective extraction of information from studies, better integration of knowledge across studies and more precise predictions of trial outcome, thereby allowing more informed decision making.

Combining the ‘bottom up’ and ‘top down’ approaches in pharmacokinetic modelling: fitting PBPK models to observed clinical data

Pharmacokinetic models range from being entirely exploratory and empirical, to semi‐mechanistic and ultimately complex physiologically based pharmacokinetic (PBPK) models. This choice is conditional on the modelling purpose as well as the amount and quality of the available data. The main advantage of PBPK models is that they can be used to extrapolate outside the studied population and experimental conditions. The trade‐off for this advantage is a complex system of differential equations with a considerable number of model parameters. When these parameters cannot be informed from in vitro or in silico experiments they are usually optimized with respect to observed clinical data. Parameter estimation in complex models is a challenging task associated with many methodological issues which are discussed here with specific recommendations. Concepts such as structural and practical identifiability are described with regards to PBPK modelling and the value of experimental design and sensitivity analyses is sketched out. Parameter estimation approaches are discussed, while we also highlight the importance of not neglecting the covariance structure between model parameters and the uncertainty and population variability that is associated with them. Finally the possibility of using model order reduction techniques and minimal semi‐mechanistic models that retain the physiological‐mechanistic nature only in the parts of the model which are relevant to the desired modelling purpose is emphasized. Careful attention to all the above issues allows us to integrate successfully information from in vitro or in silico experiments together with information deriving from observed clinical data and develop mechanistically sound models with clinical relevance.

Population pharmacokinetics of mefloquine in patients with acute falciparum malaria.

To construct a population pharmacokinetic model for mefloquine in the treatment of falciparum malaria.

Quantitative Structure-Pharmacokinetics Relationships: I. Development of a Whole-Body Physiologically Based Model to Characterize Changes in Pharmacokinetics Across a Homologous Series of Barbiturates in the Rat

As pan of an overall program to develop a framework for evaluating the contribution of structural and physicochemical properties to pharmacokinetics, the distribution kinetics of nine 5-n-alkyl-5-ethyl barbituric acids in arterial blood and 14 tissues (lung, liver, kidney, stomach, pancreas, spleen, gut, muscle, adipose, skin, bone, heart, brain, testes) was examined after iv bolus administration in rats. The barbituric acids studied form a true homologous series; therefore any differences in pharmacokinetics, noted between congeners, can be directly linked to the increase in lipophilicity, resulting from the addition of a methylene group. A whole-body physiologically based pharmacokinetic model has been developed, assuming most of the tissues to be well-stirred compartments. Brain and testes, in which distribution for the lower homologues was permeability rate-limited, were represented by two compartments. For each homologue, the model parameters have been optimized, using the tissue concentration–time data. The initial distribution processes in the system were very rapid, making it quite stiff, and essentially over before the first samples were taken. A progressively increasing redistribution from lean tissues into adipose on ascending the homologous series was observed, characterized by a tendency for a progressive decrease in the magnitude of the concentration–time profiles for some of the lean and well-perfused tissues, an increase in the adipose concentration–time profile, and an increase in the time to reach the maximum adipose concentration. A shift from permeability rate limitation to perfusion rate limitation of the distribution processes for brain and testes, as well as an increase in the intrinsic hepatic clearance and decrease in the renal clearance with the increase of lipophilicity of the homologues, were quantified. An increase in the total unbound volume of distribution on ascending the homologous series was also observed. Muscle was found to be the major drug depot at steady state, accounting for approximately 50% of the total unbound volume of distribution, regardless of the lipophilicity of the homologue; the unbound volume of distribution of adipose increases more than 10-fold with the increase of lipophilicity.

Software for population pharmacokinetics and pharmacodynamics

Pharmacokinetic-pharmacodynamic modelling is being used increasingly as a tool in drug development because often in phase III clinical trials only sparse data are available for analysis and so a nonlinear mixed effects modelling approach has to be adopted. Specialist data analytical techniques and software are required to analyse such data. This article reviews some of the software currently available for performing nonlinear mixed effects modelling.A questionnaire was devised and sent to a number of software producers and the findings are presented and discussed in this paper. The programs could be grouped into 3 main categories: parametric and nonparametric maximum likelihood and Bayesian.It was apparent from the questionnaire that software development for population data analysis is a very active area of investigation. The implementation of methodologies varied widely between the packages: some were self-contained programs, whereas others were written within another application, usually a statistical package. They also varied with respect to their ease of use and level of support offered by the software producers. Although robustness and reliability are important concerns, they were not addressed in the present review. Most of the programs surveyed are in continual development.