Optimizing study design in LPS challenge studies for quantifying drug induced inhibition of TNFα response: Did we miss the prime time?

In this work we evaluate the study design of LPS challenge experiments used for quantification of drug induced inhibition of TNFα response and provide general guidelines of how to improve the study design. Analysis of model simulated data, using a recently published TNFα turnover model, as well as the optimal design tool PopED have been used to find the optimal values of three key study design variables – time delay between drug and LPS administration, LPS dose, and sampling time points – that in turn could make the resulting TNFα response data more informative. Our findings suggest that the current rule of thumb for choosing the time delay should be reconsidered, and that the placement of the measurements after maximal TNFα response are crucial for the quality of the experiment. Furthermore, a literature study summarizing a wide range of published LPS challenge studies is provided, giving a broader perspective of how LPS challenge studies are usually conducted both in a preclinical and clinical setting

Second-generation TNFα turnover model for improved analysis of test compound interventions in LPS challenge studies

This study presents a non-linear mixed effects model describing tumour necrosis factor alpha (TNFα) release after lipopolysaccharide (LPS) provocations in absence or presence of anti-inflammatory test compounds. Inter-occasion variability and the pharmacokinetics of two test compounds have been added to this second-generation model, and the goal is to produce a framework of how to model TNFα response in LPS challenge studies in vivo and demonstrate its general applicability regardless of occasion or type of test compound. Model improvements based on experimental data were successfully implemented and provided a robust model for TNFα response after LPS provocation, as well as reliable estimates of the median pharmacodynamic parameters. The two test compounds, Test Compound A and roflumilast, showed 81.1% and 74.9% partial reduction of TNFα response, respectively, and the potency of Test Compound A was estimated to 0.166 \ub5mol/L. Comparing this study with previously published work reveals that our model leads to biologically reasonable output, handles complex data pooled from different studies, and highlights the importance of accurately distinguishing the stimulatory effect of LPS from the inhibitory effect of the test compound

Challenge model of TNFα turnover at varying LPS and drug provocations

A mechanism-based biomarker model of TNFα-response, including different external provocations of LPS challenge and test compound intervention, was developed. The model contained system properties (such as\ua0kt, kout), challenge characteristics (such as\ua0ks, kLPS, Km,\ua0LPS, Smax, SC50) and test-compound-related parameters (Imax, IC50). The exposure to test compound was modelled by means of first-order input and Michaelis–Menten type of nonlinear elimination. Test compound potency was estimated to 20\ua0nM with a 70% partial reduction in TNFα-response at the highest dose of 30\ua0mg\ub7kg−1. Future selection of drug candidates may focus the estimation on potency and efficacy by applying the selected structure consisting of TNFα\ua0system and LPS challenge characteristics. A related aim was to demonstrate how an exploratory (graphical) analysis may guide us to a tentative model structure, which enables us to better understand target biology. The analysis demonstrated how to tackle a biomarker with a baseline below the limit of detection. Repeated LPS-challenges may also reveal how the rate and extent of replenishment of TNFα\ua0pools occur. Lack of LPS exposure-time courses was solved by including a biophase model, with the underlying assumption that TNFα-response time courses, as such, contain kinetic information. A transduction type of model with non-linear stimulation of TNFα\ua0release was finally selected. Typical features of a challenge experiment were shown by means of model simulations. Experimental shortcomings of present and published designs are identified and discussed. The final model coupled to suggested guidance rules may serve as a general basis for the collection and analysis of pharmacological challenge data of future studies

Bayesian hierarchical model of oscillatory cortisol response during drug intervention

Introduction:\ua0Oscillating biomarker response-time courses challenge modelling of drug intervention. A periodically recurring pattern is typically seen for the stress hormone cortisol. This pattern can be captured by mechanism-based turnover models. However, analysing experimental data requires new mathematical techniques. Bayesian hierarchical modelling allows for full quantification of parameter uncer\uadtainty while also capturing the population aspects typical to nonlinear mixed effects modelling. Inter-occasion variability (IOV) is incorporated in addition to inter-individual variability (IIV).Objectives:- Propose a model based workflow for oscillating baseline turnover models including IIV and IOV.- Apply the workflow to cortisol- and dexamethasone time-series data obtained from horses.- An additional aim was to predict test performance of a two-sample dexamethasone suppression test-protocol (DST-protocol) [1, 2] in horses.Methods:\ua0Cortisol- and dexamethasone time courses were collected [1]. Four different doses of dexamethasone were given (no drug and 0.1, 1, 10 \ub5g/kg bolus + 0.07, 0.7, 7 \ub5g/kg infusion over three hours). The pharmacokinetic/pharmacodynamic model was adapted from [1]. Cortisol was described by a turnover model with oscillating turnover rate (average baseline kavg, amplitude α, phase-shift t0) and fractional turnover rate kout. Drug intervention was modelled with Hill-type suppression (maximum inhibition Imax, potency IC50, hill coefficient \uadn). Dexamethasone exposure was described by a two-compartment model. The model was then extended to a population model by introduction of inter-individual and inter-occasion effects. The final model was inferred from data using a Bayesian framework with the Hamiltonian Monte Carlo algorithm in Stan [3]. Ordinary differential equations were solved analytically for the case of constant drug exposure. The performance of the two-sample DST-protocol was studied by calculation of the\ua0specificity of the test.\ua0Specificity was predicted by Monte Carlo simulations and compared to two previously published experimental results.Results:\ua0The proposed model described the data well. Estimated ranges for pharmacodynamic\ua0parameters were estimated as\ua0median (95% credible intervals): kavg\ua0= 12.7 (6.44, 23.5) \ub5g L-1\ua0h-1, α = 5.40 (1.38, 17.9) \ub5g L-1\ua0h-1, t0\ua0= -3.71 (-7.54, 0.494) h, kout\ua0= 0.315 (0.221, 0.493) h-1, Imax\ua0= 0.923 (0.874, 0.965), IC50 = 0.0298 (0.00490, 0.155) \ub5g L-1, n = 1.57 (1.03, 2.61\ua0 ). Low precision was found in the standard deviations of the random effect parameters. IIV and IOV present in the data were captured by the model. The average cortisol response level and its amplitude are suppressed with respect to magnitude and variability with increasing exposure to dexamethasone. The maximum and minimum levels of cortisol response were also suppressed by increasing exposure to dexamethasone. Mathematical expressions were derived describing cortisol oscillations with inhibition and were consistent with experimental data. Dependence of predicted specificity on drug administration time and time until measurement was observed. Different levels of variability (IIV and IOV) led to a fraction of healthy subjects with positive test results. The oscillatory behaviour of cortisol response led to an oscillatory pattern in predicted specificity.\ua0Conclusions:- New techniques were developed for graphical analysis of the oscillatory cortisol response- These were successfully applied to equine cortisol data after dexamethasone intervention- Oscillatory behaviour and level of variability had great impact on the sparse-sample DST-designReferences:\ua0[1] Ekstrand, C.\ua0et al.\ua0(2015) ‘A quantitative approach to analysing cortisol response in the horse’,\ua0Journal of Veterinary Pharmacology and Therapeutics, 39, pp. 255–263. doi: 10.1111/jvp.12276[2] Carpenter, B.\ua0et al.\ua0(2017) ‘Stan: A Probabilistic Programming Language’,\ua0Journal of Statistical Software, 76(1). doi: 10.18637/jss.v076.i01[3] Dybdal, N. O.\ua0et al.\ua0(1994) ‘Diagnostic testing for pituitary pars intermedia dysfunction in horses’,\ua0Journal of the American Veterinary Medical Association1, 204, pp. 627–632[4] Frank, N.\ua0et al.\ua0(2006) ‘Evaluation of the combined dexamethasone suppression/ thyrotropin-releasing hormone stimulation test for detection of pars intermedia pituitary adenomas in horses.’,\ua0Journal of Veterinary Internal Medicine, 20(4), pp. 987–93. doi: 10.1111/j.1939-1676.2006.tb01816.x

Activated Human T Cells, B Cells, and Monocytes Produce Brain-derived Neurotrophic Factor In Vitro and in Inflammatory Brain Lesions: A Neuroprotective Role of Inflammation?

Brain-derived neurotrophic factor (BDNF) has potent effects on neuronal survival and plasticity during development and after injury. In the nervous system, neurons are considered the major cellular source of BDNF. We demonstrate here that in addition, activated human T cells, B cells, and monocytes secrete bioactive BDNF in vitro. Notably, in T helper (Th)1- and Th2-type CD4+ T cell lines specific for myelin autoantigens such as myelin basic protein or myelin oligodendrocyte glycoprotein, BDNF production is increased upon antigen stimulation. The BDNF secreted by immune cells is bioactive, as it supports neuronal survival in vitro. Using anti-BDNF monoclonal antibody and polyclonal antiserum, BDNF immunoreactivity is demonstrable in inflammatory infiltrates in the brain of patients with acute disseminated encephalitis and multiple sclerosis. The results raise the possibility that in the nervous system, inflammatory infiltrates have a neuroprotective effect, which may limit the success of nonselective immunotherapies