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Model-Informed Drug Development
Published in Wei Zhang, Fangrong Yan, Feng Chen, Shein-Chung Chow, Advanced Statistics in Regulatory Critical Clinical Initiatives, 2022
The direct response PK/PD model is based on the assumption that there is an effect compartment where drugs exhibit effects [12]. The concentration-effect relationships in the effect compartment are described by the classical dose-response models listed in the above section. For direct response PK/PD models, the measured concentration in plasma is directly linked to the effect-site concentration. Equilibrium between both concentrations is assumed to be rapidly achieved and thus their ratio is constant, under PK steady-state as well as non steady-state conditions [13]. Hence, the measured concentrations can directly serve as input function in the pharmacodynamic model component, thereby directly linking measured concentration to the observed effect. In that case, concentration and effect maxima would occur at the same time and affect vs. concentration plots would lack any hysteresis if the response is directly mediated [13].
Pharmacokinetic-Pharmacodynamic Relationships of Cardiovascular Drugs
Published in Hartmut Derendorf, Günther Hochhaus, Handbook of Pharmacokinetic/Pharmacodynamic Correlation, 2019
The Emax model has been described as obeying the “law of diminishing returns”, since the increments in pharmacologic response become gradually smaller as concentrations increase (Figure 3). This particular model has been used extensively to describe the concentration-effect relationship for cardiovascular drugs. An example is the relationship between unbound serum concentrations of propranolol and the percent inhibition of exercise heart rate shown in Figure 4.19 In this study of nine subjects, the mean Emax was 33.5% and reflects the adrenergic component of exercise-induced tachycardia. Based on the mean EC50 value of 1.7 ng/ml (approximately equivalent to 18 ng/ml for total propranolol), 80% of Emax will be achieved at unbound concentrations of 6.8 ng/ml or total concentrations of 72 ng/ml. Relatively little additional beta-blockade is produced if concentrations are increased further. Conversely at concentrations well below the EC50, there is a near-linear relationship between effect and concentrations. This is the basis for the linear model which can be considered as a submodel of the Emax model (see below). It is noteworthy that the Emax model, like the Michaelis-Menten equation, can be expressed in several different linear forms (direct linear plot, double reciprocal or Lineweaver-Burk plot, Eadie-Hofstee plot, etc.) but that nonlinear regression using Equation 16 is a more appropriate method to estimate Emax and EC50.20
Why Analyze Drugs in Biological Fluids?
Published in Joseph Chamberlain, The Analysis of Drugs in Biological Fluids, 2018
The question of correlating plasma drug levels with pharmacological response was referred to at the beginning of this discussion on the use of analysis of drugs in biological fluids in research and development. When a successful drug has been developed to the point of full clinical trials the wheel has turned full circle and there is a renewed intellectual drive to attempt to relate plasma levels with effect in human volunteers or in patients. There is greater justification in pursuing this research when the drug has proven efficacy, and a thorough understanding of the levels necessary to obtain the desired effect makes formulation design and dosage regimen more sensible. This could be extremely useful, for instance, in designing a sustained release formulation to provide exactly the right plasma concentration and to avoid the near toxic effects of large doses which may be thought necessary to achieve effective levels for a reasonable time. Apart from avoiding the short-term high concentrations, a suitably designed sustained release form will result in an overall smaller exposure of the body to the drug and therefore, presumably, a safer medication. Thus, the exercise of analysis of drugs in biological fluids is a key factor in the establishment of a concentration–effect relationship and the monitoring of dosage forms to achieve this optimal performance.
Smart design of patient-centric long-acting products: from preclinical to marketed pipeline trends and opportunities
Published in Expert Opinion on Drug Delivery, 2022
Céline Bassand, Alessia Villois, Lucas Gianola, Grit Laue, Farshad Ramazani, Bernd Riebesehl, Manuel Sanchez-Felix, Kurt Sedo, Thomas Ullrich, Marieta Duvnjak Romic
Although the positive impact of long-acting products for the patients is supported by health authorities (better adherence, better overall clinical outcomes, etc.), they represent approximately 10% of the market products in the disease areas where they are typically employed (such as cancer, infections, and blood disorders). Bringing long-acting products to the market is lengthy and expensive, complicated by challenges related to manufacturability, drug potency, deliverable dose, dosing interval, release control, tolerability and, for local delivery, accurate concentration measurements in the site of interest. Nonetheless, there is a growing tendency to design products for LAI by engineering the DS at the discovery phase, instead of readapting an immediate release drug in the context of life cycle management, which was a standard procedure in the past. To do this, it is important to invest as early as possible in (i) the collaboration between DS design and formulation teams and (ii) the understanding of dose – (local or systemic) concentration – effect relationship.
A pharmacokinetics–pharmacodynamics study of single-dose total glucosides of paeony capsule on reducing serum total bile acid in hepatic injury rats
Published in Pharmaceutical Biology, 2021
Ninghua Jiang, Bohong Zheng, Yihan Feng, Lei Yin, Yuanrong Liu, Lujing Cao, Ning Zheng, Suxiang Wu, Baoyue Ding, Xuan Huang, Jeffrey Wang, Shuyu Zhan
Pharmacokinetics–pharmacodynamics (PK-PD) modelling combines the profiles of the drugs’ concentration-time and effect-time to characterize their concentration–effect relationships in vivo. The successful PK-PD modelling with its PD parameters will reveal the drugs’ triggering effect and action time in vivo and helps to understand drugs’ pharmacological mechanisms and optimize dosage regimens. Therefore, PK-PD modelling has been widely used in drugs’ preclinical and clinical in vivo processes studies (Zhang et al. 2016). Nevertheless, it is critical to select and test suitable effect index which might be simply sampling and quantitative, and most importantly, related to drug’s pharmacological activities and real-time respond on the drug’s intervention (Agoram and van der Graaf 2012; Zhan et al. 2018). Serum biomarker is a more available effect index than others for pharmacodynamic study in PK-PD modelling because they can indirectly reflect drug’s activities as well as be synchronously monitored with drug concentrations in blood. Serum alanine transaminase (ALT), aspartate transaminase (AST) and total bile acid (TBA) are common and standard biomarkers applied in TGP’s hepatoprotection (Qin and Tian 2011; Ma et al. 2015, 2016). However, their real-time responses on single dose of TGP intervention remain unknown.
Use of quantitative clinical pharmacology to improve early clinical development success in neurodegenerative diseases
Published in Expert Review of Clinical Pharmacology, 2018
Hugo Geerts, Ronald Gieschke, Richard Peck
This form of PK modeling describes the dynamics of active drug plasma levels with an empirical model based on absorption and disposition processes where the relevant parameters are fitted to existing clinical plasma PK levels. Phase I studies provide PK data over a large dose range, data that might well be applicable to patients under the condition that the PK profiles observed in healthy controls are not affected by the disease. The large number of sampled concentrations allows a compartmental approach with specification of rate equations for the movement of drug between central and peripheral compartments. Rate constants are related to volume and clearance terms, but without physiological reference (see section 1.2). The PK profile of a drug links dosing regimens to drug concentrations in plasma (or other compartments) and provides the basis for concentration-effect relationships. This approach has been used for over 40 years [9] and respective programs have been optimized extensively.