Modeling and design of challenge tests: Inflammatory and metabolic biomarker study examples

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 µmol/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.

Evolving data analysis of an Oral Lipid Tolerance Test toward the standard for the Oral Glucose Tolerance Test: Cross species modeling effects of AZD7687 on plasma triacylglycerol

We have developed a novel mechanistic pharmacokinetic-pharmacodynamic (PK/PD) model to describe the time course of plasma triglyceride (TAG) after Oral Lipid Tolerance Test (OLTT) and the effects of AZD7687, an inhibitor of diacylglycerol acyltransferase 1 (DGAT1), in humans, rats, and mice. Pharmacokinetic and plasma TAG data were obtained both in animals and in two phase I OLTT studies. In the PK/PD model, the introduction of exogenous TAG is represented by a first order process. The endogenous production and removal of TAG from plasma are described with a turnover model. AZD7687 inhibits the contribution of exogenous TAG into circulation. One or two compartment models with first order absorption was used to describe the PK of AZD7687 for the different species. Nonlinear mixed effect modeling was used to fit the model to the data. The effects of AZD7687 on the plasma TAG time course during an OLTT as well as interindividual variability were well described by the model in all three species. Meal fat content or data from single vs repeated dosing did not affect model parameter estimates. Body mass index was found to be a significant covariate on the plasma TAG baseline. The system parameters of the model will facilitate analysis for other compounds and provide tools to bring the standard of OLTT data analysis closer to the analyses of Oral Glucose Tolerance Test data maximizing knowledge gain.

Challenge model of TNFα turnover at varying LPS and drug provocations

A mechanism-based biomarker model of TNF_α-response, including different external provocations of LPS challenge and test compound intervention, was developed. The model contained system properties (such as k_t, k_out), challenge characteristics (such as k_s, k_LPS, K_m, LPS, S_max, SC₅₀) and test-compound-related parameters (I_max, 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 nM with a 70% partial reduction in TNF_α-response at the highest dose of 30 mg·kg⁻¹. Future selection of drug candidates may focus the estimation on potency and efficacy by applying the selected structure consisting of TNF_α system 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 TNF_α pools occur. Lack of LPS exposure-time courses was solved by including a biophase model, with the underlying assumption that TNF_α-response time courses, as such, contain kinetic information. A transduction type of model with non-linear stimulation of TNF_α release 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.

Dose-Response-Time Data Analysis: An Underexploited Trinity

The most common approach to in vivo pharmacokinetic and pharmacodynamic analyses involves sequential analysis of the plasma concentration- and response-time data, such that the plasma kinetic model provides an independent function, driving the dynamics. However, in situations when plasma sampling may jeopardize the effect measurements or is scarce, nonexistent, or unlinked to the effect (e.g., in intensive care units, pediatric or frail elderly populations, or drug discovery), focusing on the response-time course alone may be an adequate alternative for pharmacodynamic analyses. Response-time data inherently contain useful information about the turnover characteristics of response (target turnover rate, half-life of response), as well as the drug's biophase kinetics (biophase availability, absorption half-life, and disposition half-life) pharmacodynamic properties (potency, efficacy). The use of pharmacodynamic time-response data circumvents the need for a direct assay method for the drug and has the additional advantage of being applicable to cases of local drug administration close to its intended targets in the immediate vicinity of target, or when target precedes systemic plasma concentrations. This review exemplifies the potential of biophase functions in pharmacodynamic analyses in both preclinical and clinical studies, with the purpose of characterizing response data and optimizing subsequent study protocols. This article illustrates crucial determinants to the success of modeling dose-response-time (DRT) data, such as the dose selection, repeated dosing, and different input rates and routes. Finally, a literature search was also performed to gauge how frequently this technique has been applied in preclinical and clinical studies. This review highlights situations in which DRT should be carefully scrutinized and discusses future perspectives of the field.

The acute glucose lowering effect of specific GPR120 activation in mice is mainly driven by glucagon-like peptide 1

The mechanism behind the glucose lowering effect occurring after specific activation of GPR120 is not completely understood. In this study, a potent and selective GPR120 agonist was developed and its pharmacological properties were compared with the previously described GPR120 agonist Metabolex-36. Effects of both compounds on signaling pathways and GLP-1 secretion were investigated in vitro. The acute glucose lowering effect was studied in lean wild-type and GPR120 null mice following oral or intravenous glucose tolerance tests. In vitro, in GPR120 overexpressing cells, both agonists signaled through Gα_q, Gα_s and the β-arrestin pathway. However, in mouse islets the signaling pathway was different since the agonists reduced cAMP production. The GPR120 agonists stimulated GLP-1 secretion both in vitro in STC-1 cells and in vivo following oral administration. In vivo GPR120 activation induced significant glucose lowering and increased insulin secretion after intravenous glucose administration in lean mice, while the agonists had no effect in GPR120 null mice. Exendin 9–39, a GLP-1 receptor antagonist, abolished the GPR120 induced effects on glucose and insulin following an intravenous glucose challenge. In conclusion, GLP-1 secretion is an important mechanism behind the acute glucose lowering effect following specific GPR120 activation.

Indazole-6-phenylcyclopropylcarboxylic Acids as Selective GPR120 Agonists with in Vivo Efficacy

GPR120 agonists have therapeutic potential for the treatment of diabetes, but few selective agonists have been reported. We identified an indazole-6-phenylcyclopropylcarboxylic acid series of GPR120 agonists and conducted SAR studies to optimize GPR120 potency. Furthermore, we identified a (S,S)-cyclopropylcarboxylic acid structural motif which gave selectivity against GPR40. Good oral exposure was obtained with some compounds displaying unexpected high CNS penetration. Increased MDCK efflux was utilized to identify compounds such as 33 with lower CNS penetration, and activity in oral glucose tolerance studies was demonstrated. Differential activity was observed in GPR120 null and wild-type mice indicating that this effect operates through a mechanism involving GPR120 agonism.

Pattern Recognition in Pharmacodynamic Data Analysis

Pattern recognition is a key element in pharmacodynamic analyses as a first step to identify drug action and selection of a pharmacodynamic model. The essence of this process is going from data to insight through exploratory data analysis. There are few formal strategies that scientists typically use when the experiment has been done and data collected. This report attempts to ameliorate this deficit by identifying the properties of a pharmacodynamic model via dissection of the pattern revealed in response-time data. Pattern recognition in pharmacodynamic analyses contrasts with pharmacokinetic analyses with respect to time course. Thus, the time course of drug in plasma usually differs markedly from the time course of the biomarker response, as a consequence of a myriad of interactions (transport to biophase, binding to target, activation of target and downstream mediators, physiological response, cascade and amplification of biosignals, homeostatic feedback) between the events of exposure to test compound and the occurrence of the biomarker response. Homing in on this important-but less often addressed-element, 20 datasets of varying complexity were analyzed, and from this, we summarize a set of points to consider, specifically addressing baseline behavior, number of phases in the response-time course, time delays between concentration- and response-time courses, peak shifts in response with increasing doses, saturation, and other potential nonlinearities. These strategies will hopefully give a better understanding of the complete pharmacodynamic response-time profile.