1. Previous studies have shown that formation of 2,3-dihydroxybenzoate (2,3-DHB) from salicylate in vivo is a sensitive and specific marker of *OH radical generation, since 2,3-DHB is formed exclusively by *OH radicals, whereas both *OH radicals and cytochrome P450 (CYP) contribute to the production of 2,5-DHB. In the present study the salicylate-hydroxylation assay was used to examine whether CYP induction by the administration of dexamethasone, phenobarbital or beta-naphthoflavone to the male rat led to oxidative stress in vivo. 2. Dexamethasone was used under conditions that induced an approximately 50-fold induction of CYP P4503A expression in liver microsomal protein. Treatment with dexamethasone caused a 17.2-fold increase in 2,3-DHB plasma concentration compared with control animals. An increase in total hydroxylated salicylate (2,3-DHB plus 2,5-DHB) of 133.5 micromol/l plasma was produced, of which--assuming that the attack by *OH in position 3 or 5 of salicylate occurs at a similar rate--10.9 micromol/l were due to *OH radical attack and 122.6 micromol/l due to metabolism by CYP. 3. Phenobarbital led to a 4.7-fold increase in 2,3-DHB plasma concentration under conditions that induced CYP P4502B and 3A. An increase in total hydroxylated salicylate of 34.3 micromol/l plasma was observed, 2.0 micromol/l due to *OH radical attack and 32.3 micromol/l due to metabolism by cytochrome P450. 4. In contrast to dexamethasone and phenobarbital, beta-naphthoflavone did not cause a significant increase in 2,3-DHB plasma concentrations. 5. SKF 525A, a mixed-function oxidase inhibitor, caused a significant reduction of mean 2,5-DHB plasma concentration by 35% (p < 0.001), whereas 2,3-DHB was not significantly reduced, indicating that in contrast to the situation after induction by dexamethasone or phenobarbital, *OH radical generation by constitutive CYP contributes only to a minor degree to total in vivo *OH radical generation. 6. This study shows for the first time, to the authors' knowledge, that induction of some (but not all) P450s is associated with the production of hydroxyl radicals in vivo.

The PreciseInhale platform enabled the administration of CHF6001 powder with good accuracy and reproducibility, with low tracheal deposition. The new platform can be used at an early discovery stage to obtain inhalatory PK data for respirable aerosols of neat NCE powder without excipients and with minimal use of dry powder formulation work.

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In recent years, global sensitivity analysis (GSA) has gained interest in physiologically based pharmacokinetics (PBPK) modelling and simulation from pharmaceutical industry, regulatory authorities, and academia. With the case study of an in-house PBPK model for inhaled compounds in rats, the aim of this work is to show how GSA can contribute in PBPK model development and daily use. We identified two types of GSA that differ in the aims and, thus, in the parameter variability: inter-compound and intra-compound GSA. The inter-compound GSA aims to understand which are the parameters that mostly influence the variability of the metrics of interest in the whole space of the drugs’ properties, and thus, it is useful during the model development. On the other hand, the intra-compound GSA aims to highlight how much the uncertainty associated with the parameters of a given drug impacts the uncertainty in the model prediction and so, it is useful during routine PBPK use. In this work, inter-compound GSA highlighted that dissolution- and formulation-related parameters were mostly important for the prediction of the fraction absorbed, while the permeability is the most important parameter for lung AUC and MRT. Intra-compound GSA highlighted that, for all the considered compounds, the permeability was one of the most important parameters for lung AUC, MRT and plasma MRT, while the extraction ratio and the dose for the plasma AUC. GSA is a crucial instrument for the quality assessment of model-based inference; for this reason, we suggest its use during both PBPK model development and use.

<p class="ADMETabstracttext">The interest in using physiologically-based pharmacokinetic (PBPK) models as a support to the drug development decision making process has rapidly increased in the last years. These kind of models are examples of the “bottom up” modelling strategy, which progressively integrates into a mechanistic framework different information as soon as they become available along the drug development. For this reason PBPK models can be used with different aims, from the early stages of drug development up to the clinical phases. Different software tools are nowadays available. They can be categorized in “designed software” and “open software”. The first ones typically include commercial platforms expressly designed to implement PBPK models, in which the model structure is pre-defined, assumptions are generally not explicitly declared and equations are hidden to the user. Even if the software is validated and routinely used in the pharmaceutical industry, sometimes they do not allow working with the flexibility needed to cope with specific applications/tasks. For this reason, some scientists prefer to define and implement their own PBPK tool in “open” software. This paper shows how to build an in-house PBPK tool from species-related physiological information available in the literature and a limited number of drug specific parameters generally made available by the drug development process. It also reports the results of an evaluation exercise that compares simulated plasma concentration-time profiles and related pharmacokinetic (PK) parameters (i.e., AUC, C_max and T_max) with literature and in-house data. This evaluation involved 25 drugs with different physico-chemical properties, intravenously or orally administrated in three different species (i.e., rat, dog and man). The comparison shows that model predictions have a good degree of accuracy, since the average fold error for all the considered PK parameters is close to 1 and only in few cases the fold error is greater than 2. In summary, the paper demonstrates that addressing specific aims when needed is possible by creation of in-house PBPK tools with satisfactory performances and it provides some suggestions how to do that.</p><p class="ADMETabstracttext"><span lang="EN-GB">This paper shows how to build an in-house PBPK tool from species-related physiological information available in the literature and a limited number of drug specific parameters generally made available by the drug development process. It also reports the results of an evaluation exercise that compares simulated plasmatic concentration-time profiles and related PK parameters (i.e., AUC, Cmax and Tmax) with literature and in-house data. This evaluation involved 25 drugs with different physico-chemical properties, intravenously or orally administrated in three different species (i.e., rat, dog and man). The comparison shows that model predictions have a good degree of accuracy, since the average fold error for all the considered PK parameters is close to 1 and only in few cases the fold error is grater than 2.</span></p><p><span lang="EN-GB">In summary, the paper shows that to address specific aims, when needed, it is possible to create in-house PBPK tools with satisfactory performances and it provides some suggestions to do that.</span></p>