Pattern Recognition in Pharmacodynamic Data Analysis

An integrated modelling approach for targeted degradation: insights on optimization, data requirements and PKPD predictions from semi- or fully-mechanistic models and exact steady state solutions

The value of an integrated mathematical modelling approach for protein degraders which combines the benefits of traditional turnover models and fully mechanistic models is presented. Firstly, we show how exact solutions of the mechanistic models of monovalent and bivalent degraders can provide insight on the role of each system parameter in driving the pharmacological response. We show how on/off binding rates and degradation rates are related to potency and maximal effect of monovalent degraders, and how such relationship can be used to suggest a compound optimization strategy. Even convoluted exact steady state solutions for bivalent degraders provide insight on the type of observations required to ensure the predictive capacity of a mechanistic approach. Specifically for PROTACs, the structure of the exact steady state solution suggests that the total remaining target at steady state, which is easily accessible experimentally, is insufficient to reconstruct the state of the whole system at equilibrium and observations on different species (such as binary/ternary complexes) are necessary. Secondly, global sensitivity analysis of fully mechanistic models for PROTACs suggests that both target and ligase baselines (actually, their ratio) are the major sources of variability in the response of non-cooperative systems, which speaks to the importance of characterizing their distribution in the target patient population. Finally, we propose a pragmatic modelling approach which incorporates the insights generated with fully mechanistic models into simpler turnover models to improve their predictive ability, hence enabling acceleration of drug discovery programs and increased probability of success in the clinic.

Translational Population-Pharmacodynamic Modeling of a Novel Long-Acting siRNA Therapy, Inclisiran, for the Treatment of Hypercholesterolemia

Inclisiran is a novel N-acetylgalactosamine (GalNAc) conjugated small-interfering ribonucleic acid (siRNA) therapy designed to specifically target proprotein convertase subtilisin/kexin type 9 (PCSK9) mRNA in the liver for the treatment of hypercholesterolemia. Inclisiran's GalNAc attachment results in a rapid uptake into the liver, and thus a short plasma half-life, but long duration of effects on PCSK9 inhibition and low-density lipoprotein cholesterol (LDL-C) lowering. The effects on PCSK9 inhibition and consequent LDL-C reduction are sustained for more than 6 months following a single subcutaneous (s.c.) dose, despite inclisiran being detectable in the plasma only for up to 48 hours. A kinetic-pharmacodynamic (K-PD) model was developed to characterize inclisiran's dose-related LDL-C lowering effects and to evaluate the impact of intrinsic and extrinsic factors on LDL-C lowering. To accommodate the long duration of action, the K-PD model incorporated an effect compartment which represents the liver. Inclisiran concentration in the liver leads to decreased production of the PCSK9 protein and allow recycling of more LDL-C receptors on the hepatocyte cell surface, which results in a reduction of circulating LDL-C. The analysis of covariates identified PCSK9 and LDL-C baseline levels as important factors for the effects of LDL-C lowering. Observations and modeling and simulation results demonstrated that PCSK9 and LDL-C reductions are achieved rapidly after dosing and sustained when patients are treated with a 300 mg s.c. dose once every 6 months.

Pharmacokinetic/Pharmacodynamic Modeling To Predict the Antiplatelet Effect of the Ticagrelor-Loaded Self-Microemulsifying Drug Delivery System in Rats

Ticagrelor (TCG) has been used as an antiplatelet agent for acute coronary syndrome patients. The aim of this research was to establish a population pharmacokinetic/pharmacodynamic (PK/PD) model of TCG and to apply the model for predicting the PD response of the TCG-loaded self-microemulsifying drug delivery system (TCG-SME) in rats. Pure TCG and TCG-SME (2, 5, and 10 mg/kg of TCG) were orally administered to male Sprague-Dawley rats. Plasma samples were collected at scheduled time-points and then analyzed for TCG plasma concentrations and antiplatelet effects. The inhibition of platelet aggregation of TCG was measured as a PD response. The PK profiles of pure TCG and TCG-SME could be well-explained with a two-compartment PK model. The accuracy of the PK model was assessed with a goodness-of-fit plot and conditional weight residual error (CWRES). Also, the visual predictive check was investigated based on the predictions. A population PK/PD model for pure TCG was established as an indirect response E_max model linked to the two-compartment PK model of pure TCG. The PK/PD model proposed a suitable fitting to link the plasma concentration of TCG simultaneously with platelet aggregation. Based on the PK data of TCG-SME, as well as the established PK/PD model of pure TCG, the PD profiles of TCG-SME were simulated. TCG-SME was more effective in inducing the antiplatelet effect than pure TCG at equivalent doses of TCG. The accuracy of the simulation was verified by comparing the simulated PD profile with the profile observed in rats. The observations were close to the model simulations. In addition, the values of CWRES were almost within ±2. In conclusion, the PK/PD modeling approach can provide a way for predicting mathematically the PD responses from PK profiles of other TCG formulations and a conceptual prediction for future clinical assessment.

Access to the CNS: Biomarker Strategies for Dopaminergic Treatments

Despite substantial research carried out over the last decades, it remains difficult to understand the wide range of pharmacological effects of dopaminergic agents. The dopaminergic system is involved in several neurological disorders, such as Parkinson's disease and schizophrenia. This complex system features multiple pathways implicated in emotion and cognition, psychomotor functions and endocrine control through activation of G protein-coupled dopamine receptors. This review focuses on the system-wide effects of dopaminergic agents on the multiple biochemical and endocrine pathways, in particular the biomarkers (i.e., indicators of a pharmacological process) that reflect these effects. Dopaminergic treatments developed over the last decades were found to be associated with numerous biochemical pathways in the brain, including the norepinephrine and the kynurenine pathway. Additionally, they have shown to affect peripheral systems, for example the hypothalamus-pituitary-adrenal (HPA) axis. Dopaminergic agents thus have a complex and broad pharmacological profile, rendering drug development challenging. Considering the complex system-wide pharmacological profile of dopaminergic agents, this review underlines the needs for systems pharmacology studies that include: i) proteomics and metabolomics analysis; ii) longitudinal data evaluation and mathematical modeling; iii) pharmacokinetics-based interpretation of drug effects; iv) simultaneous biomarker evaluation in the brain, the cerebrospinal fluid (CSF) and plasma; and v) specific attention to condition-dependent (e.g., disease) pharmacology. Such approach is considered essential to increase our understanding of central nervous system (CNS) drug effects and substantially improve CNS drug development.

Frequency-Domain Response Analysis for Quantitative Systems Pharmacology Models

Drug dosing regimen can significantly impact drug effect and, thus, the success of treatments. Nevertheless, trial and error is still the most commonly used method by conventional pharmacometric approaches to optimize dosing regimen. In this tutorial, we utilize four distinct classes of quantitative systems pharmacology models to introduce frequency-domain response analysis, a method widely used in electrical and control engineering that allows the analytical optimization of drug treatment regimen from the dynamics of the model.