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There are several antibody therapeutics in preclinical and clinical development, industry-wide, for the treatment of central nervous system (CNS) disorders. Due to the limited permeability of antibodies across brain barriers, the quantitative understanding of antibody exposure in the CNS is important for the design of antibody drug characteristics and determining appropriate dosing regimens. We have developed a minimal physiologically-based pharmacokinetic (mPBPK) model of the brain for antibody therapeutics, which was reduced from an existing multi-species platform brain PBPK model. All non-brain compartments were combined into a single tissue compartment and cerebral spinal fluid (CSF) compartments were combined into a single CSF compartment. The mPBPK model contains 16 differential equations, compared to 100 in the original PBPK model, and improved simulation speed approximately 11-fold. Area under the curve ratios for minimal versus full PBPK models were close to 1 across species for both brain and plasma compartments, which indicates the reduced model simulations are similar to those of the original model. The minimal model retained detailed physiological processes of the brain while not significantly affecting model predictability, which supports the law of parsimony in the context of balancing model complexity with added predictive power. The minimal model has a variety of applications for supporting the preclinical development of antibody therapeutics and can be expanded to include target information for evaluating target engagement to inform clinical dose selection. Supplementary Information The online version contains supplementary material available at 10.1007/s10928-021-09776-7.

BackgroundQuantitative systems pharmacology (QSP) modeling of the host-immune response against Mtb can inform rational design of host-directed therapies (HDTs). We aimed to develop a QSP framework to evaluate the effects of metformin-associated autophagy-induction in combination with antibiotics.MethodsA QSP framework for autophagy was developed by extending a model for host-immune response to include AMPK-mTOR-autophagy signalling. This model was combined with pharmacokinetic-pharmacodynamic models for metformin and antibiotics against Mtb. We compared the model predictions to mice infection experiments, and derived predictions for pathogen and host-associated dynamics in humans treated with metformin in combination with antibiotics.ResultsThe model adequately captured the observed bacterial load dynamics in mice Mtb infection models treated with metformin. Simulations for adjunctive metformin therapy in newly diagnosed patients suggested a limited yet dose-dependent effect of metformin on reducing the intracellular bacterial load and selected pro-inflammatory cytokines. Our predictions suggest that metformin may provide beneficiary effects when overall bacterial load, or extracellular-to-intracellular bacterial ratio is low, either early after infection or late during antibiotic treatment.ConclusionsWe present the first QSP framework for HDTs against Mtb, linking cellular-level autophagy effects to disease progression. This framework may be extended to guide design of HDTs against Mtb.

A phase 1 clinical trial in healthy male volunteers was conducted with a somatostatin-dopamine chimera (BIM23B065), from which information could be obtained on the concentration-effect relationship of the inhibition of pulsatile endogenous growth hormone and prolactin secretion. Endogenous growth hormone profiles were analyzed using a two-step deconvolution-analysis-informed population pharmacodynamic modeling approach, which was developed for the analyses of pulsatile profiles. Prolactin concentrations were modelled using a population pool model with a circadian component on the prolactin release. During treatment with BIM23B065, growth hormone secretion was significantly reduced (maximal effect [EMAX] = − 64.8%) with significant reductions in the pulse frequency in two out of three multiple ascending dose cohorts. A circadian component in prolactin secretion was identified, modelled using a combination of two cosine functions with 24 h and 12 h periods. Dosing of BIM23B065 strongly inhibited (EMAX = − 91%) the prolactin release and demonstrated further reduction of prolactin secretion after multiple days of dosing. This study quantified the concentration-effect relationship of BIM23B065 on the release of two pituitary hormones, providing proof of pharmacology of the chimeric actions of BIM23B065. Electronic supplementary material The online version of this article (10.1007/s10928-020-09683-3) contains supplementary material, which is available to authorized users.

Treatment failure of antibiotic therapy due to insufficient efficacy or occurrence of toxicity is a major clinical challenge, and is expected to become even more urgent with the global rise of antibiotic resistance. Strategies to optimize treatment in individual patients are therefore of crucial importance. Currently, therapeutic drug monitoring plays an important role in optimizing antibiotic exposure to reduce treatment failure and toxicity. Biomarker‐based strategies may be a powerful tool to further quantify and monitor antibiotic treatment response, and reduce variation in treatment response between patients. Host response biomarkers, such as CRP, procalcitonin, IL‐6, and presepsin, could potentially carry significant information to be utilized for treatment individualization. To achieve this, the complex interactions among immune system, pathogen, drug, and biomarker need to be better understood and characterized. The purpose of this tutorial is to discuss the use and evidence of currently available biomarker‐based approaches to inform antibiotic treatment. To this end, we also included a discussion on how treatment response biomarker data from preclinical, healthy volunteer, and patient‐based studies can be further characterized using pharmacometric and system pharmacology based modeling approaches. As an illustrative example of how such modeling strategies can be used, we describe a case study in which we quantitatively characterize procalcitonin dynamics in relation to antibiotic treatments in patients with sepsis.

Collateral sensitivity (CS)-based antibiotic treatments, where increased resistance to one antibiotic leads to increased sensitivity to a second antibiotic, may have the potential to limit the emergence of antimicrobial resistance. However, it remains unclear how to best design CS-based treatment schedules. To address this problem, we use mathematical modelling to study the effects of pathogen- and drug-specific characteristics for different treatment designs on bacterial population dynamics and resistance evolution. We confirm that simultaneous and one-day cycling treatments could supress resistance in the presence of CS. We show that the efficacy of CS-based cycling therapies depends critically on the order of drug administration. Finally, we find that reciprocal CS is not essential to suppress resistance, a result that significantly broadens treatment options given the ubiquity of one-way CS in pathogens. Overall, our analyses identify key design principles of CS-based treatment strategies and provide guidance to develop treatment schedules to suppress resistance., Collateral sensitivity-based antibiotic treatments may have the potential to limit the emergence of antimicrobial resistance. Here, the authors use mathematical modelling to study the effects of pathogen- and drug-specific characteristics for different treatment designs on bacterial population dynamics and resistance evolution.

Aims: Growth hormone (GH) secretion is pulsatile and secretion varies highly between individuals. To understand and ultimately predict GH secretion, it is important to first delineate and quantify the interaction and variability in the biological processes underlying stimulated GH secretion. This study reports on the development of a population nonlinear mixed effects model for GH stimulation, incorporating individual GH kinetics and the stimulation of GH by GH-releasing hormone (GHRH). Methods: Literature data on the systemic circulation, the median eminence, and the anterior pituitary were included as system parameters in the model. Population parameters were estimated on data from 8 healthy normal weight and 16 obese women who received a 33 μg recombinant human GH dose. The next day, a bolus injection of 100 μg GHRH was given to stimulate GH secretion. Results: The GH kinetics were best described with the addition of 2 distribution compartments with a bodyweight dependent clearance (increasing linearly from 24.7 L/h for a 60-kg subject to 32.1 L/h for a 100-kg subject). The model described the data adequately with high parameter precision and significant interindividual variability on the GH clearance and distribution volume. Additionally, high variability in the amount of secreted GH, driven by GHRH receptor activation, was identified (coefficient of variation = 90%). Conclusion: The stimulation of GH by GHRH was quantified and significant interindividual variability was identified on multiple parameters. This model sets the stage for further development of by inclusion of additional physiological components to quantify GH secretion in humans.

Immunogenicity is a major challenge in drug development and patient care. Currently, most efforts are dedicated to the elimination of the unwanted immune responses through T‐cell epitope prediction and protein engineering. However, because it is unlikely that this approach will lead to complete eradication of immunogenicity, we propose that quantitative systems pharmacology models should be developed to predict and manage immunogenicity. The potential impact of such a mechanistic model‐based approach is precedented by applications of physiologically‐based pharmacokinetics.

The quantification of the effect of pharmacological treatment on the cardiovascular system is complicated because of the high level of interindividual and circadian variability. Recently, a dopamine-somatostatin chimera, BIM23B065, was under investigation to concurrently target the somatostatin and dopamine D2 receptors for the treatment of neuroendocrine tumors. However, both dopamine and somatostatin interact with different components of the cardiovascular system. This study established the response of the heart rate and the systolic blood pressure after administration of BIM23B065 in healthy male volunteers by analysis of the rate-pressure product (RPP), in a model-informed analysis. The RPP in the supine position of placebo-treated subjects showed a clear circadian component, best described by 2 cosine functions. The pharmacokinetics of BIM23B065 and its metabolite were best described using 2-compartment models with different forms of elimination kinetics. The administration of BIM23B065 gave a statistically significant reduction in the RPP, after which the effect diminished because of the tolerance to the cardiovascular effects after prolonged exposure to BIM23B065. This model provided insight in the circadian rhythm of the RPP in the supine position and the level of interindividual variability in healthy male volunteers. The developed population pharmacokinetic/pharmacodynamic model quantified the interaction between BIM23B065 and the RPP, informing on the clinical pharmacological properties of BIM23B065.

A key challenge in the development of central nervous system drugs is the availability of drug target specific blood‐based biomarkers. As a new approach, we applied cluster‐based pharmacokinetic/pharmacodynamic (PK/PD) analysis in brain extracellular fluid (brainECF) and plasma simultaneously after 0, 0.17, and 0.86 mg/kg of the dopamine D2/3 agonist quinpirole (QP) in rats. We measured 76 biogenic amines in plasma and brainECF after single and 8‐day administration, to be analyzed by cluster‐based PK/PD analysis. Multiple concentration‐effect relations were observed with potencies ranging from 0.001–383 nM. Many biomarker responses seem to distribute over the blood‐brain barrier (BBB). Effects were observed for dopamine and glutamate signaling in brainECF, and branched‐chain amino acid metabolism and immune signaling in plasma. Altogether, we showed for the first time how cluster‐based PK/PD could describe a systems‐response across plasma and brain, thereby identifying potential blood‐based biomarkers. This concept is envisioned to provide an important connection between drug discovery and early drug development.

The lack of standardization in the way that quantitative and systems pharmacology (QSP) models are developed, tested, and documented hinders their reproducibility, reusability, and expansion or reduction to alternative contexts. This in turn undermines the potential impact of QSP in academic, industrial, and regulatory frameworks. This article presents a minimum set of recommendations from the UK Quantitative and Systems Pharmacology Network (UK QSP Network) to guide QSP practitioners seeking to maximize their impact, and stakeholders considering the use of QSP models in their environment.

Zebrafish larvae are increasingly used in pharmacological and toxicological studies, but it is often overlooked that internal exposure to exogenous compounds, rather than the incubation medium concentration, is driving observed effects. Moreover, as the zebrafish larva is a developing organism, continuous physiological changes impact pharmacokinetic or toxicokinetic processes like the absorption and elimination of exogenous compounds, influencing the interpretation of observations and conclusions drawn from experiments at different larval ages. Here, using paracetamol as paradigm compound, mathematical modelling is used to quantify absorption and elimination rates from internal exposure over time profiles after waterborne treatment, as well as changes in these parameters in post-hatching larvae of 3, 4, and 5 days post fertilisation (dpf). An increase of 106% in absorption rate was observed between 3 and 4 dpf, but no further increase at 5 dpf, and an increase of 17.5% in elimination rate for each dpf. Paracetamol clearance, determined from elimination rate constants and reported total larval volumes of 253, 263, and 300 nL at 3, 4, and 5 dpf respectively, correlates best with higher vertebrates at 5 dpf. This suggests that when studying direct effects of exogenous compounds, experiments with zebrafish larvae are best performed at 5 dpf.

Objective In rare disease research, most randomized prospective clinical trials can only use limited number of patients and are comprised of highly heterogeneous populations. Therefore, it is crucial to report the results in such a manner that it allows for comparison of treatment effectiveness and biochemical control between studies. The aim of this review was to investigate the current methods that are being applied to measure and report growth hormone (GH) and insulin-like growth factor-1 (IGF-1) as markers for drug effectiveness in clinical acromegaly research. Search strategy A systematic search of recent prospective and retrospective studies, published between 2012 and 2017, that studied the effects of somatostatin analogues or dopamine agonists in acromegaly patients was performed. The markers of interest were GH, IGF-1, and the suppression of GH after an oral glucose tolerance test (OGTT). Additionally, the use of pharmacokinetic (PK) measurements in these studies was analyzed. The sampling design, cut-off for biochemical control, reported units, and used summary statistics were summarized. Results A total of 49 articles were selected out of the 263 screened abstracts. IGF-1 concentrations were measured in all 49 studies, GH in 45 studies, and an OGTT was performed in 11 studies. A wide range of different cut-off values and sampling designs were used to determine biochemical control in acromegaly patients. The summary statistics were reported in various ways, with the percentage of biochemical control most frequently used. Nine studies sampled the PK at one or more time points. Non-compartmental analyses were commonly performed on the available PK data. Conclusions The way GH and IGF-1 are measured and reported in acromegaly research varies considerably. A consensus on how to report study results would enable better comparisons between studies, thereby improving evidence based decision making to optimize treatment in acromegaly. Electronic supplementary material The online version of this article (10.1007/s11102-018-0884-4) contains supplementary material, which is available to authorized users.

Selectivity is an important attribute of effective and safe drugs, and prediction of in vivo target and tissue selectivity would likely improve drug development success rates. However, a lack of understanding of the underlying (pharmacological) mechanisms and availability of directly applicable predictive methods complicates the prediction of selectivity. We explore the value of combining physiologically based pharmacokinetic (PBPK) modeling with quantitative structure-activity relationship (QSAR) modeling to predict the influence of the target dissociation constant (K D) and the target dissociation rate constant on target and tissue selectivity. The K D values of CB1 ligands in the ChEMBL database are predicted by QSAR random forest (RF) modeling for the CB1 receptor and known off-targets (TRPV1, mGlu5, 5-HT1a). Of these CB1 ligands, rimonabant, CP-55940, and Δ8-tetrahydrocanabinol, one of the active ingredients of cannabis, were selected for simulations of target occupancy for CB1, TRPV1, mGlu5, and 5-HT1a in three brain regions, to illustrate the principles of the combined PBPK-QSAR modeling. Our combined PBPK and target binding modeling demonstrated that the optimal values of the K D and k off for target and tissue selectivity were dependent on target concentration and tissue distribution kinetics. Interestingly, if the target concentration is high and the perfusion of the target site is low, the optimal K D value is often not the lowest K D value, suggesting that optimization towards high drug-target affinity can decrease the benefit-risk ratio. The presented integrative structure-pharmacokinetic-pharmacodynamic modeling provides an improved understanding of tissue and target selectivity.

Pain is a complex biopsychosocial phenomenon of which the intensity, location and duration depends on various underlying components. Treatment of pain is associated with considerable inter-individual variability, and as such, requires a personalized approach. However, a priori prediction of optimal analgesic treatment for individual patients is still challenging. Another challenge is the assessment and treatment of pain in patients unable to self-report pain. In this mini-review, we first provide a brief overview of the various components underlying pain, and their associated biomarkers. These include clinical, psychosocial, neurophysiological, and biochemical components. We then discuss the use of empirical and mechanism-based pharmacokinetic-pharmacodynamic modelling to support personalized treatment of pain. Finally, we propose how these concepts can be extended to a quantitative systems pharmacology (QSP) approach that integrates the components of clinical pain and treatment response. This integrative approach can support predictions of optimal pharmacotherapy of pain, compared with approaches that focus on single components of pain. Moreover, combination of QSP modelling with state-of-the-art metabolomics approaches may offer unique possibilities to identify novel pain biomarkers. Such biomarkers could support both the personalized treatment of pain and translational drug development of novel analgesic agents. In conclusion, a QSP approach will likely improve our ability to predict pain and treatment response, paving the way for personalized treatment of pain.

Novel developments in biomarkers discovery are essential in modern health care, notably in treatment individualization and precision medicine. Clinical metabolomics, which aims to identify small molecule metabolites present in patient-derived samples, has attracted much attention to support discovery of novel biomarkers. However, the step from discriminatory features of disease states towards biomarkers that can truly individualize treatments is challenging. Biomarkers used for treatment individualization can either be dynamic or static prognostic biomarkers. Dynamic biomarkers are relevant for describing the clinical response, including dynamical disease progression and associated treatment response. Static (prognostic) biomarkers do not describe but rather predict a clinical response, and typically reflect aspects of the physiological state of a patient related to drug treatment response or disease progression dynamics. Pharmacokinetic-pharmacodynamic (PK-PD) modeling represents an established approach for drug treatment individualization based on drug exposure or treatment response biomarkers, as well as for the description of disease progression dynamics. Here, we discuss how novel treatment individualization biomarkers can be identified using a clinical metabolomics-based approach, and how concepts inspired from the field of PK-PD modeling can be integrated in this process in order to increase the clinical relevance of identified biomarkers and precision medicine.

Interspecies translation of pharmacological processes needs to improve to reduce attrition in drug development. Systems pharmacology integrates systems biology and pharmacometrics to characterise and quantify system-specific behaviour upon exposure to drugs in different species. The zebrafish is a suitable vertebrate model organism for systems pharmacology, combining high-throughput potential with high genetic homology to higher vertebrates. Zebrafish larvae have been increasingly used for drug screens, but the influence of internal drug and metabolite exposure is hardly studied. Quantifying this internal exposure is essential for establishing both exposure-response and dose-exposure relationships, needed for translation. The zebrafish may also serve as a suitable model species for translational studies on the occurrence of hepatotoxicity and the influence of hepatic dysfunction on drug metabolism.

To reveal unknown and potentially important mechanisms of drug action, multi-biomarker discovery approaches are increasingly used. Time-course relationships between drug action and multi-biomarker profiles, however, are typically missing, while such relationships will provide increased insight in the underlying body processes. The aim of this study was to investigate the effect of the dopamine D2 antagonist remoxipride on the neuroendocrine system. Different doses of remoxipride (0, 0.7, 5.2, or 14 mg/kg) were administered to rats by intravenous infusion. Serial brain extracellular fluid (brainECF) and plasma samples were collected and analyzed for remoxipride pharmacokinetics (PK). Plasma samples were analyzed for concentrations of the eight pituitary-related hormones as a function of time. A Mann–Whitney test was used to identify the responding hormones, which were further analyzed by pharmacokinetic/pharmacodynamic (PK/PD) modeling. A three-compartment PK model adequately described remoxipride PK in plasma and brainECF. Not only plasma PRL, but also adrenocorticotrophic hormone (ACTH) concentrations were increased, the latter especially at higher concentrations of remoxipride. Brain-derived neurotropic factor (BDNF), follicle stimulating hormone (FSH), growth hormone (GH), luteinizing hormone (LH), and thyroid stimulating hormones (TSH) did not respond to remoxipride at the tested doses, while oxytocin (OXT) measurements were below limit of quantification. Precursor pool models were linked to brainECF remoxipride PK by Emax drug effect models, which could accurately describe the PRL and ACTH responses. To conclude, this study shows how a multi-biomarker identification approach combined with PK/PD modeling can reveal and quantify a neuroendocrine multi-biomarker response for single drug action.

Zebrafish larvae (Danio rerio) are increasingly used to translate findings regarding drug efficacy and safety from in vitro-based assays to vertebrate species, including humans. However, the limited understanding of drug exposure in this species hampers its implementation in translational research. Using paracetamol as a paradigm compound, we present a novel method to characterize pharmacokinetic processes in zebrafish larvae, by combining sensitive bioanalytical methods and nonlinear mixed effects modeling. The developed method allowed quantification of paracetamol and its two major metabolites, paracetamol-sulfate and paracetamol-glucuronide in pooled samples of five lysed zebrafish larvae of 3 days post-fertilization. Paracetamol drug uptake was quantified to be 0.289 pmole/min and paracetamol clearance was quantified to be 1.7% of the total value of the larvae. With an average volume determined to be 0.290 μL, this yields an absolute clearance of 2.96 × 10(7) L/h, which scales reasonably well with clearance rates in higher vertebrates. The developed methodology will improve the success rate of drug screens in zebrafish larvae and the translation potential of findings, by allowing the establishment of accurate exposure profiles and thereby also the establishment of concentration-effect relationships.

A recently developed system-pharmacological model for the dynamics of receptor tyrosine kinases is used to compare di erent targets for drug action: one aiming at binding endogenous ligand needed for phosphorylation and another binding re- ceptors at their kinase domains and thus preventing them to generate phosphory- lation. We obtain quantitative estimates for the e ectivity of the inhibitor which demonstrate the influence of drug-properties such as dose, a nity to target and drug-elimination rates.

Obtaining pharmacologically relevant exposure levels of antibiotics in the epithelial lining fluid (ELF) is of critical importance to ensure optimal treatment of lung infections. Our objectives were to develop a model for the prediction of the ELF-plasma concentration ratio (EPR) of antibiotics based on their chemical structure descriptors (CSDs). EPR data was obtained by aggregating ELF and plasma concentrations from historical clinical studies investigating antibiotics and associated agents. An elastic net regularized regression model was used to predict EPRs based on a large number of CSDs. The model was tuned using leave-one-drug-out cross validation, and the predictions were further evaluated using a test dataset. EPR data of 56 unique compounds was included. A high degree of variability in EPRs both between- and within drugs was apparent. No trends related to study design or pharmacokinetic factors could be identified. The model predicted 80% of the within-drug variability (R2 WDV) and 78.6% of drugs were within 3-fold difference from the observations. Key CSDs were related to molecular size and lipophilicity. When predicting EPRs for a test dataset the R2 WDV was 75%. This model is of relevance to inform dose selection and optimization during antibiotic drug development of agents targeting lung infections.

Quantitative systems pharmacology (QSP) modeling represents an emerging area of value to further streamline knowledge integration and to better inform decision making processes in drug development. QSP models reside at the interface between systems biology models and pharmacological models, yet their concrete implementation still needs to be established further. This review outlines key modeling techniques in both of these areas and to subsequently discuss challenges and opportunities for further integration, in oncology drug development.

Obesity and morbid obesity are associated with many physiological changes affecting pharmacokinetics, such as increased blood volume, cardiac output, splanchnic blood flow, and hepatic blood flow. In obesity, drug absorption appears unaltered, although recent evidence suggests that this conclusion may be premature. Volume of distribution may vary largely, but the magnitude and direction of changes seem difficult to predict, with extrapolation on the basis of total body weight being the best approach to date. Changes in clearance may be smaller than in distribution, whereas there is growing evidence that the influence of obesity on clearance can be predicted on the basis of reported changes in the metabolic or elimination pathways involved. For obese children, we propose two methods to distinguish between developmental and obesity-related changes. Future research should focus on the characterization of physiological concepts to predict the optimal dose for each drug in the obese population.

The nerve growth factor (NGF) pathway is of great interest as a potential source of drug targets, for example in the management of certain types of pain. However, selecting targets from this pathway either by intuition or by non-contextual measures is likely to be challenging. An alternative approach is to construct a mathematical model of the system and via sensitivity analysis rank order the targets in the known pathway, with respect to an endpoint such as the diphosphorylated extracellular signal-regulated kinase concentration in the nucleus. Using the published literature, a model was created and, via sensitivity analysis, it was concluded that, after NGF itself, tropomyosin receptor kinase A (TrkA) was one of the most sensitive druggable targets. This initial model was subsequently used to develop a further model incorporating physiological and pharmacological parameters. This allowed the exploration of the characteristics required for a successful hypothetical TrkA inhibitor. Using these systems models, we were able to identify candidates for the optimal drug targets in the known pathway. These conclusions were consistent with clinical and human genetic data. We also found that incorporating appropriate physiological context was essential to drawing accurate conclusions about important parameters such as the drug dose required to give pathway inhibition. Furthermore, the importance of the concentration of key reactants such as TrkA kinase means that appropriate contextual data are required before clear conclusions can be drawn. Such models could be of great utility in selecting optimal targets and in the clinical evaluation of novel drugs.

This report describes a rapid and sensitive analytical method for the quantification of the neuroactive steroids alphaxalone and pregnanolone in rat plasma using derivatization with dansyl hydrazine as fluorescent label. The method involves protein precipitation, alkaline derivatization and extraction of the compounds and internal standard pregnenolone with dichloromethane, followed by isocratic reversed-phase high-performance liquid chromatography on a 3-microm Microsphere C18 column with fluorescence detection at wavelengths 332 nm and 516 nm for excitation and emission, respectively. The mobile phase consists of a mixture of 25 mM acetate buffer (pH 3.7)-acetonitrile (45:55, v/v for alphaxalone and 40:60, v/v for pregnanolone) with a flow-rate of 1 ml/min. The total run time was approximately 35 min. In the concentration range of 0.010-10 microg ml(-1), the intra- and inter-assay coefficients of variation were less than 17% for both methods. In 50 microl plasma samples the corresponding limits of detection were 10 ng ml(-1) (signal-to-noise ratio=3). The utility of the analytical method was established by analyzing plasma samples from rats, which had received an intravenous administration of 5 mg kg(-1) alphaxalone or pregnanolone. Values for clearance, volume of distribution at steady state and terminal half life were 71.9 ml min(-1) kg(-1), 814 mg kg(-1) and 13.5 min for alphaxalone and 69.2 ml min(-1) kg(-1), 1,638 ml kg(-1) and 27.8 min for pregnanolone, respectively. Due to its simplicity and sensitivity this method can be used on a routine basis for pharmacokinetic analysis of neuroactive steroids.