Modelling the dose-response relationship: the fair share of pharmacokinetic and pharmacodynamic information

Pharmacokinetic/Pharmacodynamic Modeling of the Acute Heart Rate Effects of Delta-9 Tetrahydrocannabinol and Its Major Metabolites After Intravenous Injection in Healthy Volunteers

BACKGROUND AND OBJECTIVES

Cannabis consumption is increasing in both the recreational and medical settings. Tetrahydrocannabinol (THC) is known to produce cardiovascular effects, but the specific roles of THC and its metabolites THC-OH and THC-COOH in cannabinoid-induced cardiovascular effects remain unclear. We hypothesized that THC and THC-OH mediate a cannabinoid-induced increase in heart rate in either an additive or synergistic fashion.

METHODS

The present study uses prospectively obtained data to evaluate the effect of THC and its metabolites on heart rate in healthy volunteers through non-linear mixed-effect pharmacokinetic/pharmacodynamic (PK/PD) modeling.

RESULTS

The PK/PD models reveal that THC, THC-OH and a combination of THC and THC-OH, but not THC-COOH, are responsible for THC-induced tachycardia. The EC50 of the THC Emax model was 0.53 µM, 25-fold the EC50 for the THC-OH Emax model. The General Empiric Dynamic Model indicates that THC and THC-OH act synergistically to increase heart rate. Neither sex nor CYP2C9 polymorphism contributes to THC-induced tachycardia.

CONCLUSION

THC-OH but not THC-COOH contributes to the heart rate effect of THC and THC-OH may be acting in a synergistic manner with THC. This contributes to understanding the cardiovascular effects of THC and cannabis-induced cardiovascular events. Future research including further hemodynamic data will allow a detailed systems pharmacology or response surface model approach.

TRIAL REGISTRATION

www.isrctn.com ; registration number ISRCTN53019164.

Model-based interspecies interpretation of botulinum neurotoxin type A on muscle-contraction inhibition

Botulinum neurotoxins (BoNTs) are commonly used in therapeutic and cosmetic applications. One such neurotoxin, BoNT type A (BoNT/A), has been studied widely for its effects on muscle function and contraction. Despite the importance of BoNT/A products, determining the blood concentrations of these toxins can be challenging. To address this, researchers have focused on pharmacodynamic (PD) markers, including compound muscle action potential (CMAP) and digit abduction scoring (DAS). In this study, we aimed to develop a probabilistic kinetic-pharmacodynamic (K-PD) model to interpret CMAP and DAS data obtained from mice and rats during the development of BoNT/A products. The researchers also wanted to gain a better understanding of how the estimated parameters from the model relate to the bridging of animal models to human responses. We used female Institute of Cancer Research mice and Sprague-Dawley (SD) rats to measure CMAP and DAS levels over 32 weeks after administering BoNT/A. We developed a muscle-contraction inhibition model using a virtual pharmacokinetic (PK) compartment combined with an indirect response model and performed model diagnostics using goodness-of-fit analysis, visual predictive checks (VPC), and bootstrap analysis. The CMAP and DAS profiles were dose-dependent, with recovery times varying depending on the administered dose. The final K-PD model effectively characterized the data and provided insights into species-specific differences in the PK and PD parameters. Overall, this study demonstrated the utility of PK-PD modeling in understanding the effects of BoNT/A and provides a foundation for future research on other BoNT/A products.

Model-Based Anticancer Effect of Botulinum Neurotoxin Type A1 on Syngeneic Melanoma Mice

In recent, Botulinum Neurotoxin A1 (BoNT/A1) has been suggested as a potential anticancer agent due to neuronal innervation in tumor cells. Although potential BoNT/A1's mechanism of action for the tumor suppression has been gradually revealed so far, there were no reports to figure out the exposure-response relationships because of the difficulty of its quantitation in the biological matrix. The main objectives of this study were to measure the anticancer effect of BoNT/A1 using a syngeneic mouse model transplanted with melanoma cells (B16-F10) and developed a kinetic-pharmacodynamic (K-PD) model for quantitative exposure-response evaluation. To overcome the lack of exposure information, the K-PD model was implemented by the virtual pharmacokinetic compartment link to the pharmacodynamic compartment of Simeoni's tumor growth inhibition model and evaluated using curve-fitting for the tumor growth-time profile after intratumoral injection of BoNT/A1. The final K-PD model was adequately explained for a pattern of tumor growth depending on represented exposure parameters and simulation studies were conducted to determine the optimal dose under various scenarios considering dose strength and frequency. The optimal dose range and regimen of ≥13.8 units kg^-1 once a week or once every 3 days was predicted using the final model in B16-F10 syngeneic model and it was demonstrated with an extra in-vivo experiment. In conclusion, the K-PD model of BoNT/A1 was well developed to optimize the dosing regimen for evaluation of anticancer effect and this approach could be expandable to figure out quantitative interpretation of BoNT/A1's efficacy in various xenograft and/or syngeneic models.

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.

Modelling the dose-response relationship: the fair share of pharmacokinetic and pharmacodynamic information

AIMS

The aim of this paper is to investigate the role of drug concentration samplings in the modelling of the dose-response relationship.

METHODS

Using an initial PK/PD model, a reference dataset was simulated. PK and PD samples were extracted to create reduced datasets. PK/PD and K-PD models were fitted to theses reduced datasets. Post hoc estimates from both types of models were compared to the initial PK/PD model and performance was assessed.

RESULTS

K-PD models were largely biased when the drug has a nonlinear elimination. PK/PD models with 1 PK and 2 PD samples were superior to K-PD models with 3 PD samples. PK/PD models with 1 or 2 PK samples and 3 PD samples proved to be superior to K-PD models with 4 PD samples.

CONCLUSIONS

K-PD models should not be used when the drug has nonlinear elimination. K-PD models should not replace PK/PD modelling but are an alternative approach if the PD information is large enough.