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This study presents an extensive dose-response-time (DRT) meta-analysis of the nicotinic acid-induced inhibition of free fatty acids and insulin release. The purpose was to quantify the implications of lacking exposure data when analysing complex pharmacodynamic systems. The DRT model successfully characterised various response behaviours-including time-delays, rebound, feedback mechanisms, and adaptation-on both the individual and the population level. Comparing the fitted DRT model to an exposure-driven reference analysis showed that bias and uncertainty were introduced in the parameter estimates. However, most estimates were within one standard error from the reference. In both approaches, a few parameters suffered from practical identifiability issues, likely due to large differences in half-lives of the different rate processes. Moreover, the optimal dosing strategies predicted by the DRT model differed slightly from those of the exposure-driven analysis, having a lower optimal steady-state reduction of free fatty acids exposure.

Radiotherapy is one of the major therapy forms in oncology, and combination therapies involving radiation and chemical compounds can yield highly effective tumor eradication. In this paper, we develop a tumor growth inhibition model for combination therapy with radiation and radiosensitizing agents. Moreover, we extend previous analyses of drug combinations by introducing the tumor static exposure (TSE) curve. The TSE curve for radiation and radiosensitizer visualizes exposure combinations sufficient for tumor regression. The model and TSE analysis are then tested on xenograft data. The calibrated model indicates that the highest dose of combination therapy increases the time until tumor regrowth 10‐fold. The TSE curve shows that with an average radiosensitizer concentration of 1.0 ug/mL the radiation dose can be decreased from 2.2 Gy to 0.7 Gy . Finally, we successfully predict the effect of a clinically relevant treatment schedule, which contributes to validating both the model and the TSE concept.

Translation across species and from in vitro to in vivo is a central tenet in drug discovery pharmacology. Successful implementation requires proper assessment of both in vivo potency and efficacy. This notwith- standing, in vivo data is typically defined mostly in terms of ligand-to-target binding affinity, similar to in vitrostudies. As in vivo potency and efficacy involve a combination not only of drug, but also partitioning, target, and drug-target-complex events and processes, ignoring some of the central differences between in vivo and in vitromay result in serious miscalculations of in vivo efficacious exposure for translational predictions.We compare potency measures derived from two basic pharmacodynamic model situations: A ‘closed’ in vitrosystem defining target binding of a ligand when both concentrations remain essentially static, and an ‘open’ in vivo system where target turnover dynamics and elimination of the drug-target complex are also included. Corresponding equilibrium (steady-state) expressions in the central pharmacokinetic compartment are derived and presented. Three representative variants of ‘open’ in vivo systems are discussed, showing relationships for ligand-target complex and ligand for each of the systems and graphically illustrating corresponding shapes. The examples include i) two ligands competing for one target, ii) two targets competing for one ligand (/drug), andiii) target-ligand (/drug) interactions in a peripheral PK compartment. The expanded in vivo potency EC50 ex- pression emphasises the contribution from target-related biology parameters that need accounting for, and particularly that ‘closed’ system (in vitro) properties should not be first choice when ranking compounds in vivo(‘open’ system).

Quantitative techniques improve our understanding of tumor volume data for combination treatments and its translation across in vivo models and species. The focus of this paper is therefore on understanding in vivo data, highlighting key structural elements of pharmacodynamic tumor models, and challenging these methods from a translational point of view. We introduce the concept of Tumor Static Exposure (TSE) both for single and multiple combined anticancer agents. The TSE curve separates all possible exposure combinations into regions of tumor growth and tumor shrinkage. Moreover, the degree of curvature of the TSE curve indicates the degree of synergy or antagonism. We demonstrate the TSE approach by two case studies. The first examines a combination of the drugs cetuximab and cisplatin. The TSE curve associated with this combination reveals a weak synergistic effect, suggesting only modest gains from combination therapy. The second case study examines combinations of ionizing radiation and a radiosensitizing agent. In this case, the TSE curve exhibits a pronounced curvature, indicating a strong synergistic effect; tumor regression can be achieved at significantly lower exposure levels and/or radiation doses. Finally, an allometric approach to human dose prediction demonstrates the translational power of the model and the TSE concept. We conclude that the TSE approach, which embodies model-based measures of both drug (potency) and target properties (tumor growth rate), has a strong potential for ranking of compounds, supporting compound selection, and translating preclinical findings to humans.

Drug-discovery has become a complex disci- pline in which the amount of knowledge about human biology, physiology, and biochemistry have increased. In order to harness this complex body of knowledge mathe- matics can play a critical role, and has actually already been doing so. We demonstrate through four case studies, taken from previously published data and analyses, what we can gain from mathematical/analytical techniques when nonlinear concentration-time courses have to be trans- formed into their equilibrium concentration-response (tar- get or complex) relationships and new structures of drug potency have to be deciphered; when pattern recognition needs to be carried out for an unconventional response- time dataset; when what-if? predictions beyond the obser- vational concentration-time range need to be made; or when the behaviour of a semi-mechanistic model needs to be elucidated or challenged. These four examples are typical situations when standard approaches known to the general community of pharmacokineticists prove to be inadequate.

This study presents a dose-response-time (DRT) analysis based on a large preclinical biomarker dataset on the interaction between nicotinic acid (NiAc) and free fatty acids (FFA). Data were collected from studies that examined different rates, routes, and modes of NiAc provocations on the FFA time course. All information regarding the exposure to NiAc was excluded in order to demonstrate the utility of a DRT model. Special emphasis was placed on the selection process of the biophase model. An inhibitory I-max-model, driven by the biophase amount, acted on the turnover rate of FFA. A second generation NiAc/FFA model, which encompasses integral (slow buildup of tolerance - an extension of the previously used NiAc/FFA turnover models) and moderator (rapid and oscillatory) feedback control, was simultaneously fitted to all time courses in normal rats. The integral feedback control managed to capture an observed 90% adaptation (i.e., almost a full return to baseline) when 10 days constant-rate infusion protocols of NiAc were used. The half-life of the adaptation process had a 90% prediction interval between 3.5-12 in the present population. The pharmacodynamic parameter estimates were highly consistent when compared to an exposure-driven analysis, partly validating the DRT modelling approach and suggesting the potential of DRT analysis in areas where exposure data are not attainable. Finally, new numerical algorithms, which rely on sensitivity equations to robustly and efficiently compute the gradients in the parameter optimization, were successfully used for the mixed-effects approach in the parameter estimation.

Imprinting theory suggests that founding conditions are ‘stamped’ on organizations, and these imprinted routines often resist change. In contrast, strategic choice theory suggests that the firm can overcome organizational inertia and deliberately choose its future. Both theories offer dramatically different explanations behind an organization’s capacity for change. IPO firms provide a unique context for exploring how imprinting forces interact with strategic choice factors to address organizational capacity for change as a firm moves from private to public firm status. Juxtaposing imprinting and strategic choice perspectives, we employ fuzzy set analysis to examine the multi-level determinants of organizational capacity for change. Our cross-national data reveal three effective configurations of organizational capacity for change within IPOs, and two ineffective configurations. Our results suggest that the antecedents of organizational capacity for change in entrepreneurial threshold firms are nonlinear, interdependent, and equifinal.

To evaluate the prerequisites for using ethanol dilution to estimate total body water, we studied the within- and between-subject variation in the parameter estimates of a two-compartment model for ethanol pharmacokinetics with parallel Michaelis-Menten and first-order renal elimination. Because sampling of breath might be preferable in some clinical situations the parameter estimates derived from breath and venous blood were compared.On two occasions, ethanol 0.4 g kg-1 was given by intravenous infusion to 16 volunteers after they had fasted overnight. The proposed model was fitted by means of nonlinear regression to concentration-time data measured in the breath, venous blood and urine during 360 min. The model contained six parameters: Vmax and Km (Michaelis-Menten elimination constants), CLd (intercompartmental distribution parameter), VC and VT (volumes of the central and tissue compartment, respectively) and CLR (renal clearance). The volume of distribution, Vss, was calculated as the sum of VC and VT.The mean +/- total s.d. of the parameter estimates derived from blood data were Vmax 95 +/- 25 mg min-1, Km 27 +/- 19 mg l-1, CLd 809 +/- 232 ml min-1, VC 14.5 +/- 4.3 l, VT 21. 2 +/- 4.4 l, CLR 3.6 +/- 2.0 ml min-1 and Vss 35.8 +/- 4.3 l. The variation within subjects amounted to 3%, 9%, 21%, 21%, 17%, 26% and 2%, respectively, of the total variation. Breath samples were associated with a similar or lower variation than blood, both within and between subjects. About 1.5% of the infused ethanol was recovered in the urine.The low within-subject variation of the key parameter Vss (only 2%) suggests that ethanol dilution analysed by the pharmacokinetic model applied here may be used as an index of the total body water. Breath samples yielded at least as good reproducibility in the model parameters as venous blood.