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ABSTRACT:Current assessment of drug-drug interaction (DDI) prediction success is based on whether predictions fall within a two-fold range of the observed data. This strategy results in a potential bias toward successful prediction at lower interaction levels [ratio of the area under the concentration-time profile (AUC) in the presence of inhibitor/inducer compared with control is <2]. This scenario can bias any assessment of different DDI prediction algorithms if databases contain large proportion of interactions in this lower range. Therefore, the current study proposes an alternative method to assess prediction success with a variable prediction margin dependent on the particular AUC ratio. The method is applicable for assessment of both induction and inhibition-related algorithms. The inclusion of variability into this predictive measure is also considered using midazolam as a case study. Comparison of the traditional two-fold and the new predictive method was performed on a subset of midazolam DDIs collated from previous databases; in each case, DDIs were predicted using the dynamic model in Simcyp simulator. A 21% reduction in prediction accuracy was evident using the new predictive measure, in particular at the level of no/weak interaction (AUC ratio <2). However, inclusion of variability increased the prediction success at these levels by twofold. The trend of lower prediction accuracy at higher potency of DDIs reported in previous studies is no longer apparent when predictions are assessed via the new predictive measure. Thus, the study proposes a more logical method for the assessment of prediction success and its application for induction and inhibition DDIs. IntroductionThe current consensus for the in vitro-in vivo extrapolation of either clearance or drug-drug interactions (DDI) accepts prediction within a two-fold (or occasionally three-fold) range from the observed data as successful (Galetin et al., 2005Brown et al., 2006;Einolf, 2007;Teitelbaum et al., 2010;Wang, 2010). The commonly used metric to assess DDI is the ratio of the area under the plasma concentration-time curve (AUC) after multiple dosing of inhibitor or inducer in comparison to the control state (Rostami-Hodjegan and Tucker, 2004;Obach et al., 2006;Houston and Galetin, 2008;Fahmi et al., 2009). The assessment of different DDI algorithms involves retrospective prediction of in vivo studies, and conclusions are often made after the separation of the predictions according to the in vivo DDI potency, analogous to the approach proposed by the United States Food and Drug Administration guidelines for the classification of inhibitor potency (Bjornsson et al., 2003;Huang et al., 2007).This study considers the importance of the two-fold criterion in the assessment of DDI prediction success. Although a two-fold range may be appropriate for absolute values, the application of this method to the prediction of a "ratio" has not been fully considered. Implications and importance of these considerations for DDI prediction success are discussed. ...

Patient groups prone to polypharmacy and special subpopulations are susceptible to suboptimal treatment. Refined dosing in special populations is imperative to improve therapeutic response and/or lowering the risk of toxicity. Model-informed precision dosing (MIPD) may improve treatment outcomes by achieving the optimal dose for an individual patient. There is however relatively little published evidence of large-scale utility and impact of MIPD, where it is often implemented as local collaborative efforts between academia and healthcare.This manuscript highlights some successful applications of bringing MIPD to clinical care and proposes strategies for wider integration of MIPD in healthcare.Considerations are brought up herein that will need addressing to see MIPD become 'widespread clinical practice': amongst those, wider interdisciplinary collaborations and the necessity for further evidence-based efficacy and cost-benefit analysis of MIPD in healthcare. The implications of MIPD on regulatory policies and pharmaceutical development are also discussed as part of the roadmap.This article is protected by copyright. All rights reserved. 4 PRELUDEThis article appears in the so called 'State of the Art' section of the journal. 'State of the Art' is often considered to be cutting edge and the highest level of development in a given area. However, coining something as 'State of the Art' is a subliminal admission to the fact that the subject area has not yet become 'popular'. This article is a culmination of discussions and debates between many key opinion leaders, beyond the authorship, on the issue of model-informed precision dosing (MIPD), and why it has remained and is treated as 'State of the Art' rather than being used as 'widespread' clinical practice. It is hoped that the report provides a roadmap to advance the position of MIPD to a common clinical practice under the umbrella of precision medicine.

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.

A retrospective analysis was performed of parasite count data recorded from the first 7 days of blood or mosquito transmitted Plasmodium falciparum infections given for the treatment of neurosyphilis in the USA before 1963. The objective of this study was to characterize initial growth dynamics before host defences have significant effects on the infecting parasite population. Of the 328 patients' data available for analysis, 83 were excluded because they had received anti-malarial treatment during the first 7 days of the patent infection. Nonlinear mixed effects modelling was performed to estimate the parameters of interest; 'parasite multiplication rate per 48 h' (PMR), and length of the parasite life-cycle (periodicity). The parasitaemia versus time profiles showed great variability between patients. The mean population estimate of 'PMR' was approximately 8, and was highly dependent on the P. falciparum 'strain'. PMR also varied significantly between patients with a 90% prediction interval varying from 5.5 to 12.3-fold. Both intrinsic parasite multiplication rate (an intrinsic virulence determinant), and host susceptibility and defence contribute to expansion of the parasite biomass and thus disease severity in falciparum malaria.

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.

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 ShannonWeaver 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.Periodontitis is a generic term relating to inflammation of the tissues supporting the teeth but is widely attributed to succession by polymicrobial communities (36,58,74). The etiology of the condition is further complicated by the presence of a complex resident subgingival microbiota that underlies both periodontal health and disease (22,45). Periodontitis is often self-limiting; invasion of bacteria beyond the gingival tissue is rare (32). No single etiologic agent has been identified; rather, specific groups and combinations of bacteria including Porphyromonas gingivalis, Treponema denticola, and Tannerella forsythensis have been strongly associated with pathology (11, 32, 58). Emerging research now implicates both host genetic and immunological factors as being important in disease susceptibility (9,11,23,24), further demonstrating the complex nature of this condition.Plaque accumulates in the mouth at sites such as the gingival margin, where shear forces are low (36). Chronic bacterial colonization of this site, often in the absence of effective oral hygiene, leads to inflammation of the adjacent gingival tissue, termed gingivitis. Chronic gingivit...

1. Population pharmacokinetic parameters of tobramycin were determined in a heterogenous group of 97 patients using serum samples drawn for the routine monitoring of tobramycin concentrations, following multiple dosing regimens. 2. To describe the accumulation kinetics of tobramycin a two‐compartment pharmacokinetic model was required. The best fit to the data was obtained when drug clearance (1 h‐1) was related linearly to creatinine clearance (proportionality constant: 0.059 +/‐ 0.002 x CLcr (ml min‐1)) and initial volume of distribution (1) was related linearly to body weight (proportionality constant: 0.327 +/‐ 0.014 x body weight (kg)). The intersubject variability in these two parameters was 32% and 3%, respectively, whilst the residual or intrasubject variability amounted to 21% of the tobramycin concentration. The terminal half‐life of tobramycin, 26.6 +/‐ 9.4 h, was appreciably shorter than previously reported. 3. The population pharmacokinetic model was validated against data obtained from 34 independent patients and the predicted and observed concentrations were found to be in good agreement. The population pharmacokinetic model was used to design a priori dosing recommendations for tobramycin.